This repository provides a set of ten machine learning projects. These projects show how computers learn from data to make predictions. The collection includes both regression models for numerical output and classification models for category assignment. You can use these tools to understand how data science works in the real world. Each project operates through a simple interface built with Streamlit, which makes viewing the results straightforward.
To run this software, your computer must meet the following basic standards:
Follow these steps to set up the software on your Windows machine:
After you install the program, you can launch it from your desktop shortcut or the Start menu. When the application starts, a small window opens. Then, your default web browser will open to display the project dashboard. From this dashboard, you can click on any of the ten projects to see how they perform. You can adjust the sample data and watch the models update their results in real time. This interactive approach helps you visualize the math behind each algorithm without writing code yourself.
The software includes ten unique projects. Regression models help you predict values like house prices or future sales. Classification models help you group items, such as identifying spam messages or classifying types of flowers. Each project uses Scikit-learn for calculations and Python to handle the data logic. These tools provide a clear window into common data science tasks. The interface remains clean and removes the need for manual configuration.
If you encounter issues during installation, check your antivirus settings. Sometimes, security software blocks new programs. Ensure you have administrative rights on your machine to install software. If the dashboard does not open, try refreshing your browser window. For errors that persist, verify that you have enough disk space available.