Python and Machine Learning Lab Programs

This repository contains a collection of programs developed as part of the B.Tech Python and Machine Learning Lab. These programs cover essential Python programming skills, foundational machine learning techniques, and data preprocessing methods. By completing these exercises, students gain hands-on experience in applying supervised and unsupervised machine learning algorithms, along with using popular Python libraries for data analysis and visualization.

Check Syllabus : Click Here

Table of Contents

• Introduction
• Programs
  • 1. Introduction to Python Programming
  • 2. Familiarization of Basic Python Libraries
  • 3. Union and Intersection of Two Lists
  • 4. Word Count in a Sentence
  • 5. Matrix Multiplication
  • 6. Most Frequent Words in a Text File
  • 7. Regression Analysis
  • 8. Logistic Regression
  • 9. Naive Bayes Classifier
  • 10. Decision Tree with ID3 Algorithm
  • 11. Support Vector Machine (SVM) Classifier
  • 12. K-Nearest Neighbor (KNN) Algorithm
  • 13. K-Means Clustering
  • 14. Artificial Neural Network (ANN) using Backpropagation
  • 15. Principal Component Analysis (PCA)
• Summary
• Conclusion
• Requirements
• Usage
• License

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Introduction

This lab introduces Python programming and machine learning basics. The programs implemented here demonstrate foundational concepts in data science, including regression, classification, clustering, and dimensionality reduction. Key Python libraries like numpy, pandas, matplotlib, and scikit-learn are utilized to provide practical skills in data handling and visualization.

Programs

1. Introduction to Python Programming

Basic Python programming exercises to refresh and strengthen foundational skills.Click here

2. Familiarization of Basic Python Libraries

Exploring essential libraries for machine learning, including:

• Sklearn: Used for implementing machine learning algorithms.
• Numpy: Used for numerical operations.
• Pandas: Used for data manipulation.
• Matplotlib: Used for data visualization. Click Here

3. Union and Intersection of Two Lists

A Python program to find the union and intersection of two lists, demonstrating set operations and list manipulation. Click Here

4. Word Count in a Sentence

Counts the occurrences of each word in a given sentence to understand basic text processing techniques. Click Here

5. Matrix Multiplication

Implements matrix multiplication using nested loops, a fundamental concept for linear algebra operations in machine learning. Click Here

6. Most Frequent Words in a Text File

Reads a text file and identifies the most frequent words, an essential skill in text processing and data cleaning. Click Here

7. Regression Analysis]

Performs Single, Multivariable, and Polynomial Regression using training data from a CSV file. Evaluates the accuracy of each regression model. Click Here

8. Logistic Regression

A Python program to implement logistic regression on a dataset, useful for binary classification problems. Click Here

9. Naive Bayes Classifier

Implements the Naive Bayes classification algorithm and calculates accuracy, precision, and recall to evaluate model performance. Click Here

10. Decision Tree with ID3 Algorithm

Demonstrates the ID3 algorithm using a dataset, builds a decision tree, and applies it to classify new samples. Click Here

11. Support Vector Machine (SVM) Classifier

Applies an SVM classifier to a dataset and evaluates the classification accuracy, a common approach for high-dimensional data. Click Here

12. K-Nearest Neighbor (KNN) Algorithm

Implements the K-Nearest Neighbor algorithm to classify data points based on proximity to labeled points in the dataset. Click Here

13. K-Means Clustering

Applies K-Means clustering to group data points into clusters, showcasing unsupervised learning on unlabeled data. Click Here

14. Artificial Neural Network (ANN) using Backpropagation

Builds an artificial neural network and trains it using the backpropagation algorithm. Tests the model on a dataset to evaluate its predictive power. Click Here

15. Principal Component Analysis (PCA)

Implements PCA for dimensionality reduction, a crucial step in preprocessing large datasets to reduce computational complexity. Click Here

Summary

This repository offers a practical approach to learning machine learning techniques and data handling in Python. Each program builds on core concepts, with implementations of both supervised and unsupervised algorithms. Topics like regression, classification, and clustering are explored, alongside preprocessing methods for efficient data handling.

Conclusion

These programs provide a solid foundation in machine learning using Python. By understanding and implementing these algorithms, students gain valuable insights into real-world data processing and analysis techniques, preparing them for more advanced studies in AI and machine learning.

Requirements

• Python 3.x
• Libraries: numpy, pandas, scikit-learn, matplotlib

Usage

To use this repository, clone it to your local machine using the following command:

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

Contribution

Contributions are welcome! If you have any suggestions or improvements, feel free to fork the repository and submit a pull request.

License

This repository is licensed under the MIT License. See the LICENSE file for more details.