A sample repository for demonstration of MLOps in machine learning projects
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.
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 filehousing_data.csv
. -
The
notebooks/
directory contains theexploratory_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 theREADME.md
andsetup.md
files.
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 thenotebooks/
directory. -
Preprocess the data by running the
preprocessing.py
module in thesrc/
directory. -
Train the machine learning model using the
model.py
module. -
Evaluate the trained model using the
evaluation.py
module.
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.
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.
The reports/ directory includes the project report in PDF format (project_report.pdf). It provides a detailed description of the project, methodology, and results.
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.