DataHack Radio #16: Kaggle Grandmaster SRK’s Journey and Advice for Data Science Competitions
Who better to learn data science tips and tricks from than a Kaggle grandmaster? Join SRK as he provides invaluable advice for all data scientists!
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Improve Your Model Performance using Cross Validation (in Python and R)
Learn various methods of cross validation including k fold to improve the model performance by high prediction accuracy and reduced variance
Exclusive Interview with Data Scientist – Bishwarup Bhattacharjee (Analytics Vidhya Rank 8)
Introduction Energy and Persistence conquers all things! …
Complete Solution: How I got in Top 11% of Kaggle Telstra Competition ?
Introduction Telstra is Australia’s largest telecommunications network. Telstra Network Disruptions (TND) Competition ended on 29th February 2016. This was a recruiting competition. At Analytics Vidhya, I’ve …
New Year Resolutions for a Data Scientist
Introduction New Year is not just replacing your table calendar with a new one or waking up next morning rubbing your eyes. It’s celebrating …
Perfect way to build a Predictive Model in less than 10 minutes
Overview Hackathons involve building predictive models in a short time span The Data Preprocessing step takes up the most share while building a model …
Getting into Top 10 in Kaggle Facebook Recruiting Competition
Facebook recently wrapped up its Recruitment competition on Kaggle. This was by far the richest data I have seen on Kaggle. The amount of …
Kaggle Bike Sharing Demand Prediction – How I got in top 5 percentile of participants?
Introduction There are three types of people who take part in a Kaggle Competition: Type 1: Who are experts in machine learning and their motivation is …
Kaggle Competitions: How and where to begin?
Introduction Do I have the necessary skills to take part in Kaggle Competitions? Did …
k-Fold Cross Validation made simple
Does your high performing model degrade/perform poorly on an out of time sample? Has your Kaggle Private score come down from your Public score significantly? Not …
