Detect fraudulent credit card transactions through supervised machine learning
Description: How to detect fraudulent credit card transactions?. Detect fraudulent credit card transactions through supervised machine learning, testing 6 classification models (w/CV) and their hyperparameters. Using also techniques like:
- Example-dependent cost-sensitive learning, calculating the weights for each sample based on the feature (transaction amount) and class weight
- OverSampling (SMOTE) and undersampling
It involves Data Understanding, research and design of Artificial Intelligence supervised learning algorithms for classification problem.
A typical data science project has several phases (Cross-Industry Standard Process for Data Mining (CRISP-DM)). Phases adapted to this project
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Business Understanding: Understand the business problem and define the objectives of the project.
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Data Understanding: Understand the data and its structure. We look at the data types, missing values, and other characteristics of the data. Discover and visualize the data to gain insights.
- Load Data
- PCA for reducing features (Need for?)
- Exploratory Data Analysis (EDA): Data Analysis Process with estatistical analysis and data visualization for knowing data and obtain findings a) Data Exploration: Examining summary statistics, visualizing data distributions, and identifying patterns or relationships. b) Data Visualization: Presenting insights through plots, charts, and graphs to communicate findings effectively
- Data Preparation: Prepare the data for modeling by cleaning it up and transforming it into a format that can be used by machine learning algorithms.
- Data Cleaning: Handling missing values and categorical values, removing outliers and duplicates, and ensuring data quality.
- Data Transforming
- Feature Engineering: Transforming variables, creating new features, or selecting relevant variables for analysis. -- Feature selection: selecting the most useful features to train on among existing features. -- Feature extraction: combining existing features to produce a more useful one (e.g. handling missing data, encoding variables, dealing with categorical variables, dimensionality reduction algorithms ...). -- Creating new features by gathering new data.
- PCA for reducing features (or in the begining after loading data) (need for?)
- Data Visualization after Data Preparation: Presenting insights through plots, charts, and graphs to communicate findings effectively
- Modeling / Select and Train Models: We create a model that can be used to make predictions or classify new data.
- Retrieve Class (y) from the dataset (X)
- Split Data
- Normal Test
- OverSampling (SMOTE) and undersampling (need for?)
- Calculate the weights for each sample based on the feature (transaction amount) and class weight (need for?)
- Intermediate Results (need for?)
- Train model (w/cross-validate) (based on OverSampling (SMOTE) and undersampling) (need for?)
- Evaluation: We evaluate the model’s performance and determine whether it meets the business objectives.
- Evaluation using cross-validate
- Fine-tune Models
- Model Evaluation on Test Set
- Deployment Deploy the model into production and monitor its performance over time.
Data with the projects (to train the model and to apply the model) are in data dir.
Consider the data creditcard.csv (inzip the file) and obtain a model to detect fraudulent credit card transactions.
28 fields (only numerical input variables) which are the result of a PCA transformation. This means that your original dataset had more than 28 variables, and PCA was used to reduce it to 28 variables while retaining most of the information in the original dataset.
- Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset.
- The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning Note: The cost of a false positive (predicting fraud when there is none) might be the cost of investigating the transaction, while the cost of a false negative (failing to predict fraud when it is present) might be the cost of reimbursing the customer for the fraudulent transaction. The amount feature can be used in example-dependent cost-sensitive learning by assigning different costs to false positives and false negatives based on the amount of the transaction. For example, you might assign a higher cost to false negatives for high-value transactions because they are more costly to reimburse.
- Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
jupiter notebook (.ipynb) file in model dir
Please see the description in .ipynb about this project.