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Sam Kenkel

Data Science, Machine Learning, DevOps, CCNA, ACSR
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Anime_Rec3: Generating possible recommendations (Cosine Similarity methods)

The intro to this series explained why I was making an Anime recommendation system, part 1 gave a brief overview of the approach I was taking. Part 2 explained how I got my Data. In this part I will explore how I tuned my three different methods determining Item-Item similarity. Method 1: Item-Item similarity based on user scores. Anime_Score_Sim in my github shows the code for this. First all 0’s (or statuses without a score) are dropped.  Next, I find the average score for each user, and subtract that score from every user’s score. This is to […]

Anime_Rec2: Data Collection, EDA

The intro to these posts explained why I was making an Anime recommendation system, and Part 1  gave a brief overview of the approach I was taking.  In the next part I will start to explore how I tuned my Item-Item similarity models. Before diving into that I wanted to go through my data collection process, and initial analysis that helped guide me in this process. Even though every project like this starts with the data, and as  Data is the New Oil it’s always worth going past the platitudes to figure out where my data came from, […]

Anime_Rec: Generating recommended Animes based on MAL data.

Anime_Rec is a Data Science project to generate Anime recommendations based on publicly available data from the website myanimelist.net. I’m an Anime fan. In fact, I watch enough Anime to have hit that point where finding something to watch becomes difficult. As an Anime fan and Data Scientist,  the obvious solution was to build a Recommendation engine to recommend Anime for me to watch. This post explains my overall approach and architecture The first step in any machine learning or Data Science project is gathering the data, and thankfully for me, other Anime fans have done […]