├── 1. Data Manipulation with Python Pandas.ipynb
├── 1. Label Encoder.ipynb
├── 2. One Hot Encoding.ipynb
├── 3. Binary Encoder.ipynb
├── 4. Ordinal Encoder.ipynb
├── Bias Variance.ipynb
├── Boxplot using Python.ipynb
├── CNN Basic Overview .ipynb
├── Churn Modelling with Decision Tree.ipynb
├── Churn Modelling with Random Forest.ipynb
├── Confusion Matrix.ipynb
├── Cross Validation.ipynb
├── Datasets
    ├── Churn_Modelling.csv
    ├── car data.csv
    ├── linear_data.csv
    └── nonlinear_data.csv
├── Decision Tree Classifier.ipynb
├── Docs
    ├── Machine Learning with TensorFlow.pdf
    ├── PyTorch Cheatsheet.pdf
    ├── Readme.md
    └── 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧.pdf
├── Fit, Transforn and fit_transform.ipynb
├── Importing data from google sheet using pandas and python.ipynb
├── Linear Relationship.ipynb
├── Neural Network.pdf
├── PROJECT on Linear Regression.ipynb
├── Papers
    ├── A Proficient Approach to Detect Osteosarcoma Through Deep Learning.pdf
    └── read.txt
├── Plot accuracy in seaborn.ipynb
├── Polynomial Regression.ipynb
├── README.md
├── Random Forest & HyperparameterTuning.ipynb
├── SVM in ML.ipynb
├── Save ML Models.ipynb
├── Screen Time Data.csv
├── The Normal or Gaussian Distribution.ipynb
├── home data.csv
├── shoe.csv
├── shop data.csv
└── weight-height.csv


/1. Label Encoder.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "21070538",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# 1. Label Encoder"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": 1,
 14 |    "id": "5122882e",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "classes = ['ClassA', 'ClassB', 'ClassC', 'ClassD']\n",
 19 |     "\n",
 20 |     "instances = ['ClassA', 'ClassB', 'ClassC', 'ClassD', 'ClassA', 'ClassB', 'ClassC', 'ClassD', 'ClassA', 'ClassB']"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 2,
 26 |    "id": "0790c5cf",
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "name": "stdout",
 31 |      "output_type": "stream",
 32 |      "text": [
 33 |       "Encoded labels: [0, 1, 2, 3, 0, 1, 2, 3, 0, 1]\n"
 34 |      ]
 35 |     }
 36 |    ],
 37 |    "source": [
 38 |     "label_to_int = {label: index for index, label in enumerate(classes)} #60 Days of Python ; Day 25\n",
 39 |     "encoded_labels = [label_to_int[label] for label in instances]\n",
 40 |     "\n",
 41 |     "print(\"Encoded labels:\", encoded_labels)"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 3,
 47 |    "id": "9bdaf4f3",
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "name": "stdout",
 52 |      "output_type": "stream",
 53 |      "text": [
 54 |       "Encoded labels: [0, 1, 2, 3, 0, 1, 2, 3, 0, 1]\n",
 55 |       "Decoded labels: ['ClassA', 'ClassB', 'ClassC', 'ClassD', 'ClassA', 'ClassB', 'ClassC', 'ClassD', 'ClassA', 'ClassB']\n"
 56 |      ]
 57 |     }
 58 |    ],
 59 |    "source": [
 60 |     "int_to_label = {index: label for label, index in label_to_int.items()}\n",
 61 |     "decoded_labels = [int_to_label[index] for index in encoded_labels]\n",
 62 |     "\n",
 63 |     "print(\"Encoded labels:\", encoded_labels)\n",
 64 |     "print(\"Decoded labels:\", decoded_labels)"
 65 |    ]
 66 |   },
 67 |   {
 68 |    "cell_type": "markdown",
 69 |    "id": "63ee1325",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "# Sklearn - Label Encoder"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 4,
 78 |    "id": "60a20a81",
 79 |    "metadata": {},
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "from sklearn.preprocessing import LabelEncoder"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 5,
 88 |    "id": "ad2970f2",
 89 |    "metadata": {},
 90 |    "outputs": [
 91 |     {
 92 |      "name": "stdout",
 93 |      "output_type": "stream",
 94 |      "text": [
 95 |       "Encoded labels: [0 1 2 3 0 1 2 3 0 1]\n"
 96 |      ]
 97 |     }
 98 |    ],
 99 |    "source": [
100 |     "label_encoder = LabelEncoder()\n",
101 |     "encoded_labels = label_encoder.fit_transform(instances)\n",
102 |     "\n",
103 |     "print(\"Encoded labels:\", encoded_labels)"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": 6,
109 |    "id": "5fd9dabe",
110 |    "metadata": {},
111 |    "outputs": [
112 |     {
113 |      "name": "stdout",
114 |      "output_type": "stream",
115 |      "text": [
116 |       "Encoded labels: [0 1 2 3 0 1 2 3 0 1]\n",
117 |       "Original labels: ['ClassA' 'ClassB' 'ClassC' 'ClassD' 'ClassA' 'ClassB' 'ClassC' 'ClassD'\n",
118 |       " 'ClassA' 'ClassB']\n"
119 |      ]
120 |     }
121 |    ],
122 |    "source": [
123 |     "original_labels = label_encoder.inverse_transform(encoded_labels)\n",
124 |     "\n",
125 |     "print(\"Encoded labels:\", encoded_labels)\n",
126 |     "print(\"Original labels:\", original_labels)"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": null,
132 |    "id": "e932d2b3",
133 |    "metadata": {},
134 |    "outputs": [],
135 |    "source": []
136 |   }
137 |  ],
138 |  "metadata": {
139 |   "kernelspec": {
140 |    "display_name": "Python 3 (ipykernel)",
141 |    "language": "python",
142 |    "name": "python3"
143 |   },
144 |   "language_info": {
145 |    "codemirror_mode": {
146 |     "name": "ipython",
147 |     "version": 3
148 |    },
149 |    "file_extension": ".py",
150 |    "mimetype": "text/x-python",
151 |    "name": "python",
152 |    "nbconvert_exporter": "python",
153 |    "pygments_lexer": "ipython3",
154 |    "version": "3.9.13"
155 |   }
156 |  },
157 |  "nbformat": 4,
158 |  "nbformat_minor": 5
159 | }
160 | 


--------------------------------------------------------------------------------
/2. One Hot Encoding.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "79afcd9b",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "id": "a31b20f7",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "data = {'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C']}"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 3,
 26 |    "id": "6c33a2f7",
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "data": {
 31 |       "text/html": [
 32 |        "<div>\n",
 33 |        "<style scoped>\n",
 34 |        "    .dataframe tbody tr th:only-of-type {\n",
 35 |        "        vertical-align: middle;\n",
 36 |        "    }\n",
 37 |        "\n",
 38 |        "    .dataframe tbody tr th {\n",
 39 |        "        vertical-align: top;\n",
 40 |        "    }\n",
 41 |        "\n",
 42 |        "    .dataframe thead th {\n",
 43 |        "        text-align: right;\n",
 44 |        "    }\n",
 45 |        "</style>\n",
 46 |        "<table border=\"1\" class=\"dataframe\">\n",
 47 |        "  <thead>\n",
 48 |        "    <tr style=\"text-align: right;\">\n",
 49 |        "      <th></th>\n",
 50 |        "      <th>Category</th>\n",
 51 |        "    </tr>\n",
 52 |        "  </thead>\n",
 53 |        "  <tbody>\n",
 54 |        "    <tr>\n",
 55 |        "      <th>0</th>\n",
 56 |        "      <td>A</td>\n",
 57 |        "    </tr>\n",
 58 |        "    <tr>\n",
 59 |        "      <th>1</th>\n",
 60 |        "      <td>B</td>\n",
 61 |        "    </tr>\n",
 62 |        "    <tr>\n",
 63 |        "      <th>2</th>\n",
 64 |        "      <td>C</td>\n",
 65 |        "    </tr>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>3</th>\n",
 68 |        "      <td>A</td>\n",
 69 |        "    </tr>\n",
 70 |        "    <tr>\n",
 71 |        "      <th>4</th>\n",
 72 |        "      <td>B</td>\n",
 73 |        "    </tr>\n",
 74 |        "  </tbody>\n",
 75 |        "</table>\n",
 76 |        "</div>"
 77 |       ],
 78 |       "text/plain": [
 79 |        "  Category\n",
 80 |        "0        A\n",
 81 |        "1        B\n",
 82 |        "2        C\n",
 83 |        "3        A\n",
 84 |        "4        B"
 85 |       ]
 86 |      },
 87 |      "execution_count": 3,
 88 |      "metadata": {},
 89 |      "output_type": "execute_result"
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "df = pd.DataFrame(data)\n",
 94 |     "df.head()"
 95 |    ]
 96 |   },
 97 |   {
 98 |    "cell_type": "code",
 99 |    "execution_count": 4,
100 |    "id": "d5674ea5",
101 |    "metadata": {},
102 |    "outputs": [
103 |     {
104 |      "data": {
105 |       "text/html": [
106 |        "<div>\n",
107 |        "<style scoped>\n",
108 |        "    .dataframe tbody tr th:only-of-type {\n",
109 |        "        vertical-align: middle;\n",
110 |        "    }\n",
111 |        "\n",
112 |        "    .dataframe tbody tr th {\n",
113 |        "        vertical-align: top;\n",
114 |        "    }\n",
115 |        "\n",
116 |        "    .dataframe thead th {\n",
117 |        "        text-align: right;\n",
118 |        "    }\n",
119 |        "</style>\n",
120 |        "<table border=\"1\" class=\"dataframe\">\n",
121 |        "  <thead>\n",
122 |        "    <tr style=\"text-align: right;\">\n",
123 |        "      <th></th>\n",
124 |        "      <th>Category_A</th>\n",
125 |        "      <th>Category_B</th>\n",
126 |        "      <th>Category_C</th>\n",
127 |        "    </tr>\n",
128 |        "  </thead>\n",
129 |        "  <tbody>\n",
130 |        "    <tr>\n",
131 |        "      <th>0</th>\n",
132 |        "      <td>1</td>\n",
133 |        "      <td>0</td>\n",
134 |        "      <td>0</td>\n",
135 |        "    </tr>\n",
136 |        "    <tr>\n",
137 |        "      <th>1</th>\n",
138 |        "      <td>0</td>\n",
139 |        "      <td>1</td>\n",
140 |        "      <td>0</td>\n",
141 |        "    </tr>\n",
142 |        "    <tr>\n",
143 |        "      <th>2</th>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>0</td>\n",
146 |        "      <td>1</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>3</th>\n",
150 |        "      <td>1</td>\n",
151 |        "      <td>0</td>\n",
152 |        "      <td>0</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>4</th>\n",
156 |        "      <td>0</td>\n",
157 |        "      <td>1</td>\n",
158 |        "      <td>0</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>5</th>\n",
162 |        "      <td>0</td>\n",
163 |        "      <td>0</td>\n",
164 |        "      <td>1</td>\n",
165 |        "    </tr>\n",
166 |        "    <tr>\n",
167 |        "      <th>6</th>\n",
168 |        "      <td>1</td>\n",
169 |        "      <td>0</td>\n",
170 |        "      <td>0</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>7</th>\n",
174 |        "      <td>0</td>\n",
175 |        "      <td>1</td>\n",
176 |        "      <td>0</td>\n",
177 |        "    </tr>\n",
178 |        "    <tr>\n",
179 |        "      <th>8</th>\n",
180 |        "      <td>0</td>\n",
181 |        "      <td>0</td>\n",
182 |        "      <td>1</td>\n",
183 |        "    </tr>\n",
184 |        "  </tbody>\n",
185 |        "</table>\n",
186 |        "</div>"
187 |       ],
188 |       "text/plain": [
189 |        "   Category_A  Category_B  Category_C\n",
190 |        "0           1           0           0\n",
191 |        "1           0           1           0\n",
192 |        "2           0           0           1\n",
193 |        "3           1           0           0\n",
194 |        "4           0           1           0\n",
195 |        "5           0           0           1\n",
196 |        "6           1           0           0\n",
197 |        "7           0           1           0\n",
198 |        "8           0           0           1"
199 |       ]
200 |      },
201 |      "execution_count": 4,
202 |      "metadata": {},
203 |      "output_type": "execute_result"
204 |     }
205 |    ],
206 |    "source": [
207 |     "one_hot_encoded_df = pd.get_dummies(df, columns=['Category'])\n",
208 |     "one_hot_encoded_df"
209 |    ]
210 |   },
211 |   {
212 |    "cell_type": "code",
213 |    "execution_count": 5,
214 |    "id": "e16d27ac",
215 |    "metadata": {},
216 |    "outputs": [
217 |     {
218 |      "data": {
219 |       "text/html": [
220 |        "<div>\n",
221 |        "<style scoped>\n",
222 |        "    .dataframe tbody tr th:only-of-type {\n",
223 |        "        vertical-align: middle;\n",
224 |        "    }\n",
225 |        "\n",
226 |        "    .dataframe tbody tr th {\n",
227 |        "        vertical-align: top;\n",
228 |        "    }\n",
229 |        "\n",
230 |        "    .dataframe thead th {\n",
231 |        "        text-align: right;\n",
232 |        "    }\n",
233 |        "</style>\n",
234 |        "<table border=\"1\" class=\"dataframe\">\n",
235 |        "  <thead>\n",
236 |        "    <tr style=\"text-align: right;\">\n",
237 |        "      <th></th>\n",
238 |        "      <th>Dummy_A</th>\n",
239 |        "      <th>Dummy_B</th>\n",
240 |        "      <th>Dummy_C</th>\n",
241 |        "    </tr>\n",
242 |        "  </thead>\n",
243 |        "  <tbody>\n",
244 |        "    <tr>\n",
245 |        "      <th>0</th>\n",
246 |        "      <td>1</td>\n",
247 |        "      <td>0</td>\n",
248 |        "      <td>0</td>\n",
249 |        "    </tr>\n",
250 |        "    <tr>\n",
251 |        "      <th>1</th>\n",
252 |        "      <td>0</td>\n",
253 |        "      <td>1</td>\n",
254 |        "      <td>0</td>\n",
255 |        "    </tr>\n",
256 |        "    <tr>\n",
257 |        "      <th>2</th>\n",
258 |        "      <td>0</td>\n",
259 |        "      <td>0</td>\n",
260 |        "      <td>1</td>\n",
261 |        "    </tr>\n",
262 |        "    <tr>\n",
263 |        "      <th>3</th>\n",
264 |        "      <td>1</td>\n",
265 |        "      <td>0</td>\n",
266 |        "      <td>0</td>\n",
267 |        "    </tr>\n",
268 |        "    <tr>\n",
269 |        "      <th>4</th>\n",
270 |        "      <td>0</td>\n",
271 |        "      <td>1</td>\n",
272 |        "      <td>0</td>\n",
273 |        "    </tr>\n",
274 |        "    <tr>\n",
275 |        "      <th>5</th>\n",
276 |        "      <td>0</td>\n",
277 |        "      <td>0</td>\n",
278 |        "      <td>1</td>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <th>6</th>\n",
282 |        "      <td>1</td>\n",
283 |        "      <td>0</td>\n",
284 |        "      <td>0</td>\n",
285 |        "    </tr>\n",
286 |        "    <tr>\n",
287 |        "      <th>7</th>\n",
288 |        "      <td>0</td>\n",
289 |        "      <td>1</td>\n",
290 |        "      <td>0</td>\n",
291 |        "    </tr>\n",
292 |        "    <tr>\n",
293 |        "      <th>8</th>\n",
294 |        "      <td>0</td>\n",
295 |        "      <td>0</td>\n",
296 |        "      <td>1</td>\n",
297 |        "    </tr>\n",
298 |        "  </tbody>\n",
299 |        "</table>\n",
300 |        "</div>"
301 |       ],
302 |       "text/plain": [
303 |        "   Dummy_A  Dummy_B  Dummy_C\n",
304 |        "0        1        0        0\n",
305 |        "1        0        1        0\n",
306 |        "2        0        0        1\n",
307 |        "3        1        0        0\n",
308 |        "4        0        1        0\n",
309 |        "5        0        0        1\n",
310 |        "6        1        0        0\n",
311 |        "7        0        1        0\n",
312 |        "8        0        0        1"
313 |       ]
314 |      },
315 |      "execution_count": 5,
316 |      "metadata": {},
317 |      "output_type": "execute_result"
318 |     }
319 |    ],
320 |    "source": [
321 |     "one_hot_encoded_df = pd.get_dummies(df, columns=['Category'], prefix='Dummy')\n",
322 |     "one_hot_encoded_df"
323 |    ]
324 |   },
325 |   {
326 |    "cell_type": "code",
327 |    "execution_count": 6,
328 |    "id": "2e8cfc1d",
329 |    "metadata": {},
330 |    "outputs": [
331 |     {
332 |      "data": {
333 |       "text/html": [
334 |        "<div>\n",
335 |        "<style scoped>\n",
336 |        "    .dataframe tbody tr th:only-of-type {\n",
337 |        "        vertical-align: middle;\n",
338 |        "    }\n",
339 |        "\n",
340 |        "    .dataframe tbody tr th {\n",
341 |        "        vertical-align: top;\n",
342 |        "    }\n",
343 |        "\n",
344 |        "    .dataframe thead th {\n",
345 |        "        text-align: right;\n",
346 |        "    }\n",
347 |        "</style>\n",
348 |        "<table border=\"1\" class=\"dataframe\">\n",
349 |        "  <thead>\n",
350 |        "    <tr style=\"text-align: right;\">\n",
351 |        "      <th></th>\n",
352 |        "      <th>Dummy_B</th>\n",
353 |        "      <th>Dummy_C</th>\n",
354 |        "    </tr>\n",
355 |        "  </thead>\n",
356 |        "  <tbody>\n",
357 |        "    <tr>\n",
358 |        "      <th>0</th>\n",
359 |        "      <td>0</td>\n",
360 |        "      <td>0</td>\n",
361 |        "    </tr>\n",
362 |        "    <tr>\n",
363 |        "      <th>1</th>\n",
364 |        "      <td>1</td>\n",
365 |        "      <td>0</td>\n",
366 |        "    </tr>\n",
367 |        "    <tr>\n",
368 |        "      <th>2</th>\n",
369 |        "      <td>0</td>\n",
370 |        "      <td>1</td>\n",
371 |        "    </tr>\n",
372 |        "    <tr>\n",
373 |        "      <th>3</th>\n",
374 |        "      <td>0</td>\n",
375 |        "      <td>0</td>\n",
376 |        "    </tr>\n",
377 |        "    <tr>\n",
378 |        "      <th>4</th>\n",
379 |        "      <td>1</td>\n",
380 |        "      <td>0</td>\n",
381 |        "    </tr>\n",
382 |        "    <tr>\n",
383 |        "      <th>5</th>\n",
384 |        "      <td>0</td>\n",
385 |        "      <td>1</td>\n",
386 |        "    </tr>\n",
387 |        "    <tr>\n",
388 |        "      <th>6</th>\n",
389 |        "      <td>0</td>\n",
390 |        "      <td>0</td>\n",
391 |        "    </tr>\n",
392 |        "    <tr>\n",
393 |        "      <th>7</th>\n",
394 |        "      <td>1</td>\n",
395 |        "      <td>0</td>\n",
396 |        "    </tr>\n",
397 |        "    <tr>\n",
398 |        "      <th>8</th>\n",
399 |        "      <td>0</td>\n",
400 |        "      <td>1</td>\n",
401 |        "    </tr>\n",
402 |        "  </tbody>\n",
403 |        "</table>\n",
404 |        "</div>"
405 |       ],
406 |       "text/plain": [
407 |        "   Dummy_B  Dummy_C\n",
408 |        "0        0        0\n",
409 |        "1        1        0\n",
410 |        "2        0        1\n",
411 |        "3        0        0\n",
412 |        "4        1        0\n",
413 |        "5        0        1\n",
414 |        "6        0        0\n",
415 |        "7        1        0\n",
416 |        "8        0        1"
417 |       ]
418 |      },
419 |      "execution_count": 6,
420 |      "metadata": {},
421 |      "output_type": "execute_result"
422 |     }
423 |    ],
424 |    "source": [
425 |     "one_hot_encoded_df = pd.get_dummies(df, columns=['Category'], prefix='Dummy',drop_first=True )\n",
426 |     "one_hot_encoded_df"
427 |    ]
428 |   },
429 |   {
430 |    "cell_type": "code",
431 |    "execution_count": 7,
432 |    "id": "287f4418",
433 |    "metadata": {},
434 |    "outputs": [
435 |     {
436 |      "data": {
437 |       "text/html": [
438 |        "<div>\n",
439 |        "<style scoped>\n",
440 |        "    .dataframe tbody tr th:only-of-type {\n",
441 |        "        vertical-align: middle;\n",
442 |        "    }\n",
443 |        "\n",
444 |        "    .dataframe tbody tr th {\n",
445 |        "        vertical-align: top;\n",
446 |        "    }\n",
447 |        "\n",
448 |        "    .dataframe thead th {\n",
449 |        "        text-align: right;\n",
450 |        "    }\n",
451 |        "</style>\n",
452 |        "<table border=\"1\" class=\"dataframe\">\n",
453 |        "  <thead>\n",
454 |        "    <tr style=\"text-align: right;\">\n",
455 |        "      <th></th>\n",
456 |        "      <th>Category</th>\n",
457 |        "    </tr>\n",
458 |        "  </thead>\n",
459 |        "  <tbody>\n",
460 |        "    <tr>\n",
461 |        "      <th>0</th>\n",
462 |        "      <td>A</td>\n",
463 |        "    </tr>\n",
464 |        "    <tr>\n",
465 |        "      <th>1</th>\n",
466 |        "      <td>B</td>\n",
467 |        "    </tr>\n",
468 |        "    <tr>\n",
469 |        "      <th>2</th>\n",
470 |        "      <td>C</td>\n",
471 |        "    </tr>\n",
472 |        "    <tr>\n",
473 |        "      <th>3</th>\n",
474 |        "      <td>A</td>\n",
475 |        "    </tr>\n",
476 |        "    <tr>\n",
477 |        "      <th>4</th>\n",
478 |        "      <td>B</td>\n",
479 |        "    </tr>\n",
480 |        "  </tbody>\n",
481 |        "</table>\n",
482 |        "</div>"
483 |       ],
484 |       "text/plain": [
485 |        "  Category\n",
486 |        "0        A\n",
487 |        "1        B\n",
488 |        "2        C\n",
489 |        "3        A\n",
490 |        "4        B"
491 |       ]
492 |      },
493 |      "execution_count": 7,
494 |      "metadata": {},
495 |      "output_type": "execute_result"
496 |     }
497 |    ],
498 |    "source": [
499 |     "df.head()"
500 |    ]
501 |   },
502 |   {
503 |    "cell_type": "code",
504 |    "execution_count": null,
505 |    "id": "cb821849",
506 |    "metadata": {},
507 |    "outputs": [],
508 |    "source": []
509 |   }
510 |  ],
511 |  "metadata": {
512 |   "kernelspec": {
513 |    "display_name": "Python 3 (ipykernel)",
514 |    "language": "python",
515 |    "name": "python3"
516 |   },
517 |   "language_info": {
518 |    "codemirror_mode": {
519 |     "name": "ipython",
520 |     "version": 3
521 |    },
522 |    "file_extension": ".py",
523 |    "mimetype": "text/x-python",
524 |    "name": "python",
525 |    "nbconvert_exporter": "python",
526 |    "pygments_lexer": "ipython3",
527 |    "version": "3.9.13"
528 |   }
529 |  },
530 |  "nbformat": 4,
531 |  "nbformat_minor": 5
532 | }
533 | 


--------------------------------------------------------------------------------
/3. Binary Encoder.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "adfe09d4",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n",
 11 |     "import category_encoders as ce"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "id": "56986103",
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "data = {'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C']}\n",
 22 |     "df = pd.DataFrame(data)"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 3,
 28 |    "id": "b5813023",
 29 |    "metadata": {},
 30 |    "outputs": [
 31 |     {
 32 |      "data": {
 33 |       "text/html": [
 34 |        "<div>\n",
 35 |        "<style scoped>\n",
 36 |        "    .dataframe tbody tr th:only-of-type {\n",
 37 |        "        vertical-align: middle;\n",
 38 |        "    }\n",
 39 |        "\n",
 40 |        "    .dataframe tbody tr th {\n",
 41 |        "        vertical-align: top;\n",
 42 |        "    }\n",
 43 |        "\n",
 44 |        "    .dataframe thead th {\n",
 45 |        "        text-align: right;\n",
 46 |        "    }\n",
 47 |        "</style>\n",
 48 |        "<table border=\"1\" class=\"dataframe\">\n",
 49 |        "  <thead>\n",
 50 |        "    <tr style=\"text-align: right;\">\n",
 51 |        "      <th></th>\n",
 52 |        "      <th>Category</th>\n",
 53 |        "    </tr>\n",
 54 |        "  </thead>\n",
 55 |        "  <tbody>\n",
 56 |        "    <tr>\n",
 57 |        "      <th>0</th>\n",
 58 |        "      <td>A</td>\n",
 59 |        "    </tr>\n",
 60 |        "    <tr>\n",
 61 |        "      <th>1</th>\n",
 62 |        "      <td>B</td>\n",
 63 |        "    </tr>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>2</th>\n",
 66 |        "      <td>C</td>\n",
 67 |        "    </tr>\n",
 68 |        "    <tr>\n",
 69 |        "      <th>3</th>\n",
 70 |        "      <td>A</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>4</th>\n",
 74 |        "      <td>B</td>\n",
 75 |        "    </tr>\n",
 76 |        "  </tbody>\n",
 77 |        "</table>\n",
 78 |        "</div>"
 79 |       ],
 80 |       "text/plain": [
 81 |        "  Category\n",
 82 |        "0        A\n",
 83 |        "1        B\n",
 84 |        "2        C\n",
 85 |        "3        A\n",
 86 |        "4        B"
 87 |       ]
 88 |      },
 89 |      "execution_count": 3,
 90 |      "metadata": {},
 91 |      "output_type": "execute_result"
 92 |     }
 93 |    ],
 94 |    "source": [
 95 |     "df.head()"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": 4,
101 |    "id": "9390c11f",
102 |    "metadata": {},
103 |    "outputs": [
104 |     {
105 |      "data": {
106 |       "text/plain": [
107 |        "(9, 1)"
108 |       ]
109 |      },
110 |      "execution_count": 4,
111 |      "metadata": {},
112 |      "output_type": "execute_result"
113 |     }
114 |    ],
115 |    "source": [
116 |     "df.shape"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "code",
121 |    "execution_count": 5,
122 |    "id": "ec81a1c2",
123 |    "metadata": {},
124 |    "outputs": [],
125 |    "source": [
126 |     "encoder = ce.BinaryEncoder(cols=['Category'], return_df=True)"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": 6,
132 |    "id": "d5fe35de",
133 |    "metadata": {},
134 |    "outputs": [
135 |     {
136 |      "data": {
137 |       "text/html": [
138 |        "<div>\n",
139 |        "<style scoped>\n",
140 |        "    .dataframe tbody tr th:only-of-type {\n",
141 |        "        vertical-align: middle;\n",
142 |        "    }\n",
143 |        "\n",
144 |        "    .dataframe tbody tr th {\n",
145 |        "        vertical-align: top;\n",
146 |        "    }\n",
147 |        "\n",
148 |        "    .dataframe thead th {\n",
149 |        "        text-align: right;\n",
150 |        "    }\n",
151 |        "</style>\n",
152 |        "<table border=\"1\" class=\"dataframe\">\n",
153 |        "  <thead>\n",
154 |        "    <tr style=\"text-align: right;\">\n",
155 |        "      <th></th>\n",
156 |        "      <th>Category_0</th>\n",
157 |        "      <th>Category_1</th>\n",
158 |        "    </tr>\n",
159 |        "  </thead>\n",
160 |        "  <tbody>\n",
161 |        "    <tr>\n",
162 |        "      <th>0</th>\n",
163 |        "      <td>0</td>\n",
164 |        "      <td>1</td>\n",
165 |        "    </tr>\n",
166 |        "    <tr>\n",
167 |        "      <th>1</th>\n",
168 |        "      <td>1</td>\n",
169 |        "      <td>0</td>\n",
170 |        "    </tr>\n",
171 |        "    <tr>\n",
172 |        "      <th>2</th>\n",
173 |        "      <td>1</td>\n",
174 |        "      <td>1</td>\n",
175 |        "    </tr>\n",
176 |        "    <tr>\n",
177 |        "      <th>3</th>\n",
178 |        "      <td>0</td>\n",
179 |        "      <td>1</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <th>4</th>\n",
183 |        "      <td>1</td>\n",
184 |        "      <td>0</td>\n",
185 |        "    </tr>\n",
186 |        "    <tr>\n",
187 |        "      <th>5</th>\n",
188 |        "      <td>1</td>\n",
189 |        "      <td>1</td>\n",
190 |        "    </tr>\n",
191 |        "    <tr>\n",
192 |        "      <th>6</th>\n",
193 |        "      <td>0</td>\n",
194 |        "      <td>1</td>\n",
195 |        "    </tr>\n",
196 |        "    <tr>\n",
197 |        "      <th>7</th>\n",
198 |        "      <td>1</td>\n",
199 |        "      <td>0</td>\n",
200 |        "    </tr>\n",
201 |        "    <tr>\n",
202 |        "      <th>8</th>\n",
203 |        "      <td>1</td>\n",
204 |        "      <td>1</td>\n",
205 |        "    </tr>\n",
206 |        "  </tbody>\n",
207 |        "</table>\n",
208 |        "</div>"
209 |       ],
210 |       "text/plain": [
211 |        "   Category_0  Category_1\n",
212 |        "0           0           1\n",
213 |        "1           1           0\n",
214 |        "2           1           1\n",
215 |        "3           0           1\n",
216 |        "4           1           0\n",
217 |        "5           1           1\n",
218 |        "6           0           1\n",
219 |        "7           1           0\n",
220 |        "8           1           1"
221 |       ]
222 |      },
223 |      "execution_count": 6,
224 |      "metadata": {},
225 |      "output_type": "execute_result"
226 |     }
227 |    ],
228 |    "source": [
229 |     "df_binary_encoded = encoder.fit_transform(df)\n",
230 |     "df_binary_encoded"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "id": "201a7fa5",
237 |    "metadata": {},
238 |    "outputs": [],
239 |    "source": []
240 |   }
241 |  ],
242 |  "metadata": {
243 |   "kernelspec": {
244 |    "display_name": "Python 3 (ipykernel)",
245 |    "language": "python",
246 |    "name": "python3"
247 |   },
248 |   "language_info": {
249 |    "codemirror_mode": {
250 |     "name": "ipython",
251 |     "version": 3
252 |    },
253 |    "file_extension": ".py",
254 |    "mimetype": "text/x-python",
255 |    "name": "python",
256 |    "nbconvert_exporter": "python",
257 |    "pygments_lexer": "ipython3",
258 |    "version": "3.9.13"
259 |   }
260 |  },
261 |  "nbformat": 4,
262 |  "nbformat_minor": 5
263 | }
264 | 


--------------------------------------------------------------------------------
/4. Ordinal Encoder.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "b78c029d",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n",
 11 |     "from sklearn.preprocessing import OrdinalEncoder"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "id": "c146e9da",
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "data = [\n",
 22 |     "    ['good'], ['bad'], ['excellent'], ['average'], \n",
 23 |     "    ['good'], ['average'], ['excellent'], ['bad'], \n",
 24 |     "    ['average'], ['good']\n",
 25 |     "]"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 3,
 31 |    "id": "3ad7f2c7",
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "data": {
 36 |       "text/html": [
 37 |        "<div>\n",
 38 |        "<style scoped>\n",
 39 |        "    .dataframe tbody tr th:only-of-type {\n",
 40 |        "        vertical-align: middle;\n",
 41 |        "    }\n",
 42 |        "\n",
 43 |        "    .dataframe tbody tr th {\n",
 44 |        "        vertical-align: top;\n",
 45 |        "    }\n",
 46 |        "\n",
 47 |        "    .dataframe thead th {\n",
 48 |        "        text-align: right;\n",
 49 |        "    }\n",
 50 |        "</style>\n",
 51 |        "<table border=\"1\" class=\"dataframe\">\n",
 52 |        "  <thead>\n",
 53 |        "    <tr style=\"text-align: right;\">\n",
 54 |        "      <th></th>\n",
 55 |        "      <th>reviews</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>good</td>\n",
 62 |        "    </tr>\n",
 63 |        "    <tr>\n",
 64 |        "      <th>1</th>\n",
 65 |        "      <td>bad</td>\n",
 66 |        "    </tr>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>2</th>\n",
 69 |        "      <td>excellent</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>3</th>\n",
 73 |        "      <td>average</td>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>4</th>\n",
 77 |        "      <td>good</td>\n",
 78 |        "    </tr>\n",
 79 |        "  </tbody>\n",
 80 |        "</table>\n",
 81 |        "</div>"
 82 |       ],
 83 |       "text/plain": [
 84 |        "     reviews\n",
 85 |        "0       good\n",
 86 |        "1        bad\n",
 87 |        "2  excellent\n",
 88 |        "3    average\n",
 89 |        "4       good"
 90 |       ]
 91 |      },
 92 |      "execution_count": 3,
 93 |      "metadata": {},
 94 |      "output_type": "execute_result"
 95 |     }
 96 |    ],
 97 |    "source": [
 98 |     "data = pd.DataFrame(data=data, columns=['reviews'])\n",
 99 |     "data.head()"
100 |    ]
101 |   },
102 |   {
103 |    "cell_type": "code",
104 |    "execution_count": 4,
105 |    "id": "14b89f09",
106 |    "metadata": {},
107 |    "outputs": [
108 |     {
109 |      "data": {
110 |       "text/plain": [
111 |        "(10, 1)"
112 |       ]
113 |      },
114 |      "execution_count": 4,
115 |      "metadata": {},
116 |      "output_type": "execute_result"
117 |     }
118 |    ],
119 |    "source": [
120 |     "data.shape"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 5,
126 |    "id": "3e569b99",
127 |    "metadata": {},
128 |    "outputs": [],
129 |    "source": [
130 |     "categories = [['bad', 'average', 'good', 'excellent']]"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "code",
135 |    "execution_count": 6,
136 |    "id": "eee41b3c",
137 |    "metadata": {},
138 |    "outputs": [
139 |     {
140 |      "data": {
141 |       "text/plain": [
142 |        "[['bad', 'average', 'good', 'excellent']]"
143 |       ]
144 |      },
145 |      "execution_count": 6,
146 |      "metadata": {},
147 |      "output_type": "execute_result"
148 |     }
149 |    ],
150 |    "source": [
151 |     "categories"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "code",
156 |    "execution_count": 7,
157 |    "id": "b3a1d73e",
158 |    "metadata": {},
159 |    "outputs": [],
160 |    "source": [
161 |     "encoder = OrdinalEncoder(categories=categories)"
162 |    ]
163 |   },
164 |   {
165 |    "cell_type": "code",
166 |    "execution_count": 8,
167 |    "id": "74286b79",
168 |    "metadata": {},
169 |    "outputs": [
170 |     {
171 |      "data": {
172 |       "text/plain": [
173 |        "array([[2.],\n",
174 |        "       [0.],\n",
175 |        "       [3.],\n",
176 |        "       [1.],\n",
177 |        "       [2.],\n",
178 |        "       [1.],\n",
179 |        "       [3.],\n",
180 |        "       [0.],\n",
181 |        "       [1.],\n",
182 |        "       [2.]])"
183 |       ]
184 |      },
185 |      "execution_count": 8,
186 |      "metadata": {},
187 |      "output_type": "execute_result"
188 |     }
189 |    ],
190 |    "source": [
191 |     "encoded_data = encoder.fit_transform(data)\n",
192 |     "encoded_data"
193 |    ]
194 |   },
195 |   {
196 |    "cell_type": "code",
197 |    "execution_count": 9,
198 |    "id": "b4fa6545",
199 |    "metadata": {},
200 |    "outputs": [
201 |     {
202 |      "data": {
203 |       "text/plain": [
204 |        "array([['good'],\n",
205 |        "       ['bad'],\n",
206 |        "       ['excellent'],\n",
207 |        "       ['average'],\n",
208 |        "       ['good'],\n",
209 |        "       ['average'],\n",
210 |        "       ['excellent'],\n",
211 |        "       ['bad'],\n",
212 |        "       ['average'],\n",
213 |        "       ['good']], dtype=object)"
214 |       ]
215 |      },
216 |      "execution_count": 9,
217 |      "metadata": {},
218 |      "output_type": "execute_result"
219 |     }
220 |    ],
221 |    "source": [
222 |     "decoded_data = encoder.inverse_transform(encoded_data)\n",
223 |     "decoded_data"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": null,
229 |    "id": "525a8f59",
230 |    "metadata": {},
231 |    "outputs": [],
232 |    "source": []
233 |   }
234 |  ],
235 |  "metadata": {
236 |   "kernelspec": {
237 |    "display_name": "Python 3 (ipykernel)",
238 |    "language": "python",
239 |    "name": "python3"
240 |   },
241 |   "language_info": {
242 |    "codemirror_mode": {
243 |     "name": "ipython",
244 |     "version": 3
245 |    },
246 |    "file_extension": ".py",
247 |    "mimetype": "text/x-python",
248 |    "name": "python",
249 |    "nbconvert_exporter": "python",
250 |    "pygments_lexer": "ipython3",
251 |    "version": "3.9.13"
252 |   }
253 |  },
254 |  "nbformat": 4,
255 |  "nbformat_minor": 5
256 | }
257 | 


--------------------------------------------------------------------------------
/Bias Variance.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "id": "b1a8e3b3",
 7 |    "metadata": {},
 8 |    "outputs": [
 9 |     {
10 |      "name": "stdout",
11 |      "output_type": "stream",
12 |      "text": [
13 |       "MSE (Mean Squared Error): 0.9388721228182039\n",
14 |       "Bias^2: 0.9178184739745323\n",
15 |       "Variance: 0.021053648843671263\n"
16 |      ]
17 |     }
18 |    ],
19 |    "source": [
20 |     "import numpy as np\n",
21 |     "from sklearn.model_selection import train_test_split\n",
22 |     "from sklearn.linear_model import LinearRegression\n",
23 |     "from mlxtend.evaluate import bias_variance_decomp\n",
24 |     "\n",
25 |     "np.random.seed(0)\n",
26 |     "X = np.random.rand(100, 1) * 10\n",
27 |     "y = 2 * X.squeeze() + np.random.randn(100)  # True relationship is y = 2X + noise\n",
28 |     "\n",
29 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
30 |     "model = LinearRegression()\n",
31 |     "model.fit(X_train, y_train)\n",
32 |     "\n",
33 |     "# Calculate bias and variance using the bias_variance_decomp function\n",
34 |     "mse, bias, variance = bias_variance_decomp(model, X_train, y_train, X_test, y_test, loss='mse')\n",
35 |     "\n",
36 |     "print(\"MSE (Mean Squared Error):\", mse)\n",
37 |     "print(\"Bias^2:\", bias)\n",
38 |     "print(\"Variance:\", variance)"
39 |    ]
40 |   },
41 |   {
42 |    "cell_type": "code",
43 |    "execution_count": null,
44 |    "id": "6c73e239",
45 |    "metadata": {},
46 |    "outputs": [],
47 |    "source": []
48 |   }
49 |  ],
50 |  "metadata": {
51 |   "kernelspec": {
52 |    "display_name": "Python 3 (ipykernel)",
53 |    "language": "python",
54 |    "name": "python3"
55 |   },
56 |   "language_info": {
57 |    "codemirror_mode": {
58 |     "name": "ipython",
59 |     "version": 3
60 |    },
61 |    "file_extension": ".py",
62 |    "mimetype": "text/x-python",
63 |    "name": "python",
64 |    "nbconvert_exporter": "python",
65 |    "pygments_lexer": "ipython3",
66 |    "version": "3.9.13"
67 |   }
68 |  },
69 |  "nbformat": 4,
70 |  "nbformat_minor": 5
71 | }
72 | 


--------------------------------------------------------------------------------
/Cross Validation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "e8f71325",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Import Libraries"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": null,
 14 |    "id": "be1ca00c",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import numpy as np\n",
 19 |     "import xgboost as xgb\n",
 20 |     "from sklearn.datasets import make_classification\n",
 21 |     "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score\n",
 22 |     "import warnings\n",
 23 |     "warnings.filterwarnings('ignore')"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "id": "8169bc95",
 29 |    "metadata": {},
 30 |    "source": [
 31 |     "# Generating synthetic data & splitting into train-test"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": null,
 37 |    "id": "d78b72e8",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42)\n",
 42 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "markdown",
 47 |    "id": "74140325",
 48 |    "metadata": {},
 49 |    "source": [
 50 |     "# XGBoost Classifier"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": null,
 56 |    "id": "02b08163",
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "clf = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')\n",
 61 |     "clf.fit(X_train, y_train)"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": null,
 67 |    "id": "88846ffc",
 68 |    "metadata": {},
 69 |    "outputs": [],
 70 |    "source": [
 71 |     "clf.score(X_test,y_test)"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "markdown",
 76 |    "id": "828a0d8f",
 77 |    "metadata": {},
 78 |    "source": [
 79 |     "# Perform k-Fold Cross-Validation"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": null,
 85 |    "id": "40209a32",
 86 |    "metadata": {},
 87 |    "outputs": [],
 88 |    "source": [
 89 |     "kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
 90 |     "kf_scores = cross_val_score(clf, X, y, cv=kf)"
 91 |    ]
 92 |   },
 93 |   {
 94 |    "cell_type": "code",
 95 |    "execution_count": null,
 96 |    "id": "7f1faafa",
 97 |    "metadata": {},
 98 |    "outputs": [],
 99 |    "source": [
100 |     "kf_scores"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "markdown",
105 |    "id": "ebe2b712",
106 |    "metadata": {},
107 |    "source": [
108 |     "# Perform Stratified k-Fold Cross-Validation"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": null,
114 |    "id": "975f881f",
115 |    "metadata": {},
116 |    "outputs": [],
117 |    "source": [
118 |     "skf = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)\n",
119 |     "skf_scores = cross_val_score(clf, X, y, cv=skf)"
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": null,
125 |    "id": "038c6ded",
126 |    "metadata": {},
127 |    "outputs": [],
128 |    "source": [
129 |     "skf_scores"
130 |    ]
131 |   },
132 |   {
133 |    "cell_type": "code",
134 |    "execution_count": null,
135 |    "id": "25f4fdd9",
136 |    "metadata": {},
137 |    "outputs": [],
138 |    "source": []
139 |   }
140 |  ],
141 |  "metadata": {
142 |   "kernelspec": {
143 |    "display_name": "Python 3 (ipykernel)",
144 |    "language": "python",
145 |    "name": "python3"
146 |   },
147 |   "language_info": {
148 |    "codemirror_mode": {
149 |     "name": "ipython",
150 |     "version": 3
151 |    },
152 |    "file_extension": ".py",
153 |    "mimetype": "text/x-python",
154 |    "name": "python",
155 |    "nbconvert_exporter": "python",
156 |    "pygments_lexer": "ipython3",
157 |    "version": "3.9.13"
158 |   }
159 |  },
160 |  "nbformat": 4,
161 |  "nbformat_minor": 5
162 | }
163 | 


--------------------------------------------------------------------------------
/Datasets/car data.csv:
--------------------------------------------------------------------------------
  1 | speed,car_age,experience,risk
  2 | 172,22,8.104041735628343,42
  3 | 94,17,11.429460504100213,36
  4 | 186,20,14.819477218213366,45
  5 | 151,10,15.343897062171434,38
  6 | 100,6,16.455807037533862,47
  7 | 182,15,14.88423313886596,66
  8 | 154,11,4.750129425849399,64
  9 | 167,28,8.004457857440496,42
 10 | 196,5,9.554297817604551,77
 11 | 179,1,1.6578264338670845,95
 12 | 183,8,10.567402425944184,75
 13 | 210,21,8.726716632131119,19
 14 | 132,28,16.04218498053468,14
 15 | 81,12,19.558013375085416,83
 16 | 167,12,11.120100772851693,47
 17 | 117,5,6.453727765551673,22
 18 | 209,7,0.8680156659634553,40
 19 | 100,4,18.49286660447192,56
 20 | 168,20,13.90822246213295,65
 21 | 128,15,1.508690941795385,24
 22 | 138,3,3.3243086381850584,38
 23 | 94,23,4.336182131729824,17
 24 | 130,8,5.889878936788597,14
 25 | 187,20,19.916627500587413,38
 26 | 134,16,13.938501117276296,56
 27 | 143,13,7.6840366275843985,77
 28 | 210,18,14.742014116860386,85
 29 | 130,28,18.305086833382127,54
 30 | 214,10,19.174048148156924,11
 31 | 100,19,1.157277955154148,36
 32 | 152,17,7.890435972162598,45
 33 | 97,24,2.135121318717688,45
 34 | 211,19,6.713201295939264,35
 35 | 168,28,3.39359807791131,52
 36 | 139,26,12.936967233374476,36
 37 | 93,23,7.765367679797444,78
 38 | 88,26,4.587894914098993,29
 39 | 169,5,5.31875849928478,20
 40 | 132,26,7.206901904105365,83
 41 | 209,21,5.198997257085254,47
 42 | 163,23,9.06481693657662,15
 43 | 171,9,0.6463190221930093,81
 44 | 190,12,5.595270143378914,32
 45 | 87,21,8.224134417443725,56
 46 | 114,1,12.05563764046597,99
 47 | 160,26,5.419152850302598,55
 48 | 183,1,1.524007591983263,99
 49 | 211,15,18.81063654270767,22
 50 | 81,2,8.332781154769584,71
 51 | 213,22,11.623252423616526,91
 52 | 133,16,18.38353103671119,98
 53 | 185,25,1.6549684007061227,69
 54 | 83,8,17.533229718515855,52
 55 | 133,13,11.0317574508814,85
 56 | 123,21,3.296685222740019,77
 57 | 93,1,8.225102331707381,14
 58 | 174,16,15.55204567470451,46
 59 | 127,29,9.607401637327094,81
 60 | 94,7,19.70572101331009,40
 61 | 119,5,7.534779398758815,18
 62 | 161,22,14.99156599952144,60
 63 | 190,29,7.859788979577182,38
 64 | 132,23,16.58328441413029,87
 65 | 103,3,11.381629383314952,49
 66 | 203,12,1.270236591800027,50
 67 | 120,26,0.7364373527198276,95
 68 | 94,16,2.677042376012664,20
 69 | 124,19,0.2734392965399457,32
 70 | 144,5,1.5071812070492463,10
 71 | 168,22,13.83428794337824,55
 72 | 150,25,10.68692550058926,30
 73 | 88,29,14.998214989399436,99
 74 | 167,14,18.263315045152854,45
 75 | 208,28,11.70299064650944,63
 76 | 215,5,14.52241688143696,96
 77 | 142,15,15.141624040849118,66
 78 | 218,17,7.557010985515729,10
 79 | 215,20,4.100906570130498,63
 80 | 112,5,5.028848790235969,64
 81 | 202,12,5.494635940518176,49
 82 | 84,16,4.144552980161129,24
 83 | 120,26,17.564413349583845,30
 84 | 107,26,15.139982643444574,56
 85 | 214,16,0.9379293558243984,82
 86 | 151,21,5.373449640506147,62
 87 | 91,7,0.4436948406060326,18
 88 | 112,4,9.963303732917858,83
 89 | 127,1,9.524213935780285,61
 90 | 141,5,16.627429801250024,66
 91 | 116,23,6.1555447954209885,35
 92 | 178,26,16.327714704261247,50
 93 | 183,10,19.359475308796465,44
 94 | 114,22,1.7681644673129315,72
 95 | 180,5,15.836356789392658,34
 96 | 210,4,11.79911794275272,99
 97 | 80,2,9.600919340369504,84
 98 | 84,20,8.410715573301502,47
 99 | 182,10,15.69336527888619,11
100 | 106,26,12.787227139484846,16
101 | 216,19,16.10089335038724,91
102 | 94,26,18.06302117353253,43
103 | 169,1,12.34527424194584,26
104 | 121,24,19.60925450184756,52
105 | 203,5,12.161756990837704,68
106 | 142,13,12.732886432445651,60
107 | 175,4,11.096312170844511,63
108 | 131,16,1.8200418210266056,33
109 | 211,16,10.94892613798223,80
110 | 108,23,9.018208948322089,61
111 | 115,2,18.20942556806546,79
112 | 92,17,5.95918903083901,97
113 | 150,28,10.472045520300224,42
114 | 165,27,13.952837429869266,58
115 | 107,20,15.929435513477546,38
116 | 145,24,9.186936158923448,72
117 | 124,12,16.841828300374193,31
118 | 141,18,15.37835482630795,35
119 | 213,3,1.3247195570373171,37
120 | 107,28,0.917225328092659,94
121 | 107,1,12.416113688441826,58
122 | 187,1,6.948268164508285,80
123 | 123,29,4.182615821570284,90
124 | 109,11,6.8312642091309,58
125 | 207,12,9.20238317967144,95
126 | 171,3,11.695322118983071,72
127 | 208,1,8.006009778205627,70
128 | 200,1,13.953351473938636,58
129 | 106,8,3.6013454469859707,80
130 | 200,10,13.93002932454158,10
131 | 195,11,8.233224287150563,22
132 | 82,12,17.486352097088343,96
133 | 182,29,10.304721097284215,60
134 | 216,13,19.462206985402087,65
135 | 141,12,12.038707910819204,92
136 | 130,14,4.476981318126412,71
137 | 138,2,16.435812736888984,41
138 | 197,29,6.901652556447735,39
139 | 175,19,6.95238428965367,38
140 | 192,18,0.6360936297033026,58
141 | 141,3,10.974306184148173,54
142 | 131,23,10.68847007465046,39
143 | 91,17,7.119829685439722,25
144 | 118,26,17.884345257918515,49
145 | 209,8,2.574967965992878,28
146 | 210,29,6.601990266203108,27
147 | 192,26,6.43165529360058,10
148 | 180,10,1.8458117245749217,87
149 | 192,26,9.622907881125863,56
150 | 160,2,13.75569434192166,75
151 | 192,19,10.233140270945224,47
152 | 81,9,3.1395536543238745,60
153 | 209,7,7.545719304841452,72
154 | 133,4,0.0519004876729312,13
155 | 166,26,17.366022140711735,10
156 | 208,21,1.6903401536136389,17
157 | 205,18,11.945561645108612,38
158 | 132,11,10.73181291974665,12
159 | 147,29,18.480835182148663,41
160 | 202,24,4.722330662348087,19
161 | 117,4,15.199108178751713,83
162 | 103,25,10.62531507947068,92
163 | 148,21,14.410321227829296,43
164 | 195,29,1.246827250224829,96
165 | 177,25,2.954781838124534,64
166 | 218,4,2.662338571749565,41
167 | 176,10,13.743310108066622,59
168 | 203,5,16.888813452672576,16
169 | 149,9,14.992324640171748,17
170 | 82,3,17.344299621494464,66
171 | 175,3,7.94327665008165,97
172 | 131,16,2.097383330649618,96
173 | 207,4,14.748104121876382,68
174 | 118,18,3.645677569519161,81
175 | 161,17,11.279301840376728,63
176 | 183,7,16.814199717558623,76
177 | 208,24,1.7840865742411238,60
178 | 90,23,10.70671128031047,17
179 | 178,12,6.858537285565205,44
180 | 86,17,9.479398828611124,97
181 | 169,23,7.102086065586095,87
182 | 191,13,12.97645680583152,41
183 | 139,23,9.591642035085004,55
184 | 192,25,11.683989694283383,25
185 | 81,3,14.736449507898255,77
186 | 208,9,11.154845319008748,46
187 | 127,17,11.73070869321518,63
188 | 219,17,11.289170850452823,94
189 | 116,20,7.575452519025787,23
190 | 88,16,6.748936677438362,64
191 | 178,25,17.992947942900244,57
192 | 127,22,12.151104445871455,91
193 | 210,13,4.8870631092930905,16
194 | 133,27,9.964953956978803,83
195 | 199,19,6.60696962561828,16
196 | 195,17,18.67383647519749,42
197 | 192,12,4.506655954250565,94
198 | 183,29,7.307136393481153,28
199 | 163,9,9.75619601617476,28
200 | 191,19,17.016350356722924,45
201 | 178,12,1.7577524908028774,38
202 | 172,9,16.11729773482287,69
203 | 207,7,1.1130697871395068,91
204 | 189,28,16.846280705963633,11
205 | 161,14,1.0327095574902567,10
206 | 133,20,0.3648496285672209,56
207 | 147,19,13.93922924732843,78
208 | 112,26,19.945110695367887,29
209 | 100,15,17.932205259480416,20
210 | 127,16,11.5199683561132,11
211 | 207,21,18.347912261710636,76
212 | 215,5,0.1060001279521083,96
213 | 214,3,19.50134325890285,21
214 | 207,12,9.81497558517569,29
215 | 112,25,14.457921932435514,14
216 | 194,20,16.417229463067052,46
217 | 198,27,14.369145416731072,47
218 | 101,21,10.700734640898212,18
219 | 117,4,9.53238968252824,62
220 | 188,21,16.771651174052494,53
221 | 87,16,19.359881330084686,33
222 | 106,25,14.219049576734966,90
223 | 100,13,14.724943673969763,39
224 | 109,21,10.596800463622332,68
225 | 176,10,14.14460191288889,96
226 | 107,26,15.355590028856776,90
227 | 190,7,1.7458035917935089,90
228 | 140,14,10.12207912718694,23
229 | 127,26,18.64028684513868,18
230 | 83,5,6.412843843190286,49
231 | 114,3,11.877661951157975,75
232 | 128,23,7.384609630197922,34
233 | 96,29,9.085359444381991,82
234 | 125,11,10.972062358763065,31
235 | 196,11,10.978439597779545,13
236 | 85,18,4.034609246604717,35
237 | 178,15,13.691430957596666,67
238 | 203,12,1.7573622248531362,38
239 | 116,9,2.7764954688404075,96
240 | 172,26,2.3339212828336264,84
241 | 125,12,9.463324021037844,79
242 | 174,17,12.122037621116773,27
243 | 195,6,2.133985002268721,51
244 | 146,7,17.014549200329093,50
245 | 207,14,14.919490510974365,47
246 | 97,13,8.170365858718338,43
247 | 104,8,18.65876134199909,26
248 | 137,9,4.100036201064745,34
249 | 146,18,7.584571699276008,85
250 | 183,27,18.52898847179422,96
251 | 103,26,14.431931015025544,95
252 | 193,2,0.9618928792817538,94
253 | 111,5,15.630289635028298,36
254 | 165,29,16.558817743549838,66
255 | 206,5,15.01003849530627,40
256 | 209,6,15.990742618390312,15
257 | 96,19,16.502653533855945,49
258 | 183,8,3.728099735762882,21
259 | 216,16,4.713834921299995,62
260 | 122,13,12.675171274403546,80
261 | 118,1,18.15732260925032,19
262 | 105,22,6.323931645929893,54
263 | 178,20,11.766133515901132,26
264 | 129,17,13.659641268342648,35
265 | 92,7,9.038972025980785,94
266 | 139,25,14.27564909710652,71
267 | 214,13,17.99334890584346,55
268 | 136,4,12.482030883636751,73
269 | 99,6,8.77489350776678,63
270 | 144,28,11.549725418122511,74
271 | 87,19,7.107249036367918,60
272 | 194,29,7.829642204030039,62
273 | 171,12,10.63714959376348,45
274 | 177,22,1.3323822478163394,35
275 | 145,7,4.580507818495927,38
276 | 111,27,10.85698621226818,30
277 | 165,10,8.630562862728851,20
278 | 130,26,6.656315718249686,49
279 | 142,19,14.61098273618603,20
280 | 204,7,13.87435214806677,45
281 | 137,3,3.334615199867663,68
282 | 


--------------------------------------------------------------------------------
/Datasets/linear_data.csv:
--------------------------------------------------------------------------------
  1 | x,y
  2 | 0.0,4.4476917012367645
  3 | 0.02004008016032064,5.024612031683661
  4 | 0.04008016032064128,5.869592917849527
  5 | 0.06012024048096192,4.722490205935278
  6 | 0.08016032064128256,4.877100714416455
  7 | 0.1002004008016032,5.046555162919705
  8 | 0.12024048096192384,5.3749929985119715
  9 | 0.1402805611222445,5.54302005406743
 10 | 0.16032064128256512,5.954347109974459
 11 | 0.18036072144288576,5.610470559620101
 12 | 0.2004008016032064,5.369775040307245
 13 | 0.22044088176352705,6.070465328332461
 14 | 0.24048096192384769,5.833017434918486
 15 | 0.2605210420841683,4.773202326039828
 16 | 0.280561122244489,6.824306364666977
 17 | 0.3006012024048096,6.486163098907986
 18 | 0.32064128256513025,5.557175453746723
 19 | 0.3406813627254509,5.87031777632014
 20 | 0.3607214428857715,6.383622260769463
 21 | 0.3807615230460922,5.675422649743743
 22 | 0.4008016032064128,6.166779101601335
 23 | 0.42084168336673344,6.393972603519205
 24 | 0.4408817635270541,5.374350573186888
 25 | 0.4609218436873747,5.620677760583318
 26 | 0.48096192384769537,6.422628046600681
 27 | 0.501002004008016,6.232411246590447
 28 | 0.5210420841683366,6.503982448493647
 29 | 0.5410821643286573,6.015880321330456
 30 | 0.561122244488978,5.97774188350084
 31 | 0.5811623246492986,5.163004911506911
 32 | 0.6012024048096192,5.629404596365517
 33 | 0.6212424849699398,6.266018017269126
 34 | 0.6412825651302605,6.694843740042638
 35 | 0.6613226452905812,6.58823447384901
 36 | 0.6813627254509018,6.298604463887953
 37 | 0.7014028056112224,6.266919827977096
 38 | 0.721442885771543,6.5514755878622255
 39 | 0.7414829659318637,6.522071522409804
 40 | 0.7615230460921844,7.225318849654224
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340 | 6.773547094188377,0.8626614816789757
341 | 6.793587174348697,0.9238952788358095
342 | 6.813627254509018,1.0484140929983332
343 | 6.833667334669339,1.175978743590853
344 | 6.853707414829659,1.128254588844602
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346 | 6.8937875751503,1.139255210018654
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359 | 7.154308617234468,1.4496302155212728
360 | 7.174348697394789,1.7902716496135778
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363 | 7.234468937875751,1.722132255526509
364 | 7.254509018036072,1.6809398843692411
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366 | 7.294589178356713,1.6455451142393254
367 | 7.314629258517034,1.70860243884781
368 | 7.3346693386773545,1.7375445608559885
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392 | 7.8156312625250495,2.0426327199872087
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408 | 8.13627254509018,1.9749169799249768
409 | 8.1563126252505,1.7274823155753782
410 | 8.176352705410821,1.8920413743946485
411 | 8.196392785571142,1.9077990360253247
412 | 8.216432865731463,1.7700279792464326
413 | 8.236472945891784,2.022873922714745
414 | 8.256513026052104,1.85630047371615
415 | 8.276553106212425,1.9804152302997036
416 | 8.296593186372744,1.7282201297333017
417 | 8.316633266533065,1.6990143108675015
418 | 8.336673346693386,1.7939226903409637
419 | 8.356713426853707,1.58467035908628
420 | 8.376753507014028,1.754373676935718
421 | 8.396793587174349,1.722241055077264
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426 | 8.496993987975952,1.7718168367561646
427 | 8.517034068136272,1.4970226084706109
428 | 8.537074148296593,1.4466964487056793
429 | 8.557114228456914,1.4171551241754052
430 | 8.577154308617233,1.6111504032294273
431 | 8.597194388777554,1.420706838703299
432 | 8.617234468937875,1.3698120178039788
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442 | 8.817635270541082,1.3079713536183704
443 | 8.837675350701403,1.1381011847085876
444 | 8.857715430861724,1.135128973134417
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448 | 8.937875751503006,0.9793353196411695
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450 | 8.977955911823647,0.8675125064578089
451 | 8.997995991983968,0.7424710115675756
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458 | 9.138276553106213,0.4303546456589809
459 | 9.158316633266534,0.6531369414080328
460 | 9.178356713426853,0.4577913812410666
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462 | 9.218436873747494,0.43074491265624126
463 | 9.238476953907815,0.24638790864072269
464 | 9.258517034068136,0.35323831905765685
465 | 9.278557114228457,0.2825631608647057
466 | 9.298597194388778,0.2615301903816453
467 | 9.318637274549097,0.25002463414025744
468 | 9.338677354709418,0.17873775307438408
469 | 9.358717434869739,0.13365878528380684
470 | 9.37875751503006,0.12043985820286288
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475 | 9.478957915831662,-0.2515804523989187
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477 | 9.519038076152304,-0.09436650111172341
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481 | 9.599198396793588,-0.3142446632131576
482 | 9.619238476953907,-0.42660427541533075
483 | 9.639278557114228,-0.4573845217731056
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485 | 9.67935871743487,-0.6024080695288476
486 | 9.69939879759519,-0.5824874734962593
487 | 9.719438877755511,-0.6608390437315119
488 | 9.739478957915832,-0.7233772829079065
489 | 9.759519038076151,-0.7427570780576205
490 | 9.779559118236472,-0.6270241030282162
491 | 9.799599198396793,-0.7270303280206188
492 | 9.819639278557114,-0.8572764266889769
493 | 9.839679358717435,-0.8293095529169475
494 | 9.859719438877756,-1.0065953885016579
495 | 9.879759519038076,-0.9522227772435712
496 | 9.899799599198396,-0.6997579264519058
497 | 9.919839679358716,-0.9591963065537876
498 | 9.939879759519037,-0.9120809896866726
499 | 9.959919839679358,-1.0264750140589323
500 | 9.97995991983968,-1.0193789536220121
501 | 10.0,-1.021716412810822
502 | 


--------------------------------------------------------------------------------
/Decision Tree Classifier.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 matplotlib.pyplot as plt\n",
  11 |     "import numpy as np"
  12 |    ]
  13 |   },
  14 |   {
  15 |    "cell_type": "code",
  16 |    "execution_count": 2,
  17 |    "metadata": {},
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "df = pd.read_csv('shop data.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>age</th>\n",
  50 |        "      <th>income</th>\n",
  51 |        "      <th>gender</th>\n",
  52 |        "      <th>m_status</th>\n",
  53 |        "      <th>buys</th>\n",
  54 |        "    </tr>\n",
  55 |        "  </thead>\n",
  56 |        "  <tbody>\n",
  57 |        "    <tr>\n",
  58 |        "      <th>0</th>\n",
  59 |        "      <td>&lt;25</td>\n",
  60 |        "      <td>high</td>\n",
  61 |        "      <td>male</td>\n",
  62 |        "      <td>single</td>\n",
  63 |        "      <td>no</td>\n",
  64 |        "    </tr>\n",
  65 |        "    <tr>\n",
  66 |        "      <th>1</th>\n",
  67 |        "      <td>&lt;25</td>\n",
  68 |        "      <td>high</td>\n",
  69 |        "      <td>male</td>\n",
  70 |        "      <td>married</td>\n",
  71 |        "      <td>no</td>\n",
  72 |        "    </tr>\n",
  73 |        "    <tr>\n",
  74 |        "      <th>2</th>\n",
  75 |        "      <td>25-35</td>\n",
  76 |        "      <td>high</td>\n",
  77 |        "      <td>male</td>\n",
  78 |        "      <td>single</td>\n",
  79 |        "      <td>yes</td>\n",
  80 |        "    </tr>\n",
  81 |        "    <tr>\n",
  82 |        "      <th>3</th>\n",
  83 |        "      <td>&gt;35</td>\n",
  84 |        "      <td>medium</td>\n",
  85 |        "      <td>male</td>\n",
  86 |        "      <td>single</td>\n",
  87 |        "      <td>yes</td>\n",
  88 |        "    </tr>\n",
  89 |        "    <tr>\n",
  90 |        "      <th>4</th>\n",
  91 |        "      <td>&gt;35</td>\n",
  92 |        "      <td>low</td>\n",
  93 |        "      <td>female</td>\n",
  94 |        "      <td>single</td>\n",
  95 |        "      <td>yes</td>\n",
  96 |        "    </tr>\n",
  97 |        "    <tr>\n",
  98 |        "      <th>5</th>\n",
  99 |        "      <td>&gt;35</td>\n",
 100 |        "      <td>low</td>\n",
 101 |        "      <td>female</td>\n",
 102 |        "      <td>single</td>\n",
 103 |        "      <td>no</td>\n",
 104 |        "    </tr>\n",
 105 |        "    <tr>\n",
 106 |        "      <th>6</th>\n",
 107 |        "      <td>25-35</td>\n",
 108 |        "      <td>low</td>\n",
 109 |        "      <td>female</td>\n",
 110 |        "      <td>married</td>\n",
 111 |        "      <td>yes</td>\n",
 112 |        "    </tr>\n",
 113 |        "    <tr>\n",
 114 |        "      <th>7</th>\n",
 115 |        "      <td>&lt;25</td>\n",
 116 |        "      <td>medium</td>\n",
 117 |        "      <td>male</td>\n",
 118 |        "      <td>married</td>\n",
 119 |        "      <td>no</td>\n",
 120 |        "    </tr>\n",
 121 |        "    <tr>\n",
 122 |        "      <th>8</th>\n",
 123 |        "      <td>&lt;25</td>\n",
 124 |        "      <td>low</td>\n",
 125 |        "      <td>female</td>\n",
 126 |        "      <td>single</td>\n",
 127 |        "      <td>yes</td>\n",
 128 |        "    </tr>\n",
 129 |        "    <tr>\n",
 130 |        "      <th>9</th>\n",
 131 |        "      <td>&gt;35</td>\n",
 132 |        "      <td>medium</td>\n",
 133 |        "      <td>female</td>\n",
 134 |        "      <td>married</td>\n",
 135 |        "      <td>yes</td>\n",
 136 |        "    </tr>\n",
 137 |        "    <tr>\n",
 138 |        "      <th>10</th>\n",
 139 |        "      <td>&lt;25</td>\n",
 140 |        "      <td>medium</td>\n",
 141 |        "      <td>female</td>\n",
 142 |        "      <td>single</td>\n",
 143 |        "      <td>yes</td>\n",
 144 |        "    </tr>\n",
 145 |        "    <tr>\n",
 146 |        "      <th>11</th>\n",
 147 |        "      <td>25-35</td>\n",
 148 |        "      <td>medium</td>\n",
 149 |        "      <td>male</td>\n",
 150 |        "      <td>married</td>\n",
 151 |        "      <td>yes</td>\n",
 152 |        "    </tr>\n",
 153 |        "    <tr>\n",
 154 |        "      <th>12</th>\n",
 155 |        "      <td>25-35</td>\n",
 156 |        "      <td>high</td>\n",
 157 |        "      <td>female</td>\n",
 158 |        "      <td>single</td>\n",
 159 |        "      <td>yes</td>\n",
 160 |        "    </tr>\n",
 161 |        "    <tr>\n",
 162 |        "      <th>13</th>\n",
 163 |        "      <td>&gt;35</td>\n",
 164 |        "      <td>medium</td>\n",
 165 |        "      <td>male</td>\n",
 166 |        "      <td>married</td>\n",
 167 |        "      <td>no</td>\n",
 168 |        "    </tr>\n",
 169 |        "    <tr>\n",
 170 |        "      <th>14</th>\n",
 171 |        "      <td>&lt;25</td>\n",
 172 |        "      <td>high</td>\n",
 173 |        "      <td>male</td>\n",
 174 |        "      <td>single</td>\n",
 175 |        "      <td>no</td>\n",
 176 |        "    </tr>\n",
 177 |        "    <tr>\n",
 178 |        "      <th>15</th>\n",
 179 |        "      <td>&lt;25</td>\n",
 180 |        "      <td>high</td>\n",
 181 |        "      <td>female</td>\n",
 182 |        "      <td>married</td>\n",
 183 |        "      <td>yes</td>\n",
 184 |        "    </tr>\n",
 185 |        "    <tr>\n",
 186 |        "      <th>16</th>\n",
 187 |        "      <td>&gt;35</td>\n",
 188 |        "      <td>medium</td>\n",
 189 |        "      <td>male</td>\n",
 190 |        "      <td>married</td>\n",
 191 |        "      <td>yes</td>\n",
 192 |        "    </tr>\n",
 193 |        "    <tr>\n",
 194 |        "      <th>17</th>\n",
 195 |        "      <td>&lt;25</td>\n",
 196 |        "      <td>high</td>\n",
 197 |        "      <td>female</td>\n",
 198 |        "      <td>single</td>\n",
 199 |        "      <td>yes</td>\n",
 200 |        "    </tr>\n",
 201 |        "    <tr>\n",
 202 |        "      <th>18</th>\n",
 203 |        "      <td>25-35</td>\n",
 204 |        "      <td>medium</td>\n",
 205 |        "      <td>female</td>\n",
 206 |        "      <td>married</td>\n",
 207 |        "      <td>yes</td>\n",
 208 |        "    </tr>\n",
 209 |        "    <tr>\n",
 210 |        "      <th>19</th>\n",
 211 |        "      <td>25-35</td>\n",
 212 |        "      <td>high</td>\n",
 213 |        "      <td>male</td>\n",
 214 |        "      <td>single</td>\n",
 215 |        "      <td>yes</td>\n",
 216 |        "    </tr>\n",
 217 |        "    <tr>\n",
 218 |        "      <th>20</th>\n",
 219 |        "      <td>&gt;35</td>\n",
 220 |        "      <td>medium</td>\n",
 221 |        "      <td>female</td>\n",
 222 |        "      <td>married</td>\n",
 223 |        "      <td>no</td>\n",
 224 |        "    </tr>\n",
 225 |        "    <tr>\n",
 226 |        "      <th>21</th>\n",
 227 |        "      <td>&lt;25</td>\n",
 228 |        "      <td>low</td>\n",
 229 |        "      <td>male</td>\n",
 230 |        "      <td>single</td>\n",
 231 |        "      <td>yes</td>\n",
 232 |        "    </tr>\n",
 233 |        "  </tbody>\n",
 234 |        "</table>\n",
 235 |        "</div>"
 236 |       ],
 237 |       "text/plain": [
 238 |        "      age  income  gender m_status buys\n",
 239 |        "0     <25    high    male   single   no\n",
 240 |        "1     <25    high    male  married   no\n",
 241 |        "2   25-35    high    male   single  yes\n",
 242 |        "3     >35  medium    male   single  yes\n",
 243 |        "4     >35     low  female   single  yes\n",
 244 |        "5     >35     low  female   single   no\n",
 245 |        "6   25-35     low  female  married  yes\n",
 246 |        "7     <25  medium    male  married   no\n",
 247 |        "8     <25     low  female   single  yes\n",
 248 |        "9     >35  medium  female  married  yes\n",
 249 |        "10    <25  medium  female   single  yes\n",
 250 |        "11  25-35  medium    male  married  yes\n",
 251 |        "12  25-35    high  female   single  yes\n",
 252 |        "13    >35  medium    male  married   no\n",
 253 |        "14    <25    high    male   single   no\n",
 254 |        "15    <25    high  female  married  yes\n",
 255 |        "16    >35  medium    male  married  yes\n",
 256 |        "17    <25    high  female   single  yes\n",
 257 |        "18  25-35  medium  female  married  yes\n",
 258 |        "19  25-35    high    male   single  yes\n",
 259 |        "20    >35  medium  female  married   no\n",
 260 |        "21    <25     low    male   single  yes"
 261 |       ]
 262 |      },
 263 |      "execution_count": 3,
 264 |      "metadata": {},
 265 |      "output_type": "execute_result"
 266 |     }
 267 |    ],
 268 |    "source": [
 269 |     "df"
 270 |    ]
 271 |   },
 272 |   {
 273 |    "cell_type": "code",
 274 |    "execution_count": 4,
 275 |    "metadata": {},
 276 |    "outputs": [],
 277 |    "source": [
 278 |     "x = df.iloc[:,:-1]"
 279 |    ]
 280 |   },
 281 |   {
 282 |    "cell_type": "code",
 283 |    "execution_count": 5,
 284 |    "metadata": {},
 285 |    "outputs": [
 286 |     {
 287 |      "data": {
 288 |       "text/html": [
 289 |        "<div>\n",
 290 |        "<style scoped>\n",
 291 |        "    .dataframe tbody tr th:only-of-type {\n",
 292 |        "        vertical-align: middle;\n",
 293 |        "    }\n",
 294 |        "\n",
 295 |        "    .dataframe tbody tr th {\n",
 296 |        "        vertical-align: top;\n",
 297 |        "    }\n",
 298 |        "\n",
 299 |        "    .dataframe thead th {\n",
 300 |        "        text-align: right;\n",
 301 |        "    }\n",
 302 |        "</style>\n",
 303 |        "<table border=\"1\" class=\"dataframe\">\n",
 304 |        "  <thead>\n",
 305 |        "    <tr style=\"text-align: right;\">\n",
 306 |        "      <th></th>\n",
 307 |        "      <th>age</th>\n",
 308 |        "      <th>income</th>\n",
 309 |        "      <th>gender</th>\n",
 310 |        "      <th>m_status</th>\n",
 311 |        "    </tr>\n",
 312 |        "  </thead>\n",
 313 |        "  <tbody>\n",
 314 |        "    <tr>\n",
 315 |        "      <th>0</th>\n",
 316 |        "      <td>&lt;25</td>\n",
 317 |        "      <td>high</td>\n",
 318 |        "      <td>male</td>\n",
 319 |        "      <td>single</td>\n",
 320 |        "    </tr>\n",
 321 |        "    <tr>\n",
 322 |        "      <th>1</th>\n",
 323 |        "      <td>&lt;25</td>\n",
 324 |        "      <td>high</td>\n",
 325 |        "      <td>male</td>\n",
 326 |        "      <td>married</td>\n",
 327 |        "    </tr>\n",
 328 |        "    <tr>\n",
 329 |        "      <th>2</th>\n",
 330 |        "      <td>25-35</td>\n",
 331 |        "      <td>high</td>\n",
 332 |        "      <td>male</td>\n",
 333 |        "      <td>single</td>\n",
 334 |        "    </tr>\n",
 335 |        "    <tr>\n",
 336 |        "      <th>3</th>\n",
 337 |        "      <td>&gt;35</td>\n",
 338 |        "      <td>medium</td>\n",
 339 |        "      <td>male</td>\n",
 340 |        "      <td>single</td>\n",
 341 |        "    </tr>\n",
 342 |        "    <tr>\n",
 343 |        "      <th>4</th>\n",
 344 |        "      <td>&gt;35</td>\n",
 345 |        "      <td>low</td>\n",
 346 |        "      <td>female</td>\n",
 347 |        "      <td>single</td>\n",
 348 |        "    </tr>\n",
 349 |        "    <tr>\n",
 350 |        "      <th>5</th>\n",
 351 |        "      <td>&gt;35</td>\n",
 352 |        "      <td>low</td>\n",
 353 |        "      <td>female</td>\n",
 354 |        "      <td>single</td>\n",
 355 |        "    </tr>\n",
 356 |        "    <tr>\n",
 357 |        "      <th>6</th>\n",
 358 |        "      <td>25-35</td>\n",
 359 |        "      <td>low</td>\n",
 360 |        "      <td>female</td>\n",
 361 |        "      <td>married</td>\n",
 362 |        "    </tr>\n",
 363 |        "    <tr>\n",
 364 |        "      <th>7</th>\n",
 365 |        "      <td>&lt;25</td>\n",
 366 |        "      <td>medium</td>\n",
 367 |        "      <td>male</td>\n",
 368 |        "      <td>married</td>\n",
 369 |        "    </tr>\n",
 370 |        "    <tr>\n",
 371 |        "      <th>8</th>\n",
 372 |        "      <td>&lt;25</td>\n",
 373 |        "      <td>low</td>\n",
 374 |        "      <td>female</td>\n",
 375 |        "      <td>single</td>\n",
 376 |        "    </tr>\n",
 377 |        "    <tr>\n",
 378 |        "      <th>9</th>\n",
 379 |        "      <td>&gt;35</td>\n",
 380 |        "      <td>medium</td>\n",
 381 |        "      <td>female</td>\n",
 382 |        "      <td>married</td>\n",
 383 |        "    </tr>\n",
 384 |        "    <tr>\n",
 385 |        "      <th>10</th>\n",
 386 |        "      <td>&lt;25</td>\n",
 387 |        "      <td>medium</td>\n",
 388 |        "      <td>female</td>\n",
 389 |        "      <td>single</td>\n",
 390 |        "    </tr>\n",
 391 |        "    <tr>\n",
 392 |        "      <th>11</th>\n",
 393 |        "      <td>25-35</td>\n",
 394 |        "      <td>medium</td>\n",
 395 |        "      <td>male</td>\n",
 396 |        "      <td>married</td>\n",
 397 |        "    </tr>\n",
 398 |        "    <tr>\n",
 399 |        "      <th>12</th>\n",
 400 |        "      <td>25-35</td>\n",
 401 |        "      <td>high</td>\n",
 402 |        "      <td>female</td>\n",
 403 |        "      <td>single</td>\n",
 404 |        "    </tr>\n",
 405 |        "    <tr>\n",
 406 |        "      <th>13</th>\n",
 407 |        "      <td>&gt;35</td>\n",
 408 |        "      <td>medium</td>\n",
 409 |        "      <td>male</td>\n",
 410 |        "      <td>married</td>\n",
 411 |        "    </tr>\n",
 412 |        "    <tr>\n",
 413 |        "      <th>14</th>\n",
 414 |        "      <td>&lt;25</td>\n",
 415 |        "      <td>high</td>\n",
 416 |        "      <td>male</td>\n",
 417 |        "      <td>single</td>\n",
 418 |        "    </tr>\n",
 419 |        "    <tr>\n",
 420 |        "      <th>15</th>\n",
 421 |        "      <td>&lt;25</td>\n",
 422 |        "      <td>high</td>\n",
 423 |        "      <td>female</td>\n",
 424 |        "      <td>married</td>\n",
 425 |        "    </tr>\n",
 426 |        "    <tr>\n",
 427 |        "      <th>16</th>\n",
 428 |        "      <td>&gt;35</td>\n",
 429 |        "      <td>medium</td>\n",
 430 |        "      <td>male</td>\n",
 431 |        "      <td>married</td>\n",
 432 |        "    </tr>\n",
 433 |        "    <tr>\n",
 434 |        "      <th>17</th>\n",
 435 |        "      <td>&lt;25</td>\n",
 436 |        "      <td>high</td>\n",
 437 |        "      <td>female</td>\n",
 438 |        "      <td>single</td>\n",
 439 |        "    </tr>\n",
 440 |        "    <tr>\n",
 441 |        "      <th>18</th>\n",
 442 |        "      <td>25-35</td>\n",
 443 |        "      <td>medium</td>\n",
 444 |        "      <td>female</td>\n",
 445 |        "      <td>married</td>\n",
 446 |        "    </tr>\n",
 447 |        "    <tr>\n",
 448 |        "      <th>19</th>\n",
 449 |        "      <td>25-35</td>\n",
 450 |        "      <td>high</td>\n",
 451 |        "      <td>male</td>\n",
 452 |        "      <td>single</td>\n",
 453 |        "    </tr>\n",
 454 |        "    <tr>\n",
 455 |        "      <th>20</th>\n",
 456 |        "      <td>&gt;35</td>\n",
 457 |        "      <td>medium</td>\n",
 458 |        "      <td>female</td>\n",
 459 |        "      <td>married</td>\n",
 460 |        "    </tr>\n",
 461 |        "    <tr>\n",
 462 |        "      <th>21</th>\n",
 463 |        "      <td>&lt;25</td>\n",
 464 |        "      <td>low</td>\n",
 465 |        "      <td>male</td>\n",
 466 |        "      <td>single</td>\n",
 467 |        "    </tr>\n",
 468 |        "  </tbody>\n",
 469 |        "</table>\n",
 470 |        "</div>"
 471 |       ],
 472 |       "text/plain": [
 473 |        "      age  income  gender m_status\n",
 474 |        "0     <25    high    male   single\n",
 475 |        "1     <25    high    male  married\n",
 476 |        "2   25-35    high    male   single\n",
 477 |        "3     >35  medium    male   single\n",
 478 |        "4     >35     low  female   single\n",
 479 |        "5     >35     low  female   single\n",
 480 |        "6   25-35     low  female  married\n",
 481 |        "7     <25  medium    male  married\n",
 482 |        "8     <25     low  female   single\n",
 483 |        "9     >35  medium  female  married\n",
 484 |        "10    <25  medium  female   single\n",
 485 |        "11  25-35  medium    male  married\n",
 486 |        "12  25-35    high  female   single\n",
 487 |        "13    >35  medium    male  married\n",
 488 |        "14    <25    high    male   single\n",
 489 |        "15    <25    high  female  married\n",
 490 |        "16    >35  medium    male  married\n",
 491 |        "17    <25    high  female   single\n",
 492 |        "18  25-35  medium  female  married\n",
 493 |        "19  25-35    high    male   single\n",
 494 |        "20    >35  medium  female  married\n",
 495 |        "21    <25     low    male   single"
 496 |       ]
 497 |      },
 498 |      "execution_count": 5,
 499 |      "metadata": {},
 500 |      "output_type": "execute_result"
 501 |     }
 502 |    ],
 503 |    "source": [
 504 |     "x"
 505 |    ]
 506 |   },
 507 |   {
 508 |    "cell_type": "code",
 509 |    "execution_count": 6,
 510 |    "metadata": {},
 511 |    "outputs": [],
 512 |    "source": [
 513 |     "y = df.iloc[:,4]"
 514 |    ]
 515 |   },
 516 |   {
 517 |    "cell_type": "code",
 518 |    "execution_count": 7,
 519 |    "metadata": {},
 520 |    "outputs": [
 521 |     {
 522 |      "data": {
 523 |       "text/plain": [
 524 |        "0      no\n",
 525 |        "1      no\n",
 526 |        "2     yes\n",
 527 |        "3     yes\n",
 528 |        "4     yes\n",
 529 |        "5      no\n",
 530 |        "6     yes\n",
 531 |        "7      no\n",
 532 |        "8     yes\n",
 533 |        "9     yes\n",
 534 |        "10    yes\n",
 535 |        "11    yes\n",
 536 |        "12    yes\n",
 537 |        "13     no\n",
 538 |        "14     no\n",
 539 |        "15    yes\n",
 540 |        "16    yes\n",
 541 |        "17    yes\n",
 542 |        "18    yes\n",
 543 |        "19    yes\n",
 544 |        "20     no\n",
 545 |        "21    yes\n",
 546 |        "Name: buys, dtype: object"
 547 |       ]
 548 |      },
 549 |      "execution_count": 7,
 550 |      "metadata": {},
 551 |      "output_type": "execute_result"
 552 |     }
 553 |    ],
 554 |    "source": [
 555 |     "y"
 556 |    ]
 557 |   },
 558 |   {
 559 |    "cell_type": "code",
 560 |    "execution_count": 8,
 561 |    "metadata": {},
 562 |    "outputs": [],
 563 |    "source": [
 564 |     "from sklearn.preprocessing import LabelEncoder"
 565 |    ]
 566 |   },
 567 |   {
 568 |    "cell_type": "code",
 569 |    "execution_count": 9,
 570 |    "metadata": {},
 571 |    "outputs": [],
 572 |    "source": [
 573 |     "le_x=LabelEncoder()\n",
 574 |     "x = x.apply(LabelEncoder().fit_transform)"
 575 |    ]
 576 |   },
 577 |   {
 578 |    "cell_type": "code",
 579 |    "execution_count": 10,
 580 |    "metadata": {},
 581 |    "outputs": [],
 582 |    "source": [
 583 |     "from sklearn.model_selection import train_test_split\n",
 584 |     "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=.25,random_state=1)"
 585 |    ]
 586 |   },
 587 |   {
 588 |    "cell_type": "code",
 589 |    "execution_count": 11,
 590 |    "metadata": {},
 591 |    "outputs": [
 592 |     {
 593 |      "data": {
 594 |       "text/html": [
 595 |        "<div>\n",
 596 |        "<style scoped>\n",
 597 |        "    .dataframe tbody tr th:only-of-type {\n",
 598 |        "        vertical-align: middle;\n",
 599 |        "    }\n",
 600 |        "\n",
 601 |        "    .dataframe tbody tr th {\n",
 602 |        "        vertical-align: top;\n",
 603 |        "    }\n",
 604 |        "\n",
 605 |        "    .dataframe thead th {\n",
 606 |        "        text-align: right;\n",
 607 |        "    }\n",
 608 |        "</style>\n",
 609 |        "<table border=\"1\" class=\"dataframe\">\n",
 610 |        "  <thead>\n",
 611 |        "    <tr style=\"text-align: right;\">\n",
 612 |        "      <th></th>\n",
 613 |        "      <th>age</th>\n",
 614 |        "      <th>income</th>\n",
 615 |        "      <th>gender</th>\n",
 616 |        "      <th>m_status</th>\n",
 617 |        "    </tr>\n",
 618 |        "  </thead>\n",
 619 |        "  <tbody>\n",
 620 |        "    <tr>\n",
 621 |        "      <th>10</th>\n",
 622 |        "      <td>1</td>\n",
 623 |        "      <td>2</td>\n",
 624 |        "      <td>0</td>\n",
 625 |        "      <td>1</td>\n",
 626 |        "    </tr>\n",
 627 |        "    <tr>\n",
 628 |        "      <th>4</th>\n",
 629 |        "      <td>2</td>\n",
 630 |        "      <td>1</td>\n",
 631 |        "      <td>0</td>\n",
 632 |        "      <td>1</td>\n",
 633 |        "    </tr>\n",
 634 |        "    <tr>\n",
 635 |        "      <th>2</th>\n",
 636 |        "      <td>0</td>\n",
 637 |        "      <td>0</td>\n",
 638 |        "      <td>1</td>\n",
 639 |        "      <td>1</td>\n",
 640 |        "    </tr>\n",
 641 |        "    <tr>\n",
 642 |        "      <th>17</th>\n",
 643 |        "      <td>1</td>\n",
 644 |        "      <td>0</td>\n",
 645 |        "      <td>0</td>\n",
 646 |        "      <td>1</td>\n",
 647 |        "    </tr>\n",
 648 |        "    <tr>\n",
 649 |        "      <th>6</th>\n",
 650 |        "      <td>0</td>\n",
 651 |        "      <td>1</td>\n",
 652 |        "      <td>0</td>\n",
 653 |        "      <td>0</td>\n",
 654 |        "    </tr>\n",
 655 |        "    <tr>\n",
 656 |        "      <th>7</th>\n",
 657 |        "      <td>1</td>\n",
 658 |        "      <td>2</td>\n",
 659 |        "      <td>1</td>\n",
 660 |        "      <td>0</td>\n",
 661 |        "    </tr>\n",
 662 |        "    <tr>\n",
 663 |        "      <th>1</th>\n",
 664 |        "      <td>1</td>\n",
 665 |        "      <td>0</td>\n",
 666 |        "      <td>1</td>\n",
 667 |        "      <td>0</td>\n",
 668 |        "    </tr>\n",
 669 |        "    <tr>\n",
 670 |        "      <th>14</th>\n",
 671 |        "      <td>1</td>\n",
 672 |        "      <td>0</td>\n",
 673 |        "      <td>1</td>\n",
 674 |        "      <td>1</td>\n",
 675 |        "    </tr>\n",
 676 |        "    <tr>\n",
 677 |        "      <th>0</th>\n",
 678 |        "      <td>1</td>\n",
 679 |        "      <td>0</td>\n",
 680 |        "      <td>1</td>\n",
 681 |        "      <td>1</td>\n",
 682 |        "    </tr>\n",
 683 |        "    <tr>\n",
 684 |        "      <th>21</th>\n",
 685 |        "      <td>1</td>\n",
 686 |        "      <td>1</td>\n",
 687 |        "      <td>1</td>\n",
 688 |        "      <td>1</td>\n",
 689 |        "    </tr>\n",
 690 |        "    <tr>\n",
 691 |        "      <th>20</th>\n",
 692 |        "      <td>2</td>\n",
 693 |        "      <td>2</td>\n",
 694 |        "      <td>0</td>\n",
 695 |        "      <td>0</td>\n",
 696 |        "    </tr>\n",
 697 |        "    <tr>\n",
 698 |        "      <th>9</th>\n",
 699 |        "      <td>2</td>\n",
 700 |        "      <td>2</td>\n",
 701 |        "      <td>0</td>\n",
 702 |        "      <td>0</td>\n",
 703 |        "    </tr>\n",
 704 |        "    <tr>\n",
 705 |        "      <th>8</th>\n",
 706 |        "      <td>1</td>\n",
 707 |        "      <td>1</td>\n",
 708 |        "      <td>0</td>\n",
 709 |        "      <td>1</td>\n",
 710 |        "    </tr>\n",
 711 |        "    <tr>\n",
 712 |        "      <th>12</th>\n",
 713 |        "      <td>0</td>\n",
 714 |        "      <td>0</td>\n",
 715 |        "      <td>0</td>\n",
 716 |        "      <td>1</td>\n",
 717 |        "    </tr>\n",
 718 |        "    <tr>\n",
 719 |        "      <th>11</th>\n",
 720 |        "      <td>0</td>\n",
 721 |        "      <td>2</td>\n",
 722 |        "      <td>1</td>\n",
 723 |        "      <td>0</td>\n",
 724 |        "    </tr>\n",
 725 |        "    <tr>\n",
 726 |        "      <th>5</th>\n",
 727 |        "      <td>2</td>\n",
 728 |        "      <td>1</td>\n",
 729 |        "      <td>0</td>\n",
 730 |        "      <td>1</td>\n",
 731 |        "    </tr>\n",
 732 |        "  </tbody>\n",
 733 |        "</table>\n",
 734 |        "</div>"
 735 |       ],
 736 |       "text/plain": [
 737 |        "    age  income  gender  m_status\n",
 738 |        "10    1       2       0         1\n",
 739 |        "4     2       1       0         1\n",
 740 |        "2     0       0       1         1\n",
 741 |        "17    1       0       0         1\n",
 742 |        "6     0       1       0         0\n",
 743 |        "7     1       2       1         0\n",
 744 |        "1     1       0       1         0\n",
 745 |        "14    1       0       1         1\n",
 746 |        "0     1       0       1         1\n",
 747 |        "21    1       1       1         1\n",
 748 |        "20    2       2       0         0\n",
 749 |        "9     2       2       0         0\n",
 750 |        "8     1       1       0         1\n",
 751 |        "12    0       0       0         1\n",
 752 |        "11    0       2       1         0\n",
 753 |        "5     2       1       0         1"
 754 |       ]
 755 |      },
 756 |      "execution_count": 11,
 757 |      "metadata": {},
 758 |      "output_type": "execute_result"
 759 |     }
 760 |    ],
 761 |    "source": [
 762 |     "xtrain"
 763 |    ]
 764 |   },
 765 |   {
 766 |    "cell_type": "code",
 767 |    "execution_count": 12,
 768 |    "metadata": {},
 769 |    "outputs": [
 770 |     {
 771 |      "data": {
 772 |       "text/html": [
 773 |        "<div>\n",
 774 |        "<style scoped>\n",
 775 |        "    .dataframe tbody tr th:only-of-type {\n",
 776 |        "        vertical-align: middle;\n",
 777 |        "    }\n",
 778 |        "\n",
 779 |        "    .dataframe tbody tr th {\n",
 780 |        "        vertical-align: top;\n",
 781 |        "    }\n",
 782 |        "\n",
 783 |        "    .dataframe thead th {\n",
 784 |        "        text-align: right;\n",
 785 |        "    }\n",
 786 |        "</style>\n",
 787 |        "<table border=\"1\" class=\"dataframe\">\n",
 788 |        "  <thead>\n",
 789 |        "    <tr style=\"text-align: right;\">\n",
 790 |        "      <th></th>\n",
 791 |        "      <th>age</th>\n",
 792 |        "      <th>income</th>\n",
 793 |        "      <th>gender</th>\n",
 794 |        "      <th>m_status</th>\n",
 795 |        "    </tr>\n",
 796 |        "  </thead>\n",
 797 |        "  <tbody>\n",
 798 |        "    <tr>\n",
 799 |        "      <th>19</th>\n",
 800 |        "      <td>0</td>\n",
 801 |        "      <td>0</td>\n",
 802 |        "      <td>1</td>\n",
 803 |        "      <td>1</td>\n",
 804 |        "    </tr>\n",
 805 |        "    <tr>\n",
 806 |        "      <th>16</th>\n",
 807 |        "      <td>2</td>\n",
 808 |        "      <td>2</td>\n",
 809 |        "      <td>1</td>\n",
 810 |        "      <td>0</td>\n",
 811 |        "    </tr>\n",
 812 |        "    <tr>\n",
 813 |        "      <th>3</th>\n",
 814 |        "      <td>2</td>\n",
 815 |        "      <td>2</td>\n",
 816 |        "      <td>1</td>\n",
 817 |        "      <td>1</td>\n",
 818 |        "    </tr>\n",
 819 |        "    <tr>\n",
 820 |        "      <th>13</th>\n",
 821 |        "      <td>2</td>\n",
 822 |        "      <td>2</td>\n",
 823 |        "      <td>1</td>\n",
 824 |        "      <td>0</td>\n",
 825 |        "    </tr>\n",
 826 |        "    <tr>\n",
 827 |        "      <th>18</th>\n",
 828 |        "      <td>0</td>\n",
 829 |        "      <td>2</td>\n",
 830 |        "      <td>0</td>\n",
 831 |        "      <td>0</td>\n",
 832 |        "    </tr>\n",
 833 |        "    <tr>\n",
 834 |        "      <th>15</th>\n",
 835 |        "      <td>1</td>\n",
 836 |        "      <td>0</td>\n",
 837 |        "      <td>0</td>\n",
 838 |        "      <td>0</td>\n",
 839 |        "    </tr>\n",
 840 |        "  </tbody>\n",
 841 |        "</table>\n",
 842 |        "</div>"
 843 |       ],
 844 |       "text/plain": [
 845 |        "    age  income  gender  m_status\n",
 846 |        "19    0       0       1         1\n",
 847 |        "16    2       2       1         0\n",
 848 |        "3     2       2       1         1\n",
 849 |        "13    2       2       1         0\n",
 850 |        "18    0       2       0         0\n",
 851 |        "15    1       0       0         0"
 852 |       ]
 853 |      },
 854 |      "execution_count": 12,
 855 |      "metadata": {},
 856 |      "output_type": "execute_result"
 857 |     }
 858 |    ],
 859 |    "source": [
 860 |     "xtest"
 861 |    ]
 862 |   },
 863 |   {
 864 |    "cell_type": "code",
 865 |    "execution_count": 13,
 866 |    "metadata": {},
 867 |    "outputs": [],
 868 |    "source": [
 869 |     "from sklearn.tree import DecisionTreeClassifier"
 870 |    ]
 871 |   },
 872 |   {
 873 |    "cell_type": "code",
 874 |    "execution_count": 14,
 875 |    "metadata": {},
 876 |    "outputs": [],
 877 |    "source": [
 878 |     "dect = DecisionTreeClassifier()"
 879 |    ]
 880 |   },
 881 |   {
 882 |    "cell_type": "code",
 883 |    "execution_count": 15,
 884 |    "metadata": {},
 885 |    "outputs": [
 886 |     {
 887 |      "data": {
 888 |       "text/plain": [
 889 |        "DecisionTreeClassifier()"
 890 |       ]
 891 |      },
 892 |      "execution_count": 15,
 893 |      "metadata": {},
 894 |      "output_type": "execute_result"
 895 |     }
 896 |    ],
 897 |    "source": [
 898 |     "dect.fit(xtrain,ytrain)"
 899 |    ]
 900 |   },
 901 |   {
 902 |    "cell_type": "code",
 903 |    "execution_count": 16,
 904 |    "metadata": {},
 905 |    "outputs": [],
 906 |    "source": [
 907 |     "y_predict = dect.predict(xtest)"
 908 |    ]
 909 |   },
 910 |   {
 911 |    "cell_type": "code",
 912 |    "execution_count": 17,
 913 |    "metadata": {},
 914 |    "outputs": [],
 915 |    "source": [
 916 |     "from sklearn.metrics import confusion_matrix, accuracy_score\n",
 917 |     "cm = confusion_matrix(ytest,y_predict)"
 918 |    ]
 919 |   },
 920 |   {
 921 |    "cell_type": "code",
 922 |    "execution_count": 18,
 923 |    "metadata": {},
 924 |    "outputs": [
 925 |     {
 926 |      "data": {
 927 |       "text/plain": [
 928 |        "array([[1, 0],\n",
 929 |        "       [1, 4]], dtype=int64)"
 930 |       ]
 931 |      },
 932 |      "execution_count": 18,
 933 |      "metadata": {},
 934 |      "output_type": "execute_result"
 935 |     }
 936 |    ],
 937 |    "source": [
 938 |     "cm"
 939 |    ]
 940 |   },
 941 |   {
 942 |    "cell_type": "code",
 943 |    "execution_count": 19,
 944 |    "metadata": {},
 945 |    "outputs": [],
 946 |    "source": [
 947 |     "xinput = np.array([1,0,0,1])"
 948 |    ]
 949 |   },
 950 |   {
 951 |    "cell_type": "code",
 952 |    "execution_count": null,
 953 |    "metadata": {},
 954 |    "outputs": [],
 955 |    "source": [
 956 |     "y_predict = dect.predict([xinput])"
 957 |    ]
 958 |   },
 959 |   {
 960 |    "cell_type": "code",
 961 |    "execution_count": null,
 962 |    "metadata": {},
 963 |    "outputs": [],
 964 |    "source": [
 965 |     "y_predict"
 966 |    ]
 967 |   },
 968 |   {
 969 |    "cell_type": "code",
 970 |    "execution_count": null,
 971 |    "metadata": {},
 972 |    "outputs": [],
 973 |    "source": [
 974 |     "import seaborn as sn\n",
 975 |     "plt.figure(figsize = (10,7))\n",
 976 |     "sn.heatmap(cm, annot=True)\n",
 977 |     "plt.xlabel('Predicted')\n",
 978 |     "plt.ylabel('Truth')"
 979 |    ]
 980 |   },
 981 |   {
 982 |    "cell_type": "code",
 983 |    "execution_count": null,
 984 |    "metadata": {},
 985 |    "outputs": [],
 986 |    "source": [
 987 |     "dect.score(xtest,ytest)"
 988 |    ]
 989 |   },
 990 |   {
 991 |    "cell_type": "code",
 992 |    "execution_count": null,
 993 |    "metadata": {},
 994 |    "outputs": [],
 995 |    "source": []
 996 |   }
 997 |  ],
 998 |  "metadata": {
 999 |   "kernelspec": {
1000 |    "display_name": "Python 3",
1001 |    "language": "python",
1002 |    "name": "python3"
1003 |   },
1004 |   "language_info": {
1005 |    "codemirror_mode": {
1006 |     "name": "ipython",
1007 |     "version": 3
1008 |    },
1009 |    "file_extension": ".py",
1010 |    "mimetype": "text/x-python",
1011 |    "name": "python",
1012 |    "nbconvert_exporter": "python",
1013 |    "pygments_lexer": "ipython3",
1014 |    "version": "3.8.8"
1015 |   }
1016 |  },
1017 |  "nbformat": 4,
1018 |  "nbformat_minor": 2
1019 | }
1020 | 


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/Docs/Machine Learning with TensorFlow.pdf:
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https://raw.githubusercontent.com/rashakil-ds/Machine-Learning-with-Python/418124d517ff32f4e19c21ea3567fb106a222264/Docs/Machine Learning with TensorFlow.pdf


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/Docs/PyTorch Cheatsheet.pdf:
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https://raw.githubusercontent.com/rashakil-ds/Machine-Learning-with-Python/418124d517ff32f4e19c21ea3567fb106a222264/Docs/PyTorch Cheatsheet.pdf


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/Docs/Readme.md:
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1 | Author info: <br>
2 | KM Rashedul Alam
3 | 


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/Docs/𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧.pdf:
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https://raw.githubusercontent.com/rashakil-ds/Machine-Learning-with-Python/418124d517ff32f4e19c21ea3567fb106a222264/Docs/𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧.pdf


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/Fit, Transforn and fit_transform.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "8b4f83cb",
  7 |    "metadata": {},
  8 |    "outputs": [
  9 |     {
 10 |      "data": {
 11 |       "image/png": 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\n",
 12 |       "text/plain": [
 13 |        "<IPython.core.display.Image object>"
 14 |       ]
 15 |      },
 16 |      "execution_count": 1,
 17 |      "metadata": {},
 18 |      "output_type": "execute_result"
 19 |     }
 20 |    ],
 21 |    "source": [
 22 |     "from IPython.display import Image\n",
 23 |     "Image('minmax.png')"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 2,
 29 |    "id": "68728e9f",
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "from sklearn.preprocessing import MinMaxScaler\n",
 34 |     "import numpy as np"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "markdown",
 39 |    "id": "cdce050c",
 40 |    "metadata": {},
 41 |    "source": [
 42 |     "# Generate some example data"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 3,
 48 |    "id": "1660a307",
 49 |    "metadata": {},
 50 |    "outputs": [],
 51 |    "source": [
 52 |     "data = np.array([[1.0, 2.0, 3.0],\n",
 53 |     "                 [4.0, 5.0, 6.0],\n",
 54 |     "                 [7.0, 8.0, 9.0]])"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "markdown",
 59 |    "id": "f76cd59f",
 60 |    "metadata": {},
 61 |    "source": [
 62 |     "# Create a MinMaxScaler instance"
 63 |    ]
 64 |   },
 65 |   {
 66 |    "cell_type": "code",
 67 |    "execution_count": 4,
 68 |    "id": "076dc5c5",
 69 |    "metadata": {},
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "scaler = MinMaxScaler()"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "markdown",
 77 |    "id": "0e66a5c7",
 78 |    "metadata": {},
 79 |    "source": [
 80 |     "# Fit the scaler to the data and transform it in one step"
 81 |    ]
 82 |   },
 83 |   {
 84 |    "cell_type": "code",
 85 |    "execution_count": 5,
 86 |    "id": "7760afe8",
 87 |    "metadata": {},
 88 |    "outputs": [
 89 |     {
 90 |      "data": {
 91 |       "text/plain": [
 92 |        "array([[0. , 0. , 0. ],\n",
 93 |        "       [0.5, 0.5, 0.5],\n",
 94 |        "       [1. , 1. , 1. ]])"
 95 |       ]
 96 |      },
 97 |      "execution_count": 5,
 98 |      "metadata": {},
 99 |      "output_type": "execute_result"
100 |     }
101 |    ],
102 |    "source": [
103 |     "data_transformed = scaler.fit_transform(data)\n",
104 |     "data_transformed"
105 |    ]
106 |   },
107 |   {
108 |    "cell_type": "markdown",
109 |    "id": "ea221d4f",
110 |    "metadata": {},
111 |    "source": [
112 |     "# Alternatively, you can use fit and transform separately"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": 6,
118 |    "id": "555cfdca",
119 |    "metadata": {},
120 |    "outputs": [],
121 |    "source": [
122 |     "scaler.fit(data)\n",
123 |     "data_transformed_separate = scaler.transform(data)"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "markdown",
128 |    "id": "0bc6919a",
129 |    "metadata": {},
130 |    "source": [
131 |     "# Print the original data, transformed data using fit_transform, and transformed data using fit and transform separately"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 7,
137 |    "id": "7fd8e6aa",
138 |    "metadata": {},
139 |    "outputs": [
140 |     {
141 |      "name": "stdout",
142 |      "output_type": "stream",
143 |      "text": [
144 |       "Original Data:\n",
145 |       "[[1. 2. 3.]\n",
146 |       " [4. 5. 6.]\n",
147 |       " [7. 8. 9.]]\n",
148 |       "\n",
149 |       "Transformed Data using fit_transform:\n",
150 |       "[[0.  0.  0. ]\n",
151 |       " [0.5 0.5 0.5]\n",
152 |       " [1.  1.  1. ]]\n",
153 |       "\n",
154 |       "Transformed Data using fit and transform separately:\n",
155 |       "[[0.  0.  0. ]\n",
156 |       " [0.5 0.5 0.5]\n",
157 |       " [1.  1.  1. ]]\n"
158 |      ]
159 |     }
160 |    ],
161 |    "source": [
162 |     "print(\"Original Data:\")\n",
163 |     "print(data)\n",
164 |     "\n",
165 |     "print(\"\\nTransformed Data using fit_transform:\")\n",
166 |     "print(data_transformed)\n",
167 |     "\n",
168 |     "print(\"\\nTransformed Data using fit and transform separately:\")\n",
169 |     "print(data_transformed_separate)\n"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "markdown",
174 |    "id": "f8113f1a",
175 |    "metadata": {},
176 |    "source": [
177 |     "# Fit and Transform Seperately"
178 |    ]
179 |   },
180 |   {
181 |    "cell_type": "code",
182 |    "execution_count": 8,
183 |    "id": "928f44f7",
184 |    "metadata": {},
185 |    "outputs": [],
186 |    "source": [
187 |     "scaler.fit(data)\n",
188 |     "data_transformed_separate = scaler.transform(data)"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "code",
193 |    "execution_count": 9,
194 |    "id": "ddffb752",
195 |    "metadata": {},
196 |    "outputs": [
197 |     {
198 |      "name": "stdout",
199 |      "output_type": "stream",
200 |      "text": [
201 |       "Original Data:\n",
202 |       "[[1. 2. 3.]\n",
203 |       " [4. 5. 6.]\n",
204 |       " [7. 8. 9.]]\n",
205 |       "\n",
206 |       "Transformed Data using fit and transform separately:\n",
207 |       "[[0.  0.  0. ]\n",
208 |       " [0.5 0.5 0.5]\n",
209 |       " [1.  1.  1. ]]\n"
210 |      ]
211 |     }
212 |    ],
213 |    "source": [
214 |     "print(\"Original Data:\")\n",
215 |     "print(data)\n",
216 |     "\n",
217 |     "print(\"\\nTransformed Data using fit and transform separately:\")\n",
218 |     "print(data_transformed_separate)"
219 |    ]
220 |   },
221 |   {
222 |    "cell_type": "code",
223 |    "execution_count": null,
224 |    "id": "e616905a",
225 |    "metadata": {},
226 |    "outputs": [],
227 |    "source": []
228 |   }
229 |  ],
230 |  "metadata": {
231 |   "kernelspec": {
232 |    "display_name": "Python 3 (ipykernel)",
233 |    "language": "python",
234 |    "name": "python3"
235 |   },
236 |   "language_info": {
237 |    "codemirror_mode": {
238 |     "name": "ipython",
239 |     "version": 3
240 |    },
241 |    "file_extension": ".py",
242 |    "mimetype": "text/x-python",
243 |    "name": "python",
244 |    "nbconvert_exporter": "python",
245 |    "pygments_lexer": "ipython3",
246 |    "version": "3.9.13"
247 |   }
248 |  },
249 |  "nbformat": 4,
250 |  "nbformat_minor": 5
251 | }
252 | 


--------------------------------------------------------------------------------
/Importing data from google sheet using pandas and python.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "id": "bbdeadf4",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# Full Video: Video: https://youtu.be/3KWzYM_KafM"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 13,
 16 |    "id": "91176585",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "# !pip install gspread"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 1,
 26 |    "id": "d5bec96a",
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "import gspread as gs\n",
 31 |     "import pandas as pd"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 3,
 37 |    "id": "dd662fbd",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "gc = gs.service_account(filename=\"C:\\\\Users\\\\rashe\\\\Downloads\\\\aiquest-376023-1304ddc10d11.json\")"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 4,
 47 |    "id": "4a5cd3d8",
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1zr1M2Tdrash8S8LdbnwpzLmbCDESZaXCkA_2d-nd6fI/edit?usp=sharing')"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 5,
 57 |    "id": "3e788fdf",
 58 |    "metadata": {},
 59 |    "outputs": [
 60 |     {
 61 |      "data": {
 62 |       "text/plain": [
 63 |        "<Spreadsheet 'aiquest employees' id:1zr1M2Tdrash8S8LdbnwpzLmbCDESZaXCkA_2d-nd6fI>"
 64 |       ]
 65 |      },
 66 |      "execution_count": 5,
 67 |      "metadata": {},
 68 |      "output_type": "execute_result"
 69 |     }
 70 |    ],
 71 |    "source": [
 72 |     "sh"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 10,
 78 |    "id": "425087c0",
 79 |    "metadata": {},
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "ws = sh.worksheet('emp2')"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 11,
 88 |    "id": "4025c84d",
 89 |    "metadata": {},
 90 |    "outputs": [
 91 |     {
 92 |      "data": {
 93 |       "text/html": [
 94 |        "<div>\n",
 95 |        "<style scoped>\n",
 96 |        "    .dataframe tbody tr th:only-of-type {\n",
 97 |        "        vertical-align: middle;\n",
 98 |        "    }\n",
 99 |        "\n",
100 |        "    .dataframe tbody tr th {\n",
101 |        "        vertical-align: top;\n",
102 |        "    }\n",
103 |        "\n",
104 |        "    .dataframe thead th {\n",
105 |        "        text-align: right;\n",
106 |        "    }\n",
107 |        "</style>\n",
108 |        "<table border=\"1\" class=\"dataframe\">\n",
109 |        "  <thead>\n",
110 |        "    <tr style=\"text-align: right;\">\n",
111 |        "      <th></th>\n",
112 |        "      <th>name2</th>\n",
113 |        "      <th>age2</th>\n",
114 |        "    </tr>\n",
115 |        "  </thead>\n",
116 |        "  <tbody>\n",
117 |        "    <tr>\n",
118 |        "      <th>0</th>\n",
119 |        "      <td>noman</td>\n",
120 |        "      <td>27</td>\n",
121 |        "    </tr>\n",
122 |        "    <tr>\n",
123 |        "      <th>1</th>\n",
124 |        "      <td>rony</td>\n",
125 |        "      <td>28</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>2</th>\n",
129 |        "      <td>sohan</td>\n",
130 |        "      <td>30</td>\n",
131 |        "    </tr>\n",
132 |        "    <tr>\n",
133 |        "      <th>3</th>\n",
134 |        "      <td>shuvo</td>\n",
135 |        "      <td>31</td>\n",
136 |        "    </tr>\n",
137 |        "  </tbody>\n",
138 |        "</table>\n",
139 |        "</div>"
140 |       ],
141 |       "text/plain": [
142 |        "   name2  age2\n",
143 |        "0  noman    27\n",
144 |        "1   rony    28\n",
145 |        "2  sohan    30\n",
146 |        "3  shuvo    31"
147 |       ]
148 |      },
149 |      "execution_count": 11,
150 |      "metadata": {},
151 |      "output_type": "execute_result"
152 |     }
153 |    ],
154 |    "source": [
155 |     "df = pd.DataFrame(ws.get_all_records())\n",
156 |     "df.head()"
157 |    ]
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": null,
162 |    "id": "d8025040",
163 |    "metadata": {},
164 |    "outputs": [],
165 |    "source": []
166 |   }
167 |  ],
168 |  "metadata": {
169 |   "kernelspec": {
170 |    "display_name": "Python 3 (ipykernel)",
171 |    "language": "python",
172 |    "name": "python3"
173 |   },
174 |   "language_info": {
175 |    "codemirror_mode": {
176 |     "name": "ipython",
177 |     "version": 3
178 |    },
179 |    "file_extension": ".py",
180 |    "mimetype": "text/x-python",
181 |    "name": "python",
182 |    "nbconvert_exporter": "python",
183 |    "pygments_lexer": "ipython3",
184 |    "version": "3.9.13"
185 |   }
186 |  },
187 |  "nbformat": 4,
188 |  "nbformat_minor": 5
189 | }
190 | 


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/Neural Network.pdf:
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https://raw.githubusercontent.com/rashakil-ds/Machine-Learning-with-Python/418124d517ff32f4e19c21ea3567fb106a222264/Neural Network.pdf


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/PROJECT on Linear Regression.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# Importing the libraries\n",
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "import pandas as pd"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "# Importing the dataset\n",
 22 |     "df = pd.read_csv('online.csv')"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 4,
 28 |    "metadata": {},
 29 |    "outputs": [
 30 |     {
 31 |      "data": {
 32 |       "text/html": [
 33 |        "<div>\n",
 34 |        "<style scoped>\n",
 35 |        "    .dataframe tbody tr th:only-of-type {\n",
 36 |        "        vertical-align: middle;\n",
 37 |        "    }\n",
 38 |        "\n",
 39 |        "    .dataframe tbody tr th {\n",
 40 |        "        vertical-align: top;\n",
 41 |        "    }\n",
 42 |        "\n",
 43 |        "    .dataframe thead th {\n",
 44 |        "        text-align: right;\n",
 45 |        "    }\n",
 46 |        "</style>\n",
 47 |        "<table border=\"1\" class=\"dataframe\">\n",
 48 |        "  <thead>\n",
 49 |        "    <tr style=\"text-align: right;\">\n",
 50 |        "      <th></th>\n",
 51 |        "      <th>Marketing Spend</th>\n",
 52 |        "      <th>Administration</th>\n",
 53 |        "      <th>Transport</th>\n",
 54 |        "      <th>Area</th>\n",
 55 |        "      <th>Profit</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>114523.61</td>\n",
 62 |        "      <td>136897.80</td>\n",
 63 |        "      <td>471784.10</td>\n",
 64 |        "      <td>Dhaka</td>\n",
 65 |        "      <td>192261.83</td>\n",
 66 |        "    </tr>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>1</th>\n",
 69 |        "      <td>162597.70</td>\n",
 70 |        "      <td>151377.59</td>\n",
 71 |        "      <td>443898.53</td>\n",
 72 |        "      <td>Ctg</td>\n",
 73 |        "      <td>191792.06</td>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>2</th>\n",
 77 |        "      <td>153441.51</td>\n",
 78 |        "      <td>101145.55</td>\n",
 79 |        "      <td>407934.54</td>\n",
 80 |        "      <td>Rangpur</td>\n",
 81 |        "      <td>191050.39</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>3</th>\n",
 85 |        "      <td>144372.41</td>\n",
 86 |        "      <td>118671.85</td>\n",
 87 |        "      <td>383199.62</td>\n",
 88 |        "      <td>Dhaka</td>\n",
 89 |        "      <td>182901.99</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>4</th>\n",
 93 |        "      <td>142107.34</td>\n",
 94 |        "      <td>91391.77</td>\n",
 95 |        "      <td>366168.42</td>\n",
 96 |        "      <td>Rangpur</td>\n",
 97 |        "      <td>166187.94</td>\n",
 98 |        "    </tr>\n",
 99 |        "  </tbody>\n",
100 |        "</table>\n",
101 |        "</div>"
102 |       ],
103 |       "text/plain": [
104 |        "   Marketing Spend  Administration  Transport     Area     Profit\n",
105 |        "0        114523.61       136897.80  471784.10    Dhaka  192261.83\n",
106 |        "1        162597.70       151377.59  443898.53      Ctg  191792.06\n",
107 |        "2        153441.51       101145.55  407934.54  Rangpur  191050.39\n",
108 |        "3        144372.41       118671.85  383199.62    Dhaka  182901.99\n",
109 |        "4        142107.34        91391.77  366168.42  Rangpur  166187.94"
110 |       ]
111 |      },
112 |      "execution_count": 4,
113 |      "metadata": {},
114 |      "output_type": "execute_result"
115 |     }
116 |    ],
117 |    "source": [
118 |     "df.head()"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 5,
124 |    "metadata": {},
125 |    "outputs": [
126 |     {
127 |      "data": {
128 |       "text/plain": [
129 |        "(50, 5)"
130 |       ]
131 |      },
132 |      "execution_count": 5,
133 |      "metadata": {},
134 |      "output_type": "execute_result"
135 |     }
136 |    ],
137 |    "source": [
138 |     "df.shape"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 6,
144 |    "metadata": {},
145 |    "outputs": [
146 |     {
147 |      "data": {
148 |       "text/plain": [
149 |        "Marketing Spend    0\n",
150 |        "Administration     0\n",
151 |        "Transport          0\n",
152 |        "Area               0\n",
153 |        "Profit             0\n",
154 |        "dtype: int64"
155 |       ]
156 |      },
157 |      "execution_count": 6,
158 |      "metadata": {},
159 |      "output_type": "execute_result"
160 |     }
161 |    ],
162 |    "source": [
163 |     "df.isnull().sum()"
164 |    ]
165 |   },
166 |   {
167 |    "cell_type": "code",
168 |    "execution_count": null,
169 |    "metadata": {},
170 |    "outputs": [],
171 |    "source": [
172 |     "# if missiong\n",
173 |     "# missing = df.Administration.mean()\n",
174 |     "# df.Administration = df.Administration.fillna(missing)"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "markdown",
179 |    "metadata": {},
180 |    "source": [
181 |     "# separate x,y"
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": 7,
187 |    "metadata": {},
188 |    "outputs": [],
189 |    "source": [
190 |     "x = df.drop(['Profit'],axis=1)"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": 8,
196 |    "metadata": {},
197 |    "outputs": [
198 |     {
199 |      "data": {
200 |       "text/html": [
201 |        "<div>\n",
202 |        "<style scoped>\n",
203 |        "    .dataframe tbody tr th:only-of-type {\n",
204 |        "        vertical-align: middle;\n",
205 |        "    }\n",
206 |        "\n",
207 |        "    .dataframe tbody tr th {\n",
208 |        "        vertical-align: top;\n",
209 |        "    }\n",
210 |        "\n",
211 |        "    .dataframe thead th {\n",
212 |        "        text-align: right;\n",
213 |        "    }\n",
214 |        "</style>\n",
215 |        "<table border=\"1\" class=\"dataframe\">\n",
216 |        "  <thead>\n",
217 |        "    <tr style=\"text-align: right;\">\n",
218 |        "      <th></th>\n",
219 |        "      <th>Marketing Spend</th>\n",
220 |        "      <th>Administration</th>\n",
221 |        "      <th>Transport</th>\n",
222 |        "      <th>Area</th>\n",
223 |        "    </tr>\n",
224 |        "  </thead>\n",
225 |        "  <tbody>\n",
226 |        "    <tr>\n",
227 |        "      <th>0</th>\n",
228 |        "      <td>114523.61</td>\n",
229 |        "      <td>136897.80</td>\n",
230 |        "      <td>471784.10</td>\n",
231 |        "      <td>Dhaka</td>\n",
232 |        "    </tr>\n",
233 |        "    <tr>\n",
234 |        "      <th>1</th>\n",
235 |        "      <td>162597.70</td>\n",
236 |        "      <td>151377.59</td>\n",
237 |        "      <td>443898.53</td>\n",
238 |        "      <td>Ctg</td>\n",
239 |        "    </tr>\n",
240 |        "    <tr>\n",
241 |        "      <th>2</th>\n",
242 |        "      <td>153441.51</td>\n",
243 |        "      <td>101145.55</td>\n",
244 |        "      <td>407934.54</td>\n",
245 |        "      <td>Rangpur</td>\n",
246 |        "    </tr>\n",
247 |        "    <tr>\n",
248 |        "      <th>3</th>\n",
249 |        "      <td>144372.41</td>\n",
250 |        "      <td>118671.85</td>\n",
251 |        "      <td>383199.62</td>\n",
252 |        "      <td>Dhaka</td>\n",
253 |        "    </tr>\n",
254 |        "    <tr>\n",
255 |        "      <th>4</th>\n",
256 |        "      <td>142107.34</td>\n",
257 |        "      <td>91391.77</td>\n",
258 |        "      <td>366168.42</td>\n",
259 |        "      <td>Rangpur</td>\n",
260 |        "    </tr>\n",
261 |        "  </tbody>\n",
262 |        "</table>\n",
263 |        "</div>"
264 |       ],
265 |       "text/plain": [
266 |        "   Marketing Spend  Administration  Transport     Area\n",
267 |        "0        114523.61       136897.80  471784.10    Dhaka\n",
268 |        "1        162597.70       151377.59  443898.53      Ctg\n",
269 |        "2        153441.51       101145.55  407934.54  Rangpur\n",
270 |        "3        144372.41       118671.85  383199.62    Dhaka\n",
271 |        "4        142107.34        91391.77  366168.42  Rangpur"
272 |       ]
273 |      },
274 |      "execution_count": 8,
275 |      "metadata": {},
276 |      "output_type": "execute_result"
277 |     }
278 |    ],
279 |    "source": [
280 |     "x.head()"
281 |    ]
282 |   },
283 |   {
284 |    "cell_type": "code",
285 |    "execution_count": 9,
286 |    "metadata": {},
287 |    "outputs": [],
288 |    "source": [
289 |     "y = df['Profit']"
290 |    ]
291 |   },
292 |   {
293 |    "cell_type": "code",
294 |    "execution_count": 10,
295 |    "metadata": {},
296 |    "outputs": [
297 |     {
298 |      "data": {
299 |       "text/plain": [
300 |        "0    192261.83\n",
301 |        "1    191792.06\n",
302 |        "2    191050.39\n",
303 |        "3    182901.99\n",
304 |        "4    166187.94\n",
305 |        "Name: Profit, dtype: float64"
306 |       ]
307 |      },
308 |      "execution_count": 10,
309 |      "metadata": {},
310 |      "output_type": "execute_result"
311 |     }
312 |    ],
313 |    "source": [
314 |     "y.head()"
315 |    ]
316 |   },
317 |   {
318 |    "cell_type": "markdown",
319 |    "metadata": {},
320 |    "source": [
321 |     "# One hot encoding "
322 |    ]
323 |   },
324 |   {
325 |    "cell_type": "code",
326 |    "execution_count": 11,
327 |    "metadata": {},
328 |    "outputs": [],
329 |    "source": [
330 |     "#Convert the column into categorical columns\n",
331 |     "city = pd.get_dummies(x['Area'],drop_first=True)"
332 |    ]
333 |   },
334 |   {
335 |    "cell_type": "code",
336 |    "execution_count": 12,
337 |    "metadata": {},
338 |    "outputs": [
339 |     {
340 |      "data": {
341 |       "text/html": [
342 |        "<div>\n",
343 |        "<style scoped>\n",
344 |        "    .dataframe tbody tr th:only-of-type {\n",
345 |        "        vertical-align: middle;\n",
346 |        "    }\n",
347 |        "\n",
348 |        "    .dataframe tbody tr th {\n",
349 |        "        vertical-align: top;\n",
350 |        "    }\n",
351 |        "\n",
352 |        "    .dataframe thead th {\n",
353 |        "        text-align: right;\n",
354 |        "    }\n",
355 |        "</style>\n",
356 |        "<table border=\"1\" class=\"dataframe\">\n",
357 |        "  <thead>\n",
358 |        "    <tr style=\"text-align: right;\">\n",
359 |        "      <th></th>\n",
360 |        "      <th>Dhaka</th>\n",
361 |        "      <th>Rangpur</th>\n",
362 |        "    </tr>\n",
363 |        "  </thead>\n",
364 |        "  <tbody>\n",
365 |        "    <tr>\n",
366 |        "      <th>0</th>\n",
367 |        "      <td>1</td>\n",
368 |        "      <td>0</td>\n",
369 |        "    </tr>\n",
370 |        "    <tr>\n",
371 |        "      <th>1</th>\n",
372 |        "      <td>0</td>\n",
373 |        "      <td>0</td>\n",
374 |        "    </tr>\n",
375 |        "    <tr>\n",
376 |        "      <th>2</th>\n",
377 |        "      <td>0</td>\n",
378 |        "      <td>1</td>\n",
379 |        "    </tr>\n",
380 |        "    <tr>\n",
381 |        "      <th>3</th>\n",
382 |        "      <td>1</td>\n",
383 |        "      <td>0</td>\n",
384 |        "    </tr>\n",
385 |        "    <tr>\n",
386 |        "      <th>4</th>\n",
387 |        "      <td>0</td>\n",
388 |        "      <td>1</td>\n",
389 |        "    </tr>\n",
390 |        "  </tbody>\n",
391 |        "</table>\n",
392 |        "</div>"
393 |       ],
394 |       "text/plain": [
395 |        "   Dhaka  Rangpur\n",
396 |        "0      1        0\n",
397 |        "1      0        0\n",
398 |        "2      0        1\n",
399 |        "3      1        0\n",
400 |        "4      0        1"
401 |       ]
402 |      },
403 |      "execution_count": 12,
404 |      "metadata": {},
405 |      "output_type": "execute_result"
406 |     }
407 |    ],
408 |    "source": [
409 |     "city.head()"
410 |    ]
411 |   },
412 |   {
413 |    "cell_type": "code",
414 |    "execution_count": 13,
415 |    "metadata": {},
416 |    "outputs": [],
417 |    "source": [
418 |     "# Drop the Area coulmn\n",
419 |     "x = x.drop('Area',axis=1)"
420 |    ]
421 |   },
422 |   {
423 |    "cell_type": "code",
424 |    "execution_count": 14,
425 |    "metadata": {},
426 |    "outputs": [
427 |     {
428 |      "data": {
429 |       "text/html": [
430 |        "<div>\n",
431 |        "<style scoped>\n",
432 |        "    .dataframe tbody tr th:only-of-type {\n",
433 |        "        vertical-align: middle;\n",
434 |        "    }\n",
435 |        "\n",
436 |        "    .dataframe tbody tr th {\n",
437 |        "        vertical-align: top;\n",
438 |        "    }\n",
439 |        "\n",
440 |        "    .dataframe thead th {\n",
441 |        "        text-align: right;\n",
442 |        "    }\n",
443 |        "</style>\n",
444 |        "<table border=\"1\" class=\"dataframe\">\n",
445 |        "  <thead>\n",
446 |        "    <tr style=\"text-align: right;\">\n",
447 |        "      <th></th>\n",
448 |        "      <th>Marketing Spend</th>\n",
449 |        "      <th>Administration</th>\n",
450 |        "      <th>Transport</th>\n",
451 |        "    </tr>\n",
452 |        "  </thead>\n",
453 |        "  <tbody>\n",
454 |        "    <tr>\n",
455 |        "      <th>0</th>\n",
456 |        "      <td>114523.61</td>\n",
457 |        "      <td>136897.80</td>\n",
458 |        "      <td>471784.10</td>\n",
459 |        "    </tr>\n",
460 |        "    <tr>\n",
461 |        "      <th>1</th>\n",
462 |        "      <td>162597.70</td>\n",
463 |        "      <td>151377.59</td>\n",
464 |        "      <td>443898.53</td>\n",
465 |        "    </tr>\n",
466 |        "    <tr>\n",
467 |        "      <th>2</th>\n",
468 |        "      <td>153441.51</td>\n",
469 |        "      <td>101145.55</td>\n",
470 |        "      <td>407934.54</td>\n",
471 |        "    </tr>\n",
472 |        "    <tr>\n",
473 |        "      <th>3</th>\n",
474 |        "      <td>144372.41</td>\n",
475 |        "      <td>118671.85</td>\n",
476 |        "      <td>383199.62</td>\n",
477 |        "    </tr>\n",
478 |        "    <tr>\n",
479 |        "      <th>4</th>\n",
480 |        "      <td>142107.34</td>\n",
481 |        "      <td>91391.77</td>\n",
482 |        "      <td>366168.42</td>\n",
483 |        "    </tr>\n",
484 |        "  </tbody>\n",
485 |        "</table>\n",
486 |        "</div>"
487 |       ],
488 |       "text/plain": [
489 |        "   Marketing Spend  Administration  Transport\n",
490 |        "0        114523.61       136897.80  471784.10\n",
491 |        "1        162597.70       151377.59  443898.53\n",
492 |        "2        153441.51       101145.55  407934.54\n",
493 |        "3        144372.41       118671.85  383199.62\n",
494 |        "4        142107.34        91391.77  366168.42"
495 |       ]
496 |      },
497 |      "execution_count": 14,
498 |      "metadata": {},
499 |      "output_type": "execute_result"
500 |     }
501 |    ],
502 |    "source": [
503 |     "x.head()"
504 |    ]
505 |   },
506 |   {
507 |    "cell_type": "code",
508 |    "execution_count": 15,
509 |    "metadata": {},
510 |    "outputs": [],
511 |    "source": [
512 |     "#concatation\n",
513 |     "x = pd.concat([x,city],axis=1)"
514 |    ]
515 |   },
516 |   {
517 |    "cell_type": "code",
518 |    "execution_count": 16,
519 |    "metadata": {},
520 |    "outputs": [
521 |     {
522 |      "data": {
523 |       "text/html": [
524 |        "<div>\n",
525 |        "<style scoped>\n",
526 |        "    .dataframe tbody tr th:only-of-type {\n",
527 |        "        vertical-align: middle;\n",
528 |        "    }\n",
529 |        "\n",
530 |        "    .dataframe tbody tr th {\n",
531 |        "        vertical-align: top;\n",
532 |        "    }\n",
533 |        "\n",
534 |        "    .dataframe thead th {\n",
535 |        "        text-align: right;\n",
536 |        "    }\n",
537 |        "</style>\n",
538 |        "<table border=\"1\" class=\"dataframe\">\n",
539 |        "  <thead>\n",
540 |        "    <tr style=\"text-align: right;\">\n",
541 |        "      <th></th>\n",
542 |        "      <th>Marketing Spend</th>\n",
543 |        "      <th>Administration</th>\n",
544 |        "      <th>Transport</th>\n",
545 |        "      <th>Dhaka</th>\n",
546 |        "      <th>Rangpur</th>\n",
547 |        "    </tr>\n",
548 |        "  </thead>\n",
549 |        "  <tbody>\n",
550 |        "    <tr>\n",
551 |        "      <th>0</th>\n",
552 |        "      <td>114523.61</td>\n",
553 |        "      <td>136897.80</td>\n",
554 |        "      <td>471784.10</td>\n",
555 |        "      <td>1</td>\n",
556 |        "      <td>0</td>\n",
557 |        "    </tr>\n",
558 |        "    <tr>\n",
559 |        "      <th>1</th>\n",
560 |        "      <td>162597.70</td>\n",
561 |        "      <td>151377.59</td>\n",
562 |        "      <td>443898.53</td>\n",
563 |        "      <td>0</td>\n",
564 |        "      <td>0</td>\n",
565 |        "    </tr>\n",
566 |        "    <tr>\n",
567 |        "      <th>2</th>\n",
568 |        "      <td>153441.51</td>\n",
569 |        "      <td>101145.55</td>\n",
570 |        "      <td>407934.54</td>\n",
571 |        "      <td>0</td>\n",
572 |        "      <td>1</td>\n",
573 |        "    </tr>\n",
574 |        "    <tr>\n",
575 |        "      <th>3</th>\n",
576 |        "      <td>144372.41</td>\n",
577 |        "      <td>118671.85</td>\n",
578 |        "      <td>383199.62</td>\n",
579 |        "      <td>1</td>\n",
580 |        "      <td>0</td>\n",
581 |        "    </tr>\n",
582 |        "    <tr>\n",
583 |        "      <th>4</th>\n",
584 |        "      <td>142107.34</td>\n",
585 |        "      <td>91391.77</td>\n",
586 |        "      <td>366168.42</td>\n",
587 |        "      <td>0</td>\n",
588 |        "      <td>1</td>\n",
589 |        "    </tr>\n",
590 |        "  </tbody>\n",
591 |        "</table>\n",
592 |        "</div>"
593 |       ],
594 |       "text/plain": [
595 |        "   Marketing Spend  Administration  Transport  Dhaka  Rangpur\n",
596 |        "0        114523.61       136897.80  471784.10      1        0\n",
597 |        "1        162597.70       151377.59  443898.53      0        0\n",
598 |        "2        153441.51       101145.55  407934.54      0        1\n",
599 |        "3        144372.41       118671.85  383199.62      1        0\n",
600 |        "4        142107.34        91391.77  366168.42      0        1"
601 |       ]
602 |      },
603 |      "execution_count": 16,
604 |      "metadata": {},
605 |      "output_type": "execute_result"
606 |     }
607 |    ],
608 |    "source": [
609 |     "x.head()"
610 |    ]
611 |   },
612 |   {
613 |    "cell_type": "code",
614 |    "execution_count": 17,
615 |    "metadata": {},
616 |    "outputs": [],
617 |    "source": [
618 |     "# Splitting the dataset into the Training set and Test set"
619 |    ]
620 |   },
621 |   {
622 |    "cell_type": "code",
623 |    "execution_count": 18,
624 |    "metadata": {},
625 |    "outputs": [],
626 |    "source": [
627 |     "#import library\n",
628 |     "from sklearn.model_selection import train_test_split\n"
629 |    ]
630 |   },
631 |   {
632 |    "cell_type": "code",
633 |    "execution_count": 19,
634 |    "metadata": {},
635 |    "outputs": [],
636 |    "source": [
637 |     "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.25, random_state = 0)"
638 |    ]
639 |   },
640 |   {
641 |    "cell_type": "code",
642 |    "execution_count": 20,
643 |    "metadata": {},
644 |    "outputs": [],
645 |    "source": [
646 |     "# Fitting Multiple Linear Regression to the Training set\n",
647 |     "from sklearn.linear_model import LinearRegression\n"
648 |    ]
649 |   },
650 |   {
651 |    "cell_type": "code",
652 |    "execution_count": 21,
653 |    "metadata": {},
654 |    "outputs": [],
655 |    "source": [
656 |     "regressor = LinearRegression()"
657 |    ]
658 |   },
659 |   {
660 |    "cell_type": "code",
661 |    "execution_count": 22,
662 |    "metadata": {},
663 |    "outputs": [
664 |     {
665 |      "data": {
666 |       "text/plain": [
667 |        "LinearRegression()"
668 |       ]
669 |      },
670 |      "execution_count": 22,
671 |      "metadata": {},
672 |      "output_type": "execute_result"
673 |     }
674 |    ],
675 |    "source": [
676 |     "regressor.fit(xtrain, ytrain)"
677 |    ]
678 |   },
679 |   {
680 |    "cell_type": "code",
681 |    "execution_count": 23,
682 |    "metadata": {},
683 |    "outputs": [
684 |     {
685 |      "data": {
686 |       "text/html": [
687 |        "<div>\n",
688 |        "<style scoped>\n",
689 |        "    .dataframe tbody tr th:only-of-type {\n",
690 |        "        vertical-align: middle;\n",
691 |        "    }\n",
692 |        "\n",
693 |        "    .dataframe tbody tr th {\n",
694 |        "        vertical-align: top;\n",
695 |        "    }\n",
696 |        "\n",
697 |        "    .dataframe thead th {\n",
698 |        "        text-align: right;\n",
699 |        "    }\n",
700 |        "</style>\n",
701 |        "<table border=\"1\" class=\"dataframe\">\n",
702 |        "  <thead>\n",
703 |        "    <tr style=\"text-align: right;\">\n",
704 |        "      <th></th>\n",
705 |        "      <th>Marketing Spend</th>\n",
706 |        "      <th>Administration</th>\n",
707 |        "      <th>Transport</th>\n",
708 |        "      <th>Dhaka</th>\n",
709 |        "      <th>Rangpur</th>\n",
710 |        "    </tr>\n",
711 |        "  </thead>\n",
712 |        "  <tbody>\n",
713 |        "    <tr>\n",
714 |        "      <th>28</th>\n",
715 |        "      <td>66051.52</td>\n",
716 |        "      <td>182645.56</td>\n",
717 |        "      <td>118148.20</td>\n",
718 |        "      <td>0</td>\n",
719 |        "      <td>1</td>\n",
720 |        "    </tr>\n",
721 |        "    <tr>\n",
722 |        "      <th>11</th>\n",
723 |        "      <td>100671.96</td>\n",
724 |        "      <td>91790.61</td>\n",
725 |        "      <td>249744.55</td>\n",
726 |        "      <td>0</td>\n",
727 |        "      <td>0</td>\n",
728 |        "    </tr>\n",
729 |        "    <tr>\n",
730 |        "      <th>10</th>\n",
731 |        "      <td>101913.08</td>\n",
732 |        "      <td>110594.11</td>\n",
733 |        "      <td>229160.95</td>\n",
734 |        "      <td>0</td>\n",
735 |        "      <td>1</td>\n",
736 |        "    </tr>\n",
737 |        "    <tr>\n",
738 |        "      <th>41</th>\n",
739 |        "      <td>27892.92</td>\n",
740 |        "      <td>84710.77</td>\n",
741 |        "      <td>164470.71</td>\n",
742 |        "      <td>0</td>\n",
743 |        "      <td>1</td>\n",
744 |        "    </tr>\n",
745 |        "    <tr>\n",
746 |        "      <th>2</th>\n",
747 |        "      <td>153441.51</td>\n",
748 |        "      <td>101145.55</td>\n",
749 |        "      <td>407934.54</td>\n",
750 |        "      <td>0</td>\n",
751 |        "      <td>1</td>\n",
752 |        "    </tr>\n",
753 |        "  </tbody>\n",
754 |        "</table>\n",
755 |        "</div>"
756 |       ],
757 |       "text/plain": [
758 |        "    Marketing Spend  Administration  Transport  Dhaka  Rangpur\n",
759 |        "28         66051.52       182645.56  118148.20      0        1\n",
760 |        "11        100671.96        91790.61  249744.55      0        0\n",
761 |        "10        101913.08       110594.11  229160.95      0        1\n",
762 |        "41         27892.92        84710.77  164470.71      0        1\n",
763 |        "2         153441.51       101145.55  407934.54      0        1"
764 |       ]
765 |      },
766 |      "execution_count": 23,
767 |      "metadata": {},
768 |      "output_type": "execute_result"
769 |     }
770 |    ],
771 |    "source": [
772 |     "xtest.head()"
773 |    ]
774 |   },
775 |   {
776 |    "cell_type": "code",
777 |    "execution_count": 24,
778 |    "metadata": {},
779 |    "outputs": [
780 |     {
781 |      "data": {
782 |       "text/plain": [
783 |        "28    103282.38\n",
784 |        "11    144259.40\n",
785 |        "10    146121.95\n",
786 |        "41     77798.83\n",
787 |        "2     191050.39\n",
788 |        "Name: Profit, dtype: float64"
789 |       ]
790 |      },
791 |      "execution_count": 24,
792 |      "metadata": {},
793 |      "output_type": "execute_result"
794 |     }
795 |    ],
796 |    "source": [
797 |     "ytest.head()"
798 |    ]
799 |   },
800 |   {
801 |    "cell_type": "code",
802 |    "execution_count": 25,
803 |    "metadata": {},
804 |    "outputs": [],
805 |    "source": [
806 |     "# Predicting the Test set results\n",
807 |     "pred = regressor.predict(xtest)"
808 |    ]
809 |   },
810 |   {
811 |    "cell_type": "code",
812 |    "execution_count": 26,
813 |    "metadata": {},
814 |    "outputs": [
815 |     {
816 |      "data": {
817 |       "text/plain": [
818 |        "array([103501.0825284 , 128011.28068627, 126695.43891127,  70573.91718775,\n",
819 |        "       173381.96874259, 124238.07860872,  69298.09250304,  98399.41936876,\n",
820 |        "       116419.1480864 , 161430.98134847,  94740.73303076,  89920.22800514,\n",
821 |        "       105956.86065332])"
822 |       ]
823 |      },
824 |      "execution_count": 26,
825 |      "metadata": {},
826 |      "output_type": "execute_result"
827 |     }
828 |    ],
829 |    "source": [
830 |     "pred"
831 |    ]
832 |   },
833 |   {
834 |    "cell_type": "code",
835 |    "execution_count": 27,
836 |    "metadata": {},
837 |    "outputs": [
838 |     {
839 |      "data": {
840 |       "text/plain": [
841 |        "0.8840978623923469"
842 |       ]
843 |      },
844 |      "execution_count": 27,
845 |      "metadata": {},
846 |      "output_type": "execute_result"
847 |     }
848 |    ],
849 |    "source": [
850 |     "regressor.score(xtest,ytest)"
851 |    ]
852 |   },
853 |   {
854 |    "cell_type": "markdown",
855 |    "metadata": {},
856 |    "source": [
857 |     "# R-Squared Value"
858 |    ]
859 |   },
860 |   {
861 |    "cell_type": "code",
862 |    "execution_count": 31,
863 |    "metadata": {},
864 |    "outputs": [],
865 |    "source": [
866 |     "from sklearn.metrics import r2_score\n",
867 |     "from sklearn.metrics import mean_squared_error"
868 |    ]
869 |   },
870 |   {
871 |    "cell_type": "code",
872 |    "execution_count": 32,
873 |    "metadata": {},
874 |    "outputs": [],
875 |    "source": [
876 |     "score=r2_score(ytest,pred)"
877 |    ]
878 |   },
879 |   {
880 |    "cell_type": "code",
881 |    "execution_count": 33,
882 |    "metadata": {},
883 |    "outputs": [
884 |     {
885 |      "data": {
886 |       "text/plain": [
887 |        "0.8840978623923469"
888 |       ]
889 |      },
890 |      "execution_count": 33,
891 |      "metadata": {},
892 |      "output_type": "execute_result"
893 |     }
894 |    ],
895 |    "source": [
896 |     "score"
897 |    ]
898 |   },
899 |   {
900 |    "cell_type": "code",
901 |    "execution_count": null,
902 |    "metadata": {},
903 |    "outputs": [],
904 |    "source": [
905 |     "mean_squared_error(xtest)"
906 |    ]
907 |   },
908 |   {
909 |    "cell_type": "code",
910 |    "execution_count": null,
911 |    "metadata": {},
912 |    "outputs": [],
913 |    "source": []
914 |   }
915 |  ],
916 |  "metadata": {
917 |   "kernelspec": {
918 |    "display_name": "Python 3",
919 |    "language": "python",
920 |    "name": "python3"
921 |   },
922 |   "language_info": {
923 |    "codemirror_mode": {
924 |     "name": "ipython",
925 |     "version": 3
926 |    },
927 |    "file_extension": ".py",
928 |    "mimetype": "text/x-python",
929 |    "name": "python",
930 |    "nbconvert_exporter": "python",
931 |    "pygments_lexer": "ipython3",
932 |    "version": "3.8.8"
933 |   }
934 |  },
935 |  "nbformat": 4,
936 |  "nbformat_minor": 2
937 | }
938 | 


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/Papers/A Proficient Approach to Detect Osteosarcoma Through Deep Learning.pdf:
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https://raw.githubusercontent.com/rashakil-ds/Machine-Learning-with-Python/418124d517ff32f4e19c21ea3567fb106a222264/Papers/A Proficient Approach to Detect Osteosarcoma Through Deep Learning.pdf


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/Papers/read.txt:
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1 | Research Paper
2 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | <!DOCTYPE html>
 2 | <html>
 3 | <body>
 4 |   <h1>Machine Learning with Python by Study Mart & aiQuest Intelligence</h1>
 5 |   <p>Welcome to the <strong>Machine Learning with Python</strong> repository! This repository contains resources and materials to help you learn and master machine learning using Python. Curated by Study Mart and aiQuest Intelligence, these resources are perfect for both beginners and advanced learners.</p>
 6 | 
 7 |   <h2>Topics Covered</h2>
 8 |   <ul>
 9 |     <li>Introduction to Machine Learning</li>
10 |     <li>Data Preprocessing</li>
11 |     <li>Supervised Learning</li>
12 |     <li>Unsupervised Learning</li>
13 |     <li>Model Evaluation and Validation</li>
14 |     <li>Advanced Machine Learning Techniques</li>
15 |   </ul>
16 | 
17 |   <h2>YouTube Video Playlist</h2>
18 |   <p>Watch the complete video playlist on YouTube: <a href="https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V" target="_blank">Machine Learning with Python Playlist</a></p>
19 | 
20 |   <h2>Repository Link</h2>
21 |   <p>Explore the resources in detail <a href="https://github.com/rashakil-ds/Machine-Learning-with-Python" target="_blank">here</a>.</p>
22 | 
23 |   <h2>Additional Resources</h2>
24 |   <p>We also offer a variety of paid courses on data science on our website. Visit <a href="https://aiquest.org/" target="_blank">AIQuest</a> for more details.</p>
25 |   <p>For free resources, check out our YouTube channel: <a href="https://www.youtube.com/StudyMart" target="_blank">StudyMart</a>.</p>
26 |   <p>Join our Facebook group for more discussions and resources: <a href="https://www.facebook.com/groups/StudyMart" target="_blank">StudyMart Facebook Group</a>.</p>
27 | 
28 |   <footer>
29 |     <p>Visit our <a href="https://aiquest.org/" target="_blank">paid course website</a> for more courses on data science.</p>
30 |     <p>Subscribe to our YouTube channel for free resources: <a href="https://www.youtube.com/StudyMart" target="_blank">StudyMart</a>.</p>
31 |     <p>Join our Facebook group: <a href="https://www.facebook.com/groups/StudyMart" target="_blank">StudyMart Facebook Group</a>.</p>
32 |   </footer>
33 | </body>
34 | </html>
35 | 


--------------------------------------------------------------------------------
/Save ML Models.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "8f8d056b",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Random Forest"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": 13,
 14 |    "id": "cb7d7f37",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "from sklearn.ensemble import RandomForestClassifier"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 14,
 24 |    "id": "99140e4e",
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "ran = RandomForestClassifier(n_estimators=15)"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 15,
 34 |    "id": "9ca3fdcd",
 35 |    "metadata": {},
 36 |    "outputs": [
 37 |     {
 38 |      "data": {
 39 |       "text/plain": [
 40 |        "RandomForestClassifier(n_estimators=15)"
 41 |       ]
 42 |      },
 43 |      "execution_count": 15,
 44 |      "metadata": {},
 45 |      "output_type": "execute_result"
 46 |     }
 47 |    ],
 48 |    "source": [
 49 |     "ran.fit(x,y)"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 16,
 55 |    "id": "525c42fe",
 56 |    "metadata": {},
 57 |    "outputs": [
 58 |     {
 59 |      "data": {
 60 |       "text/plain": [
 61 |        "array(['Yes'], dtype=object)"
 62 |       ]
 63 |      },
 64 |      "execution_count": 16,
 65 |      "metadata": {},
 66 |      "output_type": "execute_result"
 67 |     }
 68 |    ],
 69 |    "source": [
 70 |     "ran.predict([[1,0,1]])"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 17,
 76 |    "id": "f5babf51",
 77 |    "metadata": {},
 78 |    "outputs": [
 79 |     {
 80 |      "data": {
 81 |       "text/plain": [
 82 |        "array(['No'], dtype=object)"
 83 |       ]
 84 |      },
 85 |      "execution_count": 17,
 86 |      "metadata": {},
 87 |      "output_type": "execute_result"
 88 |     }
 89 |    ],
 90 |    "source": [
 91 |     "ran.predict([[0,1,0]])"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": null,
 97 |    "id": "de29bfc4",
 98 |    "metadata": {},
 99 |    "outputs": [],
100 |    "source": []
101 |   },
102 |   {
103 |    "cell_type": "markdown",
104 |    "id": "f7febbe2",
105 |    "metadata": {},
106 |    "source": [
107 |     "# Save Machine Learning Models"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 18,
113 |    "id": "f9edb713",
114 |    "metadata": {},
115 |    "outputs": [],
116 |    "source": [
117 |     "import pickle as pk\n",
118 |     "\n",
119 |     "with open('My_Model1','wb') as file:\n",
120 |     "    pk.dump(ran,file)"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 19,
126 |    "id": "4e31af7e",
127 |    "metadata": {},
128 |    "outputs": [],
129 |    "source": [
130 |     "with open('My_Model1','rb') as file:\n",
131 |     "    model1 = pk.load(file)"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 20,
137 |    "id": "b94ba55a",
138 |    "metadata": {},
139 |    "outputs": [
140 |     {
141 |      "data": {
142 |       "text/plain": [
143 |        "array(['Yes'], dtype=object)"
144 |       ]
145 |      },
146 |      "execution_count": 20,
147 |      "metadata": {},
148 |      "output_type": "execute_result"
149 |     }
150 |    ],
151 |    "source": [
152 |     "model1.predict([[1,0,1]])"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 21,
158 |    "id": "60cdf548",
159 |    "metadata": {},
160 |    "outputs": [
161 |     {
162 |      "data": {
163 |       "text/plain": [
164 |        "'C:\\\\Users\\\\study mart\\\\Downloads\\\\New Folder'"
165 |       ]
166 |      },
167 |      "execution_count": 21,
168 |      "metadata": {},
169 |      "output_type": "execute_result"
170 |     }
171 |    ],
172 |    "source": [
173 |     "import os\n",
174 |     "os.getcwd()"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": 22,
180 |    "id": "ed465725",
181 |    "metadata": {},
182 |    "outputs": [],
183 |    "source": [
184 |     "pk.dump(ran,open('My_Model2','wb'))"
185 |    ]
186 |   },
187 |   {
188 |    "cell_type": "code",
189 |    "execution_count": 23,
190 |    "id": "b319f8ca",
191 |    "metadata": {},
192 |    "outputs": [],
193 |    "source": [
194 |     "model2 = pk.load(open('My_Model2','rb'))"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": 24,
200 |    "id": "35a79a67",
201 |    "metadata": {},
202 |    "outputs": [
203 |     {
204 |      "data": {
205 |       "text/plain": [
206 |        "array(['No'], dtype=object)"
207 |       ]
208 |      },
209 |      "execution_count": 24,
210 |      "metadata": {},
211 |      "output_type": "execute_result"
212 |     }
213 |    ],
214 |    "source": [
215 |     "model2.predict([[0,1,0]])"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": null,
221 |    "id": "7b292b64",
222 |    "metadata": {},
223 |    "outputs": [],
224 |    "source": []
225 |   },
226 |   {
227 |    "cell_type": "markdown",
228 |    "id": "49ae4874",
229 |    "metadata": {},
230 |    "source": [
231 |     "# Joblib"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": 25,
237 |    "id": "0a9782b1",
238 |    "metadata": {},
239 |    "outputs": [
240 |     {
241 |      "data": {
242 |       "text/plain": [
243 |        "['My_Model3']"
244 |       ]
245 |      },
246 |      "execution_count": 25,
247 |      "metadata": {},
248 |      "output_type": "execute_result"
249 |     }
250 |    ],
251 |    "source": [
252 |     "import joblib as jbl\n",
253 |     "\n",
254 |     "jbl.dump(ran,'My_Model3')"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": 26,
260 |    "id": "d2acfb96",
261 |    "metadata": {},
262 |    "outputs": [],
263 |    "source": [
264 |     "model3 = jbl.load('My_Model3')"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 27,
270 |    "id": "2768c18e",
271 |    "metadata": {},
272 |    "outputs": [
273 |     {
274 |      "data": {
275 |       "text/plain": [
276 |        "array(['No'], dtype=object)"
277 |       ]
278 |      },
279 |      "execution_count": 27,
280 |      "metadata": {},
281 |      "output_type": "execute_result"
282 |     }
283 |    ],
284 |    "source": [
285 |     "model3.predict([[0,1,0]])"
286 |    ]
287 |   },
288 |   {
289 |    "cell_type": "markdown",
290 |    "id": "176e33a0",
291 |    "metadata": {},
292 |    "source": [
293 |     "# Access File From Diffrent Folder"
294 |    ]
295 |   },
296 |   {
297 |    "cell_type": "code",
298 |    "execution_count": 28,
299 |    "id": "14390d9c",
300 |    "metadata": {},
301 |    "outputs": [
302 |     {
303 |      "data": {
304 |       "text/plain": [
305 |        "'C:\\\\Users\\\\study mart\\\\Downloads\\\\New Folder'"
306 |       ]
307 |      },
308 |      "execution_count": 28,
309 |      "metadata": {},
310 |      "output_type": "execute_result"
311 |     }
312 |    ],
313 |    "source": [
314 |     "import os\n",
315 |     "os.getcwd()"
316 |    ]
317 |   },
318 |   {
319 |    "cell_type": "code",
320 |    "execution_count": 29,
321 |    "id": "60a7b572",
322 |    "metadata": {},
323 |    "outputs": [],
324 |    "source": [
325 |     "os.chdir('C:\\\\Users\\\\study mart\\\\Downloads\\\\new 2')"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "code",
330 |    "execution_count": 30,
331 |    "id": "756553a7",
332 |    "metadata": {},
333 |    "outputs": [
334 |     {
335 |      "data": {
336 |       "text/plain": [
337 |        "'C:\\\\Users\\\\study mart\\\\Downloads\\\\new 2'"
338 |       ]
339 |      },
340 |      "execution_count": 30,
341 |      "metadata": {},
342 |      "output_type": "execute_result"
343 |     }
344 |    ],
345 |    "source": [
346 |     "os.getcwd()"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": 31,
352 |    "id": "d1eb0e87",
353 |    "metadata": {},
354 |    "outputs": [],
355 |    "source": [
356 |     "with open('My_Model1','rb') as file:\n",
357 |     "    model4 = pk.load(file)"
358 |    ]
359 |   },
360 |   {
361 |    "cell_type": "code",
362 |    "execution_count": 32,
363 |    "id": "9df5e9fc",
364 |    "metadata": {},
365 |    "outputs": [
366 |     {
367 |      "data": {
368 |       "text/plain": [
369 |        "array(['No'], dtype=object)"
370 |       ]
371 |      },
372 |      "execution_count": 32,
373 |      "metadata": {},
374 |      "output_type": "execute_result"
375 |     }
376 |    ],
377 |    "source": [
378 |     "model4.predict([[0,1,0]])"
379 |    ]
380 |   },
381 |   {
382 |    "cell_type": "markdown",
383 |    "id": "167acab7",
384 |    "metadata": {},
385 |    "source": [
386 |     "Vist: https://youtube.com/StudyMart"
387 |    ]
388 |   },
389 |   {
390 |    "cell_type": "code",
391 |    "execution_count": null,
392 |    "id": "b040d926",
393 |    "metadata": {},
394 |    "outputs": [],
395 |    "source": []
396 |   }
397 |  ],
398 |  "metadata": {
399 |   "kernelspec": {
400 |    "display_name": "Python 3",
401 |    "language": "python",
402 |    "name": "python3"
403 |   },
404 |   "language_info": {
405 |    "codemirror_mode": {
406 |     "name": "ipython",
407 |     "version": 3
408 |    },
409 |    "file_extension": ".py",
410 |    "mimetype": "text/x-python",
411 |    "name": "python",
412 |    "nbconvert_exporter": "python",
413 |    "pygments_lexer": "ipython3",
414 |    "version": "3.8.8"
415 |   }
416 |  },
417 |  "nbformat": 4,
418 |  "nbformat_minor": 5
419 | }
420 | 


--------------------------------------------------------------------------------
/Screen Time Data.csv:
--------------------------------------------------------------------------------
 1 | index,Date,Week Day,Total Screen Time ,Social Networking,Reading and Reference,Other,Productivity,Health and Fitness,Entertainment,Creativity,Yoga
 2 | 0,04/17/19,Wednesday,187,89,17,41,22,0,0,0,0
 3 | 1,04/18/19,Thursday,123,78,17,8,9,0,0,0,0
 4 | 2,04/19/19,Friday,112,52,40,8,4,0,3,0,0
 5 | 3,04/20/19,Saturday,101,69,9,38,2,0,3,0,0
 6 | 4,04/21/19,Sunday,56,35,2,43,3,0,1,1,0
 7 | 5,04/22/19,Monday,189,68,0,9,3,4,0,0,0
 8 | 6,04/23/19,Tuesday,158,56,18,41,12,15,0,0,0
 9 | 7,04/24/19,Wednesday,135,98,3,33,16,0,0,0,0
10 | 8,04/25/19,Thursday,52,25,7,3,16,0,0,0,0
11 | 9,04/26/19,Friday,198,76,8,29,15,0,32,0,0
12 | 10,04/27/19,Saturday,116,75,10,20,5,0,0,0,0
13 | 11,04/28/19,Sunday,85,42,22,4,2,0,0,0,0
14 | 12,04/29/19,Monday,109,46,8,13,9,15,1,0,1
15 | 13,04/30/19,Tuesday,79,40,2,9,12,0,0,0,1
16 | 14,05/01/19,Wednesday,127,90,0,10,7,0,0,0,1
17 | 15,05/02/19,Thursday,170,60,3,2,11,0,0,0,1
18 | 16,05/03/19,Friday,91,64,2,18,5,1,1,2,1
19 | 17,05/04/19,Saturday,58,34,4,5,3,0,1,0,1
20 | 18,05/05/19,Sunday,133,109,5,1,3,0,0,0,1
21 | 19,05/06/19,Monday,144,81,4,5,3,0,0,0,1
22 | 20,05/07/19,Tuesday,110,70,5,6,15,0,9,0,1
23 | 21,05/08/19,Wednesday,122,53,25,26,15,0,0,0,1
24 | 22,05/09/19,Thursday,96,42,15,16,19,0,0,0,1
25 | 23,05/10/19,Friday,161,93,13,17,16,1,0,0,1
26 | 24,05/11/19,Saturday,58,49,1,2,2,0,0,2,1
27 | 25,05/12/19,Sunday,52,28,1,1,6,0,0,1,1
28 | 26,05/13/19,Monday,61,37,1,0,4,0,0,0,1
29 | 27,05/14/19,Tuesday,88,41,2,7,15,0,0,0,1
30 | 


--------------------------------------------------------------------------------
/home data.csv:
--------------------------------------------------------------------------------
  1 | x,y
  2 | 48.95588857,60.72360244
  3 | 44.68719623,82.89250373
  4 | 60.29732685,97.37989686
  5 | 45.61864377,48.84715332
  6 | 38.81681754,56.87721319
  7 | 53.42680403,68.77759598
  8 | 61.53035803,62.5623823
  9 | 47.47563963,71.54663223
 10 | 52.55001444,71.30087989
 11 | 45.41973014,55.16567715
 12 | 54.35163488,82.47884676
 13 | 44.1640495,62.00892325
 14 | 58.16847072,75.39287043
 15 | 56.72720806,81.43619216
 16 | 59.81320787,87.23092513
 17 | 55.14218841,78.21151827
 18 | 52.21179669,79.64197305
 19 | 39.29956669,59.17148932
 20 | 48.10504169,75.3312423
 21 | 66.18981661,83.87856466
 22 | 65.41605175,118.5912173
 23 | 47.48120861,57.25181946
 24 | 41.57564262,51.39174408
 25 | 51.84518691,75.38065167
 26 | 59.37082201,74.76556403
 27 | 57.31000344,95.45505292
 28 | 63.61556125,95.22936602
 29 | 46.73761941,79.05240617
 30 | 50.55676015,83.43207142
 31 | 52.22399609,63.35879032
 32 | 35.56783005,41.4128853
 33 | 42.43647694,76.61734128
 34 | 58.16454011,96.76956643
 35 | 57.50444762,74.08413012
 36 | 45.44053073,66.58814441
 37 | 61.89622268,77.76848242
 38 | 33.09383174,50.71958891
 39 | 36.43600951,62.12457082
 40 | 37.67565486,60.81024665
 41 | 44.55560838,52.68298337
 42 | 43.31828263,58.56982472
 43 | 50.07314563,82.90598149
 44 | 43.87061265,61.4247098
 45 | 62.99748075,115.2441528
 46 | 32.66904376,45.57058882
 47 | 40.16689901,54.0840548
 48 | 53.57507753,87.99445276
 49 | 33.86421497,52.72549438
 50 | 64.70713867,93.57611869
 51 | 38.11982403,80.16627545
 52 | 44.50253806,65.10171157
 53 | 40.59953838,65.56230126
 54 | 41.72067636,65.28088692
 55 | 51.08863468,73.43464155
 56 | 55.0780959,71.13972786
 57 | 41.37772653,79.10282968
 58 | 62.49469743,86.52053844
 59 | 49.20388754,84.74269781
 60 | 41.10268519,59.35885025
 61 | 41.18201611,61.68403752
 62 | 50.18638949,69.84760416
 63 | 52.37844622,86.09829121
 64 | 50.13548549,59.10883927
 65 | 33.64470601,69.89968164
 66 | 39.55790122,44.86249071
 67 | 56.13038882,85.49806778
 68 | 57.36205213,95.53668685
 69 | 60.26921439,70.25193442
 70 | 35.67809389,52.72173496
 71 | 31.588117,50.39267014
 72 | 53.66093226,63.64239878
 73 | 46.68222865,72.24725107
 74 | 43.10782022,57.81251298
 75 | 70.34607562,104.2571016
 76 | 44.49285588,86.64202032
 77 | 57.5045333,91.486778
 78 | 36.93007661,55.23166089
 79 | 55.80573336,79.55043668
 80 | 38.95476907,44.84712424
 81 | 56.9012147,80.20752314
 82 | 56.86890066,83.14274979
 83 | 34.3331247,55.72348926
 84 | 59.04974121,77.63418251
 85 | 57.78822399,99.05141484
 86 | 54.28232871,79.12064627
 87 | 51.0887199,69.58889785
 88 | 50.28283635,69.51050331
 89 | 44.21174175,73.68756432
 90 | 38.00548801,61.36690454
 91 | 32.94047994,67.17065577
 92 | 53.69163957,85.66820315
 93 | 68.76573427,114.8538712
 94 | 46.2309665,90.12357207
 95 | 68.31936082,97.91982104
 96 | 50.03017434,81.53699078
 97 | 49.23976534,72.11183247
 98 | 50.03957594,85.23200734
 99 | 48.14985889,66.22495789
100 | 25.12848465,53.45439421
101 | 


--------------------------------------------------------------------------------
/shoe.csv:
--------------------------------------------------------------------------------
 1 | size(cm),class(y)
 2 | 9.5,Female
 3 | 10.125,Male
 4 | 10.41,Male
 5 | 9.81,Female
 6 | 11.05,Male
 7 | 9.15,Female
 8 | 9.45,Female
 9 | 10.57,Male
10 | 9.71,Female
11 | 9.65,Female
12 | 9.82,Female
13 | 10.42,Male
14 | 10.19,Male
15 | 10.91,Male
16 | 10.55,Male
17 | 10.73,Male
18 | 10.02,Female
19 | 9.93,Female
20 | 10.3,Male
21 | 10.59,Male
22 | 10.15,Male
23 | 9.35,Female
24 | 9.2,Female
25 | 10.66,Male
26 | 9.62,Female
27 | 10.46,Male
28 | 10.29,Male
29 | 10.81,Male
30 | 10.45,Male
31 | 10.73,Male
32 | 10.04,Female
33 | 9.91,Female
34 | 10.4,Male
35 | 9.59,Female
36 | 10.16,Male
37 | 9.3,Female
38 | 9.21,Female
39 | 10.56,Male
40 | 9.6,Female
41 | 9.32,Male
42 | 


--------------------------------------------------------------------------------
/shop data.csv:
--------------------------------------------------------------------------------
 1 | age,income,gender,m_status,buys
 2 | <25,high,male,single,no
 3 | <25,high,male,married,no
 4 | 25-35,high,male,single,yes
 5 | >35,medium,male,single,yes
 6 | >35,low,female,single,yes
 7 | >35,low,female,single,no
 8 | 25-35,low,female,married,yes
 9 | <25,medium,male,married,no
10 | <25,low,female,single,yes
11 | >35,medium,female,married,yes
12 | <25,medium,female,single,yes
13 | 25-35,medium,male,married,yes
14 | 25-35,high,female,single,yes
15 | >35,medium,male,married,no
16 | <25,high,male,single,no
17 | <25,high,female,married,yes
18 | >35,medium,male,married,yes
19 | <25,high,female,single,yes
20 | 25-35,medium,female,married,yes
21 | 25-35,high,male,single,yes
22 | >35,medium,female,married,no
23 | <25,low,male,single,yes
24 | 


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