As promised, this is the second post on my two part blog series on time series modelling and forecasting. In my first blog post I discussed the basics of time series analysis and gave a theoretical overview. In case you missed it you can find it here - Understanding Time Series Modelling and Forecasting, Part 1 … Continue reading Understanding Time Series Modelling and Forecasting, Part 2
Time series forecasting is extensively used in numerous practical fields such as business, economics, finance, science and engineering. The main aim of a time series analysis is to forecast future values of a variable using its past values. In this post, I will give you a detailed introduction to time series modelling. This would be the … Continue reading Understanding Time Series Modelling and Forecasting – Part 1
It has been more than 13 years since the last episode of Friends aired. But we never stop talking about it. Do we? I do not remember the last time I had a pizza without watching a random episode of Friends. Last night, I was watching one of my favorite episodes, "The One With Ross' … Continue reading Who was the lead character in Friends? The Data Science Answer
In this post, I will discuss a very common problem that we face when dealing with a machine learning task - How to handle categorical data especially when the entire dataset is too large to fit in memory? I will talk about how to represent categorical variables, the common problems we face while one hot … Continue reading How to One Hot Encode Categorical Variables of a Large Dataset in Python?
We are often interested in population parameters. For example, the mean salary of all adults in a country. But collecting data of the entire population is almost always infeasible. Therefore, we use samples of the population to get a point estimate of our parameter of interest. But, what is the 95% confidence interval of your … Continue reading Bootstrapping – A Powerful Resampling Method in Statistics