Make a logistic regression and report the accuracy.
Task 1: Train Data Transformation
Perform the pre-processing to transform the original data into a new feature space by doing feature engineering so the features are linear in the new space. Confirm four assumptions required for a linear classifier.
Task 2: Linear Parametric Classification
Implement logistic regression model using Scikit-learn. Using the GridSearchCV, optimize the model.
1. Make a logistic regression model. Report the weights and the accuracy of the model.
2. Using the GridSearchCV at various 100 a values from 10-5 to 10, build a logistic
regression model. Visualize how the model accuracy behaviors. Then report the best
model. If the accuracy is 100%, then the model is overfitted. In this case, the model
should be regularized.
3. Using the best model, classify the test data set.
Task 3: Transformation using Kernel Method
Kernelize the original to a Kernel space using five different valid Kernel functions. Then repeat Task 2.
Task 4: Non-parametric KNN Classification
1. Classify the original data with K values from 1 to 200. Then report the accuracy with
visualization.
2. Repeat step 1 with the final train data sets from Tasks 1 and 3.
Report:
Write a report summarizing the work. In the report, all steps must be explicitly explained with
visualizations.