This repository contains a machine learning project focused on predicting house prices using the Random Forest Regressor algorithm. The goal of this project is to build a robust model that can accurately estimate the price of a house based on various features such as location, size, number of bedrooms, and other relevant factors.
- Machine Learning Algorithm: Utilizes the Random Forest Regressor algorithm for prediction.
- Data Preprocessing: Includes scripts for cleaning, preprocessing, and transforming the dataset.
- Feature Engineering: Demonstrates techniques for selecting and engineering features to improve model performance.
- Model Training: Detailed implementation of the Random Forest Regressor, including parameter tuning and model evaluation.
- Prediction and Evaluation: Provides methods for predicting house prices and evaluating model accuracy using metrics like Mean Squared Error (MSE) and R-squared.
- Incorporate additional algorithms for comparison and improvement.
- Enhance feature engineering techniques for better accuracy.
- Deploy the model as a web application for real-time predictions.
~ Feel free to explore the project, provide feedback, and contribute to further improvements!