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ML Lab Questions

1. Using matplotlib and seaborn to perform data visualization on the standard dataset

a. Perform the preprocessing

b. Print the no of rows and columns

c. Plot box plot

d. Heat map

e. Scatter plot

f. Bubble chart

g. Area chart


2. Build a Linear Regression model using Gradient Descent methods in Python for a wine data set



3. Build a Linear Regression model using an ordinary least-squared model in Python for a wine data set 


4. Implement quadratic Regression for the wine dataset


5. Implement Logistic Regression for the wine data set


6. Implement classification using SVM for Iris Dataset


7. Implement Decision-tree learning for the Tip Dataset


8. Implement Bagging using Random Forests 


9.  Implement K-means Clustering 

 

10.  Implement DBSCAN clustering 


11.  Implement the Gaussian Mixture Model 


12. Solve the curse of Dimensionality by implementing the PCA algorithm on a high-dimensional


13. Comparison of Classification algorithms 


14. Comparison of Clustering algorithms  


Data Set: Energy Consumption

https://drive.google.com/file/d/1i5iBDN9OJXNfvVLtIsV9HbfaKQy31dnR/view?usp=sharing

wine dataset

https://ocw.mit.edu/courses/15-097-prediction-machine-learning-and-statistics-spring-2012/bb222ea44cb14f4d9f8729328cd08a21_wine.csv

https://github.com/jbrownlee/Datasets/blob/master/wine.csv


weather dataset

https://corgis-edu.github.io/corgis/datasets/csv/weather/weather.csv





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