Credit-Card-Fraud-Detection-using-Scikit-Learn-and-Snap-ML

Description:

I will use two models are: Decision Tree and Support Vector Machine, and a real dataset to train each of these models. The dataset includes information about transactions made by credit cards in September 2013 by European cardholders. I will use the trained model to assess if a credit card transaction is legitimate or not.

Objectives

• Perform basic data preprocessing in Python
• Model a classification task using the Scikit-Learn and Snap ML Python APIs
• Train Suppport Vector Machine and Decision Tree models using Scikit-Learn and Snap ML
• Run inference and assess the quality of the trained models

Table of contents

1. Import Libraries
2. Dataset Analysis
3. Dataset Preprocessing
4. Dataset Train/Test Split
5. Build a Decision Tree Classifier model with Scikit-Learn
6. Build a Decision Tree Classifier model with Snap ML
7. Evaluate the Scikit-Learn and Snap ML Decision Tree Classifiers
8. Build a Support Vector Machine model with Scikit-Learn
9. Build a Support Vector Machine model with Snap ML
10. Evaluate the Scikit-Learn and Snap ML Support Vector Machine Models

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