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import numpy as np # For arithmetics and arrays | |
import math # For inbuilt math functions | |
import pandas as pd # For handling data frames | |
import collections # used for dictionaries and counters | |
from itertools import permutations # used to find permutations | |
from sklearn.ensemble import GradientBoostingClassifier | |
from sklearn.ensemble import RandomForestClassifier | |
from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier | |
from sklearn.model_selection import train_test_split # Import train_test_split function to easily split data into training and testing samples | |
from sklearn.decomposition import PCA # Principal component analysis used to reduce the number of features in a model | |
from sklearn.preprocessing import StandardScaler # used to scale data to be used in the model | |
from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation | |
from sklearn.metrics import confusion_matrix | |
from sklearn.metrics import classification_report | |
from sklearn.metrics import roc_auc_score | |
from sklearn.metrics import roc_curve | |
from sklearn.metrics import accuracy_score | |
from sklearn.metrics import log_loss | |
import pickle # To save the trained model and then read it | |
import seaborn as sns # Create plots | |
sns.set(style="ticks") | |
import matplotlib.pyplot as plt |
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