🏠 House Price Prediction using Machine Learning

📖 Problem Statement

Predict house prices based on various features such as location, size, number of rooms, and amenities to help buyers and sellers make informed decisions.

🎯 Objective

To build a regression model that accurately predicts house prices.

🗂 Dataset

• Source: Kaggle
• Features: Area, Bedrooms, Location, Bathrooms, Price
• Rows: ~2000+

🛠 Tools & Technologies

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

🔍 Approach

1. Data cleaning and handling missing values
2. Exploratory Data Analysis
3. Feature scaling
4. Model training using Linear Regression & Random Forest
5. Model evaluation using RMSE

📊 Key Insights

• Location and area significantly impact price
• Random Forest performed better than Linear Regression

🤖 Model Used

• Linear Regression
• Random Forest Regressor

🚀 Results

The Random Forest model achieved the lowest RMSE, making it the best-performing model.

📌 Conclusion

This project demonstrates how machine learning can help estimate property prices and assist real estate decision-making.