$(1$ point$)$ Machine Learning Application

Logistic regression $($LR$)$ is a type of model used to compute the probabitity that a class or an event is observed. LR is commonly used in machine learning applications. In thi. problem, we will implement a logistic regression models and then we will apply it$.$

a$.$ A company is interested in determining the probability that a person will rent their e$-$scooters for a trip from Terrace to UPRM. They have a LR model that computes the probability that a person rents an e$-$scooter; we will use $p$ to represent this probability. Create a function called $lr$ model that implements the following LR model:

$p=\frac{1}{1+e^{-z}}$

where $z={}_0+{}_1{x}_1+{}_2{x}_2.$ The variable $x_1$ represents a person's income and the variable $x_2$ is the price of an e$-$scooter trip. The ${}_0,{}_1,{}_2,x_1,$ and $x_2$ variables must be represented as the function parameters. The function must return the value of $p.$

b$.$ The model classifies people as renters and non$-$renters of the company's e$-$scooters based on each person renting probability $(p).$ The "decision rule" is as follows: if the probability $p$ is greater than $0\mathrm{.}5,$ the person is classified as a renter; otherwise, the person is classified as a non$-$renter. Create a function called classification that implements the decision rule. The function must return the value of a variable called decision. decision is $1$ if $p$ is greater than $0\mathrm{.}5($i$.$e$.,$ the person is classified as a renter$),$ and $0$ otherwise $($i$.$e$.,$ the person is classified as a non$-$renter$).$

c$.$ Add the lr$\_$model function and the classification function within function called demand$\_$forecast. This function predicts the level of demand for scooters at different price levels. should be simil