Time Series Forecasting on CoronaVirus Dataset





We all know How much impact Covid-19 had in our day to day life. The datasets has been taken from Covid-19 Kaggle. The talk will go through predicting the confirmed cases during covid -19. I have attached the Video Link and my Blog links with the proposal.

The take away from the talks will be mostly about forecasting using data:

  1. difference between prediction and forecasting,
  2. why its tough to achieve better accuracy in forecasting than in prediction,
  3. where we mostly use forecasting ,
  4. what other tools and algorithm we can use ,
  5. all about algorithms and how they work.

The flow of the talk will be like this:

Introduction and contents : 1 min

  1. Exponential Growth in Epidemic -- 1 min
  2. History of Pandemics -- 1 min
  3. Flatten the curve -- 1 min
  4. Understanding difference between Prediction and Forecasting -- 3 mins
  5. Why Forecasting accuracy is more difficult than Prediction -- 2 mins
  6. TimeSeries Forecasting using Prophet -- 5 mins
    • notebook explanation
  7. SVM (Support Vector Machine) -- 5 mins
    • Working of algorithms
    • Notebook explanation
  8. What is LSTM -- 3 mins
    • Working of the algorithms
    • Notebook Explanation
  9. Time Series forecasting using LSTM -- 3 mins

QnA -- 5 minutes


Basic python!!

Video URL:


Speaker Info:

The Speaker has been working as Software Lead at IIT Madras Incubated Company. She is passionate about machine learning and deep learning algorithms and its deployment on Edge. She has been speaker at PyCon India 2019 and PyData BBSR 2020.

She had previously worked on the development of Level 4 autonomy for self driving vehicles, also she was appointed as an Adjunct Professor/ Visiting Professor for robotics. She is a blend of both technology and entrepreneurship , which in her term is all about solving problems in the most efficient way.

PyCon 2019 : " Solving Industrial Problems with Machine Learning" : How she used concepts of support vector machine for Inspection tools.

PyData 2020: "Supervised and Unsupervised machine learning algorithms " : Different types and applications of both the algorithms

She has been actively involved in Intel Edge AI Community where she taught use of ml in edge computing basic architecture of edge applications.

Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Intermediate
Last Updated: