feat: Add new Isolation Forest script

[rashi-repo]

AI-Based Fraud Detection with Isolation Forest

This script provides an end-to-end example of building and evaluating an AI model for credit card fraud detection. It uses the Isolation Forest algorithm, which is highly effective for identifying anomalies in large datasets.

Core Components
The script is structured into several key sections to handle the entire machine learning workflow:

Data Loading: It begins by securely loading the creditcard.csv dataset, with built-in error handling to ensure the file is found.

Feature Engineering:** This is a crucial step where new features are created from the raw data to improve the model's performance. Two new features, TransactionHour and Time_Since_Last_Trans, are calculated to provide behavioral context for each transaction.

Model Training: An Isolation Forest model is trained on the prepared data. This unsupervised learning algorithm is ideal for fraud detection because it can learn to identify anomalies (fraudulent transactions) without needing them to be explicitly labeled in the training data.

Model Evaluation: After training, the model's performance is evaluated using a confusion matrix and a classification report. These metrics provide a clear view of how well the model is performing, especially in catching fraud cases.

Live Transaction Test: A new section has been added to create a mock transaction with values designed to mimic fraudulent activity. This transaction is then fed to the trained model to demonstrate its real-time predictive capability.

How it Works
The Isolation Forest works by randomly selecting features and splitting the data into subsets. Anomalies, or fraudulent transactions, are typically isolated in fewer steps because they are "different" from the rest of the data. This is what makes the model so effective. The new TransactionHour and Time_Since_Last_Trans features provide the model with a richer understanding of a transaction's context, helping it make more accurate predictions.

How to Run
To run this script, ensure you have the required libraries (pandas, scikit-learn, numpy) installed and the creditcard.csv file in the same directory. Then, simply execute the script from your terminal:

python updated.py