23AM51-MACHINE LEARNING LAB

Course Objectives: 

• To learn about computing central tendency measures and Data preprocessing techniques
 • To learn about classification and regression algorithms 
• To apply different clustering algorithms for a problem.

CO1: Apply the appropriate pre-processing techniques on dataset.(Apply–L3)
CO2: Implement supervised Machine Learning algorithms.(Apply–L3)
CO3: Implement advanced Machine Learning algorithms.(Apply–L3)
CO4: Improve individual/teamwork skills, communication & report writing skills with ethical values.

TEXTBOOKS:
T1“MachineLearningTheoryandPractice”,MNMurthy,VS Ananthanarayana,UniversitiesPress(India), 2024.
REFERENCEBOOKS:
R1“Machine Learning”,Tom M.Mitchell,McGraw-HillPublication,2017
R2“MachineLearning inAction”,PeterHarrington,DreamTech.
R3“IntroductiontoDataMining”, Pang-NingTan,MichelStenbach, VipinKumar,7^thEdition,2019.

Software Required: Python/R/Weka Lab should cover the concepts studied in the course work, sample list of Experiments:

 1. Compute Central Tendency Measures: Mean, Median, Mode Measure of Dispersion: Variance, Standard Deviation Cycle-1 Program. 

2.  Apply the following Pre-processing techniques for a given dataset. a. Attribute selection b. Handling Missing Values c. Discretization d. Elimination of Outliers.Cycle-2 Program1.py, Cycle -2 Program .ipynb file, Iris Dataset, Kidney-stone-Dataset.csv, Iris-Flower Dataset, Train_loan-Dataset, Cycle-2.1_ipynb, Cycle-2.2_ipynb, Cycle-2-Word, Cycle-2.1_word

3.  Apply KNN algorithm for classification and regression.

Cycle-3 Program link , Cycle-3 Program .py file

4. Demonstrate decision tree algorithm for a classification problem and perform parameter tuning for better results  Cycle -4 & 5 Programs Link, Play-Tennis, Housing Data, Heart Disease Prediction, Iris, Program link2
5. Demonstrate decision tree algorithm for a regression problem 
6. Apply Random Forest algorithm for classification and regression 
7. Demonstrate Naïve Bayes Classification algorithm. 
8. Apply Support Vector algorithm for classification
 9. Demonstrate simple linear regression algorithm for a regression problem 
10. Apply Logistic regression algorithm for a classification problem 
11. Demonstrate Multi-layer Perceptron algorithm for a classification problem 
12.Implement the K-means algorithm and apply it to the data you selected. Evaluate performance by measuring the sum of the Euclidean distance of each example from its class center. Test the performance of the algorithm as a function of the parameters K. 
13. Demonstrate the use of Fuzzy C-Means Clustering 
14. Demonstrate the use of Expectation Maximization based clustering algorithm