AutoML

AutoML is an open-source project that aims to simplify the machine learning process by providing a streamlined and user-friendly interface for data analysis, exploratory data profiling, and model building. This project leverages the power of:

Features 👨‍💻

• Upload Your Dataset: Easily upload your dataset in CSV format to initiate the data analysis process.
• Exploratory Data Analysis (EDA): Perform in-depth exploratory data analysis using the Pandas Profiling library to gain insights into the dataset's characteristics.
• Machine Learning Modeling: Build regression models effortlessly with PyCaret's streamlined setup and comparison tools. Choose a target variable and let AutoML handle the rest.
• Model Comparison: Automatically compare and evaluate multiple regression models to identify the best-performing model for your dataset.
• Download Trained Model: Download the trained regression model as a pickle file for future use and deployment.

How to Use 🤔

1. Clone this repository to your local machine.
2. Install the required libraries using pip install -r requirements.txt.
3. Run the Streamlit app using streamlit run app.py.
4. Navigate through the intuitive sidebar menu to upload your dataset, perform exploratory data analysis, build machine learning models, and download the trained model.

Getting Started 🤓

1. Start by uploading your dataset through the "Upload" option in the sidebar.
2. Explore the dataset's characteristics and gain insights using the "Profile" section.
3. Choose the target variable and initiate the machine learning modeling process under the "Machine Learning" section.
4. Compare different regression models and save the best-performing model.
5. Download the trained model for future use.

Contributors 🫂

This project welcomes contributions from the open-source community. If you encounter issues, have suggestions, or would like to enhance the project, feel free to create issues, pull requests, or reach out to us.

License📃

AutoML is released under the MIT License.

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Disclaimer: This project is a template and may require further customization and error handling based on specific use cases.