🧠 Machine Learning Lab

A structured repository documenting my hands-on journey through Machine Learning concepts, algorithms, and experimentation.

This lab focuses on understanding how models work internally, not just using libraries.

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🎯 Purpose

This repository serves as a personal ML experimentation space where I:

• Implement core machine learning algorithms
• Experiment with datasets
• Study model behavior & evaluation techniques
• Strengthen intuition behind ML systems

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🧭 Machine Learning Roadmap

Machine Learning Algorithm Practice
│
├── 📘 Fundamentals
│   ├── Saving and Loading Models
│   │   ├── SAVING_MODEL.ipynb
│   │   ├── pickle_model
│   │   └── joblib_model
│   │
│   └── Cross Validation
│       └── CROSS_VALIDATION.ipynb
│
├── 📊 Regression Algorithms (Supervised Learning)
│   ├── Linear Regression
│   │   ├── LINEAR_REGRESSION_1.ipynb
│   │   └── LINEAR_REGRESSION_2.ipynb
│   │
│   ├── Polynomial Regression
│   │   ├── POLYNOMIAL_REGRESSION_1.ipynb
│   │   └── POLYNOMIAL_REGRESSION_2.ipynb
│   │
│   └── Regularization Techniques
│       ├── RIDGE_LASSO_REGRESSION_1.ipynb
│       └── RIDGE_LASSO_REGRESSION_2.ipynb
│
├── 🤖 Classification Algorithms (Supervised Learning)
│   ├── Logistic Regression
│   │   └── LOGISTIC_REGRESSION.ipynb
│   │
│   ├── K-Nearest Neighbors
│   │   └── KNN_ALGORITHM.ipynb
│   │
│   ├── Support Vector Machine
│   │   └── SVM_ALGORITHM.ipynb
│   │
│   ├── Naive Bayes
│   │   ├── NAIVE_BAYES_1.ipynb
│   │   └── NAIVE_BAYES_2.ipynb
│   │
│   └── Decision Trees
│       ├── DECSION_TREE_CLASSIFIER_1.ipynb
│       └── DECSION_TREE_CLASSIFIER_2.ipynb
│
├── 🌲 Ensemble Learning
│   ├── Random Forest
│   │   └── RANDOM_FOREST_CLASSIFIER.ipynb
│   │
│   ├── Gradient Boosting
│   │   └── GRADIENT_BOOSTING.ipynb
│   │
│   └── AdaBoost
│       └── ADA_BOOSTING.ipynb
│
├── 🧩 Unsupervised Learning
│   ├── Clustering
│   │   └── K_MEANS_CLUSTERING.ipynb
        └── DBSCAN_CLUSTERING.ipynb
        └── HIERARCHICAL_CLUSTERING.ipynb
        └── SILHOUETTE_CLUSTERING.ipynb
│   │
│   └── Dimensionality Reduction
│       ├── PCA_ALGORITHM.ipynb
│       └── LDA.ipynb
│
└── ⚙️ Model Optimization
    └── Hyperparameter Tuning
        └── HYPER_PARAMETER_TUNING.ipynb

🔬 Areas Covered

• Supervised Learning
• Unsupervised Learning
• Feature Engineering
• Model Evaluation
• Hyperparameter Tuning
• Statistical Foundations

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⚙️ Tech Stack

Python • Pandas • NumPy • Scikit-learn • Matplotlib

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📌 Repository Philosophy

Learn → Experiment → Understand → Improve

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⚠️ Usage Notice

This repository is shared publicly for learning and reference purposes only.
Reuse or redistribution of the code is not permitted without permission.

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🚀 Status

Actively evolving as part of continuous ML learning.