Auto-ML-App

Overview

This project provides an Automatic Machine Learning (AutoML) application built using Streamlit. Users can upload datasets in CSV/XLSX format, select a model, and train it automatically with preprocessing and evaluation metrics. The trained model and its performance metrics can be downloaded for further use.

Features

• Supports multiple models:
  • Random Forest (Classification & Regression)
  • Logistic Regression (Classification)
  • Support Vector Machine (SVM) (Classification & Regression)
  • Linear Regression (Regression)
• Automatic Preprocessing:
  • Handles missing values
  • Encodes categorical variables
  • Scales numerical features
• Performance Metrics:
  • Classification: Accuracy, Precision, Recall, F1-score, Confusion Matrix
  • Regression: Mean Squared Error (MSE), R² Score
• Visualizations:
  • Confusion matrix for classification
• Downloadable Outputs:
  • Trained model (.pkl file)
  • Model performance report (metrics.txt)

Installation

Prerequisites

Make sure you have Python installed (>=3.7). You can install the required dependencies using:

pip install -r requirements.txt

Usage

Run the application with:

streamlit run app.py

Steps to Use:

1. Upload a dataset (CSV/XLSX format).
2. Select a model type (Random Forest, Logistic Regression, SVM, or Linear Regression).
3. Click "Train Model" to start training.
4. View the metrics and confusion matrix (for classification models).
5. Download the trained model and performance report.

Example Dataset

For testing, you can use datasets such as:

• Iris Dataset (for classification)
• Boston Housing Dataset (for regression)

Technologies Used

• Streamlit (UI framework)
• Scikit-Learn (Machine learning models)
• Pandas & NumPy (Data handling)
• Seaborn & Matplotlib (Visualization)
• Joblib (Model serialization)

License

This project is open-source and licensed under the MIT License.

Contributions

Feel free to contribute by submitting issues or pull requests!

Author

Developed by Tharun Bala.