To build a fraud detection model using the provided dataset and help FriendPay, a competitor of PayPal, and increase revenue from transaction fees.

The following steps were followed:

1. Data Collection/Cleaning: Obtain the credit card transaction dataset and clean it by removing any missing values, duplicates, or outliers.

2. Data Exploration: Explore the data to gain insights into its structure, distribution, and relationships between the variables. This will help determine which features are most important in predicting fraud.

3. Data Visualization: Visualize the data to identify patterns, correlations, and anomalies that may be useful in detecting fraud. Use graphs, charts, and other visual tools to present your findings.

4. Machine Learning: Train and test various machine learning algorithms such as logistic regression, decision trees, and random forests on the data to develop a fraud detection model. Evaluate the model's performance using the AUPRC.

5. Communication: Prepare a presentation with slides on how the model works, including assumptions, implications, and other important information. Provide the DevOps team with code that can be pushed to production, a transaction data simulator, and a technical specification for integrating the model into FriendPay's existing system.

import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt import matplotlib.pyplot as plt

import re import string

!pip install scikit-learn from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score from sklearn.metrics import confusion_matrix

• Loading and inspecting the dataset using pandas to understand the data structure and format.
• Checking for missing values, outliers, and imbalanced classes in the dataset.
• Visualizing the distribution of the features using histograms, boxplots, and density plots to identify any patterns or anomalies in the data.
• Examining the correlation between features using a correlation matrix and heatmap to identify any highly correlated features.
• Visualizing the class distribution using a bar chart to understand the balance between fraudulent and non-fraudulent transactions.
• Using scatter plots to visualize the relationship between two features and how they relate to the target variable.
• Using Principal Component Analysis (PCA) to visualize the dataset in a 2D and 3D space.
• Data preprocessing: Standardization of the data using StandardScaler() to make sure all features are on the same scale, and reducing the number of dimensions using principal component analysis (PCA) to two components.
• Data splitting: The data is split into training and testing sets using train_test_split().
• Model training: A logistic regression model is trained on the training data using LogisticRegression(), DecisionTreeClassifier(), and RandomForestClassifier().
• Model evaluation: The performance of the model is evaluated on the test data using evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix using the evaluate_classification_model() function.

./my_project argument1 argument2

Murphy Ogbeide

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