├── README.md
├── SALES PREDICTION USING PYTHON.csv
└── SALES PREDICTION USING PYTHON TASK 4.ipynb


/README.md:
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1 | # SALES-PREDICTION-USING-PYTHON-CODSOFT_INTERN
2 | They utilize machine learning techniques in Python to analyze and interpret data, allowing them to make informed decisions regarding advertising costs. By leveraging these predictions, businesses can optimize their advertising strategies and maximize sales potential.
3 | 


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/SALES PREDICTION USING PYTHON.csv:
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  1 | TV,Radio,Newspaper,Sales
  2 | 230.1,37.8,69.2,22.1
  3 | 44.5,39.3,45.1,10.4
  4 | 17.2,45.9,69.3,12
  5 | 151.5,41.3,58.5,16.5
  6 | 180.8,10.8,58.4,17.9
  7 | 8.7,48.9,75,7.2
  8 | 57.5,32.8,23.5,11.8
  9 | 120.2,19.6,11.6,13.2
 10 | 8.6,2.1,1,4.8
 11 | 199.8,2.6,21.2,15.6
 12 | 66.1,5.8,24.2,12.6
 13 | 214.7,24,4,17.4
 14 | 23.8,35.1,65.9,9.2
 15 | 97.5,7.6,7.2,13.7
 16 | 204.1,32.9,46,19
 17 | 195.4,47.7,52.9,22.4
 18 | 67.8,36.6,114,12.5
 19 | 281.4,39.6,55.8,24.4
 20 | 69.2,20.5,18.3,11.3
 21 | 147.3,23.9,19.1,14.6
 22 | 218.4,27.7,53.4,18
 23 | 237.4,5.1,23.5,17.5
 24 | 13.2,15.9,49.6,5.6
 25 | 228.3,16.9,26.2,20.5
 26 | 62.3,12.6,18.3,9.7
 27 | 262.9,3.5,19.5,17
 28 | 142.9,29.3,12.6,15
 29 | 240.1,16.7,22.9,20.9
 30 | 248.8,27.1,22.9,18.9
 31 | 70.6,16,40.8,10.5
 32 | 292.9,28.3,43.2,21.4
 33 | 112.9,17.4,38.6,11.9
 34 | 97.2,1.5,30,13.2
 35 | 265.6,20,0.3,17.4
 36 | 95.7,1.4,7.4,11.9
 37 | 290.7,4.1,8.5,17.8
 38 | 266.9,43.8,5,25.4
 39 | 74.7,49.4,45.7,14.7
 40 | 43.1,26.7,35.1,10.1
 41 | 228,37.7,32,21.5
 42 | 202.5,22.3,31.6,16.6
 43 | 177,33.4,38.7,17.1
 44 | 293.6,27.7,1.8,20.7
 45 | 206.9,8.4,26.4,17.9
 46 | 25.1,25.7,43.3,8.5
 47 | 175.1,22.5,31.5,16.1
 48 | 89.7,9.9,35.7,10.6
 49 | 239.9,41.5,18.5,23.2
 50 | 227.2,15.8,49.9,19.8
 51 | 66.9,11.7,36.8,9.7
 52 | 199.8,3.1,34.6,16.4
 53 | 100.4,9.6,3.6,10.7
 54 | 216.4,41.7,39.6,22.6
 55 | 182.6,46.2,58.7,21.2
 56 | 262.7,28.8,15.9,20.2
 57 | 198.9,49.4,60,23.7
 58 | 7.3,28.1,41.4,5.5
 59 | 136.2,19.2,16.6,13.2
 60 | 210.8,49.6,37.7,23.8
 61 | 210.7,29.5,9.3,18.4
 62 | 53.5,2,21.4,8.1
 63 | 261.3,42.7,54.7,24.2
 64 | 239.3,15.5,27.3,20.7
 65 | 102.7,29.6,8.4,14
 66 | 131.1,42.8,28.9,16
 67 | 69,9.3,0.9,11.3
 68 | 31.5,24.6,2.2,11
 69 | 139.3,14.5,10.2,13.4
 70 | 237.4,27.5,11,18.9
 71 | 216.8,43.9,27.2,22.3
 72 | 199.1,30.6,38.7,18.3
 73 | 109.8,14.3,31.7,12.4
 74 | 26.8,33,19.3,8.8
 75 | 129.4,5.7,31.3,11
 76 | 213.4,24.6,13.1,17
 77 | 16.9,43.7,89.4,8.7
 78 | 27.5,1.6,20.7,6.9
 79 | 120.5,28.5,14.2,14.2
 80 | 5.4,29.9,9.4,5.3
 81 | 116,7.7,23.1,11
 82 | 76.4,26.7,22.3,11.8
 83 | 239.8,4.1,36.9,17.3
 84 | 75.3,20.3,32.5,11.3
 85 | 68.4,44.5,35.6,13.6
 86 | 213.5,43,33.8,21.7
 87 | 193.2,18.4,65.7,20.2
 88 | 76.3,27.5,16,12
 89 | 110.7,40.6,63.2,16
 90 | 88.3,25.5,73.4,12.9
 91 | 109.8,47.8,51.4,16.7
 92 | 134.3,4.9,9.3,14
 93 | 28.6,1.5,33,7.3
 94 | 217.7,33.5,59,19.4
 95 | 250.9,36.5,72.3,22.2
 96 | 107.4,14,10.9,11.5
 97 | 163.3,31.6,52.9,16.9
 98 | 197.6,3.5,5.9,16.7
 99 | 184.9,21,22,20.5
100 | 289.7,42.3,51.2,25.4
101 | 135.2,41.7,45.9,17.2
102 | 222.4,4.3,49.8,16.7
103 | 296.4,36.3,100.9,23.8
104 | 280.2,10.1,21.4,19.8
105 | 187.9,17.2,17.9,19.7
106 | 238.2,34.3,5.3,20.7
107 | 137.9,46.4,59,15
108 | 25,11,29.7,7.2
109 | 90.4,0.3,23.2,12
110 | 13.1,0.4,25.6,5.3
111 | 255.4,26.9,5.5,19.8
112 | 225.8,8.2,56.5,18.4
113 | 241.7,38,23.2,21.8
114 | 175.7,15.4,2.4,17.1
115 | 209.6,20.6,10.7,20.9
116 | 78.2,46.8,34.5,14.6
117 | 75.1,35,52.7,12.6
118 | 139.2,14.3,25.6,12.2
119 | 76.4,0.8,14.8,9.4
120 | 125.7,36.9,79.2,15.9
121 | 19.4,16,22.3,6.6
122 | 141.3,26.8,46.2,15.5
123 | 18.8,21.7,50.4,7
124 | 224,2.4,15.6,16.6
125 | 123.1,34.6,12.4,15.2
126 | 229.5,32.3,74.2,19.7
127 | 87.2,11.8,25.9,10.6
128 | 7.8,38.9,50.6,6.6
129 | 80.2,0,9.2,11.9
130 | 220.3,49,3.2,24.7
131 | 59.6,12,43.1,9.7
132 | 0.7,39.6,8.7,1.6
133 | 265.2,2.9,43,17.7
134 | 8.4,27.2,2.1,5.7
135 | 219.8,33.5,45.1,19.6
136 | 36.9,38.6,65.6,10.8
137 | 48.3,47,8.5,11.6
138 | 25.6,39,9.3,9.5
139 | 273.7,28.9,59.7,20.8
140 | 43,25.9,20.5,9.6
141 | 184.9,43.9,1.7,20.7
142 | 73.4,17,12.9,10.9
143 | 193.7,35.4,75.6,19.2
144 | 220.5,33.2,37.9,20.1
145 | 104.6,5.7,34.4,10.4
146 | 96.2,14.8,38.9,12.3
147 | 140.3,1.9,9,10.3
148 | 240.1,7.3,8.7,18.2
149 | 243.2,49,44.3,25.4
150 | 38,40.3,11.9,10.9
151 | 44.7,25.8,20.6,10.1
152 | 280.7,13.9,37,16.1
153 | 121,8.4,48.7,11.6
154 | 197.6,23.3,14.2,16.6
155 | 171.3,39.7,37.7,16
156 | 187.8,21.1,9.5,20.6
157 | 4.1,11.6,5.7,3.2
158 | 93.9,43.5,50.5,15.3
159 | 149.8,1.3,24.3,10.1
160 | 11.7,36.9,45.2,7.3
161 | 131.7,18.4,34.6,12.9
162 | 172.5,18.1,30.7,16.4
163 | 85.7,35.8,49.3,13.3
164 | 188.4,18.1,25.6,19.9
165 | 163.5,36.8,7.4,18
166 | 117.2,14.7,5.4,11.9
167 | 234.5,3.4,84.8,16.9
168 | 17.9,37.6,21.6,8
169 | 206.8,5.2,19.4,17.2
170 | 215.4,23.6,57.6,17.1
171 | 284.3,10.6,6.4,20
172 | 50,11.6,18.4,8.4
173 | 164.5,20.9,47.4,17.5
174 | 19.6,20.1,17,7.6
175 | 168.4,7.1,12.8,16.7
176 | 222.4,3.4,13.1,16.5
177 | 276.9,48.9,41.8,27
178 | 248.4,30.2,20.3,20.2
179 | 170.2,7.8,35.2,16.7
180 | 276.7,2.3,23.7,16.8
181 | 165.6,10,17.6,17.6
182 | 156.6,2.6,8.3,15.5
183 | 218.5,5.4,27.4,17.2
184 | 56.2,5.7,29.7,8.7
185 | 287.6,43,71.8,26.2
186 | 253.8,21.3,30,17.6
187 | 205,45.1,19.6,22.6
188 | 139.5,2.1,26.6,10.3
189 | 191.1,28.7,18.2,17.3
190 | 286,13.9,3.7,20.9
191 | 18.7,12.1,23.4,6.7
192 | 39.5,41.1,5.8,10.8
193 | 75.5,10.8,6,11.9
194 | 17.2,4.1,31.6,5.9
195 | 166.8,42,3.6,19.6
196 | 149.7,35.6,6,17.3
197 | 38.2,3.7,13.8,7.6
198 | 94.2,4.9,8.1,14
199 | 177,9.3,6.4,14.8
200 | 283.6,42,66.2,25.5
201 | 232.1,8.6,8.7,18.4
202 | 


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/SALES PREDICTION USING PYTHON TASK 4.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "raw",
  5 |    "id": "cbf06209-bf31-4a51-810e-632bc3490c11",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "\n",
 10 |     "▶TASK-4 : SALES PREDICTION USING PYTHON"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "raw",
 15 |    "id": "4fb8e9f3-d604-44e0-964a-09fc55f46b1e",
 16 |    "metadata": {},
 17 |    "source": [
 18 |     "IMPORTING IMPORTANT LIBRARIES"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 1,
 24 |    "id": "b7b4cd25-d06a-4c01-be56-911eaedc641f",
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "import numpy as np\n",
 29 |     "import pandas as pd\n",
 30 |     "import matplotlib.pyplot as plt\n",
 31 |     "import seaborn as sns\n",
 32 |     "     "
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "raw",
 37 |    "id": "fadf3c91-d5b4-42f4-9670-392d2748d822",
 38 |    "metadata": {},
 39 |    "source": [
 40 |     "IMPORTING DATASET"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 2,
 46 |    "id": "8ca10932-1f68-4ef0-b4a7-592eea3b28db",
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "df = pd.read_csv(\"advertising.csv\")"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 3,
 56 |    "id": "82e61595-62cf-4506-b029-56ac9f433938",
 57 |    "metadata": {},
 58 |    "outputs": [
 59 |     {
 60 |      "data": {
 61 |       "text/html": [
 62 |        "<div>\n",
 63 |        "<style scoped>\n",
 64 |        "    .dataframe tbody tr th:only-of-type {\n",
 65 |        "        vertical-align: middle;\n",
 66 |        "    }\n",
 67 |        "\n",
 68 |        "    .dataframe tbody tr th {\n",
 69 |        "        vertical-align: top;\n",
 70 |        "    }\n",
 71 |        "\n",
 72 |        "    .dataframe thead th {\n",
 73 |        "        text-align: right;\n",
 74 |        "    }\n",
 75 |        "</style>\n",
 76 |        "<table border=\"1\" class=\"dataframe\">\n",
 77 |        "  <thead>\n",
 78 |        "    <tr style=\"text-align: right;\">\n",
 79 |        "      <th></th>\n",
 80 |        "      <th>TV</th>\n",
 81 |        "      <th>Radio</th>\n",
 82 |        "      <th>Newspaper</th>\n",
 83 |        "      <th>Sales</th>\n",
 84 |        "    </tr>\n",
 85 |        "  </thead>\n",
 86 |        "  <tbody>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>0</th>\n",
 89 |        "      <td>230.1</td>\n",
 90 |        "      <td>37.8</td>\n",
 91 |        "      <td>69.2</td>\n",
 92 |        "      <td>22.1</td>\n",
 93 |        "    </tr>\n",
 94 |        "    <tr>\n",
 95 |        "      <th>1</th>\n",
 96 |        "      <td>44.5</td>\n",
 97 |        "      <td>39.3</td>\n",
 98 |        "      <td>45.1</td>\n",
 99 |        "      <td>10.4</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>2</th>\n",
103 |        "      <td>17.2</td>\n",
104 |        "      <td>45.9</td>\n",
105 |        "      <td>69.3</td>\n",
106 |        "      <td>12.0</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>3</th>\n",
110 |        "      <td>151.5</td>\n",
111 |        "      <td>41.3</td>\n",
112 |        "      <td>58.5</td>\n",
113 |        "      <td>16.5</td>\n",
114 |        "    </tr>\n",
115 |        "    <tr>\n",
116 |        "      <th>4</th>\n",
117 |        "      <td>180.8</td>\n",
118 |        "      <td>10.8</td>\n",
119 |        "      <td>58.4</td>\n",
120 |        "      <td>17.9</td>\n",
121 |        "    </tr>\n",
122 |        "  </tbody>\n",
123 |        "</table>\n",
124 |        "</div>"
125 |       ],
126 |       "text/plain": [
127 |        "      TV  Radio  Newspaper  Sales\n",
128 |        "0  230.1   37.8       69.2   22.1\n",
129 |        "1   44.5   39.3       45.1   10.4\n",
130 |        "2   17.2   45.9       69.3   12.0\n",
131 |        "3  151.5   41.3       58.5   16.5\n",
132 |        "4  180.8   10.8       58.4   17.9"
133 |       ]
134 |      },
135 |      "execution_count": 3,
136 |      "metadata": {},
137 |      "output_type": "execute_result"
138 |     }
139 |    ],
140 |    "source": [
141 |     "df.head()"
142 |    ]
143 |   },
144 |   {
145 |    "cell_type": "code",
146 |    "execution_count": 4,
147 |    "id": "3ee82c48-4e56-49fe-816c-bbde1c537ed0",
148 |    "metadata": {},
149 |    "outputs": [
150 |     {
151 |      "data": {
152 |       "text/html": [
153 |        "<div>\n",
154 |        "<style scoped>\n",
155 |        "    .dataframe tbody tr th:only-of-type {\n",
156 |        "        vertical-align: middle;\n",
157 |        "    }\n",
158 |        "\n",
159 |        "    .dataframe tbody tr th {\n",
160 |        "        vertical-align: top;\n",
161 |        "    }\n",
162 |        "\n",
163 |        "    .dataframe thead th {\n",
164 |        "        text-align: right;\n",
165 |        "    }\n",
166 |        "</style>\n",
167 |        "<table border=\"1\" class=\"dataframe\">\n",
168 |        "  <thead>\n",
169 |        "    <tr style=\"text-align: right;\">\n",
170 |        "      <th></th>\n",
171 |        "      <th>TV</th>\n",
172 |        "      <th>Radio</th>\n",
173 |        "      <th>Newspaper</th>\n",
174 |        "      <th>Sales</th>\n",
175 |        "    </tr>\n",
176 |        "  </thead>\n",
177 |        "  <tbody>\n",
178 |        "    <tr>\n",
179 |        "      <th>0</th>\n",
180 |        "      <td>230.1</td>\n",
181 |        "      <td>37.8</td>\n",
182 |        "      <td>69.2</td>\n",
183 |        "      <td>22.1</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>1</th>\n",
187 |        "      <td>44.5</td>\n",
188 |        "      <td>39.3</td>\n",
189 |        "      <td>45.1</td>\n",
190 |        "      <td>10.4</td>\n",
191 |        "    </tr>\n",
192 |        "    <tr>\n",
193 |        "      <th>2</th>\n",
194 |        "      <td>17.2</td>\n",
195 |        "      <td>45.9</td>\n",
196 |        "      <td>69.3</td>\n",
197 |        "      <td>12.0</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>3</th>\n",
201 |        "      <td>151.5</td>\n",
202 |        "      <td>41.3</td>\n",
203 |        "      <td>58.5</td>\n",
204 |        "      <td>16.5</td>\n",
205 |        "    </tr>\n",
206 |        "    <tr>\n",
207 |        "      <th>4</th>\n",
208 |        "      <td>180.8</td>\n",
209 |        "      <td>10.8</td>\n",
210 |        "      <td>58.4</td>\n",
211 |        "      <td>17.9</td>\n",
212 |        "    </tr>\n",
213 |        "    <tr>\n",
214 |        "      <th>5</th>\n",
215 |        "      <td>8.7</td>\n",
216 |        "      <td>48.9</td>\n",
217 |        "      <td>75.0</td>\n",
218 |        "      <td>7.2</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <th>6</th>\n",
222 |        "      <td>57.5</td>\n",
223 |        "      <td>32.8</td>\n",
224 |        "      <td>23.5</td>\n",
225 |        "      <td>11.8</td>\n",
226 |        "    </tr>\n",
227 |        "    <tr>\n",
228 |        "      <th>7</th>\n",
229 |        "      <td>120.2</td>\n",
230 |        "      <td>19.6</td>\n",
231 |        "      <td>11.6</td>\n",
232 |        "      <td>13.2</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>8</th>\n",
236 |        "      <td>8.6</td>\n",
237 |        "      <td>2.1</td>\n",
238 |        "      <td>1.0</td>\n",
239 |        "      <td>4.8</td>\n",
240 |        "    </tr>\n",
241 |        "    <tr>\n",
242 |        "      <th>9</th>\n",
243 |        "      <td>199.8</td>\n",
244 |        "      <td>2.6</td>\n",
245 |        "      <td>21.2</td>\n",
246 |        "      <td>15.6</td>\n",
247 |        "    </tr>\n",
248 |        "  </tbody>\n",
249 |        "</table>\n",
250 |        "</div>"
251 |       ],
252 |       "text/plain": [
253 |        "      TV  Radio  Newspaper  Sales\n",
254 |        "0  230.1   37.8       69.2   22.1\n",
255 |        "1   44.5   39.3       45.1   10.4\n",
256 |        "2   17.2   45.9       69.3   12.0\n",
257 |        "3  151.5   41.3       58.5   16.5\n",
258 |        "4  180.8   10.8       58.4   17.9\n",
259 |        "5    8.7   48.9       75.0    7.2\n",
260 |        "6   57.5   32.8       23.5   11.8\n",
261 |        "7  120.2   19.6       11.6   13.2\n",
262 |        "8    8.6    2.1        1.0    4.8\n",
263 |        "9  199.8    2.6       21.2   15.6"
264 |       ]
265 |      },
266 |      "execution_count": 4,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "df.head(10)"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 5,
278 |    "id": "daf8316b-04ca-4763-aa98-2ba72454b279",
279 |    "metadata": {},
280 |    "outputs": [
281 |     {
282 |      "data": {
283 |       "text/plain": [
284 |        "(200, 4)"
285 |       ]
286 |      },
287 |      "execution_count": 5,
288 |      "metadata": {},
289 |      "output_type": "execute_result"
290 |     }
291 |    ],
292 |    "source": [
293 |     "df.shape"
294 |    ]
295 |   },
296 |   {
297 |    "cell_type": "code",
298 |    "execution_count": 6,
299 |    "id": "70e7e3de-2564-4fc8-a359-6ac3dc9b5000",
300 |    "metadata": {},
301 |    "outputs": [
302 |     {
303 |      "data": {
304 |       "text/html": [
305 |        "<div>\n",
306 |        "<style scoped>\n",
307 |        "    .dataframe tbody tr th:only-of-type {\n",
308 |        "        vertical-align: middle;\n",
309 |        "    }\n",
310 |        "\n",
311 |        "    .dataframe tbody tr th {\n",
312 |        "        vertical-align: top;\n",
313 |        "    }\n",
314 |        "\n",
315 |        "    .dataframe thead th {\n",
316 |        "        text-align: right;\n",
317 |        "    }\n",
318 |        "</style>\n",
319 |        "<table border=\"1\" class=\"dataframe\">\n",
320 |        "  <thead>\n",
321 |        "    <tr style=\"text-align: right;\">\n",
322 |        "      <th></th>\n",
323 |        "      <th>TV</th>\n",
324 |        "      <th>Radio</th>\n",
325 |        "      <th>Newspaper</th>\n",
326 |        "      <th>Sales</th>\n",
327 |        "    </tr>\n",
328 |        "  </thead>\n",
329 |        "  <tbody>\n",
330 |        "    <tr>\n",
331 |        "      <th>count</th>\n",
332 |        "      <td>200.000000</td>\n",
333 |        "      <td>200.000000</td>\n",
334 |        "      <td>200.000000</td>\n",
335 |        "      <td>200.000000</td>\n",
336 |        "    </tr>\n",
337 |        "    <tr>\n",
338 |        "      <th>mean</th>\n",
339 |        "      <td>147.042500</td>\n",
340 |        "      <td>23.264000</td>\n",
341 |        "      <td>30.554000</td>\n",
342 |        "      <td>15.130500</td>\n",
343 |        "    </tr>\n",
344 |        "    <tr>\n",
345 |        "      <th>std</th>\n",
346 |        "      <td>85.854236</td>\n",
347 |        "      <td>14.846809</td>\n",
348 |        "      <td>21.778621</td>\n",
349 |        "      <td>5.283892</td>\n",
350 |        "    </tr>\n",
351 |        "    <tr>\n",
352 |        "      <th>min</th>\n",
353 |        "      <td>0.700000</td>\n",
354 |        "      <td>0.000000</td>\n",
355 |        "      <td>0.300000</td>\n",
356 |        "      <td>1.600000</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>25%</th>\n",
360 |        "      <td>74.375000</td>\n",
361 |        "      <td>9.975000</td>\n",
362 |        "      <td>12.750000</td>\n",
363 |        "      <td>11.000000</td>\n",
364 |        "    </tr>\n",
365 |        "    <tr>\n",
366 |        "      <th>50%</th>\n",
367 |        "      <td>149.750000</td>\n",
368 |        "      <td>22.900000</td>\n",
369 |        "      <td>25.750000</td>\n",
370 |        "      <td>16.000000</td>\n",
371 |        "    </tr>\n",
372 |        "    <tr>\n",
373 |        "      <th>75%</th>\n",
374 |        "      <td>218.825000</td>\n",
375 |        "      <td>36.525000</td>\n",
376 |        "      <td>45.100000</td>\n",
377 |        "      <td>19.050000</td>\n",
378 |        "    </tr>\n",
379 |        "    <tr>\n",
380 |        "      <th>max</th>\n",
381 |        "      <td>296.400000</td>\n",
382 |        "      <td>49.600000</td>\n",
383 |        "      <td>114.000000</td>\n",
384 |        "      <td>27.000000</td>\n",
385 |        "    </tr>\n",
386 |        "  </tbody>\n",
387 |        "</table>\n",
388 |        "</div>"
389 |       ],
390 |       "text/plain": [
391 |        "               TV       Radio   Newspaper       Sales\n",
392 |        "count  200.000000  200.000000  200.000000  200.000000\n",
393 |        "mean   147.042500   23.264000   30.554000   15.130500\n",
394 |        "std     85.854236   14.846809   21.778621    5.283892\n",
395 |        "min      0.700000    0.000000    0.300000    1.600000\n",
396 |        "25%     74.375000    9.975000   12.750000   11.000000\n",
397 |        "50%    149.750000   22.900000   25.750000   16.000000\n",
398 |        "75%    218.825000   36.525000   45.100000   19.050000\n",
399 |        "max    296.400000   49.600000  114.000000   27.000000"
400 |       ]
401 |      },
402 |      "execution_count": 6,
403 |      "metadata": {},
404 |      "output_type": "execute_result"
405 |     }
406 |    ],
407 |    "source": [
408 |     "df.describe()"
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "raw",
413 |    "id": "99db4bae-d253-461d-a480-18c1dc9958d4",
414 |    "metadata": {},
415 |    "source": [
416 |     "Basic Observation"
417 |    ]
418 |   },
419 |   {
420 |    "cell_type": "raw",
421 |    "id": "5f5def96-4141-4906-a11e-2af9000165dd",
422 |    "metadata": {},
423 |    "source": [
424 |     "\n",
425 |     "\n",
426 |     "    Avg expense spend is highest on TV\n",
427 |     "\n",
428 |     "    Avg expense spend is lowest on Radio\n",
429 |     "\n",
430 |     "    Max sale is 27 and min is 1.6\n"
431 |    ]
432 |   },
433 |   {
434 |    "cell_type": "code",
435 |    "execution_count": 7,
436 |    "id": "116fd4cb-d9ee-4eb7-bf24-b7f3ae02d1aa",
437 |    "metadata": {},
438 |    "outputs": [
439 |     {
440 |      "data": {
441 |       "image/png": 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",
442 |       "text/plain": [
443 |        "<Figure size 750x250 with 3 Axes>"
444 |       ]
445 |      },
446 |      "metadata": {},
447 |      "output_type": "display_data"
448 |     }
449 |    ],
450 |    "source": [
451 |     "sns.pairplot(df, x_vars=['TV', 'Radio','Newspaper'], y_vars='Sales', kind='scatter')\n",
452 |     "plt.show()\n"
453 |    ]
454 |   },
455 |   {
456 |    "cell_type": "code",
457 |    "execution_count": 8,
458 |    "id": "d6cb613a-44b9-40be-9ef1-d385582c7002",
459 |    "metadata": {},
460 |    "outputs": [
461 |     {
462 |      "data": {
463 |       "text/plain": [
464 |        "<AxesSubplot: ylabel='Frequency'>"
465 |       ]
466 |      },
467 |      "execution_count": 8,
468 |      "metadata": {},
469 |      "output_type": "execute_result"
470 |     },
471 |     {
472 |      "data": {
473 |       "image/png": 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",
474 |       "text/plain": [
475 |        "<Figure size 640x480 with 1 Axes>"
476 |       ]
477 |      },
478 |      "metadata": {},
479 |      "output_type": "display_data"
480 |     }
481 |    ],
482 |    "source": [
483 |     "df['TV'].plot.hist(bins=10)\n"
484 |    ]
485 |   },
486 |   {
487 |    "cell_type": "code",
488 |    "execution_count": 9,
489 |    "id": "e3567bdf-2a9b-4e6e-aa98-503d27a48c8d",
490 |    "metadata": {},
491 |    "outputs": [
492 |     {
493 |      "data": {
494 |       "text/plain": [
495 |        "<AxesSubplot: ylabel='Frequency'>"
496 |       ]
497 |      },
498 |      "execution_count": 9,
499 |      "metadata": {},
500 |      "output_type": "execute_result"
501 |     },
502 |     {
503 |      "data": {
504 |       "image/png": 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",
505 |       "text/plain": [
506 |        "<Figure size 640x480 with 1 Axes>"
507 |       ]
508 |      },
509 |      "metadata": {},
510 |      "output_type": "display_data"
511 |     }
512 |    ],
513 |    "source": [
514 |     "df['Radio'].plot.hist(bins=10, color=\"green\", xlabel=\"Radio\")"
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 10,
520 |    "id": "da1af576-7cbc-4724-8a0a-0bf6a118fabd",
521 |    "metadata": {},
522 |    "outputs": [
523 |     {
524 |      "data": {
525 |       "text/plain": [
526 |        "<AxesSubplot: ylabel='Frequency'>"
527 |       ]
528 |      },
529 |      "execution_count": 10,
530 |      "metadata": {},
531 |      "output_type": "execute_result"
532 |     },
533 |     {
534 |      "data": {
535 |       "image/png": 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",
536 |       "text/plain": [
537 |        "<Figure size 640x480 with 1 Axes>"
538 |       ]
539 |      },
540 |      "metadata": {},
541 |      "output_type": "display_data"
542 |     }
543 |    ],
544 |    "source": [
545 |     "df['Newspaper'].plot.hist(bins=10,color=\"purple\", xlabel=\"newspaper\")"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "code",
550 |    "execution_count": 11,
551 |    "id": "d553c2f0-a660-488c-90d6-6742ce92ceaa",
552 |    "metadata": {},
553 |    "outputs": [
554 |     {
555 |      "data": {
556 |       "image/png": 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",
557 |       "text/plain": [
558 |        "<Figure size 640x480 with 2 Axes>"
559 |       ]
560 |      },
561 |      "metadata": {},
562 |      "output_type": "display_data"
563 |     }
564 |    ],
565 |    "source": [
566 |     "sns.heatmap(df.corr(),annot = True)\n",
567 |     "plt.show()"
568 |    ]
569 |   },
570 |   {
571 |    "cell_type": "code",
572 |    "execution_count": 12,
573 |    "id": "0353e6bb-5194-4bbb-84da-a82cefb63ec5",
574 |    "metadata": {},
575 |    "outputs": [],
576 |    "source": [
577 |     "\n",
578 |     "\n",
579 |     "from sklearn.model_selection import train_test_split\n",
580 |     "X_train, X_test, y_train, y_test = train_test_split(df[['TV']], df[['Sales']], test_size = 0.3,random_state=0)\n",
581 |     "     \n"
582 |    ]
583 |   },
584 |   {
585 |    "cell_type": "code",
586 |    "execution_count": 13,
587 |    "id": "3ae4a73e-632a-41ce-85de-b3a4b8469c23",
588 |    "metadata": {},
589 |    "outputs": [
590 |     {
591 |      "name": "stdout",
592 |      "output_type": "stream",
593 |      "text": [
594 |       "        TV\n",
595 |       "131  265.2\n",
596 |       "96   197.6\n",
597 |       "181  218.5\n",
598 |       "19   147.3\n",
599 |       "153  171.3\n",
600 |       "..     ...\n",
601 |       "67   139.3\n",
602 |       "192   17.2\n",
603 |       "117   76.4\n",
604 |       "47   239.9\n",
605 |       "172   19.6\n",
606 |       "\n",
607 |       "[140 rows x 1 columns]\n"
608 |      ]
609 |     }
610 |    ],
611 |    "source": [
612 |     "\n",
613 |     "\n",
614 |     "print(X_train)\n",
615 |     "     \n"
616 |    ]
617 |   },
618 |   {
619 |    "cell_type": "code",
620 |    "execution_count": 14,
621 |    "id": "618d505d-70ca-4c17-a5ab-f5142d9c9202",
622 |    "metadata": {},
623 |    "outputs": [
624 |     {
625 |      "name": "stdout",
626 |      "output_type": "stream",
627 |      "text": [
628 |       "     Sales\n",
629 |       "131   17.7\n",
630 |       "96    16.7\n",
631 |       "181   17.2\n",
632 |       "19    14.6\n",
633 |       "153   16.0\n",
634 |       "..     ...\n",
635 |       "67    13.4\n",
636 |       "192    5.9\n",
637 |       "117    9.4\n",
638 |       "47    23.2\n",
639 |       "172    7.6\n",
640 |       "\n",
641 |       "[140 rows x 1 columns]\n"
642 |      ]
643 |     }
644 |    ],
645 |    "source": [
646 |     "print(y_train)"
647 |    ]
648 |   },
649 |   {
650 |    "cell_type": "code",
651 |    "execution_count": 15,
652 |    "id": "f7c91a26-9059-4c10-aa38-251e2ccc7d82",
653 |    "metadata": {},
654 |    "outputs": [
655 |     {
656 |      "name": "stdout",
657 |      "output_type": "stream",
658 |      "text": [
659 |       "        TV\n",
660 |       "18    69.2\n",
661 |       "170   50.0\n",
662 |       "107   90.4\n",
663 |       "98   289.7\n",
664 |       "177  170.2\n",
665 |       "182   56.2\n",
666 |       "5      8.7\n",
667 |       "146  240.1\n",
668 |       "12    23.8\n",
669 |       "152  197.6\n",
670 |       "61   261.3\n",
671 |       "125   87.2\n",
672 |       "180  156.6\n",
673 |       "154  187.8\n",
674 |       "80    76.4\n",
675 |       "7    120.2\n",
676 |       "33   265.6\n",
677 |       "130    0.7\n",
678 |       "37    74.7\n",
679 |       "74   213.4\n",
680 |       "183  287.6\n",
681 |       "145  140.3\n",
682 |       "45   175.1\n",
683 |       "159  131.7\n",
684 |       "60    53.5\n",
685 |       "123  123.1\n",
686 |       "179  165.6\n",
687 |       "185  205.0\n",
688 |       "122  224.0\n",
689 |       "44    25.1\n",
690 |       "16    67.8\n",
691 |       "55   198.9\n",
692 |       "150  280.7\n",
693 |       "111  241.7\n",
694 |       "22    13.2\n",
695 |       "189   18.7\n",
696 |       "129   59.6\n",
697 |       "4    180.8\n",
698 |       "83    68.4\n",
699 |       "106   25.0\n",
700 |       "134   36.9\n",
701 |       "66    31.5\n",
702 |       "26   142.9\n",
703 |       "113  209.6\n",
704 |       "168  215.4\n",
705 |       "63   102.7\n",
706 |       "8      8.6\n",
707 |       "75    16.9\n",
708 |       "118  125.7\n",
709 |       "143  104.6\n",
710 |       "71   109.8\n",
711 |       "124  229.5\n",
712 |       "184  253.8\n",
713 |       "97   184.9\n",
714 |       "149   44.7\n",
715 |       "24    62.3\n",
716 |       "30   292.9\n",
717 |       "160  172.5\n",
718 |       "40   202.5\n",
719 |       "56     7.3\n"
720 |      ]
721 |     }
722 |    ],
723 |    "source": [
724 |     "print(X_test)\n",
725 |     "     "
726 |    ]
727 |   },
728 |   {
729 |    "cell_type": "code",
730 |    "execution_count": 16,
731 |    "id": "59545614-76c3-4e68-8873-7b5d532731b5",
732 |    "metadata": {},
733 |    "outputs": [
734 |     {
735 |      "name": "stdout",
736 |      "output_type": "stream",
737 |      "text": [
738 |       "     Sales\n",
739 |       "18    11.3\n",
740 |       "170    8.4\n",
741 |       "107   12.0\n",
742 |       "98    25.4\n",
743 |       "177   16.7\n",
744 |       "182    8.7\n",
745 |       "5      7.2\n",
746 |       "146   18.2\n",
747 |       "12     9.2\n",
748 |       "152   16.6\n",
749 |       "61    24.2\n",
750 |       "125   10.6\n",
751 |       "180   15.5\n",
752 |       "154   20.6\n",
753 |       "80    11.8\n",
754 |       "7     13.2\n",
755 |       "33    17.4\n",
756 |       "130    1.6\n",
757 |       "37    14.7\n",
758 |       "74    17.0\n",
759 |       "183   26.2\n",
760 |       "145   10.3\n",
761 |       "45    16.1\n",
762 |       "159   12.9\n",
763 |       "60     8.1\n",
764 |       "123   15.2\n",
765 |       "179   17.6\n",
766 |       "185   22.6\n",
767 |       "122   16.6\n",
768 |       "44     8.5\n",
769 |       "16    12.5\n",
770 |       "55    23.7\n",
771 |       "150   16.1\n",
772 |       "111   21.8\n",
773 |       "22     5.6\n",
774 |       "189    6.7\n",
775 |       "129    9.7\n",
776 |       "4     17.9\n",
777 |       "83    13.6\n",
778 |       "106    7.2\n",
779 |       "134   10.8\n",
780 |       "66    11.0\n",
781 |       "26    15.0\n",
782 |       "113   20.9\n",
783 |       "168   17.1\n",
784 |       "63    14.0\n",
785 |       "8      4.8\n",
786 |       "75     8.7\n",
787 |       "118   15.9\n",
788 |       "143   10.4\n",
789 |       "71    12.4\n",
790 |       "124   19.7\n",
791 |       "184   17.6\n",
792 |       "97    20.5\n",
793 |       "149   10.1\n",
794 |       "24     9.7\n",
795 |       "30    21.4\n",
796 |       "160   16.4\n",
797 |       "40    16.6\n",
798 |       "56     5.5\n"
799 |      ]
800 |     }
801 |    ],
802 |    "source": [
803 |     "print(y_test)\n",
804 |     "     "
805 |    ]
806 |   },
807 |   {
808 |    "cell_type": "code",
809 |    "execution_count": 17,
810 |    "id": "c1160828-7b9b-4524-8dab-ffac8ea680c2",
811 |    "metadata": {},
812 |    "outputs": [
813 |     {
814 |      "data": {
815 |       "text/html": [
816 |        "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
817 |       ],
818 |       "text/plain": [
819 |        "LinearRegression()"
820 |       ]
821 |      },
822 |      "execution_count": 17,
823 |      "metadata": {},
824 |      "output_type": "execute_result"
825 |     }
826 |    ],
827 |    "source": [
828 |     "from sklearn.linear_model import LinearRegression\n",
829 |     "model = LinearRegression()\n",
830 |     "model.fit(X_train,y_train)\n",
831 |     "     "
832 |    ]
833 |   },
834 |   {
835 |    "cell_type": "code",
836 |    "execution_count": 18,
837 |    "id": "d361e3d3-1027-4a94-ab6f-3f17d83b8887",
838 |    "metadata": {},
839 |    "outputs": [
840 |     {
841 |      "name": "stdout",
842 |      "output_type": "stream",
843 |      "text": [
844 |       "[[10.93127621]\n",
845 |       " [ 9.88042193]\n",
846 |       " [12.09159447]\n",
847 |       " [22.99968079]\n",
848 |       " [16.45920756]\n",
849 |       " [10.21976029]\n",
850 |       " [ 7.6199906 ]\n",
851 |       " [20.28497391]\n",
852 |       " [ 8.4464437 ]\n",
853 |       " [17.95886418]\n",
854 |       " [21.44529217]\n",
855 |       " [11.91645209]\n",
856 |       " [15.71485245]\n",
857 |       " [17.42249065]\n",
858 |       " [11.32534656]\n",
859 |       " [13.72260788]\n",
860 |       " [21.68063975]\n",
861 |       " [ 7.18213465]\n",
862 |       " [11.23230217]\n",
863 |       " [18.82362968]\n",
864 |       " [22.88474361]\n",
865 |       " [14.82272095]\n",
866 |       " [16.72739433]\n",
867 |       " [14.35202581]\n",
868 |       " [10.07198391]\n",
869 |       " [13.88133066]\n",
870 |       " [16.20744039]\n",
871 |       " [18.36388094]\n",
872 |       " [19.40378881]\n",
873 |       " [ 8.51759529]\n",
874 |       " [10.85465142]\n",
875 |       " [18.03001578]\n",
876 |       " [22.50709285]\n",
877 |       " [20.3725451 ]\n",
878 |       " [ 7.86628457]\n",
879 |       " [ 8.16731053]\n",
880 |       " [10.40584907]\n",
881 |       " [17.03936669]\n",
882 |       " [10.88749061]\n",
883 |       " [ 8.51212209]\n",
884 |       " [ 9.16343282]\n",
885 |       " [ 8.86788005]\n",
886 |       " [14.96502414]\n",
887 |       " [18.61564811]\n",
888 |       " [18.93309367]\n",
889 |       " [12.76479799]\n",
890 |       " [ 7.6145174 ]\n",
891 |       " [ 8.06879294]\n",
892 |       " [14.02363385]\n",
893 |       " [12.86878878]\n",
894 |       " [13.15339515]\n",
895 |       " [19.70481478]\n",
896 |       " [21.03480222]\n",
897 |       " [17.26376787]\n",
898 |       " [ 9.59034237]\n",
899 |       " [10.55362545]\n",
900 |       " [23.17482317]\n",
901 |       " [16.58509115]\n",
902 |       " [18.22705095]\n",
903 |       " [ 7.54336581]]\n"
904 |      ]
905 |     }
906 |    ],
907 |    "source": [
908 |     "res= model.predict(X_test)\n",
909 |     "print(res)"
910 |    ]
911 |   },
912 |   {
913 |    "cell_type": "code",
914 |    "execution_count": null,
915 |    "id": "a28a6bdf-4b7a-49a0-a215-478d2fd1e8d5",
916 |    "metadata": {},
917 |    "outputs": [],
918 |    "source": []
919 |   }
920 |  ],
921 |  "metadata": {
922 |   "kernelspec": {
923 |    "display_name": "Python 3 (ipykernel)",
924 |    "language": "python",
925 |    "name": "python3"
926 |   },
927 |   "language_info": {
928 |    "codemirror_mode": {
929 |     "name": "ipython",
930 |     "version": 3
931 |    },
932 |    "file_extension": ".py",
933 |    "mimetype": "text/x-python",
934 |    "name": "python",
935 |    "nbconvert_exporter": "python",
936 |    "pygments_lexer": "ipython3",
937 |    "version": "3.10.8"
938 |   }
939 |  },
940 |  "nbformat": 4,
941 |  "nbformat_minor": 5
942 | }
943 | 


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