Data Science Credit Card Fraud Detection – Model Building. A practical hands-on Data Science Project on Credit Card Fraud Detection using different sampling and Model Building
What you’ll learn
- Data Analysis and Understanding
- Data Preprocessing Techniques
- Model Building using Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models
- RepeatedKFold and StratifiedKFold
- Random Oversampler, SMOTE, ADASYN
- Classification Metrics
- Model Evaluation
this course I will cover, how to develop a Credit Card Fraud Detection model to categorize a transaction as Fraud or Legitimate with very high accuracy using different Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model.
This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation. We will explore RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.
I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.
Task 1 : Installing Packages.
Task 2 : Importing Libraries.
Task 3 : Loading the data from source.
Task 4 : Understanding the data
Task 5 : Checking the class distribution of the target variable
Task 6 : Finding correlation and plotting Heat Map
Task 7 : Performing Feature engineering.
Task 8 : Train Test Split
Task 9 : Plotting the distribution of a variable
Task 10 : About Confusion Matrix, Classification Report, AUC-ROC
Task 11 : Created a common function to plot confusion matrix
Task 12 : About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models
Task 13 : Created a common function to fit and predict on a Logistic Regression model
Task 14 : Created a common function to fit and predict on a KNN model
Task 15 : Created a common function to fit and predict on a Tree models
Task 16 : Created a common function to fit and predict on a Random Forest model
Task 17 : Created a common function to fit and predict on a XGBoost model
Task 18 : Created a common function to fit and predict on a SVM model
Task 19 : About RepeatedKFold and StratifiedKFold.
Task 20 : Performing cross validation with RepeatedKFold and Model Evaluation
Task 21 : Performing cross validation with StratifiedKFold and Model Evaluation
Task 22 : Proceeding with the model which shows the best result till now
Task 23 : About Random Oversampler, SMOTE, ADASYN.
Task 24 : Performing oversampling with Random Oversampler with StratifiedKFold cross
validation and Model Evaluation.
Task 25 : Performing oversampling with SMOTE and Model Evaluation.
Task 26 : Performing oversampling with ADASYN and Model Evaluation.
Task 27 : Hyperparameter Tuning.
Task 28 : Extracting most important features
Task 29 : Final Inference.
Data Analysis, Model Building is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of Machine learning in just a few hours!
You will receive :
1. Certificate of completion from AutomationGig.
2. All the datasets used in the course are in the resources section.
3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.
So what are you waiting for?
Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!
Happy Learning !![Music : bensound]
Who this course is for:
- Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation.
- Students and professionals who wants to learn RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN