However, Machine Learning algorithms usually work best when the different classes contained in the dataset are more or less equally present. Fraud occurrences are fortunately an extreme minority in these transactions. In this chapter, you will work on creditcard_sampledata.csv, a dataset containing credit card transactions data. Verify the veracity of rules based labels.Existing sources can be used as inputs into the ML model.Sometimes use machine learning algorithms to detect fraud or suspicious behavior.suspicious names or track an external hit list from police to reference check against the client base.Receive external lists of fraudulent accounts and names.Often use rules based systems, based on manually set thresholds and experience.Fraud detection requires training an algorithm to identify concealed observations from any normal observations.Fraudulent activities perpetrated by networks - organized crime.

e-commerce businesses must continuously assess the legitimacy of client transactions.

Import pandas as pd import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import numpy as np from pprint import pprint as pp import csv from pathlib import Path import seaborn as sns from itertools import product import string import nltk from rpus import stopwords from import WordNetLemmatizer from imblearn.over_sampling import SMOTE from imblearn.over_sampling import BorderlineSMOTE from imblearn.pipeline import Pipeline from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from ee import DecisionTreeClassifier from trics import r2_score, classification_report, confusion_matrix, accuracy_score, roc_auc_score, roc_curve, precision_recall_curve, average_precision_score from trics import homogeneity_score, silhouette_score from sklearn.ensemble import RandomForestClassifier, VotingClassifier from sklearn.preprocessing import MinMaxScaler from sklearn.cluster import MiniBatchKMeans, DBSCAN import gensim from gensim import corpora

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