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Machine Learning Streamlit Application

This repository contains a Streamlit-based Machine Learning Application designed to provide an intuitive and interactive interface for performing essential machine learning tasks, including Classification, Clustering, and Regression. It is ideal for beginners, educators, and professionals looking for a quick way to experiment with various machine learning models.

Features

1. Homepage

The homepage serves as the entry point to the app, providing a brief overview of the available tasks and guidance on how to use the application.

2. Classification

Perform classification using popular machine learning models:

  • Random Forest Classifier
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes Classifier
  • Logistic Regression Classifier

Each model has its own dedicated page with an easy-to-follow interface to:

  • Upload datasets in CSV format.
  • Select features and target variables.
  • Configure model-specific parameters.
  • View detailed classification reports with metrics like accuracy, precision, recall, and F1-score.

3. Clustering

Explore unsupervised learning models to group data points into clusters. Users can upload datasets and visualize clustering results dynamically.

  • K Means
  • DBSCAN
  • Agglomerative Clustering
  • Gaussian Mixtures

4. Regression

Experiment with regression models to predict continuous target variables. The app allows you to train and evaluate models interactively.

5. Interactive Widgets

The app uses Streamlit’s widgets (sliders, dropdowns, and file uploaders) for a seamless user experience.

6. Modular Design

Each task and model is implemented in a modular structure, making the app easy to extend or customize.

Technologies Used

  • Python
    The backbone of the application.

  • Streamlit
    A powerful framework for building interactive web applications.

  • Scikit-learn
    For implementing machine learning models.

  • Pandas
    For data preprocessing and manipulation.

  • Matplotlib & Seaborn
    For data visualization.

How to Run the Application

1. Clone the repository

git clone https://github.com/your-username/ml-streamlit-app.git
cd ml-streamlit-app

# Install dependencies
pip install -r requirements.txt

# Run the application
 streamlit run app.py