🏠 House Price Prediction using Linear Regression

📌 Project Overview

This project predicts house prices based on various features such as area, number of bedrooms, bathrooms, parking spaces, furnishing status, and other amenities. The model is built using Linear Regression and demonstrates the complete machine learning workflow from data preprocessing to model evaluation.

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🚀 Features

• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering and Encoding
• Correlation Analysis
• Linear Regression Model Training
• Model Evaluation using MAE, MSE, and R² Score
• House Price Prediction for new data
• Model Saving using Pickle

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🛠️ Tech Stack

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-Learn
• Pickle

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📂 Project Structure

House-Price-Prediction/ ├── housing.csv ├── house_price_prediction.ipynb ├── house_price_model.pkl └── README.md

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📊 Dataset Features

• Area
• Bedrooms
• Bathrooms
• Stories
• Parking
• Main Road Access
• Guest Room
• Basement
• Hot Water Heating
• Air Conditioning
• Preferred Area
• Furnishing Status

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📈 Model Performance

• Algorithm: Linear Regression
• R² Score: 65.29%
• Successfully predicts house prices based on input features.

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🎯 Key Insights

• Bathrooms had the highest positive impact on house prices.
• Houses with air conditioning and hot water heating tend to have higher prices.
• Preferred location and furnishing status significantly influence house prices.
• Larger house areas generally correspond to higher prices.

👩‍💻 Author

Simran Singh