Car Price Prediction using Machine Learning with Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fff96efc",
   "metadata": {},
   "source": [
    "# Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f98c39ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6507c157",
   "metadata": {},
   "source": [
    "# Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e358a7ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the data from csv file to pandas dataframe\n",
    "car_dataset = pd.read_csv('D:\\KaggleX\\car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e02dbf6b",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspecting the first 5 rows of the dataframe\n",
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9191356d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of rows and columns\n",
    "car_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "821e59c5",
   "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 about the dataset\n",
    "car_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e979080",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of missing values\n",
    "car_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a7dca8a",
   "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",
    "print(car_dataset.Fuel_Type.value_counts())\n",
    "print(car_dataset.Seller_Type.value_counts())\n",
    "print(car_dataset.Transmission.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b311000",
   "metadata": {},
   "source": [
    "# Encoding the Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ca75891",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding \"Fuel_Type\" Column\n",
    "car_dataset.replace({'Fuel_Type':{'Petrol':0,'Diesel':1,'CNG':2}},inplace=True)\n",
    "\n",
    "# encoding \"Seller_Type\" Column\n",
    "car_dataset.replace({'Seller_Type':{'Dealer':0,'Individual':1}},inplace=True)\n",
    "\n",
    "# encoding \"Transmission\" Column\n",
    "car_dataset.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c53ae000",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c668ba",
   "metadata": {},
   "source": [
    "# Splitting the data and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2936826",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = car_dataset.drop(['Car_Name','Selling_Price'],axis=1)\n",
    "Y = car_dataset['Selling_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47850fb4",
   "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": 13,
   "id": "c469b855",
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660b3e22",
   "metadata": {},
   "source": [
    "# Splitting Training and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da65d4cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c59e8cf",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1549debe",
   "metadata": {},
   "source": [
    "1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2472a631",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the linear regression model\n",
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3db792a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08893006",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "242d8966",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "training_data_prediction = lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71be06ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8799451660493698\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c943e6",
   "metadata": {},
   "source": [
    "# Visualize the actual prices and Predicted prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1c43a772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, training_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a7b89bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "test_data_prediction = lin_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a27f5789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8365766715026903\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac79fa14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, test_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1eca90",
   "metadata": {},
   "source": [
    "2. Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc073c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the linear regression model\n",
    "lass_reg_model = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6604a1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lass_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce88383a",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "51931629",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "training_data_prediction = lass_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0d2c2db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8427856123435794\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c00203",
   "metadata": {},
   "source": [
    "# Visualize the actual prices and Predicted prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66e43498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, training_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6d05de4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "test_data_prediction = lass_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6672e7e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8709167941173195\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e8e7608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, test_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0b65d5",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}