June 2015; DOI: 10.13140/RG.2.1.1748.6326. Organisation for … The goal was to accurately make class predictions on roughly 144,000 unlabeled products based on 93 features. The resource of the dataset comes from an open competition Otto Group Product Classification Challenge, which can be retrieved on www kaggle.com. Hence, the low AUC (~70%) of Naive Bayes is justified. 1 任务描述 Kaggle 2015年举办的Otto Group Product Classification Challenge竞赛数据。 Although high leaderboard score was desirable, our primary focus was to take a hands-on learning approach to a wide variety of machine learning algorithms and gain practice using them to solve real-world problems. Choosing different values of K or different distance metrics could produce multiple meta features that other models could use. It might also be worth standardizing the value ranges for all features if we were to use lasso regression for feature selection. It contains: Neural Networks; XGBoost; Random Forest; SVM; Regularized Greedy Forest; Linear model; However only top four kind of algorithms were used to build final ensemble. The objective is to build a predictive model which is able to distinguish between our main product categories. Otto Group is one of the world’s biggest e-commerce companies. Top 10 placement in a data science competition with over 4000 competing data scientists all around the world. The Otto Group Product Classification Challenge is a competition sponsored by the Otto Group that asks participants to build a predictive model which is capable of classifying a list of more than 200,000 products with 93 features into their correct product categories. Learn more. The objective is to … The final model uses an ensemble of two levels by stacking. Otto group product classification challenge, Yicheng (Jason) Wang, Chenxiao Wang, Axel Chauvin 15. Bike Sharing Demand. Procedurally, we broke the problem down into nine binomial regression problems. 3,505 teams; 6 years ago; Overview Data Notebooks Discussion Leaderboard Rules. A high number could lead to overfitting very quickly. An inspection of the response variable revealed an imbalance in class membership. In total, there were nine possible product lines. This function effectively stops the program from fitting additional models if the objective function has not improved in the specified number of rounds. I like that I can write Markdown, but the syntax is cumbersome. He now works full-time at an engineering consulting firm while enrolled in the NYCDSA's 2017 January to May online cohort,... © 2020 NYC Data Science Academy Rules. Use Git or checkout with SVN using the web URL. Kaggle's Otto Group Product Classification Challenge Introduction. Organisation for … Ultimately, no ridge or lasso penalization was implemented. Otto Group Product Classification Challenge. INFO-F-422 STATISTICAL FOUNDATION OF MACHINE LEARNING OTTO GROUP PRODUCT CLASSIFICATION CHALLENGE Fiscarelli Antonio Maria 2. The winning models will be open sourced. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. One obvious limitation is inherent in the kNN implementation of several R packages. Authors: Philip Chan. Posted by. can be conveniently accessed via an R package, h2o’s machine learning methods were used for the next three models. This is my code for kaggle's Product Classification Challenge. Class_2 was the most frequently-observed product class, and Class_1 was the least frequently-observed. Used Tanh with Dropout as the activation function. between main product categories in an e­commerce dataset. Although grid search was performed over a range of alpha (penalization type between L1 and L2 norm) and lambda (amount of coefficient shrinkage), predictive accuracy was not improved while computation time increased. For instance, neural networks are bad with sparse data and such. Given daily bike rental and weather records predict future daily bike rental demand. This blog post presents several different approaches and analyzes the pros and cons of each with their respective outcomes. Unsupervised Data Analysis -- Otto Group Product Classification Challenge. 5th/3514 teams on Otto Group Product Classification Challenge - Classifying products into the correct category, kaggle.com. Game sales prediction, Ningyuan Jiang 17. Join Competition. As a data-set, we have chosen “Otto Group Product Classification Challenge” [1]. ###Pre-processing 3rd/377 teams on Microsoft Malware Classification Challenge (BIG 2015) - Classifying malware into families based on file content and characteristics, kaggle.com. Despite sharing many of the same tuning parameters and using similar sampling methods, random forest modeling on the Otto dataset – even at a small number of 50 trees – was computationally slow and provided only average predictive accuracy. This helps us understand more about our data and possible class imbalance that may pose a problem in doing classification. A quick presentation of the winner's solution of the most popular Kaggle challenge (yet): the Otto Group Product Classification Challenge. Combining high predictive accuracy gradient boosting without added computational efficiency, the xgboost package provided a quick and accurate method for this project, ultimately providing the best logloss value of all models attempted. The objective is to build a predictive model which is able to distinguish between our main product categories. We then aggregated the probabilities of the nine classes, weighted by the deviance of these nine models, into one single final probability matrix. Kaggle required the submission file to be a probability matrix of all nine classes for the given observations. This competition challenges participants to correctly classify products into 1 of 9 classes based on data in 93 features. The better the classification, the more insights we can generate about our product range. START PROJECT . Given more time, it might be better to use kNN in the process of feature engineering to create meta features for this competition. I competed in the Otto Group Product Classification Challenge that ended on May 18th, 2015. Alternatively, down sampling are used in tree.R. On this site of Otto Group Product Classification Challenge, it is shown that best accuracy was possible with RandomForest method, but it was relatively low at 0.83. Python. My Kaggle profile can be seen here. Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Liberty Mutual Group: Property Inspection Prediction. Numerous parameters had to be tuned to achieve better predictive accuracy. 学習データ(20万個)から商品カテゴリを推定するモデルを作成 2. Otto Group Product Classification Challenge Classify products into the correct category. The challenge boiled down to a supervised, multinomial classification exercise. This model was implemented with ntrees = 100 and the default learn rate of 0.1. The challenge boiled down to a supervised, multinomial classification exercise. This gave us a rough idea that the data was biased toward certain classes and would require some method of sampling when we fit it to the models down the road. Model averaging is a strategy often employed to diversify, or generalize, model prediction. $10,000 Prize Money. On this site of Otto Group Product Classification Challenge, it is shown that best accuracy was possible with RandomForest method, but it was relatively low at 0.83. My score was sufficient to land in the top 10%, so I’ve completed one of the requirements for Kaggle master. H2o proved to be a powerful tool in reducing training time and addressing computational challenges on the large Otto training set, as compared to native R packages. This competition challenges participants to correctly classify products into 1 of 9 classes based on data in 93 features. Given the points of interest of examined properties foresee a peril score for properties. Principal component analysis and resulting scree plot revealed a "cutoff point" of around 68 components. Each had 93 numeric features and a labeled categorical outcome class (product lines). Since high-performance machine learning platform h2o can be conveniently accessed via an R package, h2o’s machine learning methods were used for the next three models. Deep learning. Although grid search was performed over a range of alpha (penalization type between L1 and L2 norm) and lambda (amount of coefficient shrinkage), predictive accuracy was not improved while computation time increased. $10,000 Prize Money. By clicking on the "I understand and accept" button, you indicate that you agree to be bound with the rules outlined below. Given this required format, we attempted to develop methods to combine individual model predictions to a single submission probability matrix. Quoted from https://www.kaggle.com/c/otto-group-product-classification-challenge/data. AIC for stepwise feature selection; used deviance for weights. Stacking Algorithms. The objective was to build a predictive model which is able to distinguish between Otto Group main product categories. 1 year experience. In order to conduct our own test before submitting to Kaggle, we partitioned the 62,000 rows of training data into a training set of 70 percent and a test set of the remaining 30 percent. By following the example below, you should be able to achieve scores that will put you on the top 1% in the leaderboard. . I had a write-up about the solution in my blog. h2o.randomForest function with default parameters. The default value was 6. In this step, we import a TabularPrediction task. The inability to return predicted probabilities for each class made the model a less useful candidate in this competition. With only a predicted probability of one of nine classes for each observation, there was an insufficient basis to predict probabilities well for the other eight classes. Kaggle uses multi-class logarithmic loss to evaluate classification accuracy. About. Quoted from https://www.kaggle.com/c/otto-group-product-classification-challenge/data Each row corresponds to a single product. My Kaggle profile can be seen here. Given more time, it might be better to use kNN in the process of feature engineering to create meta features for this competition. Grid search proved to expensive, especially at high number of trees. This blog post presents several different approaches and analyzes the pros and cons of each with their respective outcomes. at Wesleyan University, focusing on molecular neuroscience while completing additional coursework in math and economics. The h2o package's deeplearning function offers many parameters for neural network modeling along with high computational speeds due to h2o's ability to dedicate all of a CPU’s processing power to model computation. Second Annual Data Science Bowl. Running one binomial regression model with stepwise feature selection could take up to an hour for the training set. The time required to compute distances between each observation in the test dataset and the training dataset for all 93 features was significant, and limited the opportunity to use grid search to select an optimal value of K and an ideal distance measure. Otto Group Product Classification Challenge Classify products into the correct category. Streamlit Magic⌗ Otto Group Product Classification Challenge Nov 2014 – Dec 2014-Conducted descriptive analysis to identify the high influential points and imputed missing values. Using the base R lm() function, we found this approach to be extremely time consuming. 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