Write complete code for it in Python and use algorithm(classifier and feature reduction technique) that gives best result.

Do not copy paste from others, write your own complete code.

Problem Statement

Whenever any new treatment is proposed, there are numerous steps involved that include massive research. The last step of this research include involvement of volunteers who participate in controlled testing. Some of these patients are given the treatment while others are given a placebo.

We have a dataset where patients are blindly divided into treatment and placebo groups and some values are calculated. Patients have been divided into numerous groups and all of you have been assigned a different group to conduct your analysis.

Deliverables:

Fit best ML algorithm to verify the changes before and after treatment/placebo

You have been given an excel sheet for the calculated features.

You would be assigned one of many feature sets by your instructor and you would conduct your testing on it. Some may get placebo data and some may get treatment data

Use T testing on all features as a first step to identify the differences are significant or not.

You have to apply the whole ML cycle (figure 1) to select the best features (USE FEATURE SELECTION TOO) and ML algorithm and calculate validation parameters for each technique.

In your opinion which of your technique is best out of 3 feature reduction and 3 classification techniques, state your reasons?

algorithmMethod/process/codethatperformsstatisticalanalysisofdata.predictions.trainProcessthatiteratesanalgorithmoverdatacalculatingamodel.modelPersistabletraining.inferenceProcessingnewmodeltomake