bcsl607-ml 1.0.0

Machine Learning lab programs for BCSL607 - NIE Mysuru (KNN, Linear/Polynomial Regression, Decision Tree, K-Means, Naive Bayes)

bcsl607_ml — Machine Learning Lab Library

Subject: BCSL607 — Machine Learning
Department: CS&E, National Institute of Engineering, Mysuru

A clean Python package containing all 7 lab programs, each importable and runnable independently.

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📁 Folder Structure

bcsl607_ml/
├── __init__.py
└── labs/
    ├── __init__.py
    ├── lab1_knn_basic.py           # Lab 1 - KNN (k=3)
    ├── lab2_knn_multi_k.py         # Lab 2 - KNN (k=1,2,3,4,5,20,30)
    ├── lab3_linear_regression.py   # Lab 3 - Linear Regression
    ├── lab4_polynomial_regression.py # Lab 4 - Polynomial Regression
    ├── lab5_decision_tree.py       # Lab 5 - Decision Tree
    ├── lab6_kmeans_clustering.py   # Lab 6 - K-Means Clustering
    └── lab7_naive_bayes.py         # Lab 7 - Naive Bayes

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⚡ Installation

No installation needed. Just place the bcsl607_ml/ folder in your project directory.

Install dependencies:

pip install numpy matplotlib scikit-learn pandas

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🚀 Usage

Run a specific lab

from bcsl607_ml.labs import lab1_knn_basic
lab1_knn_basic.run()

Run all labs (except Lab 7)

import bcsl607_ml
bcsl607_ml.run_all()

Lab 7 — Naive Bayes (needs CSV)

from bcsl607_ml.labs import lab7_naive_bayes
lab7_naive_bayes.run("path/to/diabetes_data.csv")

Download dataset from: https://www.kaggle.com/datasets/himanshunakrani/naive-bayes-classificationdata

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🧪 Lab Summary

Lab	Topic	Algorithm	Dataset
1	KNN Basic	KNeighborsClassifier (k=3)	Random 100 points
2	KNN Multi-k	KNeighborsClassifier	Random 100 points
3	Linear Regression	LinearRegression	Synthetic
4	Polynomial Regression	PolynomialFeatures + LR	Synthetic
5	Decision Tree	DecisionTreeClassifier	Breast Cancer
6	K-Means Clustering	KMeans + PCA	Breast Cancer
7	Naive Bayes	GaussianNB	Diabetes CSV

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📦 Dependencies

• numpy
• matplotlib
• scikit-learn
• pandas