Advanced Machine Learning Experiments

A curated collection of practical laboratory experiments, data preprocessing workflows, and machine learning implementations developed for the Advanced Machine Learning curriculum.

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Overview

This repository documents weekly practical lab experiments focusing on classical advanced machine learning, statistical modeling, algorithmic optimization, and data preprocessing pipelines. Each experiment is self-contained with end-to-end data manipulation, model training, and performance evaluation.

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Experiments Index

Exp #	Experiment Title	Notebook	Key Concepts & Techniques	Metrics / Evaluation	Status
01	Data Wrangling & Preprocessing	aml_exp1_data_wrangling.ipynb	Missing value imputation, standard scaling, categorical encoding, EDA	Data distributions, skewness analysis	✅ Completed
02	Linear Regression & Iris Classification	exp2_linear_regression_iris_classification.ipynb	Ordinary Least Squares, hyper-plane fitting, feature mapping	MSE, RMSE, $R^2$ Score	✅ Completed
03	Decision Tree Classifier (ID3)	exp_3_decision_tree_ID3.ipynb	Information Gain, Shannon Entropy, recursive node splitting, tree pruning	Accuracy, Confusion Matrix	✅ Completed
04	Decision Tree Mushroom Classification	exp_4_decision-tree_mushroom-classification.ipynb	Categorical feature encoding, Gini Impurity/Entropy, binary decision trees	Precision, Recall, Classification Report	✅ Completed

(New experiments will be added as lab assignments are completed)

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Repository Structure

advanced-machine-learning-experiments/
│
├── .gitignore
├── README.md
├── aml_exp1_data_wrangling.ipynb                          # Exp 1: Data Preprocessing & Manipulation
├── exp2_linear_regression_iris_classification.ipynb        # Exp 2: Linear Regression & Classification
├── exp_3_decision_tree_ID3.ipynb                          # Exp 3: Decision Tree via ID3 Algorithm
└── exp_4_decision-tree_mushroom-classification.ipynb      # Exp 4: Decision Tree on Mushroom Dataset