Classification models

This case study aims to use different classification models to solve the same problem. You will need to check if your best models are generalized correctly or have under/overfitting. You will need to use the AUC to compare the model’s performances and tell which model you will be choosing to deploy. In this case study, we went on to implement random forest and SVM, k-Nearest Neighbors Steps to do: In [1] : ) import pandas as pd import numpy as np from sklearn.

Neighbors import KNeighborsClassifier from sklearn. metrics import accuracy_score from sklearn. metrics import confusion_matrix from sklearn. svm import SVC from sklearn. ensemble import RandomForestClassifier from sklearn. pipeline import make_pipeline from sklearn. model_selection import cross_val_score from sklearn. model_selection import validation_curve from sklearn. preprocessing import StandardScaler from sklearn. model_selection import GridSearchCV from sklearn. model_selection import train_test_split import matplotlib. pyplot as plt %matplotlib inline import seaborn as sns %config InlineBackend . figure_format= ‘ retina’ sns . set() # Revert to matplotlib defaults pit . rcParams [ ‘ figure . figsize’ ] = (9, 6) pit . rcParams[ ‘ axes . labelpad’ ] = 10 sns . set_style( “darkgrid”)