8.2 多元线性回归 广告数据集.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93e0e4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4dce934f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split  # 数据划分\n",
    "from sklearn.metrics import mean_squared_error  # 均方误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fc8514b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5aa6ba9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./dataset/Advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0719a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29a947edd50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.TV, data.sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42664cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29a948ab550>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.radio, data.sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55e2558f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29a97aa8e80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.newspaper, data.sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c243707",
   "metadata": {},
   "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>Unnamed: 0</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\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>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     TV  radio  newspaper  sales\n",
       "0             1  230.1   37.8       69.2   22.1\n",
       "1             2   44.5   39.3       45.1   10.4\n",
       "2             3   17.2   45.9       69.3    9.3\n",
       "3             4  151.5   41.3       58.5   18.5\n",
       "4             5  180.8   10.8       58.4   12.9\n",
       "..          ...    ...    ...        ...    ...\n",
       "195         196   38.2    3.7       13.8    7.6\n",
       "196         197   94.2    4.9        8.1    9.7\n",
       "197         198  177.0    9.3        6.4   12.8\n",
       "198         199  283.6   42.0       66.2   25.5\n",
       "199         200  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8de5d41d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[['TV','radio','newspaper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "314088b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4806f394",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "adb16107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 150)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train),len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0bd5a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9f0e78a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a5c1e186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0bce416d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04360884, 0.1919157 , 0.00078185])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9497a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('TV', 0.0436088434424217)\n",
      "('radio', 0.19191569983572265)\n",
      "('newspaper', 0.0007818490609210749)\n"
     ]
    }
   ],
   "source": [
    "for i in zip(x_train.columns, model.coef_):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cc1e5727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.171246979713524"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(model.predict(x_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a677514",
   "metadata": {},
   "source": [
    "# 模型改进"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a1a0dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[['TV', 'radio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9deb4233",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "944c0e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c890d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1bf731e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44ded074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04662603, 0.17581585])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "27f2cacb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2629269821059164"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(model2.predict(x_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0551da",
   "metadata": {},
   "source": [
    "所以并不是特征越多越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30aff690",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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