CarPricePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "245c0a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the dependencies\n",
    "\n",
    "# to make dataframe - more structured data\n",
    "import pandas as pd\n",
    "# for making plots\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "# To split our original data into training data and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# importing LinearRegression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# another regression model\n",
    "from sklearn.linear_model import Lasso\n",
    "# metrics is used to evaluate our model- to find the accuracy, error, etc\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf027a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Collection and Processing\n",
    "\n",
    "# Loading the data from csv file to pandas Dataframe\n",
    "car_dataset= pd.read_csv(\"Car-Price-Prediction/car data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb898f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspecting the first 5 rows of the dataframe\n",
    "\n",
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4c1ebc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of rows and cols\n",
    "\n",
    "car_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8d8d984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# getting some information abt the dataset\n",
    "\n",
    "car_dataset.info()\n",
    "\n",
    "# objects are categorical data\n",
    "# 301 entries with no data missing as all fields are non-null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00e28c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of missing values \n",
    "\n",
    "car_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2cb0bbdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Petrol    239\n",
      "Diesel     60\n",
      "CNG         2\n",
      "Name: Fuel_Type, dtype: int64\n",
      "Dealer        195\n",
      "Individual    106\n",
      "Name: Seller_Type, dtype: int64\n",
      "Manual       261\n",
      "Automatic     40\n",
      "Name: Transmission, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#checking the distribution of categorical data\n",
    "\n",
    "print (car_dataset.Fuel_Type.value_counts())\n",
    "print(car_dataset.Seller_Type.value_counts())\n",
    "print (car_dataset.Transmission.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9632bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the categorical data\n",
    "\n",
    "# we encode our categorical data because ml models does not understand text \n",
    "# properly - So we assign numerical values\n",
    "\n",
    "#encoding Fuel_type coloumn\n",
    "\n",
    "car_dataset.replace({'Fuel_Type':{'Petrol':0,'Diesel':1,'CNG':2}},inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "607afeec",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_dataset.replace({'Seller_Type':{'Dealer':0,'Individual':1}},inplace=True)\n",
    "car_dataset.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d7e0628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven  Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000          0   \n",
       "1      sx4  2013           4.75           9.54       43000          1   \n",
       "2     ciaz  2017           7.25           9.85        6900          0   \n",
       "3  wagon r  2011           2.85           4.15        5200          0   \n",
       "4    swift  2014           4.60           6.87       42450          1   \n",
       "\n",
       "   Seller_Type  Transmission  Owner  \n",
       "0            0             0      0  \n",
       "1            0             0      0  \n",
       "2            0             0      0  \n",
       "3            0             0      0  \n",
       "4            0             0      0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95fe3554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data and target\n",
    "\n",
    "# Selling price becomes our target- which we are going to predict\n",
    "\n",
    "# The other features become the data\n",
    "\n",
    "# Remove the Car_Name and Selling_Price\n",
    "\n",
    "X= car_dataset.drop(['Car_Name','Selling_Price'],axis=1) \n",
    "# car name and selling price is removed from X\n",
    "Y= car_dataset['Selling_Price']\n",
    "# Selling price is stored in Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75861ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
      "0    2014           5.59       27000          0            0             0   \n",
      "1    2013           9.54       43000          1            0             0   \n",
      "2    2017           9.85        6900          0            0             0   \n",
      "3    2011           4.15        5200          0            0             0   \n",
      "4    2014           6.87       42450          1            0             0   \n",
      "..    ...            ...         ...        ...          ...           ...   \n",
      "296  2016          11.60       33988          1            0             0   \n",
      "297  2015           5.90       60000          0            0             0   \n",
      "298  2009          11.00       87934          0            0             0   \n",
      "299  2017          12.50        9000          1            0             0   \n",
      "300  2016           5.90        5464          0            0             0   \n",
      "\n",
      "     Owner  \n",
      "0        0  \n",
      "1        0  \n",
      "2        0  \n",
      "3        0  \n",
      "4        0  \n",
      "..     ...  \n",
      "296      0  \n",
      "297      0  \n",
      "298      0  \n",
      "299      0  \n",
      "300      0  \n",
      "\n",
      "[301 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3724f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3.35\n",
      "1       4.75\n",
      "2       7.25\n",
      "3       2.85\n",
      "4       4.60\n",
      "       ...  \n",
      "296     9.50\n",
      "297     4.00\n",
      "298     3.35\n",
      "299    11.50\n",
      "300     5.30\n",
      "Name: Selling_Price, Length: 301, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y) # in lakhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5bdca28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting Training and Test data\n",
    "\n",
    "# only 10 % of data is used for testing\n",
    "X_train,X_test, Y_train,Y_test= train_test_split(X,Y,test_size=0.1, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9389a6b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Training\n",
    "\n",
    "# 1. Linear Regression model\n",
    "\n",
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4de1e52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit() is used to train our model\n",
    "lin_reg_model.fit(X_train,Y_train) # trains the regression model\n",
    "# and finds the line - y= mx+c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5a7fdb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Evaluation\n",
    "\n",
    "# prediction on Training Data\n",
    "\n",
    "training_data_prediction= lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78946f7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error :  0.8799451660493701\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "\n",
    "# depends on magnitude of values we have in training and Y_train\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print('R Squared Error : ', error_score) \n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9b5d5c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the actual and predicted prices\n",
    "\n",
    "plt.scatter(Y_train,training_data_prediction)\n",
    "plt.xlabel('Actual prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title(\"Actual vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f7c1825e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on training data\n",
    "\n",
    "test_data_prediction= lin_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3701f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error :  0.836576671502687\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "\n",
    "# depends on magnitude of values we have in training and Y_test\n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print('R Squared Error : ', error_score) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7fd7e3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the actual and predicted prices\n",
    " \n",
    "# we have only 30 data points for test data\n",
    "plt.scatter(Y_test,test_data_prediction)\n",
    "plt.xlabel('Actual prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title(\"Actual vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e089990",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Lasso Regression Model - \n",
    "# we use this when we have multiple variables\n",
    "\n",
    "#loading the lasso regression model\n",
    "\n",
    "lass_reg_model= Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23b76533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit() is used to train our model\n",
    "lass_reg_model.fit(X_train,Y_train) # trains the regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3918e93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "training_data_prediction1 = lass_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae340bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error :  0.8427856123435794\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "\n",
    "error_score1 = metrics.r2_score(Y_train,training_data_prediction1)\n",
    "print('R Squared Error : ', error_score1) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12e75f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the actual and predicted prices\n",
    "\n",
    "plt.scatter(Y_train,training_data_prediction1)\n",
    "plt.xlabel('Actual prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title(\"Actual vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3ea4ca2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction on test data\n",
    "test_data_prediction1= lass_reg_model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "48c5cb40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared Error :  0.8709167941173195\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "\n",
    "error_score1 = metrics.r2_score(Y_test,test_data_prediction1)\n",
    "print('R Squared Error : ', error_score1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1ec5858a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the actual and predicted prices\n",
    "\n",
    "plt.scatter(Y_test,test_data_prediction1)\n",
    "plt.xlabel('Actual prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title(\"Actual vs Predicted Prices\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "e8bba4f2be892cbd4f39a9f240acc2c7ebb8cef7264d56c87dbb8c6b0bcbe231"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}