Q1. Fit a predictive linear regression model to estimate weight of the fish from its length, height and width? (The data source fish.csv can be found here: https://www.kaggle.com/aungpyaeap/fish-marke

Assignment #1 Possible Points: 100

CS-697AB ML Due date: 25th February 2022

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Assignment should be done on individual basis.

Q1 Fit a predictive linear regression model to estimate weight of the fish from its length, height and width? (The data source fish.csv can be found here: https://www.kaggle.com/aungpyaeap/fish-market) (50 points)

-Report the coefficients values by using the standard Least Square Estimates

-What is the standard error of the estimated coefficients, R-squared term, and the 95% confidence interval?

-Is there any dependence between the length and weight of the fish?

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Q2 Using the data source in Q1 fit the Ridge and Lasso Regression Models. (25 points)

• Report the coefficients for both the models

• Report the attribute(s) least impacting the weight of the fish.

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Q3 Modify the example code for Logistic Regression to include all the four attributes in iris dataset for two class and multi-class classification. Report any difference in the performance if noted. (25 points)

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