Automated-ML-Modelling

This is the Streamlit web application that allows users to upload a dataset, generate an automated exploratory data analysis (EDA) report using the pandas-profiling library, and and train a machine learning model for regression or classification tasks.

Installation

To run this application, you need to have Python 3.x and the following packages installed:

• Streamlit
• Pandas
• Pandas-profiling
• Pycaret

Otherwise you can install all the requirements by executing the following Command

pip install -r requirements.txt

Getting Started

This is make you understand how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

1. Clone this repository

git clone https://github.com/KalyanMurapaka45/Automated-ML-Modelling.git

2. Install the required Python libraries listed in the requirements.txt

pip install -r requirements.txt

3. Run the ML Model.py file

streamlit run ML Model.py

Usage Instructions

After running the ML Model.py file, a Streamlit app will be launched in your web browser. The app will have a sidebar with three options:

• Upload: Clicking on the "Upload" option allows you to upload a CSV file with your dataset. Once the file is uploaded, it displays the datafarame.

• Profile Report: Clicking on the "Profile Report" option generates an automated EDA report using the pandas-profiling library. You can download the report as an HTML file by clicking the "Download Report" button.

• Automatic Model Training: Selecting the "Automatic Model Training" option will train an automatic machine learning model using the Pycaret Library. You will need to select a target feature and a problem type (regression or classification) in the sidebar. The app will then train an automatic machine learning model using the pycaret library and display a table with the performance metrics of several models. You can download the best model as a pickle file by clicking the "Download Model" button.

Note: The app requires a CSV file as input. Make sure to have a CSV file ready before running the app.

Contributing

If you find a bug or have a feature request, please open an issue on this repository. Pull requests are also welcome.

License

This Repository licensed under the MIT License. See the LICENSE file for more information.