🤖 Machine Learning Lab RTU (Python)

Machine Learning Lab Experiments - RTU

Rajasthan Technical University (RTU)

B.Tech Artificial Intelligence & Data Science

Python | Machine Learning | Scikit-Learn | NumPy | Pandas | Matplotlib

⭐ Complete RTU Machine Learning Lab Programs with Source Code, Datasets, Output Screenshots and Documentation.

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📖 About This Repository

This repository contains the complete implementation of Machine Learning Lab (RTU) experiments for Rajasthan Technical University (RTU).

The repository is designed for:

• B.Tech AI & DS Students
• Computer Science Students
• RTU Practical Lab
• Machine Learning Beginners
• Python Machine Learning Practice
• Placement Preparation
• Academic Projects

Every experiment includes:

• ✅ Python Source Code
• ✅ Dataset
• ✅ Output Screenshots
• ✅ Individual README
• ✅ Step-by-Step Documentation

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📚 RTU Machine Learning Lab Experiments

This repository contains the implementation of the following Machine Learning Laboratory experiments prescribed by Rajasthan Technical University (RTU).

S.No	Experiment	Status
<<<<<<< HEAD
01	Implement and demonstrate the FIND-Salgorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.	✅ Completed
02	For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithmto output a description of the set of all hypotheses consistent with the training examples.	✅ Completed
03	Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge toclassify a new sample.	✅ Completed
04	Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.	✅ Completed
05	Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.	✅ Completed
06	Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data set.	✅ Completed
07	Write a program to construct aBayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Java/Python ML library classes/API.	✅ Completed
08	Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.	✅ Completed
09	Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem.	✅ Completed
10	Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.	✅ Completed
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01	Implement and demonstrate the FIND-Salgorithm for finding the most specific
hypothesis based on a given set of training data samples. Read the training
data from a .CSV file.	✅ Completed
02	For a given set of training data examples stored in a .CSV file, implement and
demonstrate the Candidate-Elimination algorithmto output a description of the
set of all hypotheses consistent with the training example.	✅ Completed
03	Demonstrate the Decision Tree (ID3) Algorithm using an appropriate dataset and classify a new sample.	✅ Completed
04	Build an Artificial Neural Network (ANN) by implementing the Backpropagation Algorithm and test it using an appropriate dataset.	✅ Completed
05	Implement the Naïve Bayesian Classifier using a CSV dataset and compute the classification accuracy.	✅ Completed
06	Perform Document Classification using the Naïve Bayesian Classifier and calculate Accuracy, Precision, and Recall.	✅ Completed
07	Construct a Bayesian Network using the Heart Disease dataset to diagnose heart disease using Python ML libraries.	✅ Completed
08	Apply the Expectation Maximization (EM) Algorithm and compare its clustering performance with the K-Means Algorithm on the same dataset.	✅ Completed
09	Implement the k-Nearest Neighbour (KNN) Algorithm to classify the Iris dataset and display correct as well as incorrect predictions.	✅ Completed
10	Implement the Non-Parametric Locally Weighted Regression (LWR) Algorithm to fit data points and visualize the regression graph.	✅ Completed

ed86a42 (Updated repository README and added second LWR output)

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🚀 Repository Highlights

• ✅ All 10 RTU Machine Learning Lab Experiments
• ✅ Python Implementations
• ✅ Well-Documented Source Code
• ✅ Datasets Included
• ✅ Output Screenshots
• ✅ Individual README for Every Experiment
• ✅ Easy-to-Understand Folder Structure
• ✅ Beginner-Friendly Machine Learning Programs
• ✅ Suitable for RTU Practical Exams and Academic Reference

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🎯 Keywords

RTU Machine Learning Lab, Machine Learning Lab RTU, RTU ML Lab Programs, Python Machine Learning, FIND-S Algorithm, Candidate Elimination Algorithm, Decision Tree ID3, Artificial Neural Network, Backpropagation, Naïve Bayes Classifier, Document Classification, Bayesian Network, Heart Disease Prediction, EM Algorithm, K-Means Clustering, KNN Iris Classification, Locally Weighted Regression, Machine Learning Practical, RTU AI & DS Lab.

🚀 Features

• Complete RTU Machine Learning Lab Programs
• Python Implementation
• Beginner Friendly Code
• Proper Folder Structure
• Output Screenshots
• Datasets Included
• Detailed README for Every Experiment
• Easy to Understand
• Ready for Practical Examination
• Recruiter Friendly Repository

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🛠 Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Scikit-Learn
• pgmpy
• VS Code
• Git & GitHub

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📁 Repository Structure

Machine-Learning-Lab-RTU
│
├── Experiment-01-FIND-S-Algorithm
├── Experiment-02-Candidate-Elimination
├── Experiment-03-Decision-Tree-ID3
├── Experiment-04-Artificial-Neural-Network
├── Experiment-05-Naive-Bayes-Classifier
├── Experiment-06-Document-Classification-Naive-Bayes
├── Experiment-07-Bayesian-Network-Heart-Disease-Prediction
├── Experiment-08-EM-vs-KMeans-Clustering
├── Experiment-09-KNN-Iris-Classification
├── Experiment-10-Locally-Weighted-Regression
│
└── README.md

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▶️ Getting Started

Clone the repository:

git clone https://github.com/ziyaur-12/Machine-Learning-Lab-RTU.git

Go to the project directory:

cd Machine-Learning-Lab-RTU

Install dependencies:

pip install numpy pandas matplotlib scikit-learn pgmpy

Run any experiment:

python Experiment-05-Naive-Bayes-Classifier/naive_based_exp5.py

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🎯 Learning Outcomes

After completing these experiments, students will understand:

• Concept Learning
• Classification Algorithms
• Regression Algorithms
• Clustering Algorithms
• Bayesian Learning
• Artificial Neural Networks
• Machine Learning Model Evaluation
• Data Visualization using Python

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🎓 RTU Machine Learning Lab Topics Covered

• FIND-S Algorithm
• Candidate Elimination
• Decision Tree (ID3)
• Artificial Neural Network
• Backpropagation Algorithm
• Naïve Bayes Classifier
• Bayesian Network
• Expectation Maximization (EM)
• K-Means Clustering
• K-Nearest Neighbour (KNN)
• Locally Weighted Regression

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👨‍💻 Author

Ziyaurrahman

B.Tech – Artificial Intelligence & Data Science

Arya College of Engineering & I.T., Jaipur

GitHub: https://github.com/ziyaur-12

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⭐ Support

If this repository helped you in your RTU Machine Learning Lab practicals or Python Machine Learning learning journey, consider giving it a ⭐ on GitHub.