Machine Learning Project – Car Price Prediction
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Program 1
import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics #pandas 2.2.2 #numpy 1.24.3 #matplotlib 3.7.1 #scikit-learn 1.5.1 # Read data set df=pd.read_csv("D://scikit_data/car/car_data.csv") df.head() type(df) # No of Rows & Columns df.shape # Dispaly information about dataset df.info() # Infomrmation about missing values df.isnull().sum() # Check categorical data print(df.Fuel_Type.value_counts()) print(df.Seller_Type.value_counts()) print(df.Transmission.value_counts()) # Replace Character values df.replace({'Fuel_Type':{'Petrol':0,'Diesel':1,'CNG':2}},inplace=True) df.replace({'Seller_Type':{'Dealer':0,'Individual':1}},inplace=True) df.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True) df.head() # Find Depended and Independed variables x=df.drop(['Car_Name','Selling_Price'],axis='columns') # Independed variables y=df['Selling_Price'] # Depended variables x y # Split data into training and testing data x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=2) # x_train ---> training independed variable # y_train ---> training depended variable # x_test ---> test independed variable # y_test ---> test depended variable len(x_train) len(x_test) # Model Prepration model=LinearRegression() model.fit(x_train,y_train) y_pred_train=model.predict(x_train) y_train plt.scatter(y_train,y_pred_train,marker='.',color='blue') plt.xlabel("Actual Price") plt.ylabel("Predicated Price") plt.title("Actual Price vs Predicated Price") plt.show() error_score=metrics.r2_score(y_train,y_pred_train) print("R squared Error for training data set ", error_score) # Predication an testing data y_pred_test=model.predict(x_test) plt.scatter(y_test,y_pred_test,marker='.',color='red') plt.xlabel("Actual Price") plt.ylabel("Predicated Price") plt.title("Actual Price vs Predicated Price") plt.show() error_score=metrics.r2_score(y_test,y_pred_test) print("R squared Error for training data set ", error_score) model.score(x_test,y_test)
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