House Price Prediction Project

This repository contains the code and documentation for a machine learning project that aims to predict house prices using various regression models. The project involves data preprocessing, feature engineering, model evaluation, and feature selection.

Table of Contents

• Project Overview
• Getting Started
• Data Preprocessing
• Feature Engineering
• Model Evaluation
• Feature Selection
• Saving the Model
• Conclusion

Project Overview

The main objective of this project is to predict house prices based on a set of features related to the properties. The project involves the following steps:

1. Data preprocessing to handle missing values and encode categorical features.
2. Feature engineering to create new features and transform existing ones.
3. Evaluation of various regression models to identify the best-performing one.
4. Feature selection to identify the most important features for prediction.
5. Saving the trained model for future use.

Getting Started

To get started with this project, follow these steps:

1. Clone this repository to your local machine.
2. Install the required libraries and dependencies mentioned in the requirements.txt file.
3. Run the Jupyter Notebook file house-price-prediction.ipynb to execute the code.

Data Preprocessing

The data preprocessing phase involves handling missing values and encoding categorical features. Various techniques, such as imputation and one-hot encoding, are applied to prepare the data for model training.

Feature Engineering

Feature engineering is a critical step in improving model performance. New features are created, and existing features are transformed to capture important information. This step ensures that the data is in a suitable format for modeling.

Model Evaluation

Multiple regression models are evaluated to identify the best-performing one for house price prediction. Models such as Linear Regression, Ridge Regression, K Nearest Neighbors, Support Vector Regression, Decision Tree Regression, XGBoost Regressor, Random Forest Regressor, and LightGBM Regressor are assessed using metrics such as Mean Absolute Error, R^2 score, and Root Mean Squared Error.

Feature Selection

Feature selection techniques are applied to narrow down the set of features used for model training. The most important features are identified and used for predicting house prices.

Saving the Model

The trained Random Forest Regressor model, which performed best in our evaluations, is saved for future use. This saved model can be loaded and used to make predictions on new data.

Demo of API Testing:

Postman-Demo.mp4

Conclusion

This project showcases the process of predicting house prices using machine learning techniques. By following the steps outlined in this repository, you can gain insights into data preprocessing, feature engineering, model evaluation, and feature selection. Feel free to explore the Jupyter Notebook files to dive deeper into the code and understand the project in more detail.

If you have any questions or need further assistance, please don't hesitate to reach out.

Happy coding!