Let’s first import all of the libraries we will need to create and train the model.

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score

import urllib.request

Now we can import our data. Alexey Grigorev, from Data Talks Club has the data on his GitHub account.

url = 'https://raw.githubusercontent.com/alexeygrigorev/mlbookcamp-code/master/chapter-03-churn-prediction/WA_Fn-UseC_-Telco-Customer-Churn.csv'

filename = 'data-week-3.csv'

df = pd.read_csv(url)

Now that we have our data let’s clean up the data by replacing spaces with ‘_’. With the categorical features, we will do another replacement of spaces with ‘_’. During our exploration, we noticed that the ‘totalcharges’ feature contained ‘NAN’s, which we can’t have, so we will fill those with a ‘0’. The last action we will take is to turn our target variable, ‘churn’ into an integer using the ‘astype(int)‘ function

df.columns = df.columns.str.lower().str.replace(' ', '_')

categorical_columns = list(df.dtypes[df.dtypes == 'object'].index)

for c in categorical_columns:
    df[c] = df[c].str.lower().str.replace(' ', '_')

df.totalcharges = pd.to_numeric(df.totalcharges, errors='coerce')
df.totalcharges = df.totalcharges.fillna(0)

df.churn = (df.churn == 'yes').astype(int)

It is time to split our data into train, validation, and test datasets using ‘train_test_split‘. First, we split our data into 80% and 20% and assign each to df_full_train and df_test, respectively. Second, we split our df_train_full dataset into 67% and 33%, respectively. Since this was a training class, the random_state was used so that we were all working with the same splits. We have also reset the index on each dataset with the ‘.reset_index’ function just for appearances, it isn’t required. We then assign y_train and y_val the target variable, again ‘churn’, and then we delete the target variable from our train and val set.

df_train_full, df_test = train_test_split(df, test_size=0.2, random_state=11)

df_train_full = df_train_full.reset_index(drop=True)
df_test = df_test.reset_index(drop=True)

df_train, df_val = train_test_split(df_train_full, test_size=0.33, random_state=11)

df_train = df_train.reset_index(drop=True)
df_val = df_val.reset_index(drop=True)

y_train = df_train.churn.values
y_val = df_val.churn.values

del df_train['churn']
del df_val['churn']

We will assign our different types of features to two variables here. ‘Categorical’ will contain all features that are considered categorical, i.e. ‘gender’ will be assigned as male=0, female=1, ‘seniorcitizen’ will be no=0, yes=1. The other features are numerical so therefore will be added to the numerical variable.

categorical = ['gender', 'seniorcitizen', 'partner', 'dependents',
               'phoneservice', 'multiplelines', 'internetservice',
               'onlinesecurity', 'onlinebackup', 'deviceprotection',
               'techsupport', 'streamingtv', 'streamingmovies',
               'contract', 'paperlessbilling', 'paymentmethod']
numerical = ['tenure', 'monthlycharges', 'totalcharges']

Let’s create a function that will turn our categorical and numerical features into a dictionary and assign that dictionary to the variable ‘cat’. Next, we will assign the Dictionary Vectorizer, ‘DictVectorizer’ function, which will turn our dictionary into a vector, to ‘dv’. The ‘fit’ method

def train(df, y, C=1.0):
    cat = df[categorical + numerical].to_dict(orient='rows')
    
    dv = DictVectorizer(sparse=False)
    dv.fit(cat)

    X = dv.transform(cat)

    model = LogisticRegression(solver='liblinear', C=C)
    model.fit(X, y)

    return dv, model


def predict(df, dv, model):
    cat = df[categorical + numerical].to_dict(orient='rows')
    
    X = dv.transform(cat)

    y_pred = model.predict_proba(X)[:, 1]

    return y_pred

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