Machine Learning lab (BAIL606)

Machine Learning lab

Course Code BAIL606 
CIE Marks 50
Teaching Hours/Week (L:T:P: S) 0:0:2:0 
SEE Marks 50
Credits 01 
Exam Hours 100
Examination type (SEE) Practical

Experiments

1 Develop a program to Load a dataset and select one numerical column. Compute mean, median, mode, standard deviation, variance, and range for a given numerical column in a dataset. Generate a histogram and boxplot to understand the distribution of the data. Identify any outliers in the data using IQR. Select a categorical variable from a dataset. Compute the frequency of each category and display it as a bar chart or pie chart.

2 Develop a program to Load a dataset with at least two numerical columns (e.g., Iris, Titanic). Plot a scatter plot of two variables and calculate their Pearson correlation coefficient. Write a program to compute the covariance and correlation matrix for a dataset. Visualize the correlation matrix using a heatmap to know which variables have strong positive/negative correlations.

3 Develop a program to implement Principal Component Analysis (PCA) for reducing the dimensionality of the Iris dataset from 4 features to 2.

4 Develop a program to load the Iris dataset. Implement the k-Nearest Neighbors (k-NN) algorithm for classifying flowers based on their features. Split the dataset into training and testing sets and evaluate the model using metrics like accuracy and F1-score. Test it for different values of 𝑘 (e.g., k=1,3,5) and evaluate the accuracy. Extend the k-NN algorithm to assign weights based on the distance of neighbors (e.g.,𝑤𝑒𝑖𝑔ℎ𝑡=1/𝑑2 ). Compare the performance of weighted k-NN and regular k-NN on a synthetic or real-world dataset.

6 Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.

7 Develop a program to demonstrate the working of Linear Regression and Polynomial Regression. Use

Boston Housing Dataset for Linear Regression and Auto MPG Dataset (for vehicle fuel efficiency prediction) for Polynomial Regression.

8 Develop a program to load the Titanic dataset. Split the data into training and test sets. Train a decision tree classifier. Visualize the tree structure. Evaluate accuracy, precision, recall, and F1-score.

9 Develop a program to implement the Naive Bayesian classifier considering Iris dataset for training. Compute the accuracy of the classifier, considering the test data.

10 Develop a program to implement k-means clustering using Wisconsin Breast Cancer data set and visualize the clustering result.

Suggested Learning Resources:

Books:

1. S Sridhar and M Vijayalakshmi, “Machine Learning”, Oxford University Press, 2021.

2. M N Murty and Ananthanarayana V S, “Machine Learning: Theory and Practice”, Universities Press (India) Pvt. Limited, 2024.