Sample regression

.dockerignore

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+# Git
+.git
+.gitignore
+.github
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+pip-log.txt
+pip-delete-this-directory.txt
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Node.js
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnp
+.pnp.js
+frontend/build
+frontend/.env.local
+frontend/.env.development.local
+frontend/.env.test.local
+frontend/.env.production.local
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+.DS_Store
+
+# Environment
+.env
+.env.local
+.env.*.local
+
+# Uploads (will be handled by volumes)
+backend/uploads/*
+!backend/uploads/.gitkeep
+
+# Database
+*.db
+*.sqlite
+*.sqlite3
+
+# Logs
+*.log
+logs/
+
+# Documentation
+docs/assets/
+*.md
+!README.md
+
+# Testing
+.pytest_cache/
+coverage/
+.coverage
+
+# OS
+Thumbs.db
+.DS_Store

Dockerfile.txt

@@ -1,18 +1,70 @@
-# Use official Python base image
-FROM python:3.11-slim
+# ML Simulator - Multi-stage Dockerfile
+# Author: Akshit
+# Date: October 13, 2025
+# Purpose: Containerize the ML Simulator application
+
+# ================================
+# Stage 1: Build Frontend
+# ================================
+FROM node:18-alpine AS frontend-build
 
 # Set working directory
+WORKDIR /app/frontend
+
+# Copy frontend package files
+COPY frontend/package*.json ./
+
+# Install dependencies
+RUN npm ci --only=production
+
+# Copy frontend source code
+COPY frontend/ ./
+
+# Build the React app
+RUN npm run build
+
+# ================================
+# Stage 2: Backend + Frontend
+# ================================
+FROM python:3.9-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create app directory
 WORKDIR /app
 
-# Copy requirements file and install dependencies
+# Copy backend requirements and install dependencies
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy the rest of the app files
-COPY . .
+# Copy backend code
+COPY backend/ ./backend/
+
+# Copy built frontend from previous stage
+COPY --from=frontend-build /app/frontend/build ./frontend/build
+
+# Create uploads directory
+RUN mkdir -p backend/uploads/datasets backend/uploads/resumes
+
+# Expose port
+EXPOSE 5000
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
+    CMD curl -f http://localhost:5000/health || exit 1
 
-# Expose a port if your app runs a server (optional)
-# EXPOSE 5000
+# Set working directory to backend
+WORKDIR /app/backend
 
-# Default command to run the simulator
-CMD ["python", "main.py"]
+# Run the application
+CMD ["python", "app.py"]

Docs/Knn.md

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+# K-Nearest Neighbors (KNN) - Documentation
+
+## 📋 Overview
+
+KNN is a simple, instance-based learning algorithm that classifies data points based on the classes of their k nearest neighbors[web:100][web:102].
+
+**Key Characteristics:**
+- **Type**: Instance-based Learning
+- **Algorithm**: Distance-based classification
+- **Output**: Class based on neighbor voting
+- **Best For**: Small to medium datasets, pattern recognition
+
+## 🎯 Purpose and Use Cases
+
+- **Recommendation Systems**: Similar user preferences
+- **Pattern Recognition**: Handwriting, image recognition
+- **Anomaly Detection**: Identifying outliers
+- **Medical Diagnosis**: Similar patient cases
+- **Text Classification**: Document similarity
+
+## 📊 Key Parameters
+
+| Parameter | Description | Default | Recommendation |
+|-----------|-------------|---------|----------------|
+| **n_neighbors (k)** | Number of neighbors | 5 | 3-15 (odd numbers) |
+| **weights** | Vote weighting | uniform | uniform/distance |
+| **metric** | Distance measure | euclidean | euclidean/manhattan |
+
+## 💡 Choosing K Value
+
+- **Small k (3-5)**: More sensitive to noise, complex boundaries
+- **Large k (10-20)**: Smoother boundaries, may miss patterns
+- **Rule of thumb**: √n where n = number of samples
+- **Use odd k**: Avoids tie votes in binary classification
+
+## 🐛 Common Issues
+
+### Slow Prediction
+- Reduce training data size
+- Use approximate methods
+- Try other algorithms for large datasets
+
+### Poor Performance
+- Scale features (very important for KNN!)
+- Try different k values
+- Check for irrelevant features
+
+---
+
+**Last Updated**: October 13, 2025  
+**Version**: 1.0  
+**Author**: Akshit  
+**Hacktoberfest 2025 Contribution** 🎃

Docs/Readme.md

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+# ML Simulator - Model Documentation
+
+Welcome to the ML Simulator documentation! This directory contains comprehensive guides for each machine learning model available in the simulator.
+
+## 📚 Available Models
+
+| Model | Type | Documentation | Use Case |
+|-------|------|---------------|----------|
+| [Logistic Regression](logistic_regression.md) | Classification | Binary classification | Disease prediction, spam detection |
+| [Linear Regression](linear_regression.md) | Regression | Continuous prediction | Price prediction, trend analysis |
+| [Decision Tree](decision_tree.md) | Classification/Regression | Tree-based decisions | Credit scoring, diagnosis |
+| [Random Forest](random_forest.md) | Ensemble | Multiple trees | Complex classification tasks |
+| [K-Nearest Neighbors](knn.md) | Classification/Regression | Instance-based | Pattern recognition |
+| [Support Vector Machine](svm.md) | Classification | Maximum margin | Text classification, image recognition |
+
+## 🚀 Quick Start
+
+Each model documentation includes:
+- ✅ **Overview**: What the model does and when to use it
+- ✅ **How to Run**: Step-by-step instructions
+- ✅ **Parameter Explanations**: What each setting means
+- ✅ **Plot Interpretations**: Understanding visualizations
+- ✅ **Performance Metrics**: Evaluating model quality
+- ✅ **Troubleshooting**: Common issues and solutions
+- ✅ **Examples**: Real-world use cases
+
+## 📖 How to Use This Documentation
+
+1. Select the model you want to learn about from the table above
+2. Click on the documentation link
+3. Follow the step-by-step guide
+4. Review the screenshot examples
+5. Apply to your own dataset
+
+## 🎯 Contributing
+
+Found an error or want to improve the documentation? See our [CONTRIBUTING.md](../CONTRIBUTING.md) for guidelines.
+
+---
+
+**Last Updated**: October 13, 2025  
+**Version**: 1.0  
+**Author**: Akshit  
+**Hacktoberfest 2025 Contribution** 🎃