Dynamic Machine Learning Model Selector

Overview

The Dynamic Machine Learning Model Selector is an interactive web application designed to facilitate the selection and evaluation of various machine learning models. This tool allows users to easily upload datasets, select features and target variables, and dynamically apply regression and classification models. It aims to streamline the model selection process by providing essential performance metrics and visualizations.

Features

• Dataset Upload: Users can upload their own CSV datasets for analysis.
• Model Selection: Choose from multiple machine learning models, including:
  • Linear Regression
  • Logistic Regression
  • Decision Tree Classifier/Regressor
  • Random Forest Classifier/Regressor (with hyperparameter tuning)
• Visualization: Visualize model performance through confusion matrices, bar graphs of Mean Squared Error, and other metrics.
• User-Friendly Interface: Built with Streamlit for an intuitive user experience.

Getting Started

Prerequisites

• Python 3.x installed on your system. Download it from python.org.

Installation

Follow the steps below to set up the project:

1. Open a terminal and navigate to your project folder:

   cd myproject

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • Windows PowerShell:

     .\venv\Scripts\Activate.ps1

   • macOS/Linux:

     source venv/bin/activate

4. Install the required libraries:

   pip install streamlit pandas scikit-learn matplotlib seaborn plotly

5. Launch the application:

   streamlit run app.py

Requirements

The following Python libraries are required for this application:

• streamlit
• pandas
• scikit-learn
• matplotlib
• seaborn
• plotly

These can be installed using:

pip install -r requirements.txt