https://github.com/shliakhovai/house-price-prediction

This repository contains a complete machine learning pipeline for predicting housing prices. It includes data preprocessing, feature engineering, and model training and evaluation components, designed to provide a robust solution for regression tasks.
https://github.com/shliakhovai/house-price-prediction

data-science machine-learning matplotlib numpy pandas prediction python regression scikit-learn seaborn

Last synced: 14 days ago
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This repository contains a complete machine learning pipeline for predicting housing prices. It includes data preprocessing, feature engineering, and model training and evaluation components, designed to provide a robust solution for regression tasks.

• Host: GitHub
• URL: https://github.com/shliakhovai/house-price-prediction
• Owner: shliakhovai
• Created: 2024-08-28T20:23:49.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-29T20:33:57.000Z (4 months ago)
• Last Synced: 2024-09-27T20:04:43.723Z (3 months ago)
• Topics: data-science, machine-learning, matplotlib, numpy, pandas, prediction, python, regression, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 3.57 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Housing Price Prediction

## Overview

This repository contains a complete machine learning pipeline for predicting housing prices. It includes data preprocessing, feature engineering, and model training and evaluation components, designed to provide a robust solution for regression tasks.

## Project Structure

- **`data/`**: Contains raw and processed data files.

- **`notebooks/`**: Contains Jupyter Notebooks for data exploration, preprocessing and model development with evaluation.

- **`scripts`**: Contains python classes with data preprocessing, model training, evaluation and running pipeline.

- **`requirements.txt`**: Contains all dependencies to this project.

- **`README.md`**: This file.

## Installation

1. **Clone the repository:**

```python

   git clone https://github.com/shliakhovai/house-price-prediction.git

   cd house-price-prediction

```

2. **Install dependencies:**

   Ensure you have Python installed. Install the required libraries using pip:

```

   pip install -r requirements.txt

```

3. **Run the pipeline:**

  Execute the ```main_pipeline.py``` script to run the entire workflow:

```python

   python main_pipeline.py

```

4. **Check the results:**

 - Processed data will be saved in the ```../data/processed/``` directory.

 - The best model will be saved in the ```../models/``` directory.