├── README.md
├── classification
    ├── decision_tree.ipynb
    ├── gradient_boosted_decision_tree.ipynb
    ├── logistic_regression.ipynb
    ├── naive_bayes.ipynb
    ├── neural_networks_classifier.ipynb
    ├── random_forest.ipynb
    └── svm.ipynb
├── regression
    ├── lasso_regression.ipynb
    ├── linear_regression.ipynb
    ├── neural_networks_regressor.ipynb
    └── ridge_regression.ipynb
└── supplements
    └── data
        ├── cars.csv
        └── heart.csv


/README.md:
--------------------------------------------------------------------------------
 1 | This repository contains templates for supervised learning algorithms - for both regression and classification problems.
 2 | 
 3 | # List of Algorithms 
 4 | ### Classification
 5 | 
 6 | 1. [Decision Trees](classification/decision_tree.ipynb)
 7 | 2. [Random Forests](classification/random_forest.ipynb)
 8 | 3. [Logistic Regression](classification/logistic_regression.ipynb)
 9 | 4. [Naive Bayes Classifiers](classification/naive_bayes.ipynb)
10 | 5. [Neural Network Classifier](classification/neural_networks_classifier.ipynb)
11 | 6. [Support Vector Machines](classification/svm.ipynb)
12 | 
13 | ### Regression
14 | 1. [Linear Regression](regression/linear_regression.ipynb)
15 | 2. [Ridge Regression](regression/ridge_regression.ipynb)
16 | 3. [Lasso Regression ](regression/lasso_regression.ipynb)
17 | 4. [Polynomial Regression](regression/linear_vs_%20polynomial_regressions.ipynb)
18 | 5. [Neural Network Regressor](regression/neural_networks_regressor.ipynb)
19 | 


--------------------------------------------------------------------------------
/classification/naive_bayes.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Naive Bayes"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "- This is a supplement material for my lectures on ML. It sheds light on Python implementations of the supervised machine learning algorithms. \n",
 15 |     "- I assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n",
 16 |     "- This material is represented as a Jupyter notebook, which you can easily download to reproduce the code and play around with it. "
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "## 1. Libraries"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "To build a model, we need \n",
 31 |     "- `pandas` library to work with panel dataframes\n",
 32 |     "- `sklearn.model_selection.train_test_split` to split the dataset into train and test\n",
 33 |     "- `sklearn.naive_bayes.GaussianNB` to build a model"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 1,
 39 |    "metadata": {},
 40 |    "outputs": [],
 41 |    "source": [
 42 |     "import pandas as pd\n",
 43 |     "from sklearn.naive_bayes import GaussianNB\n",
 44 |     "from sklearn.model_selection import train_test_split"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "markdown",
 49 |    "metadata": {},
 50 |    "source": [
 51 |     "## 2. Data Load & Overview"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "markdown",
 56 |    "metadata": {},
 57 |    "source": [
 58 |     "Let's load the dataset and understand it."
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "code",
 63 |    "execution_count": 2,
 64 |    "metadata": {},
 65 |    "outputs": [],
 66 |    "source": [
 67 |     "# Load the dataset\n",
 68 |     "df = pd.read_csv('https://raw.githubusercontent.com/5x12/ml-cookbook/master/supplements/data/heart.csv')"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": 25,
 74 |    "metadata": {},
 75 |    "outputs": [
 76 |     {
 77 |      "data": {
 78 |       "text/html": [
 79 |        "<div>\n",
 80 |        "<style scoped>\n",
 81 |        "    .dataframe tbody tr th:only-of-type {\n",
 82 |        "        vertical-align: middle;\n",
 83 |        "    }\n",
 84 |        "\n",
 85 |        "    .dataframe tbody tr th {\n",
 86 |        "        vertical-align: top;\n",
 87 |        "    }\n",
 88 |        "\n",
 89 |        "    .dataframe thead th {\n",
 90 |        "        text-align: right;\n",
 91 |        "    }\n",
 92 |        "</style>\n",
 93 |        "<table border=\"1\" class=\"dataframe\">\n",
 94 |        "  <thead>\n",
 95 |        "    <tr style=\"text-align: right;\">\n",
 96 |        "      <th></th>\n",
 97 |        "      <th>age</th>\n",
 98 |        "      <th>sex</th>\n",
 99 |        "      <th>cp</th>\n",
100 |        "      <th>trestbps</th>\n",
101 |        "      <th>chol</th>\n",
102 |        "      <th>fbs</th>\n",
103 |        "      <th>restecg</th>\n",
104 |        "      <th>thalach</th>\n",
105 |        "      <th>exang</th>\n",
106 |        "      <th>oldpeak</th>\n",
107 |        "      <th>slope</th>\n",
108 |        "      <th>ca</th>\n",
109 |        "      <th>thal</th>\n",
110 |        "      <th>target</th>\n",
111 |        "    </tr>\n",
112 |        "  </thead>\n",
113 |        "  <tbody>\n",
114 |        "    <tr>\n",
115 |        "      <th>0</th>\n",
116 |        "      <td>52</td>\n",
117 |        "      <td>1</td>\n",
118 |        "      <td>0</td>\n",
119 |        "      <td>125</td>\n",
120 |        "      <td>212</td>\n",
121 |        "      <td>0</td>\n",
122 |        "      <td>1</td>\n",
123 |        "      <td>168</td>\n",
124 |        "      <td>0</td>\n",
125 |        "      <td>1.0</td>\n",
126 |        "      <td>2</td>\n",
127 |        "      <td>2</td>\n",
128 |        "      <td>3</td>\n",
129 |        "      <td>0</td>\n",
130 |        "    </tr>\n",
131 |        "    <tr>\n",
132 |        "      <th>1</th>\n",
133 |        "      <td>53</td>\n",
134 |        "      <td>1</td>\n",
135 |        "      <td>0</td>\n",
136 |        "      <td>140</td>\n",
137 |        "      <td>203</td>\n",
138 |        "      <td>1</td>\n",
139 |        "      <td>0</td>\n",
140 |        "      <td>155</td>\n",
141 |        "      <td>1</td>\n",
142 |        "      <td>3.1</td>\n",
143 |        "      <td>0</td>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>3</td>\n",
146 |        "      <td>0</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>2</th>\n",
150 |        "      <td>70</td>\n",
151 |        "      <td>1</td>\n",
152 |        "      <td>0</td>\n",
153 |        "      <td>145</td>\n",
154 |        "      <td>174</td>\n",
155 |        "      <td>0</td>\n",
156 |        "      <td>1</td>\n",
157 |        "      <td>125</td>\n",
158 |        "      <td>1</td>\n",
159 |        "      <td>2.6</td>\n",
160 |        "      <td>0</td>\n",
161 |        "      <td>0</td>\n",
162 |        "      <td>3</td>\n",
163 |        "      <td>0</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>3</th>\n",
167 |        "      <td>61</td>\n",
168 |        "      <td>1</td>\n",
169 |        "      <td>0</td>\n",
170 |        "      <td>148</td>\n",
171 |        "      <td>203</td>\n",
172 |        "      <td>0</td>\n",
173 |        "      <td>1</td>\n",
174 |        "      <td>161</td>\n",
175 |        "      <td>0</td>\n",
176 |        "      <td>0.0</td>\n",
177 |        "      <td>2</td>\n",
178 |        "      <td>1</td>\n",
179 |        "      <td>3</td>\n",
180 |        "      <td>0</td>\n",
181 |        "    </tr>\n",
182 |        "    <tr>\n",
183 |        "      <th>4</th>\n",
184 |        "      <td>62</td>\n",
185 |        "      <td>0</td>\n",
186 |        "      <td>0</td>\n",
187 |        "      <td>138</td>\n",
188 |        "      <td>294</td>\n",
189 |        "      <td>1</td>\n",
190 |        "      <td>1</td>\n",
191 |        "      <td>106</td>\n",
192 |        "      <td>0</td>\n",
193 |        "      <td>1.9</td>\n",
194 |        "      <td>1</td>\n",
195 |        "      <td>3</td>\n",
196 |        "      <td>2</td>\n",
197 |        "      <td>0</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>5</th>\n",
201 |        "      <td>58</td>\n",
202 |        "      <td>0</td>\n",
203 |        "      <td>0</td>\n",
204 |        "      <td>100</td>\n",
205 |        "      <td>248</td>\n",
206 |        "      <td>0</td>\n",
207 |        "      <td>0</td>\n",
208 |        "      <td>122</td>\n",
209 |        "      <td>0</td>\n",
210 |        "      <td>1.0</td>\n",
211 |        "      <td>1</td>\n",
212 |        "      <td>0</td>\n",
213 |        "      <td>2</td>\n",
214 |        "      <td>1</td>\n",
215 |        "    </tr>\n",
216 |        "    <tr>\n",
217 |        "      <th>6</th>\n",
218 |        "      <td>58</td>\n",
219 |        "      <td>1</td>\n",
220 |        "      <td>0</td>\n",
221 |        "      <td>114</td>\n",
222 |        "      <td>318</td>\n",
223 |        "      <td>0</td>\n",
224 |        "      <td>2</td>\n",
225 |        "      <td>140</td>\n",
226 |        "      <td>0</td>\n",
227 |        "      <td>4.4</td>\n",
228 |        "      <td>0</td>\n",
229 |        "      <td>3</td>\n",
230 |        "      <td>1</td>\n",
231 |        "      <td>0</td>\n",
232 |        "    </tr>\n",
233 |        "  </tbody>\n",
234 |        "</table>\n",
235 |        "</div>"
236 |       ],
237 |       "text/plain": [
238 |        "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
239 |        "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
240 |        "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
241 |        "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
242 |        "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
243 |        "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
244 |        "5   58    0   0       100   248    0        0      122      0      1.0      1   \n",
245 |        "6   58    1   0       114   318    0        2      140      0      4.4      0   \n",
246 |        "\n",
247 |        "   ca  thal  target  \n",
248 |        "0   2     3       0  \n",
249 |        "1   0     3       0  \n",
250 |        "2   0     3       0  \n",
251 |        "3   1     3       0  \n",
252 |        "4   3     2       0  \n",
253 |        "5   0     2       1  \n",
254 |        "6   3     1       0  "
255 |       ]
256 |      },
257 |      "execution_count": 25,
258 |      "metadata": {},
259 |      "output_type": "execute_result"
260 |     }
261 |    ],
262 |    "source": [
263 |     "# Print the first 10 rows of the dataset\n",
264 |     "df.head(10)"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "markdown",
269 |    "metadata": {},
270 |    "source": [
271 |     "This Public Health Dataset represents people's medical records.\n",
272 |     "\n",
273 |     "- age: age in years\n",
274 |     "- sex: (1 = male; 0 = female)\n",
275 |     "- cp: chest pain type\n",
276 |     "- trestbps: resting blood pressure (in mm Hg on admission to the hospital)\n",
277 |     "- chol: serum cholestoral in mg/dl\n",
278 |     "- fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
279 |     "- restecg: resting electrocardiographic results\n",
280 |     "- thalach: maximum heart rate achieved\n",
281 |     "- exang: exercise induced angina (1 = yes; 0 = no)\n",
282 |     "- oldpeak: ST depression induced by exercise relative to rest\n",
283 |     "- slope: the slope of the peak exercise ST segment\n",
284 |     "- ca: number of major vessels (0-3) colored by flourosopy\n",
285 |     "- thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
286 |     "- target: 1 or 0. It refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease.\n",
287 |     "\n",
288 |     "The main aim is to build **a model that predicts a heart disease of a patient** (target column) based on independent variables."
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "markdown",
293 |    "metadata": {},
294 |    "source": [
295 |     "## 3. Variables"
296 |    ]
297 |   },
298 |   {
299 |    "cell_type": "markdown",
300 |    "metadata": {},
301 |    "source": [
302 |     "Let's split the dataset into X and y, where \n",
303 |     "- $X$ is a set of independent variables (all columns but the last one)\n",
304 |     "- $y$ is a dependent, or target variable (the last column)"
305 |    ]
306 |   },
307 |   {
308 |    "cell_type": "code",
309 |    "execution_count": 4,
310 |    "metadata": {},
311 |    "outputs": [
312 |     {
313 |      "data": {
314 |       "text/html": [
315 |        "<div>\n",
316 |        "<style scoped>\n",
317 |        "    .dataframe tbody tr th:only-of-type {\n",
318 |        "        vertical-align: middle;\n",
319 |        "    }\n",
320 |        "\n",
321 |        "    .dataframe tbody tr th {\n",
322 |        "        vertical-align: top;\n",
323 |        "    }\n",
324 |        "\n",
325 |        "    .dataframe thead th {\n",
326 |        "        text-align: right;\n",
327 |        "    }\n",
328 |        "</style>\n",
329 |        "<table border=\"1\" class=\"dataframe\">\n",
330 |        "  <thead>\n",
331 |        "    <tr style=\"text-align: right;\">\n",
332 |        "      <th></th>\n",
333 |        "      <th>age</th>\n",
334 |        "      <th>sex</th>\n",
335 |        "      <th>cp</th>\n",
336 |        "      <th>trestbps</th>\n",
337 |        "      <th>chol</th>\n",
338 |        "      <th>fbs</th>\n",
339 |        "      <th>restecg</th>\n",
340 |        "      <th>thalach</th>\n",
341 |        "      <th>exang</th>\n",
342 |        "      <th>oldpeak</th>\n",
343 |        "      <th>slope</th>\n",
344 |        "      <th>ca</th>\n",
345 |        "      <th>thal</th>\n",
346 |        "    </tr>\n",
347 |        "  </thead>\n",
348 |        "  <tbody>\n",
349 |        "    <tr>\n",
350 |        "      <th>0</th>\n",
351 |        "      <td>52</td>\n",
352 |        "      <td>1</td>\n",
353 |        "      <td>0</td>\n",
354 |        "      <td>125</td>\n",
355 |        "      <td>212</td>\n",
356 |        "      <td>0</td>\n",
357 |        "      <td>1</td>\n",
358 |        "      <td>168</td>\n",
359 |        "      <td>0</td>\n",
360 |        "      <td>1.0</td>\n",
361 |        "      <td>2</td>\n",
362 |        "      <td>2</td>\n",
363 |        "      <td>3</td>\n",
364 |        "    </tr>\n",
365 |        "    <tr>\n",
366 |        "      <th>1</th>\n",
367 |        "      <td>53</td>\n",
368 |        "      <td>1</td>\n",
369 |        "      <td>0</td>\n",
370 |        "      <td>140</td>\n",
371 |        "      <td>203</td>\n",
372 |        "      <td>1</td>\n",
373 |        "      <td>0</td>\n",
374 |        "      <td>155</td>\n",
375 |        "      <td>1</td>\n",
376 |        "      <td>3.1</td>\n",
377 |        "      <td>0</td>\n",
378 |        "      <td>0</td>\n",
379 |        "      <td>3</td>\n",
380 |        "    </tr>\n",
381 |        "    <tr>\n",
382 |        "      <th>2</th>\n",
383 |        "      <td>70</td>\n",
384 |        "      <td>1</td>\n",
385 |        "      <td>0</td>\n",
386 |        "      <td>145</td>\n",
387 |        "      <td>174</td>\n",
388 |        "      <td>0</td>\n",
389 |        "      <td>1</td>\n",
390 |        "      <td>125</td>\n",
391 |        "      <td>1</td>\n",
392 |        "      <td>2.6</td>\n",
393 |        "      <td>0</td>\n",
394 |        "      <td>0</td>\n",
395 |        "      <td>3</td>\n",
396 |        "    </tr>\n",
397 |        "    <tr>\n",
398 |        "      <th>3</th>\n",
399 |        "      <td>61</td>\n",
400 |        "      <td>1</td>\n",
401 |        "      <td>0</td>\n",
402 |        "      <td>148</td>\n",
403 |        "      <td>203</td>\n",
404 |        "      <td>0</td>\n",
405 |        "      <td>1</td>\n",
406 |        "      <td>161</td>\n",
407 |        "      <td>0</td>\n",
408 |        "      <td>0.0</td>\n",
409 |        "      <td>2</td>\n",
410 |        "      <td>1</td>\n",
411 |        "      <td>3</td>\n",
412 |        "    </tr>\n",
413 |        "    <tr>\n",
414 |        "      <th>4</th>\n",
415 |        "      <td>62</td>\n",
416 |        "      <td>0</td>\n",
417 |        "      <td>0</td>\n",
418 |        "      <td>138</td>\n",
419 |        "      <td>294</td>\n",
420 |        "      <td>1</td>\n",
421 |        "      <td>1</td>\n",
422 |        "      <td>106</td>\n",
423 |        "      <td>0</td>\n",
424 |        "      <td>1.9</td>\n",
425 |        "      <td>1</td>\n",
426 |        "      <td>3</td>\n",
427 |        "      <td>2</td>\n",
428 |        "    </tr>\n",
429 |        "    <tr>\n",
430 |        "      <th>...</th>\n",
431 |        "      <td>...</td>\n",
432 |        "      <td>...</td>\n",
433 |        "      <td>...</td>\n",
434 |        "      <td>...</td>\n",
435 |        "      <td>...</td>\n",
436 |        "      <td>...</td>\n",
437 |        "      <td>...</td>\n",
438 |        "      <td>...</td>\n",
439 |        "      <td>...</td>\n",
440 |        "      <td>...</td>\n",
441 |        "      <td>...</td>\n",
442 |        "      <td>...</td>\n",
443 |        "      <td>...</td>\n",
444 |        "    </tr>\n",
445 |        "    <tr>\n",
446 |        "      <th>1020</th>\n",
447 |        "      <td>59</td>\n",
448 |        "      <td>1</td>\n",
449 |        "      <td>1</td>\n",
450 |        "      <td>140</td>\n",
451 |        "      <td>221</td>\n",
452 |        "      <td>0</td>\n",
453 |        "      <td>1</td>\n",
454 |        "      <td>164</td>\n",
455 |        "      <td>1</td>\n",
456 |        "      <td>0.0</td>\n",
457 |        "      <td>2</td>\n",
458 |        "      <td>0</td>\n",
459 |        "      <td>2</td>\n",
460 |        "    </tr>\n",
461 |        "    <tr>\n",
462 |        "      <th>1021</th>\n",
463 |        "      <td>60</td>\n",
464 |        "      <td>1</td>\n",
465 |        "      <td>0</td>\n",
466 |        "      <td>125</td>\n",
467 |        "      <td>258</td>\n",
468 |        "      <td>0</td>\n",
469 |        "      <td>0</td>\n",
470 |        "      <td>141</td>\n",
471 |        "      <td>1</td>\n",
472 |        "      <td>2.8</td>\n",
473 |        "      <td>1</td>\n",
474 |        "      <td>1</td>\n",
475 |        "      <td>3</td>\n",
476 |        "    </tr>\n",
477 |        "    <tr>\n",
478 |        "      <th>1022</th>\n",
479 |        "      <td>47</td>\n",
480 |        "      <td>1</td>\n",
481 |        "      <td>0</td>\n",
482 |        "      <td>110</td>\n",
483 |        "      <td>275</td>\n",
484 |        "      <td>0</td>\n",
485 |        "      <td>0</td>\n",
486 |        "      <td>118</td>\n",
487 |        "      <td>1</td>\n",
488 |        "      <td>1.0</td>\n",
489 |        "      <td>1</td>\n",
490 |        "      <td>1</td>\n",
491 |        "      <td>2</td>\n",
492 |        "    </tr>\n",
493 |        "    <tr>\n",
494 |        "      <th>1023</th>\n",
495 |        "      <td>50</td>\n",
496 |        "      <td>0</td>\n",
497 |        "      <td>0</td>\n",
498 |        "      <td>110</td>\n",
499 |        "      <td>254</td>\n",
500 |        "      <td>0</td>\n",
501 |        "      <td>0</td>\n",
502 |        "      <td>159</td>\n",
503 |        "      <td>0</td>\n",
504 |        "      <td>0.0</td>\n",
505 |        "      <td>2</td>\n",
506 |        "      <td>0</td>\n",
507 |        "      <td>2</td>\n",
508 |        "    </tr>\n",
509 |        "    <tr>\n",
510 |        "      <th>1024</th>\n",
511 |        "      <td>54</td>\n",
512 |        "      <td>1</td>\n",
513 |        "      <td>0</td>\n",
514 |        "      <td>120</td>\n",
515 |        "      <td>188</td>\n",
516 |        "      <td>0</td>\n",
517 |        "      <td>1</td>\n",
518 |        "      <td>113</td>\n",
519 |        "      <td>0</td>\n",
520 |        "      <td>1.4</td>\n",
521 |        "      <td>1</td>\n",
522 |        "      <td>1</td>\n",
523 |        "      <td>3</td>\n",
524 |        "    </tr>\n",
525 |        "  </tbody>\n",
526 |        "</table>\n",
527 |        "<p>1025 rows × 13 columns</p>\n",
528 |        "</div>"
529 |       ],
530 |       "text/plain": [
531 |        "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
532 |        "0      52    1   0       125   212    0        1      168      0      1.0   \n",
533 |        "1      53    1   0       140   203    1        0      155      1      3.1   \n",
534 |        "2      70    1   0       145   174    0        1      125      1      2.6   \n",
535 |        "3      61    1   0       148   203    0        1      161      0      0.0   \n",
536 |        "4      62    0   0       138   294    1        1      106      0      1.9   \n",
537 |        "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
538 |        "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
539 |        "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
540 |        "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
541 |        "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
542 |        "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
543 |        "\n",
544 |        "      slope  ca  thal  \n",
545 |        "0         2   2     3  \n",
546 |        "1         0   0     3  \n",
547 |        "2         0   0     3  \n",
548 |        "3         2   1     3  \n",
549 |        "4         1   3     2  \n",
550 |        "...     ...  ..   ...  \n",
551 |        "1020      2   0     2  \n",
552 |        "1021      1   1     3  \n",
553 |        "1022      1   1     2  \n",
554 |        "1023      2   0     2  \n",
555 |        "1024      1   1     3  \n",
556 |        "\n",
557 |        "[1025 rows x 13 columns]"
558 |       ]
559 |      },
560 |      "execution_count": 4,
561 |      "metadata": {},
562 |      "output_type": "execute_result"
563 |     }
564 |    ],
565 |    "source": [
566 |     "# Filter out target column\n",
567 |     "X = df.iloc[:, :-1]\n",
568 |     "\n",
569 |     "# Print X\n",
570 |     "X"
571 |    ]
572 |   },
573 |   {
574 |    "cell_type": "code",
575 |    "execution_count": 5,
576 |    "metadata": {},
577 |    "outputs": [
578 |     {
579 |      "data": {
580 |       "text/plain": [
581 |        "0       0\n",
582 |        "1       0\n",
583 |        "2       0\n",
584 |        "3       0\n",
585 |        "4       0\n",
586 |        "       ..\n",
587 |        "1020    1\n",
588 |        "1021    0\n",
589 |        "1022    0\n",
590 |        "1023    1\n",
591 |        "1024    0\n",
592 |        "Name: target, Length: 1025, dtype: int64"
593 |       ]
594 |      },
595 |      "execution_count": 5,
596 |      "metadata": {},
597 |      "output_type": "execute_result"
598 |     }
599 |    ],
600 |    "source": [
601 |     "# Select target column\n",
602 |     "y = df['target']\n",
603 |     "\n",
604 |     "# Print y\n",
605 |     "y"
606 |    ]
607 |   },
608 |   {
609 |    "cell_type": "markdown",
610 |    "metadata": {},
611 |    "source": [
612 |     "## 4. Model"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "markdown",
617 |    "metadata": {},
618 |    "source": [
619 |     "In this section we are going to \n",
620 |     "- build a Naive Bayes model, \n",
621 |     "- evaluate its accuracy, and \n",
622 |     "- make a prediction"
623 |    ]
624 |   },
625 |   {
626 |    "cell_type": "markdown",
627 |    "metadata": {},
628 |    "source": [
629 |     "### 4.1. Building the model"
630 |    ]
631 |   },
632 |   {
633 |    "cell_type": "markdown",
634 |    "metadata": {},
635 |    "source": [
636 |     "Let's recall three simple steps:\n",
637 |     "\n",
638 |     "- Split the $X$ & $y$ variables into train and test sets\n",
639 |     "- Initialize the model\n",
640 |     "- Train the model with the varialbes"
641 |    ]
642 |   },
643 |   {
644 |    "cell_type": "code",
645 |    "execution_count": 16,
646 |    "metadata": {},
647 |    "outputs": [],
648 |    "source": [
649 |     "# Split variables into train and test\n",
650 |     "X_train, X_test, y_train, y_test = train_test_split(X, #independent variables\n",
651 |     "                                                    y, #dependent variable\n",
652 |     "                                                    random_state = 3\n",
653 |     "                                                   )"
654 |    ]
655 |   },
656 |   {
657 |    "cell_type": "code",
658 |    "execution_count": 17,
659 |    "metadata": {},
660 |    "outputs": [],
661 |    "source": [
662 |     "# Initialize the model\n",
663 |     "clf = GaussianNB()"
664 |    ]
665 |   },
666 |   {
667 |    "cell_type": "code",
668 |    "execution_count": 18,
669 |    "metadata": {},
670 |    "outputs": [
671 |     {
672 |      "data": {
673 |       "text/html": [
674 |        "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
675 |       ],
676 |       "text/plain": [
677 |        "GaussianNB()"
678 |       ]
679 |      },
680 |      "execution_count": 18,
681 |      "metadata": {},
682 |      "output_type": "execute_result"
683 |     }
684 |    ],
685 |    "source": [
686 |     "# Train the model\n",
687 |     "clf.fit(X_train, y_train)"
688 |    ]
689 |   },
690 |   {
691 |    "cell_type": "markdown",
692 |    "metadata": {},
693 |    "source": [
694 |     "### 4.2. Checking models accuracy"
695 |    ]
696 |   },
697 |   {
698 |    "cell_type": "markdown",
699 |    "metadata": {},
700 |    "source": [
701 |     "After the model has trained with the data, it's essential to understand how precisely it predicts heart disease. For that, we need to check model's accuracy. "
702 |    ]
703 |   },
704 |   {
705 |    "cell_type": "code",
706 |    "execution_count": 19,
707 |    "metadata": {},
708 |    "outputs": [
709 |     {
710 |      "name": "stdout",
711 |      "output_type": "stream",
712 |      "text": [
713 |       "Accuracy of Naive Bayes classifier on training set: 0.85\n",
714 |       "Accuracy of Naive Bayes classifier on test set: 0.83\n"
715 |      ]
716 |     }
717 |    ],
718 |    "source": [
719 |     "print(f'Accuracy of Naive Bayes classifier on training set: {clf.score(X_train, y_train):.2f}')\n",
720 |     "print(f'Accuracy of Naive Bayes classifier on test set: {clf.score(X_test, y_test):.2f}')"
721 |    ]
722 |   },
723 |   {
724 |    "cell_type": "markdown",
725 |    "metadata": {},
726 |    "source": [
727 |     "### 4.3. Making a prediction"
728 |    ]
729 |   },
730 |   {
731 |    "cell_type": "markdown",
732 |    "metadata": {},
733 |    "source": [
734 |     "Now that we know the model is accurate enough, we can predict whether or not a patient is having a heart disease by passing independent varialbes to the model. The method `predict` returns such a prediction - 0 for NO, 1 for YES."
735 |    ]
736 |   },
737 |   {
738 |    "cell_type": "code",
739 |    "execution_count": 21,
740 |    "metadata": {},
741 |    "outputs": [
742 |     {
743 |      "name": "stderr",
744 |      "output_type": "stream",
745 |      "text": [
746 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
747 |       "  warnings.warn(\n"
748 |      ]
749 |     },
750 |     {
751 |      "data": {
752 |       "text/plain": [
753 |        "array([0])"
754 |       ]
755 |      },
756 |      "execution_count": 21,
757 |      "metadata": {},
758 |      "output_type": "execute_result"
759 |     }
760 |    ],
761 |    "source": [
762 |     "clf.predict([[59, 1, 0, 101, 234, 0, 1, 143, 0, 3.4, 0, 0, 0]])"
763 |    ]
764 |   },
765 |   {
766 |    "cell_type": "markdown",
767 |    "metadata": {},
768 |    "source": [
769 |     "In the array:\n",
770 |     "- value 0 means a patient does not have a heart disease, \n",
771 |     "- value 1 means a patient does not have a heart disease.\n",
772 |     "\n",
773 |     "We can also check the probability of a patient having a heart disease. The method `predict_proba` can be used to infer the class probabilities (i.e. the probability that a particular data point falls into the underlying classes)."
774 |    ]
775 |   },
776 |   {
777 |    "cell_type": "code",
778 |    "execution_count": 23,
779 |    "metadata": {},
780 |    "outputs": [
781 |     {
782 |      "name": "stderr",
783 |      "output_type": "stream",
784 |      "text": [
785 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
786 |       "  warnings.warn(\n"
787 |      ]
788 |     },
789 |     {
790 |      "data": {
791 |       "text/plain": [
792 |        "array([[9.99964049e-01, 3.59509641e-05]])"
793 |       ]
794 |      },
795 |      "execution_count": 23,
796 |      "metadata": {},
797 |      "output_type": "execute_result"
798 |     }
799 |    ],
800 |    "source": [
801 |     "clf.predict_proba([[59, 1, 0, 101, 234, 0, 1, 143, 0, 3.4, 0, 0, 0]])"
802 |    ]
803 |   },
804 |   {
805 |    "cell_type": "markdown",
806 |    "metadata": {},
807 |    "source": [
808 |     "More on Naive Bayes at https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB"
809 |    ]
810 |   }
811 |  ],
812 |  "metadata": {
813 |   "kernelspec": {
814 |    "display_name": "Python 3 (ipykernel)",
815 |    "language": "python",
816 |    "name": "python3"
817 |   },
818 |   "language_info": {
819 |    "codemirror_mode": {
820 |     "name": "ipython",
821 |     "version": 3
822 |    },
823 |    "file_extension": ".py",
824 |    "mimetype": "text/x-python",
825 |    "name": "python",
826 |    "nbconvert_exporter": "python",
827 |    "pygments_lexer": "ipython3",
828 |    "version": "3.10.7"
829 |   }
830 |  },
831 |  "nbformat": 4,
832 |  "nbformat_minor": 2
833 | }
834 | 


--------------------------------------------------------------------------------
/regression/lasso_regression.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Lasso Regression"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "- This is a supplement material for my lectures on ML. It sheds light on Python implementations of the supervised machine learning algorithms. \n",
  15 |     "- I assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n",
  16 |     "- This material is represented as a Jupyter notebook, which you can easily download to reproduce the code and play around with it. "
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "markdown",
  21 |    "metadata": {},
  22 |    "source": [
  23 |     "## 1. Libraries"
  24 |    ]
  25 |   },
  26 |   {
  27 |    "cell_type": "markdown",
  28 |    "metadata": {},
  29 |    "source": [
  30 |     "To build a model, we need \n",
  31 |     "- `pandas` library to work with panel dataframes\n",
  32 |     "- `sklearn.model_selection.train_test_split` to split the dataset into train and test\n",
  33 |     "- `sklearn.linear_model.Lasso` to build a model\n",
  34 |     "\n",
  35 |     "<!-- We will also need `matplotlib.pyplot` to visualize a trained model. -->"
  36 |    ]
  37 |   },
  38 |   {
  39 |    "cell_type": "code",
  40 |    "execution_count": 30,
  41 |    "metadata": {},
  42 |    "outputs": [],
  43 |    "source": [
  44 |     "import pandas as pd\n",
  45 |     "import matplotlib.pyplot as plt\n",
  46 |     "from sklearn.linear_model import Lasso\n",
  47 |     "from sklearn.model_selection import train_test_split"
  48 |    ]
  49 |   },
  50 |   {
  51 |    "cell_type": "markdown",
  52 |    "metadata": {},
  53 |    "source": [
  54 |     "## 2. Data Load & Overview"
  55 |    ]
  56 |   },
  57 |   {
  58 |    "cell_type": "markdown",
  59 |    "metadata": {},
  60 |    "source": [
  61 |     "Let's load the dataset and understand it."
  62 |    ]
  63 |   },
  64 |   {
  65 |    "cell_type": "code",
  66 |    "execution_count": 2,
  67 |    "metadata": {},
  68 |    "outputs": [],
  69 |    "source": [
  70 |     "# Load the dataset\n",
  71 |     "df = pd.read_csv('https://raw.githubusercontent.com/5x12/ml-cookbook/master/supplements/data/cars.csv')"
  72 |    ]
  73 |   },
  74 |   {
  75 |    "cell_type": "code",
  76 |    "execution_count": 3,
  77 |    "metadata": {},
  78 |    "outputs": [
  79 |     {
  80 |      "data": {
  81 |       "text/html": [
  82 |        "<div>\n",
  83 |        "<style scoped>\n",
  84 |        "    .dataframe tbody tr th:only-of-type {\n",
  85 |        "        vertical-align: middle;\n",
  86 |        "    }\n",
  87 |        "\n",
  88 |        "    .dataframe tbody tr th {\n",
  89 |        "        vertical-align: top;\n",
  90 |        "    }\n",
  91 |        "\n",
  92 |        "    .dataframe thead th {\n",
  93 |        "        text-align: right;\n",
  94 |        "    }\n",
  95 |        "</style>\n",
  96 |        "<table border=\"1\" class=\"dataframe\">\n",
  97 |        "  <thead>\n",
  98 |        "    <tr style=\"text-align: right;\">\n",
  99 |        "      <th></th>\n",
 100 |        "      <th>car_ID</th>\n",
 101 |        "      <th>symboling</th>\n",
 102 |        "      <th>CarName</th>\n",
 103 |        "      <th>fueltype</th>\n",
 104 |        "      <th>aspiration</th>\n",
 105 |        "      <th>doornumber</th>\n",
 106 |        "      <th>carbody</th>\n",
 107 |        "      <th>drivewheel</th>\n",
 108 |        "      <th>enginelocation</th>\n",
 109 |        "      <th>wheelbase</th>\n",
 110 |        "      <th>...</th>\n",
 111 |        "      <th>enginesize</th>\n",
 112 |        "      <th>fuelsystem</th>\n",
 113 |        "      <th>boreratio</th>\n",
 114 |        "      <th>stroke</th>\n",
 115 |        "      <th>compressionratio</th>\n",
 116 |        "      <th>horsepower</th>\n",
 117 |        "      <th>peakrpm</th>\n",
 118 |        "      <th>citympg</th>\n",
 119 |        "      <th>highwaympg</th>\n",
 120 |        "      <th>price</th>\n",
 121 |        "    </tr>\n",
 122 |        "  </thead>\n",
 123 |        "  <tbody>\n",
 124 |        "    <tr>\n",
 125 |        "      <th>0</th>\n",
 126 |        "      <td>1</td>\n",
 127 |        "      <td>3</td>\n",
 128 |        "      <td>alfa-romero giulia</td>\n",
 129 |        "      <td>gas</td>\n",
 130 |        "      <td>std</td>\n",
 131 |        "      <td>two</td>\n",
 132 |        "      <td>convertible</td>\n",
 133 |        "      <td>rwd</td>\n",
 134 |        "      <td>front</td>\n",
 135 |        "      <td>88.6</td>\n",
 136 |        "      <td>...</td>\n",
 137 |        "      <td>130</td>\n",
 138 |        "      <td>mpfi</td>\n",
 139 |        "      <td>3.47</td>\n",
 140 |        "      <td>2.68</td>\n",
 141 |        "      <td>9.0</td>\n",
 142 |        "      <td>111</td>\n",
 143 |        "      <td>5000</td>\n",
 144 |        "      <td>21</td>\n",
 145 |        "      <td>27</td>\n",
 146 |        "      <td>13495.000</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>1</th>\n",
 150 |        "      <td>2</td>\n",
 151 |        "      <td>3</td>\n",
 152 |        "      <td>alfa-romero stelvio</td>\n",
 153 |        "      <td>gas</td>\n",
 154 |        "      <td>std</td>\n",
 155 |        "      <td>two</td>\n",
 156 |        "      <td>convertible</td>\n",
 157 |        "      <td>rwd</td>\n",
 158 |        "      <td>front</td>\n",
 159 |        "      <td>88.6</td>\n",
 160 |        "      <td>...</td>\n",
 161 |        "      <td>130</td>\n",
 162 |        "      <td>mpfi</td>\n",
 163 |        "      <td>3.47</td>\n",
 164 |        "      <td>2.68</td>\n",
 165 |        "      <td>9.0</td>\n",
 166 |        "      <td>111</td>\n",
 167 |        "      <td>5000</td>\n",
 168 |        "      <td>21</td>\n",
 169 |        "      <td>27</td>\n",
 170 |        "      <td>16500.000</td>\n",
 171 |        "    </tr>\n",
 172 |        "    <tr>\n",
 173 |        "      <th>2</th>\n",
 174 |        "      <td>3</td>\n",
 175 |        "      <td>1</td>\n",
 176 |        "      <td>alfa-romero Quadrifoglio</td>\n",
 177 |        "      <td>gas</td>\n",
 178 |        "      <td>std</td>\n",
 179 |        "      <td>two</td>\n",
 180 |        "      <td>hatchback</td>\n",
 181 |        "      <td>rwd</td>\n",
 182 |        "      <td>front</td>\n",
 183 |        "      <td>94.5</td>\n",
 184 |        "      <td>...</td>\n",
 185 |        "      <td>152</td>\n",
 186 |        "      <td>mpfi</td>\n",
 187 |        "      <td>2.68</td>\n",
 188 |        "      <td>3.47</td>\n",
 189 |        "      <td>9.0</td>\n",
 190 |        "      <td>154</td>\n",
 191 |        "      <td>5000</td>\n",
 192 |        "      <td>19</td>\n",
 193 |        "      <td>26</td>\n",
 194 |        "      <td>16500.000</td>\n",
 195 |        "    </tr>\n",
 196 |        "    <tr>\n",
 197 |        "      <th>3</th>\n",
 198 |        "      <td>4</td>\n",
 199 |        "      <td>2</td>\n",
 200 |        "      <td>audi 100 ls</td>\n",
 201 |        "      <td>gas</td>\n",
 202 |        "      <td>std</td>\n",
 203 |        "      <td>four</td>\n",
 204 |        "      <td>sedan</td>\n",
 205 |        "      <td>fwd</td>\n",
 206 |        "      <td>front</td>\n",
 207 |        "      <td>99.8</td>\n",
 208 |        "      <td>...</td>\n",
 209 |        "      <td>109</td>\n",
 210 |        "      <td>mpfi</td>\n",
 211 |        "      <td>3.19</td>\n",
 212 |        "      <td>3.40</td>\n",
 213 |        "      <td>10.0</td>\n",
 214 |        "      <td>102</td>\n",
 215 |        "      <td>5500</td>\n",
 216 |        "      <td>24</td>\n",
 217 |        "      <td>30</td>\n",
 218 |        "      <td>13950.000</td>\n",
 219 |        "    </tr>\n",
 220 |        "    <tr>\n",
 221 |        "      <th>4</th>\n",
 222 |        "      <td>5</td>\n",
 223 |        "      <td>2</td>\n",
 224 |        "      <td>audi 100ls</td>\n",
 225 |        "      <td>gas</td>\n",
 226 |        "      <td>std</td>\n",
 227 |        "      <td>four</td>\n",
 228 |        "      <td>sedan</td>\n",
 229 |        "      <td>4wd</td>\n",
 230 |        "      <td>front</td>\n",
 231 |        "      <td>99.4</td>\n",
 232 |        "      <td>...</td>\n",
 233 |        "      <td>136</td>\n",
 234 |        "      <td>mpfi</td>\n",
 235 |        "      <td>3.19</td>\n",
 236 |        "      <td>3.40</td>\n",
 237 |        "      <td>8.0</td>\n",
 238 |        "      <td>115</td>\n",
 239 |        "      <td>5500</td>\n",
 240 |        "      <td>18</td>\n",
 241 |        "      <td>22</td>\n",
 242 |        "      <td>17450.000</td>\n",
 243 |        "    </tr>\n",
 244 |        "    <tr>\n",
 245 |        "      <th>5</th>\n",
 246 |        "      <td>6</td>\n",
 247 |        "      <td>2</td>\n",
 248 |        "      <td>audi fox</td>\n",
 249 |        "      <td>gas</td>\n",
 250 |        "      <td>std</td>\n",
 251 |        "      <td>two</td>\n",
 252 |        "      <td>sedan</td>\n",
 253 |        "      <td>fwd</td>\n",
 254 |        "      <td>front</td>\n",
 255 |        "      <td>99.8</td>\n",
 256 |        "      <td>...</td>\n",
 257 |        "      <td>136</td>\n",
 258 |        "      <td>mpfi</td>\n",
 259 |        "      <td>3.19</td>\n",
 260 |        "      <td>3.40</td>\n",
 261 |        "      <td>8.5</td>\n",
 262 |        "      <td>110</td>\n",
 263 |        "      <td>5500</td>\n",
 264 |        "      <td>19</td>\n",
 265 |        "      <td>25</td>\n",
 266 |        "      <td>15250.000</td>\n",
 267 |        "    </tr>\n",
 268 |        "    <tr>\n",
 269 |        "      <th>6</th>\n",
 270 |        "      <td>7</td>\n",
 271 |        "      <td>1</td>\n",
 272 |        "      <td>audi 100ls</td>\n",
 273 |        "      <td>gas</td>\n",
 274 |        "      <td>std</td>\n",
 275 |        "      <td>four</td>\n",
 276 |        "      <td>sedan</td>\n",
 277 |        "      <td>fwd</td>\n",
 278 |        "      <td>front</td>\n",
 279 |        "      <td>105.8</td>\n",
 280 |        "      <td>...</td>\n",
 281 |        "      <td>136</td>\n",
 282 |        "      <td>mpfi</td>\n",
 283 |        "      <td>3.19</td>\n",
 284 |        "      <td>3.40</td>\n",
 285 |        "      <td>8.5</td>\n",
 286 |        "      <td>110</td>\n",
 287 |        "      <td>5500</td>\n",
 288 |        "      <td>19</td>\n",
 289 |        "      <td>25</td>\n",
 290 |        "      <td>17710.000</td>\n",
 291 |        "    </tr>\n",
 292 |        "    <tr>\n",
 293 |        "      <th>7</th>\n",
 294 |        "      <td>8</td>\n",
 295 |        "      <td>1</td>\n",
 296 |        "      <td>audi 5000</td>\n",
 297 |        "      <td>gas</td>\n",
 298 |        "      <td>std</td>\n",
 299 |        "      <td>four</td>\n",
 300 |        "      <td>wagon</td>\n",
 301 |        "      <td>fwd</td>\n",
 302 |        "      <td>front</td>\n",
 303 |        "      <td>105.8</td>\n",
 304 |        "      <td>...</td>\n",
 305 |        "      <td>136</td>\n",
 306 |        "      <td>mpfi</td>\n",
 307 |        "      <td>3.19</td>\n",
 308 |        "      <td>3.40</td>\n",
 309 |        "      <td>8.5</td>\n",
 310 |        "      <td>110</td>\n",
 311 |        "      <td>5500</td>\n",
 312 |        "      <td>19</td>\n",
 313 |        "      <td>25</td>\n",
 314 |        "      <td>18920.000</td>\n",
 315 |        "    </tr>\n",
 316 |        "    <tr>\n",
 317 |        "      <th>8</th>\n",
 318 |        "      <td>9</td>\n",
 319 |        "      <td>1</td>\n",
 320 |        "      <td>audi 4000</td>\n",
 321 |        "      <td>gas</td>\n",
 322 |        "      <td>turbo</td>\n",
 323 |        "      <td>four</td>\n",
 324 |        "      <td>sedan</td>\n",
 325 |        "      <td>fwd</td>\n",
 326 |        "      <td>front</td>\n",
 327 |        "      <td>105.8</td>\n",
 328 |        "      <td>...</td>\n",
 329 |        "      <td>131</td>\n",
 330 |        "      <td>mpfi</td>\n",
 331 |        "      <td>3.13</td>\n",
 332 |        "      <td>3.40</td>\n",
 333 |        "      <td>8.3</td>\n",
 334 |        "      <td>140</td>\n",
 335 |        "      <td>5500</td>\n",
 336 |        "      <td>17</td>\n",
 337 |        "      <td>20</td>\n",
 338 |        "      <td>23875.000</td>\n",
 339 |        "    </tr>\n",
 340 |        "    <tr>\n",
 341 |        "      <th>9</th>\n",
 342 |        "      <td>10</td>\n",
 343 |        "      <td>0</td>\n",
 344 |        "      <td>audi 5000s (diesel)</td>\n",
 345 |        "      <td>gas</td>\n",
 346 |        "      <td>turbo</td>\n",
 347 |        "      <td>two</td>\n",
 348 |        "      <td>hatchback</td>\n",
 349 |        "      <td>4wd</td>\n",
 350 |        "      <td>front</td>\n",
 351 |        "      <td>99.5</td>\n",
 352 |        "      <td>...</td>\n",
 353 |        "      <td>131</td>\n",
 354 |        "      <td>mpfi</td>\n",
 355 |        "      <td>3.13</td>\n",
 356 |        "      <td>3.40</td>\n",
 357 |        "      <td>7.0</td>\n",
 358 |        "      <td>160</td>\n",
 359 |        "      <td>5500</td>\n",
 360 |        "      <td>16</td>\n",
 361 |        "      <td>22</td>\n",
 362 |        "      <td>17859.167</td>\n",
 363 |        "    </tr>\n",
 364 |        "  </tbody>\n",
 365 |        "</table>\n",
 366 |        "<p>10 rows × 26 columns</p>\n",
 367 |        "</div>"
 368 |       ],
 369 |       "text/plain": [
 370 |        "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
 371 |        "0       1          3        alfa-romero giulia      gas        std        two   \n",
 372 |        "1       2          3       alfa-romero stelvio      gas        std        two   \n",
 373 |        "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
 374 |        "3       4          2               audi 100 ls      gas        std       four   \n",
 375 |        "4       5          2                audi 100ls      gas        std       four   \n",
 376 |        "5       6          2                  audi fox      gas        std        two   \n",
 377 |        "6       7          1                audi 100ls      gas        std       four   \n",
 378 |        "7       8          1                 audi 5000      gas        std       four   \n",
 379 |        "8       9          1                 audi 4000      gas      turbo       four   \n",
 380 |        "9      10          0       audi 5000s (diesel)      gas      turbo        two   \n",
 381 |        "\n",
 382 |        "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
 383 |        "0  convertible        rwd          front       88.6  ...         130   \n",
 384 |        "1  convertible        rwd          front       88.6  ...         130   \n",
 385 |        "2    hatchback        rwd          front       94.5  ...         152   \n",
 386 |        "3        sedan        fwd          front       99.8  ...         109   \n",
 387 |        "4        sedan        4wd          front       99.4  ...         136   \n",
 388 |        "5        sedan        fwd          front       99.8  ...         136   \n",
 389 |        "6        sedan        fwd          front      105.8  ...         136   \n",
 390 |        "7        wagon        fwd          front      105.8  ...         136   \n",
 391 |        "8        sedan        fwd          front      105.8  ...         131   \n",
 392 |        "9    hatchback        4wd          front       99.5  ...         131   \n",
 393 |        "\n",
 394 |        "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
 395 |        "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
 396 |        "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
 397 |        "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
 398 |        "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
 399 |        "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
 400 |        "5        mpfi       3.19    3.40              8.5        110     5500      19   \n",
 401 |        "6        mpfi       3.19    3.40              8.5        110     5500      19   \n",
 402 |        "7        mpfi       3.19    3.40              8.5        110     5500      19   \n",
 403 |        "8        mpfi       3.13    3.40              8.3        140     5500      17   \n",
 404 |        "9        mpfi       3.13    3.40              7.0        160     5500      16   \n",
 405 |        "\n",
 406 |        "   highwaympg      price  \n",
 407 |        "0          27  13495.000  \n",
 408 |        "1          27  16500.000  \n",
 409 |        "2          26  16500.000  \n",
 410 |        "3          30  13950.000  \n",
 411 |        "4          22  17450.000  \n",
 412 |        "5          25  15250.000  \n",
 413 |        "6          25  17710.000  \n",
 414 |        "7          25  18920.000  \n",
 415 |        "8          20  23875.000  \n",
 416 |        "9          22  17859.167  \n",
 417 |        "\n",
 418 |        "[10 rows x 26 columns]"
 419 |       ]
 420 |      },
 421 |      "execution_count": 3,
 422 |      "metadata": {},
 423 |      "output_type": "execute_result"
 424 |     }
 425 |    ],
 426 |    "source": [
 427 |     "# Print the first 10 rows of the dataset\n",
 428 |     "df.head(10)"
 429 |    ]
 430 |   },
 431 |   {
 432 |    "cell_type": "markdown",
 433 |    "metadata": {},
 434 |    "source": [
 435 |     "This Cars Dataset represents cars characteristics, along with their prices.\n",
 436 |     "\n",
 437 |     "- `car_ID`: Unique id of each observation (Interger)\n",
 438 |     "- `symboling`: Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.(Categorical) \t\t\n",
 439 |     "- `carCompany`: Name of car company (Categorical)\t\t\n",
 440 |     "- `fueltype`: Car fuel type i.e gas or diesel (Categorical)\t\t\n",
 441 |     "- `aspiration`: Aspiration used in a car (Categorical)\t\t\n",
 442 |     "- `doornumber`: Number of doors in a car (Categorical)\t\t\n",
 443 |     "- `carbody`: body of car (Categorical)\t\t\n",
 444 |     "- `drivewheel`: type of drive wheel (Categorical)\t\t\n",
 445 |     "- `enginelocation`: Location of car engine (Categorical)\t\t\n",
 446 |     "- `wheelbase`: Weelbase of car (Numeric)\t\t\n",
 447 |     "- `carlength`: Length of car (Numeric)\t\t\n",
 448 |     "- `carwidth`: Width of car (Numeric)\t\t\n",
 449 |     "- `carheight` Height of car (Numeric)\t\t\n",
 450 |     "- `curbweight` The weight of a car without occupants or baggage. (Numeric)\t\t\n",
 451 |     "- `enginetype`: Type of engine. (Categorical)\t\t\n",
 452 |     "- `cylindernumber`: Cylinder placed in the car (Categorical)\t\t\n",
 453 |     "- `enginesize`: Size of car (Numeric)\t\t\n",
 454 |     "- `fuelsystem`: Fuel system of car (Categorical)\t\t\n",
 455 |     "- `boreratio`: Boreratio of car (Numeric)\t\t\n",
 456 |     "- `stroke`: Stroke or volume inside the engine (Numeric)\t\t\n",
 457 |     "- `compressionratio`: Compression ratio of car (Numeric)\t\t\n",
 458 |     "- `horsepower`: Horsepower (Numeric)\t\t\n",
 459 |     "- `peakrpm`: Car peak rpm (Numeric)\t\t\n",
 460 |     "- `citympg`: Mileage in city (Numeric)\t\t\n",
 461 |     "- `highwaympg`: Mileage on highway (Numeric)\t\t\n",
 462 |     "- `price` (Dependent variable): Price of car in \\$ (Numeric)\n",
 463 |     "\n",
 464 |     "The main aim is to build **a model that predicts a price of a car** (`price` column) based on independent variables."
 465 |    ]
 466 |   },
 467 |   {
 468 |    "cell_type": "markdown",
 469 |    "metadata": {},
 470 |    "source": [
 471 |     "## 3. Variables"
 472 |    ]
 473 |   },
 474 |   {
 475 |    "cell_type": "markdown",
 476 |    "metadata": {},
 477 |    "source": [
 478 |     "Let's split the dataset into X and y, where \n",
 479 |     "- $X$ is a set of independent variables\n",
 480 |     "- $y$ is a dependent, or target variable (the last column)\n",
 481 |     "\n",
 482 |     "To make things simpler, let's pick 3 independent variables."
 483 |    ]
 484 |   },
 485 |   {
 486 |    "cell_type": "code",
 487 |    "execution_count": 5,
 488 |    "metadata": {},
 489 |    "outputs": [
 490 |     {
 491 |      "data": {
 492 |       "text/html": [
 493 |        "<div>\n",
 494 |        "<style scoped>\n",
 495 |        "    .dataframe tbody tr th:only-of-type {\n",
 496 |        "        vertical-align: middle;\n",
 497 |        "    }\n",
 498 |        "\n",
 499 |        "    .dataframe tbody tr th {\n",
 500 |        "        vertical-align: top;\n",
 501 |        "    }\n",
 502 |        "\n",
 503 |        "    .dataframe thead th {\n",
 504 |        "        text-align: right;\n",
 505 |        "    }\n",
 506 |        "</style>\n",
 507 |        "<table border=\"1\" class=\"dataframe\">\n",
 508 |        "  <thead>\n",
 509 |        "    <tr style=\"text-align: right;\">\n",
 510 |        "      <th></th>\n",
 511 |        "      <th>symboling</th>\n",
 512 |        "      <th>enginesize</th>\n",
 513 |        "      <th>horsepower</th>\n",
 514 |        "    </tr>\n",
 515 |        "  </thead>\n",
 516 |        "  <tbody>\n",
 517 |        "    <tr>\n",
 518 |        "      <th>0</th>\n",
 519 |        "      <td>3</td>\n",
 520 |        "      <td>130</td>\n",
 521 |        "      <td>111</td>\n",
 522 |        "    </tr>\n",
 523 |        "    <tr>\n",
 524 |        "      <th>1</th>\n",
 525 |        "      <td>3</td>\n",
 526 |        "      <td>130</td>\n",
 527 |        "      <td>111</td>\n",
 528 |        "    </tr>\n",
 529 |        "    <tr>\n",
 530 |        "      <th>2</th>\n",
 531 |        "      <td>1</td>\n",
 532 |        "      <td>152</td>\n",
 533 |        "      <td>154</td>\n",
 534 |        "    </tr>\n",
 535 |        "    <tr>\n",
 536 |        "      <th>3</th>\n",
 537 |        "      <td>2</td>\n",
 538 |        "      <td>109</td>\n",
 539 |        "      <td>102</td>\n",
 540 |        "    </tr>\n",
 541 |        "    <tr>\n",
 542 |        "      <th>4</th>\n",
 543 |        "      <td>2</td>\n",
 544 |        "      <td>136</td>\n",
 545 |        "      <td>115</td>\n",
 546 |        "    </tr>\n",
 547 |        "    <tr>\n",
 548 |        "      <th>...</th>\n",
 549 |        "      <td>...</td>\n",
 550 |        "      <td>...</td>\n",
 551 |        "      <td>...</td>\n",
 552 |        "    </tr>\n",
 553 |        "    <tr>\n",
 554 |        "      <th>200</th>\n",
 555 |        "      <td>-1</td>\n",
 556 |        "      <td>141</td>\n",
 557 |        "      <td>114</td>\n",
 558 |        "    </tr>\n",
 559 |        "    <tr>\n",
 560 |        "      <th>201</th>\n",
 561 |        "      <td>-1</td>\n",
 562 |        "      <td>141</td>\n",
 563 |        "      <td>160</td>\n",
 564 |        "    </tr>\n",
 565 |        "    <tr>\n",
 566 |        "      <th>202</th>\n",
 567 |        "      <td>-1</td>\n",
 568 |        "      <td>173</td>\n",
 569 |        "      <td>134</td>\n",
 570 |        "    </tr>\n",
 571 |        "    <tr>\n",
 572 |        "      <th>203</th>\n",
 573 |        "      <td>-1</td>\n",
 574 |        "      <td>145</td>\n",
 575 |        "      <td>106</td>\n",
 576 |        "    </tr>\n",
 577 |        "    <tr>\n",
 578 |        "      <th>204</th>\n",
 579 |        "      <td>-1</td>\n",
 580 |        "      <td>141</td>\n",
 581 |        "      <td>114</td>\n",
 582 |        "    </tr>\n",
 583 |        "  </tbody>\n",
 584 |        "</table>\n",
 585 |        "<p>205 rows × 3 columns</p>\n",
 586 |        "</div>"
 587 |       ],
 588 |       "text/plain": [
 589 |        "     symboling  enginesize  horsepower\n",
 590 |        "0            3         130         111\n",
 591 |        "1            3         130         111\n",
 592 |        "2            1         152         154\n",
 593 |        "3            2         109         102\n",
 594 |        "4            2         136         115\n",
 595 |        "..         ...         ...         ...\n",
 596 |        "200         -1         141         114\n",
 597 |        "201         -1         141         160\n",
 598 |        "202         -1         173         134\n",
 599 |        "203         -1         145         106\n",
 600 |        "204         -1         141         114\n",
 601 |        "\n",
 602 |        "[205 rows x 3 columns]"
 603 |       ]
 604 |      },
 605 |      "execution_count": 5,
 606 |      "metadata": {},
 607 |      "output_type": "execute_result"
 608 |     }
 609 |    ],
 610 |    "source": [
 611 |     "# Filter out target column\n",
 612 |     "X = df[['symboling', 'enginesize', 'horsepower']]\n",
 613 |     "\n",
 614 |     "# Print X\n",
 615 |     "X"
 616 |    ]
 617 |   },
 618 |   {
 619 |    "cell_type": "code",
 620 |    "execution_count": 6,
 621 |    "metadata": {},
 622 |    "outputs": [
 623 |     {
 624 |      "data": {
 625 |       "text/plain": [
 626 |        "0      13495.0\n",
 627 |        "1      16500.0\n",
 628 |        "2      16500.0\n",
 629 |        "3      13950.0\n",
 630 |        "4      17450.0\n",
 631 |        "        ...   \n",
 632 |        "200    16845.0\n",
 633 |        "201    19045.0\n",
 634 |        "202    21485.0\n",
 635 |        "203    22470.0\n",
 636 |        "204    22625.0\n",
 637 |        "Name: price, Length: 205, dtype: float64"
 638 |       ]
 639 |      },
 640 |      "execution_count": 6,
 641 |      "metadata": {},
 642 |      "output_type": "execute_result"
 643 |     }
 644 |    ],
 645 |    "source": [
 646 |     "# Select target column\n",
 647 |     "y = df['price']\n",
 648 |     "\n",
 649 |     "# Print y\n",
 650 |     "y"
 651 |    ]
 652 |   },
 653 |   {
 654 |    "cell_type": "markdown",
 655 |    "metadata": {},
 656 |    "source": [
 657 |     "## 4. Model"
 658 |    ]
 659 |   },
 660 |   {
 661 |    "cell_type": "markdown",
 662 |    "metadata": {},
 663 |    "source": [
 664 |     "In this section we are going to \n",
 665 |     "- build a lasso regression model, \n",
 666 |     "- evaluate its accuracy, and \n",
 667 |     "- make a prediction"
 668 |    ]
 669 |   },
 670 |   {
 671 |    "cell_type": "markdown",
 672 |    "metadata": {},
 673 |    "source": [
 674 |     "### 4.1. Building the model"
 675 |    ]
 676 |   },
 677 |   {
 678 |    "cell_type": "markdown",
 679 |    "metadata": {},
 680 |    "source": [
 681 |     "Let's recall three simple steps:\n",
 682 |     "\n",
 683 |     "- Split the $X$ & $y$ variables into train and test sets\n",
 684 |     "- Initialize the model\n",
 685 |     "- Train the model with the varialbes"
 686 |    ]
 687 |   },
 688 |   {
 689 |    "cell_type": "code",
 690 |    "execution_count": 10,
 691 |    "metadata": {},
 692 |    "outputs": [],
 693 |    "source": [
 694 |     "# Split variables into train and test\n",
 695 |     "X_train, X_test, y_train, y_test = train_test_split(X, #independent variables\n",
 696 |     "                                                    y, #dependent variable\n",
 697 |     "                                                    random_state = 3\n",
 698 |     "                                                   )"
 699 |    ]
 700 |   },
 701 |   {
 702 |    "cell_type": "code",
 703 |    "execution_count": 11,
 704 |    "metadata": {},
 705 |    "outputs": [],
 706 |    "source": [
 707 |     "# Initialize the model\n",
 708 |     "reg = Lasso(alpha=2.0, max_iter = 10000)"
 709 |    ]
 710 |   },
 711 |   {
 712 |    "cell_type": "code",
 713 |    "execution_count": 12,
 714 |    "metadata": {},
 715 |    "outputs": [
 716 |     {
 717 |      "data": {
 718 |       "text/html": [
 719 |        "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=2.0, max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=2.0, max_iter=10000)</pre></div></div></div></div></div>"
 720 |       ],
 721 |       "text/plain": [
 722 |        "Lasso(alpha=2.0, max_iter=10000)"
 723 |       ]
 724 |      },
 725 |      "execution_count": 12,
 726 |      "metadata": {},
 727 |      "output_type": "execute_result"
 728 |     }
 729 |    ],
 730 |    "source": [
 731 |     "# Train the model\n",
 732 |     "reg.fit(X_train, y_train)"
 733 |    ]
 734 |   },
 735 |   {
 736 |    "cell_type": "markdown",
 737 |    "metadata": {},
 738 |    "source": [
 739 |     "### 4.2. Checking models accuracy"
 740 |    ]
 741 |   },
 742 |   {
 743 |    "cell_type": "markdown",
 744 |    "metadata": {},
 745 |    "source": [
 746 |     "After the model has trained with the data, it's essential to understand how precisely it predicts car prices. For that, we need to check model's accuracy. "
 747 |    ]
 748 |   },
 749 |   {
 750 |    "cell_type": "code",
 751 |    "execution_count": 13,
 752 |    "metadata": {},
 753 |    "outputs": [
 754 |     {
 755 |      "name": "stdout",
 756 |      "output_type": "stream",
 757 |      "text": [
 758 |       "Accuracy (R-squared score) of Lasso Regression model on training set: 0.78\n",
 759 |       "Accuracy (R-squared score) of Lasso Regression model on test set: 0.82\n"
 760 |      ]
 761 |     }
 762 |    ],
 763 |    "source": [
 764 |     "print(f'Accuracy (R-squared score) of Lasso Regression model on training set: {reg.score(X_train, y_train):.2f}')\n",
 765 |     "print(f'Accuracy (R-squared score) of Lasso Regression model on test set: {reg.score(X_test, y_test):.2f}')"
 766 |    ]
 767 |   },
 768 |   {
 769 |    "cell_type": "markdown",
 770 |    "metadata": {},
 771 |    "source": [
 772 |     "### 4.3. Making a prediction"
 773 |    ]
 774 |   },
 775 |   {
 776 |    "cell_type": "markdown",
 777 |    "metadata": {},
 778 |    "source": [
 779 |     "Now that we know the model is accurate enough, we can predict the price of any car by passing independent varialbes `symboling`, `enginesize`, `horsepower` to the model. \n",
 780 |     "\n",
 781 |     "\n",
 782 |     "The method `predict` returns such a prediction."
 783 |    ]
 784 |   },
 785 |   {
 786 |    "cell_type": "code",
 787 |    "execution_count": 17,
 788 |    "metadata": {},
 789 |    "outputs": [
 790 |     {
 791 |      "name": "stderr",
 792 |      "output_type": "stream",
 793 |      "text": [
 794 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but Lasso was fitted with feature names\n",
 795 |       "  warnings.warn(\n"
 796 |      ]
 797 |     },
 798 |     {
 799 |      "data": {
 800 |       "text/plain": [
 801 |        "array([29358.44608125])"
 802 |       ]
 803 |      },
 804 |      "execution_count": 17,
 805 |      "metadata": {},
 806 |      "output_type": "execute_result"
 807 |     }
 808 |    ],
 809 |    "source": [
 810 |     "reg.predict([[0, 180, 249]])"
 811 |    ]
 812 |   },
 813 |   {
 814 |    "cell_type": "markdown",
 815 |    "metadata": {},
 816 |    "source": [
 817 |     "### 4.4. Understanding Features"
 818 |    ]
 819 |   },
 820 |   {
 821 |    "cell_type": "markdown",
 822 |    "metadata": {},
 823 |    "source": [
 824 |     "We can use Lasso as a tool to check the prediction power of a feature. "
 825 |    ]
 826 |   },
 827 |   {
 828 |    "cell_type": "code",
 829 |    "execution_count": 18,
 830 |    "metadata": {},
 831 |    "outputs": [
 832 |     {
 833 |      "name": "stdout",
 834 |      "output_type": "stream",
 835 |      "text": [
 836 |       "Features with non-zero weight, sorted by absolute magnitude:\n",
 837 |       "\tsymboling, -492.567\n",
 838 |       "\tenginesize, 110.565\n",
 839 |       "\thorsepower, 68.667\n"
 840 |      ]
 841 |     }
 842 |    ],
 843 |    "source": [
 844 |     "print('Features with non-zero weight, sorted by absolute magnitude:')\n",
 845 |     "\n",
 846 |     "for e in sorted (list(zip(list(X), reg.coef_)),\n",
 847 |     "                key = lambda e: -abs(e[1])):\n",
 848 |     "    if e[1] != 0:\n",
 849 |     "        print('\\t{}, {:.3f}'.format(e[0], e[1]))"
 850 |    ]
 851 |   },
 852 |   {
 853 |    "cell_type": "markdown",
 854 |    "metadata": {},
 855 |    "source": [
 856 |     "## 5. Hyperparameters Tuning"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "markdown",
 861 |    "metadata": {},
 862 |    "source": [
 863 |     "Hyperparameter Tuning is one of the critical steps in model building. It helps in increasing model's accuracy even further."
 864 |    ]
 865 |   },
 866 |   {
 867 |    "cell_type": "markdown",
 868 |    "metadata": {},
 869 |    "source": [
 870 |     "### 5.1. Playing around with regularization parameter alpha"
 871 |    ]
 872 |   },
 873 |   {
 874 |    "cell_type": "code",
 875 |    "execution_count": 34,
 876 |    "metadata": {},
 877 |    "outputs": [
 878 |     {
 879 |      "name": "stdout",
 880 |      "output_type": "stream",
 881 |      "text": [
 882 |       "Lasso regression: effect of alpha regularization parameter:\n",
 883 |       "\n",
 884 |       "Alpha = 0.00\n",
 885 |       "              Features kept: 3, \n",
 886 |       "              r-squared training: 0.78,\n",
 887 |       "              r-squared test: 0.82 \n",
 888 |       "\n",
 889 |       "Lasso regression: effect of alpha regularization parameter:\n",
 890 |       "\n",
 891 |       "Alpha = 1.00\n",
 892 |       "              Features kept: 3, \n",
 893 |       "              r-squared training: 0.78,\n",
 894 |       "              r-squared test: 0.82 \n",
 895 |       "\n",
 896 |       "Lasso regression: effect of alpha regularization parameter:\n",
 897 |       "\n",
 898 |       "Alpha = 10.00\n",
 899 |       "              Features kept: 3, \n",
 900 |       "              r-squared training: 0.78,\n",
 901 |       "              r-squared test: 0.82 \n",
 902 |       "\n",
 903 |       "Lasso regression: effect of alpha regularization parameter:\n",
 904 |       "\n",
 905 |       "Alpha = 20.00\n",
 906 |       "              Features kept: 3, \n",
 907 |       "              r-squared training: 0.78,\n",
 908 |       "              r-squared test: 0.82 \n",
 909 |       "\n",
 910 |       "Lasso regression: effect of alpha regularization parameter:\n",
 911 |       "\n",
 912 |       "Alpha = 50.00\n",
 913 |       "              Features kept: 3, \n",
 914 |       "              r-squared training: 0.78,\n",
 915 |       "              r-squared test: 0.82 \n",
 916 |       "\n",
 917 |       "Lasso regression: effect of alpha regularization parameter:\n",
 918 |       "\n",
 919 |       "Alpha = 100.00\n",
 920 |       "              Features kept: 3, \n",
 921 |       "              r-squared training: 0.78,\n",
 922 |       "              r-squared test: 0.82 \n",
 923 |       "\n"
 924 |      ]
 925 |     },
 926 |     {
 927 |      "name": "stderr",
 928 |      "output_type": "stream",
 929 |      "text": [
 930 |       "/var/folders/5y/7zvhsc3x5nx162713kvx9c1m0000gn/T/ipykernel_44918/1770655528.py:7: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
 931 |       "  reg.fit(X_train, y_train)\n",
 932 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:634: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
 933 |       "  model = cd_fast.enet_coordinate_descent(\n",
 934 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:634: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.002e+09, tolerance: 9.162e+05 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n",
 935 |       "  model = cd_fast.enet_coordinate_descent(\n"
 936 |      ]
 937 |     }
 938 |    ],
 939 |    "source": [
 940 |     "# Lasso regression: effect of alpha regularization parameter:\n",
 941 |     "\n",
 942 |     "for this_alpha in [0, 1, 10, 20, 50, 100]:\n",
 943 |     "    \n",
 944 |     "    # Initialize the model\n",
 945 |     "    reg = Lasso(alpha=this_alpha, max_iter = 10000)\n",
 946 |     "    \n",
 947 |     "    # Train the model\n",
 948 |     "    reg.fit(X_train, y_train)\n",
 949 |     "    \n",
 950 |     "    #Save accuracy\n",
 951 |     "    r2_train = reg.score(X_train, y_train)\n",
 952 |     "    r2_test = reg.score(X_test, y_test)\n",
 953 |     "    \n",
 954 |     "    # Print accuracy\n",
 955 |     "    print(f'''Alpha = {this_alpha:.2f}\n",
 956 |     "              Features kept: {len(reg.coef_ != 0)}, \n",
 957 |     "              r-squared training: {r2_train:.2f},\n",
 958 |     "              r-squared test: {r2_test:.2f} \\n''')"
 959 |    ]
 960 |   },
 961 |   {
 962 |    "cell_type": "markdown",
 963 |    "metadata": {},
 964 |    "source": [
 965 |     "### 5.2. All Hyperparameters of Lasso Regression"
 966 |    ]
 967 |   },
 968 |   {
 969 |    "cell_type": "markdown",
 970 |    "metadata": {},
 971 |    "source": [
 972 |     "There are many hyperparameters in Lasso Regression. They are all presented below  - as you can see, they have values by default. Feel free to play around with them. To learn more about hyperparameters and their meaning, visit https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso."
 973 |    ]
 974 |   },
 975 |   {
 976 |    "cell_type": "code",
 977 |    "execution_count": 33,
 978 |    "metadata": {},
 979 |    "outputs": [],
 980 |    "source": [
 981 |     "reg = Lasso(alpha=1.0, \n",
 982 |     "            fit_intercept=True, \n",
 983 |     "            precompute=False, \n",
 984 |     "            copy_X=True, \n",
 985 |     "            max_iter=1000, \n",
 986 |     "            tol=0.0001, \n",
 987 |     "            warm_start=False, \n",
 988 |     "            positive=False, \n",
 989 |     "            random_state=None, \n",
 990 |     "            selection='cyclic')"
 991 |    ]
 992 |   }
 993 |  ],
 994 |  "metadata": {
 995 |   "kernelspec": {
 996 |    "display_name": "Python 3 (ipykernel)",
 997 |    "language": "python",
 998 |    "name": "python3"
 999 |   },
1000 |   "language_info": {
1001 |    "codemirror_mode": {
1002 |     "name": "ipython",
1003 |     "version": 3
1004 |    },
1005 |    "file_extension": ".py",
1006 |    "mimetype": "text/x-python",
1007 |    "name": "python",
1008 |    "nbconvert_exporter": "python",
1009 |    "pygments_lexer": "ipython3",
1010 |    "version": "3.10.7"
1011 |   }
1012 |  },
1013 |  "nbformat": 4,
1014 |  "nbformat_minor": 2
1015 | }
1016 | 


--------------------------------------------------------------------------------
/regression/neural_networks_regressor.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Neural Networks Regression"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "- This is a supplement material for my lectures on ML. It sheds light on Python implementations of the supervised machine learning algorithms. \n",
  15 |     "- I assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n",
  16 |     "- This material is represented as a Jupyter notebook, which you can easily download to reproduce the code and play around with it. "
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "markdown",
  21 |    "metadata": {},
  22 |    "source": [
  23 |     "## 1. Libraries"
  24 |    ]
  25 |   },
  26 |   {
  27 |    "cell_type": "markdown",
  28 |    "metadata": {},
  29 |    "source": [
  30 |     "To build a model, we need \n",
  31 |     "- `pandas` library to work with panel dataframes\n",
  32 |     "- `sklearn.model_selection.train_test_split` to split the dataset into train and test\n",
  33 |     "- `sklearn.neural_network.MLPRegressor` to build a model\n",
  34 |     "\n",
  35 |     "<!-- In addition, I'd like to show you `sklearn.preprocessing.StandardScaler` - a library for data normalization. -->"
  36 |    ]
  37 |   },
  38 |   {
  39 |    "cell_type": "code",
  40 |    "execution_count": 1,
  41 |    "metadata": {},
  42 |    "outputs": [],
  43 |    "source": [
  44 |     "import pandas as pd\n",
  45 |     "from sklearn.neural_network import MLPRegressor\n",
  46 |     "from sklearn.model_selection import train_test_split"
  47 |    ]
  48 |   },
  49 |   {
  50 |    "cell_type": "markdown",
  51 |    "metadata": {},
  52 |    "source": [
  53 |     "## 2. Data Load & Overview"
  54 |    ]
  55 |   },
  56 |   {
  57 |    "cell_type": "markdown",
  58 |    "metadata": {},
  59 |    "source": [
  60 |     "Let's load the dataset and understand it."
  61 |    ]
  62 |   },
  63 |   {
  64 |    "cell_type": "code",
  65 |    "execution_count": 2,
  66 |    "metadata": {},
  67 |    "outputs": [],
  68 |    "source": [
  69 |     "# Load the dataset\n",
  70 |     "df = pd.read_csv('https://raw.githubusercontent.com/5x12/ml-cookbook/master/supplements/data/cars.csv')"
  71 |    ]
  72 |   },
  73 |   {
  74 |    "cell_type": "code",
  75 |    "execution_count": 3,
  76 |    "metadata": {},
  77 |    "outputs": [
  78 |     {
  79 |      "data": {
  80 |       "text/html": [
  81 |        "<div>\n",
  82 |        "<style scoped>\n",
  83 |        "    .dataframe tbody tr th:only-of-type {\n",
  84 |        "        vertical-align: middle;\n",
  85 |        "    }\n",
  86 |        "\n",
  87 |        "    .dataframe tbody tr th {\n",
  88 |        "        vertical-align: top;\n",
  89 |        "    }\n",
  90 |        "\n",
  91 |        "    .dataframe thead th {\n",
  92 |        "        text-align: right;\n",
  93 |        "    }\n",
  94 |        "</style>\n",
  95 |        "<table border=\"1\" class=\"dataframe\">\n",
  96 |        "  <thead>\n",
  97 |        "    <tr style=\"text-align: right;\">\n",
  98 |        "      <th></th>\n",
  99 |        "      <th>car_ID</th>\n",
 100 |        "      <th>symboling</th>\n",
 101 |        "      <th>CarName</th>\n",
 102 |        "      <th>fueltype</th>\n",
 103 |        "      <th>aspiration</th>\n",
 104 |        "      <th>doornumber</th>\n",
 105 |        "      <th>carbody</th>\n",
 106 |        "      <th>drivewheel</th>\n",
 107 |        "      <th>enginelocation</th>\n",
 108 |        "      <th>wheelbase</th>\n",
 109 |        "      <th>...</th>\n",
 110 |        "      <th>enginesize</th>\n",
 111 |        "      <th>fuelsystem</th>\n",
 112 |        "      <th>boreratio</th>\n",
 113 |        "      <th>stroke</th>\n",
 114 |        "      <th>compressionratio</th>\n",
 115 |        "      <th>horsepower</th>\n",
 116 |        "      <th>peakrpm</th>\n",
 117 |        "      <th>citympg</th>\n",
 118 |        "      <th>highwaympg</th>\n",
 119 |        "      <th>price</th>\n",
 120 |        "    </tr>\n",
 121 |        "  </thead>\n",
 122 |        "  <tbody>\n",
 123 |        "    <tr>\n",
 124 |        "      <th>0</th>\n",
 125 |        "      <td>1</td>\n",
 126 |        "      <td>3</td>\n",
 127 |        "      <td>alfa-romero giulia</td>\n",
 128 |        "      <td>gas</td>\n",
 129 |        "      <td>std</td>\n",
 130 |        "      <td>two</td>\n",
 131 |        "      <td>convertible</td>\n",
 132 |        "      <td>rwd</td>\n",
 133 |        "      <td>front</td>\n",
 134 |        "      <td>88.6</td>\n",
 135 |        "      <td>...</td>\n",
 136 |        "      <td>130</td>\n",
 137 |        "      <td>mpfi</td>\n",
 138 |        "      <td>3.47</td>\n",
 139 |        "      <td>2.68</td>\n",
 140 |        "      <td>9.0</td>\n",
 141 |        "      <td>111</td>\n",
 142 |        "      <td>5000</td>\n",
 143 |        "      <td>21</td>\n",
 144 |        "      <td>27</td>\n",
 145 |        "      <td>13495.000</td>\n",
 146 |        "    </tr>\n",
 147 |        "    <tr>\n",
 148 |        "      <th>1</th>\n",
 149 |        "      <td>2</td>\n",
 150 |        "      <td>3</td>\n",
 151 |        "      <td>alfa-romero stelvio</td>\n",
 152 |        "      <td>gas</td>\n",
 153 |        "      <td>std</td>\n",
 154 |        "      <td>two</td>\n",
 155 |        "      <td>convertible</td>\n",
 156 |        "      <td>rwd</td>\n",
 157 |        "      <td>front</td>\n",
 158 |        "      <td>88.6</td>\n",
 159 |        "      <td>...</td>\n",
 160 |        "      <td>130</td>\n",
 161 |        "      <td>mpfi</td>\n",
 162 |        "      <td>3.47</td>\n",
 163 |        "      <td>2.68</td>\n",
 164 |        "      <td>9.0</td>\n",
 165 |        "      <td>111</td>\n",
 166 |        "      <td>5000</td>\n",
 167 |        "      <td>21</td>\n",
 168 |        "      <td>27</td>\n",
 169 |        "      <td>16500.000</td>\n",
 170 |        "    </tr>\n",
 171 |        "    <tr>\n",
 172 |        "      <th>2</th>\n",
 173 |        "      <td>3</td>\n",
 174 |        "      <td>1</td>\n",
 175 |        "      <td>alfa-romero Quadrifoglio</td>\n",
 176 |        "      <td>gas</td>\n",
 177 |        "      <td>std</td>\n",
 178 |        "      <td>two</td>\n",
 179 |        "      <td>hatchback</td>\n",
 180 |        "      <td>rwd</td>\n",
 181 |        "      <td>front</td>\n",
 182 |        "      <td>94.5</td>\n",
 183 |        "      <td>...</td>\n",
 184 |        "      <td>152</td>\n",
 185 |        "      <td>mpfi</td>\n",
 186 |        "      <td>2.68</td>\n",
 187 |        "      <td>3.47</td>\n",
 188 |        "      <td>9.0</td>\n",
 189 |        "      <td>154</td>\n",
 190 |        "      <td>5000</td>\n",
 191 |        "      <td>19</td>\n",
 192 |        "      <td>26</td>\n",
 193 |        "      <td>16500.000</td>\n",
 194 |        "    </tr>\n",
 195 |        "    <tr>\n",
 196 |        "      <th>3</th>\n",
 197 |        "      <td>4</td>\n",
 198 |        "      <td>2</td>\n",
 199 |        "      <td>audi 100 ls</td>\n",
 200 |        "      <td>gas</td>\n",
 201 |        "      <td>std</td>\n",
 202 |        "      <td>four</td>\n",
 203 |        "      <td>sedan</td>\n",
 204 |        "      <td>fwd</td>\n",
 205 |        "      <td>front</td>\n",
 206 |        "      <td>99.8</td>\n",
 207 |        "      <td>...</td>\n",
 208 |        "      <td>109</td>\n",
 209 |        "      <td>mpfi</td>\n",
 210 |        "      <td>3.19</td>\n",
 211 |        "      <td>3.40</td>\n",
 212 |        "      <td>10.0</td>\n",
 213 |        "      <td>102</td>\n",
 214 |        "      <td>5500</td>\n",
 215 |        "      <td>24</td>\n",
 216 |        "      <td>30</td>\n",
 217 |        "      <td>13950.000</td>\n",
 218 |        "    </tr>\n",
 219 |        "    <tr>\n",
 220 |        "      <th>4</th>\n",
 221 |        "      <td>5</td>\n",
 222 |        "      <td>2</td>\n",
 223 |        "      <td>audi 100ls</td>\n",
 224 |        "      <td>gas</td>\n",
 225 |        "      <td>std</td>\n",
 226 |        "      <td>four</td>\n",
 227 |        "      <td>sedan</td>\n",
 228 |        "      <td>4wd</td>\n",
 229 |        "      <td>front</td>\n",
 230 |        "      <td>99.4</td>\n",
 231 |        "      <td>...</td>\n",
 232 |        "      <td>136</td>\n",
 233 |        "      <td>mpfi</td>\n",
 234 |        "      <td>3.19</td>\n",
 235 |        "      <td>3.40</td>\n",
 236 |        "      <td>8.0</td>\n",
 237 |        "      <td>115</td>\n",
 238 |        "      <td>5500</td>\n",
 239 |        "      <td>18</td>\n",
 240 |        "      <td>22</td>\n",
 241 |        "      <td>17450.000</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>5</th>\n",
 245 |        "      <td>6</td>\n",
 246 |        "      <td>2</td>\n",
 247 |        "      <td>audi fox</td>\n",
 248 |        "      <td>gas</td>\n",
 249 |        "      <td>std</td>\n",
 250 |        "      <td>two</td>\n",
 251 |        "      <td>sedan</td>\n",
 252 |        "      <td>fwd</td>\n",
 253 |        "      <td>front</td>\n",
 254 |        "      <td>99.8</td>\n",
 255 |        "      <td>...</td>\n",
 256 |        "      <td>136</td>\n",
 257 |        "      <td>mpfi</td>\n",
 258 |        "      <td>3.19</td>\n",
 259 |        "      <td>3.40</td>\n",
 260 |        "      <td>8.5</td>\n",
 261 |        "      <td>110</td>\n",
 262 |        "      <td>5500</td>\n",
 263 |        "      <td>19</td>\n",
 264 |        "      <td>25</td>\n",
 265 |        "      <td>15250.000</td>\n",
 266 |        "    </tr>\n",
 267 |        "    <tr>\n",
 268 |        "      <th>6</th>\n",
 269 |        "      <td>7</td>\n",
 270 |        "      <td>1</td>\n",
 271 |        "      <td>audi 100ls</td>\n",
 272 |        "      <td>gas</td>\n",
 273 |        "      <td>std</td>\n",
 274 |        "      <td>four</td>\n",
 275 |        "      <td>sedan</td>\n",
 276 |        "      <td>fwd</td>\n",
 277 |        "      <td>front</td>\n",
 278 |        "      <td>105.8</td>\n",
 279 |        "      <td>...</td>\n",
 280 |        "      <td>136</td>\n",
 281 |        "      <td>mpfi</td>\n",
 282 |        "      <td>3.19</td>\n",
 283 |        "      <td>3.40</td>\n",
 284 |        "      <td>8.5</td>\n",
 285 |        "      <td>110</td>\n",
 286 |        "      <td>5500</td>\n",
 287 |        "      <td>19</td>\n",
 288 |        "      <td>25</td>\n",
 289 |        "      <td>17710.000</td>\n",
 290 |        "    </tr>\n",
 291 |        "    <tr>\n",
 292 |        "      <th>7</th>\n",
 293 |        "      <td>8</td>\n",
 294 |        "      <td>1</td>\n",
 295 |        "      <td>audi 5000</td>\n",
 296 |        "      <td>gas</td>\n",
 297 |        "      <td>std</td>\n",
 298 |        "      <td>four</td>\n",
 299 |        "      <td>wagon</td>\n",
 300 |        "      <td>fwd</td>\n",
 301 |        "      <td>front</td>\n",
 302 |        "      <td>105.8</td>\n",
 303 |        "      <td>...</td>\n",
 304 |        "      <td>136</td>\n",
 305 |        "      <td>mpfi</td>\n",
 306 |        "      <td>3.19</td>\n",
 307 |        "      <td>3.40</td>\n",
 308 |        "      <td>8.5</td>\n",
 309 |        "      <td>110</td>\n",
 310 |        "      <td>5500</td>\n",
 311 |        "      <td>19</td>\n",
 312 |        "      <td>25</td>\n",
 313 |        "      <td>18920.000</td>\n",
 314 |        "    </tr>\n",
 315 |        "    <tr>\n",
 316 |        "      <th>8</th>\n",
 317 |        "      <td>9</td>\n",
 318 |        "      <td>1</td>\n",
 319 |        "      <td>audi 4000</td>\n",
 320 |        "      <td>gas</td>\n",
 321 |        "      <td>turbo</td>\n",
 322 |        "      <td>four</td>\n",
 323 |        "      <td>sedan</td>\n",
 324 |        "      <td>fwd</td>\n",
 325 |        "      <td>front</td>\n",
 326 |        "      <td>105.8</td>\n",
 327 |        "      <td>...</td>\n",
 328 |        "      <td>131</td>\n",
 329 |        "      <td>mpfi</td>\n",
 330 |        "      <td>3.13</td>\n",
 331 |        "      <td>3.40</td>\n",
 332 |        "      <td>8.3</td>\n",
 333 |        "      <td>140</td>\n",
 334 |        "      <td>5500</td>\n",
 335 |        "      <td>17</td>\n",
 336 |        "      <td>20</td>\n",
 337 |        "      <td>23875.000</td>\n",
 338 |        "    </tr>\n",
 339 |        "    <tr>\n",
 340 |        "      <th>9</th>\n",
 341 |        "      <td>10</td>\n",
 342 |        "      <td>0</td>\n",
 343 |        "      <td>audi 5000s (diesel)</td>\n",
 344 |        "      <td>gas</td>\n",
 345 |        "      <td>turbo</td>\n",
 346 |        "      <td>two</td>\n",
 347 |        "      <td>hatchback</td>\n",
 348 |        "      <td>4wd</td>\n",
 349 |        "      <td>front</td>\n",
 350 |        "      <td>99.5</td>\n",
 351 |        "      <td>...</td>\n",
 352 |        "      <td>131</td>\n",
 353 |        "      <td>mpfi</td>\n",
 354 |        "      <td>3.13</td>\n",
 355 |        "      <td>3.40</td>\n",
 356 |        "      <td>7.0</td>\n",
 357 |        "      <td>160</td>\n",
 358 |        "      <td>5500</td>\n",
 359 |        "      <td>16</td>\n",
 360 |        "      <td>22</td>\n",
 361 |        "      <td>17859.167</td>\n",
 362 |        "    </tr>\n",
 363 |        "  </tbody>\n",
 364 |        "</table>\n",
 365 |        "<p>10 rows × 26 columns</p>\n",
 366 |        "</div>"
 367 |       ],
 368 |       "text/plain": [
 369 |        "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
 370 |        "0       1          3        alfa-romero giulia      gas        std        two   \n",
 371 |        "1       2          3       alfa-romero stelvio      gas        std        two   \n",
 372 |        "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
 373 |        "3       4          2               audi 100 ls      gas        std       four   \n",
 374 |        "4       5          2                audi 100ls      gas        std       four   \n",
 375 |        "5       6          2                  audi fox      gas        std        two   \n",
 376 |        "6       7          1                audi 100ls      gas        std       four   \n",
 377 |        "7       8          1                 audi 5000      gas        std       four   \n",
 378 |        "8       9          1                 audi 4000      gas      turbo       four   \n",
 379 |        "9      10          0       audi 5000s (diesel)      gas      turbo        two   \n",
 380 |        "\n",
 381 |        "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
 382 |        "0  convertible        rwd          front       88.6  ...         130   \n",
 383 |        "1  convertible        rwd          front       88.6  ...         130   \n",
 384 |        "2    hatchback        rwd          front       94.5  ...         152   \n",
 385 |        "3        sedan        fwd          front       99.8  ...         109   \n",
 386 |        "4        sedan        4wd          front       99.4  ...         136   \n",
 387 |        "5        sedan        fwd          front       99.8  ...         136   \n",
 388 |        "6        sedan        fwd          front      105.8  ...         136   \n",
 389 |        "7        wagon        fwd          front      105.8  ...         136   \n",
 390 |        "8        sedan        fwd          front      105.8  ...         131   \n",
 391 |        "9    hatchback        4wd          front       99.5  ...         131   \n",
 392 |        "\n",
 393 |        "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
 394 |        "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
 395 |        "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
 396 |        "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
 397 |        "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
 398 |        "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
 399 |        "5        mpfi       3.19    3.40              8.5        110     5500      19   \n",
 400 |        "6        mpfi       3.19    3.40              8.5        110     5500      19   \n",
 401 |        "7        mpfi       3.19    3.40              8.5        110     5500      19   \n",
 402 |        "8        mpfi       3.13    3.40              8.3        140     5500      17   \n",
 403 |        "9        mpfi       3.13    3.40              7.0        160     5500      16   \n",
 404 |        "\n",
 405 |        "   highwaympg      price  \n",
 406 |        "0          27  13495.000  \n",
 407 |        "1          27  16500.000  \n",
 408 |        "2          26  16500.000  \n",
 409 |        "3          30  13950.000  \n",
 410 |        "4          22  17450.000  \n",
 411 |        "5          25  15250.000  \n",
 412 |        "6          25  17710.000  \n",
 413 |        "7          25  18920.000  \n",
 414 |        "8          20  23875.000  \n",
 415 |        "9          22  17859.167  \n",
 416 |        "\n",
 417 |        "[10 rows x 26 columns]"
 418 |       ]
 419 |      },
 420 |      "execution_count": 3,
 421 |      "metadata": {},
 422 |      "output_type": "execute_result"
 423 |     }
 424 |    ],
 425 |    "source": [
 426 |     "# Print the first 10 rows of the dataset\n",
 427 |     "df.head(10)"
 428 |    ]
 429 |   },
 430 |   {
 431 |    "cell_type": "markdown",
 432 |    "metadata": {},
 433 |    "source": [
 434 |     "This Cars Dataset represents cars characteristics, along with their prices.\n",
 435 |     "\n",
 436 |     "- `car_ID`: Unique id of each observation (Interger)\n",
 437 |     "- `symboling`: Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.(Categorical) \t\t\n",
 438 |     "- `carCompany`: Name of car company (Categorical)\t\t\n",
 439 |     "- `fueltype`: Car fuel type i.e gas or diesel (Categorical)\t\t\n",
 440 |     "- `aspiration`: Aspiration used in a car (Categorical)\t\t\n",
 441 |     "- `doornumber`: Number of doors in a car (Categorical)\t\t\n",
 442 |     "- `carbody`: body of car (Categorical)\t\t\n",
 443 |     "- `drivewheel`: type of drive wheel (Categorical)\t\t\n",
 444 |     "- `enginelocation`: Location of car engine (Categorical)\t\t\n",
 445 |     "- `wheelbase`: Weelbase of car (Numeric)\t\t\n",
 446 |     "- `carlength`: Length of car (Numeric)\t\t\n",
 447 |     "- `carwidth`: Width of car (Numeric)\t\t\n",
 448 |     "- `carheight` Height of car (Numeric)\t\t\n",
 449 |     "- `curbweight` The weight of a car without occupants or baggage. (Numeric)\t\t\n",
 450 |     "- `enginetype`: Type of engine. (Categorical)\t\t\n",
 451 |     "- `cylindernumber`: Cylinder placed in the car (Categorical)\t\t\n",
 452 |     "- `enginesize`: Size of car (Numeric)\t\t\n",
 453 |     "- `fuelsystem`: Fuel system of car (Categorical)\t\t\n",
 454 |     "- `boreratio`: Boreratio of car (Numeric)\t\t\n",
 455 |     "- `stroke`: Stroke or volume inside the engine (Numeric)\t\t\n",
 456 |     "- `compressionratio`: Compression ratio of car (Numeric)\t\t\n",
 457 |     "- `horsepower`: Horsepower (Numeric)\t\t\n",
 458 |     "- `peakrpm`: Car peak rpm (Numeric)\t\t\n",
 459 |     "- `citympg`: Mileage in city (Numeric)\t\t\n",
 460 |     "- `highwaympg`: Mileage on highway (Numeric)\t\t\n",
 461 |     "- `price` (Dependent variable): Price of car in \\$ (Numeric)\n",
 462 |     "\n",
 463 |     "The main aim is to build **a model that predicts a price of a car** (`price` column) based on independent variables."
 464 |    ]
 465 |   },
 466 |   {
 467 |    "cell_type": "markdown",
 468 |    "metadata": {},
 469 |    "source": [
 470 |     "## 3. Variables"
 471 |    ]
 472 |   },
 473 |   {
 474 |    "cell_type": "markdown",
 475 |    "metadata": {},
 476 |    "source": [
 477 |     "Let's split the dataset into X and y, where \n",
 478 |     "- $X$ is a set of independent variables\n",
 479 |     "- $y$ is a dependent, or target variable (the last column)\n",
 480 |     "\n",
 481 |     "To make things simpler, let's pick 3 independent variables."
 482 |    ]
 483 |   },
 484 |   {
 485 |    "cell_type": "code",
 486 |    "execution_count": 4,
 487 |    "metadata": {},
 488 |    "outputs": [
 489 |     {
 490 |      "data": {
 491 |       "text/html": [
 492 |        "<div>\n",
 493 |        "<style scoped>\n",
 494 |        "    .dataframe tbody tr th:only-of-type {\n",
 495 |        "        vertical-align: middle;\n",
 496 |        "    }\n",
 497 |        "\n",
 498 |        "    .dataframe tbody tr th {\n",
 499 |        "        vertical-align: top;\n",
 500 |        "    }\n",
 501 |        "\n",
 502 |        "    .dataframe thead th {\n",
 503 |        "        text-align: right;\n",
 504 |        "    }\n",
 505 |        "</style>\n",
 506 |        "<table border=\"1\" class=\"dataframe\">\n",
 507 |        "  <thead>\n",
 508 |        "    <tr style=\"text-align: right;\">\n",
 509 |        "      <th></th>\n",
 510 |        "      <th>symboling</th>\n",
 511 |        "      <th>enginesize</th>\n",
 512 |        "      <th>horsepower</th>\n",
 513 |        "    </tr>\n",
 514 |        "  </thead>\n",
 515 |        "  <tbody>\n",
 516 |        "    <tr>\n",
 517 |        "      <th>0</th>\n",
 518 |        "      <td>3</td>\n",
 519 |        "      <td>130</td>\n",
 520 |        "      <td>111</td>\n",
 521 |        "    </tr>\n",
 522 |        "    <tr>\n",
 523 |        "      <th>1</th>\n",
 524 |        "      <td>3</td>\n",
 525 |        "      <td>130</td>\n",
 526 |        "      <td>111</td>\n",
 527 |        "    </tr>\n",
 528 |        "    <tr>\n",
 529 |        "      <th>2</th>\n",
 530 |        "      <td>1</td>\n",
 531 |        "      <td>152</td>\n",
 532 |        "      <td>154</td>\n",
 533 |        "    </tr>\n",
 534 |        "    <tr>\n",
 535 |        "      <th>3</th>\n",
 536 |        "      <td>2</td>\n",
 537 |        "      <td>109</td>\n",
 538 |        "      <td>102</td>\n",
 539 |        "    </tr>\n",
 540 |        "    <tr>\n",
 541 |        "      <th>4</th>\n",
 542 |        "      <td>2</td>\n",
 543 |        "      <td>136</td>\n",
 544 |        "      <td>115</td>\n",
 545 |        "    </tr>\n",
 546 |        "    <tr>\n",
 547 |        "      <th>...</th>\n",
 548 |        "      <td>...</td>\n",
 549 |        "      <td>...</td>\n",
 550 |        "      <td>...</td>\n",
 551 |        "    </tr>\n",
 552 |        "    <tr>\n",
 553 |        "      <th>200</th>\n",
 554 |        "      <td>-1</td>\n",
 555 |        "      <td>141</td>\n",
 556 |        "      <td>114</td>\n",
 557 |        "    </tr>\n",
 558 |        "    <tr>\n",
 559 |        "      <th>201</th>\n",
 560 |        "      <td>-1</td>\n",
 561 |        "      <td>141</td>\n",
 562 |        "      <td>160</td>\n",
 563 |        "    </tr>\n",
 564 |        "    <tr>\n",
 565 |        "      <th>202</th>\n",
 566 |        "      <td>-1</td>\n",
 567 |        "      <td>173</td>\n",
 568 |        "      <td>134</td>\n",
 569 |        "    </tr>\n",
 570 |        "    <tr>\n",
 571 |        "      <th>203</th>\n",
 572 |        "      <td>-1</td>\n",
 573 |        "      <td>145</td>\n",
 574 |        "      <td>106</td>\n",
 575 |        "    </tr>\n",
 576 |        "    <tr>\n",
 577 |        "      <th>204</th>\n",
 578 |        "      <td>-1</td>\n",
 579 |        "      <td>141</td>\n",
 580 |        "      <td>114</td>\n",
 581 |        "    </tr>\n",
 582 |        "  </tbody>\n",
 583 |        "</table>\n",
 584 |        "<p>205 rows × 3 columns</p>\n",
 585 |        "</div>"
 586 |       ],
 587 |       "text/plain": [
 588 |        "     symboling  enginesize  horsepower\n",
 589 |        "0            3         130         111\n",
 590 |        "1            3         130         111\n",
 591 |        "2            1         152         154\n",
 592 |        "3            2         109         102\n",
 593 |        "4            2         136         115\n",
 594 |        "..         ...         ...         ...\n",
 595 |        "200         -1         141         114\n",
 596 |        "201         -1         141         160\n",
 597 |        "202         -1         173         134\n",
 598 |        "203         -1         145         106\n",
 599 |        "204         -1         141         114\n",
 600 |        "\n",
 601 |        "[205 rows x 3 columns]"
 602 |       ]
 603 |      },
 604 |      "execution_count": 4,
 605 |      "metadata": {},
 606 |      "output_type": "execute_result"
 607 |     }
 608 |    ],
 609 |    "source": [
 610 |     "# Filter out target column\n",
 611 |     "X = df[['symboling', 'enginesize', 'horsepower']]\n",
 612 |     "\n",
 613 |     "# Print X\n",
 614 |     "X"
 615 |    ]
 616 |   },
 617 |   {
 618 |    "cell_type": "code",
 619 |    "execution_count": 5,
 620 |    "metadata": {},
 621 |    "outputs": [
 622 |     {
 623 |      "data": {
 624 |       "text/plain": [
 625 |        "0      13495.0\n",
 626 |        "1      16500.0\n",
 627 |        "2      16500.0\n",
 628 |        "3      13950.0\n",
 629 |        "4      17450.0\n",
 630 |        "        ...   \n",
 631 |        "200    16845.0\n",
 632 |        "201    19045.0\n",
 633 |        "202    21485.0\n",
 634 |        "203    22470.0\n",
 635 |        "204    22625.0\n",
 636 |        "Name: price, Length: 205, dtype: float64"
 637 |       ]
 638 |      },
 639 |      "execution_count": 5,
 640 |      "metadata": {},
 641 |      "output_type": "execute_result"
 642 |     }
 643 |    ],
 644 |    "source": [
 645 |     "# Select target column\n",
 646 |     "y = df['price']\n",
 647 |     "\n",
 648 |     "# Print y\n",
 649 |     "y"
 650 |    ]
 651 |   },
 652 |   {
 653 |    "cell_type": "markdown",
 654 |    "metadata": {},
 655 |    "source": [
 656 |     "## 4. Model"
 657 |    ]
 658 |   },
 659 |   {
 660 |    "cell_type": "markdown",
 661 |    "metadata": {},
 662 |    "source": [
 663 |     "In this section we are going to \n",
 664 |     "- build a neural nets model, \n",
 665 |     "- evaluate its accuracy, and \n",
 666 |     "- make a prediction"
 667 |    ]
 668 |   },
 669 |   {
 670 |    "cell_type": "markdown",
 671 |    "metadata": {},
 672 |    "source": [
 673 |     "### 4.1. Building the model"
 674 |    ]
 675 |   },
 676 |   {
 677 |    "cell_type": "markdown",
 678 |    "metadata": {},
 679 |    "source": [
 680 |     "Let's recall three simple steps:\n",
 681 |     "\n",
 682 |     "- Split the $X$ & $y$ variables into train and test sets\n",
 683 |     "- Initialize the model\n",
 684 |     "- Train the model with the varialbes"
 685 |    ]
 686 |   },
 687 |   {
 688 |    "cell_type": "code",
 689 |    "execution_count": 6,
 690 |    "metadata": {},
 691 |    "outputs": [],
 692 |    "source": [
 693 |     "# Split variables into train and test\n",
 694 |     "X_train, X_test, y_train, y_test = train_test_split(X, #independent variables\n",
 695 |     "                                                    y, #dependent variable\n",
 696 |     "                                                    random_state = 3\n",
 697 |     "                                                   )"
 698 |    ]
 699 |   },
 700 |   {
 701 |    "cell_type": "code",
 702 |    "execution_count": 9,
 703 |    "metadata": {},
 704 |    "outputs": [],
 705 |    "source": [
 706 |     "# Initialize the model\n",
 707 |     "reg = MLPRegressor(hidden_layer_sizes = [100, 100], \n",
 708 |     "                   alpha = 5.0, \n",
 709 |     "                   random_state = 3\n",
 710 |     "                  )"
 711 |    ]
 712 |   },
 713 |   {
 714 |    "cell_type": "code",
 715 |    "execution_count": 10,
 716 |    "metadata": {},
 717 |    "outputs": [
 718 |     {
 719 |      "data": {
 720 |       "text/html": [
 721 |        "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPRegressor(alpha=5.0, hidden_layer_sizes=[100, 100], random_state=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPRegressor</label><div class=\"sk-toggleable__content\"><pre>MLPRegressor(alpha=5.0, hidden_layer_sizes=[100, 100], random_state=3)</pre></div></div></div></div></div>"
 722 |       ],
 723 |       "text/plain": [
 724 |        "MLPRegressor(alpha=5.0, hidden_layer_sizes=[100, 100], random_state=3)"
 725 |       ]
 726 |      },
 727 |      "execution_count": 10,
 728 |      "metadata": {},
 729 |      "output_type": "execute_result"
 730 |     }
 731 |    ],
 732 |    "source": [
 733 |     "# Train the model\n",
 734 |     "reg.fit(X_train, y_train)"
 735 |    ]
 736 |   },
 737 |   {
 738 |    "cell_type": "markdown",
 739 |    "metadata": {},
 740 |    "source": [
 741 |     "### 4.2. Checking models accuracy"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "markdown",
 746 |    "metadata": {},
 747 |    "source": [
 748 |     "After the model has trained with the data, it's essential to understand how precisely it predicts car prices. For that, we need to check model's accuracy. "
 749 |    ]
 750 |   },
 751 |   {
 752 |    "cell_type": "code",
 753 |    "execution_count": 11,
 754 |    "metadata": {},
 755 |    "outputs": [
 756 |     {
 757 |      "name": "stdout",
 758 |      "output_type": "stream",
 759 |      "text": [
 760 |       "Accuracy (R-squared score) of NN regression model on training set: 0.67\n",
 761 |       "Accuracy (R-squared score) of NN regression model on test set: 0.71\n"
 762 |      ]
 763 |     }
 764 |    ],
 765 |    "source": [
 766 |     "print(f'Accuracy (R-squared score) of NN regression model on training set: {reg.score(X_train, y_train):.2f}')\n",
 767 |     "print(f'Accuracy (R-squared score) of NN regression model on test set: {reg.score(X_test, y_test):.2f}')"
 768 |    ]
 769 |   },
 770 |   {
 771 |    "cell_type": "markdown",
 772 |    "metadata": {},
 773 |    "source": [
 774 |     "### 4.3. Making a prediction"
 775 |    ]
 776 |   },
 777 |   {
 778 |    "cell_type": "markdown",
 779 |    "metadata": {},
 780 |    "source": [
 781 |     "Now that we know the model is accurate enough, we can predict the price of any car by passing independent varialbes `symboling`, `enginesize`, `horsepower` to the model. \n",
 782 |     "\n",
 783 |     "\n",
 784 |     "The method `predict` returns such a prediction."
 785 |    ]
 786 |   },
 787 |   {
 788 |    "cell_type": "code",
 789 |    "execution_count": 12,
 790 |    "metadata": {},
 791 |    "outputs": [
 792 |     {
 793 |      "name": "stderr",
 794 |      "output_type": "stream",
 795 |      "text": [
 796 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but MLPRegressor was fitted with feature names\n",
 797 |       "  warnings.warn(\n"
 798 |      ]
 799 |     },
 800 |     {
 801 |      "data": {
 802 |       "text/plain": [
 803 |        "array([25421.43933963])"
 804 |       ]
 805 |      },
 806 |      "execution_count": 12,
 807 |      "metadata": {},
 808 |      "output_type": "execute_result"
 809 |     }
 810 |    ],
 811 |    "source": [
 812 |     "reg.predict([[0, 180, 249]])"
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "markdown",
 817 |    "metadata": {},
 818 |    "source": [
 819 |     "## 5. Hyperparameters Tuning"
 820 |    ]
 821 |   },
 822 |   {
 823 |    "cell_type": "markdown",
 824 |    "metadata": {},
 825 |    "source": [
 826 |     "Hyperparameter Tuning is one of the critical steps in model building. It helps in increasing model's accuracy even further."
 827 |    ]
 828 |   },
 829 |   {
 830 |    "cell_type": "markdown",
 831 |    "metadata": {},
 832 |    "source": [
 833 |     "### 5.1. Playing around with activation functions and regularization parameter alpha"
 834 |    ]
 835 |   },
 836 |   {
 837 |    "cell_type": "code",
 838 |    "execution_count": 16,
 839 |    "metadata": {},
 840 |    "outputs": [
 841 |     {
 842 |      "name": "stderr",
 843 |      "output_type": "stream",
 844 |      "text": [
 845 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
 846 |       "  warnings.warn(\n"
 847 |      ]
 848 |     },
 849 |     {
 850 |      "name": "stdout",
 851 |      "output_type": "stream",
 852 |      "text": [
 853 |       "activation funtion = tanh || alpha = 0.0001\n",
 854 |       "              r-squared training: -2.83,\n",
 855 |       "              r-squared test: -2.64 \n",
 856 |       "\n"
 857 |      ]
 858 |     },
 859 |     {
 860 |      "name": "stderr",
 861 |      "output_type": "stream",
 862 |      "text": [
 863 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
 864 |       "  warnings.warn(\n"
 865 |      ]
 866 |     },
 867 |     {
 868 |      "name": "stdout",
 869 |      "output_type": "stream",
 870 |      "text": [
 871 |       "activation funtion = tanh || alpha = 1.0\n",
 872 |       "              r-squared training: -2.83,\n",
 873 |       "              r-squared test: -2.64 \n",
 874 |       "\n"
 875 |      ]
 876 |     },
 877 |     {
 878 |      "name": "stderr",
 879 |      "output_type": "stream",
 880 |      "text": [
 881 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
 882 |       "  warnings.warn(\n",
 883 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
 884 |       "  warnings.warn(\n"
 885 |      ]
 886 |     },
 887 |     {
 888 |      "name": "stdout",
 889 |      "output_type": "stream",
 890 |      "text": [
 891 |       "activation funtion = tanh || alpha = 100\n",
 892 |       "              r-squared training: -2.83,\n",
 893 |       "              r-squared test: -2.64 \n",
 894 |       "\n",
 895 |       "activation funtion = relu || alpha = 0.0001\n",
 896 |       "              r-squared training: 0.65,\n",
 897 |       "              r-squared test: 0.65 \n",
 898 |       "\n",
 899 |       "activation funtion = relu || alpha = 1.0\n",
 900 |       "              r-squared training: 0.67,\n",
 901 |       "              r-squared test: 0.68 \n",
 902 |       "\n",
 903 |       "activation funtion = relu || alpha = 100\n",
 904 |       "              r-squared training: 0.67,\n",
 905 |       "              r-squared test: 0.71 \n",
 906 |       "\n"
 907 |      ]
 908 |     },
 909 |     {
 910 |      "name": "stderr",
 911 |      "output_type": "stream",
 912 |      "text": [
 913 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
 914 |       "  warnings.warn(\n",
 915 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
 916 |       "  warnings.warn(\n"
 917 |      ]
 918 |     }
 919 |    ],
 920 |    "source": [
 921 |     "# Accuracy with different activation functions and regularization parameter alpha\n",
 922 |     "\n",
 923 |     "for this_activation in ['tanh', 'relu']:\n",
 924 |     "    for this_alpha in [0.0001, 1.0, 100]:\n",
 925 |     "        \n",
 926 |     "        # Initialize the model\n",
 927 |     "        reg = MLPRegressor(hidden_layer_sizes = [100,100],\n",
 928 |     "                             activation = this_activation,\n",
 929 |     "                             alpha = this_alpha)\n",
 930 |     "        \n",
 931 |     "        # Train the model\n",
 932 |     "        reg.fit(X_train, y_train)\n",
 933 |     "        \n",
 934 |     "        # Print results\n",
 935 |     "        print(f'''activation funtion = {this_activation} || alpha = {this_alpha}\n",
 936 |     "              r-squared training: {reg.score(X_train, y_train):.2f},\n",
 937 |     "              r-squared test: {reg.score(X_test, y_test):.2f} \\n''')"
 938 |    ]
 939 |   },
 940 |   {
 941 |    "cell_type": "markdown",
 942 |    "metadata": {},
 943 |    "source": [
 944 |     "### 5.2. All Hyperparameters of NN Regression"
 945 |    ]
 946 |   },
 947 |   {
 948 |    "cell_type": "markdown",
 949 |    "metadata": {},
 950 |    "source": [
 951 |     "There are many hyperparameters in Ridge Regression. They are all presented below  - as you can see, they have values by default. Feel free to play around with them. To learn more about hyperparameters and their meaning, visit https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor."
 952 |    ]
 953 |   },
 954 |   {
 955 |    "cell_type": "code",
 956 |    "execution_count": 17,
 957 |    "metadata": {},
 958 |    "outputs": [],
 959 |    "source": [
 960 |     "reg = MLPRegressor(hidden_layer_sizes=(100,), \n",
 961 |     "                   activation='relu',\n",
 962 |     "                   solver='adam', \n",
 963 |     "                   alpha=0.0001, \n",
 964 |     "                   batch_size='auto', \n",
 965 |     "                   learning_rate='constant', \n",
 966 |     "                   learning_rate_init=0.001, \n",
 967 |     "                   power_t=0.5, \n",
 968 |     "                   max_iter=200, \n",
 969 |     "                   shuffle=True, \n",
 970 |     "                   random_state=None, \n",
 971 |     "                   tol=0.0001, \n",
 972 |     "                   verbose=False, \n",
 973 |     "                   warm_start=False, \n",
 974 |     "                   momentum=0.9, \n",
 975 |     "                   nesterovs_momentum=True, \n",
 976 |     "                   early_stopping=False, \n",
 977 |     "                   validation_fraction=0.1, \n",
 978 |     "                   beta_1=0.9, \n",
 979 |     "                   beta_2=0.999, \n",
 980 |     "                   epsilon=1e-08, \n",
 981 |     "                   n_iter_no_change=10, \n",
 982 |     "                   max_fun=15000\n",
 983 |     "                  )"
 984 |    ]
 985 |   },
 986 |   {
 987 |    "cell_type": "markdown",
 988 |    "metadata": {},
 989 |    "source": [
 990 |     "## 6. NN Regression with normalized data"
 991 |    ]
 992 |   },
 993 |   {
 994 |    "cell_type": "markdown",
 995 |    "metadata": {},
 996 |    "source": [
 997 |     "To do"
 998 |    ]
 999 |   },
1000 |   {
1001 |    "cell_type": "code",
1002 |    "execution_count": 21,
1003 |    "metadata": {},
1004 |    "outputs": [
1005 |     {
1006 |      "name": "stdout",
1007 |      "output_type": "stream",
1008 |      "text": [
1009 |       "Accuracy of NN regressor on training set: -2.84\n",
1010 |       "Accuracy of NN regressor on test set: -2.64\n"
1011 |      ]
1012 |     },
1013 |     {
1014 |      "name": "stderr",
1015 |      "output_type": "stream",
1016 |      "text": [
1017 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
1018 |       "  warnings.warn(\n"
1019 |      ]
1020 |     }
1021 |    ],
1022 |    "source": [
1023 |     "from sklearn.neural_network import MLPClassifier\n",
1024 |     "from sklearn.preprocessing import MinMaxScaler\n",
1025 |     "from sklearn.neural_network import MLPRegressor\n",
1026 |     "\n",
1027 |     "#initialize scaler\n",
1028 |     "scaler = MinMaxScaler()\n",
1029 |     "\n",
1030 |     "#transform variables\n",
1031 |     "X_train_scaled = scaler.fit_transform(X_train)\n",
1032 |     "X_test_scaled = scaler.transform(X_test)\n",
1033 |     "\n",
1034 |     "#initialize the model\n",
1035 |     "clf = MLPRegressor(hidden_layer_sizes = [100, 100], alpha = 5.0, random_state = 3)\n",
1036 |     "\n",
1037 |     "#train the model\n",
1038 |     "reg.fit(X_train_scaled, y_train)\n",
1039 |     "\n",
1040 |     "#print results\n",
1041 |     "print(f'Accuracy of NN regressor on training set: {reg.score(X_train_scaled, y_train):.2f}')\n",
1042 |     "print(f'Accuracy of NN regressor on test set: {reg.score(X_test_scaled, y_test):.2f}')"
1043 |    ]
1044 |   },
1045 |   {
1046 |    "cell_type": "code",
1047 |    "execution_count": null,
1048 |    "metadata": {},
1049 |    "outputs": [],
1050 |    "source": []
1051 |   }
1052 |  ],
1053 |  "metadata": {
1054 |   "kernelspec": {
1055 |    "display_name": "Python 3 (ipykernel)",
1056 |    "language": "python",
1057 |    "name": "python3"
1058 |   },
1059 |   "language_info": {
1060 |    "codemirror_mode": {
1061 |     "name": "ipython",
1062 |     "version": 3
1063 |    },
1064 |    "file_extension": ".py",
1065 |    "mimetype": "text/x-python",
1066 |    "name": "python",
1067 |    "nbconvert_exporter": "python",
1068 |    "pygments_lexer": "ipython3",
1069 |    "version": "3.10.7"
1070 |   }
1071 |  },
1072 |  "nbformat": 4,
1073 |  "nbformat_minor": 2
1074 | }
1075 | 


--------------------------------------------------------------------------------
/regression/ridge_regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Ridge Regression"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "- This is a supplement material for my lectures on ML. It sheds light on Python implementations of the supervised machine learning algorithms. \n",
 15 |     "- I assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n",
 16 |     "- This material is represented as a Jupyter notebook, which you can easily download to reproduce the code and play around with it. "
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "## 1. Libraries"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "To build a model, we need \n",
 31 |     "- `pandas` library to work with panel dataframes\n",
 32 |     "- `sklearn.model_selection.train_test_split` to split the dataset into train and test\n",
 33 |     "- `sklearn.linear_model.Ridge` to build a model\n",
 34 |     "\n",
 35 |     "In addition, I'd like to show you `sklearn.preprocessing.StandardScaler` - a library for data normalization."
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": 15,
 41 |    "metadata": {},
 42 |    "outputs": [],
 43 |    "source": [
 44 |     "import pandas as pd\n",
 45 |     "from sklearn.preprocessing import StandardScaler\n",
 46 |     "from sklearn.linear_model import Ridge\n",
 47 |     "from sklearn.model_selection import train_test_split"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "markdown",
 52 |    "metadata": {},
 53 |    "source": [
 54 |     "## 2. Data Load & Overview"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "markdown",
 59 |    "metadata": {},
 60 |    "source": [
 61 |     "Let's load the dataset and understand it."
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 3,
 67 |    "metadata": {},
 68 |    "outputs": [],
 69 |    "source": [
 70 |     "# Load the dataset\n",
 71 |     "df = pd.read_csv('https://raw.githubusercontent.com/5x12/ml-cookbook/master/supplements/data/cars.csv')"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "code",
 76 |    "execution_count": 4,
 77 |    "metadata": {},
 78 |    "outputs": [
 79 |     {
 80 |      "data": {
 81 |       "text/html": [
 82 |        "<div>\n",
 83 |        "<style scoped>\n",
 84 |        "    .dataframe tbody tr th:only-of-type {\n",
 85 |        "        vertical-align: middle;\n",
 86 |        "    }\n",
 87 |        "\n",
 88 |        "    .dataframe tbody tr th {\n",
 89 |        "        vertical-align: top;\n",
 90 |        "    }\n",
 91 |        "\n",
 92 |        "    .dataframe thead th {\n",
 93 |        "        text-align: right;\n",
 94 |        "    }\n",
 95 |        "</style>\n",
 96 |        "<table border=\"1\" class=\"dataframe\">\n",
 97 |        "  <thead>\n",
 98 |        "    <tr style=\"text-align: right;\">\n",
 99 |        "      <th></th>\n",
100 |        "      <th>car_ID</th>\n",
101 |        "      <th>symboling</th>\n",
102 |        "      <th>CarName</th>\n",
103 |        "      <th>fueltype</th>\n",
104 |        "      <th>aspiration</th>\n",
105 |        "      <th>doornumber</th>\n",
106 |        "      <th>carbody</th>\n",
107 |        "      <th>drivewheel</th>\n",
108 |        "      <th>enginelocation</th>\n",
109 |        "      <th>wheelbase</th>\n",
110 |        "      <th>...</th>\n",
111 |        "      <th>enginesize</th>\n",
112 |        "      <th>fuelsystem</th>\n",
113 |        "      <th>boreratio</th>\n",
114 |        "      <th>stroke</th>\n",
115 |        "      <th>compressionratio</th>\n",
116 |        "      <th>horsepower</th>\n",
117 |        "      <th>peakrpm</th>\n",
118 |        "      <th>citympg</th>\n",
119 |        "      <th>highwaympg</th>\n",
120 |        "      <th>price</th>\n",
121 |        "    </tr>\n",
122 |        "  </thead>\n",
123 |        "  <tbody>\n",
124 |        "    <tr>\n",
125 |        "      <th>0</th>\n",
126 |        "      <td>1</td>\n",
127 |        "      <td>3</td>\n",
128 |        "      <td>alfa-romero giulia</td>\n",
129 |        "      <td>gas</td>\n",
130 |        "      <td>std</td>\n",
131 |        "      <td>two</td>\n",
132 |        "      <td>convertible</td>\n",
133 |        "      <td>rwd</td>\n",
134 |        "      <td>front</td>\n",
135 |        "      <td>88.6</td>\n",
136 |        "      <td>...</td>\n",
137 |        "      <td>130</td>\n",
138 |        "      <td>mpfi</td>\n",
139 |        "      <td>3.47</td>\n",
140 |        "      <td>2.68</td>\n",
141 |        "      <td>9.0</td>\n",
142 |        "      <td>111</td>\n",
143 |        "      <td>5000</td>\n",
144 |        "      <td>21</td>\n",
145 |        "      <td>27</td>\n",
146 |        "      <td>13495.000</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>1</th>\n",
150 |        "      <td>2</td>\n",
151 |        "      <td>3</td>\n",
152 |        "      <td>alfa-romero stelvio</td>\n",
153 |        "      <td>gas</td>\n",
154 |        "      <td>std</td>\n",
155 |        "      <td>two</td>\n",
156 |        "      <td>convertible</td>\n",
157 |        "      <td>rwd</td>\n",
158 |        "      <td>front</td>\n",
159 |        "      <td>88.6</td>\n",
160 |        "      <td>...</td>\n",
161 |        "      <td>130</td>\n",
162 |        "      <td>mpfi</td>\n",
163 |        "      <td>3.47</td>\n",
164 |        "      <td>2.68</td>\n",
165 |        "      <td>9.0</td>\n",
166 |        "      <td>111</td>\n",
167 |        "      <td>5000</td>\n",
168 |        "      <td>21</td>\n",
169 |        "      <td>27</td>\n",
170 |        "      <td>16500.000</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>2</th>\n",
174 |        "      <td>3</td>\n",
175 |        "      <td>1</td>\n",
176 |        "      <td>alfa-romero Quadrifoglio</td>\n",
177 |        "      <td>gas</td>\n",
178 |        "      <td>std</td>\n",
179 |        "      <td>two</td>\n",
180 |        "      <td>hatchback</td>\n",
181 |        "      <td>rwd</td>\n",
182 |        "      <td>front</td>\n",
183 |        "      <td>94.5</td>\n",
184 |        "      <td>...</td>\n",
185 |        "      <td>152</td>\n",
186 |        "      <td>mpfi</td>\n",
187 |        "      <td>2.68</td>\n",
188 |        "      <td>3.47</td>\n",
189 |        "      <td>9.0</td>\n",
190 |        "      <td>154</td>\n",
191 |        "      <td>5000</td>\n",
192 |        "      <td>19</td>\n",
193 |        "      <td>26</td>\n",
194 |        "      <td>16500.000</td>\n",
195 |        "    </tr>\n",
196 |        "    <tr>\n",
197 |        "      <th>3</th>\n",
198 |        "      <td>4</td>\n",
199 |        "      <td>2</td>\n",
200 |        "      <td>audi 100 ls</td>\n",
201 |        "      <td>gas</td>\n",
202 |        "      <td>std</td>\n",
203 |        "      <td>four</td>\n",
204 |        "      <td>sedan</td>\n",
205 |        "      <td>fwd</td>\n",
206 |        "      <td>front</td>\n",
207 |        "      <td>99.8</td>\n",
208 |        "      <td>...</td>\n",
209 |        "      <td>109</td>\n",
210 |        "      <td>mpfi</td>\n",
211 |        "      <td>3.19</td>\n",
212 |        "      <td>3.40</td>\n",
213 |        "      <td>10.0</td>\n",
214 |        "      <td>102</td>\n",
215 |        "      <td>5500</td>\n",
216 |        "      <td>24</td>\n",
217 |        "      <td>30</td>\n",
218 |        "      <td>13950.000</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <th>4</th>\n",
222 |        "      <td>5</td>\n",
223 |        "      <td>2</td>\n",
224 |        "      <td>audi 100ls</td>\n",
225 |        "      <td>gas</td>\n",
226 |        "      <td>std</td>\n",
227 |        "      <td>four</td>\n",
228 |        "      <td>sedan</td>\n",
229 |        "      <td>4wd</td>\n",
230 |        "      <td>front</td>\n",
231 |        "      <td>99.4</td>\n",
232 |        "      <td>...</td>\n",
233 |        "      <td>136</td>\n",
234 |        "      <td>mpfi</td>\n",
235 |        "      <td>3.19</td>\n",
236 |        "      <td>3.40</td>\n",
237 |        "      <td>8.0</td>\n",
238 |        "      <td>115</td>\n",
239 |        "      <td>5500</td>\n",
240 |        "      <td>18</td>\n",
241 |        "      <td>22</td>\n",
242 |        "      <td>17450.000</td>\n",
243 |        "    </tr>\n",
244 |        "    <tr>\n",
245 |        "      <th>5</th>\n",
246 |        "      <td>6</td>\n",
247 |        "      <td>2</td>\n",
248 |        "      <td>audi fox</td>\n",
249 |        "      <td>gas</td>\n",
250 |        "      <td>std</td>\n",
251 |        "      <td>two</td>\n",
252 |        "      <td>sedan</td>\n",
253 |        "      <td>fwd</td>\n",
254 |        "      <td>front</td>\n",
255 |        "      <td>99.8</td>\n",
256 |        "      <td>...</td>\n",
257 |        "      <td>136</td>\n",
258 |        "      <td>mpfi</td>\n",
259 |        "      <td>3.19</td>\n",
260 |        "      <td>3.40</td>\n",
261 |        "      <td>8.5</td>\n",
262 |        "      <td>110</td>\n",
263 |        "      <td>5500</td>\n",
264 |        "      <td>19</td>\n",
265 |        "      <td>25</td>\n",
266 |        "      <td>15250.000</td>\n",
267 |        "    </tr>\n",
268 |        "    <tr>\n",
269 |        "      <th>6</th>\n",
270 |        "      <td>7</td>\n",
271 |        "      <td>1</td>\n",
272 |        "      <td>audi 100ls</td>\n",
273 |        "      <td>gas</td>\n",
274 |        "      <td>std</td>\n",
275 |        "      <td>four</td>\n",
276 |        "      <td>sedan</td>\n",
277 |        "      <td>fwd</td>\n",
278 |        "      <td>front</td>\n",
279 |        "      <td>105.8</td>\n",
280 |        "      <td>...</td>\n",
281 |        "      <td>136</td>\n",
282 |        "      <td>mpfi</td>\n",
283 |        "      <td>3.19</td>\n",
284 |        "      <td>3.40</td>\n",
285 |        "      <td>8.5</td>\n",
286 |        "      <td>110</td>\n",
287 |        "      <td>5500</td>\n",
288 |        "      <td>19</td>\n",
289 |        "      <td>25</td>\n",
290 |        "      <td>17710.000</td>\n",
291 |        "    </tr>\n",
292 |        "    <tr>\n",
293 |        "      <th>7</th>\n",
294 |        "      <td>8</td>\n",
295 |        "      <td>1</td>\n",
296 |        "      <td>audi 5000</td>\n",
297 |        "      <td>gas</td>\n",
298 |        "      <td>std</td>\n",
299 |        "      <td>four</td>\n",
300 |        "      <td>wagon</td>\n",
301 |        "      <td>fwd</td>\n",
302 |        "      <td>front</td>\n",
303 |        "      <td>105.8</td>\n",
304 |        "      <td>...</td>\n",
305 |        "      <td>136</td>\n",
306 |        "      <td>mpfi</td>\n",
307 |        "      <td>3.19</td>\n",
308 |        "      <td>3.40</td>\n",
309 |        "      <td>8.5</td>\n",
310 |        "      <td>110</td>\n",
311 |        "      <td>5500</td>\n",
312 |        "      <td>19</td>\n",
313 |        "      <td>25</td>\n",
314 |        "      <td>18920.000</td>\n",
315 |        "    </tr>\n",
316 |        "    <tr>\n",
317 |        "      <th>8</th>\n",
318 |        "      <td>9</td>\n",
319 |        "      <td>1</td>\n",
320 |        "      <td>audi 4000</td>\n",
321 |        "      <td>gas</td>\n",
322 |        "      <td>turbo</td>\n",
323 |        "      <td>four</td>\n",
324 |        "      <td>sedan</td>\n",
325 |        "      <td>fwd</td>\n",
326 |        "      <td>front</td>\n",
327 |        "      <td>105.8</td>\n",
328 |        "      <td>...</td>\n",
329 |        "      <td>131</td>\n",
330 |        "      <td>mpfi</td>\n",
331 |        "      <td>3.13</td>\n",
332 |        "      <td>3.40</td>\n",
333 |        "      <td>8.3</td>\n",
334 |        "      <td>140</td>\n",
335 |        "      <td>5500</td>\n",
336 |        "      <td>17</td>\n",
337 |        "      <td>20</td>\n",
338 |        "      <td>23875.000</td>\n",
339 |        "    </tr>\n",
340 |        "    <tr>\n",
341 |        "      <th>9</th>\n",
342 |        "      <td>10</td>\n",
343 |        "      <td>0</td>\n",
344 |        "      <td>audi 5000s (diesel)</td>\n",
345 |        "      <td>gas</td>\n",
346 |        "      <td>turbo</td>\n",
347 |        "      <td>two</td>\n",
348 |        "      <td>hatchback</td>\n",
349 |        "      <td>4wd</td>\n",
350 |        "      <td>front</td>\n",
351 |        "      <td>99.5</td>\n",
352 |        "      <td>...</td>\n",
353 |        "      <td>131</td>\n",
354 |        "      <td>mpfi</td>\n",
355 |        "      <td>3.13</td>\n",
356 |        "      <td>3.40</td>\n",
357 |        "      <td>7.0</td>\n",
358 |        "      <td>160</td>\n",
359 |        "      <td>5500</td>\n",
360 |        "      <td>16</td>\n",
361 |        "      <td>22</td>\n",
362 |        "      <td>17859.167</td>\n",
363 |        "    </tr>\n",
364 |        "  </tbody>\n",
365 |        "</table>\n",
366 |        "<p>10 rows × 26 columns</p>\n",
367 |        "</div>"
368 |       ],
369 |       "text/plain": [
370 |        "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
371 |        "0       1          3        alfa-romero giulia      gas        std        two   \n",
372 |        "1       2          3       alfa-romero stelvio      gas        std        two   \n",
373 |        "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
374 |        "3       4          2               audi 100 ls      gas        std       four   \n",
375 |        "4       5          2                audi 100ls      gas        std       four   \n",
376 |        "5       6          2                  audi fox      gas        std        two   \n",
377 |        "6       7          1                audi 100ls      gas        std       four   \n",
378 |        "7       8          1                 audi 5000      gas        std       four   \n",
379 |        "8       9          1                 audi 4000      gas      turbo       four   \n",
380 |        "9      10          0       audi 5000s (diesel)      gas      turbo        two   \n",
381 |        "\n",
382 |        "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
383 |        "0  convertible        rwd          front       88.6  ...         130   \n",
384 |        "1  convertible        rwd          front       88.6  ...         130   \n",
385 |        "2    hatchback        rwd          front       94.5  ...         152   \n",
386 |        "3        sedan        fwd          front       99.8  ...         109   \n",
387 |        "4        sedan        4wd          front       99.4  ...         136   \n",
388 |        "5        sedan        fwd          front       99.8  ...         136   \n",
389 |        "6        sedan        fwd          front      105.8  ...         136   \n",
390 |        "7        wagon        fwd          front      105.8  ...         136   \n",
391 |        "8        sedan        fwd          front      105.8  ...         131   \n",
392 |        "9    hatchback        4wd          front       99.5  ...         131   \n",
393 |        "\n",
394 |        "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
395 |        "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
396 |        "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
397 |        "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
398 |        "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
399 |        "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
400 |        "5        mpfi       3.19    3.40              8.5        110     5500      19   \n",
401 |        "6        mpfi       3.19    3.40              8.5        110     5500      19   \n",
402 |        "7        mpfi       3.19    3.40              8.5        110     5500      19   \n",
403 |        "8        mpfi       3.13    3.40              8.3        140     5500      17   \n",
404 |        "9        mpfi       3.13    3.40              7.0        160     5500      16   \n",
405 |        "\n",
406 |        "   highwaympg      price  \n",
407 |        "0          27  13495.000  \n",
408 |        "1          27  16500.000  \n",
409 |        "2          26  16500.000  \n",
410 |        "3          30  13950.000  \n",
411 |        "4          22  17450.000  \n",
412 |        "5          25  15250.000  \n",
413 |        "6          25  17710.000  \n",
414 |        "7          25  18920.000  \n",
415 |        "8          20  23875.000  \n",
416 |        "9          22  17859.167  \n",
417 |        "\n",
418 |        "[10 rows x 26 columns]"
419 |       ]
420 |      },
421 |      "execution_count": 4,
422 |      "metadata": {},
423 |      "output_type": "execute_result"
424 |     }
425 |    ],
426 |    "source": [
427 |     "# Print the first 10 rows of the dataset\n",
428 |     "df.head(10)"
429 |    ]
430 |   },
431 |   {
432 |    "cell_type": "markdown",
433 |    "metadata": {},
434 |    "source": [
435 |     "This Cars Dataset represents cars characteristics, along with their prices.\n",
436 |     "\n",
437 |     "- `car_ID`: Unique id of each observation (Interger)\n",
438 |     "- `symboling`: Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.(Categorical) \t\t\n",
439 |     "- `carCompany`: Name of car company (Categorical)\t\t\n",
440 |     "- `fueltype`: Car fuel type i.e gas or diesel (Categorical)\t\t\n",
441 |     "- `aspiration`: Aspiration used in a car (Categorical)\t\t\n",
442 |     "- `doornumber`: Number of doors in a car (Categorical)\t\t\n",
443 |     "- `carbody`: body of car (Categorical)\t\t\n",
444 |     "- `drivewheel`: type of drive wheel (Categorical)\t\t\n",
445 |     "- `enginelocation`: Location of car engine (Categorical)\t\t\n",
446 |     "- `wheelbase`: Weelbase of car (Numeric)\t\t\n",
447 |     "- `carlength`: Length of car (Numeric)\t\t\n",
448 |     "- `carwidth`: Width of car (Numeric)\t\t\n",
449 |     "- `carheight` Height of car (Numeric)\t\t\n",
450 |     "- `curbweight` The weight of a car without occupants or baggage. (Numeric)\t\t\n",
451 |     "- `enginetype`: Type of engine. (Categorical)\t\t\n",
452 |     "- `cylindernumber`: Cylinder placed in the car (Categorical)\t\t\n",
453 |     "- `enginesize`: Size of car (Numeric)\t\t\n",
454 |     "- `fuelsystem`: Fuel system of car (Categorical)\t\t\n",
455 |     "- `boreratio`: Boreratio of car (Numeric)\t\t\n",
456 |     "- `stroke`: Stroke or volume inside the engine (Numeric)\t\t\n",
457 |     "- `compressionratio`: Compression ratio of car (Numeric)\t\t\n",
458 |     "- `horsepower`: Horsepower (Numeric)\t\t\n",
459 |     "- `peakrpm`: Car peak rpm (Numeric)\t\t\n",
460 |     "- `citympg`: Mileage in city (Numeric)\t\t\n",
461 |     "- `highwaympg`: Mileage on highway (Numeric)\t\t\n",
462 |     "- `price` (Dependent variable): Price of car in \\$ (Numeric)\n",
463 |     "\n",
464 |     "The main aim is to build **a model that predicts a price of a car** (`price` column) based on independent variables."
465 |    ]
466 |   },
467 |   {
468 |    "cell_type": "markdown",
469 |    "metadata": {},
470 |    "source": [
471 |     "## 3. Variables"
472 |    ]
473 |   },
474 |   {
475 |    "cell_type": "markdown",
476 |    "metadata": {},
477 |    "source": [
478 |     "Let's split the dataset into X and y, where \n",
479 |     "- $X$ is a set of independent variables\n",
480 |     "- $y$ is a dependent, or target variable (the last column)\n",
481 |     "\n",
482 |     "To make things simpler, let's pick 3 independent variables."
483 |    ]
484 |   },
485 |   {
486 |    "cell_type": "code",
487 |    "execution_count": 5,
488 |    "metadata": {},
489 |    "outputs": [
490 |     {
491 |      "data": {
492 |       "text/html": [
493 |        "<div>\n",
494 |        "<style scoped>\n",
495 |        "    .dataframe tbody tr th:only-of-type {\n",
496 |        "        vertical-align: middle;\n",
497 |        "    }\n",
498 |        "\n",
499 |        "    .dataframe tbody tr th {\n",
500 |        "        vertical-align: top;\n",
501 |        "    }\n",
502 |        "\n",
503 |        "    .dataframe thead th {\n",
504 |        "        text-align: right;\n",
505 |        "    }\n",
506 |        "</style>\n",
507 |        "<table border=\"1\" class=\"dataframe\">\n",
508 |        "  <thead>\n",
509 |        "    <tr style=\"text-align: right;\">\n",
510 |        "      <th></th>\n",
511 |        "      <th>symboling</th>\n",
512 |        "      <th>enginesize</th>\n",
513 |        "      <th>horsepower</th>\n",
514 |        "    </tr>\n",
515 |        "  </thead>\n",
516 |        "  <tbody>\n",
517 |        "    <tr>\n",
518 |        "      <th>0</th>\n",
519 |        "      <td>3</td>\n",
520 |        "      <td>130</td>\n",
521 |        "      <td>111</td>\n",
522 |        "    </tr>\n",
523 |        "    <tr>\n",
524 |        "      <th>1</th>\n",
525 |        "      <td>3</td>\n",
526 |        "      <td>130</td>\n",
527 |        "      <td>111</td>\n",
528 |        "    </tr>\n",
529 |        "    <tr>\n",
530 |        "      <th>2</th>\n",
531 |        "      <td>1</td>\n",
532 |        "      <td>152</td>\n",
533 |        "      <td>154</td>\n",
534 |        "    </tr>\n",
535 |        "    <tr>\n",
536 |        "      <th>3</th>\n",
537 |        "      <td>2</td>\n",
538 |        "      <td>109</td>\n",
539 |        "      <td>102</td>\n",
540 |        "    </tr>\n",
541 |        "    <tr>\n",
542 |        "      <th>4</th>\n",
543 |        "      <td>2</td>\n",
544 |        "      <td>136</td>\n",
545 |        "      <td>115</td>\n",
546 |        "    </tr>\n",
547 |        "    <tr>\n",
548 |        "      <th>...</th>\n",
549 |        "      <td>...</td>\n",
550 |        "      <td>...</td>\n",
551 |        "      <td>...</td>\n",
552 |        "    </tr>\n",
553 |        "    <tr>\n",
554 |        "      <th>200</th>\n",
555 |        "      <td>-1</td>\n",
556 |        "      <td>141</td>\n",
557 |        "      <td>114</td>\n",
558 |        "    </tr>\n",
559 |        "    <tr>\n",
560 |        "      <th>201</th>\n",
561 |        "      <td>-1</td>\n",
562 |        "      <td>141</td>\n",
563 |        "      <td>160</td>\n",
564 |        "    </tr>\n",
565 |        "    <tr>\n",
566 |        "      <th>202</th>\n",
567 |        "      <td>-1</td>\n",
568 |        "      <td>173</td>\n",
569 |        "      <td>134</td>\n",
570 |        "    </tr>\n",
571 |        "    <tr>\n",
572 |        "      <th>203</th>\n",
573 |        "      <td>-1</td>\n",
574 |        "      <td>145</td>\n",
575 |        "      <td>106</td>\n",
576 |        "    </tr>\n",
577 |        "    <tr>\n",
578 |        "      <th>204</th>\n",
579 |        "      <td>-1</td>\n",
580 |        "      <td>141</td>\n",
581 |        "      <td>114</td>\n",
582 |        "    </tr>\n",
583 |        "  </tbody>\n",
584 |        "</table>\n",
585 |        "<p>205 rows × 3 columns</p>\n",
586 |        "</div>"
587 |       ],
588 |       "text/plain": [
589 |        "     symboling  enginesize  horsepower\n",
590 |        "0            3         130         111\n",
591 |        "1            3         130         111\n",
592 |        "2            1         152         154\n",
593 |        "3            2         109         102\n",
594 |        "4            2         136         115\n",
595 |        "..         ...         ...         ...\n",
596 |        "200         -1         141         114\n",
597 |        "201         -1         141         160\n",
598 |        "202         -1         173         134\n",
599 |        "203         -1         145         106\n",
600 |        "204         -1         141         114\n",
601 |        "\n",
602 |        "[205 rows x 3 columns]"
603 |       ]
604 |      },
605 |      "execution_count": 5,
606 |      "metadata": {},
607 |      "output_type": "execute_result"
608 |     }
609 |    ],
610 |    "source": [
611 |     "# Filter out target column\n",
612 |     "X = df[['symboling', 'enginesize', 'horsepower']]\n",
613 |     "\n",
614 |     "# Print X\n",
615 |     "X"
616 |    ]
617 |   },
618 |   {
619 |    "cell_type": "code",
620 |    "execution_count": 6,
621 |    "metadata": {},
622 |    "outputs": [
623 |     {
624 |      "data": {
625 |       "text/plain": [
626 |        "0      13495.0\n",
627 |        "1      16500.0\n",
628 |        "2      16500.0\n",
629 |        "3      13950.0\n",
630 |        "4      17450.0\n",
631 |        "        ...   \n",
632 |        "200    16845.0\n",
633 |        "201    19045.0\n",
634 |        "202    21485.0\n",
635 |        "203    22470.0\n",
636 |        "204    22625.0\n",
637 |        "Name: price, Length: 205, dtype: float64"
638 |       ]
639 |      },
640 |      "execution_count": 6,
641 |      "metadata": {},
642 |      "output_type": "execute_result"
643 |     }
644 |    ],
645 |    "source": [
646 |     "# Select target column\n",
647 |     "y = df['price']\n",
648 |     "\n",
649 |     "# Print y\n",
650 |     "y"
651 |    ]
652 |   },
653 |   {
654 |    "cell_type": "markdown",
655 |    "metadata": {},
656 |    "source": [
657 |     "## 4. Model"
658 |    ]
659 |   },
660 |   {
661 |    "cell_type": "markdown",
662 |    "metadata": {},
663 |    "source": [
664 |     "In this section we are going to \n",
665 |     "- build a ridge regression model, \n",
666 |     "- evaluate its accuracy, and \n",
667 |     "- make a prediction"
668 |    ]
669 |   },
670 |   {
671 |    "cell_type": "markdown",
672 |    "metadata": {},
673 |    "source": [
674 |     "### 4.1. Building the model"
675 |    ]
676 |   },
677 |   {
678 |    "cell_type": "markdown",
679 |    "metadata": {},
680 |    "source": [
681 |     "Let's recall three simple steps:\n",
682 |     "\n",
683 |     "- Split the $X$ & $y$ variables into train and test sets\n",
684 |     "- Initialize the model\n",
685 |     "- Train the model with the varialbes"
686 |    ]
687 |   },
688 |   {
689 |    "cell_type": "code",
690 |    "execution_count": 7,
691 |    "metadata": {},
692 |    "outputs": [],
693 |    "source": [
694 |     "# Split variables into train and test\n",
695 |     "X_train, X_test, y_train, y_test = train_test_split(X, #independent variables\n",
696 |     "                                                    y, #dependent variable\n",
697 |     "                                                    random_state = 3\n",
698 |     "                                                   )"
699 |    ]
700 |   },
701 |   {
702 |    "cell_type": "code",
703 |    "execution_count": 8,
704 |    "metadata": {},
705 |    "outputs": [],
706 |    "source": [
707 |     "# Initialize the model\n",
708 |     "reg = Ridge(alpha=2.0, max_iter = 10000)"
709 |    ]
710 |   },
711 |   {
712 |    "cell_type": "code",
713 |    "execution_count": 9,
714 |    "metadata": {},
715 |    "outputs": [
716 |     {
717 |      "data": {
718 |       "text/html": [
719 |        "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=2.0, max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=2.0, max_iter=10000)</pre></div></div></div></div></div>"
720 |       ],
721 |       "text/plain": [
722 |        "Ridge(alpha=2.0, max_iter=10000)"
723 |       ]
724 |      },
725 |      "execution_count": 9,
726 |      "metadata": {},
727 |      "output_type": "execute_result"
728 |     }
729 |    ],
730 |    "source": [
731 |     "# Train the model\n",
732 |     "reg.fit(X_train, y_train)"
733 |    ]
734 |   },
735 |   {
736 |    "cell_type": "markdown",
737 |    "metadata": {},
738 |    "source": [
739 |     "### 4.2. Checking models accuracy"
740 |    ]
741 |   },
742 |   {
743 |    "cell_type": "markdown",
744 |    "metadata": {},
745 |    "source": [
746 |     "After the model has trained with the data, it's essential to understand how precisely it predicts car prices. For that, we need to check model's accuracy. "
747 |    ]
748 |   },
749 |   {
750 |    "cell_type": "code",
751 |    "execution_count": 10,
752 |    "metadata": {},
753 |    "outputs": [
754 |     {
755 |      "name": "stdout",
756 |      "output_type": "stream",
757 |      "text": [
758 |       "Accuracy (R-squared score) of Ridge Regression model on training set: 0.78\n",
759 |       "Accuracy (R-squared score) of Ridge Regression model on test set: 0.82\n"
760 |      ]
761 |     }
762 |    ],
763 |    "source": [
764 |     "print(f'Accuracy (R-squared score) of Ridge Regression model on training set: {reg.score(X_train, y_train):.2f}')\n",
765 |     "print(f'Accuracy (R-squared score) of Ridge Regression model on test set: {reg.score(X_test, y_test):.2f}')"
766 |    ]
767 |   },
768 |   {
769 |    "cell_type": "markdown",
770 |    "metadata": {},
771 |    "source": [
772 |     "### 4.3. Making a prediction"
773 |    ]
774 |   },
775 |   {
776 |    "cell_type": "markdown",
777 |    "metadata": {},
778 |    "source": [
779 |     "Now that we know the model is accurate enough, we can predict the price of any car by passing independent varialbes `symboling`, `enginesize`, `horsepower` to the model. \n",
780 |     "\n",
781 |     "\n",
782 |     "The method `predict` returns such a prediction."
783 |    ]
784 |   },
785 |   {
786 |    "cell_type": "code",
787 |    "execution_count": 11,
788 |    "metadata": {},
789 |    "outputs": [
790 |     {
791 |      "name": "stderr",
792 |      "output_type": "stream",
793 |      "text": [
794 |       "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but Ridge was fitted with feature names\n",
795 |       "  warnings.warn(\n"
796 |      ]
797 |     },
798 |     {
799 |      "data": {
800 |       "text/plain": [
801 |        "array([29353.06594801])"
802 |       ]
803 |      },
804 |      "execution_count": 11,
805 |      "metadata": {},
806 |      "output_type": "execute_result"
807 |     }
808 |    ],
809 |    "source": [
810 |     "reg.predict([[0, 180, 249]])"
811 |    ]
812 |   },
813 |   {
814 |    "cell_type": "markdown",
815 |    "metadata": {},
816 |    "source": [
817 |     "## 5. Hyperparameters Tuning"
818 |    ]
819 |   },
820 |   {
821 |    "cell_type": "markdown",
822 |    "metadata": {},
823 |    "source": [
824 |     "Hyperparameter Tuning is one of the critical steps in model building. It helps in increasing model's accuracy even further."
825 |    ]
826 |   },
827 |   {
828 |    "cell_type": "markdown",
829 |    "metadata": {},
830 |    "source": [
831 |     "### 5.1. Playing around with regularization parameter alpha"
832 |    ]
833 |   },
834 |   {
835 |    "cell_type": "code",
836 |    "execution_count": 26,
837 |    "metadata": {},
838 |    "outputs": [
839 |     {
840 |      "name": "stdout",
841 |      "output_type": "stream",
842 |      "text": [
843 |       "Alpha = 0.00\n",
844 |       "              r-squared training: 0.78,\n",
845 |       "              r-squared test: 0.82 \n",
846 |       "\n",
847 |       "Alpha = 1.00\n",
848 |       "              r-squared training: 0.78,\n",
849 |       "              r-squared test: 0.82 \n",
850 |       "\n",
851 |       "Alpha = 10.00\n",
852 |       "              r-squared training: 0.78,\n",
853 |       "              r-squared test: 0.82 \n",
854 |       "\n",
855 |       "Alpha = 20.00\n",
856 |       "              r-squared training: 0.78,\n",
857 |       "              r-squared test: 0.82 \n",
858 |       "\n",
859 |       "Alpha = 50.00\n",
860 |       "              r-squared training: 0.78,\n",
861 |       "              r-squared test: 0.82 \n",
862 |       "\n",
863 |       "Alpha = 100.00\n",
864 |       "              r-squared training: 0.78,\n",
865 |       "              r-squared test: 0.82 \n",
866 |       "\n"
867 |      ]
868 |     }
869 |    ],
870 |    "source": [
871 |     "# Ridge regression: effect of alpha regularization parameter\n",
872 |     "\n",
873 |     "for this_alpha in [0, 1, 10, 20, 50, 100]:\n",
874 |     "    \n",
875 |     "    # Initialize the model\n",
876 |     "    reg = Ridge(alpha=this_alpha, max_iter = 10000)\n",
877 |     "    \n",
878 |     "    # Train the model\n",
879 |     "    reg.fit(X_train, y_train)\n",
880 |     "    \n",
881 |     "    #Save accuracy\n",
882 |     "    r2_train = reg.score(X_train, y_train)\n",
883 |     "    r2_test = reg.score(X_test, y_test)\n",
884 |     "    \n",
885 |     "    # Print results\n",
886 |     "    print(f'''Alpha = {this_alpha:.2f}\n",
887 |     "              r-squared training: {r2_train:.2f},\n",
888 |     "              r-squared test: {r2_test:.2f} \\n''')"
889 |    ]
890 |   },
891 |   {
892 |    "cell_type": "markdown",
893 |    "metadata": {},
894 |    "source": [
895 |     "### 5.2. All Hyperparameters of Ridge Regression"
896 |    ]
897 |   },
898 |   {
899 |    "cell_type": "markdown",
900 |    "metadata": {},
901 |    "source": [
902 |     "There are many hyperparameters in Ridge Regression. They are all presented below  - as you can see, they have values by default. Feel free to play around with them. To learn more about hyperparameters and their meaning, visit https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge."
903 |    ]
904 |   },
905 |   {
906 |    "cell_type": "code",
907 |    "execution_count": 21,
908 |    "metadata": {},
909 |    "outputs": [],
910 |    "source": [
911 |     "reg = Ridge(alpha=1.0,\n",
912 |     "            fit_intercept=True, \n",
913 |     "            copy_X=True, \n",
914 |     "            max_iter=None, \n",
915 |     "            tol=0.0001, \n",
916 |     "            solver='auto', \n",
917 |     "            positive=False, \n",
918 |     "            random_state=None)"
919 |    ]
920 |   },
921 |   {
922 |    "cell_type": "markdown",
923 |    "metadata": {},
924 |    "source": [
925 |     "## 6. Ridge Regression with normalized data"
926 |    ]
927 |   },
928 |   {
929 |    "cell_type": "markdown",
930 |    "metadata": {},
931 |    "source": [
932 |     "(feature preprocessing using minmax scaling)"
933 |    ]
934 |   },
935 |   {
936 |    "cell_type": "code",
937 |    "execution_count": 19,
938 |    "metadata": {},
939 |    "outputs": [
940 |     {
941 |      "name": "stdout",
942 |      "output_type": "stream",
943 |      "text": [
944 |       "Ridge regression linear model intercept: 13045.07625490196\n",
945 |       "Ridge regression linear model coeff: [-582.02217923 3782.60816914 2880.08326775]\n",
946 |       "\n",
947 |       "R-squared score (training): 0.777\n",
948 |       "R-squared score (test): 0.806\n",
949 |       "\n"
950 |      ]
951 |     }
952 |    ],
953 |    "source": [
954 |     "#initialize scaler\n",
955 |     "scaler = StandardScaler()\n",
956 |     "\n",
957 |     "#transform variables\n",
958 |     "X_train_scaled = scaler.fit_transform(X_train)\n",
959 |     "X_test_scaled = scaler.transform(X_test)\n",
960 |     "\n",
961 |     "#initialize the model\n",
962 |     "reg = Ridge(alpha=20.0)\n",
963 |     "\n",
964 |     "#train the model\n",
965 |     "reg.fit(X_train_scaled, y_train)\n",
966 |     "\n",
967 |     "#print results\n",
968 |     "print(f'Ridge regression linear model intercept: {(reg.intercept_)}')\n",
969 |     "print(f'Ridge regression linear model coeff: {(reg.coef_)}\\n')\n",
970 |     "print(f'R-squared score (training): {reg.score(X_train_scaled, y_train):.3f}')\n",
971 |     "print(f'R-squared score (test): {reg.score(X_test_scaled, y_test):.3f}\\n')"
972 |    ]
973 |   }
974 |  ],
975 |  "metadata": {
976 |   "kernelspec": {
977 |    "display_name": "Python 3 (ipykernel)",
978 |    "language": "python",
979 |    "name": "python3"
980 |   },
981 |   "language_info": {
982 |    "codemirror_mode": {
983 |     "name": "ipython",
984 |     "version": 3
985 |    },
986 |    "file_extension": ".py",
987 |    "mimetype": "text/x-python",
988 |    "name": "python",
989 |    "nbconvert_exporter": "python",
990 |    "pygments_lexer": "ipython3",
991 |    "version": "3.10.7"
992 |   }
993 |  },
994 |  "nbformat": 4,
995 |  "nbformat_minor": 2
996 | }
997 | 


--------------------------------------------------------------------------------
/supplements/data/cars.csv:
--------------------------------------------------------------------------------
  1 | car_ID,symboling,CarName,fueltype,aspiration,doornumber,carbody,drivewheel,enginelocation,wheelbase,carlength,carwidth,carheight,curbweight,enginetype,cylindernumber,enginesize,fuelsystem,boreratio,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,price
  2 | 1,3,alfa-romero giulia,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,13495
  3 | 2,3,alfa-romero stelvio,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,16500
  4 | 3,1,alfa-romero Quadrifoglio,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26,16500
  5 | 4,2,audi 100 ls,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10,102,5500,24,30,13950
  6 | 5,2,audi 100ls,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8,115,5500,18,22,17450
  7 | 6,2,audi fox,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,15250
  8 | 7,1,audi 100ls,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,17710
  9 | 8,1,audi 5000,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,18920
 10 | 9,1,audi 4000,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140,5500,17,20,23875
 11 | 10,0,audi 5000s (diesel),gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52,3053,ohc,five,131,mpfi,3.13,3.4,7,160,5500,16,22,17859.167
 12 | 11,2,bmw 320i,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16430
 13 | 12,0,bmw 320i,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16925
 14 | 13,0,bmw x1,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,20970
 15 | 14,0,bmw x3,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,21105
 16 | 15,1,bmw z4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9,121,4250,20,25,24565
 17 | 16,0,bmw x4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,30760
 18 | 17,0,bmw x5,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,41315
 19 | 18,0,bmw x3,gas,std,four,sedan,rwd,front,110,197,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8,182,5400,15,20,36880
 20 | 19,2,chevrolet impala,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151
 21 | 20,1,chevrolet monte carlo,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295
 22 | 21,0,chevrolet vega 2300,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575
 23 | 22,1,dodge rampage,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572
 24 | 23,1,dodge challenger se,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377
 25 | 24,1,dodge d200,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957
 26 | 25,1,dodge monaco (sw),gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229
 27 | 26,1,dodge colt hardtop,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692
 28 | 27,1,dodge colt (sw),gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609
 29 | 28,1,dodge coronet custom,gas,turbo,two,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558
 30 | 29,-1,dodge dart custom,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88,5000,24,30,8921
 31 | 30,3,dodge coronet custom (sw),gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2811,ohc,four,156,mfi,3.6,3.9,7,145,5000,19,24,12964
 32 | 31,2,honda civic,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58,4800,49,54,6479
 33 | 32,2,honda civic cvcc,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,31,38,6855
 34 | 33,1,honda civic,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60,5500,38,42,5399
 35 | 34,1,honda accord cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,6529
 36 | 35,1,honda civic cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7129
 37 | 36,0,honda accord lx,gas,std,four,sedan,fwd,front,96.5,163.4,64,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7295
 38 | 37,0,honda civic 1500 gl,gas,std,four,wagon,fwd,front,96.5,157.1,63.9,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76,6000,30,34,7295
 39 | 38,0,honda accord,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,7895
 40 | 39,0,honda civic 1300,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,9095
 41 | 40,0,honda prelude,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,8845
 42 | 41,0,honda accord,gas,std,four,sedan,fwd,front,96.5,175.4,62.5,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,10295
 43 | 42,0,honda civic,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9,101,5800,24,28,12945
 44 | 43,1,honda civic (auto),gas,std,two,sedan,fwd,front,96.5,169.1,66,51,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100,5500,25,31,10345
 45 | 44,0,isuzu MU-X,gas,std,four,sedan,rwd,front,94.3,170.7,61.8,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78,4800,24,29,6785
 46 | 45,1,isuzu D-Max ,gas,std,two,sedan,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5
 47 | 46,0,isuzu D-Max V-Cross,gas,std,four,sedan,fwd,front,94.5,155.9,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5
 48 | 47,2,isuzu D-Max ,gas,std,two,hatchback,rwd,front,96,172.6,65.2,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90,5000,24,29,11048
 49 | 48,0,jaguar xj,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,32250
 50 | 49,0,jaguar xf,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,35550
 51 | 50,0,jaguar xk,gas,std,two,sedan,rwd,front,102,191.7,70.6,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262,5000,13,17,36000
 52 | 51,1,maxda rx3,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9,68,5000,30,31,5195
 53 | 52,1,maxda glc deluxe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6095
 54 | 53,1,mazda rx2 coupe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6795
 55 | 54,1,mazda rx-4,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6695
 56 | 55,1,mazda glc deluxe,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9,68,5000,31,38,7395
 57 | 56,3,mazda 626,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,10945
 58 | 57,3,mazda glc,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,11845
 59 | 58,3,mazda rx-7 gs,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2385,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,13645
 60 | 59,3,mazda glc 4,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2500,rotor,two,80,mpfi,3.33,3.255,9.4,135,6000,16,23,15645
 61 | 60,1,mazda 626,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8845
 62 | 61,0,mazda glc custom l,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8495
 63 | 62,1,mazda glc custom,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10595
 64 | 63,0,mazda rx-4,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10245
 65 | 64,0,mazda glc deluxe,diesel,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64,4650,36,42,10795
 66 | 65,0,mazda 626,gas,std,four,hatchback,fwd,front,98.8,177.8,66.5,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,11245
 67 | 66,0,mazda glc,gas,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8,120,5000,19,27,18280
 68 | 67,0,mazda rx-7 gs,diesel,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2700,ohc,four,134,idi,3.43,3.64,22,72,4200,31,39,18344
 69 | 68,-1,buick electra 225 custom,diesel,turbo,four,sedan,rwd,front,110,190.9,70.3,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,25552
 70 | 69,-1,buick century luxus (sw),diesel,turbo,four,wagon,rwd,front,110,190.9,70.3,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28248
 71 | 70,0,buick century,diesel,turbo,two,hardtop,rwd,front,106.7,187.5,70.3,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28176
 72 | 71,-1,buick skyhawk,diesel,turbo,four,sedan,rwd,front,115.6,202.6,71.7,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,31600
 73 | 72,-1,buick opel isuzu deluxe,gas,std,four,sedan,rwd,front,115.6,202.6,71.7,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,34184
 74 | 73,3,buick skylark,gas,std,two,convertible,rwd,front,96.6,180.3,70.5,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,35056
 75 | 74,0,buick century special,gas,std,four,sedan,rwd,front,120.9,208.1,71.7,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8,184,4500,14,16,40960
 76 | 75,1,buick regal sport coupe (turbo),gas,std,two,hardtop,rwd,front,112,199.2,72,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8,184,4500,14,16,45400
 77 | 76,1,mercury cougar,gas,turbo,two,hatchback,rwd,front,102.7,178.4,68,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8,175,5000,19,24,16503
 78 | 77,2,mitsubishi mirage,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,37,41,5389
 79 | 78,2,mitsubishi lancer,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6189
 80 | 79,2,mitsubishi outlander,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6669
 81 | 80,1,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,93,157.3,63.8,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7689
 82 | 81,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,96.3,173,65.4,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9959
 83 | 82,3,mitsubishi g4,gas,std,two,hatchback,fwd,front,96.3,173,65.4,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8499
 84 | 83,3,mitsubishi outlander,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2833,ohc,four,156,spdi,3.58,3.86,7,145,5000,19,24,12629
 85 | 84,3,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2921,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14869
 86 | 85,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2926,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14489
 87 | 86,1,mitsubishi montero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,6989
 88 | 87,1,mitsubishi pajero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8189
 89 | 88,1,mitsubishi outlander,gas,turbo,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279
 90 | 89,-1,mitsubishi mirage g4,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279
 91 | 90,1,Nissan versa,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,5499
 92 | 91,1,nissan gt-r,diesel,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55,4800,45,50,7099
 93 | 92,1,nissan rogue,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6649
 94 | 93,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6849
 95 | 94,1,nissan titan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7349
 96 | 95,1,nissan leaf,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7299
 97 | 96,1,nissan juke,gas,std,two,hatchback,fwd,front,94.5,165.6,63.8,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7799
 98 | 97,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7499
 99 | 98,1,nissan note,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7999
100 | 99,2,nissan clipper,gas,std,two,hardtop,fwd,front,95.1,162.4,63.8,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,8249
101 | 100,0,nissan rogue,gas,std,four,hatchback,fwd,front,97.2,173.4,65.2,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,8949
102 | 101,0,nissan nv200,gas,std,four,sedan,fwd,front,97.2,173.4,65.2,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,9549
103 | 102,0,nissan dayz,gas,std,four,sedan,fwd,front,100.4,181.7,66.5,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,13499
104 | 103,0,nissan fuga,gas,std,four,wagon,fwd,front,100.4,184.6,66.5,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,14399
105 | 104,0,nissan otti,gas,std,four,sedan,fwd,front,100.4,184.6,66.5,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,19,25,13499
106 | 105,3,nissan teana,gas,std,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,17199
107 | 106,3,nissan kicks,gas,turbo,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200,5200,17,23,19699
108 | 107,1,nissan clipper,gas,std,two,hatchback,rwd,front,99.2,178.5,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,18399
109 | 108,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,11900
110 | 109,0,peugeot 304,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3197,l,four,152,idi,3.7,3.52,21,95,4150,28,33,13200
111 | 110,0,peugeot 504 (sw),gas,std,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,12440
112 | 111,0,peugeot 504,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3430,l,four,152,idi,3.7,3.52,21,95,4150,25,25,13860
113 | 112,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,15580
114 | 113,0,peugeot 604sl,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,16900
115 | 114,0,peugeot 504,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,16695
116 | 115,0,peugeot 505s turbo diesel,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3485,l,four,152,idi,3.7,3.52,21,95,4150,25,25,17075
117 | 116,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,16630
118 | 117,0,peugeot 504,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,17950
119 | 118,0,peugeot 604sl,gas,turbo,four,sedan,rwd,front,108,186.7,68.3,56,3130,l,four,134,mpfi,3.61,3.21,7,142,5600,18,24,18150
120 | 119,1,plymouth fury iii,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,37,41,5572
121 | 120,1,plymouth cricket,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7957
122 | 121,1,plymouth fury iii,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229
123 | 122,1,plymouth satellite custom (sw),gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692
124 | 123,1,plymouth fury gran sedan,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609
125 | 124,-1,plymouth valiant,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921
126 | 125,3,plymouth duster,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,12764
127 | 126,3,porsche macan,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018
128 | 127,3,porcshce panamera,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,32528
129 | 128,3,porsche cayenne,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,34028
130 | 129,3,porsche boxter,gas,std,two,convertible,rwd,rear,89.5,168.9,65,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,37028
131 | 130,1,porsche cayenne,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,31400.5
132 | 131,0,renault 12tl,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9295
133 | 132,2,renault 5 gtl,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9895
134 | 133,3,saab 99e,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850
135 | 134,2,saab 99le,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170
136 | 135,3,saab 99le,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040
137 | 136,2,saab 99gle,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510
138 | 137,3,saab 99gle,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18150
139 | 138,2,saab 99e,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18620
140 | 139,2,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9,69,4900,31,36,5118
141 | 140,2,subaru dl,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053
142 | 141,2,subaru dl,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603
143 | 142,0,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126
144 | 143,0,subaru brz,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775
145 | 144,0,subaru baja,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,26,32,9960
146 | 145,0,subaru r1,gas,std,four,sedan,4wd,front,97,172,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,24,25,9233
147 | 146,0,subaru r2,gas,turbo,four,sedan,4wd,front,97,172,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259
148 | 147,0,subaru trezia,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2290,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,28,32,7463
149 | 148,0,subaru tribeca,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2455,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,25,31,10198
150 | 149,0,subaru dl,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,23,29,8013
151 | 150,0,subaru dl,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694
152 | 151,1,toyota corona mark ii,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9,62,4800,35,39,5348
153 | 152,1,toyota corona,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6338
154 | 153,1,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6488
155 | 154,0,toyota corona hardtop,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,37,6918
156 | 155,0,toyota corolla 1600 (sw),gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,7898
157 | 156,0,toyota carina,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,8778
158 | 157,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2081,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,6938
159 | 158,0,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,7198
160 | 159,0,toyota corona,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898
161 | 160,0,toyota corolla,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788
162 | 161,0,toyota corona,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2094,ohc,four,98,2bbl,3.19,3.03,9,70,4800,38,47,7738
163 | 162,0,toyota corolla,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,8358
164 | 163,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,9258
165 | 164,1,toyota corolla liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8058
166 | 165,1,toyota corona,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8238
167 | 166,1,toyota celica gt liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298
168 | 167,1,toyota corolla tercel,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538
169 | 168,2,toyota corona liftback,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,8449
170 | 169,2,toyota corolla,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9639
171 | 170,2,toyota starlet,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9989
172 | 171,2,toyota tercel,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11199
173 | 172,2,toyota corolla,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11549
174 | 173,2,toyota cressida,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,17669
175 | 174,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948
176 | 175,-1,toyota celica gt,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698
177 | 176,-1,toyota corona,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988
178 | 177,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898
179 | 178,-1,toyota mark ii,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248
180 | 179,3,toyota corolla liftback,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558
181 | 180,3,toyota corona,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998
182 | 181,-1,toyota starlet,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690
183 | 182,-1,toyouta tercel,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750
184 | 183,2,vokswagen rabbit,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7775
185 | 184,2,volkswagen 1131 deluxe sedan,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,7975
186 | 185,2,volkswagen model 111,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7995
187 | 186,2,volkswagen type 3,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8195
188 | 187,2,volkswagen 411 (sw),gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8495
189 | 188,2,volkswagen super beetle,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.4,23,68,4500,37,42,9495
190 | 189,2,volkswagen dasher,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10,100,5500,26,32,9995
191 | 190,3,vw dasher,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,11595
192 | 191,3,vw rabbit,gas,std,two,hatchback,fwd,front,94.5,165.7,64,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,9980
193 | 192,0,volkswagen rabbit,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,24,13295
194 | 193,0,volkswagen rabbit custom,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.4,23,68,4500,33,38,13845
195 | 194,0,volkswagen dasher,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9,88,5500,25,31,12290
196 | 195,-2,volvo 145e (sw),gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940
197 | 196,-1,volvo 144ea,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415
198 | 197,-2,volvo 244dl,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985
199 | 198,-1,volvo 245,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515
200 | 199,-2,volvo 264gl,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420
201 | 200,-1,volvo diesel,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950
202 | 201,-1,volvo 145e (sw),gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845
203 | 202,-1,volvo 144ea,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045
204 | 203,-1,volvo 244dl,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485
205 | 204,-1,volvo 246,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.4,23,106,4800,26,27,22470
206 | 205,-1,volvo 264gl,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625
207 | 


--------------------------------------------------------------------------------
/supplements/data/heart.csv:
--------------------------------------------------------------------------------
   1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
   2 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0
   3 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
   4 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
   5 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0
   6 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0
   7 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1
   8 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
   9 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
  10 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
  11 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
  12 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
  13 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0
  14 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1
  15 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0
  16 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0
  17 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1
  18 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1
  19 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0
  20 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1
  21 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1
  22 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0
  23 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1
  24 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1
  25 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1
  26 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1
  27 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0
  28 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1
  29 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0
  30 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
  31 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
  32 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
  33 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1
  34 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
  35 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0
  36 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1
  37 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0
  38 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1
  39 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1
  40 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1
  41 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
  42 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1
  43 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1
  44 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0
  45 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
  46 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
  47 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1
  48 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1
  49 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0
  50 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1
  51 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
  52 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1
  53 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0
  54 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
  55 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0
  56 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
  57 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
  58 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1
  59 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1
  60 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
  61 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0
  62 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1
  63 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1
  64 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0
  65 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1
  66 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1
  67 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
  68 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1
  69 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
  70 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1
  71 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0
  72 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0
  73 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
  74 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
  75 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0
  76 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0
  77 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1
  78 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1
  79 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0
  80 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1
  81 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1
  82 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0
  83 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
  84 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0
  85 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
  86 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1
  87 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1
  88 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1
  89 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0
  90 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
  91 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0
  92 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1
  93 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1
  94 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0
  95 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
  96 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1
  97 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1
  98 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1
  99 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0
 100 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0
 101 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1
 102 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1
 103 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1
 104 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1
 105 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1
 106 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1
 107 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0
 108 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
 109 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
 110 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0
 111 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0
 112 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0
 113 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1
 114 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0
 115 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0
 116 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
 117 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0
 118 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
 119 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
 120 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1
 121 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1
 122 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1
 123 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
 124 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0
 125 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1
 126 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
 127 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
 128 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1
 129 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1
 130 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1
 131 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1
 132 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
 133 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1
 134 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1
 135 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1
 136 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1
 137 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
 138 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
 139 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1
 140 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1
 141 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1
 142 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0
 143 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0
 144 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
 145 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1
 146 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1
 147 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0
 148 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1
 149 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0
 150 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1
 151 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1
 152 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
 153 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0
 154 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0
 155 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1
 156 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
 157 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1
 158 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1
 159 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1
 160 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1
 161 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1
 162 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0
 163 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1
 164 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0
 165 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0
 166 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
 167 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0
 168 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0
 169 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1
 170 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1
 171 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1
 172 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1
 173 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
 174 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1
 175 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1
 176 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
 177 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0
 178 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
 179 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
 180 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0
 181 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0
 182 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0
 183 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1
 184 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
 185 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1
 186 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1
 187 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0
 188 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0
 189 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
 190 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
 191 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
 192 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1
 193 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1
 194 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1
 195 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1
 196 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
 197 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1
 198 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
 199 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1
 200 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1
 201 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0
 202 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1
 203 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1
 204 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1
 205 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
 206 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1
 207 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1
 208 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
 209 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1
 210 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
 211 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
 212 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1
 213 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0
 214 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0
 215 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1
 216 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1
 217 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1
 218 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
 219 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1
 220 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
 221 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
 222 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0
 223 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
 224 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1
 225 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1
 226 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1
 227 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1
 228 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
 229 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1
 230 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1
 231 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0
 232 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0
 233 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1
 234 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0
 235 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1
 236 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1
 237 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
 238 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
 239 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
 240 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
 241 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0
 242 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1
 243 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
 244 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
 245 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0
 246 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1
 247 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1
 248 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0
 249 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
 250 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1
 251 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1
 252 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0
 253 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1
 254 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
 255 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0
 256 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
 257 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1
 258 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1
 259 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1
 260 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
 261 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1
 262 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1
 263 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1
 264 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1
 265 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
 266 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
 267 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1
 268 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0
 269 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0
 270 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
 271 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1
 272 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1
 273 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1
 274 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1
 275 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0
 276 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
 277 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
 278 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1
 279 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1
 280 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
 281 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1
 282 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1
 283 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1
 284 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1
 285 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1
 286 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
 287 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1
 288 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1
 289 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1
 290 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1
 291 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0
 292 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1
 293 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0
 294 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1
 295 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1
 296 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0
 297 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0
 298 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0
 299 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0
 300 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1
 301 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1
 302 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1
 303 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1
 304 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
 305 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0
 306 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1
 307 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0
 308 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1
 309 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1
 310 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
 311 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1
 312 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
 313 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0
 314 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0
 315 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1
 316 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1
 317 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1
 318 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1
 319 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1
 320 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0
 321 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1
 322 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1
 323 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1
 324 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0
 325 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0
 326 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1
 327 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1
 328 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0
 329 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
 330 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
 331 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1
 332 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1
 333 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
 334 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1
 335 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1
 336 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
 337 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0
 338 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1
 339 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1
 340 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
 341 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0
 342 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
 343 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1
 344 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1
 345 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1
 346 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1
 347 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0
 348 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0
 349 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1
 350 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0
 351 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
 352 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1
 353 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0
 354 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0
 355 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1
 356 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
 357 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1
 358 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0
 359 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
 360 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
 361 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1
 362 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1
 363 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1
 364 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1
 365 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1
 366 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0
 367 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1
 368 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0
 369 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0
 370 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1
 371 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1
 372 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0
 373 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
 374 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1
 375 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
 376 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1
 377 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
 378 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1
 379 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1
 380 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0
 381 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1
 382 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0
 383 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
 384 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0
 385 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0
 386 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0
 387 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1
 388 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1
 389 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0
 390 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
 391 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1
 392 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0
 393 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
 394 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1
 395 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0
 396 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
 397 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1
 398 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0
 399 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0
 400 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
 401 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1
 402 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0
 403 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
 404 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1
 405 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1
 406 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
 407 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
 408 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1
 409 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
 410 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1
 411 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0
 412 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1
 413 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
 414 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
 415 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
 416 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
 417 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1
 418 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1
 419 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1
 420 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1
 421 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1
 422 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1
 423 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1
 424 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1
 425 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0
 426 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0
 427 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0
 428 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1
 429 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1
 430 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0
 431 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0
 432 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
 433 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0
 434 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1
 435 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1
 436 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1
 437 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1
 438 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0
 439 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
 440 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1
 441 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
 442 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0
 443 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
 444 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0
 445 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1
 446 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1
 447 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1
 448 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1
 449 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
 450 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1
 451 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0
 452 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0
 453 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1
 454 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0
 455 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1
 456 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0
 457 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1
 458 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0
 459 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1
 460 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1
 461 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1
 462 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0
 463 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1
 464 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1
 465 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1
 466 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1
 467 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
 468 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1
 469 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0
 470 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1
 471 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
 472 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
 473 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1
 474 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
 475 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1
 476 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0
 477 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1
 478 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0
 479 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
 480 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1
 481 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0
 482 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0
 483 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0
 484 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0
 485 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1
 486 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0
 487 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
 488 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0
 489 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
 490 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1
 491 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1
 492 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1
 493 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1
 494 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
 495 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
 496 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1
 497 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1
 498 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0
 499 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0
 500 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1
 501 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1
 502 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
 503 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1
 504 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1
 505 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1
 506 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0
 507 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1
 508 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
 509 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1
 510 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0
 511 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
 512 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1
 513 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
 514 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0
 515 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0
 516 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1
 517 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0
 518 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
 519 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1
 520 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0
 521 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0
 522 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0
 523 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
 524 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1
 525 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0
 526 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0
 527 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1
 528 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0
 529 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1
 530 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1
 531 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1
 532 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0
 533 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1
 534 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0
 535 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1
 536 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1
 537 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1
 538 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1
 539 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1
 540 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
 541 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
 542 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0
 543 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1
 544 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1
 545 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
 546 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1
 547 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0
 548 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
 549 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1
 550 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1
 551 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1
 552 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0
 553 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
 554 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0
 555 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1
 556 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
 557 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0
 558 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0
 559 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1
 560 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1
 561 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0
 562 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
 563 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1
 564 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1
 565 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0
 566 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0
 567 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1
 568 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1
 569 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1
 570 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1
 571 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1
 572 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1
 573 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0
 574 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1
 575 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0
 576 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0
 577 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1
 578 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1
 579 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
 580 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1
 581 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0
 582 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1
 583 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1
 584 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1
 585 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
 586 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
 587 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1
 588 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
 589 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0
 590 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0
 591 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
 592 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1
 593 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
 594 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0
 595 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
 596 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0
 597 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0
 598 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1
 599 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
 600 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
 601 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1
 602 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
 603 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
 604 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1
 605 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1
 606 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1
 607 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1
 608 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
 609 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
 610 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0
 611 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
 612 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0
 613 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0
 614 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
 615 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
 616 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0
 617 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1
 618 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1
 619 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1
 620 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1
 621 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0
 622 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0
 623 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0
 624 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0
 625 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
 626 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1
 627 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0
 628 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
 629 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
 630 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1
 631 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
 632 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
 633 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1
 634 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1
 635 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
 636 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0
 637 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1
 638 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0
 639 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1
 640 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
 641 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
 642 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1
 643 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0
 644 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1
 645 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1
 646 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1
 647 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0
 648 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0
 649 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1
 650 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
 651 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1
 652 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0
 653 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1
 654 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1
 655 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
 656 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1
 657 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1
 658 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0
 659 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1
 660 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
 661 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1
 662 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
 663 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
 664 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1
 665 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1
 666 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1
 667 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1
 668 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1
 669 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
 670 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1
 671 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0
 672 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0
 673 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0
 674 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1
 675 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1
 676 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
 677 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
 678 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0
 679 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
 680 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1
 681 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0
 682 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1
 683 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0
 684 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0
 685 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
 686 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0
 687 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0
 688 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0
 689 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0
 690 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0
 691 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1
 692 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1
 693 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1
 694 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
 695 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1
 696 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
 697 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0
 698 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1
 699 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
 700 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
 701 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0
 702 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1
 703 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
 704 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1
 705 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1
 706 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1
 707 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0
 708 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
 709 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1
 710 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1
 711 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
 712 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1
 713 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
 714 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1
 715 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1
 716 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1
 717 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1
 718 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0
 719 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
 720 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1
 721 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1
 722 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0
 723 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1
 724 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1
 725 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1
 726 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1
 727 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0
 728 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0
 729 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1
 730 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
 731 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1
 732 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1
 733 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0
 734 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1
 735 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1
 736 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0
 737 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1
 738 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0
 739 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
 740 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0
 741 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0
 742 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1
 743 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1
 744 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
 745 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
 746 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1
 747 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1
 748 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0
 749 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
 750 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1
 751 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
 752 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1
 753 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1
 754 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1
 755 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1
 756 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1
 757 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1
 758 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
 759 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
 760 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1
 761 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0
 762 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
 763 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1
 764 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1
 765 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1
 766 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0
 767 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0
 768 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0
 769 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0
 770 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1
 771 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1
 772 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1
 773 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1
 774 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0
 775 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0
 776 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1
 777 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1
 778 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0
 779 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0
 780 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
 781 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1
 782 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
 783 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0
 784 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1
 785 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1
 786 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1
 787 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
 788 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0
 789 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0
 790 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0
 791 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
 792 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
 793 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
 794 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0
 795 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1
 796 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
 797 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1
 798 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1
 799 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0
 800 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
 801 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1
 802 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
 803 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1
 804 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0
 805 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0
 806 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
 807 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1
 808 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
 809 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1
 810 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1
 811 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1
 812 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0
 813 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
 814 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
 815 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
 816 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1
 817 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1
 818 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1
 819 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1
 820 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1
 821 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
 822 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
 823 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
 824 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0
 825 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1
 826 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
 827 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1
 828 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1
 829 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
 830 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1
 831 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0
 832 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1
 833 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
 834 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1
 835 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
 836 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
 837 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
 838 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1
 839 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1
 840 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1
 841 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1
 842 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
 843 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1
 844 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0
 845 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0
 846 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
 847 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0
 848 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1
 849 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
 850 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1
 851 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0
 852 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
 853 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1
 854 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0
 855 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
 856 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
 857 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1
 858 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1
 859 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1
 860 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1
 861 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
 862 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0
 863 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0
 864 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
 865 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0
 866 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0
 867 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
 868 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1
 869 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1
 870 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1
 871 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1
 872 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1
 873 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1
 874 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
 875 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1
 876 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0
 877 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
 878 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
 879 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1
 880 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0
 881 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1
 882 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1
 883 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0
 884 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
 885 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0
 886 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
 887 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0
 888 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
 889 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1
 890 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0
 891 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0
 892 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0
 893 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1
 894 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
 895 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0
 896 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
 897 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
 898 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1
 899 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1
 900 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1
 901 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1
 902 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
 903 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1
 904 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
 905 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
 906 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1
 907 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
 908 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1
 909 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1
 910 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0
 911 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0
 912 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0
 913 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0
 914 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
 915 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1
 916 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
 917 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
 918 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
 919 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1
 920 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
 921 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
 922 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
 923 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0
 924 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1
 925 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1
 926 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0
 927 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
 928 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0
 929 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1
 930 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0
 931 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
 932 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
 933 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0
 934 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1
 935 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
 936 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1
 937 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1
 938 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1
 939 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0
 940 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1
 941 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1
 942 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0
 943 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1
 944 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1
 945 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1
 946 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0
 947 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1
 948 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1
 949 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1
 950 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
 951 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1
 952 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
 953 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
 954 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1
 955 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1
 956 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1
 957 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1
 958 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1
 959 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0
 960 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1
 961 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1
 962 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1
 963 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1
 964 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1
 965 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0
 966 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1
 967 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1
 968 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0
 969 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1
 970 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
 971 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1
 972 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
 973 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1
 974 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1
 975 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1
 976 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1
 977 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
 978 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
 979 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1
 980 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0
 981 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0
 982 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1
 983 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
 984 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1
 985 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1
 986 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1
 987 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1
 988 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
 989 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0
 990 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
 991 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1
 992 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1
 993 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
 994 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1
 995 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0
 996 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0
 997 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1
 998 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0
 999 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0
1000 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
1001 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0
1002 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0
1003 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1
1004 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
1005 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1
1006 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1
1007 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0
1008 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1
1009 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1
1010 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1
1011 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0
1012 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
1013 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1
1014 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0
1015 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
1016 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1
1017 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0
1018 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
1019 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0
1020 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0
1021 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1
1022 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1
1023 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0
1024 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0
1025 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1
1026 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0
1027 | 


--------------------------------------------------------------------------------