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HOUSE PRICE PREDICTION

This Python project is designed to predict house prices using machine learning algorithms. It takes input features such as the size of the house, the number of bedrooms, the location, and other relevant parameters to predict the price of the house.

Table of Contents

Overview

The goal of this project is to provide a tool for predicting house prices based on input parameters. It utilizes machine learning techniques to train a model on historical data and then uses that model to predict house prices for new data points.

Dependencies

This project requires the following dependencies:

  • Python (>= 3.6)
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib (for visualization, optional)

Installation

  1. Clone the repository to your local machine:
git clone https://github.com/your_username/HOUSE_PRICE_PREDICTION.git
  1. Navigate to the project directory:
cd HOUSE_PRICE_PREDICTION
  1. Install the required dependencies:
pip install -r requirements.txt

Usage

  1. Prepare your dataset:

    • Ensure that you have a dataset with relevant features (e.g., size, number of bedrooms, location, etc.) and corresponding house prices.
    • The dataset should be in a format compatible with Pandas DataFrame.

  2. Train the model:

    • Run the training script to train the machine learning model using your dataset.
    python train.py --dataset path_to_your_dataset.csv

  3. Predict house prices:

    • Once the model is trained, you can use it to predict house prices for new data points.
    python predict.py

  4. Visualize results (optional):

    • If you want to visualize the results or analyze the model's performance, you can run the visualization script.
    python visualize.py

File Descriptions

  • train.py: Script for training the machine learning model.
  • predict.py: Script for making predictions using the trained model.
  • visualize.py: Script for visualizing the results and analyzing the model's performance.
  • requirements.txt: List of dependencies required for the project.
  • README.md: Documentation file providing an overview of the project and instructions for usage.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvement, please feel free to open an issue or create a pull request.

Feel free to customize this README according to your specific project requirements and structure. Happy coding!