├── .gitattributes
├── student_scores.csv
├── .ipynb_checkpoints
    └── linear-checkpoint.ipynb
└── linear.ipynb


/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


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/student_scores.csv:
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 1 | Hours,Scores
 2 | 2.5,21
 3 | 5.1,47
 4 | 3.2,27
 5 | 8.5,75
 6 | 3.5,30
 7 | 1.5,20
 8 | 9.2,88
 9 | 5.5,60
10 | 8.3,81
11 | 2.7,25
12 | 7.7,85
13 | 5.9,62
14 | 4.5,41
15 | 3.3,42
16 | 1.1,17
17 | 8.9,95
18 | 2.5,30
19 | 1.9,24
20 | 6.1,67
21 | 7.4,69
22 | 2.7,30
23 | 4.8,54
24 | 3.8,35
25 | 6.9,76
26 | 7.8,86
27 | 


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/.ipynb_checkpoints/linear-checkpoint.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 6,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "data": {
 21 |       "text/plain": [
 22 |        "(25, 2)"
 23 |       ]
 24 |      },
 25 |      "execution_count": 6,
 26 |      "metadata": {},
 27 |      "output_type": "execute_result"
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "dataset = pd.read_csv('student_scores.csv')\n",
 32 |     "dataset.shape"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 7,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "data": {
 42 |       "text/html": [
 43 |        "<div>\n",
 44 |        "<style scoped>\n",
 45 |        "    .dataframe tbody tr th:only-of-type {\n",
 46 |        "        vertical-align: middle;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe tbody tr th {\n",
 50 |        "        vertical-align: top;\n",
 51 |        "    }\n",
 52 |        "\n",
 53 |        "    .dataframe thead th {\n",
 54 |        "        text-align: right;\n",
 55 |        "    }\n",
 56 |        "</style>\n",
 57 |        "<table border=\"1\" class=\"dataframe\">\n",
 58 |        "  <thead>\n",
 59 |        "    <tr style=\"text-align: right;\">\n",
 60 |        "      <th></th>\n",
 61 |        "      <th>Hours</th>\n",
 62 |        "      <th>Scores</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>0</th>\n",
 68 |        "      <td>2.5</td>\n",
 69 |        "      <td>21</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>1</th>\n",
 73 |        "      <td>5.1</td>\n",
 74 |        "      <td>47</td>\n",
 75 |        "    </tr>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>2</th>\n",
 78 |        "      <td>3.2</td>\n",
 79 |        "      <td>27</td>\n",
 80 |        "    </tr>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>3</th>\n",
 83 |        "      <td>8.5</td>\n",
 84 |        "      <td>75</td>\n",
 85 |        "    </tr>\n",
 86 |        "    <tr>\n",
 87 |        "      <th>4</th>\n",
 88 |        "      <td>3.5</td>\n",
 89 |        "      <td>30</td>\n",
 90 |        "    </tr>\n",
 91 |        "  </tbody>\n",
 92 |        "</table>\n",
 93 |        "</div>"
 94 |       ],
 95 |       "text/plain": [
 96 |        "   Hours  Scores\n",
 97 |        "0    2.5      21\n",
 98 |        "1    5.1      47\n",
 99 |        "2    3.2      27\n",
100 |        "3    8.5      75\n",
101 |        "4    3.5      30"
102 |       ]
103 |      },
104 |      "execution_count": 7,
105 |      "metadata": {},
106 |      "output_type": "execute_result"
107 |     }
108 |    ],
109 |    "source": [
110 |     "dataset.head()"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 8,
116 |    "metadata": {},
117 |    "outputs": [
118 |     {
119 |      "data": {
120 |       "text/html": [
121 |        "<div>\n",
122 |        "<style scoped>\n",
123 |        "    .dataframe tbody tr th:only-of-type {\n",
124 |        "        vertical-align: middle;\n",
125 |        "    }\n",
126 |        "\n",
127 |        "    .dataframe tbody tr th {\n",
128 |        "        vertical-align: top;\n",
129 |        "    }\n",
130 |        "\n",
131 |        "    .dataframe thead th {\n",
132 |        "        text-align: right;\n",
133 |        "    }\n",
134 |        "</style>\n",
135 |        "<table border=\"1\" class=\"dataframe\">\n",
136 |        "  <thead>\n",
137 |        "    <tr style=\"text-align: right;\">\n",
138 |        "      <th></th>\n",
139 |        "      <th>Hours</th>\n",
140 |        "      <th>Scores</th>\n",
141 |        "    </tr>\n",
142 |        "  </thead>\n",
143 |        "  <tbody>\n",
144 |        "    <tr>\n",
145 |        "      <th>count</th>\n",
146 |        "      <td>25.000000</td>\n",
147 |        "      <td>25.000000</td>\n",
148 |        "    </tr>\n",
149 |        "    <tr>\n",
150 |        "      <th>mean</th>\n",
151 |        "      <td>5.012000</td>\n",
152 |        "      <td>51.480000</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>std</th>\n",
156 |        "      <td>2.525094</td>\n",
157 |        "      <td>25.286887</td>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <th>min</th>\n",
161 |        "      <td>1.100000</td>\n",
162 |        "      <td>17.000000</td>\n",
163 |        "    </tr>\n",
164 |        "    <tr>\n",
165 |        "      <th>25%</th>\n",
166 |        "      <td>2.700000</td>\n",
167 |        "      <td>30.000000</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>50%</th>\n",
171 |        "      <td>4.800000</td>\n",
172 |        "      <td>47.000000</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>75%</th>\n",
176 |        "      <td>7.400000</td>\n",
177 |        "      <td>75.000000</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>max</th>\n",
181 |        "      <td>9.200000</td>\n",
182 |        "      <td>95.000000</td>\n",
183 |        "    </tr>\n",
184 |        "  </tbody>\n",
185 |        "</table>\n",
186 |        "</div>"
187 |       ],
188 |       "text/plain": [
189 |        "           Hours     Scores\n",
190 |        "count  25.000000  25.000000\n",
191 |        "mean    5.012000  51.480000\n",
192 |        "std     2.525094  25.286887\n",
193 |        "min     1.100000  17.000000\n",
194 |        "25%     2.700000  30.000000\n",
195 |        "50%     4.800000  47.000000\n",
196 |        "75%     7.400000  75.000000\n",
197 |        "max     9.200000  95.000000"
198 |       ]
199 |      },
200 |      "execution_count": 8,
201 |      "metadata": {},
202 |      "output_type": "execute_result"
203 |     }
204 |    ],
205 |    "source": [
206 |     "dataset.describe() "
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": null,
212 |    "metadata": {},
213 |    "outputs": [],
214 |    "source": []
215 |   }
216 |  ],
217 |  "metadata": {
218 |   "interpreter": {
219 |    "hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119"
220 |   },
221 |   "kernelspec": {
222 |    "display_name": "Python 3 (ipykernel)",
223 |    "language": "python",
224 |    "name": "python3"
225 |   },
226 |   "language_info": {
227 |    "codemirror_mode": {
228 |     "name": "ipython",
229 |     "version": 3
230 |    },
231 |    "file_extension": ".py",
232 |    "mimetype": "text/x-python",
233 |    "name": "python",
234 |    "nbconvert_exporter": "python",
235 |    "pygments_lexer": "ipython3",
236 |    "version": "3.9.7"
237 |   }
238 |  },
239 |  "nbformat": 4,
240 |  "nbformat_minor": 2
241 | }
242 | 


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/linear.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "%matplotlib inline"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 6,
 18 |    "metadata": {},
 19 |    "outputs": [
 20 |     {
 21 |      "data": {
 22 |       "text/plain": [
 23 |        "(25, 2)"
 24 |       ]
 25 |      },
 26 |      "execution_count": 6,
 27 |      "metadata": {},
 28 |      "output_type": "execute_result"
 29 |     }
 30 |    ],
 31 |    "source": [
 32 |     "dataset = pd.read_csv('student_scores.csv')\n",
 33 |     "dataset.shape"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 7,
 39 |    "metadata": {},
 40 |    "outputs": [
 41 |     {
 42 |      "data": {
 43 |       "text/html": [
 44 |        "<div>\n",
 45 |        "<style scoped>\n",
 46 |        "    .dataframe tbody tr th:only-of-type {\n",
 47 |        "        vertical-align: middle;\n",
 48 |        "    }\n",
 49 |        "\n",
 50 |        "    .dataframe tbody tr th {\n",
 51 |        "        vertical-align: top;\n",
 52 |        "    }\n",
 53 |        "\n",
 54 |        "    .dataframe thead th {\n",
 55 |        "        text-align: right;\n",
 56 |        "    }\n",
 57 |        "</style>\n",
 58 |        "<table border=\"1\" class=\"dataframe\">\n",
 59 |        "  <thead>\n",
 60 |        "    <tr style=\"text-align: right;\">\n",
 61 |        "      <th></th>\n",
 62 |        "      <th>Hours</th>\n",
 63 |        "      <th>Scores</th>\n",
 64 |        "    </tr>\n",
 65 |        "  </thead>\n",
 66 |        "  <tbody>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>0</th>\n",
 69 |        "      <td>2.5</td>\n",
 70 |        "      <td>21</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>1</th>\n",
 74 |        "      <td>5.1</td>\n",
 75 |        "      <td>47</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>2</th>\n",
 79 |        "      <td>3.2</td>\n",
 80 |        "      <td>27</td>\n",
 81 |        "    </tr>\n",
 82 |        "    <tr>\n",
 83 |        "      <th>3</th>\n",
 84 |        "      <td>8.5</td>\n",
 85 |        "      <td>75</td>\n",
 86 |        "    </tr>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>4</th>\n",
 89 |        "      <td>3.5</td>\n",
 90 |        "      <td>30</td>\n",
 91 |        "    </tr>\n",
 92 |        "  </tbody>\n",
 93 |        "</table>\n",
 94 |        "</div>"
 95 |       ],
 96 |       "text/plain": [
 97 |        "   Hours  Scores\n",
 98 |        "0    2.5      21\n",
 99 |        "1    5.1      47\n",
100 |        "2    3.2      27\n",
101 |        "3    8.5      75\n",
102 |        "4    3.5      30"
103 |       ]
104 |      },
105 |      "execution_count": 7,
106 |      "metadata": {},
107 |      "output_type": "execute_result"
108 |     }
109 |    ],
110 |    "source": [
111 |     "dataset.head()"
112 |    ]
113 |   },
114 |   {
115 |    "cell_type": "code",
116 |    "execution_count": 8,
117 |    "metadata": {},
118 |    "outputs": [
119 |     {
120 |      "data": {
121 |       "text/html": [
122 |        "<div>\n",
123 |        "<style scoped>\n",
124 |        "    .dataframe tbody tr th:only-of-type {\n",
125 |        "        vertical-align: middle;\n",
126 |        "    }\n",
127 |        "\n",
128 |        "    .dataframe tbody tr th {\n",
129 |        "        vertical-align: top;\n",
130 |        "    }\n",
131 |        "\n",
132 |        "    .dataframe thead th {\n",
133 |        "        text-align: right;\n",
134 |        "    }\n",
135 |        "</style>\n",
136 |        "<table border=\"1\" class=\"dataframe\">\n",
137 |        "  <thead>\n",
138 |        "    <tr style=\"text-align: right;\">\n",
139 |        "      <th></th>\n",
140 |        "      <th>Hours</th>\n",
141 |        "      <th>Scores</th>\n",
142 |        "    </tr>\n",
143 |        "  </thead>\n",
144 |        "  <tbody>\n",
145 |        "    <tr>\n",
146 |        "      <th>count</th>\n",
147 |        "      <td>25.000000</td>\n",
148 |        "      <td>25.000000</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>mean</th>\n",
152 |        "      <td>5.012000</td>\n",
153 |        "      <td>51.480000</td>\n",
154 |        "    </tr>\n",
155 |        "    <tr>\n",
156 |        "      <th>std</th>\n",
157 |        "      <td>2.525094</td>\n",
158 |        "      <td>25.286887</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>min</th>\n",
162 |        "      <td>1.100000</td>\n",
163 |        "      <td>17.000000</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>25%</th>\n",
167 |        "      <td>2.700000</td>\n",
168 |        "      <td>30.000000</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>50%</th>\n",
172 |        "      <td>4.800000</td>\n",
173 |        "      <td>47.000000</td>\n",
174 |        "    </tr>\n",
175 |        "    <tr>\n",
176 |        "      <th>75%</th>\n",
177 |        "      <td>7.400000</td>\n",
178 |        "      <td>75.000000</td>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>max</th>\n",
182 |        "      <td>9.200000</td>\n",
183 |        "      <td>95.000000</td>\n",
184 |        "    </tr>\n",
185 |        "  </tbody>\n",
186 |        "</table>\n",
187 |        "</div>"
188 |       ],
189 |       "text/plain": [
190 |        "           Hours     Scores\n",
191 |        "count  25.000000  25.000000\n",
192 |        "mean    5.012000  51.480000\n",
193 |        "std     2.525094  25.286887\n",
194 |        "min     1.100000  17.000000\n",
195 |        "25%     2.700000  30.000000\n",
196 |        "50%     4.800000  47.000000\n",
197 |        "75%     7.400000  75.000000\n",
198 |        "max     9.200000  95.000000"
199 |       ]
200 |      },
201 |      "execution_count": 8,
202 |      "metadata": {},
203 |      "output_type": "execute_result"
204 |     }
205 |    ],
206 |    "source": [
207 |     "dataset.describe() "
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 9,
213 |    "metadata": {},
214 |    "outputs": [
215 |     {
216 |      "data": {
217 |       "image/png": 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\n",
218 |       "text/plain": [
219 |        "<Figure size 432x288 with 1 Axes>"
220 |       ]
221 |      },
222 |      "metadata": {
223 |       "needs_background": "light"
224 |      },
225 |      "output_type": "display_data"
226 |     }
227 |    ],
228 |    "source": [
229 |     "dataset.plot(x='Hours', y='Scores', style='o')\n",
230 |     "plt.title('Hours vs Percentage')\n",
231 |     "plt.xlabel('Hours Studied')\n",
232 |     "plt.ylabel('Percentage Score')\n",
233 |     "plt.show()"
234 |    ]
235 |   },
236 |   {
237 |    "cell_type": "code",
238 |    "execution_count": 10,
239 |    "metadata": {},
240 |    "outputs": [],
241 |    "source": [
242 |     "X = dataset.iloc[:, :-1].values\n",
243 |     "y = dataset.iloc[:, 1].values"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": 12,
249 |    "metadata": {},
250 |    "outputs": [
251 |     {
252 |      "data": {
253 |       "text/plain": [
254 |        "LinearRegression()"
255 |       ]
256 |      },
257 |      "execution_count": 12,
258 |      "metadata": {},
259 |      "output_type": "execute_result"
260 |     }
261 |    ],
262 |    "source": [
263 |     "# Training the LinearRegression Algorithm\n",
264 |     "from sklearn.model_selection import train_test_split\n",
265 |     "from sklearn.linear_model import LinearRegression\n",
266 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
267 |     "regressor = LinearRegression()\n",
268 |     "regressor.fit(X_train, y_train)"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 15,
274 |    "metadata": {},
275 |    "outputs": [
276 |     {
277 |      "name": "stdout",
278 |      "output_type": "stream",
279 |      "text": [
280 |       "Intercept: -2.018160041434662\n",
281 |       "Slope: -[9.91065648]\n"
282 |      ]
283 |     }
284 |    ],
285 |    "source": [
286 |     "print('Intercept: -{}'.format(regressor.intercept_))\n",
287 |     "print('Slope: -{}'.format(regressor.coef_))"
288 |    ]
289 |   },
290 |   {
291 |    "cell_type": "code",
292 |    "execution_count": 19,
293 |    "metadata": {},
294 |    "outputs": [
295 |     {
296 |      "data": {
297 |       "text/html": [
298 |        "<div>\n",
299 |        "<style scoped>\n",
300 |        "    .dataframe tbody tr th:only-of-type {\n",
301 |        "        vertical-align: middle;\n",
302 |        "    }\n",
303 |        "\n",
304 |        "    .dataframe tbody tr th {\n",
305 |        "        vertical-align: top;\n",
306 |        "    }\n",
307 |        "\n",
308 |        "    .dataframe thead th {\n",
309 |        "        text-align: right;\n",
310 |        "    }\n",
311 |        "</style>\n",
312 |        "<table border=\"1\" class=\"dataframe\">\n",
313 |        "  <thead>\n",
314 |        "    <tr style=\"text-align: right;\">\n",
315 |        "      <th></th>\n",
316 |        "      <th>Actual</th>\n",
317 |        "      <th>Predicted</th>\n",
318 |        "    </tr>\n",
319 |        "  </thead>\n",
320 |        "  <tbody>\n",
321 |        "    <tr>\n",
322 |        "      <th>0</th>\n",
323 |        "      <td>20</td>\n",
324 |        "      <td>16.884145</td>\n",
325 |        "    </tr>\n",
326 |        "    <tr>\n",
327 |        "      <th>1</th>\n",
328 |        "      <td>27</td>\n",
329 |        "      <td>33.732261</td>\n",
330 |        "    </tr>\n",
331 |        "    <tr>\n",
332 |        "      <th>2</th>\n",
333 |        "      <td>69</td>\n",
334 |        "      <td>75.357018</td>\n",
335 |        "    </tr>\n",
336 |        "    <tr>\n",
337 |        "      <th>3</th>\n",
338 |        "      <td>30</td>\n",
339 |        "      <td>26.794801</td>\n",
340 |        "    </tr>\n",
341 |        "    <tr>\n",
342 |        "      <th>4</th>\n",
343 |        "      <td>62</td>\n",
344 |        "      <td>60.491033</td>\n",
345 |        "    </tr>\n",
346 |        "  </tbody>\n",
347 |        "</table>\n",
348 |        "</div>"
349 |       ],
350 |       "text/plain": [
351 |        "   Actual  Predicted\n",
352 |        "0      20  16.884145\n",
353 |        "1      27  33.732261\n",
354 |        "2      69  75.357018\n",
355 |        "3      30  26.794801\n",
356 |        "4      62  60.491033"
357 |       ]
358 |      },
359 |      "execution_count": 19,
360 |      "metadata": {},
361 |      "output_type": "execute_result"
362 |     }
363 |    ],
364 |    "source": [
365 |     "# Making Predictions\n",
366 |     "y_pred = regressor.predict(X_test)\n",
367 |     "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
368 |     "df"
369 |    ]
370 |   },
371 |   {
372 |    "cell_type": "code",
373 |    "execution_count": 20,
374 |    "metadata": {},
375 |    "outputs": [
376 |     {
377 |      "name": "stdout",
378 |      "output_type": "stream",
379 |      "text": [
380 |       "Mean Absolute Error: 4.183859899002982\n",
381 |       "Mean Squared Error: 21.598769307217456\n",
382 |       "Root Mean Squared Error: 4.647447612100373\n"
383 |      ]
384 |     }
385 |    ],
386 |    "source": [
387 |     "# Evaluating the Errors\n",
388 |     "from sklearn import metrics\n",
389 |     "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
390 |     "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
391 |     "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
392 |    ]
393 |   },
394 |   {
395 |    "cell_type": "code",
396 |    "execution_count": null,
397 |    "metadata": {},
398 |    "outputs": [],
399 |    "source": []
400 |   },
401 |   {
402 |    "cell_type": "code",
403 |    "execution_count": null,
404 |    "metadata": {},
405 |    "outputs": [],
406 |    "source": []
407 |   }
408 |  ],
409 |  "metadata": {
410 |   "interpreter": {
411 |    "hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119"
412 |   },
413 |   "kernelspec": {
414 |    "display_name": "Python 3 (ipykernel)",
415 |    "language": "python",
416 |    "name": "python3"
417 |   },
418 |   "language_info": {
419 |    "codemirror_mode": {
420 |     "name": "ipython",
421 |     "version": 3
422 |    },
423 |    "file_extension": ".py",
424 |    "mimetype": "text/x-python",
425 |    "name": "python",
426 |    "nbconvert_exporter": "python",
427 |    "pygments_lexer": "ipython3",
428 |    "version": "3.9.7"
429 |   }
430 |  },
431 |  "nbformat": 4,
432 |  "nbformat_minor": 2
433 | }
434 | 


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