Abdelrahman-Amen / Housing-Price

Predicting housing prices with machine learning regression models. This project implements Linear Regression, Random Forest, and Decision Tree models for accurate predictions.

Housing Price Prediction 🏡

Predict housing prices using machine learning.

Algorithms Used

• Linear Regression
• Random Forest
• Decision Tree (Best Performing)

Features

• Data exploration with Pandas and NumPy.
• Preprocessing for optimal model performance.
• Min-Max scaling and feature selection using scikit-learn.
• Regression models: Linear Regression, Random Forest, and Decision Tree.

Technologies

• Python, scikit-learn, Pandas, NumPy, Matplotlib, Seaborn.

Project Overview

This project aims to provide accurate predictions for housing prices based on a variety of features. The main focus is on leveraging machine learning techniques, with particular attention given to the Decision Tree algorithm, which has shown superior performance in our analysis.

Goals

• Predict housing prices with high accuracy.
• Analyze the impact of different regression algorithms on prediction results.
• Provide a reliable and interpretable model for real estate trends.

Methodology

• Data Exploration: In-depth analysis using Pandas and NumPy to understand the dataset.
• Preprocessing: Clean and transform data for optimal model performance.
• Feature Scaling and Selection: Utilize Min-Max scaling and scikit-learn's SelectFromModel.
• Regression Models: Implement Linear Regression, Random Forest, and Decision Tree models.