Predicting House Rent Using ML: Random Forest vs Linear Regression

Introduction:

In this post, I’ll walk through a machine learning project where I built a rent prediction model using Random Forest and Linear Regression. We’ll load the dataset, clean it, train two models, and compare their performance using Mean Squared Error (MSE).

Dataset Overview:

• Source: Kaggle dataset - House Rent Prediction

• Features:

  • BHK (No. of Bedrooms)

  • Size (sq ft)

  • City

  • Furnishing Status

  • Floor Info

  • Area Type / Locality

  • Bathroom Count

  • Tenant Preferences

Preprocessing:

• Encoded categorical features like City, Furnishing Status

• Extracted floor info

• One-hot encoding and label encoding

• Used train_test_split(test_size=0.2, random_state=42)

Models:

• Trained two models:

  • LinearRegression()

  • RandomForestRegressor(n_estimators=100)

Collab Link: link

Conclusion:

Random Forest is a strong choice for house rent prediction. For production, I'd next explore hyperparameter tuning and feature importance analysis.

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