sales-prediction-using-regression.ipynb

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    "<center>\n",
    "  <h1 style=\"background-color:black; color:yellow; font-size:40px; font-weight:bold; font-family:Times New Roman; border:2px solid black;\"> Sales Prediction Using Regression</h1>\n",
    "</center>"
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    "tags": []
   },
   "source": [
    "# Importing Libraries and Dataset"
   ]
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  {
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   "source": [
    "# Supresses Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import the numpy and pandas package\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Data Visualisation\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
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       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
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       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>17.9</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>11.8</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>13.2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>15.6</td>\n",
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       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3   12.0\n",
       "3  151.5   41.3       58.5   16.5\n",
       "4  180.8   10.8       58.4   17.9\n",
       "5    8.7   48.9       75.0    7.2\n",
       "6   57.5   32.8       23.5   11.8\n",
       "7  120.2   19.6       11.6   13.2\n",
       "8    8.6    2.1        1.0    4.8\n",
       "9  199.8    2.6       21.2   15.6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "advertising = pd.DataFrame(pd.read_csv(\"/kaggle/input/sales-data/advertising.csv\"))\n",
    "advertising.head(10)"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.8</td>\n",
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       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>75.5</td>\n",
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       "      <th>192</th>\n",
       "      <td>17.2</td>\n",
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       "      <td>31.6</td>\n",
       "      <td>5.9</td>\n",
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       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>166.8</td>\n",
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       "      <th>194</th>\n",
       "      <td>149.7</td>\n",
       "      <td>35.6</td>\n",
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       "      <td>17.3</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
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       "      <td>7.6</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.4</td>\n",
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       "        TV  Radio  Newspaper  Sales\n",
       "190   39.5   41.1        5.8   10.8\n",
       "191   75.5   10.8        6.0   11.9\n",
       "192   17.2    4.1       31.6    5.9\n",
       "193  166.8   42.0        3.6   19.6\n",
       "194  149.7   35.6        6.0   17.3\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1   14.0\n",
       "197  177.0    9.3        6.4   14.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   18.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.tail(10)"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Exploring the dataset"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   Radio      200 non-null    float64\n",
      " 2   Newspaper  200 non-null    float64\n",
      " 3   Sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "advertising.info()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "f0646d06",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "800"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3c1ef93",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.shape"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
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       "      <th>Sales</th>\n",
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       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>15.130500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.283892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>16.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>19.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
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       "               TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   15.130500\n",
       "std     85.854236   14.846809   21.778621    5.283892\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   11.000000\n",
       "50%    149.750000   22.900000   25.750000   16.000000\n",
       "75%    218.825000   36.525000   45.100000   19.050000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85af5917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:25.112583Z",
     "iopub.status.busy": "2024-02-25T19:57:25.111778Z",
     "iopub.status.idle": "2024-02-25T19:57:25.130883Z",
     "shell.execute_reply": "2024-02-25T19:57:25.129967Z"
    },
    "papermill": {
     "duration": 0.03185,
     "end_time": "2024-02-25T19:57:25.132856",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.101006",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>15.130500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.283892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>19.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   15.130500\n",
       "std     85.854236   14.846809   21.778621    5.283892\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   11.000000\n",
       "50%    149.750000   22.900000   25.750000   16.000000\n",
       "75%    218.825000   36.525000   45.100000   19.050000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bd2ddb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:25.154729Z",
     "iopub.status.busy": "2024-02-25T19:57:25.154117Z",
     "iopub.status.idle": "2024-02-25T19:57:25.161006Z",
     "shell.execute_reply": "2024-02-25T19:57:25.160105Z"
    },
    "papermill": {
     "duration": 0.019834,
     "end_time": "2024-02-25T19:57:25.162983",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.143149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.nunique of 0      230.1\n",
       "1       44.5\n",
       "2       17.2\n",
       "3      151.5\n",
       "4      180.8\n",
       "       ...  \n",
       "195     38.2\n",
       "196     94.2\n",
       "197    177.0\n",
       "198    283.6\n",
       "199    232.1\n",
       "Name: TV, Length: 200, dtype: float64>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.TV.nunique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223ff09e",
   "metadata": {
    "papermill": {
     "duration": 0.010154,
     "end_time": "2024-02-25T19:57:25.182716",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.172562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "618be2f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:25.203881Z",
     "iopub.status.busy": "2024-02-25T19:57:25.203461Z",
     "iopub.status.idle": "2024-02-25T19:57:25.212165Z",
     "shell.execute_reply": "2024-02-25T19:57:25.211100Z"
    },
    "papermill": {
     "duration": 0.02173,
     "end_time": "2024-02-25T19:57:25.214138",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.192408",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0.0\n",
       "Radio        0.0\n",
       "Newspaper    0.0\n",
       "Sales        0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the percentage of null values for every column\n",
    "advertising.isnull().sum()*100/advertising.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50e8c872",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:25.235611Z",
     "iopub.status.busy": "2024-02-25T19:57:25.235240Z",
     "iopub.status.idle": "2024-02-25T19:57:25.658588Z",
     "shell.execute_reply": "2024-02-25T19:57:25.657472Z"
    },
    "papermill": {
     "duration": 0.436988,
     "end_time": "2024-02-25T19:57:25.660900",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.223912",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outliers Analysis using seaborn as sns\n",
    "fig, axs = plt.subplots(3, figsize = (5,5))\n",
    "plt1 = sns.boxplot(advertising['TV'], ax = axs[0])\n",
    "plt2 = sns.boxplot(advertising['Newspaper'], ax = axs[1])\n",
    "plt3 = sns.boxplot(advertising['Radio'], ax = axs[2])\n",
    "plt.tight_layout()\n",
    "#there are no outliers there as such"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c657fa",
   "metadata": {
    "papermill": {
     "duration": 0.009917,
     "end_time": "2024-02-25T19:57:25.681321",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.671404",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# EDA\n",
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f27ed4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:25.703472Z",
     "iopub.status.busy": "2024-02-25T19:57:25.703071Z",
     "iopub.status.idle": "2024-02-25T19:57:25.817421Z",
     "shell.execute_reply": "2024-02-25T19:57:25.816452Z"
    },
    "papermill": {
     "duration": 0.128139,
     "end_time": "2024-02-25T19:57:25.819764",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.691625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(advertising['Sales']) #target variable is sales\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a406223f",
   "metadata": {
    "papermill": {
     "duration": 0.010803,
     "end_time": "2024-02-25T19:57:25.841350",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.830547",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c48a2d15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:25.865167Z",
     "iopub.status.busy": "2024-02-25T19:57:25.864761Z",
     "iopub.status.idle": "2024-02-25T19:57:26.494984Z",
     "shell.execute_reply": "2024-02-25T19:57:26.493917Z"
    },
    "papermill": {
     "duration": 0.645227,
     "end_time": "2024-02-25T19:57:26.497516",
     "exception": false,
     "start_time": "2024-02-25T19:57:25.852289",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see how Sales are related with other variables using scatter plot.\n",
    "sns.pairplot(advertising, x_vars=['TV', 'Newspaper', 'Radio'], y_vars='Sales', height=5, aspect=0.7, kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d35e6b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:26.521785Z",
     "iopub.status.busy": "2024-02-25T19:57:26.521372Z",
     "iopub.status.idle": "2024-02-25T19:57:26.754339Z",
     "shell.execute_reply": "2024-02-25T19:57:26.753327Z"
    },
    "papermill": {
     "duration": 0.247863,
     "end_time": "2024-02-25T19:57:26.756687",
     "exception": false,
     "start_time": "2024-02-25T19:57:26.508824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation between different variables. 1st argument creates correlation matrix, second argument gives color, third tells if values of correlation will be shown on the heatmap\n",
    "sns.heatmap(advertising.corr(), cmap=\"viridis\", annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "352aa4c3",
   "metadata": {
    "papermill": {
     "duration": 0.011263,
     "end_time": "2024-02-25T19:57:26.779985",
     "exception": false,
     "start_time": "2024-02-25T19:57:26.768722",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As is visible from the pairplot and the heatmap, the variable TV seems to be most correlated with Sales. So let's perform simple linear regression using TV as our feature variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1931d935",
   "metadata": {
    "papermill": {
     "duration": 0.011295,
     "end_time": "2024-02-25T19:57:26.802837",
     "exception": false,
     "start_time": "2024-02-25T19:57:26.791542",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Building Model for Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5eb2a2bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:26.827900Z",
     "iopub.status.busy": "2024-02-25T19:57:26.827550Z",
     "iopub.status.idle": "2024-02-25T19:57:26.832265Z",
     "shell.execute_reply": "2024-02-25T19:57:26.831019Z"
    },
    "papermill": {
     "duration": 0.019904,
     "end_time": "2024-02-25T19:57:26.834430",
     "exception": false,
     "start_time": "2024-02-25T19:57:26.814526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = advertising['TV']\n",
    "y = advertising['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9a6575a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:26.859663Z",
     "iopub.status.busy": "2024-02-25T19:57:26.859318Z",
     "iopub.status.idle": "2024-02-25T19:57:27.077252Z",
     "shell.execute_reply": "2024-02-25T19:57:27.076196Z"
    },
    "papermill": {
     "duration": 0.233436,
     "end_time": "2024-02-25T19:57:27.079675",
     "exception": false,
     "start_time": "2024-02-25T19:57:26.846239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b6c5db51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:27.105057Z",
     "iopub.status.busy": "2024-02-25T19:57:27.104145Z",
     "iopub.status.idle": "2024-02-25T19:57:27.112440Z",
     "shell.execute_reply": "2024-02-25T19:57:27.111461Z"
    },
    "papermill": {
     "duration": 0.022946,
     "end_time": "2024-02-25T19:57:27.114382",
     "exception": false,
     "start_time": "2024-02-25T19:57:27.091436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74     17.0\n",
       "3      16.5\n",
       "185    22.6\n",
       "26     15.0\n",
       "90     14.0\n",
       "127    11.9\n",
       "81     17.3\n",
       "88     12.9\n",
       "119     6.6\n",
       "110    18.4\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(10)\n",
    "y_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9edd0917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:27.139889Z",
     "iopub.status.busy": "2024-02-25T19:57:27.139075Z",
     "iopub.status.idle": "2024-02-25T19:57:28.582064Z",
     "shell.execute_reply": "2024-02-25T19:57:28.581156Z"
    },
    "papermill": {
     "duration": 1.458078,
     "end_time": "2024-02-25T19:57:28.584294",
     "exception": false,
     "start_time": "2024-02-25T19:57:27.126216",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24fda87a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:28.613379Z",
     "iopub.status.busy": "2024-02-25T19:57:28.612769Z",
     "iopub.status.idle": "2024-02-25T19:57:28.621988Z",
     "shell.execute_reply": "2024-02-25T19:57:28.621080Z"
    },
    "papermill": {
     "duration": 0.027943,
     "end_time": "2024-02-25T19:57:28.624056",
     "exception": false,
     "start_time": "2024-02-25T19:57:28.596113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add a constant to get an intercept\n",
    "X_train_sm = sm.add_constant(X_train)\n",
    "\n",
    "# Fit the resgression line using 'OLS'\n",
    "lr = sm.OLS(y_train, X_train_sm).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2fc5358d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:28.649569Z",
     "iopub.status.busy": "2024-02-25T19:57:28.648909Z",
     "iopub.status.idle": "2024-02-25T19:57:28.656649Z",
     "shell.execute_reply": "2024-02-25T19:57:28.655640Z"
    },
    "papermill": {
     "duration": 0.022542,
     "end_time": "2024-02-25T19:57:28.658584",
     "exception": false,
     "start_time": "2024-02-25T19:57:28.636042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    6.948683\n",
       "TV       0.054546\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the parameters, i.e. the intercept and the slope of the regression line fitted\n",
    "lr.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5e6bf1",
   "metadata": {
    "papermill": {
     "duration": 0.064735,
     "end_time": "2024-02-25T19:57:28.735291",
     "exception": false,
     "start_time": "2024-02-25T19:57:28.670556",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c199c311",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:28.762286Z",
     "iopub.status.busy": "2024-02-25T19:57:28.761315Z",
     "iopub.status.idle": "2024-02-25T19:57:28.778499Z",
     "shell.execute_reply": "2024-02-25T19:57:28.777552Z"
    },
    "papermill": {
     "duration": 0.032899,
     "end_time": "2024-02-25T19:57:28.780583",
     "exception": false,
     "start_time": "2024-02-25T19:57:28.747684",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Sales   R-squared:                       0.816\n",
      "Model:                            OLS   Adj. R-squared:                  0.814\n",
      "Method:                 Least Squares   F-statistic:                     611.2\n",
      "Date:                Sun, 25 Feb 2024   Prob (F-statistic):           1.52e-52\n",
      "Time:                        19:57:28   Log-Likelihood:                -321.12\n",
      "No. Observations:                 140   AIC:                             646.2\n",
      "Df Residuals:                     138   BIC:                             652.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          6.9487      0.385     18.068      0.000       6.188       7.709\n",
      "TV             0.0545      0.002     24.722      0.000       0.050       0.059\n",
      "==============================================================================\n",
      "Omnibus:                        0.027   Durbin-Watson:                   2.196\n",
      "Prob(Omnibus):                  0.987   Jarque-Bera (JB):                0.150\n",
      "Skew:                          -0.006   Prob(JB):                        0.928\n",
      "Kurtosis:                       2.840   Cond. No.                         328.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lr.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "30cf7c83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:28.806363Z",
     "iopub.status.busy": "2024-02-25T19:57:28.805979Z",
     "iopub.status.idle": "2024-02-25T19:57:29.117139Z",
     "shell.execute_reply": "2024-02-25T19:57:29.115008Z"
    },
    "papermill": {
     "duration": 0.326861,
     "end_time": "2024-02-25T19:57:29.119552",
     "exception": false,
     "start_time": "2024-02-25T19:57:28.792691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, y_train)\n",
    "plt.plot(X_train, 6.948 + 0.054*X_train, 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e741d7",
   "metadata": {
    "papermill": {
     "duration": 0.012516,
     "end_time": "2024-02-25T19:57:29.144800",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.132284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0484bacb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.171577Z",
     "iopub.status.busy": "2024-02-25T19:57:29.170910Z",
     "iopub.status.idle": "2024-02-25T19:57:29.176764Z",
     "shell.execute_reply": "2024-02-25T19:57:29.175581Z"
    },
    "papermill": {
     "duration": 0.021657,
     "end_time": "2024-02-25T19:57:29.178955",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.157298",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_train_pred = lr.predict(X_train_sm)\n",
    "res = (y_train - y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a27e0074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.208324Z",
     "iopub.status.busy": "2024-02-25T19:57:29.207912Z",
     "iopub.status.idle": "2024-02-25T19:57:29.489918Z",
     "shell.execute_reply": "2024-02-25T19:57:29.488872Z"
    },
    "papermill": {
     "duration": 0.299681,
     "end_time": "2024-02-25T19:57:29.492185",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.192504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(res, bins = 15)\n",
    "fig.suptitle('Error Terms', fontsize = 15)                  # Plot heading \n",
    "plt.xlabel('y_train - y_train_pred', fontsize = 15)         # X-label\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f5a3771",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.520628Z",
     "iopub.status.busy": "2024-02-25T19:57:29.519860Z",
     "iopub.status.idle": "2024-02-25T19:57:29.685743Z",
     "shell.execute_reply": "2024-02-25T19:57:29.684708Z"
    },
    "papermill": {
     "duration": 0.182455,
     "end_time": "2024-02-25T19:57:29.687918",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.505463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4fa573",
   "metadata": {
    "papermill": {
     "duration": 0.012973,
     "end_time": "2024-02-25T19:57:29.714236",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.701263",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a42f94c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.742675Z",
     "iopub.status.busy": "2024-02-25T19:57:29.741603Z",
     "iopub.status.idle": "2024-02-25T19:57:29.748162Z",
     "shell.execute_reply": "2024-02-25T19:57:29.747452Z"
    },
    "papermill": {
     "duration": 0.022631,
     "end_time": "2024-02-25T19:57:29.749917",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.727286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add a constant to X_test\n",
    "X_test_sm = sm.add_constant(X_test)\n",
    "\n",
    "# Predict the y values corresponding to X_test_sm\n",
    "y_pred = lr.predict(X_test_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "68069850",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.778103Z",
     "iopub.status.busy": "2024-02-25T19:57:29.777157Z",
     "iopub.status.idle": "2024-02-25T19:57:29.784676Z",
     "shell.execute_reply": "2024-02-25T19:57:29.783653Z"
    },
    "papermill": {
     "duration": 0.023668,
     "end_time": "2024-02-25T19:57:29.786715",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.763047",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126     7.374140\n",
       "104    19.941482\n",
       "99     14.323269\n",
       "92     18.823294\n",
       "111    20.132392\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "952caf84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.815105Z",
     "iopub.status.busy": "2024-02-25T19:57:29.814419Z",
     "iopub.status.idle": "2024-02-25T19:57:29.819109Z",
     "shell.execute_reply": "2024-02-25T19:57:29.818428Z"
    },
    "papermill": {
     "duration": 0.021096,
     "end_time": "2024-02-25T19:57:29.821037",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.799941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a14c074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.849520Z",
     "iopub.status.busy": "2024-02-25T19:57:29.849149Z",
     "iopub.status.idle": "2024-02-25T19:57:29.856239Z",
     "shell.execute_reply": "2024-02-25T19:57:29.855179Z"
    },
    "papermill": {
     "duration": 0.023613,
     "end_time": "2024-02-25T19:57:29.858230",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.834617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.019296008966232"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returns the mean squared error; we'll take a square root\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a20fb2ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.889362Z",
     "iopub.status.busy": "2024-02-25T19:57:29.888009Z",
     "iopub.status.idle": "2024-02-25T19:57:29.895121Z",
     "shell.execute_reply": "2024-02-25T19:57:29.894351Z"
    },
    "papermill": {
     "duration": 0.025077,
     "end_time": "2024-02-25T19:57:29.896888",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.871811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.792103160124566"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_squared = r2_score(y_test, y_pred)\n",
    "r_squared # r_square value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "52614016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-25T19:57:29.926029Z",
     "iopub.status.busy": "2024-02-25T19:57:29.925173Z",
     "iopub.status.idle": "2024-02-25T19:57:30.100809Z",
     "shell.execute_reply": "2024-02-25T19:57:30.099723Z"
    },
    "papermill": {
     "duration": 0.192738,
     "end_time": "2024-02-25T19:57:30.103221",
     "exception": false,
     "start_time": "2024-02-25T19:57:29.910483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test)\n",
    "plt.plot(X_test, 6.948 + 0.054 * X_test, 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75da824",
   "metadata": {
    "papermill": {
     "duration": 0.013978,
     "end_time": "2024-02-25T19:57:30.131302",
     "exception": false,
     "start_time": "2024-02-25T19:57:30.117324",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 4494539,
     "sourceId": 7699868,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30646,
   "isGpuEnabled": false,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 10.596791,
   "end_time": "2024-02-25T19:57:30.767228",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-02-25T19:57:20.170437",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}