mak-mini-ml 0.1.1

Beginner-friendly Machine Learning utility library built from scratch using pure Python

Custom_ML_Suite

Custom_ML_Suite is a beginner-friendly Machine Learning utility library built completely from scratch using pure Python.

This project focuses on understanding the mathematical foundations of Machine Learning by manually implementing core ML algorithms, preprocessing methods, distance metrics, activation functions, statistical operations, and evaluation metrics without using external ML libraries like scikit-learn.

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Features

• Pure Python implementation
• Beginner-friendly code structure
• Mathematical formulas included
• Well-commented educational code
• Modular package structure
• ML utilities from scratch
• Edge-case handling
• Easy to understand and extend

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Modules Included

1. activations.py

Activation functions used in neural networks.

Functions

• sigmoid()
• relu()
• tanh()
• softmax()
• log_loss()

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2. distances.py

Distance and similarity metrics.

Functions

• euclidean_distance()
• manhattan_distance()
• minkowski_distance()
• cosine_similarity()
• hamming_distance()

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3. preprocessing.py

Data preprocessing and scaling methods.

Functions

• standardization()
• mean()
• min_max_scaling()
• range()
• normalization()

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4. stats.py

Basic statistical operations.

Functions

• mean()
• variance()
• std_dev()
• covariance()
• correlation()

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5. metrics.py

Machine Learning evaluation metrics.

Functions

• accuracy_score()
• precision_score()
• recall_score()
• f1_score()
• confusion_matrix()
• mean_absolute_error()
• mean_squared_error()
• root_mean_squared_error()
• r2_score()
• binary_crossentropy()

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6. model_selection.py

Dataset splitting and validation utilities.

Functions

• train_test_split()
• shuffle_data()
• batch_iterator()
• k_fold_split()
• stratified_split()

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7. linear_model.py

Basic regression models and optimization.

Functions

• linear_regression()
• predict()
• gradient_descent()
• logistic_regression()
• logistic_update()

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8. neighbors.py

K-Nearest Neighbors utilities.

Functions

• knn_distance()
• knn_predict()
• probability()

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9. tree.py

Basic Decision Tree utilities.

Functions

• gini_impurity()
• entropy()
• information_gain()
• best_split()
• build_tree()
• predict_tree()
• majority_vote()

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Project Structure

Custom_ML_Suite/
│
├── examples/
│   ├── demo.py
│   └── Formula.py
│
├── src/
│   └── Custom_ML_Suite/
│       ├── __init__.py
│       ├── activations.py
│       ├── distances.py
│       ├── linear_model.py
│       ├── metrics.py
│       ├── model_selection.py
│       ├── neighbors.py
│       ├── preprocessing.py
│       ├── stats.py
│       └── tree.py
│
├── tests/
│   └── test_all.py
│
├── README.md
├── pyproject.toml
└── .gitignore

# Authors

Aryan Kakade  
Kishor Handge