MACHINE LEARNING FOR DATA SCIENCE Lab

 SEMESTER :  IV                                         HOURS/WEEK   : 4

CODE :  P20CA4P7                                        CREDITS    : 3

 

Core Practical VII :  MACHINE LEARNING FOR DATA SCIENCE LAB

 

           List of Practical:

1. Vector operations in R

2. Managing data in R

•          Descriptive Statistics
  
•          Managing the Data
•             Movies.csv 
•            Genres.csv 

3. Forecasting using Regression techniques

4. Classification using Naives Bayes

5. Classification using Decision trees

6. Finding patterns using Association Rules

7. Clustering with K-Means

MACHINE LEARNING FOR DATA SCIENCE

SEMESTER:  IV                                           HOURS/WEEK: 4

CODE:  P20CA411                                              CREDITS    : 3

 

Core XI: MACHINE LEARNING FOR DATA SCIENCE

Syllabus: ML for DS - Theory

MCQs: ML MCQs

Sample Code: Unit 1

UNIT 1: Journey from Statistics to Machine Learning

UNIT 3:  kNN

                        kNN Code

                    Filtering mobile phone spam with the naive Bayes algorithm

Unit 4:  Regression, Neural Networks & Support Vector Machines and Finding Patterns

        Code for Predicting medical expenses using linear regression

        Code for Estimating quality of wine

        Code for Modeling the strength of concrete with ANNs

        

Unit 5:  Evaluating Model Performance