Description

• Conducted exploratory data analysis (EDA) on financial datasets, implementing data cleaning techniques and detecting outliers.
• Employed feature selection methods such as Information Gain and Correlational Analysis to identify relevant features.
• Utilized unsupervised algorithms including Isolation Forest and Local Outlier Factor for detecting anomalies within the data.
• Implemented a Random Forest classifier specifically tailored for the detection of financial fraud.
• Addressed data imbalance issues using Synthetic Minority Over-sampling Technique (SMOTE) and evaluated its impact on model performance compared to non-balanced datasets.

Results

Additional Information

The dataset is used from kaggle. The link to the notebook Link