MLOps Project

A sample repository for demonstration of MLOps in machine learning projects

Housing Price Prediction Project

This project aims to develop a machine learning model to predict housing prices based on various features such as size, bedrooms, bathrooms, garage, and neighborhood.

Project Structure

The project is organized as follows:

data/
- housing_data.csv
notebooks/
- exploratory_analysis.ipynb
src/
- preprocessing.py
- model.py
- evaluation.py
- generatedata.py
reports/
- project_report.pdf
models/
- trained_model.pkl
docs
- README.md
- stup.md
The data/ directory contains the dataset file housing_data.csv.
The notebooks/ directory contains the exploratory_analysis.ipynb Jupyter Notebook for exploring the dataset and gaining insights.
The src/ directory contains the Python modules for preprocessing the data (preprocessing.py), training the model (model.py), evaluating the model (evaluation.py), and generating dummy data (generatedata.py).
The reports/ directory contains the project report in PDF format (project_report.pdf).
The models/ directory stores the trained machine learning model (trained_model.pkl).
The docs/ directory includes the project documentation, including the README.md and setup.md files.

Getting Started

To get started with the project, follow these steps:

Clone the repository:

git clone https://github.com/Tonyloyt/mlopsProject.git

Install the required dependencies. You can use the following command to install the necessary dependencies:
```
pip install -r requirements.txt
```
Explore the dataset using the exploratory_analysis.ipynb notebook in the notebooks/ directory.
Preprocess the data by running the preprocessing.py module in the src/ directory.
Train the machine learning model using the model.py module.
Evaluate the trained model using the evaluation.py module.

Results and Evaluation

The trained model can be evaluated using various metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The evaluation.py module provides functions to calculate these metrics and visualize the true versus predicted values.

Documentation

The docs/ directory contains the project documentation, including the README.md file for an overview of the project and the setup.md file for installation and setup instructions.

Reports

The reports/ directory includes the project report in PDF format (project_report.pdf). It provides a detailed description of the project, methodology, and results.

Conclusion

This project demonstrates the process of developing a machine learning model for housing price prediction. By following the steps outlined in the project structure, you can explore the dataset, preprocess the data, train the model, and evaluate its performance.

Feel free to customize the project structure and modules according to your specific requirements.

Please make sure to update the repository URL and any other placeholder information with the actual details of your project.