ML Learning Lab

A web application for university students to upload CSV datasets and see the step-by-step mathematical workings of machine learning models, starting with Linear Regression.

Features

• Upload CSV datasets with drag-and-drop
• Interactive data preview with column statistics
• Step-by-step mathematical explanations with LaTeX rendering
• Visualizations including scatter plots, residual plots, and actual vs predicted charts
• Full derivation of the Normal Equation for Linear Regression

Tech Stack

• Frontend: React + Vite, Tailwind CSS, KaTeX, Recharts
• Backend: FastAPI (Python), NumPy, Pandas

Getting Started

Prerequisites

• Python 3.8+
• Node.js 18+
• npm or yarn

Backend Setup

cd backend

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the server
uvicorn main:app --reload

The API will be available at http://localhost:8000

Frontend Setup

cd frontend

# Install dependencies
npm install

# Run the development server
npm run dev

The app will be available at http://localhost:5173

Usage

1. Open the app in your browser at http://localhost:5173
2. Upload a CSV file (a sample file sample_data.csv is provided in the root directory)
3. Select your feature column(s) (X) and target column (Y)
4. Click "Train Linear Regression"
5. Explore the step-by-step mathematical explanation

Sample Data

A sample CSV file (sample_data.csv) is included with student study data:

• hours_studied: Hours spent studying
• practice_tests: Number of practice tests taken
• previous_score: Score on previous exam
• final_score: Final exam score (target variable)

API Endpoints

• POST /api/upload - Upload a CSV file
• POST /api/train/linear-regression - Train a linear regression model
• GET /api/sessions/{session_id} - Get session data
• DELETE /api/sessions/{session_id} - Delete a session

What You'll Learn

The app explains:

1. Data Overview - Basic statistics of your dataset
2. Problem Setup - The linear model equation and matrix notation
3. Cost Function - Mean Squared Error (MSE) and why we use it
4. Normal Equation - Step-by-step derivation with actual matrix calculations
5. Results - Coefficient interpretation and model evaluation metrics