Linerar_Regression_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdd9d885-62db-4aa0-98ac-e7f582376da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32a9a92f-a687-478d-a1b0-f01fd0f8eeca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read preset dataframe from pandas\n",
    "\n",
    "data = pd.read_csv('Advertising.csv', index_col=0)\n",
    "data.head()\n",
    "data.columns = ['TV', 'Radio', 'Newspaper', 'Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0afa741-2e4c-40a2-9be7-be35ef031635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f12141c3-ccd8-46be-a801-e935055a1e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Newspaper', ylabel='Sales'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plots\n",
    "\n",
    "fig,axs = plt.subplots(1,3,sharey=True)\n",
    "data.plot(kind='scatter', x='TV', y='Sales', ax=axs[0], figsize=(16,8))\n",
    "data.plot(kind='scatter', x='Radio', y='Sales', ax=axs[1])\n",
    "data.plot(kind='scatter', x='Newspaper', y='Sales', ax=axs[2])\n",
    "\n",
    "#from this we do see some relationship between ads and sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98d0964a-f4bd-4c92-b669-6c3046b0a8e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying linear regression\n",
    "\n",
    "feature_cols=['TV']\n",
    "x= data[feature_cols]\n",
    "y = data.Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20cc1d9e-670c-4aaa-93b9-ea862dec9eb4",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "90a23af0-c1f4-4899-884a-5f227f2f8ca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.032593549127695\n",
      "[0.04753664]\n"
     ]
    }
   ],
   "source": [
    "#intercept is b in y=mx+b\n",
    "print(lm.intercept_) \n",
    "#coef_ is m in y=mx+b\n",
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "90186750-b0d3-4a04-89a3-38fd8a671d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.409393999999999"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict increase in tv spending by 50,000 dollars (50 in our dataframes case)\n",
    "#simply plug in equation\n",
    "#this is our new value for sales\n",
    "7.032594+0.047536*50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f6a421fd-e3c0-4de5-8ab6-19acbe2c4461",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TV\n",
       "0  50"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now predicting new X values\n",
    "X_new = pd.DataFrame({'TV':[50]})\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a3b0d389-16bf-4260-b651-91f2a7ee107e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.40942557])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.predict(X_new) #this does the same thing we just did but in one command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5f104d26-2bad-49c6-9c78-f8d1514f96fe",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV\n",
       "0    0.7\n",
       "1  296.4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding largest and smallest oberved values and using them to plot the least squares line\n",
    "X_new = pd.DataFrame({'TV':[data.TV.min(), data.TV.max()]})\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f6188673-25d0-434b-8a0e-8655ec7ae948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.0658692 , 21.12245377])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = lm.predict(X_new) #calculate linear regression equation given new y values from x min and x max\n",
    "preds #this prints our new intecept and slope value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7df55d33-4469-4b73-a11a-f9e3db4c1b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8f476b7c70>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter', x='TV', y='Sales')\n",
    "plt.plot(X_new,preds, c='red', linewidth=2)\n",
    "#this plot shows we have a high bias and low variance model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c28d9bbf-d850-4c32-9544-95e9212cbe7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing hypothisis\n",
    "import statsmodels.formula.api as smf\n",
    "lm = smf.ols(formula='Sales~TV', data=data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "578c5a89-90c3-492b-bcd9-84db481adce9",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>6.129719</td>\n",
       "      <td>7.935468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>0.042231</td>\n",
       "      <td>0.052843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "Intercept  6.129719  7.935468\n",
       "TV         0.042231  0.052843"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c1de5ddc-8e3c-4a3f-97cb-f2045c7305c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    1.406300e-35\n",
       "TV           1.467390e-42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing p values\n",
    "lm.pvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22770513-a5c7-49fd-b15b-d23600fd0052",
   "metadata": {},
   "source": [
    "## From the PValue of TV being far less than 0.05, we can confrim there is infact a siginificat relationship between money spent on TV ads, and Sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "87e3c9da-1b46-4fdb-b2c9-44cfd2b97dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.611875050850071"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R squared value for testing how well fit this model is\n",
    "lm.rsquared\n",
    "#however, it's hard to say how good the fit is from just 1 r squared value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae882564-e941-4d6e-8b50-86c6134997da",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ffb3efe4-08a3-4de9-99f4-e35a4c8bf7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we begin multi linear regression\n",
    "feature_cols =['TV', 'Radio', 'Newspaper']\n",
    "X = data[feature_cols]\n",
    "y = data.Sales\n",
    "\n",
    "from sklearn import model_selection\n",
    "xtrain,xtest,ytrain,ytest = model_selection.train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1f79d5f0-507d-4f0a-bdf1-6c01cc74f39b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9388893694594085\n",
      "[ 0.04576465  0.18853002 -0.00103749]\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X,y)\n",
    "print(lm.intercept_)\n",
    "print(lm.coef_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "82d073c3-04b4-46a4-a45f-a92acce29a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1a4ba00b-a270-4a34-b59e-9b2118c20302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7089490925159065\n",
      "[0.04405928 0.1992875  0.00688245]\n",
      "1.9485372043446387\n"
     ]
    }
   ],
   "source": [
    "print(lm.intercept_)\n",
    "print(lm.coef_)\n",
    "\n",
    "predictions = lm.predict(xtest)\n",
    "print(sqrt(mean_squared_error(ytest,predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f10540f2-66bc-4ede-a776-5ef7e0316f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 08 Aug 2023</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:29:01</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     570.3\n",
       "Date:                Tue, 08 Aug 2023   Prob (F-statistic):           1.58e-96\n",
       "Time:                        22:29:01   Log-Likelihood:                -386.18\n",
       "No. Observations:                 200   AIC:                             780.4\n",
       "Df Residuals:                     196   BIC:                             793.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
       "Radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
       "Newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
       "==============================================================================\n",
       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
       "Kurtosis:                       6.332   Cond. No.                         454.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = smf.ols(formula='Sales~TV+Radio+Newspaper',data=data).fit()\n",
    "lm.conf_int()\n",
    "lm.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7481ef7a-8671-43ac-a0e7-30203c2a3077",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Results from multi linear regression\n",
    "TV and radio have siginifcant pvales with Sales while newspaper does not (it was greater than 0.05)\n",
    "\n",
    "This means we can reject the Null for TV and Radio\n",
    "There is no association between these features and sales\n",
    "\n",
    "TV and Radio sales have a positive correlation with Sales\n",
    "\n",
    "Newspaper has a slightly negative correlation with Sales (This is irrelavent because we could NOT reject the Null hypothisis)\n",
    "\n",
    "The R-squared value for this model (0.897) is larger than the previous model\n",
    "This means this current multi-linear regression model is a BETTER fit than our previous model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d835da-878c-41a8-ae91-bc82b70b3c4a",
   "metadata": {},
   "source": [
    "# Feature selection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc85ac4-d87f-4776-98b3-16c471ebbaf7",
   "metadata": {},
   "source": [
    "How do we know what features to use in a linear regression model?\n",
    "\n",
    "Ideas:\n",
    "Try out different models, only keep predictors if they have small pvalues\n",
    "Check weather R-squared value increases if you add new predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9c8d5d23-55ba-4ada-848b-7cfec2f89d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8971942610828956"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test\n",
    "lm = smf.ols(formula='Sales~TV+Radio', data=data).fit()\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e308d48-79b0-4472-b213-6a35873a5bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8972106381789522"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = smf.ols(formula='Sales~TV+Radio+Newspaper', data=data).fit()\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d3cb2ac2-8d4b-4b66-936f-87b3d6892dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#notice, rsquared values will ALWAYS increase the more predictors you add\n",
    "# thus choosing the model with the highest Rsquared value alone is not sufficent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff38458-6f38-4e0d-a1af-21006be10673",
   "metadata": {},
   "source": [
    "## Categorical Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3dc92cf8-ddfa-4a38-a965-fbb871da51d1",
   "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",
       "      <th>Size</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",
       "      <td>large</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",
       "      <td>small</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",
       "      <td>small</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",
       "      <td>small</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",
       "      <td>large</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales   Size\n",
       "1  230.1   37.8       69.2   22.1  large\n",
       "2   44.5   39.3       45.1   10.4  small\n",
       "3   17.2   45.9       69.3    9.3  small\n",
       "4  151.5   41.3       58.5   18.5  small\n",
       "5  180.8   10.8       58.4   12.9  large"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating random categorical varibale \"size\" into our dataframe\n",
    "import numpy as np\n",
    "np.random.seed(12345)\n",
    "\n",
    "nums = np.random.rand(len(data))\n",
    "mask_large=nums >0.5\n",
    "data['Size'] = 'small'\n",
    "data.loc[mask_large,'Size']='large'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "824b30f7-005d-4caf-88e3-899f5f4d0d0f",
   "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",
       "      <th>Size</th>\n",
       "      <th>Islarge</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",
       "      <td>large</td>\n",
       "      <td>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",
       "      <td>small</td>\n",
       "      <td>0</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",
       "      <td>small</td>\n",
       "      <td>0</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",
       "      <td>small</td>\n",
       "      <td>0</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",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales   Size  Islarge\n",
       "1  230.1   37.8       69.2   22.1  large        1\n",
       "2   44.5   39.3       45.1   10.4  small        0\n",
       "3   17.2   45.9       69.3    9.3  small        0\n",
       "4  151.5   41.3       58.5   18.5  small        0\n",
       "5  180.8   10.8       58.4   12.9  large        1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#turing value into binary for ease of read\n",
    "data['Islarge'] =data.Size.map({'small':0,'large':1})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4b8010c4-bab4-4ede-8879-a5be1120af97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7f8f4782d380>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#perform multi linear regression on categorical data\n",
    "\n",
    "feature_cols =['TV', 'Radio', 'Newspaper', 'Islarge']\n",
    "X = data[feature_cols]\n",
    "y=data.Sales\n",
    "\n",
    "lm=LinearRegression()\n",
    "lm.fit(X,y)\n",
    "\n",
    "zip(feature_cols,lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "db465a7d-8ebb-49d4-9141-5707eaa351f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\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",
       "      <th>Size</th>\n",
       "      <th>Islarge</th>\n",
       "      <th>Area</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",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>rural</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",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</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",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>rural</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",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</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",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales   Size  Islarge      Area\n",
       "1  230.1   37.8       69.2   22.1  large        1     rural\n",
       "2   44.5   39.3       45.1   10.4  small        0     urban\n",
       "3   17.2   45.9       69.3    9.3  small        0     rural\n",
       "4  151.5   41.3       58.5   18.5  small        0     urban\n",
       "5  180.8   10.8       58.4   12.9  large        1  suburban"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new feature 'area'\n",
    "\n",
    "np.random.seed(123456)\n",
    "\n",
    "nums = np.random.rand(len(data))\n",
    "mask_suburban = (nums>0.33) & (nums< 0.66)\n",
    "mask_urban= nums >0.66\n",
    "data['Area']='rural'\n",
    "data.loc[mask_suburban,'Area']='suburban'\n",
    "data.loc[mask_urban,'Area']='urban'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cec04b3f-4775-4ab9-ac22-2cad513cdcb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "      <th>Size</th>\n",
       "      <th>Islarge</th>\n",
       "      <th>Area</th>\n",
       "      <th>Area_suburban</th>\n",
       "      <th>Area_urban</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",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>rural</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>9.3</td>\n",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>rural</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>small</td>\n",
       "      <td>0</td>\n",
       "      <td>urban</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "      <td>suburban</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales   Size  Islarge      Area  Area_suburban  \\\n",
       "1  230.1   37.8       69.2   22.1  large        1     rural              0   \n",
       "2   44.5   39.3       45.1   10.4  small        0     urban              0   \n",
       "3   17.2   45.9       69.3    9.3  small        0     rural              0   \n",
       "4  151.5   41.3       58.5   18.5  small        0     urban              0   \n",
       "5  180.8   10.8       58.4   12.9  large        1  suburban              1   \n",
       "\n",
       "   Area_urban  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "5           0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#turing into binary again\n",
    "area_dummies = pd.get_dummies(data.Area,prefix='Area').iloc[:,1:]\n",
    "\n",
    "data=pd.concat([data,area_dummies],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4ba9b86b-a1ad-473a-bb25-6bf9e8d82d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TV', 'Radio', 'Newspaper', 'Islarge', 'Area_suburban', 'Area_urban'] [ 0.04574401  0.1878667  -0.0010877   0.07739661 -0.10656299  0.26813802]\n"
     ]
    }
   ],
   "source": [
    "#now including dummy variables into linear regression model\n",
    "feature_cols =['TV', 'Radio', 'Newspaper', 'Islarge', 'Area_suburban', 'Area_urban']\n",
    "X = data[feature_cols]\n",
    "y=data.Sales\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,y)\n",
    "print(feature_cols,lm.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff5cf2c-886f-45b9-a223-23315b116667",
   "metadata": {},
   "source": [
    "## Interpretation of linear model with categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a48510c-93bc-499d-9e8d-71002ef0c763",
   "metadata": {},
   "source": [
    "Being in an area marked as suburban is associated with a decreas in sales by about 106.5 widgest (-0.1065)\n",
    "\n",
    "Being in an area marked as urban is associated with an increase in sales by about 268.1 widgets (0.2681)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6543ab-aec6-4f22-8947-531f50879982",
   "metadata": {},
   "source": [
    "# Recap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4db999a-3440-4c59-a6cf-ffae8700aec3",
   "metadata": {},
   "source": [
    "Step 1: Visualize the relationship among the features and the tagret variable\n",
    "Step 2: apply linear and multiple Linear Regression\n",
    "Step 3: Hypothesis Testing\n",
    "Step 4: Feature Seletcion\n",
    "Setp 5: Handeling categorical Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b2f9208-a0ea-4ee9-af40-f39041b5a9bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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