Logistic Regression
You are interviewing with Google’s data science team having the responsibility of predicting the Click Through Rate (CTR) of ads they place on multiple web properties. Your hiring manager keen on testing you out, suggests to download this dataset and asks you to code up a model that predicts the CTR based on Logistic Regression.
Task 1: Data Preprocessing
Preprocess the data you are given to your liking. This may include dropping some columns you wont use, addressing noisy or missing data etc.
Use Pandas as a dataframe abstraction for this task and you can easily convert dataframes to pytorch tensors for later processing You can learn about Pandas here:
Task 2: Logistic Regression
Implement the logistic regression solution to the prediction problem that can work with Stochastic Gradient Descent (SGD).
Show clearly all equations of the gradient and include comments in markdown explaining every stage of processing. Also, highlight any enhancements you may have done to improve performance.
Plot the final precision vs recall curve of your classifier. Clearly explain the tradeoff between the two quantities and the shape of the curve.