Housing Real Estate Prediction Project

Overview

Welcome to the Housing Real Estate Prediction Project! This repository contains my initial foray into the world of machine learning, focusing on predicting housing prices using various models. As a beginner in machine learning, this project serves as a practical learning experience where I explore different algorithms and methodologies for making accurate predictions.

Project Description

The primary goal of this project is to predict housing prices based on various features using three different models:

Random Forest Linear Regression Support Vector Regression (SVR) By comparing the performance of these models, I aim to understand the strengths and weaknesses of each approach and improve my machine learning skills.

Getting Started Prerequisites To run this project, you will need to have the following installed:

Python 3.6 or higher Jupyter Notebook or Jupyter Lab Installation Clone the repository to your local machine:

bash Copy code git clone https://github.com/yourusername/housing-real-estate-prediction.git cd housing-real-estate-prediction Install the required Python packages:

bash Copy code pip install -r requirements.txt Dataset For this project, I used the California Housing Prices dataset. Download the dataset and place it in the data directory.

Usage Data Exploration

Linear Regression notebook contains the implementation of the Linear Regression model. It includes data preprocessing, model training, and evaluation.

Random Forest notebook, you will find the implementation of the Random Forest model. Similar to the Linear Regression notebook, it covers data preprocessing, model training, and evaluation.

Evaluation The models are evaluated based on the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The performance of each model is compared to determine which one provides the most accurate predictions.

Conclusion This project is a great starting point for anyone new to machine learning. It provides hands-on experience with different algorithms and a deeper understanding of the predictive modeling process. I welcome any feedback or suggestions for improving this project.

Contributing If you would like to contribute to this project, please fork the repository and create a pull request. Any contributions, whether they be new features, bug fixes, or improvements, are greatly appreciated.

Acknowledgements The dataset used in this project is sourced from Kaggle. Special thanks to the open-source community for providing the tools and libraries used in this project. Feel free to reach out if you have any questions or suggestions!

Many thanks to Shashank Kalanithi | @kalamari95

Hands-on Machine Learning with Scikit-Learn Keras & Tensorflow