House Price Prediction

Overview

This project offers a machine-learning model that predicts house prices based on various features. It utilizes a Gradient Boosting Regressor trained on a curated dataset of housing information.

Dataset

• Dataset Name: house price dataset
• Data Source: upload on git .
• The dataset contains the following attributes:
  • Feature columns (13): Numerical values representing various house price-related features.
  • Target column: price of houses.

Project Structure

• README.md: Documentation of the project.
• main.py: Python script for making diabetes predictions.
• data.joblib : weights of transformer used to transfer data before traning.
• model.joblib: Pre-trained logistic regression model for diabetes prediction.

Setup

1. Clone the repository:

   git clone PricePrediction
   cd House Price Prediction

Create a virtual environment (recommended) and install the required dependencies: python -m venv venv source venv/bin/activate # On Windows, use: venv\Scripts\activate

Usage

Clone this repository to your local machine. Ensure you have the pre-trained logistic regression model ('model.pkl') in the same directory as the script ('diabetes_prediction.py'). Open a command prompt or terminal and navigate to the directory where the script is located. Run the script with the --value argument followed by a comma-separated list of feature values that you want to classify.

For example:

python main.py --value "6,148,72,35,0,33.6,0.627,50"

Follow the instructions in the script to make predictions.

Model Training

The project uses a logistic regression model to predict the price of the house. The pre-trained model is saved as 'model.pkl'.

Evaluation

The script provides binary predictions. You can evaluate the model's performance using metrics like accuracy, precision, and F2 score.

Results

The project provides predictions for house prices based on the input features. The performance of the model may vary depending on the dataset used.

Future Improvements

There are several ways to improve the model and the project:

Explore more advanced machine learning techniques. Fine-tune hyperparameters for better model performance. Gather more labeled data for improved accuracy. References Author: Muhammad Mubashir Ali Contact: muhammadmubashirali63@gmail.com Feel free to customize this README to include any additional information you want to provide about the project.