machine Learning by networking using javapython programming  
Criteria Points
Part 1 Question 1
Write the code to find the summary statistics and observations based on that.
2
Part 1 Question 2
r observations for the below heatmap
3
Part 1 Question 3
 scatter plot to visualize the relationship between the remaining features having significant correlations (>= 0.7 or <= -0.7) - INDUS and NOX - AGE and NOX - DIS and NOX
6
Part 1 Question 4
Drop the column 'TAX' from the training data and check if multicollinearity is removed?
1
Part 1 Question 5
Write the code to create the linear regression model and print the model summary.
3
Part 1 Question 6
Drop insignificant variables (variables with p-value > 0.05) from the above model and create the regression model again.
2
Part 1 Question 7
Write the code to check the above linear regression assumptions and provide insights.
4
Part 1 Question 8
Wr observations by comparing model performance of train and test dataset
2
Part 1 Question 9
Get model Coefficients in a pandas dataframe with column 'Feature' having all the features and column 'Coefs' with all the corresponding Coefs.
2
Part 1 Question 10
Write the conclusions and business recommendations derived from the model.
5
Part 2 Question 1
Write the observations from the below summary statistics
2
Part 2 Question 2
Write the code to check the percentage of each category for columns mentioned below (cat_cols) r observations
4
Part 2 Question 3
Drop the target variable from the original data and store it in a separate dataframe X Store the target variable in a separate series Y
2
Part 2 Question 4
Fit the logistic regression model on the train dataset using random_state=1
2
Part 2 Question 5
ur observations on the below coefficients obtained from the logistic regression model
3
Part 2 Question 6
ur interpretations of the odds calculated from the logistic regression model coefficients
3
Part 2 Question 7
Check the performance on the training data and bservations from the below classification report and confusion matrix for the training set
3
Part 2 Question 8
Compare the performance of the model on the training set after changing the threshold and check the performance on the testing set
2
Part 2 Question 9
Fit the KNN model on the scaled training data using the optimal values of hyperparameters obtained from GridSearchCV Check the performance of the model on the scaled training and testing sets Check the performance of the model on the scaled training and testing sets Compare the performance andr observations
4
Part 2 Question 10
Write the conclusion on the key factors that are driving the cancellations and  recommendations to the business on how they can minimize the number of cancellations.achine Learning
Criteria Points
Part 1 Question 1
Write the code to find the summary statistics and r observations based on that.
2
Part 1 Question 2
 observations for the below heatmap
3
Part 1 Question 3
 scatter plot to visualize the relationship between the remaining features having significant correlations (>= 0.7 or <= -0.7) - INDUS and NOX - AGE and NOX - DIS and NOX
6
Part 1 Question 4
Drop the column 'TAX' from the training data and check if multicollinearity is removed?
1
Part 1 Question 5
Write the code to create the linear regression model and print the model summary.  observations from the model.
3
Part 1 Question 6
Drop insignificant variables (variables with p-value > 0.05) from the above model and create the regression model again.
2
Part 1 Question 7
Write the code to check the above linear regression assumptions and provide insights.
4
Part 1 Question 8
r observations by comparing model performance of train and test dataset
2
Part 1 Question 9
Get model Coefficients in a pandas dataframe with column 'Feature' having all the features and column 'Coefs' with all the corresponding Coefs.
2
Part 1 Question 10
Write the conclusions and business recommendations derived from the model.
5
Part 2 Question 1
Write the observations from the below summary statistics
2
Part 2 Question 2
Write the code to check the percentage of each category for columns mentioned below (cat_cols) r observations
4
Part 2 Question 3
Drop the target variable from the original data and store it in a separate dataframe X Store the target variable in a separate series Y
2
Part 2 Question 4
Fit the logistic regression model on the train dataset using random_state=1
2
Part 2 Question 5
r observations on the below coefficients obtained from the logistic regression model
3
Part 2 Question 6
 interpretations of the odds calculated from the logistic regression model coefficients
3
Part 2 Question 7
Check the performance on the training data and  observations from the below classification report and confusion matrix for the training set
3
Part 2 Question 8
Compare the performance of the model on the training set after changing the threshold and check the performance on the testing set
2
Part 2 Question 9
Fit the KNN model on the scaled training data using the optimal values of hyperparameters obtained from GridSearchCV Check the performance of the model on the scaled training and testing sets Check the performance of the model on the scaled training and testing sets Compare the performance and observations
4
Part 2 Question 10
Write the conclusion on the key factors that are driving the cancellations and w recommendations to the business on how they can minimize the number of cancellations.