🚀 Current Projects

🏠 House Price Prediction

Status: ✅ Complete | Type: Regression | Competition: House Prices - Advanced Regression Techniques

Objective: Predict the sales price for each house based on various features

Key Features:

• Comprehensive exploratory data analysis (EDA)
• Feature engineering and selection
• Advanced regression techniques
• Model performance optimization

Techniques Used:

• Data preprocessing and cleaning
• Missing value imputation
• Feature scaling and transformation
• Regression modeling
• Cross-validation
• Model evaluation metrics (RMSE, MAE)

Kaggle Link: [Link to competition]

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🛡️ Cyber Threat Analysis

Status: ✅ Complete | Type: Security Analytics | Domain: Cybersecurity

Objective: Analyze and detect cyber threats using machine learning techniques

Key Features:

• Network traffic analysis
• Anomaly detection algorithms
• Threat pattern recognition
• Security incident classification

Techniques Used:

• Feature extraction from network logs
• Statistical analysis of security events
• Pattern recognition and clustering
• Threat intelligence correlation
• Visualization of attack patterns

Applications:

• Intrusion detection systems
• Malware classification
• Network behavior analysis
• Security monitoring dashboards

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🌳 Decision Tree Analysis

Status: ✅ Complete | Type: Classification/Regression | Algorithm Focus: Tree-based Methods

Objective: Implement and analyze decision tree algorithms for various prediction tasks

Key Features:

• Decision tree implementation from scratch
• Comparison of different splitting criteria
• Tree pruning techniques
• Feature importance analysis

Techniques Used:

• Information gain and Gini impurity
• Entropy-based splitting
• Pre-pruning and post-pruning
• Random Forest implementation
• Feature selection using tree-based methods

Algorithms Covered:

• CART (Classification and Regression Trees)
• ID3 and C4.5 algorithms
• Random Forest ensemble
• Extra Trees (Extremely Randomized Trees)

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🧠 Neural Network Implementation

Status: ✅ Complete | Type: Deep Learning | Domain: Neural Networks

Objective: Build neural networks from scratch and explore deep learning architectures

Key Features:

• Neural network implementation from fundamentals
• Backpropagation algorithm
• Various activation functions
• Optimization techniques

Techniques Used:

• Forward and backward propagation
• Gradient descent optimization
• Regularization techniques (Dropout, L1/L2)
• Batch normalization
• Different network architectures

Network Types:

• Feedforward Neural Networks
• Multi-layer Perceptrons (MLP)
• Convolutional Neural Networks (CNN) - if applicable
• Custom activation functions

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📊 Supervised Machine Learning

Status: ✅ Complete | Type: Comprehensive ML Study | Domain: Supervised Learning

Objective: Comprehensive exploration of supervised learning algorithms and techniques

Key Features:

• Multiple algorithm implementations
• Comparative analysis of models
• Hyperparameter optimization
• Cross-validation strategies

Algorithms Covered:

• Linear Models: Linear/Logistic Regression, Ridge, Lasso
• Tree-based: Decision Trees, Random Forest, Gradient Boosting
• Instance-based: K-Nearest Neighbors (KNN)
• Ensemble Methods: Bagging, Boosting, Stacking
• Support Vector Machines: SVM for classification and regression

Techniques Used:

• Feature engineering and selection
• Model evaluation metrics
• Cross-validation techniques
• Hyperparameter tuning (Grid Search, Random Search)
• Bias-variance tradeoff analysis