Machine Learning — Logistic Regression with Python

# Importing packages import pandas as pd # data processing import numpy as np # working with arrays import itertools import matplotlib.pyplot as plt # visualizations from matplotlib import rcParams # plot size customization from termcolor import colored as cl # text customization from sklearn.model_selection import train_test_split # splitting the data from sklearn.linear_model import LogisticRegression # model algorithm from sklearn.preprocessing import StandardScaler # data normalization from sklearn.metrics import jaccard_similarity_score as jss # evaluation metric from sklearn.metrics import precision_score # evaluation metric from sklearn.metrics import classification_report # evaluation metric from sklearn.metrics import confusion_matrix # evaluation metric from sklearn.metrics import log_loss # evaluation metric rcParams['figure.figsize'] = (20, 10) # Importing the data and EDA df = pd.read_csv('tele_customer_data.csv') df.drop(['Unnamed: 0', 'loglong', 'callwait', 'logtoll', 'voice', 'ebill', 'lninc', 'confer', 'internet', 'cardten', 'pager', 'longten', 'wiremon', 'equipmon', 'tollten', 'wireless', 'callcard', 'cardmon', 'tollmon'], axis = 1, inplace = True) for i in df.columns: df[i] = df[i].astype(int) df.head() df.describe() df.info() # Splitting the data X_var = np.asarray(df[['tenure', 'age', 'income', 'ed', 'employ', 'longmon', 'custcat']]) y_var = np.asarray(df['churn']) print(cl('X_var samples : ', attrs = ['bold']), X_var[:5]) print(cl('y_var samples : ', attrs = ['bold']), y_var[:5]) X_var = StandardScaler().fit(X_var).transform(X_var) print(cl(X_var[:5], attrs = ['bold'])) X_train, X_test, y_train, y_test = train_test_split(X_var, y_var, test_size = 0.3, random_state = 4) print(cl('X_train samples : ', attrs = ['bold']), X_train[:5]) print(cl('X_test samples : ', attrs = ['bold']), X_test[:5]) print(cl('y_train samples : ', attrs = ['bold']), y_train[:10]) print(cl('y_test samples : ', attrs = ['bold']), y_test[:10]) # Modelling lr = LogisticRegression(C = 0.1, solver = 'liblinear') lr.fit(X_train,y_train) yhat = lr.predict(X_test) yhat_prob = lr.predict_proba(X_test) print(cl('yhat samples : ', attrs = ['bold']), yhat[:10]) print(cl('yhat_prob samples : ', attrs = ['bold']), yhat_prob[:10]) # Evaluation # 1. Jaccard Index print(cl('Jaccard Similarity Score of our model is {}'.format(jss(y_test, yhat).round(2)), attrs = ['bold'])) # 2. Precision Score print(cl('Precision Score of our model is {}'.format(precision_score(y_test, yhat).round(2)), attrs = ['bold'])) # 3. Log loss print(cl('Log Loss of our model is {}'.format(log_loss(y_test, yhat).round(2)), attrs = ['bold'])) # 4. Classificaton report print(cl(classification_report(y_test, yhat), attrs = ['bold'])) def plot_confusion_matrix(cm, classes,normalize = False, title = 'Confusion matrix', cmap = plt.cm.Blues): if normalize: cm = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis] plt.imshow(cm, interpolation = 'nearest', cmap = cmap) plt.title(title, fontsize = 22) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation = 45, fontsize = 13) plt.yticks(tick_marks, classes, fontsize = 13) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment = 'center', fontsize = 15, color = 'white' if cm[i, j] > thresh else 'black') plt.tight_layout() plt.ylabel('True label', fontsize = 16) plt.xlabel('Predicted label', fontsize = 16) # Compute confusion matrix cnf_matrix = confusion_matrix(y_test, yhat, labels = [1,0]) np.set_printoptions(precision = 2) # Plot non-normalized confusion matrix plt.figure() plot_confusion_matrix(cnf_matrix, classes = ['churn=1','churn=0'], normalize = False, title = 'Confusion matrix') plt.savefig('confusion_matrix.png')