import pandas as pd 
# Load the dataset 
df = pd.read_csv('Pune_rent.csv') 
# Display the first few rows and basic information 
print(df.head()) 
print(df.describe()) 
print(df.info()) 
# Check for missing values 
print(df.isnull().sum()) 
# Convert categorical variables to dummy/indicator variables if necessary 
# For demonstration, assuming 'location', 'house_type' are categorical variables 
df = pd.get_dummies(df, columns=['location', 'house_type'], drop_first=True) 
from sklearn.model_selection import train_test_split 
# Separate features and target 
X = df.drop(columns=['Rent']) 
y = df['Rent'] 
# Split into training and testing sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 
from sklearn.preprocessing import StandardScaler 
# Standardize the features 
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 
from sklearn.linear_model import LinearRegression 
# Initialize and train the model 
model = LinearRegression() 
model.fit(X_train_scaled, y_train) 
# Predict on the test set 
y_pred = model.predict(X_test_scaled) 
from sklearn.metrics import mean_absolute_error, mean_squared_error 
import numpy as np 
# Calculate MAE and RMSE 
mae = mean_absolute_error(y_test, y_pred) 
rmse = np.sqrt(mean_squared_error(y_test, y_pred)) 
print("Mean Absolute Error (MAE):", mae) 
print("Root Mean Squared Error (RMSE):", rmse)