23ADM4 - Predictive Machine Learning Algorithms Lab
Pre-requisite:Probability and Statistics, Python Programming
Course Educational Objective: The objective of this lab is to Make use of Data sets in implementing the machine learning algorithms in any suitable language of choice.
Course Outcomes(CO):At the end of this course,the student will be able to:
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
Experiments:
1. Basic statistical functions for data exploration.
2. Data Visualization:Boxplot,scatter plot,histogram.
3. Data Pre-processing:Handling missing values,outliers,normalization,Scaling.
4. Principal Component Analysis(PCA).
5. Linear Discriminant Analysis(LDA).
6. Regression Analysis:Linear regression,Logistic regression,Polynomial regression.
7. K-Nearest Neighbour(kNN) Classifier.
8. Support Vector Machines(SVMs).
9. Random Forest model.
10. AdaBoost Classifier and XGBoost.