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https://github.com/pramod858/house-price-prediction
https://github.com/pramod858/house-price-prediction
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/pramod858/house-price-prediction
- Owner: Pramod858
- Created: 2023-11-27T11:30:19.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-21T12:53:11.000Z (about 1 year ago)
- Last Synced: 2023-12-21T15:43:15.523Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 5.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# House Price Prediction
## Overview
This project is a simple web application for predicting house prices based on various features. It uses a machine learning model trained on a dataset of house prices. The web interface allows users to input details about a house, and the system will predict the price.
## Prerequisites
Before running the application, ensure you have the following installed:
- Python (version 3.9.18)
- Docker
## Getting Started
1. **Clone the Repository:**
```bash
git clone https://github.com/Pramod858/House-Price-Prediction.git
cd House-Price-Prediction
```
2. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the Flask App Locally:**
```bash
python app.py
```
The application will be accessible at `http://127.0.0.1:5000/` in your browser.
## Using Docker
Alternatively, you can run the application using Docker.
1. **Build the Docker Image:**
```bash
docker build -t house_price_prediction -f Dockerfile.txt .
```
2. **Run the Docker Container:**
```bash
docker run -p 5000:5000 house_price_prediction
```
The application will be available at `http://127.0.0.1:5000/` in your browser.
## Extra :
Here is the [link](https://github.com/Pramod858/House-Price-Prediction-MLflow.git) for House Price Prediction with MLflow.
## Usage
- Open your browser and navigate to `http://127.0.0.1:5000/`.
- Input the details for the house (bedrooms, bathrooms, etc.).
- Click the "Predict Price" button to get the predicted house price.