Multiple_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e5a5978",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd2ed2a7-9e29-4478-b435-17d18673f5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# pip install scikit-learn in cmd\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75a679e6-e56c-4deb-9916-e6c10d9488f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data loading & inspecction\n",
    "house = pd.read_csv(r\"C:\\Users\\akars\\OneDrive\\Desktop\\5th SEM\\DATA VISUALIZATION, ANALYTICS\\LAB\\DataSet\\Boston.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7967918-dc24-426a-88b1-64f7254f9c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS     NX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 data\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce007ca7-598a-4656-8fd6-9e8c7106e872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NX       506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "# Printing additional info of columns\n",
    "house.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d13541a-8769-497c-93ac-47e40a2a8fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      24.0\n",
       "1      21.6\n",
       "2      34.7\n",
       "3      33.4\n",
       "4      36.2\n",
       "       ... \n",
       "501    22.4\n",
       "502    20.6\n",
       "503    23.9\n",
       "504    22.0\n",
       "505    11.9\n",
       "Name: MEDV, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing specific coloumn \n",
    "house[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf6b2c7c-d53e-40cd-ab81-de06489c9008",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Storing that column in a variable\n",
    "y = house[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c7823b3-4fa6-4f53-a857-3482a951128f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new dataframe\n",
    "x = house.drop([\"MEDV\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62b01083-883f-46e2-b024-78696b470750",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into training & testing dataset\n",
    "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, random_state = 47)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "955b9800-ddb8-4562-8c36-2610398164f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((354, 13), (152, 13), (354,), (152,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensions check of training % testing dataset\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7ee06e1-c7f1-4e91-a869-ba3b6911ee07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "096132a5-2aee-41b7-93e4-8553dd8214a7",
   "metadata": {},
   "outputs": [
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       "  /* Arrow on the left of the label */\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
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       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the linear regression model\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6dc5c73-af47-49fb-8ccc-388c0567ab8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.7906603378433"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bais term\n",
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2911b38-3451-43ed-9626-848ed7862aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.15968373e-01,  3.91763972e-02,  9.92241514e-03,  3.69277779e+00,\n",
       "       -1.62691788e+01,  4.78053783e+00, -1.19792977e-02, -1.44207710e+00,\n",
       "        2.72172220e-01, -1.18274581e-02, -9.41453989e-01,  9.62039782e-03,\n",
       "       -4.16393266e-01])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficient\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "625d454d-4d71-42f1-b6fc-2be4be670685",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions on the dataset \n",
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35852884-8c68-48f3-b264-be1770de6e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.81514678, 32.37926288, 24.54278296, 30.88413252, 27.26011146,\n",
       "       19.76212755, 23.31307468, 31.97370285, 32.65758864, 22.75069597,\n",
       "       24.85887301, 27.23536024, 20.22285096, 28.08655284, 32.5882359 ,\n",
       "       10.05223034, 36.15983992, 14.83486957, 11.83733767, 15.57322269,\n",
       "       35.22027225, 22.2839578 , 38.82814794, 20.63852269, 28.51388028,\n",
       "       19.04788806, 24.02567988, 20.81360377, 19.96505736, 26.44688259,\n",
       "       14.1372452 , 25.79715435, 17.88787452,  8.25431166, 33.24604875,\n",
       "       19.02215238, 12.44584599, 27.06613733, 22.74975077, 18.73764887,\n",
       "       23.59779718, 25.26180231, 37.47827976, 21.21937484, 32.3495946 ,\n",
       "       20.66902648,  7.57860895, 28.17219192, 36.57927934, 21.479344  ,\n",
       "       28.94285403, 23.7785523 ,  7.38613363, 24.26071508,  6.35437587,\n",
       "       21.93110088, 18.32956854, 13.5009917 ,  6.23384088, 30.11250948,\n",
       "       12.44448301, 18.1954519 , 18.4025497 , 19.18851855, 42.32084082,\n",
       "       22.51430919, 20.4121436 , 23.8282114 , 25.44033918, 27.73518745,\n",
       "       30.24174873, 19.46819297, 24.61216622, 22.73638771, 30.9484349 ,\n",
       "       13.85510283, 17.8072725 , 20.31388563, 16.71164957, 17.29243801,\n",
       "        2.59897321, 33.98043191, 31.55824968, 21.85553707, 32.96214412,\n",
       "       16.8057403 , 22.85502348, 25.89799064, 22.55913873, 20.38227345,\n",
       "       17.2330436 , 20.55263607, 14.09754513, 17.18413887, 32.97264104,\n",
       "       18.51953594, 41.67584512, 28.53420821,  5.73776504, 22.15066954,\n",
       "       25.28657534, 15.1464206 , 21.17468154, 16.041732  , 15.38755254,\n",
       "       34.33983926, 20.30032615, 16.84348625, 12.81769198, 18.25010953,\n",
       "       12.5062968 , 20.93828987, 33.02837915, 21.23042523, 14.55560869,\n",
       "       17.52018897, 19.9645306 , 28.5097547 , 37.41245732, 19.50333796,\n",
       "       13.13362657, 20.47615212, 27.68039136, 24.53601327,  9.33623936,\n",
       "       19.64411343, 33.63144904, 18.4350426 , 30.45923977, 19.13578295,\n",
       "       14.14212852, 13.36806705, 13.23620979, 21.23495428, 15.53497058,\n",
       "       33.32958342, 18.69627303, 20.6149461 , 18.81100573, 12.92566905,\n",
       "       39.65803827, 21.63750933, 18.80181837, 27.38514683, 17.44975198,\n",
       "       23.65766691, 17.26345221, 18.4515094 , 30.11141562, 34.5191159 ,\n",
       "       23.06025825, 37.53636152])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5da22a0-3ff6-4877-b48e-9dc919cf7eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2157913543296113"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the mean absolute error\n",
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "beea19cc-a6ba-457c-b52f-b0b5f808eed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15159979142916444"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculation of error in percentage\n",
    "mean_absolute_percentage_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54bcf731-a91e-4622-9596-e1e3d146140d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.618650352106137"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating mean squared error\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "948cbaa5-a91a-47b9-8452-96ec70c5c342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the actual vs predicted values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, color='blue', edgecolor='k', alpha=0.6, s=100)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linewidth=2)\n",
    "plt.xlabel('Actual MEDV')\n",
    "plt.ylabel('Predicted MEDV')\n",
    "plt.title('Actual vs Predicted MEDV')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16e97642-c40b-44d3-b8bc-19e9b40c1c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the residuals\n",
    "residuals = y_test - y_pred\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_pred, residuals, color='blue', edgecolor='k', alpha=0.6, s=100)\n",
    "plt.hlines(y=0, xmin=y_pred.min(), xmax=y_pred.max(), color='red', linewidth=2)\n",
    "plt.xlabel('Predicted MEDV')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residuals vs Predicted MEDV')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26da0b3a-dfc4-4fa7-9f8e-ec74589d3b46",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}