Important facts:
Anomaly detection is a very important and active business metric for various fields. A technique that is used to identify the unusual patterns that are not in sync with the expectations.
It has many applications in business-like health (detecting health discrepancies), cybersecurity(intrusions), electricity (huge and sudden surges), finances (fraud detection), manufacturing (fault detection), etc. This shows that there is more to anomaly detection in everyday life and important concepts to be looked at.
The data science application in anomaly Detection combines multiple concepts like classification, regression, and clustering.
Important Facts:
Works best on 30 house amenities classes (as this model is trained on pre-defined amenities)
Achieved mAP@0.5:0.95 - 0.495 – close enough to be a viable product
Built on Pytorch, trained using GCP (1 GPU - P1000), developed API using Flask
Returns the inference (results) under 200 milliseconds using CPU
After thorough exploration of the data and time-series visualization, now I will try my hand at forecasting methods and predicting the demand for the three states and three products seperately. We will discuss various different approach present, used and develop one model appraoch for forecasting product sales demand time series. Lets get started!!
After having explored this huge dataset, I wanted to explore with more of time-series components to understand the distribution much better. So, lets dive in.
For Initial Data Exploration of this data, please read here - PART-I
This is from a kaggle competition, where I wanted to participated and apply my learnings in forecasting methods. In this 1st part post, I am exploring the data with various visualization and trying to understand the dataset.