How can I implement anomaly detection for the sales data?
There is a scenario where I am working as a data scientist for a particular e-commerce platform. My task is to implement anomaly detection for the sales data so that I can identify unusual patterns or outliers that may indicate fraudulent activity or technical issues. How can I execute anomaly detection for the sales data time series?
In the context of data science, you can implement anomaly detection for the sales data by using the steps which are given below:-
Data processing
You can clean the sales data, handle missing values and you also can ensure a consistent time interval.
Feature engineering
You can try to extract relevant features from the sales data, such as total sales, and average sales per day.
Choose an anomaly detection method
You should select an appropriate anomaly detection method for the time series data. You can choose among machine learning models, statistical methods, time series models, and hybrid approaches.
Threshold selection
You can try to determine an appropriate threshold to define anomalies based on the chosen method and the characteristics of the sales data.
Detect anomalies
You can apply the trained model for the entire sales date time series to detect anomalies.
From sklearn.ensemble import IsolationForest
# Load and preprocess sales data
# Assuming sales_data is a panda DataFrame with columns: ‘timestamp’ and ‘sales_amount’
# Extract features
X = sales_data[[‘sales_amount’]]
# Train Isolation Forest model
Model = IsolationForest(contamination=0.05) # Adjust contamination parameter as needed
Model.fit(X)