├── README.md
├── 50_startups Success Rate Prediction
    ├── 50_Startups.csv
    └── Multiple_Linear_Regression.ipynb
├── House_Price_Prediction Detailed Analysis
    └── Feature_Selection.ipynb
└── Fraud Detection Using ML
    └── fraud_detection.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # Machine Learning Projects
2 | 
3 | Welcome to my Machine Learning Projects repository! This repository contains a collection of machine learning projects that cover various topics.
4 | 


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/50_startups Success Rate Prediction/50_Startups.csv:
--------------------------------------------------------------------------------
 1 | R&D Spend,Administration,Marketing Spend,State,Profit
 2 | 165349.2,136897.8,471784.1,New York,192261.83
 3 | 162597.7,151377.59,443898.53,California,191792.06
 4 | 153441.51,101145.55,407934.54,Florida,191050.39
 5 | 144372.41,118671.85,383199.62,New York,182901.99
 6 | 142107.34,91391.77,366168.42,Florida,166187.94
 7 | 131876.9,99814.71,362861.36,New York,156991.12
 8 | 134615.46,147198.87,127716.82,California,156122.51
 9 | 130298.13,145530.06,323876.68,Florida,155752.6
10 | 120542.52,148718.95,311613.29,New York,152211.77
11 | 123334.88,108679.17,304981.62,California,149759.96
12 | 101913.08,110594.11,229160.95,Florida,146121.95
13 | 100671.96,91790.61,249744.55,California,144259.4
14 | 93863.75,127320.38,249839.44,Florida,141585.52
15 | 91992.39,135495.07,252664.93,California,134307.35
16 | 119943.24,156547.42,256512.92,Florida,132602.65
17 | 114523.61,122616.84,261776.23,New York,129917.04
18 | 78013.11,121597.55,264346.06,California,126992.93
19 | 94657.16,145077.58,282574.31,New York,125370.37
20 | 91749.16,114175.79,294919.57,Florida,124266.9
21 | 86419.7,153514.11,0,New York,122776.86
22 | 76253.86,113867.3,298664.47,California,118474.03
23 | 78389.47,153773.43,299737.29,New York,111313.02
24 | 73994.56,122782.75,303319.26,Florida,110352.25
25 | 67532.53,105751.03,304768.73,Florida,108733.99
26 | 77044.01,99281.34,140574.81,New York,108552.04
27 | 64664.71,139553.16,137962.62,California,107404.34
28 | 75328.87,144135.98,134050.07,Florida,105733.54
29 | 72107.6,127864.55,353183.81,New York,105008.31
30 | 66051.52,182645.56,118148.2,Florida,103282.38
31 | 65605.48,153032.06,107138.38,New York,101004.64
32 | 61994.48,115641.28,91131.24,Florida,99937.59
33 | 61136.38,152701.92,88218.23,New York,97483.56
34 | 63408.86,129219.61,46085.25,California,97427.84
35 | 55493.95,103057.49,214634.81,Florida,96778.92
36 | 46426.07,157693.92,210797.67,California,96712.8
37 | 46014.02,85047.44,205517.64,New York,96479.51
38 | 28663.76,127056.21,201126.82,Florida,90708.19
39 | 44069.95,51283.14,197029.42,California,89949.14
40 | 20229.59,65947.93,185265.1,New York,81229.06
41 | 38558.51,82982.09,174999.3,California,81005.76
42 | 28754.33,118546.05,172795.67,California,78239.91
43 | 27892.92,84710.77,164470.71,Florida,77798.83
44 | 23640.93,96189.63,148001.11,California,71498.49
45 | 15505.73,127382.3,35534.17,New York,69758.98
46 | 22177.74,154806.14,28334.72,California,65200.33
47 | 1000.23,124153.04,1903.93,New York,64926.08
48 | 1315.46,115816.21,297114.46,Florida,49490.75
49 | 0,135426.92,0,California,42559.73
50 | 542.05,51743.15,0,New York,35673.41
51 | 0,116983.8,45173.06,California,14681.4


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/50_startups Success Rate Prediction/Multiple_Linear_Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "import pandas as pd"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "df = pd.read_csv('Startups.csv')"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 3,
 26 |    "metadata": {},
 27 |    "outputs": [
 28 |     {
 29 |      "data": {
 30 |       "text/html": [
 31 |        "<div>\n",
 32 |        "<style scoped>\n",
 33 |        "    .dataframe tbody tr th:only-of-type {\n",
 34 |        "        vertical-align: middle;\n",
 35 |        "    }\n",
 36 |        "\n",
 37 |        "    .dataframe tbody tr th {\n",
 38 |        "        vertical-align: top;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe thead th {\n",
 42 |        "        text-align: right;\n",
 43 |        "    }\n",
 44 |        "</style>\n",
 45 |        "<table border=\"1\" class=\"dataframe\">\n",
 46 |        "  <thead>\n",
 47 |        "    <tr style=\"text-align: right;\">\n",
 48 |        "      <th></th>\n",
 49 |        "      <th>R&amp;D Spend</th>\n",
 50 |        "      <th>Administration</th>\n",
 51 |        "      <th>Marketing Spend</th>\n",
 52 |        "      <th>State</th>\n",
 53 |        "      <th>Profit</th>\n",
 54 |        "    </tr>\n",
 55 |        "  </thead>\n",
 56 |        "  <tbody>\n",
 57 |        "    <tr>\n",
 58 |        "      <th>0</th>\n",
 59 |        "      <td>165349.20</td>\n",
 60 |        "      <td>136897.80</td>\n",
 61 |        "      <td>471784.10</td>\n",
 62 |        "      <td>New York</td>\n",
 63 |        "      <td>192261.83</td>\n",
 64 |        "    </tr>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>1</th>\n",
 67 |        "      <td>162597.70</td>\n",
 68 |        "      <td>151377.59</td>\n",
 69 |        "      <td>443898.53</td>\n",
 70 |        "      <td>California</td>\n",
 71 |        "      <td>191792.06</td>\n",
 72 |        "    </tr>\n",
 73 |        "    <tr>\n",
 74 |        "      <th>2</th>\n",
 75 |        "      <td>153441.51</td>\n",
 76 |        "      <td>101145.55</td>\n",
 77 |        "      <td>407934.54</td>\n",
 78 |        "      <td>Florida</td>\n",
 79 |        "      <td>191050.39</td>\n",
 80 |        "    </tr>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>3</th>\n",
 83 |        "      <td>144372.41</td>\n",
 84 |        "      <td>118671.85</td>\n",
 85 |        "      <td>383199.62</td>\n",
 86 |        "      <td>New York</td>\n",
 87 |        "      <td>182901.99</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>4</th>\n",
 91 |        "      <td>142107.34</td>\n",
 92 |        "      <td>91391.77</td>\n",
 93 |        "      <td>366168.42</td>\n",
 94 |        "      <td>Florida</td>\n",
 95 |        "      <td>166187.94</td>\n",
 96 |        "    </tr>\n",
 97 |        "  </tbody>\n",
 98 |        "</table>\n",
 99 |        "</div>"
100 |       ],
101 |       "text/plain": [
102 |        "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
103 |        "0  165349.20       136897.80        471784.10    New York  192261.83\n",
104 |        "1  162597.70       151377.59        443898.53  California  191792.06\n",
105 |        "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
106 |        "3  144372.41       118671.85        383199.62    New York  182901.99\n",
107 |        "4  142107.34        91391.77        366168.42     Florida  166187.94"
108 |       ]
109 |      },
110 |      "execution_count": 3,
111 |      "metadata": {},
112 |      "output_type": "execute_result"
113 |     }
114 |    ],
115 |    "source": [
116 |     "df.head()"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "code",
121 |    "execution_count": 4,
122 |    "metadata": {},
123 |    "outputs": [
124 |     {
125 |      "data": {
126 |       "text/plain": [
127 |        "(50, 5)"
128 |       ]
129 |      },
130 |      "execution_count": 4,
131 |      "metadata": {},
132 |      "output_type": "execute_result"
133 |     }
134 |    ],
135 |    "source": [
136 |     "df.shape"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "code",
141 |    "execution_count": 5,
142 |    "metadata": {},
143 |    "outputs": [],
144 |    "source": [
145 |     "X = df.iloc[:, :-1]   # independent data\n",
146 |     "y = df.iloc[:, -1]    # dependent data"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 6,
152 |    "metadata": {},
153 |    "outputs": [
154 |     {
155 |      "data": {
156 |       "text/plain": [
157 |        "New York      17\n",
158 |        "California    17\n",
159 |        "Florida       16\n",
160 |        "Name: State, dtype: int64"
161 |       ]
162 |      },
163 |      "execution_count": 6,
164 |      "metadata": {},
165 |      "output_type": "execute_result"
166 |     }
167 |    ],
168 |    "source": [
169 |     "df['State'].value_counts() ## count the cities"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": 7,
175 |    "metadata": {},
176 |    "outputs": [
177 |     {
178 |      "data": {
179 |       "text/plain": [
180 |        "R&D Spend          0\n",
181 |        "Administration     0\n",
182 |        "Marketing Spend    0\n",
183 |        "State              0\n",
184 |        "Profit             0\n",
185 |        "dtype: int64"
186 |       ]
187 |      },
188 |      "execution_count": 7,
189 |      "metadata": {},
190 |      "output_type": "execute_result"
191 |     }
192 |    ],
193 |    "source": [
194 |     "df.isnull().sum() ## checking null values"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": 9,
200 |    "metadata": {},
201 |    "outputs": [],
202 |    "source": [
203 |     "states = pd.get_dummies(X['State'], drop_first=True) # Converting State column into one-hot-encoding"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 12,
209 |    "metadata": {},
210 |    "outputs": [],
211 |    "source": [
212 |     "X = X.drop('State', axis=1) # droping state column"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 14,
218 |    "metadata": {},
219 |    "outputs": [],
220 |    "source": [
221 |     "X = pd.concat([X, states], axis=1)"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": 15,
227 |    "metadata": {},
228 |    "outputs": [
229 |     {
230 |      "data": {
231 |       "text/html": [
232 |        "<div>\n",
233 |        "<style scoped>\n",
234 |        "    .dataframe tbody tr th:only-of-type {\n",
235 |        "        vertical-align: middle;\n",
236 |        "    }\n",
237 |        "\n",
238 |        "    .dataframe tbody tr th {\n",
239 |        "        vertical-align: top;\n",
240 |        "    }\n",
241 |        "\n",
242 |        "    .dataframe thead th {\n",
243 |        "        text-align: right;\n",
244 |        "    }\n",
245 |        "</style>\n",
246 |        "<table border=\"1\" class=\"dataframe\">\n",
247 |        "  <thead>\n",
248 |        "    <tr style=\"text-align: right;\">\n",
249 |        "      <th></th>\n",
250 |        "      <th>R&amp;D Spend</th>\n",
251 |        "      <th>Administration</th>\n",
252 |        "      <th>Marketing Spend</th>\n",
253 |        "      <th>Florida</th>\n",
254 |        "      <th>New York</th>\n",
255 |        "    </tr>\n",
256 |        "  </thead>\n",
257 |        "  <tbody>\n",
258 |        "    <tr>\n",
259 |        "      <th>0</th>\n",
260 |        "      <td>165349.20</td>\n",
261 |        "      <td>136897.80</td>\n",
262 |        "      <td>471784.10</td>\n",
263 |        "      <td>0</td>\n",
264 |        "      <td>1</td>\n",
265 |        "    </tr>\n",
266 |        "    <tr>\n",
267 |        "      <th>1</th>\n",
268 |        "      <td>162597.70</td>\n",
269 |        "      <td>151377.59</td>\n",
270 |        "      <td>443898.53</td>\n",
271 |        "      <td>0</td>\n",
272 |        "      <td>0</td>\n",
273 |        "    </tr>\n",
274 |        "    <tr>\n",
275 |        "      <th>2</th>\n",
276 |        "      <td>153441.51</td>\n",
277 |        "      <td>101145.55</td>\n",
278 |        "      <td>407934.54</td>\n",
279 |        "      <td>1</td>\n",
280 |        "      <td>0</td>\n",
281 |        "    </tr>\n",
282 |        "    <tr>\n",
283 |        "      <th>3</th>\n",
284 |        "      <td>144372.41</td>\n",
285 |        "      <td>118671.85</td>\n",
286 |        "      <td>383199.62</td>\n",
287 |        "      <td>0</td>\n",
288 |        "      <td>1</td>\n",
289 |        "    </tr>\n",
290 |        "    <tr>\n",
291 |        "      <th>4</th>\n",
292 |        "      <td>142107.34</td>\n",
293 |        "      <td>91391.77</td>\n",
294 |        "      <td>366168.42</td>\n",
295 |        "      <td>1</td>\n",
296 |        "      <td>0</td>\n",
297 |        "    </tr>\n",
298 |        "  </tbody>\n",
299 |        "</table>\n",
300 |        "</div>"
301 |       ],
302 |       "text/plain": [
303 |        "   R&D Spend  Administration  Marketing Spend  Florida  New York\n",
304 |        "0  165349.20       136897.80        471784.10        0         1\n",
305 |        "1  162597.70       151377.59        443898.53        0         0\n",
306 |        "2  153441.51       101145.55        407934.54        1         0\n",
307 |        "3  144372.41       118671.85        383199.62        0         1\n",
308 |        "4  142107.34        91391.77        366168.42        1         0"
309 |       ]
310 |      },
311 |      "execution_count": 15,
312 |      "metadata": {},
313 |      "output_type": "execute_result"
314 |     }
315 |    ],
316 |    "source": [
317 |     "X.head()"
318 |    ]
319 |   },
320 |   {
321 |    "cell_type": "code",
322 |    "execution_count": 16,
323 |    "metadata": {},
324 |    "outputs": [],
325 |    "source": [
326 |     "from sklearn.model_selection import train_test_split"
327 |    ]
328 |   },
329 |   {
330 |    "cell_type": "code",
331 |    "execution_count": 17,
332 |    "metadata": {},
333 |    "outputs": [],
334 |    "source": [
335 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
336 |    ]
337 |   },
338 |   {
339 |    "cell_type": "code",
340 |    "execution_count": 18,
341 |    "metadata": {},
342 |    "outputs": [],
343 |    "source": [
344 |     "from sklearn.linear_model import LinearRegression"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "code",
349 |    "execution_count": 19,
350 |    "metadata": {},
351 |    "outputs": [],
352 |    "source": [
353 |     "regressor = LinearRegression()"
354 |    ]
355 |   },
356 |   {
357 |    "cell_type": "code",
358 |    "execution_count": 20,
359 |    "metadata": {},
360 |    "outputs": [
361 |     {
362 |      "data": {
363 |       "text/plain": [
364 |        "LinearRegression()"
365 |       ]
366 |      },
367 |      "execution_count": 20,
368 |      "metadata": {},
369 |      "output_type": "execute_result"
370 |     }
371 |    ],
372 |    "source": [
373 |     "regressor.fit(X_train, y_train)"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "code",
378 |    "execution_count": 21,
379 |    "metadata": {},
380 |    "outputs": [],
381 |    "source": [
382 |     "y_pred = regressor.predict(X_test)"
383 |    ]
384 |   },
385 |   {
386 |    "cell_type": "code",
387 |    "execution_count": 24,
388 |    "metadata": {},
389 |    "outputs": [
390 |     {
391 |      "name": "stdout",
392 |      "output_type": "stream",
393 |      "text": [
394 |       "[103015.20159796 132582.27760816 132447.73845174  71976.09851258\n",
395 |       " 178537.48221055]\n",
396 |       "28    103282.38\n",
397 |       "11    144259.40\n",
398 |       "10    146121.95\n",
399 |       "41     77798.83\n",
400 |       "2     191050.39\n",
401 |       "27    105008.31\n",
402 |       "38     81229.06\n",
403 |       "31     97483.56\n",
404 |       "22    110352.25\n",
405 |       "4     166187.94\n",
406 |       "Name: Profit, dtype: float64\n"
407 |      ]
408 |     }
409 |    ],
410 |    "source": [
411 |     "print(y_pred[:5])\n",
412 |     "print(y_test[:])"
413 |    ]
414 |   },
415 |   {
416 |    "cell_type": "code",
417 |    "execution_count": 28,
418 |    "metadata": {},
419 |    "outputs": [],
420 |    "source": [
421 |     "from sklearn.metrics import r2_score"
422 |    ]
423 |   },
424 |   {
425 |    "cell_type": "code",
426 |    "execution_count": 29,
427 |    "metadata": {},
428 |    "outputs": [],
429 |    "source": [
430 |     "score = r2_score(y_test, y_pred)"
431 |    ]
432 |   },
433 |   {
434 |    "cell_type": "code",
435 |    "execution_count": 30,
436 |    "metadata": {},
437 |    "outputs": [
438 |     {
439 |      "name": "stdout",
440 |      "output_type": "stream",
441 |      "text": [
442 |       "0.9347068473282423\n"
443 |      ]
444 |     }
445 |    ],
446 |    "source": [
447 |     "print(score)"
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "code",
452 |    "execution_count": null,
453 |    "metadata": {},
454 |    "outputs": [],
455 |    "source": []
456 |   }
457 |  ],
458 |  "metadata": {
459 |   "kernelspec": {
460 |    "display_name": "Python 3",
461 |    "language": "python",
462 |    "name": "python3"
463 |   },
464 |   "language_info": {
465 |    "codemirror_mode": {
466 |     "name": "ipython",
467 |     "version": 3
468 |    },
469 |    "file_extension": ".py",
470 |    "mimetype": "text/x-python",
471 |    "name": "python",
472 |    "nbconvert_exporter": "python",
473 |    "pygments_lexer": "ipython3",
474 |    "version": "3.8.5"
475 |   }
476 |  },
477 |  "nbformat": 4,
478 |  "nbformat_minor": 4
479 | }
480 | 


--------------------------------------------------------------------------------
/House_Price_Prediction Detailed Analysis/Feature_Selection.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "%matplotlib inline\n",
 14 |     "\n",
 15 |     "# for feature selection\n",
 16 |     "from sklearn.linear_model import Lasso\n",
 17 |     "from sklearn.feature_selection import SelectFromModel\n",
 18 |     "\n",
 19 |     "# to visualize all the column of the dataframe\n",
 20 |     "pd.pandas.set_option('display.max_columns', None)"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 2,
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "dataset = pd.read_csv('train.csv')"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 3,
 35 |    "metadata": {},
 36 |    "outputs": [
 37 |     {
 38 |      "data": {
 39 |       "text/html": [
 40 |        "<div>\n",
 41 |        "<style scoped>\n",
 42 |        "    .dataframe tbody tr th:only-of-type {\n",
 43 |        "        vertical-align: middle;\n",
 44 |        "    }\n",
 45 |        "\n",
 46 |        "    .dataframe tbody tr th {\n",
 47 |        "        vertical-align: top;\n",
 48 |        "    }\n",
 49 |        "\n",
 50 |        "    .dataframe thead th {\n",
 51 |        "        text-align: right;\n",
 52 |        "    }\n",
 53 |        "</style>\n",
 54 |        "<table border=\"1\" class=\"dataframe\">\n",
 55 |        "  <thead>\n",
 56 |        "    <tr style=\"text-align: right;\">\n",
 57 |        "      <th></th>\n",
 58 |        "      <th>Unnamed: 0</th>\n",
 59 |        "      <th>Id</th>\n",
 60 |        "      <th>SalePrice</th>\n",
 61 |        "      <th>MSSubClass</th>\n",
 62 |        "      <th>MSZoning</th>\n",
 63 |        "      <th>LotFrontage</th>\n",
 64 |        "      <th>LotArea</th>\n",
 65 |        "      <th>Street</th>\n",
 66 |        "      <th>Alley</th>\n",
 67 |        "      <th>LotShape</th>\n",
 68 |        "      <th>LandContour</th>\n",
 69 |        "      <th>Utilities</th>\n",
 70 |        "      <th>LotConfig</th>\n",
 71 |        "      <th>LandSlope</th>\n",
 72 |        "      <th>Neighborhood</th>\n",
 73 |        "      <th>Condition1</th>\n",
 74 |        "      <th>Condition2</th>\n",
 75 |        "      <th>BldgType</th>\n",
 76 |        "      <th>HouseStyle</th>\n",
 77 |        "      <th>OverallQual</th>\n",
 78 |        "      <th>OverallCond</th>\n",
 79 |        "      <th>YearBuilt</th>\n",
 80 |        "      <th>YearRemodAdd</th>\n",
 81 |        "      <th>RoofStyle</th>\n",
 82 |        "      <th>RoofMatl</th>\n",
 83 |        "      <th>Exterior1st</th>\n",
 84 |        "      <th>Exterior2nd</th>\n",
 85 |        "      <th>MasVnrType</th>\n",
 86 |        "      <th>MasVnrArea</th>\n",
 87 |        "      <th>ExterQual</th>\n",
 88 |        "      <th>ExterCond</th>\n",
 89 |        "      <th>Foundation</th>\n",
 90 |        "      <th>BsmtQual</th>\n",
 91 |        "      <th>BsmtCond</th>\n",
 92 |        "      <th>BsmtExposure</th>\n",
 93 |        "      <th>BsmtFinType1</th>\n",
 94 |        "      <th>BsmtFinSF1</th>\n",
 95 |        "      <th>BsmtFinType2</th>\n",
 96 |        "      <th>BsmtFinSF2</th>\n",
 97 |        "      <th>BsmtUnfSF</th>\n",
 98 |        "      <th>TotalBsmtSF</th>\n",
 99 |        "      <th>Heating</th>\n",
100 |        "      <th>HeatingQC</th>\n",
101 |        "      <th>CentralAir</th>\n",
102 |        "      <th>Electrical</th>\n",
103 |        "      <th>1stFlrSF</th>\n",
104 |        "      <th>2ndFlrSF</th>\n",
105 |        "      <th>LowQualFinSF</th>\n",
106 |        "      <th>GrLivArea</th>\n",
107 |        "      <th>BsmtFullBath</th>\n",
108 |        "      <th>BsmtHalfBath</th>\n",
109 |        "      <th>FullBath</th>\n",
110 |        "      <th>HalfBath</th>\n",
111 |        "      <th>BedroomAbvGr</th>\n",
112 |        "      <th>KitchenAbvGr</th>\n",
113 |        "      <th>KitchenQual</th>\n",
114 |        "      <th>TotRmsAbvGrd</th>\n",
115 |        "      <th>Functional</th>\n",
116 |        "      <th>Fireplaces</th>\n",
117 |        "      <th>FireplaceQu</th>\n",
118 |        "      <th>GarageType</th>\n",
119 |        "      <th>GarageYrBlt</th>\n",
120 |        "      <th>GarageFinish</th>\n",
121 |        "      <th>GarageCars</th>\n",
122 |        "      <th>GarageArea</th>\n",
123 |        "      <th>GarageQual</th>\n",
124 |        "      <th>GarageCond</th>\n",
125 |        "      <th>PavedDrive</th>\n",
126 |        "      <th>WoodDeckSF</th>\n",
127 |        "      <th>OpenPorchSF</th>\n",
128 |        "      <th>EnclosedPorch</th>\n",
129 |        "      <th>3SsnPorch</th>\n",
130 |        "      <th>ScreenPorch</th>\n",
131 |        "      <th>PoolArea</th>\n",
132 |        "      <th>PoolQC</th>\n",
133 |        "      <th>Fence</th>\n",
134 |        "      <th>MiscFeature</th>\n",
135 |        "      <th>MiscVal</th>\n",
136 |        "      <th>MoSold</th>\n",
137 |        "      <th>YrSold</th>\n",
138 |        "      <th>SaleType</th>\n",
139 |        "      <th>SaleCondition</th>\n",
140 |        "      <th>LotFrontagenan</th>\n",
141 |        "      <th>MasVnrAreanan</th>\n",
142 |        "      <th>GarageYrBltnan</th>\n",
143 |        "    </tr>\n",
144 |        "  </thead>\n",
145 |        "  <tbody>\n",
146 |        "    <tr>\n",
147 |        "      <th>0</th>\n",
148 |        "      <td>0</td>\n",
149 |        "      <td>1</td>\n",
150 |        "      <td>12.247694</td>\n",
151 |        "      <td>0.235294</td>\n",
152 |        "      <td>0.5</td>\n",
153 |        "      <td>0.418208</td>\n",
154 |        "      <td>0.366344</td>\n",
155 |        "      <td>0.0</td>\n",
156 |        "      <td>0.5</td>\n",
157 |        "      <td>1.0</td>\n",
158 |        "      <td>1.0</td>\n",
159 |        "      <td>0.0</td>\n",
160 |        "      <td>0.75</td>\n",
161 |        "      <td>0.0</td>\n",
162 |        "      <td>0.181818</td>\n",
163 |        "      <td>0.4</td>\n",
164 |        "      <td>0.0</td>\n",
165 |        "      <td>0.0</td>\n",
166 |        "      <td>0.4</td>\n",
167 |        "      <td>0.666667</td>\n",
168 |        "      <td>0.500</td>\n",
169 |        "      <td>0.036765</td>\n",
170 |        "      <td>0.098361</td>\n",
171 |        "      <td>0.0</td>\n",
172 |        "      <td>0.0</td>\n",
173 |        "      <td>0.8</td>\n",
174 |        "      <td>0.8</td>\n",
175 |        "      <td>0.25</td>\n",
176 |        "      <td>0.12250</td>\n",
177 |        "      <td>0.333333</td>\n",
178 |        "      <td>1.0</td>\n",
179 |        "      <td>0.50</td>\n",
180 |        "      <td>0.5</td>\n",
181 |        "      <td>1.00</td>\n",
182 |        "      <td>1.00</td>\n",
183 |        "      <td>0.333333</td>\n",
184 |        "      <td>0.125089</td>\n",
185 |        "      <td>1.0</td>\n",
186 |        "      <td>0.0</td>\n",
187 |        "      <td>0.064212</td>\n",
188 |        "      <td>0.140098</td>\n",
189 |        "      <td>0.0</td>\n",
190 |        "      <td>0.0</td>\n",
191 |        "      <td>1.0</td>\n",
192 |        "      <td>1.0</td>\n",
193 |        "      <td>0.356155</td>\n",
194 |        "      <td>0.413559</td>\n",
195 |        "      <td>0.0</td>\n",
196 |        "      <td>0.577712</td>\n",
197 |        "      <td>0.333333</td>\n",
198 |        "      <td>0.0</td>\n",
199 |        "      <td>0.666667</td>\n",
200 |        "      <td>0.5</td>\n",
201 |        "      <td>0.375</td>\n",
202 |        "      <td>0.333333</td>\n",
203 |        "      <td>0.666667</td>\n",
204 |        "      <td>0.500000</td>\n",
205 |        "      <td>1.0</td>\n",
206 |        "      <td>0.000000</td>\n",
207 |        "      <td>0.6</td>\n",
208 |        "      <td>0.0</td>\n",
209 |        "      <td>0.046729</td>\n",
210 |        "      <td>0.666667</td>\n",
211 |        "      <td>0.50</td>\n",
212 |        "      <td>0.386460</td>\n",
213 |        "      <td>1.0</td>\n",
214 |        "      <td>1.0</td>\n",
215 |        "      <td>1.0</td>\n",
216 |        "      <td>0.000000</td>\n",
217 |        "      <td>0.111517</td>\n",
218 |        "      <td>0.000000</td>\n",
219 |        "      <td>0.0</td>\n",
220 |        "      <td>0.0</td>\n",
221 |        "      <td>0.0</td>\n",
222 |        "      <td>0.0</td>\n",
223 |        "      <td>0.5</td>\n",
224 |        "      <td>0.0</td>\n",
225 |        "      <td>0.0</td>\n",
226 |        "      <td>0.090909</td>\n",
227 |        "      <td>0.50</td>\n",
228 |        "      <td>1.0</td>\n",
229 |        "      <td>0.5</td>\n",
230 |        "      <td>0.0</td>\n",
231 |        "      <td>0.0</td>\n",
232 |        "      <td>0.0</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>1</th>\n",
236 |        "      <td>1</td>\n",
237 |        "      <td>2</td>\n",
238 |        "      <td>12.109011</td>\n",
239 |        "      <td>0.000000</td>\n",
240 |        "      <td>0.5</td>\n",
241 |        "      <td>0.495064</td>\n",
242 |        "      <td>0.391317</td>\n",
243 |        "      <td>0.0</td>\n",
244 |        "      <td>0.5</td>\n",
245 |        "      <td>1.0</td>\n",
246 |        "      <td>1.0</td>\n",
247 |        "      <td>0.0</td>\n",
248 |        "      <td>0.50</td>\n",
249 |        "      <td>0.0</td>\n",
250 |        "      <td>0.727273</td>\n",
251 |        "      <td>0.2</td>\n",
252 |        "      <td>0.0</td>\n",
253 |        "      <td>0.0</td>\n",
254 |        "      <td>0.2</td>\n",
255 |        "      <td>0.555556</td>\n",
256 |        "      <td>0.875</td>\n",
257 |        "      <td>0.227941</td>\n",
258 |        "      <td>0.524590</td>\n",
259 |        "      <td>0.0</td>\n",
260 |        "      <td>0.0</td>\n",
261 |        "      <td>0.4</td>\n",
262 |        "      <td>0.4</td>\n",
263 |        "      <td>0.50</td>\n",
264 |        "      <td>0.00000</td>\n",
265 |        "      <td>1.000000</td>\n",
266 |        "      <td>1.0</td>\n",
267 |        "      <td>0.25</td>\n",
268 |        "      <td>0.5</td>\n",
269 |        "      <td>1.00</td>\n",
270 |        "      <td>0.25</td>\n",
271 |        "      <td>0.000000</td>\n",
272 |        "      <td>0.173281</td>\n",
273 |        "      <td>1.0</td>\n",
274 |        "      <td>0.0</td>\n",
275 |        "      <td>0.121575</td>\n",
276 |        "      <td>0.206547</td>\n",
277 |        "      <td>0.0</td>\n",
278 |        "      <td>0.0</td>\n",
279 |        "      <td>1.0</td>\n",
280 |        "      <td>1.0</td>\n",
281 |        "      <td>0.503056</td>\n",
282 |        "      <td>0.000000</td>\n",
283 |        "      <td>0.0</td>\n",
284 |        "      <td>0.470245</td>\n",
285 |        "      <td>0.000000</td>\n",
286 |        "      <td>0.5</td>\n",
287 |        "      <td>0.666667</td>\n",
288 |        "      <td>0.0</td>\n",
289 |        "      <td>0.375</td>\n",
290 |        "      <td>0.333333</td>\n",
291 |        "      <td>1.000000</td>\n",
292 |        "      <td>0.333333</td>\n",
293 |        "      <td>1.0</td>\n",
294 |        "      <td>0.333333</td>\n",
295 |        "      <td>1.0</td>\n",
296 |        "      <td>0.0</td>\n",
297 |        "      <td>0.289720</td>\n",
298 |        "      <td>0.666667</td>\n",
299 |        "      <td>0.50</td>\n",
300 |        "      <td>0.324401</td>\n",
301 |        "      <td>1.0</td>\n",
302 |        "      <td>1.0</td>\n",
303 |        "      <td>1.0</td>\n",
304 |        "      <td>0.347725</td>\n",
305 |        "      <td>0.000000</td>\n",
306 |        "      <td>0.000000</td>\n",
307 |        "      <td>0.0</td>\n",
308 |        "      <td>0.0</td>\n",
309 |        "      <td>0.0</td>\n",
310 |        "      <td>0.0</td>\n",
311 |        "      <td>0.5</td>\n",
312 |        "      <td>0.0</td>\n",
313 |        "      <td>0.0</td>\n",
314 |        "      <td>0.363636</td>\n",
315 |        "      <td>0.25</td>\n",
316 |        "      <td>1.0</td>\n",
317 |        "      <td>0.5</td>\n",
318 |        "      <td>0.0</td>\n",
319 |        "      <td>0.0</td>\n",
320 |        "      <td>0.0</td>\n",
321 |        "    </tr>\n",
322 |        "    <tr>\n",
323 |        "      <th>2</th>\n",
324 |        "      <td>2</td>\n",
325 |        "      <td>3</td>\n",
326 |        "      <td>12.317167</td>\n",
327 |        "      <td>0.235294</td>\n",
328 |        "      <td>0.5</td>\n",
329 |        "      <td>0.434909</td>\n",
330 |        "      <td>0.422359</td>\n",
331 |        "      <td>0.0</td>\n",
332 |        "      <td>0.5</td>\n",
333 |        "      <td>0.0</td>\n",
334 |        "      <td>1.0</td>\n",
335 |        "      <td>0.0</td>\n",
336 |        "      <td>0.75</td>\n",
337 |        "      <td>0.0</td>\n",
338 |        "      <td>0.181818</td>\n",
339 |        "      <td>0.4</td>\n",
340 |        "      <td>0.0</td>\n",
341 |        "      <td>0.0</td>\n",
342 |        "      <td>0.4</td>\n",
343 |        "      <td>0.666667</td>\n",
344 |        "      <td>0.500</td>\n",
345 |        "      <td>0.051471</td>\n",
346 |        "      <td>0.114754</td>\n",
347 |        "      <td>0.0</td>\n",
348 |        "      <td>0.0</td>\n",
349 |        "      <td>0.8</td>\n",
350 |        "      <td>0.8</td>\n",
351 |        "      <td>0.25</td>\n",
352 |        "      <td>0.10125</td>\n",
353 |        "      <td>0.333333</td>\n",
354 |        "      <td>1.0</td>\n",
355 |        "      <td>0.50</td>\n",
356 |        "      <td>0.5</td>\n",
357 |        "      <td>1.00</td>\n",
358 |        "      <td>0.75</td>\n",
359 |        "      <td>0.333333</td>\n",
360 |        "      <td>0.086109</td>\n",
361 |        "      <td>1.0</td>\n",
362 |        "      <td>0.0</td>\n",
363 |        "      <td>0.185788</td>\n",
364 |        "      <td>0.150573</td>\n",
365 |        "      <td>0.0</td>\n",
366 |        "      <td>0.0</td>\n",
367 |        "      <td>1.0</td>\n",
368 |        "      <td>1.0</td>\n",
369 |        "      <td>0.383441</td>\n",
370 |        "      <td>0.419370</td>\n",
371 |        "      <td>0.0</td>\n",
372 |        "      <td>0.593095</td>\n",
373 |        "      <td>0.333333</td>\n",
374 |        "      <td>0.0</td>\n",
375 |        "      <td>0.666667</td>\n",
376 |        "      <td>0.5</td>\n",
377 |        "      <td>0.375</td>\n",
378 |        "      <td>0.333333</td>\n",
379 |        "      <td>0.666667</td>\n",
380 |        "      <td>0.333333</td>\n",
381 |        "      <td>1.0</td>\n",
382 |        "      <td>0.333333</td>\n",
383 |        "      <td>1.0</td>\n",
384 |        "      <td>0.0</td>\n",
385 |        "      <td>0.065421</td>\n",
386 |        "      <td>0.666667</td>\n",
387 |        "      <td>0.50</td>\n",
388 |        "      <td>0.428773</td>\n",
389 |        "      <td>1.0</td>\n",
390 |        "      <td>1.0</td>\n",
391 |        "      <td>1.0</td>\n",
392 |        "      <td>0.000000</td>\n",
393 |        "      <td>0.076782</td>\n",
394 |        "      <td>0.000000</td>\n",
395 |        "      <td>0.0</td>\n",
396 |        "      <td>0.0</td>\n",
397 |        "      <td>0.0</td>\n",
398 |        "      <td>0.0</td>\n",
399 |        "      <td>0.5</td>\n",
400 |        "      <td>0.0</td>\n",
401 |        "      <td>0.0</td>\n",
402 |        "      <td>0.727273</td>\n",
403 |        "      <td>0.50</td>\n",
404 |        "      <td>1.0</td>\n",
405 |        "      <td>0.5</td>\n",
406 |        "      <td>0.0</td>\n",
407 |        "      <td>0.0</td>\n",
408 |        "      <td>0.0</td>\n",
409 |        "    </tr>\n",
410 |        "    <tr>\n",
411 |        "      <th>3</th>\n",
412 |        "      <td>3</td>\n",
413 |        "      <td>4</td>\n",
414 |        "      <td>11.849398</td>\n",
415 |        "      <td>0.294118</td>\n",
416 |        "      <td>0.5</td>\n",
417 |        "      <td>0.388581</td>\n",
418 |        "      <td>0.390295</td>\n",
419 |        "      <td>0.0</td>\n",
420 |        "      <td>0.5</td>\n",
421 |        "      <td>0.0</td>\n",
422 |        "      <td>1.0</td>\n",
423 |        "      <td>0.0</td>\n",
424 |        "      <td>0.00</td>\n",
425 |        "      <td>0.0</td>\n",
426 |        "      <td>0.227273</td>\n",
427 |        "      <td>0.4</td>\n",
428 |        "      <td>0.0</td>\n",
429 |        "      <td>0.0</td>\n",
430 |        "      <td>0.4</td>\n",
431 |        "      <td>0.666667</td>\n",
432 |        "      <td>0.500</td>\n",
433 |        "      <td>0.669118</td>\n",
434 |        "      <td>0.606557</td>\n",
435 |        "      <td>0.0</td>\n",
436 |        "      <td>0.0</td>\n",
437 |        "      <td>0.9</td>\n",
438 |        "      <td>1.0</td>\n",
439 |        "      <td>0.50</td>\n",
440 |        "      <td>0.00000</td>\n",
441 |        "      <td>1.000000</td>\n",
442 |        "      <td>1.0</td>\n",
443 |        "      <td>0.00</td>\n",
444 |        "      <td>1.0</td>\n",
445 |        "      <td>0.25</td>\n",
446 |        "      <td>1.00</td>\n",
447 |        "      <td>0.000000</td>\n",
448 |        "      <td>0.038271</td>\n",
449 |        "      <td>1.0</td>\n",
450 |        "      <td>0.0</td>\n",
451 |        "      <td>0.231164</td>\n",
452 |        "      <td>0.123732</td>\n",
453 |        "      <td>0.0</td>\n",
454 |        "      <td>0.5</td>\n",
455 |        "      <td>1.0</td>\n",
456 |        "      <td>1.0</td>\n",
457 |        "      <td>0.399941</td>\n",
458 |        "      <td>0.366102</td>\n",
459 |        "      <td>0.0</td>\n",
460 |        "      <td>0.579157</td>\n",
461 |        "      <td>0.333333</td>\n",
462 |        "      <td>0.0</td>\n",
463 |        "      <td>0.333333</td>\n",
464 |        "      <td>0.0</td>\n",
465 |        "      <td>0.375</td>\n",
466 |        "      <td>0.333333</td>\n",
467 |        "      <td>0.666667</td>\n",
468 |        "      <td>0.416667</td>\n",
469 |        "      <td>1.0</td>\n",
470 |        "      <td>0.333333</td>\n",
471 |        "      <td>0.4</td>\n",
472 |        "      <td>0.6</td>\n",
473 |        "      <td>0.074766</td>\n",
474 |        "      <td>1.000000</td>\n",
475 |        "      <td>0.75</td>\n",
476 |        "      <td>0.452750</td>\n",
477 |        "      <td>1.0</td>\n",
478 |        "      <td>1.0</td>\n",
479 |        "      <td>1.0</td>\n",
480 |        "      <td>0.000000</td>\n",
481 |        "      <td>0.063985</td>\n",
482 |        "      <td>0.492754</td>\n",
483 |        "      <td>0.0</td>\n",
484 |        "      <td>0.0</td>\n",
485 |        "      <td>0.0</td>\n",
486 |        "      <td>0.0</td>\n",
487 |        "      <td>0.5</td>\n",
488 |        "      <td>0.0</td>\n",
489 |        "      <td>0.0</td>\n",
490 |        "      <td>0.090909</td>\n",
491 |        "      <td>0.00</td>\n",
492 |        "      <td>1.0</td>\n",
493 |        "      <td>0.0</td>\n",
494 |        "      <td>0.0</td>\n",
495 |        "      <td>0.0</td>\n",
496 |        "      <td>0.0</td>\n",
497 |        "    </tr>\n",
498 |        "    <tr>\n",
499 |        "      <th>4</th>\n",
500 |        "      <td>4</td>\n",
501 |        "      <td>5</td>\n",
502 |        "      <td>12.429216</td>\n",
503 |        "      <td>0.235294</td>\n",
504 |        "      <td>0.5</td>\n",
505 |        "      <td>0.513123</td>\n",
506 |        "      <td>0.468761</td>\n",
507 |        "      <td>0.0</td>\n",
508 |        "      <td>0.5</td>\n",
509 |        "      <td>0.0</td>\n",
510 |        "      <td>1.0</td>\n",
511 |        "      <td>0.0</td>\n",
512 |        "      <td>0.50</td>\n",
513 |        "      <td>0.0</td>\n",
514 |        "      <td>0.590909</td>\n",
515 |        "      <td>0.4</td>\n",
516 |        "      <td>0.0</td>\n",
517 |        "      <td>0.0</td>\n",
518 |        "      <td>0.4</td>\n",
519 |        "      <td>0.777778</td>\n",
520 |        "      <td>0.500</td>\n",
521 |        "      <td>0.058824</td>\n",
522 |        "      <td>0.147541</td>\n",
523 |        "      <td>0.0</td>\n",
524 |        "      <td>0.0</td>\n",
525 |        "      <td>0.8</td>\n",
526 |        "      <td>0.8</td>\n",
527 |        "      <td>0.25</td>\n",
528 |        "      <td>0.21875</td>\n",
529 |        "      <td>0.333333</td>\n",
530 |        "      <td>1.0</td>\n",
531 |        "      <td>0.50</td>\n",
532 |        "      <td>0.5</td>\n",
533 |        "      <td>1.00</td>\n",
534 |        "      <td>0.00</td>\n",
535 |        "      <td>0.333333</td>\n",
536 |        "      <td>0.116052</td>\n",
537 |        "      <td>1.0</td>\n",
538 |        "      <td>0.0</td>\n",
539 |        "      <td>0.209760</td>\n",
540 |        "      <td>0.187398</td>\n",
541 |        "      <td>0.0</td>\n",
542 |        "      <td>0.0</td>\n",
543 |        "      <td>1.0</td>\n",
544 |        "      <td>1.0</td>\n",
545 |        "      <td>0.466237</td>\n",
546 |        "      <td>0.509927</td>\n",
547 |        "      <td>0.0</td>\n",
548 |        "      <td>0.666523</td>\n",
549 |        "      <td>0.333333</td>\n",
550 |        "      <td>0.0</td>\n",
551 |        "      <td>0.666667</td>\n",
552 |        "      <td>0.5</td>\n",
553 |        "      <td>0.500</td>\n",
554 |        "      <td>0.333333</td>\n",
555 |        "      <td>0.666667</td>\n",
556 |        "      <td>0.583333</td>\n",
557 |        "      <td>1.0</td>\n",
558 |        "      <td>0.333333</td>\n",
559 |        "      <td>1.0</td>\n",
560 |        "      <td>0.0</td>\n",
561 |        "      <td>0.074766</td>\n",
562 |        "      <td>0.666667</td>\n",
563 |        "      <td>0.75</td>\n",
564 |        "      <td>0.589563</td>\n",
565 |        "      <td>1.0</td>\n",
566 |        "      <td>1.0</td>\n",
567 |        "      <td>1.0</td>\n",
568 |        "      <td>0.224037</td>\n",
569 |        "      <td>0.153565</td>\n",
570 |        "      <td>0.000000</td>\n",
571 |        "      <td>0.0</td>\n",
572 |        "      <td>0.0</td>\n",
573 |        "      <td>0.0</td>\n",
574 |        "      <td>0.0</td>\n",
575 |        "      <td>0.5</td>\n",
576 |        "      <td>0.0</td>\n",
577 |        "      <td>0.0</td>\n",
578 |        "      <td>1.000000</td>\n",
579 |        "      <td>0.50</td>\n",
580 |        "      <td>1.0</td>\n",
581 |        "      <td>0.5</td>\n",
582 |        "      <td>0.0</td>\n",
583 |        "      <td>0.0</td>\n",
584 |        "      <td>0.0</td>\n",
585 |        "    </tr>\n",
586 |        "  </tbody>\n",
587 |        "</table>\n",
588 |        "</div>"
589 |       ],
590 |       "text/plain": [
591 |        "   Unnamed: 0  Id  SalePrice  MSSubClass  MSZoning  LotFrontage   LotArea  \\\n",
592 |        "0           0   1  12.247694    0.235294       0.5     0.418208  0.366344   \n",
593 |        "1           1   2  12.109011    0.000000       0.5     0.495064  0.391317   \n",
594 |        "2           2   3  12.317167    0.235294       0.5     0.434909  0.422359   \n",
595 |        "3           3   4  11.849398    0.294118       0.5     0.388581  0.390295   \n",
596 |        "4           4   5  12.429216    0.235294       0.5     0.513123  0.468761   \n",
597 |        "\n",
598 |        "   Street  Alley  LotShape  LandContour  Utilities  LotConfig  LandSlope  \\\n",
599 |        "0     0.0    0.5       1.0          1.0        0.0       0.75        0.0   \n",
600 |        "1     0.0    0.5       1.0          1.0        0.0       0.50        0.0   \n",
601 |        "2     0.0    0.5       0.0          1.0        0.0       0.75        0.0   \n",
602 |        "3     0.0    0.5       0.0          1.0        0.0       0.00        0.0   \n",
603 |        "4     0.0    0.5       0.0          1.0        0.0       0.50        0.0   \n",
604 |        "\n",
605 |        "   Neighborhood  Condition1  Condition2  BldgType  HouseStyle  OverallQual  \\\n",
606 |        "0      0.181818         0.4         0.0       0.0         0.4     0.666667   \n",
607 |        "1      0.727273         0.2         0.0       0.0         0.2     0.555556   \n",
608 |        "2      0.181818         0.4         0.0       0.0         0.4     0.666667   \n",
609 |        "3      0.227273         0.4         0.0       0.0         0.4     0.666667   \n",
610 |        "4      0.590909         0.4         0.0       0.0         0.4     0.777778   \n",
611 |        "\n",
612 |        "   OverallCond  YearBuilt  YearRemodAdd  RoofStyle  RoofMatl  Exterior1st  \\\n",
613 |        "0        0.500   0.036765      0.098361        0.0       0.0          0.8   \n",
614 |        "1        0.875   0.227941      0.524590        0.0       0.0          0.4   \n",
615 |        "2        0.500   0.051471      0.114754        0.0       0.0          0.8   \n",
616 |        "3        0.500   0.669118      0.606557        0.0       0.0          0.9   \n",
617 |        "4        0.500   0.058824      0.147541        0.0       0.0          0.8   \n",
618 |        "\n",
619 |        "   Exterior2nd  MasVnrType  MasVnrArea  ExterQual  ExterCond  Foundation  \\\n",
620 |        "0          0.8        0.25     0.12250   0.333333        1.0        0.50   \n",
621 |        "1          0.4        0.50     0.00000   1.000000        1.0        0.25   \n",
622 |        "2          0.8        0.25     0.10125   0.333333        1.0        0.50   \n",
623 |        "3          1.0        0.50     0.00000   1.000000        1.0        0.00   \n",
624 |        "4          0.8        0.25     0.21875   0.333333        1.0        0.50   \n",
625 |        "\n",
626 |        "   BsmtQual  BsmtCond  BsmtExposure  BsmtFinType1  BsmtFinSF1  BsmtFinType2  \\\n",
627 |        "0       0.5      1.00          1.00      0.333333    0.125089           1.0   \n",
628 |        "1       0.5      1.00          0.25      0.000000    0.173281           1.0   \n",
629 |        "2       0.5      1.00          0.75      0.333333    0.086109           1.0   \n",
630 |        "3       1.0      0.25          1.00      0.000000    0.038271           1.0   \n",
631 |        "4       0.5      1.00          0.00      0.333333    0.116052           1.0   \n",
632 |        "\n",
633 |        "   BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  CentralAir  \\\n",
634 |        "0         0.0   0.064212     0.140098      0.0        0.0         1.0   \n",
635 |        "1         0.0   0.121575     0.206547      0.0        0.0         1.0   \n",
636 |        "2         0.0   0.185788     0.150573      0.0        0.0         1.0   \n",
637 |        "3         0.0   0.231164     0.123732      0.0        0.5         1.0   \n",
638 |        "4         0.0   0.209760     0.187398      0.0        0.0         1.0   \n",
639 |        "\n",
640 |        "   Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  \\\n",
641 |        "0         1.0  0.356155  0.413559           0.0   0.577712      0.333333   \n",
642 |        "1         1.0  0.503056  0.000000           0.0   0.470245      0.000000   \n",
643 |        "2         1.0  0.383441  0.419370           0.0   0.593095      0.333333   \n",
644 |        "3         1.0  0.399941  0.366102           0.0   0.579157      0.333333   \n",
645 |        "4         1.0  0.466237  0.509927           0.0   0.666523      0.333333   \n",
646 |        "\n",
647 |        "   BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  KitchenQual  \\\n",
648 |        "0           0.0  0.666667       0.5         0.375      0.333333     0.666667   \n",
649 |        "1           0.5  0.666667       0.0         0.375      0.333333     1.000000   \n",
650 |        "2           0.0  0.666667       0.5         0.375      0.333333     0.666667   \n",
651 |        "3           0.0  0.333333       0.0         0.375      0.333333     0.666667   \n",
652 |        "4           0.0  0.666667       0.5         0.500      0.333333     0.666667   \n",
653 |        "\n",
654 |        "   TotRmsAbvGrd  Functional  Fireplaces  FireplaceQu  GarageType  GarageYrBlt  \\\n",
655 |        "0      0.500000         1.0    0.000000          0.6         0.0     0.046729   \n",
656 |        "1      0.333333         1.0    0.333333          1.0         0.0     0.289720   \n",
657 |        "2      0.333333         1.0    0.333333          1.0         0.0     0.065421   \n",
658 |        "3      0.416667         1.0    0.333333          0.4         0.6     0.074766   \n",
659 |        "4      0.583333         1.0    0.333333          1.0         0.0     0.074766   \n",
660 |        "\n",
661 |        "   GarageFinish  GarageCars  GarageArea  GarageQual  GarageCond  PavedDrive  \\\n",
662 |        "0      0.666667        0.50    0.386460         1.0         1.0         1.0   \n",
663 |        "1      0.666667        0.50    0.324401         1.0         1.0         1.0   \n",
664 |        "2      0.666667        0.50    0.428773         1.0         1.0         1.0   \n",
665 |        "3      1.000000        0.75    0.452750         1.0         1.0         1.0   \n",
666 |        "4      0.666667        0.75    0.589563         1.0         1.0         1.0   \n",
667 |        "\n",
668 |        "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
669 |        "0    0.000000     0.111517       0.000000        0.0          0.0       0.0   \n",
670 |        "1    0.347725     0.000000       0.000000        0.0          0.0       0.0   \n",
671 |        "2    0.000000     0.076782       0.000000        0.0          0.0       0.0   \n",
672 |        "3    0.000000     0.063985       0.492754        0.0          0.0       0.0   \n",
673 |        "4    0.224037     0.153565       0.000000        0.0          0.0       0.0   \n",
674 |        "\n",
675 |        "   PoolQC  Fence  MiscFeature  MiscVal    MoSold  YrSold  SaleType  \\\n",
676 |        "0     0.0    0.5          0.0      0.0  0.090909    0.50       1.0   \n",
677 |        "1     0.0    0.5          0.0      0.0  0.363636    0.25       1.0   \n",
678 |        "2     0.0    0.5          0.0      0.0  0.727273    0.50       1.0   \n",
679 |        "3     0.0    0.5          0.0      0.0  0.090909    0.00       1.0   \n",
680 |        "4     0.0    0.5          0.0      0.0  1.000000    0.50       1.0   \n",
681 |        "\n",
682 |        "   SaleCondition  LotFrontagenan  MasVnrAreanan  GarageYrBltnan  \n",
683 |        "0            0.5             0.0            0.0             0.0  \n",
684 |        "1            0.5             0.0            0.0             0.0  \n",
685 |        "2            0.5             0.0            0.0             0.0  \n",
686 |        "3            0.0             0.0            0.0             0.0  \n",
687 |        "4            0.5             0.0            0.0             0.0  "
688 |       ]
689 |      },
690 |      "execution_count": 3,
691 |      "metadata": {},
692 |      "output_type": "execute_result"
693 |     }
694 |    ],
695 |    "source": [
696 |     "dataset.head()"
697 |    ]
698 |   },
699 |   {
700 |    "cell_type": "code",
701 |    "execution_count": 4,
702 |    "metadata": {},
703 |    "outputs": [],
704 |    "source": [
705 |     "# capture the dependent dataset\n",
706 |     "y_train = dataset[['SalePrice']]"
707 |    ]
708 |   },
709 |   {
710 |    "cell_type": "code",
711 |    "execution_count": 5,
712 |    "metadata": {},
713 |    "outputs": [],
714 |    "source": [
715 |     "# drop the dependent feature from dataset\n",
716 |     "X_train = dataset.drop(['Id', 'SalePrice'], axis=1)"
717 |    ]
718 |   },
719 |   {
720 |    "cell_type": "code",
721 |    "execution_count": 6,
722 |    "metadata": {},
723 |    "outputs": [
724 |     {
725 |      "data": {
726 |       "text/plain": [
727 |        "SelectFromModel(estimator=Lasso(alpha=0.05, random_state=0))"
728 |       ]
729 |      },
730 |      "execution_count": 6,
731 |      "metadata": {},
732 |      "output_type": "execute_result"
733 |     }
734 |    ],
735 |    "source": [
736 |     "# Apply Feature Selection \n",
737 |     "# First, I specify the Lasso Regression model, and I \n",
738 |     "# Selected a suitable alpha (equivalent of penalty).\n",
739 |     "# The bigger the alpha the less features that will be selected\n",
740 |     "\n",
741 |     "# Then I use selectfromModel object from sklearn which\n",
742 |     "# will select the feature which co-officients are non zero\n",
743 |     "\n",
744 |     "feature_sel_model = SelectFromModel(Lasso(alpha=0.05, random_state=0))\n",
745 |     "feature_sel_model.fit(X_train, y_train)"
746 |    ]
747 |   },
748 |   {
749 |    "cell_type": "code",
750 |    "execution_count": 7,
751 |    "metadata": {},
752 |    "outputs": [
753 |     {
754 |      "data": {
755 |       "text/plain": [
756 |        "array([ True, False, False, False, False, False, False, False, False,\n",
757 |        "       False, False, False, False, False, False, False, False, False,\n",
758 |        "       False, False,  True, False, False, False, False, False, False,\n",
759 |        "        True, False, False,  True, False, False, False, False, False,\n",
760 |        "       False, False, False, False, False, False, False, False, False,\n",
761 |        "       False, False, False, False, False, False, False, False, False,\n",
762 |        "       False, False, False, False, False, False, False, False, False,\n",
763 |        "       False, False, False, False, False, False, False, False, False,\n",
764 |        "       False, False, False, False, False, False, False, False, False,\n",
765 |        "       False, False])"
766 |       ]
767 |      },
768 |      "execution_count": 7,
769 |      "metadata": {},
770 |      "output_type": "execute_result"
771 |     }
772 |    ],
773 |    "source": [
774 |     "feature_sel_model.get_support()"
775 |    ]
776 |   },
777 |   {
778 |    "cell_type": "code",
779 |    "execution_count": 14,
780 |    "metadata": {},
781 |    "outputs": [
782 |     {
783 |      "name": "stdout",
784 |      "output_type": "stream",
785 |      "text": [
786 |       "Total features: 83\n",
787 |       "selected featrues: 4\n",
788 |       "features with cofficients shrank to zero: 79\n"
789 |      ]
790 |     }
791 |    ],
792 |    "source": [
793 |     "# Let's print the number of total and selected features\n",
794 |     "# this is how we can make a list of the selected features\n",
795 |     "\n",
796 |     "selected_feat = X_train.columns[(feature_sel_model.get_support())]\n",
797 |     "\n",
798 |     "# let's print some stats\n",
799 |     "print('Total features: {}'.format(X_train.shape[1]))\n",
800 |     "print(\"selected featrues: {}\".format(len(selected_feat)))\n",
801 |     "print('features with cofficients shrank to zero: {}'.format(\n",
802 |     "    np.sum(feature_sel_model.estimator_.coef_ == 0)))"
803 |    ]
804 |   },
805 |   {
806 |    "cell_type": "code",
807 |    "execution_count": 16,
808 |    "metadata": {},
809 |    "outputs": [],
810 |    "source": [
811 |     "X_train = X_train[selected_feat]"
812 |    ]
813 |   },
814 |   {
815 |    "cell_type": "code",
816 |    "execution_count": 17,
817 |    "metadata": {},
818 |    "outputs": [
819 |     {
820 |      "data": {
821 |       "text/html": [
822 |        "<div>\n",
823 |        "<style scoped>\n",
824 |        "    .dataframe tbody tr th:only-of-type {\n",
825 |        "        vertical-align: middle;\n",
826 |        "    }\n",
827 |        "\n",
828 |        "    .dataframe tbody tr th {\n",
829 |        "        vertical-align: top;\n",
830 |        "    }\n",
831 |        "\n",
832 |        "    .dataframe thead th {\n",
833 |        "        text-align: right;\n",
834 |        "    }\n",
835 |        "</style>\n",
836 |        "<table border=\"1\" class=\"dataframe\">\n",
837 |        "  <thead>\n",
838 |        "    <tr style=\"text-align: right;\">\n",
839 |        "      <th></th>\n",
840 |        "      <th>Unnamed: 0</th>\n",
841 |        "      <th>YearRemodAdd</th>\n",
842 |        "      <th>ExterQual</th>\n",
843 |        "      <th>BsmtQual</th>\n",
844 |        "    </tr>\n",
845 |        "  </thead>\n",
846 |        "  <tbody>\n",
847 |        "    <tr>\n",
848 |        "      <th>0</th>\n",
849 |        "      <td>0</td>\n",
850 |        "      <td>0.098361</td>\n",
851 |        "      <td>0.333333</td>\n",
852 |        "      <td>0.5</td>\n",
853 |        "    </tr>\n",
854 |        "    <tr>\n",
855 |        "      <th>1</th>\n",
856 |        "      <td>1</td>\n",
857 |        "      <td>0.524590</td>\n",
858 |        "      <td>1.000000</td>\n",
859 |        "      <td>0.5</td>\n",
860 |        "    </tr>\n",
861 |        "    <tr>\n",
862 |        "      <th>2</th>\n",
863 |        "      <td>2</td>\n",
864 |        "      <td>0.114754</td>\n",
865 |        "      <td>0.333333</td>\n",
866 |        "      <td>0.5</td>\n",
867 |        "    </tr>\n",
868 |        "    <tr>\n",
869 |        "      <th>3</th>\n",
870 |        "      <td>3</td>\n",
871 |        "      <td>0.606557</td>\n",
872 |        "      <td>1.000000</td>\n",
873 |        "      <td>1.0</td>\n",
874 |        "    </tr>\n",
875 |        "    <tr>\n",
876 |        "      <th>4</th>\n",
877 |        "      <td>4</td>\n",
878 |        "      <td>0.147541</td>\n",
879 |        "      <td>0.333333</td>\n",
880 |        "      <td>0.5</td>\n",
881 |        "    </tr>\n",
882 |        "  </tbody>\n",
883 |        "</table>\n",
884 |        "</div>"
885 |       ],
886 |       "text/plain": [
887 |        "   Unnamed: 0  YearRemodAdd  ExterQual  BsmtQual\n",
888 |        "0           0      0.098361   0.333333       0.5\n",
889 |        "1           1      0.524590   1.000000       0.5\n",
890 |        "2           2      0.114754   0.333333       0.5\n",
891 |        "3           3      0.606557   1.000000       1.0\n",
892 |        "4           4      0.147541   0.333333       0.5"
893 |       ]
894 |      },
895 |      "execution_count": 17,
896 |      "metadata": {},
897 |      "output_type": "execute_result"
898 |     }
899 |    ],
900 |    "source": [
901 |     "X_train.head()"
902 |    ]
903 |   },
904 |   {
905 |    "cell_type": "code",
906 |    "execution_count": null,
907 |    "metadata": {},
908 |    "outputs": [],
909 |    "source": []
910 |   }
911 |  ],
912 |  "metadata": {
913 |   "kernelspec": {
914 |    "display_name": "Python 3",
915 |    "language": "python",
916 |    "name": "python3"
917 |   },
918 |   "language_info": {
919 |    "codemirror_mode": {
920 |     "name": "ipython",
921 |     "version": 3
922 |    },
923 |    "file_extension": ".py",
924 |    "mimetype": "text/x-python",
925 |    "name": "python",
926 |    "nbconvert_exporter": "python",
927 |    "pygments_lexer": "ipython3",
928 |    "version": "3.8.5"
929 |   }
930 |  },
931 |  "nbformat": 4,
932 |  "nbformat_minor": 4
933 | }
934 | 


--------------------------------------------------------------------------------
/Fraud Detection Using ML/fraud_detection.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np \n",
 10 |     "import pandas as pd \n",
 11 |     "import seaborn as sns \n",
 12 |     "import  matplotlib.pyplot as plt \n",
 13 |     "%matplotlib inline\n",
 14 |     "from sklearn.model_selection import train_test_split \n",
 15 |     "from sklearn.preprocessing import StandardScaler\n",
 16 |     "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
 17 |     "from sklearn.linear_model import LogisticRegression \n",
 18 |     "\n",
 19 |     "sns.set_style('darkgrid')"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 35,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "## Dataset \n",
 29 |     "df = pd.read_csv('payment_fraud.csv')"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 36,
 35 |    "metadata": {},
 36 |    "outputs": [
 37 |     {
 38 |      "data": {
 39 |       "text/html": [
 40 |        "<div>\n",
 41 |        "<style scoped>\n",
 42 |        "    .dataframe tbody tr th:only-of-type {\n",
 43 |        "        vertical-align: middle;\n",
 44 |        "    }\n",
 45 |        "\n",
 46 |        "    .dataframe tbody tr th {\n",
 47 |        "        vertical-align: top;\n",
 48 |        "    }\n",
 49 |        "\n",
 50 |        "    .dataframe thead th {\n",
 51 |        "        text-align: right;\n",
 52 |        "    }\n",
 53 |        "</style>\n",
 54 |        "<table border=\"1\" class=\"dataframe\">\n",
 55 |        "  <thead>\n",
 56 |        "    <tr style=\"text-align: right;\">\n",
 57 |        "      <th></th>\n",
 58 |        "      <th>accountAgeDays</th>\n",
 59 |        "      <th>numItems</th>\n",
 60 |        "      <th>localTime</th>\n",
 61 |        "      <th>paymentMethod</th>\n",
 62 |        "      <th>paymentMethodAgeDays</th>\n",
 63 |        "      <th>label</th>\n",
 64 |        "    </tr>\n",
 65 |        "  </thead>\n",
 66 |        "  <tbody>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>0</th>\n",
 69 |        "      <td>29</td>\n",
 70 |        "      <td>1</td>\n",
 71 |        "      <td>4.745402</td>\n",
 72 |        "      <td>paypal</td>\n",
 73 |        "      <td>28.204861</td>\n",
 74 |        "      <td>0</td>\n",
 75 |        "    </tr>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>1</th>\n",
 78 |        "      <td>725</td>\n",
 79 |        "      <td>1</td>\n",
 80 |        "      <td>4.742303</td>\n",
 81 |        "      <td>storecredit</td>\n",
 82 |        "      <td>0.000000</td>\n",
 83 |        "      <td>0</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2</th>\n",
 87 |        "      <td>845</td>\n",
 88 |        "      <td>1</td>\n",
 89 |        "      <td>4.921318</td>\n",
 90 |        "      <td>creditcard</td>\n",
 91 |        "      <td>0.000000</td>\n",
 92 |        "      <td>0</td>\n",
 93 |        "    </tr>\n",
 94 |        "    <tr>\n",
 95 |        "      <th>3</th>\n",
 96 |        "      <td>503</td>\n",
 97 |        "      <td>1</td>\n",
 98 |        "      <td>4.886641</td>\n",
 99 |        "      <td>creditcard</td>\n",
100 |        "      <td>0.000000</td>\n",
101 |        "      <td>0</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>4</th>\n",
105 |        "      <td>2000</td>\n",
106 |        "      <td>1</td>\n",
107 |        "      <td>5.040929</td>\n",
108 |        "      <td>creditcard</td>\n",
109 |        "      <td>0.000000</td>\n",
110 |        "      <td>0</td>\n",
111 |        "    </tr>\n",
112 |        "  </tbody>\n",
113 |        "</table>\n",
114 |        "</div>"
115 |       ],
116 |       "text/plain": [
117 |        "   accountAgeDays  numItems  localTime paymentMethod  paymentMethodAgeDays  \\\n",
118 |        "0              29         1   4.745402        paypal             28.204861   \n",
119 |        "1             725         1   4.742303   storecredit              0.000000   \n",
120 |        "2             845         1   4.921318    creditcard              0.000000   \n",
121 |        "3             503         1   4.886641    creditcard              0.000000   \n",
122 |        "4            2000         1   5.040929    creditcard              0.000000   \n",
123 |        "\n",
124 |        "   label  \n",
125 |        "0      0  \n",
126 |        "1      0  \n",
127 |        "2      0  \n",
128 |        "3      0  \n",
129 |        "4      0  "
130 |       ]
131 |      },
132 |      "execution_count": 36,
133 |      "metadata": {},
134 |      "output_type": "execute_result"
135 |     }
136 |    ],
137 |    "source": [
138 |     "df.head()"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 37,
144 |    "metadata": {},
145 |    "outputs": [
146 |     {
147 |      "data": {
148 |       "text/plain": [
149 |        "accountAgeDays          0\n",
150 |        "numItems                0\n",
151 |        "localTime               0\n",
152 |        "paymentMethod           0\n",
153 |        "paymentMethodAgeDays    0\n",
154 |        "label                   0\n",
155 |        "dtype: int64"
156 |       ]
157 |      },
158 |      "execution_count": 37,
159 |      "metadata": {},
160 |      "output_type": "execute_result"
161 |     }
162 |    ],
163 |    "source": [
164 |     "df.isnull().sum() ## checking the null valeus "
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 39,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "data": {
174 |       "text/plain": [
175 |        "0    38661\n",
176 |        "1      560\n",
177 |        "Name: label, dtype: int64"
178 |       ]
179 |      },
180 |      "execution_count": 39,
181 |      "metadata": {},
182 |      "output_type": "execute_result"
183 |     }
184 |    ],
185 |    "source": [
186 |     "df.label.value_counts() ## count the number of 0's and 1's"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "code",
191 |    "execution_count": 40,
192 |    "metadata": {},
193 |    "outputs": [],
194 |    "source": [
195 |     "## coverting paymentMethod column into label encoding\n",
196 |     "paymthd_label = {v:k for k, v in enumerate(df.paymentMethod.unique())}\n",
197 |     "\n",
198 |     "df.paymentMethod = df.paymentMethod.map(paymthd_label)"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "code",
203 |    "execution_count": 41,
204 |    "metadata": {},
205 |    "outputs": [
206 |     {
207 |      "data": {
208 |       "text/html": [
209 |        "<div>\n",
210 |        "<style scoped>\n",
211 |        "    .dataframe tbody tr th:only-of-type {\n",
212 |        "        vertical-align: middle;\n",
213 |        "    }\n",
214 |        "\n",
215 |        "    .dataframe tbody tr th {\n",
216 |        "        vertical-align: top;\n",
217 |        "    }\n",
218 |        "\n",
219 |        "    .dataframe thead th {\n",
220 |        "        text-align: right;\n",
221 |        "    }\n",
222 |        "</style>\n",
223 |        "<table border=\"1\" class=\"dataframe\">\n",
224 |        "  <thead>\n",
225 |        "    <tr style=\"text-align: right;\">\n",
226 |        "      <th></th>\n",
227 |        "      <th>accountAgeDays</th>\n",
228 |        "      <th>numItems</th>\n",
229 |        "      <th>localTime</th>\n",
230 |        "      <th>paymentMethod</th>\n",
231 |        "      <th>paymentMethodAgeDays</th>\n",
232 |        "      <th>label</th>\n",
233 |        "    </tr>\n",
234 |        "  </thead>\n",
235 |        "  <tbody>\n",
236 |        "    <tr>\n",
237 |        "      <th>0</th>\n",
238 |        "      <td>29</td>\n",
239 |        "      <td>1</td>\n",
240 |        "      <td>4.745402</td>\n",
241 |        "      <td>0</td>\n",
242 |        "      <td>28.204861</td>\n",
243 |        "      <td>0</td>\n",
244 |        "    </tr>\n",
245 |        "    <tr>\n",
246 |        "      <th>1</th>\n",
247 |        "      <td>725</td>\n",
248 |        "      <td>1</td>\n",
249 |        "      <td>4.742303</td>\n",
250 |        "      <td>1</td>\n",
251 |        "      <td>0.000000</td>\n",
252 |        "      <td>0</td>\n",
253 |        "    </tr>\n",
254 |        "    <tr>\n",
255 |        "      <th>2</th>\n",
256 |        "      <td>845</td>\n",
257 |        "      <td>1</td>\n",
258 |        "      <td>4.921318</td>\n",
259 |        "      <td>2</td>\n",
260 |        "      <td>0.000000</td>\n",
261 |        "      <td>0</td>\n",
262 |        "    </tr>\n",
263 |        "    <tr>\n",
264 |        "      <th>3</th>\n",
265 |        "      <td>503</td>\n",
266 |        "      <td>1</td>\n",
267 |        "      <td>4.886641</td>\n",
268 |        "      <td>2</td>\n",
269 |        "      <td>0.000000</td>\n",
270 |        "      <td>0</td>\n",
271 |        "    </tr>\n",
272 |        "    <tr>\n",
273 |        "      <th>4</th>\n",
274 |        "      <td>2000</td>\n",
275 |        "      <td>1</td>\n",
276 |        "      <td>5.040929</td>\n",
277 |        "      <td>2</td>\n",
278 |        "      <td>0.000000</td>\n",
279 |        "      <td>0</td>\n",
280 |        "    </tr>\n",
281 |        "  </tbody>\n",
282 |        "</table>\n",
283 |        "</div>"
284 |       ],
285 |       "text/plain": [
286 |        "   accountAgeDays  numItems  localTime  paymentMethod  paymentMethodAgeDays  \\\n",
287 |        "0              29         1   4.745402              0             28.204861   \n",
288 |        "1             725         1   4.742303              1              0.000000   \n",
289 |        "2             845         1   4.921318              2              0.000000   \n",
290 |        "3             503         1   4.886641              2              0.000000   \n",
291 |        "4            2000         1   5.040929              2              0.000000   \n",
292 |        "\n",
293 |        "   label  \n",
294 |        "0      0  \n",
295 |        "1      0  \n",
296 |        "2      0  \n",
297 |        "3      0  \n",
298 |        "4      0  "
299 |       ]
300 |      },
301 |      "execution_count": 41,
302 |      "metadata": {},
303 |      "output_type": "execute_result"
304 |     }
305 |    ],
306 |    "source": [
307 |     "df.head()"
308 |    ]
309 |   },
310 |   {
311 |    "cell_type": "code",
312 |    "execution_count": 42,
313 |    "metadata": {},
314 |    "outputs": [
315 |     {
316 |      "data": {
317 |       "image/png": 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",
318 |       "text/plain": [
319 |        "<Figure size 720x720 with 2 Axes>"
320 |       ]
321 |      },
322 |      "metadata": {},
323 |      "output_type": "display_data"
324 |     }
325 |    ],
326 |    "source": [
327 |     "## corr(): it gives the correlation between the featuers\n",
328 |     "plt.figure(figsize=(10, 10))\n",
329 |     "sns.heatmap(df.corr(), annot=True);"
330 |    ]
331 |   },
332 |   {
333 |    "cell_type": "code",
334 |    "execution_count": 43,
335 |    "metadata": {},
336 |    "outputs": [
337 |     {
338 |      "data": {
339 |       "text/html": [
340 |        "<div>\n",
341 |        "<style scoped>\n",
342 |        "    .dataframe tbody tr th:only-of-type {\n",
343 |        "        vertical-align: middle;\n",
344 |        "    }\n",
345 |        "\n",
346 |        "    .dataframe tbody tr th {\n",
347 |        "        vertical-align: top;\n",
348 |        "    }\n",
349 |        "\n",
350 |        "    .dataframe thead th {\n",
351 |        "        text-align: right;\n",
352 |        "    }\n",
353 |        "</style>\n",
354 |        "<table border=\"1\" class=\"dataframe\">\n",
355 |        "  <thead>\n",
356 |        "    <tr style=\"text-align: right;\">\n",
357 |        "      <th></th>\n",
358 |        "      <th>accountAgeDays</th>\n",
359 |        "      <th>numItems</th>\n",
360 |        "      <th>localTime</th>\n",
361 |        "      <th>paymentMethod</th>\n",
362 |        "      <th>paymentMethodAgeDays</th>\n",
363 |        "      <th>label</th>\n",
364 |        "    </tr>\n",
365 |        "  </thead>\n",
366 |        "  <tbody>\n",
367 |        "    <tr>\n",
368 |        "      <th>count</th>\n",
369 |        "      <td>39221.000000</td>\n",
370 |        "      <td>39221.000000</td>\n",
371 |        "      <td>39221.000000</td>\n",
372 |        "      <td>39221.000000</td>\n",
373 |        "      <td>39221.000000</td>\n",
374 |        "      <td>39221.000000</td>\n",
375 |        "    </tr>\n",
376 |        "    <tr>\n",
377 |        "      <th>mean</th>\n",
378 |        "      <td>857.563984</td>\n",
379 |        "      <td>1.084751</td>\n",
380 |        "      <td>4.748232</td>\n",
381 |        "      <td>1.476811</td>\n",
382 |        "      <td>122.641326</td>\n",
383 |        "      <td>0.014278</td>\n",
384 |        "    </tr>\n",
385 |        "    <tr>\n",
386 |        "      <th>std</th>\n",
387 |        "      <td>804.788212</td>\n",
388 |        "      <td>0.566899</td>\n",
389 |        "      <td>0.389360</td>\n",
390 |        "      <td>0.850805</td>\n",
391 |        "      <td>283.569177</td>\n",
392 |        "      <td>0.118636</td>\n",
393 |        "    </tr>\n",
394 |        "    <tr>\n",
395 |        "      <th>min</th>\n",
396 |        "      <td>1.000000</td>\n",
397 |        "      <td>1.000000</td>\n",
398 |        "      <td>0.421214</td>\n",
399 |        "      <td>0.000000</td>\n",
400 |        "      <td>0.000000</td>\n",
401 |        "      <td>0.000000</td>\n",
402 |        "    </tr>\n",
403 |        "    <tr>\n",
404 |        "      <th>25%</th>\n",
405 |        "      <td>72.000000</td>\n",
406 |        "      <td>1.000000</td>\n",
407 |        "      <td>4.742303</td>\n",
408 |        "      <td>1.000000</td>\n",
409 |        "      <td>0.000000</td>\n",
410 |        "      <td>0.000000</td>\n",
411 |        "    </tr>\n",
412 |        "    <tr>\n",
413 |        "      <th>50%</th>\n",
414 |        "      <td>603.000000</td>\n",
415 |        "      <td>1.000000</td>\n",
416 |        "      <td>4.886641</td>\n",
417 |        "      <td>2.000000</td>\n",
418 |        "      <td>0.012500</td>\n",
419 |        "      <td>0.000000</td>\n",
420 |        "    </tr>\n",
421 |        "    <tr>\n",
422 |        "      <th>75%</th>\n",
423 |        "      <td>1804.000000</td>\n",
424 |        "      <td>1.000000</td>\n",
425 |        "      <td>4.962055</td>\n",
426 |        "      <td>2.000000</td>\n",
427 |        "      <td>87.510417</td>\n",
428 |        "      <td>0.000000</td>\n",
429 |        "    </tr>\n",
430 |        "    <tr>\n",
431 |        "      <th>max</th>\n",
432 |        "      <td>2000.000000</td>\n",
433 |        "      <td>29.000000</td>\n",
434 |        "      <td>5.040929</td>\n",
435 |        "      <td>2.000000</td>\n",
436 |        "      <td>1999.580556</td>\n",
437 |        "      <td>1.000000</td>\n",
438 |        "    </tr>\n",
439 |        "  </tbody>\n",
440 |        "</table>\n",
441 |        "</div>"
442 |       ],
443 |       "text/plain": [
444 |        "       accountAgeDays      numItems     localTime  paymentMethod  \\\n",
445 |        "count    39221.000000  39221.000000  39221.000000   39221.000000   \n",
446 |        "mean       857.563984      1.084751      4.748232       1.476811   \n",
447 |        "std        804.788212      0.566899      0.389360       0.850805   \n",
448 |        "min          1.000000      1.000000      0.421214       0.000000   \n",
449 |        "25%         72.000000      1.000000      4.742303       1.000000   \n",
450 |        "50%        603.000000      1.000000      4.886641       2.000000   \n",
451 |        "75%       1804.000000      1.000000      4.962055       2.000000   \n",
452 |        "max       2000.000000     29.000000      5.040929       2.000000   \n",
453 |        "\n",
454 |        "       paymentMethodAgeDays         label  \n",
455 |        "count          39221.000000  39221.000000  \n",
456 |        "mean             122.641326      0.014278  \n",
457 |        "std              283.569177      0.118636  \n",
458 |        "min                0.000000      0.000000  \n",
459 |        "25%                0.000000      0.000000  \n",
460 |        "50%                0.012500      0.000000  \n",
461 |        "75%               87.510417      0.000000  \n",
462 |        "max             1999.580556      1.000000  "
463 |       ]
464 |      },
465 |      "execution_count": 43,
466 |      "metadata": {},
467 |      "output_type": "execute_result"
468 |     }
469 |    ],
470 |    "source": [
471 |     "df.describe()"
472 |    ]
473 |   },
474 |   {
475 |    "cell_type": "code",
476 |    "execution_count": 44,
477 |    "metadata": {},
478 |    "outputs": [],
479 |    "source": [
480 |     "## independent and dependent features\n",
481 |     "X = df.iloc[:, :-1].values\n",
482 |     "y = df.iloc[:, -1].values"
483 |    ]
484 |   },
485 |   {
486 |    "cell_type": "code",
487 |    "execution_count": 45,
488 |    "metadata": {},
489 |    "outputs": [],
490 |    "source": [
491 |     "## scaling \n",
492 |     "\n",
493 |     "sc = StandardScaler()\n",
494 |     "X = sc.fit_transform(X)"
495 |    ]
496 |   },
497 |   {
498 |    "cell_type": "code",
499 |    "execution_count": 46,
500 |    "metadata": {},
501 |    "outputs": [],
502 |    "source": [
503 |     "## train test split \n",
504 |     "\n",
505 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
506 |    ]
507 |   },
508 |   {
509 |    "cell_type": "code",
510 |    "execution_count": 48,
511 |    "metadata": {},
512 |    "outputs": [
513 |     {
514 |      "name": "stdout",
515 |      "output_type": "stream",
516 |      "text": [
517 |       "X_train shape:  (29415, 5)\n",
518 |       "X_test shape:  (9806, 5)\n",
519 |       "y_train shape:  (29415,)\n",
520 |       "y_test shape:  (9806,)\n"
521 |      ]
522 |     }
523 |    ],
524 |    "source": [
525 |     "print(\"X_train shape: \", X_train.shape)\n",
526 |     "print(\"X_test shape: \", X_test.shape)\n",
527 |     "print(\"y_train shape: \", y_train.shape)\n",
528 |     "print(\"y_test shape: \", y_test.shape)"
529 |    ]
530 |   },
531 |   {
532 |    "cell_type": "code",
533 |    "execution_count": 49,
534 |    "metadata": {},
535 |    "outputs": [
536 |     {
537 |      "data": {
538 |       "text/plain": [
539 |        "LogisticRegression()"
540 |       ]
541 |      },
542 |      "execution_count": 49,
543 |      "metadata": {},
544 |      "output_type": "execute_result"
545 |     }
546 |    ],
547 |    "source": [
548 |     "## logisticRegression Model\n",
549 |     "lg = LogisticRegression()\n",
550 |     "\n",
551 |     "## training\n",
552 |     "lg.fit(X_train, y_train)"
553 |    ]
554 |   },
555 |   {
556 |    "cell_type": "code",
557 |    "execution_count": 50,
558 |    "metadata": {},
559 |    "outputs": [],
560 |    "source": [
561 |     "## prediction \n",
562 |     "pred = lg.predict(X_test)"
563 |    ]
564 |   },
565 |   {
566 |    "cell_type": "code",
567 |    "execution_count": null,
568 |    "metadata": {},
569 |    "outputs": [],
570 |    "source": []
571 |   },
572 |   {
573 |    "cell_type": "code",
574 |    "execution_count": null,
575 |    "metadata": {},
576 |    "outputs": [],
577 |    "source": []
578 |   }
579 |  ],
580 |  "metadata": {
581 |   "kernelspec": {
582 |    "display_name": "Python 3",
583 |    "language": "python",
584 |    "name": "python3"
585 |   },
586 |   "language_info": {
587 |    "codemirror_mode": {
588 |     "name": "ipython",
589 |     "version": 3
590 |    },
591 |    "file_extension": ".py",
592 |    "mimetype": "text/x-python",
593 |    "name": "python",
594 |    "nbconvert_exporter": "python",
595 |    "pygments_lexer": "ipython3",
596 |    "version": "3.8.5"
597 |   }
598 |  },
599 |  "nbformat": 4,
600 |  "nbformat_minor": 4
601 | }
602 | 


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