Welcome to the Machine Learning Laboratory (21AIL66) repository. This repository contains a list of problems and solutions developed as part of the Machine Learning lab coursework.
Here is a list of programs covered in this lab course:
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Find-S Algorithm
- Aim: Illustrate and demonstrate the working model and principle of the Find-S algorithm.
- Program: Implement the Find-S algorithm for a given set of training data examples stored in a .CSV file.
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Candidate Elimination Algorithm
- Aim: Demonstrate the working model and principle of the Candidate Elimination algorithm.
- Program: Implement the Candidate Elimination algorithm for a given set of training data examples stored in a .CSV file.
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Decision Tree (ID3 Algorithm)
- Aim: Construct the decision tree using training data sets under supervised learning.
- Program: Write a program to demonstrate the ID3 algorithm. Use an appropriate data set for building the decision tree and classify a new sample.
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Artificial Neural Network (Backpropagation)
- Aim: Understand the working principle of Artificial Neural Networks with feed-forward and feed-backward principles.
- Program: Build an Artificial Neural Network using the Backpropagation algorithm and test it with appropriate datasets.
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Naive Bayes Classifier
- Aim: Demonstrate the text classifier using the Naïve Bayes classifier algorithm.
- Program: Implement the Naive Bayes classifier for a sample training data set stored in a .CSV file and compute its accuracy.
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Bayesian Belief Network
- Aim: Demonstrate and analyze the results sets obtained from Bayesian belief network principles.
- Program: Construct a Bayesian network using medical data and diagnose heart patients with a standard Heart Disease Data Set.
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K-Means Clustering (Expectation Maximization)
- Aim: Implement and demonstrate the working model of K-means clustering algorithm with Expectation Maximization concept.
- Program: Apply the EM algorithm and K-Means clustering to a dataset stored in a .CSV file, compare the results, and analyze the quality of clustering.
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K-Nearest Neighbour (KNN)
- Aim: Demonstrate and analyze the results of classification based on the KNN Algorithm.
- Program: Implement the KNN algorithm to classify the iris dataset, printing both correct and wrong predictions.
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Locally Weighted Regression
- Aim: Understand and analyze the concept of Regression algorithm techniques.
- Program: Implement the Locally Weighted Regression algorithm to fit data points and visualize the results with appropriate graphs.
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Support Vector Machine (SVM)
- Aim: Implement and demonstrate classification algorithm using Support Vector Machine Algorithm.
- Program: Implement and demonstrate the working of SVM algorithm for classification purposes.
You can view and run all the programs in a Kaggle notebook. Click the link below to access the notebook:
Please upvote the notebook and follow me on Kaggle if you find it useful.
For detailed viva questions and answers, you can refer to the Viva Questions Wiki.
- Clone the repository:
git clone https://github.com/FarhaKousar1601/Machine-Learning-Laboratory-21AIL66-.git
- Navigate to the project directory:
cd Machine-Learning-Laboratory-21AIL66- - Open the relevant program file and run it using your preferred Python environment.
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Python 3.x
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Required libraries: numpy, pandas, scikit-learn, matplotlib (Install using pip if not already installed)
pip install numpy pandas scikit-learn matplotlib
Contributions are welcome! Please fork the repository, star it, learn from the code, discuss any improvements, and create a pull request with your changes.
This project is licensed under the MIT License - see the LICENSE file for details.
© 2024 Department of AIML, KNS Institute of Technology