California House Price Prediction

This project uses a machine learning model to predict house prices in California based on various features like median income, house age, average rooms, average bedrooms, population, average occupancy, latitude, and longitude.

Table of Contents

• Overview
• Installation
• Usage
• Project Structure
• Features
• License

Overview

The California House Price Prediction project utilizes a linear regression model trained on the California housing dataset to predict the prices of houses. This project includes a Flask web application where users can input the required features to get the predicted house price.

##Installation: To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/california-house-price-prediction.git
   cd california-house-price-prediction

python -m venv venv

2.Create and activate a virtual environment:

venv\Scripts\activate

3.Install the dependencies:

pip install -r requirements.txt

4.Usage

To start the Flask web application, run: python app.py

5.Project Structure

california-house-price-prediction/ │ ├── venv/ # Virtual environment directory ├── templates/ │ └── home.html # HTML file for the web application ├── .gitignore ├── app.py # Flask application ├── california_house_price.ipynb # Jupyter notebook for data analysis and model training ├── LICENSE ├── README.md # Project readme file ├── regmodel.pkl # Serialized trained model ├── requirements.txt # Project dependencies

6.Features:

Predict house prices based on user input features. Simple and user-friendly web interface built with Flask. Model trained on the California housing dataset.

7.Deployment LInk:

https://california-house-price-3.onrender.com/