Hello readers, we are entering another Kaggle playground competition, so get your Yorkshire tea ready and enjoy the process of joining. This month the competition I entered is this one https://www.kaggle.com/competitions/playground-series-s3e7It’seiew It’s looks like looks are canncellations from hotels and spoiler alert – I had a lot of fun with this dataset. EDA First, I […]
Kaggle January Playground Series – Tidymodels
Hello, hope you have your Yorkshire tea ready this is going to be a new series on the blog in which each month I am going to be tackling Kaggles monthly playground series. Find the link to Januarys below feel free https://www.kaggle.com/competitions/playground-series-s3e1 So let’s get started EDA Above is the structure of the training dataset. […]
Sliced – New York Air BnB
Hello, welcome to todays blog which I am going to go through my attmept at sliced. If you never hear of sliced its competitive data science which you have 2 hours to create a machine learning model. Catch the show here on tuesdays late a night for us Europeans https://www.twitch.tv/nickwan_datasci One of the recent rounds […]
Twenty20 Win Probability Added
Hello readers, welcome to today’s blog. I am going to implement a win probability added model for twenty 20 cricket. Now this is nothing new a quick google and there are many sources for it. Cricviz is possibly the most famous version which you may have seen on the app. The idea is the model […]
Cricket Moneyball, pt 2
Hello readers, today we have part 2 of this cricket moneyball series. If you missed the first one its here: https://wordpress.com/view/theparttimeanalyst.com In it I looked at calculating the Pythagorean win percentage for each team in the IPL and then moving that forward to calculating the how many extra runs are needed to win one extra […]
Predicting Qualifying — 2
In the last blog I outlined creating a model which predicts the fastest time for each driver in F1 qualifying. theparttimeanalyst.com/2019/07/10/predicting-f1-qualifying/ Today I am going to be dissecting the model to understands its strengths and weaknesses and to look if their is any bias within the model. First lets look at the importance matrix The […]
Predicting F1 Qualifying
Hello, welcome to this blog a few days ago I tweeted the below graph in a tweet It was the output from the model I have created which predicts the qualifying time for each driver. I will get into the review of the outputs of the model in the next blog but today im going […]
Replacing Nikita – 2
Today I’m going to doing the second part, if you didn’t read the first part go check it out: Replacing Nikita 1 I’m summary, Nikita Parris is leaving her team last season Man City, and I need to find players suitable to replace her. I did a bit of an exploratory analysis of Nikita Parris […]
Tidy Tuesday Board Games – XGBoost Model
Hello, Today we are going to ask a question and try to answer it with a analysis of data. I want to create a board game. In order do that I want to understand what make a board game more highly rated then another. I want to create a popular board game afterall! To do […]
Does the Dog Get Adopted?? — P2
Today we are going to looking into the second part of creating the classification tree to look at the outcomes of dogs in the Dallas animal shelter. Today it’s the exciting stuff, creating the actual classification tree. If you want to understand how I have prepared the data, go and check out the first blog […]
Sport Data Science 