| 001 About Confusion Matrix, Classification Report, AUC-ROC.mp4 | 27.1 MB | ||
| 001 About Confusion Matrix, Classification Report, AUC-ROC_en.srt | 6.6 KB | ||
| 001 About RepeatedKFold and StratifiedKFold.mp4 | 7 MB | ||
| 001 About RepeatedKFold and StratifiedKFold_en.srt | 2.1 KB | ||
| 001 Checking the class distribution of the target variable.mp4 | 21.1 MB | ||
| 001 Checking the class distribution of the target variable_en.srt | 4 KB | ||
| 001 Full Project Code.html | 0 B | ||
| 001 Importing Libraries.mp4 | 4.9 MB | ||
| 001 Importing Libraries_en.srt | 2.3 KB | ||
| 001 Project Overview.mp4 | 3.9 MB | ||
| 001 Project Overview_en.srt | 1.5 KB | ||
| 001 Train Test Split.mp4 | 11.3 MB | ||
| 001 Train Test Split_en.srt | 2 KB | ||
| 002 Created a common function to plot confusion matrix.mp4 | 33.3 MB | ||
| 002 Created a common function to plot confusion matrix_en.srt | 6.3 KB | ||
| 002 Finding correlation and plotting Heat Map.mp4 | 26 MB | ||
| 002 Finding correlation and plotting Heat Map_en.srt | 2.9 KB | ||
| 002 High Level Overview of the steps to be performed.mp4 | 27 MB | ||
| 002 High Level Overview of the steps to be performed_en.srt | 4.9 KB | ||
| 002 Loading the data from source.mp4 | 13.1 MB | ||
| 002 Loading the data from source_en.srt | 2.3 KB | ||
| 002 Performing cross validation with RepeatedKFold and Model Evaluation.mp4 | 61 MB | ||
| 002 Performing cross validation with RepeatedKFold and Model Evaluation_en.srt | 8.8 KB | ||
| 002 Plotting the distribution of a variable.mp4 | 13.4 MB | ||
| 002 Plotting the distribution of a variable_en.srt | 2.7 KB | ||
| 003 About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models.mp4 | 20.3 MB | ||
| 003 About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models_en.srt | 5 KB | ||
| 003 Installing Packages.mp4 | 19.2 MB | ||
| 003 Installing Packages_en.srt | 5.1 KB | ||
| 003 Performing Feature engineering.mp4 | 19.5 MB | ||
| 003 Performing Feature engineering_en.srt | 3.3 KB | ||
| 003 Performing cross validation with StratifiedKFold and Model Evaluation.mp4 | 11.7 MB | ||
| 003 Performing cross validation with StratifiedKFold and Model Evaluation_en.srt | 4.3 KB | ||
| 003 Understanding the data.mp4 | 17.1 MB | ||
| 003 Understanding the data_en.srt | 2.4 KB | ||
| 004 Created a common function to fit and predict on a Logistic Regression model.mp4 | 61.3 MB | ||
| 004 Created a common function to fit and predict on a Logistic Regression model_en.srt | 10.3 KB | ||
| 004 Proceeding with the model which shows the best result till now.mp4 | 34.1 MB | ||
| 004 Proceeding with the model which shows the best result till now_en.srt | 6.4 KB | ||
| 005 About Random Oversampler, SMOTE, ADASYN.mp4 | 24.1 MB | ||
| 005 About Random Oversampler, SMOTE, ADASYN_en.srt | 4.3 KB | ||
| 005 Created a common function to fit and predict on a KNN model.mp4 | 34.7 MB | ||
| 005 Created a common function to fit and predict on a KNN model_en.srt | 6.7 KB | ||
| 006 Created a common function to fit and predict on a Tree models.mp4 | 23.7 MB | ||
| 006 Created a common function to fit and predict on a Tree models_en.srt | 3.9 KB | ||
| 006 Performing Oversampling with Random Oversampler with StratifiedKFold.mp4 | 33.7 MB | ||
| 006 Performing Oversampling with Random Oversampler with StratifiedKFold_en.srt | 5.5 KB | ||
| 007 Created a common function to fit and predict on a Random Forest model.mp4 | 19.1 MB | ||
| 007 Created a common function to fit and predict on a Random Forest model_en.srt | 3.6 KB | ||
| 007 Performing oversampling with SMOTE and Model Evaluation.mp4 | 33.6 MB | ||
| 007 Performing oversampling with SMOTE and Model Evaluation_en.srt | 5.1 KB | ||
| 008 Created a common function to fit and predict on a XGBoost model.mp4 | 15.2 MB | ||
| 008 Created a common function to fit and predict on a XGBoost model_en.srt | 2.3 KB | ||
| 008 Performing oversampling with ADASYN and Model Evaluation.mp4 | 27.9 MB | ||
| 008 Performing oversampling with ADASYN and Model Evaluation_en.srt | 4.4 KB | ||
| 009 Created a common function to fit and predict on a SVM model.mp4 | 26.7 MB | ||
| 009 Created a common function to fit and predict on a SVM model_en.srt | 4.5 KB | ||
| 009 Hyperparameter Tuning.mp4 | 35.9 MB | ||
| 009 Hyperparameter Tuning_en.srt | 5.9 KB | ||
| 010 Extracting most important features.mp4 | 17.1 MB | ||
| 010 Extracting most important features_en.srt | 2.9 KB | ||
| 011 Final Inference.mp4 | 14.1 MB | ||
| 011 Final Inference_en.srt | 3.4 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Credit_Card_Fraud_Detection.ipynb | 2.1 MB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Kaggle Link.txt | 0 B | ||
| creditcard.csv | 143.8 MB | ||
| requirements.txt | 102.4 B | ||
| ▲ 69 total files | |||
Data Science: Credit Card Fraud Detection - Model Building
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 32 lectures (1h 40m) | Size: 658.4 MB
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
Requirements
Knowledge of Python
Description
In 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.
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