import pandas as pd

import numpy as np

import os

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.tree import DecisionTreeClassifier

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.metrics import accuracy$\_$score

from sklearn.model$\_$selection import GridSearchCV

filename $=$ os$.$path.join$($os$.$getcwd$(),$ "data", "cell$2$celltrain.csv$")$

df $=$ pd$.$read$\_$csv$($filename$,$ header$=0)$

y $=$ df$[\text{'}$Churn$\text{'}]$

X $=$ df$.$drop$($columns $=$ 'Churn', axis$=1)$

X$.$head$()$

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}10,$ random$\_$state$=1234)$

md $=[2**$n for n in range$(2,6)]$

msl $=[25*2**$n for n in range$(0,3)]$

param$\_$grid$=\{\text{'}$max$\_$depth':md$,$ 'min$\_$samples$\_$leaf':msl$\}$

param$\_$grid

print$(\text{'}$Running Grid Search...$\text{'})$

# $1.$ Create a DecisionTreeClassifier model object without supplying arguments

model $=$ DecisionTreeClassifier$()$

# $2.$ Run a Grid Search with $5-$fold cross$-$validation using our the model.

# Pass all relevant parameters to GridSearchCV and assign the output to the object 'grid'

grid $=$ GridSearchCV$($model$,$ param$\_$grid, cv$=5)$

# $3.$ Fit the model to the training data and assign the fitted model to the

# variable grid$\_$search

grid$\_$search $=$ grid.fit$($X$\_$train, y$\_$train$)$

print$(\text{'}$Done$\text{'})$