House Price Prediction Model

Developed a real estate price prediction model using machine learning models
such as linear regression ,random forest etc

Developed in a Jupyter Notebook using Pandas, Imputer, Matplotlib, Pipeline and Scikitlearn, this project employed three regression algorithms: linear regression, decision tree regressor, and random forest regressor.

The random forest regressor was chosen for its strong predictive capabilities, utilizing ensemble learning to combine insights from multiple decision trees and capture complex relationships within the housing dataset. This decision aligns with the model's goal of accurate and robust housing price prediction.

Built With

• jupyter
• matplotlib
• pandas
• python
• scikit-learn