import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score

train_data = pd.read_csv(r"C:\Users\aishwarya amin\house-prices-advanced-regression-techniques\train.csv") test_data = pd.read_csv(r"C:\Users\aishwarya amin\house-prices-advanced-regression-techniques\train.csv")

for column in train_data.columns: if train_data[column].dtype == 'object': #fill missing values with the mode for categorical features train_data[column].fillna(train_data[column].mode()[0]) if column in test_data.columns: test_data[column].fillna(test_data[column].mode()[0]) else: #fill the missing value with the mean for numeric features train_data[column].fillna(train_data[column].mean()) if column in test_data.columns: test_data[column].fillna(test_data[column].mean())

Select features

features=['GrLivArea','BedroomAbvGr','FullBath','HalfBath','TotRmsAbvGrd'] X = train_data[features] y = train_data['SalePrice']

#Split the training data for validation X_train,X_val,y_train,y_val = train_test_split(X, y,test_size=0.2,random_state=42)

Train the model

model=LinearRegression() model.fit(X_train,y_train)

y_pred = model.predict(X_val) mae = mean_absolute_error(y_val,y_pred) mse = mean_squared_error(y_val,y_pred) r2 = r2_score(y_val, y_pred)

plt.figure(figsize=(10,6)) plt.scatter(y_val,y_pred,alpha=0.5) plt.xlabel('Actual Sale Price') plt.ylabel('Prediction Sale Price') plt.title('Actual vs Predicted Sale Price') plt.plot([y.min(),y.max()],[y.min(),y.max()],'r--') plt.show()