/Fraud-Detection-using-ML

Random Forest Classifier

Primary LanguageJupyter Notebook

Machine Learning Advanced

Exploratory Data Analysis

  1. Counted the Null values.
  2. Grouped by various properties.

Visualization

Used the Seaborn and Matplotlib libraries for plotting graphs!

Implementation

  1. Merged the two csv files.
  2. features significant for determining fraud

Algorithm

Random Forest Classifier

  1. Predicting on Train Set
  2. Predicting on Test Set
  3. Confusion Matrix
  4. Precision, Recall, F-measure and Support
  5. Plotting Important Features