anomaly_detection

Achieved accurate anomaly prediction in datasets through the application of diverse unsupervised machine learning techniques. Employed various dimensionality reduction methods to detect outliers within the dataset effectively. Conducted comprehensive Exploratory Data Analysis and proficiently preprocessed the dataset to ensure high data quality. Utilized advanced techniques such as Dbscan and Isolation Forest to identify outliers, classifying them as anomalies. Additionally, applied dimensionality reduction methods like PCA, SVC, and ICA to calculate the reconstruction error, which served as a basis for predicting anomalies accurately.