House Price Prediction Using Multiple Linear Regression

📌 Project Overview

This project implements Multiple Linear Regression to predict house prices based on numerical property and neighborhood characteristics. The goal is to understand how various factors influence property value and evaluate model performance using standard regression metrics.

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🎯 Problem Statement

Given a dataset of residential properties with numerical features, the objective is to:

• Predict house prices accurately
• Identify the most influential factors affecting property value
• Evaluate model performance using regression metrics

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📂 Dataset Details

• File Name: Houses.xlsx / linear_regression_numeric.csv
• Target Variable: price (in $1000s)

Features:

• area_sqft – Total built-up area
• bedrooms – Number of bedrooms
• bathrooms – Number of bathrooms
• age_years – Age of the house
• distance_city_center_km – Distance from city center
• num_schools_nearby – Schools within 3 km
• crime_rate_index – Crime severity index

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🛠️ Tech Stack

• Language: Python
• Libraries:
  • pandas
  • numpy
  • matplotlib / seaborn
  • scikit-learn
• Environment: Jupyter Notebook

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🔍 Methodology

1. Data Loading & Cleaning

• Imported numerical dataset
• Checked for missing values and data consistency

2. Exploratory Data Analysis

• Analyzed feature distributions
• Studied correlations between features and house price

3. Model Building

• Implemented Multiple Linear Regression
• Split data into training and testing sets
• Trained regression model on training data

4. Model Evaluation

• R² Score: Measures variance explained by the model
• MAE: Average absolute prediction error
• RMSE: Penalizes larger prediction errors

5. Model Interpretation

• Analyzed regression coefficients
• Identified positive and negative price drivers

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📊 Key Results & Insights

• Model explains ~95% of price variance
• Area, bedrooms, bathrooms strongly increase price
• Distance from city center and crime rate reduce price
• Model shows strong generalization on test data

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📈 Final Conclusion

The Multiple Linear Regression model provides accurate and interpretable house price predictions. It highlights key real-estate drivers and serves as a solid foundation for predictive analytics in property valuation.

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🚀 How to Run

1. Clone the repository
2. Open the notebook in Jupyter
3. Install required libraries
4. Run all cells sequentially

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