This repository contains a simple House Price Prediction model implemented using Python. The project follows a structured process, including data cleaning, model development using Ridge regression, and the creation of a web-based user interface using Flask.
Key Components
Project Overview:Provide a brief introduction to the project, explaining its purpose and objectives.The dataset used for training and testing the model is taken from kaggle.
Dataset Description: The dataset used, including the features (e.g., number of bedrooms, square footage, location) and the target variable (house price). Information about the source of the dataset and any preprocessing steps applied (cleaning, feature engineering, etc.).
Model Architecture: The machine learning model(s) used for house price prediction (e.g., linear regression, decision trees, neural networks).
Data Cleaning: The model utilizes a dataset from Kaggle (Seattle House Price Prediction). The dataset undergoes cleaning to handle missing values, categorical data, and other preprocessing steps.
Model Development: The machine learning model is implemented using Ridge regression, leveraging the scikit-learn library. The trained model is saved for later use.
Flask Web Application: The project incorporates a Flask web application, providing a user-friendly interface for predicting house prices. Users can input details such as the number of bedrooms, bathrooms, house size, and zip code to receive a price prediction.
Usage Clone the repository:
git clone https://github.com/yourusername/HousePrice_Prediction.git cd HousePrice_Prediction
Install dependencies:
pip install -r requirements.txt
Run the Flask application:
python main.py
Open your web browser and visit http://127.0.0.1:5000/ to interact with the House Price Prediction interface.
Datasets Used Seattle House Price Prediction Dataset [Kaggle] Feel free to explore and adapt the project for your own use. If you have any questions or suggestions, please create an issue or reach out to yourusername. Happy coding!