Fm_ftrl
Hashed Factorization Machine with Follow The Regularized Leader for Kaggle Avazu Click-Through Rate Competition
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Fm_ftrl | 240 | 7 years ago | 2 | mit | Python | |||||
| Hashed Factorization Machine with Follow The Regularized Leader for Kaggle Avazu Click-Through Rate Competition | ||||||||||
| Movie Recommendation Netflix | 29 | 4 years ago | n,ull | gpl-3.0 | Jupyter Notebook | |||||
| 🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧 | ||||||||||
| Click Through Rate Prediction | 21 | 8 years ago | 1 | apache-2.0 | Scala | |||||
| Kaggle's click through rate prediction with Spark Pipeline API | ||||||||||
| Kaggle Yelp Business Rating Prediction | 19 | 11 years ago | Python | |||||||
| Code for Kaggle Contest "RecSys2013: Yelp Business Rating Prediction" -- Predicting Business Ratings on Yelp.com Using Machine Learning. My final submission scored an RMSE of 1.2307 which earned a rank of #7 out of ~250 teams. This a collection of code that is meant to be run from python console, it is not a stand-alone program | ||||||||||
| Ftrl Kaggle | 13 | 9 years ago | C++ | |||||||
| The code that I used in Click-Through Rate Prediction (http://www.kaggle.com/c/avazu-ctr-prediction/) (C++). It implements the Follow The Regularized Leader algorithm for predicting web advertisement's perfomance. | ||||||||||
| Kaggle Protein Image Classification | 10 | 3 years ago | Jupyter Notebook | |||||||
| This is the solution for Kaggle human protein classification competition. | ||||||||||
| Spark_linear_regression | 7 | 9 years ago | Python | |||||||
| Spark (pyspark) linear regression on clickthrough rate (CTR) prediction form Kaggle | ||||||||||
| Predict_movie_ratings | 6 | 9 years ago | mit | Python | ||||||
| Kaggle in Class - Predict Movie Ratings from Movielens dataset | ||||||||||
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Python
Rating
Kaggle