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🧪 Python and Machine Learning Lab Programs

This repository contains a comprehensive set of programs developed as part of the B.Tech Python and Machine Learning Lab. These exercises cover essential Python programming skills, foundational machine learning techniques, and data preprocessing methods. By completing them, students gain practical exposure to supervised, unsupervised, and neural network-based learning techniques using popular Python libraries.

📄 Check Syllabus: Click Here

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📚 Table of Contents

• Introduction
• Program List
• Summary
• Conclusion
• Requirements
• Usage
• Contribution
• License

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🔍 Introduction

This lab provides a hands-on introduction to Python programming and machine learning fundamentals. You'll explore regression, classification, clustering, and dimensionality reduction using libraries like:

• NumPy for numerical computing
• Pandas for data manipulation
• Matplotlib for data visualization
• Scikit-learn for ML models

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🧾 Program List

#	Title	Description	Link
1	Introduction to Python Programming	Basic syntax and foundational concepts in Python	View
2	Familiarization of Basic Python Libraries	Intro to numpy, pandas, matplotlib, and sklearn	View
3	Union and Intersection of Two Lists	Demonstrates set operations using Python lists	View
4	Word Count in a Sentence	Counts word occurrences in a given sentence	View
5	Matrix Multiplication	Matrix multiplication using nested loops	View
6	Most Frequent Words in a Text File	Identifies the most common words in a text file	View
7	Regression Analysis	Implements Linear, Multivariable & Polynomial Regression	View
8	Logistic Regression	Binary classification using logistic regression	View
9	Naive Bayes Classifier	Implements Naive Bayes and evaluates performance using metrics	View
10	Decision Tree with ID3 Algorithm	Constructs and tests a decision tree using ID3	View
11	Support Vector Machine (SVM) Classifier	SVM-based classification with performance evaluation	View
12	K-Nearest Neighbor (KNN) Algorithm	Implements KNN for classification	View
13	K-Means Clustering	Unsupervised learning via clustering	View
14	Artificial Neural Network (ANN) using Backpropagation	Implements a simple neural network using backpropagation	View
15	Principal Component Analysis (PCA)	Dimensionality reduction using PCA	View

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🧾 Summary

This collection provides practical exposure to implementing core machine learning algorithms. It builds essential Python skills while exploring diverse ML tasks such as:

• Regression & Classification
• Clustering & Dimensionality Reduction
• Model Evaluation Techniques
• Use of Python libraries in real-world datasets

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🎓 Conclusion

These lab programs give students the tools and experience needed to confidently work with machine learning algorithms and data science workflows. They lay the groundwork for more advanced AI and ML projects.

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💻 Requirements

• Python 3.x
• Install Required Libraries:

  pip install numpy pandas matplotlib scikit-learn

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🚀 Usage

Clone the repository:

git clone https://github.com/venkideshVenu/S5-KTU-Python-and-Machine-Learining-Lab.git
cd S5-KTU-Python-and-Machine-Learining-Lab

Run any .ipynb file using Jupyter Notebook or any Python IDE of your choice.

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🤝 Contribution

Contributions are welcome! Feel free to:

• Fork the repo
• Add or enhance any notebook
• Submit a pull request for review

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📜 License

This project is licensed under the MIT License.

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