HOURICEP -The House-Price-Prediction APP

This repository contains the code for a House Price Prediction application built using Streamlit. The app predicts house prices based on various input parameters provided by the user.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Data
• Machine Learning Models
• File Descriptions
• Contributing
• License

Introduction

The House Price Prediction app is a web application that allows users to input various parameters related to a house and predicts its price using a trained machine learning model. The app uses Gradient Boosting Regressor, which was selected after trying multiple models to achieve the best performance.

Features

• User-friendly web interface built with Streamlit.
• Input parameters include Construction status, RERA status, BHK No. , Square Feet, Readiness to move, Resale Status, Longitude, Latitude, Posted by, Type of house (BHK or RK), and City.
• Predicts the house price based on the input parameters by using ML Model

Installation

1.Clone the repository:

git clone https://github.com/Prasadayus/House-Price-Prediction.git 

2.Create and activate a virtual environment:

python -m venv myenv
source myenv/bin/activate

3.Install the required packages:

pip install -r requirements.txt

Usage

1.Run the Streamlit app:

streamlit run House_pred.py

2.Open your web browser and go to http://localhost:8501 to use the app.

Data

The dataset is 'House Price Prediction Challenge.csv' file in this repo The dataset used for training the model contains the following columns:

• POSTED_BY: The person who posted the listing (Owner, Dealer, Builder).
• UNDER_CONSTRUCTION: Whether the house is under construction (0 or 1).
• RERA: RERA status (0 or 1).
• BHK_NO.: Number of bedrooms.
• BHK_OR_RK: Type of house (BHK or RK).
• SQUARE_FT: Square footage of the house.
• READY_TO_MOVE: Whether the house is ready to move in (0 or 1).
• RESALE: Whether the house is a resale property (0 or 1).
• ADDRESS: Address of the house.
• LONGITUDE: Longitude of the house location.
• LATITUDE: Latitude of the house location.
• TARGET(PRICE_IN_LACS): Price of the house in lakhs.

File Descriptions

• House_pred.py: Main script to run the Streamlit app.
• House_pred.ipynb: Jupyter notebook for machine learning model training and evaluation.
• label_house_encoder.pkl: Pickle file containing the LabelEncoder for categorical variables.
• gbr_house_model.pkl: Pickle file containing the trained Gradient Boosting Regressor model.
• requirements.txt: List of required packages for the project.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.