Update README.md

README.md

@@ -9,4 +9,19 @@ app_file: app.py
 pinned: false
 ---
 
+### What is an Anomaly?
+
+Anomaly detection (or outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such as e.g. bank fraud, medical problems, structural defects, malfunctioning equipment etc. This connection makes it very interesting to be able to pick out which data points can be considered anomalies, as identifying these events are typically very interesting from a business perspective.
+
+### The Approach
+
+A sudden spike or dip in a metric is an anomalous behavior and both the cases needs attention. Detection of anomaly can be solved by supervised learning algorithms if we have information on anomalous behavior before modeling, but initially without feedback its difficult to identify that points. So we model this as an unsupervised problem using algorithms like Isolation Forest, Local Outlier Factor (LOFs) etc. Here we are identifying anomalies using Isolation Forest as well as LOF and K-Nearest Neighbours (KNN).
+
+### Objective
+
+Our primary objective was to train a anomaly detection model that would help us detect anomalies in SuperStore sales.
+
+So, in a superstore, anomalies can be a sudden upsurge in sales or a negetive profit. Any amount of negetive profit is an anomaly.
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference