CodeDetails-fraud_detection_model.md

Code Analysis

The given code is a Python script for building and evaluating fraud detection models using machine learning algorithms. It uses libraries such as pandas, scikit-learn, and imbalanced-learn for data manipulation, model training, and evaluation.

Here's a breakdown of what the code does:

Data Loading and Preprocessing:

Loads a CSV file named fraud_detection1.csv using pandas. Separates the dataset into features (input variables) and labels (target variable). Encodes non-numeric categorical columns using LabelEncoder. Converts the string value "PAYMENT" to the float value 0 in categorical columns.

Data Splitting and Resampling:

Splits the data into training and test sets using train_test_split. Applies Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution by oversampling the fraud transactions.

Model Creation and Training:

Creates three different machine learning models: Logistic Regression, Decision Tree, and Random Forest. Trains each of the models on the training data.

Model Evaluation:

Evaluates the trained models on the test set. Calculates metrics such as Accuracy, Precision, Recall, and F1-score for each model.

Results Presentation:

Stores the evaluation results in a dictionary and creates a Pandas DataFrame from the dictionary. Displays the evaluation results in tabular format using the DataFrame. Plots a graph to visualize the Accuracy of each model using matplotlib.