Documentation

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** 🎃

Docs/decision_tree.md

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+# Decision Tree - Documentation
+
+## 📋 Overview
+
+Decision Tree is a supervised learning algorithm that creates a tree-like model of decisions. It splits data based on feature values to make predictions for both classification and regression tasks[web:100][web:102].
+
+**Key Characteristics:**
+- **Type**: Supervised Learning - Classification or Regression
+- **Output**: Class label or continuous value
+- **Algorithm**: Recursive splitting based on information gain
+- **Best For**: Non-linear relationships, interpretable models
+
+## 🎯 Purpose and Use Cases
+
+### Primary Use
+Creating interpretable models that make decisions through a series of yes/no questions.
+
+### Common Applications
+- **Medical Diagnosis**: Decision pathways for treatment
+- **Credit Approval**: Loan decision logic
+- **Customer Segmentation**: Marketing strategy decisions
+- **Fraud Detection**: Rule-based fraud identification
+- **Product Recommendations**: Decision logic for suggestions
+
+## 🚀 How to Run
+
+### Step 1: Access the Model
+1. Navigate to ML Simulator
+2. Select **"Decision Tree"** from sidebar
+
+### Step 2: Choose Data Source
+- Upload CSV or use sample dataset
+- For classification: binary or multi-class target
+- For regression: continuous target
+
+### Step 3: Configure Parameters
+
+| Parameter | Description | Default | Range |
+|-----------|-------------|---------|-------|
+| **Max Depth** | Maximum tree depth | 5 | 1-20 |
+| **Min Samples Split** | Minimum samples to split | 2 | 2-20 |
+| **Min Samples Leaf** | Minimum samples in leaf | 1 | 1-10 |
+| **Criterion** | Splitting metric | gini/mse | gini/entropy |
+
+### Step 4: Train and Visualize
+1. Configure parameters
+2. Click **Train Model**
+3. View tree structure and results
+
+## 📊 What Each Plot Shows
+
+### 1. Tree Visualization
+
+**What You See:**
+Visual representation of the decision tree structure.
+
+**Components:**
+- **Root node**: Top of tree (all data)
+- **Internal nodes**: Decision points
+- **Leaf nodes**: Final predictions
+- **Branches**: Decision paths
+
+**How to Read:**
+- Each node shows:
+  - Feature and threshold used for split
+  - Number of samples
+  - Class distribution or value
+- Follow branches from top to bottom
+- Leaf nodes contain predictions
+
+### 2. Feature Importance
+
+**What You See:**
+Bar chart showing which features are most important[web:99][web:101].
+
+**Interpretation:**
+- Longer bars: More important for decisions
+- Features at top of tree: Usually most important
+- Zero importance: Feature not used
+
+### 3. Confusion Matrix (Classification)
+
+**Same as Logistic Regression**
+Shows prediction accuracy breakdown.
+
+### 4. Performance Metrics
+
+**Classification:**
+- Accuracy, Precision, Recall, F1-Score
+
+**Regression:**
+- R², MSE, RMSE, MAE
+
+## 🔧 Model Parameters Explained
+
+### max_depth
+**Purpose**: Limit tree depth to prevent overfitting  
+**Lower values**: Simpler, more general model  
+**Higher values**: More complex, may overfit  
+**Recommendation**: Start with 3-7
+
+### min_samples_split
+**Purpose**: Minimum samples required to split a node  
+**Lower values**: More splits, complex tree  
+**Higher values**: Fewer splits, simpler tree  
+**Recommendation**: 2-10 depending on data size
+
+### min_samples_leaf
+**Purpose**: Minimum samples required in leaf node  
+**Effect**: Smooths model, prevents overfitting  
+**Recommendation**: 1-5
+
+### criterion
+**Classification:**
+- **gini**: Gini impurity (default, faster)
+- **entropy**: Information gain (more precise)
+
+**Regression:**
+- **mse**: Mean squared error (default)
+- **mae**: Mean absolute error (robust to outliers)
+
+## 💡 Tips and Best Practices
+
+### Advantages
+✅ Easy to understand and interpret  
+✅ Handles non-linear relationships  
+✅ No feature scaling required  
+✅ Handles mixed data types  
+✅ Provides feature importance
+
+### Limitations
+❌ Prone to overfitting  
+❌ Unstable (small data changes affect tree)  
+❌ Biased toward dominant classes  
+❌ Not optimal for linear relationships
+
+### Best Practices
+- **Start shallow**: Begin with max_depth=3-5
+- **Prune the tree**: Use min_samples parameters
+- **Cross-validate**: Check performance on multiple splits
+- **Ensemble methods**: Consider Random Forest for better stability
+- **Visualize tree**: Understand decision logic
+
+## 🐛 Troubleshooting
+
+### Issue: Perfect Training Accuracy, Poor Test Accuracy
+
+**Diagnosis:** Severe overfitting
+
+**Solutions:**
+1. Reduce max_depth (try 3-7)
+2. Increase min_samples_split (try 10-20)
+3. Increase min_samples_leaf (try 5-10)
+4. Use Random Forest instead
+
+### Issue: Tree Too Large to Visualize
+
+**Solutions:**
+1. Reduce max_depth
+2. Export tree to graphical format
+3. Focus on top levels only
+
+### Issue: Low Accuracy
+
+**Solutions:**
+1. Increase max_depth (try up to 15)
+2. Check feature quality
+3. Add more relevant features
+4. Try ensemble methods
+
+## 📚 Additional Resources
+
+- [Scikit-learn Decision Trees](https://scikit-learn.org/stable/modules/tree.html)
+- [Understanding Decision Trees](https://developers.google.com/machine-learning/decision-forests/decision-trees)
+- [Tree Visualization Guide](https://mljar.com/blog/visualize-decision-tree/)
+
+## 🎯 Example Use Case
+
+### Scenario: Loan Approval System
+
+**Features:**
+- income, credit_score, debt_ratio, employment_years
+
+**Tree might learn:**