Machine Learning Lab

COURSE OBJECTIVES:

To make the student to get a clear understanding of the core concepts of python like import data in various formats for statistical computing, data manipulation, business analytics, machine learning algorithms and data visualization etc.

COURSE OUTCOMES:

After successful completion of this course, the students will be able to:

CO 1: Analyze exploratory data analysis. [K4]

CO 2: Analyze the real word datasets presented in different formats using python libraries to
Perform exploratory data analysis.[K4]

CO 3: Apply the machine learning algorithms on various real time data sets. [K3]

CO 4: Analyze the data by using visualization tools or libraries. [K4]

 List of Experiments 

1. Perform Basic Visualizations (bar chart, scatter plot, box plot,histogram etc) for all the columns (numerical data only) on the specified dataset and draw the inferences for the visualizations in excel.

2. Build a prediction model for simple linear regression.

3. Build a prediction model for multiple linear regression.

4. Build a prediction model to perform logistic regression.

5. Build a model to generate association rules by using apriori algorithm on the Movies data sets

i. Try different values of support and confidence. Observe the change in number of rules for 

               different support, confidence values

     ii. Change the minimum length in apriori algorithm Visulize the obtained rules using different plots

6. Perform clustering using k-means clustering algorithm.

7. Perform Principle Component Analysis and then perform clustering.

8. Prepare a Classification model using decision tree Classifier.

9. Prepare a Classification model using Navie Bayes Classifier

Data Sets Required can download from below

1. Salary_Data.csv 

2.  5_my_movies.csv

3. 7_wine.csv

4. 50_Startups.csv

5. Clutsering_gmm.csv

6.  Data.csv

7.  diabetes.csv 

8. DTree.csv 

9. enjoyspots.csv 

10. id3.csv

11. id3_test.csv

12. pima-indians.csv

13. heart.csv