Anomaly Detection in Data using Machine Learning
Project Overview.
Anomaly detection refers to identifying rare events or observations that differ significantly from the majority of the data. These “anomalies” can indicate critical incidents, such as fraud, technical glitches, or operational failures. This project uses unsupervised learning techniques like Isolation Forest SVM etc. to detect such anomalies across datasets.
Tools & Technologies Used.
Methodology / Process
- Loading Datasets and Merging Training and Testing Datasets.
- Checking Outliers and Data Correlation.
- Downloading Cleaned Datasets.
- Machine learning models : Decision Tree, Random Forest, XGBoost, LGBM, Ada Boost and Comparison
- Logistic Regression
- ANN
- Saving The Model and Finding Users Prediction.
Loading Datasets and Merging Training and Testing Datasets.
Checking Outliers and Data Correlation.
Downloading Cleaned Datasets.
Machine learning models :
- Decision Tree
- Random Forest
- XGBoost
- LGBM
- Ada Boost.
- Logistic Regression
- ANN
Decision Tree and Random Forest
XGBoost and LGBM
Ada Boost and Logistic Regression
ANN
All Models Comparison
Feature Importances
Checking Random Forest Classifier
Saving The Model and Finding Users Prediction.
📦 Free Project Package
Download all files including project code, dataset, model file (.pkl) etc.
⬇️ Download Project Bundle (ZIP)
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