🧠 Machine Learning Notebooks

📖 Overview

This repository contains my personal learning notebooks for Machine Learning.
It includes experiments with data preprocessing, regression, classification, clustering, ensemble learning, hyperparameter tuning,association rule learning and many many more topics.

These notebooks were created during my learning journey in ML to practice concepts and visualize results.

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⚡ Learning Topics Covered

Part 1: Data Handling & Basic ML

• Outlier handling (IQR, Z-score)
• Feature scaling (StandardScaler, MinMaxScaler)
• Duplicates & missing data handling
• Linear, Multiple, and Polynomial Regression
• Logistic Regression & Binary Classification
• Confusion Matrix, Precision, Recall, F1 Score
• Handling Imbalanced Datasets
• and many more...........

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Part 2: Advanced ML Techniques

• Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
• Cross-validation (KFold, LOO, LPO)
• Unsupervised Learning:
  • K-Means Clustering
  • Hierarchical Clustering (Dendrograms)
  • DBSCAN clustering
• Cluster evaluation and visualization
• and many more........

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Part 3: Association Rule Learning & Ensemble Learning

• Association Rule Learning:
  • Apriori Algorithm
  • FP-Growth Algorithm
• Ensemble Learning:
  • Voting Classifier & Regressor
  • Bagging Classifier & Regressor
• Polynomial Regression & Feature Transformation
• Regression & Classification experiments on sample datasets
• and many more........

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🛠️ Requirements

• Python 3.10+
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn
  • scipy
  • mlxtend
  • imblearn

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Install with:

pip install numpy pandas matplotlib seaborn scikit-learn mlxtend imbalanced-learn scipy