Bike Rides and the Poisson Model

To help the urban planners, you are called to model the daily bike rides in NYC using this dataset. The dataset contains date, day of the week, high and low temp, precipitation and bike ride counts as columns.

dataset: https://gist.github.com/sachinsdate/c17931a3f000492c1c42cf78bf4ce9fe/archive/7a5131d3f02575668b3c7e8c146b6a285acd2cd7.zip

Maximum Likelihood I (50 points)

The obvious choice in distributions is the Poisson distribution which depends only on one parameter, , which is the average number of occurrences per interval. We want to estimate this parameter using Maximum Likelihood Estimation.

Implement a Gradient Descent algorithm from scratch that will estimate the Poisson distribution according to the Maximum Likelihood criterion. Plot the estimated mean vs iterations to showcase convergence towards the true mean.