Machine learning and statistics with python

Introduction: This week, I took part in a competition playfully and thought that I will spend a good weekend on this problem. But the competition grew complex from a simple enough problem and ended up me being tackled by the problem; instead of me tackling the problem. This article will summarize my mistakes and the 2 specific things I learned from this. Summary of the article: Recently I took part in a hackathon, and tried my ml expertise with a tabular data. But for some reasons, I wasn't able to tackle the problem. The main findings were I used a wrong encoding method, as well as didn't do enough model testing. The good: In this project, the problem was to predict for a healthcare company whether they will get a customer or not; based on different policy based data. The dataset consisted of different data about the customer and policy amount etc. The customer related data were maximum and minimum ages mentioned in the policy, for how many years the policy have been work...

                             5 mistakes of a ml beginner Introduction: This is a lightweight non-technical post. I have been practicing the craft named machine learning for the last 2.5 years now. I recently have started working with a few data science aspirants, and found them making a lot of similar mistakes like I would do. In this article, I will share 5 mistakes I did, and a lot of machine learning and data science beginners do. First mistake: not reading your data: Data science is exciting and machine learning starts with learning a lot of models and algorithms. Hence often when we start learning machine learning and data science, we don't learn the most important step. The most important step is to read your data. Now, I used to find it stupid to read the data, because I didn't know what does reading data mean.  Reading data means u...