LinearRegression_AdvertisingDataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>1</th>\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>2</th>\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>3</th>\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>4</th>\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>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "1  230.1   37.8       69.2   22.1\n",
       "2   44.5   39.3       45.1   10.4\n",
       "3   17.2   45.9       69.3    9.3\n",
       "4  151.5   41.3       58.5   18.5\n",
       "5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Advertising.csv', index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\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>197</th>\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>198</th>\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>199</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>200</th>\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",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "196   38.2    3.7       13.8    7.6\n",
       "197   94.2    4.9        8.1    9.7\n",
       "198  177.0    9.3        6.4   12.8\n",
       "199  283.6   42.0       66.2   25.5\n",
       "200  232.1    8.6        8.7   13.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x22793bd1278>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1058.4x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sales is our response variable which is continuous and thus we will use Regression\n",
    "#But first visualize the relationship between the features and the response variable using scatterplots\n",
    "sns.pairplot(data, x_vars=['TV', 'Radio', 'Newspaper'], y_vars='Sales', height=7, aspect=0.7, kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper\n",
       "1  230.1   37.8       69.2\n",
       "2   44.5   39.3       45.1\n",
       "3   17.2   45.9       69.3\n",
       "4  151.5   41.3       58.5\n",
       "5  180.8   10.8       58.4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a list of feature names\n",
    "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
    "#select a subset of the original dataframe\n",
    "X = data[feature_cols] # or X = data[['TV', 'Radio', 'Newspaper']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(200, 3)\n"
     ]
    }
   ],
   "source": [
    "print(type(X))\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    22.1\n",
       "2    10.4\n",
       "3     9.3\n",
       "4    18.5\n",
       "5    12.9\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select a Series of the df\n",
    "y = data['Sales'] #or y = data.Sales if there is no space in the name of the Series\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "(200,)\n"
     ]
    }
   ],
   "source": [
    "print(type(y))\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split X and y into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 3)\n",
      "(50, 3)\n",
      "(150,)\n",
      "(50,)\n"
     ]
    }
   ],
   "source": [
    "#default split is 75% training and 25% testing\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#instantiate the model\n",
    "linreg = LinearRegression()\n",
    "#fit the model (learn the coefficients for the line of best fit)\n",
    "linreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8769666223179318\n",
      "[0.04656457 0.17915812 0.00345046]\n"
     ]
    }
   ],
   "source": [
    "#print the intecept and the coefficients\n",
    "print(linreg.intercept_)\n",
    "print(linreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('TV', 0.04656456787415028), ('Radio', 0.17915812245088836), ('Newspaper', 0.0034504647111804347)]\n"
     ]
    }
   ],
   "source": [
    "#pair the feature columns with the coefficients\n",
    "print(list(zip(feature_cols, linreg.coef_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making predictions on the testing set\n",
    "y_pred = linreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#evaluation metrics for linear regression\n",
    "true = [100, 50, 30, 20]\n",
    "pred = [90, 50, 50, 30]\n",
    "#Mean Absolute Error (MAE)\n",
    "metrics.mean_absolute_error(true, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean Squared Error (MSE)\n",
    "metrics.mean_squared_error(true, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.24744871391589"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Root Mean Squared Error (RMSE)\n",
    "#RMSE larger that MAE cause it squares the Errors and thus increased the weight of larger errors\n",
    "np.sqrt(metrics.mean_squared_error(true, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4046514230328955\n"
     ]
    }
   ],
   "source": [
    "#Computing RMSE for the sales predictions\n",
    "#1.4 is pretty good giving that the sales ranged from about 5 to 25\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3879034699382888\n"
     ]
    }
   ],
   "source": [
    "#see if Newspaper improves the quality of the model or not\n",
    "#feature selection or remove features\n",
    "X = feature_cols = data[['TV', 'Radio']]\n",
    "y = data.Sales\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "linreg.fit(X_train, y_train)\n",
    "y_pred = linreg.predict(X_test)\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature selection with cross-validation\n",
    "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
    "X = data[feature_cols]\n",
    "y = data.Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.56038438 -3.29767522 -2.08943356 -2.82474283 -1.3027754  -1.74163618\n",
      " -8.17338214 -2.11409746 -3.04273109 -2.45281793]\n"
     ]
    }
   ],
   "source": [
    "#10-fold cross-validation\n",
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X, y, cv=10, scoring='neg_mean_squared_error')\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.56038438 3.29767522 2.08943356 2.82474283 1.3027754  1.74163618\n",
      " 8.17338214 2.11409746 3.04273109 2.45281793]\n"
     ]
    }
   ],
   "source": [
    "#work around the negative values to root them cause we need RMSE\n",
    "mse_scores = -scores\n",
    "print(mse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6913531708051792\n"
     ]
    }
   ],
   "source": [
    "#convert MSE to RMSE and take the average value\n",
    "rmse_scores = np.sqrt(mse_scores)\n",
    "print(rmse_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare the model without the feature 'Newspaper'\n",
    "feature_cols = ['TV', 'Radio']\n",
    "X = data[feature_cols]\n",
    "y = data.Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6796748419090766\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(-cross_val_score(lr, X, y, cv=10, scoring='neg_mean_squared_error')).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Conclusion: Without the feature 'Newspaper' the model is better. Repeated cross-validation, though, is a more\n",
    "reliable estimate of out-of-sample performance, since it is reducing the variance associated with a single trial. Another\n",
    "reliable procedure is the hold-out set. Finally feature selection and engineering is better to occur within each cross-\n",
    "validation iteration. Cost-benefit choise to make here!'''"
   ]
  }
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