ML-InsightHub - Automated Machine Learning with Streamlit, Pandas Profiling, and PyCaret

ML-Insight Hub is an application for Automated Machine Learning (AutoML) built using Streamlit, Pandas Profiling, and PyCaret. This application allows users to upload a dataset, perform Exploratory Data Analysis (EDA), build machine learning models for regression or classification, and download the trained models.

Getting Started

• To run the application locally, make sure you have the required libraries installed. You can install them using:

pip install streamlit pandas ydata_profiling streamlit_pandas_profiling pycaret

• After installing the libraries, you can run the application by executing:

streamlit run app.py

Application Structure

Sidebar Navigation

• Upload: Upload your dataset for modeling.
• Profiling: Perform Automated Exploratory Data Analysis (EDA) on the dataset.
• ML Regression: Build automated machine learning models for regression.
• ML Classification: Build automated machine learning models for classification.
• Download: Download the trained machine learning model.

Upload

• Upload your dataset using the file uploader.
• The uploaded data will be saved as "source_data.csv."

Profiling

• Conduct Automated Exploratory Data Analysis (EDA) using Pandas Profiling.
• Gain insights into the dataset's structure, distribution, and statistics.

ML Regression

• Train a machine learning model for regression.
• Select the target column and click "Train model."
• Compare different regression models and save the best one as "best_model_regression.pkl."

ML Classification

• Train a machine learning model for classification.
• Select the target column and click "Train model."
• Compare different classification models and save the best one as "best_model_classification.pkl."

Download

• Download the trained machine learning model based on the last chosen type (regression or classification).
• Use the provided code snippets to load and use the trained model in your own code.

Usage Instructions

• Follow the sidebar navigation to perform specific tasks.
• After training a model, use the "Download" section to download the trained model file.
• Refer to the code snippets provided for using the trained model in your code.

Note

• Ensure you have trained a model before attempting to download it.
• The application provides flexibility for regression tasks only. The application is still under development for classification tasks.

Contributing

1. Fork the repository.
2. Create a new branch: git checkout -b feature_branch.
3. Commit your changes: git commit -m 'Add new feature'.
4. Push to the branch: git push origin feature_branch.
5. Open a pull request.