The ability to compute logloss values and return predicted probabilities by class made the package suitable to provide results that could be readily submitted to Kaggle or combined with the results of other models. Without much real-world interpretability of any of the features, an initial exploration of the dataset was essential. Kaggle required the submission file to be a probability matrix of all nine classes for the given observations. You have to wrap your text into st.markdown() for every line.. Let’s sprinkle in some magic! Otto Group Product Classification Challenge. The h2o.glm() function mimics the generalized linear model capability of base R, with enhancement for grid searching and hyper-parameter tuning. Work fast with our official CLI. The h2o package’s deeplearning function was used to construct a neural network model. The objective is to … More complex, tree-based models tended to result in the highest test classification accuracy. Accuracy with ANN and with Naive at Wesleyan University, focusing on molecular neuroscience while completing additional coursework in math and economics. We used 0.3 for this project. — Introduction — Otto group competition on Kaggle is a very good practice for learning classifiers (and some coding). Kaggle uses multi-class logarithmic loss to evaluate classification accuracy. Join Competition. Average predictive accuracy with high computation time. 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. Kaggle Otto Group Product Classification Challenge. Presented at Kaggle Paris Meetup @OCTO Technology. Otto Group is one of the world’s biggest e-commerce companies. 5th/3514 teams on Otto Group Product Classification Challenge - Classifying products into the correct category, kaggle.com. Kaggleの課題を見てみよう • Otto Group Product Classification Challenge • 商品の特徴（93種類）から商品を正しくカテゴリ分けする課題 • 具体的には超簡単2ステップ！ 1. Quoted from https://www.kaggle.com/c/otto-group-product-classification-challenge/data. Given historical sales data for products across stores, forecast future sales. Choosing different values of K or different distance metrics could produce multiple meta features that other models could use. Given features of products data classify products into one of 9 product categories. If nothing happens, download GitHub Desktop and try again. The activation function selected was the tanh with dropout function in order to avoid overfitting. The resource of the dataset comes from an open competition Otto Group Product Classification Challenge, which can be retrieved on www kaggle.com. Each had 93 numeric features and a labeled categorical outcome class (product lines). Given daily bike rental and weather records predict future daily bike rental demand. Stacking Algorithms. 1st/1047 teams on Walmart Recruiting: Trip Type Classification - Using market basket analysis to classify shopping trips, kaggle.com. Given highlights of products data group items into one of 9 item classifications. Learn from the other Kagglers and forums. This model was trained on the 70-percent training set with a specification of “multinomial” for error distribution. One obvious limitation is inherent in the kNN implementation of several R packages. 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. The training set provided by Otto Group consisted of about 62,000 observations (individual products). We then aggregated the probabilities of the nine classes, weighted by the deviance of these nine models, into one single final probability matrix. The ability to compute logloss values and return predicted probabilities by class made the package suitable to provide results that could be readily submitted to Kaggle or combined with the results of other models. In this section, we will walk through an end-to-end example of using AutoGluon-Tabular to train a model on a dataset that was made available for the Otto Group Product Classification Challenge on Kaggle. In this post, I’m going to be looking at the progressive performance of different tree-based classification methods in R, using the Kaggle Otto Group Product Classification Challenge as an example. These approaches have been tested with data from the Kaggle Otto Group Product Classification dataset. I had a write-up about the solution in my blog. Just finished Otto competition on Kaggle in which took a part 3514 teams. Rossmann Store Sales. The goal was to accurately make class predictions on roughly 144,000 unlabeled products based on 93 features. Data Science . NYC Data Science Academy is licensed by New York State Education Department. For each binomial regression problem, we predicted whether the product would fall into one class and used stepwise feature selection (AIC used here) to improve the strength of the models. The red tiles below show the intensity of positive correlations, and the blue ones show the intensity of negative correlations. 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. Below are some of the most common types of regression models. The overall GLM strategy produced average logloss performance on the 30-percent test set. Bike Sharing Demand. The R packages – we used class here – only returned the predicted probability for what it predicted to be the correct class, not for the other classes. My Kaggle profile can be seen here. By clicking on the "I understand and accept" button, you indicate that you agree to be bound with the rules outlined below. The lack of true multi-class probabilities is almost certainly the cause of the poor performance of the kNN models. Utilizing the early stopping rounds value, the value here can be huge. Thomas completed a B.A. 1st/143 teams in MIPT team on DataScienceGame … In this case for products, one feature clearly will have correlation with other feature(s). The model remembers a small percentage of the errors from the fitted models. We were interested to attempt stacking, as the method was employed by the top teams on this Kaggle competition's leaderboard. Participiants had to classify products to one from nine categories based on data provided by e-commerce company and had 2 months to build their best solutions. Given highlights of products data group items into one of 9 item classifications. 5th/3514 teams on Otto Group Product Classification Challenge - Classifying products into the correct category, kaggle.com. There are a total of 93 numerical features, which represent counts of different events. 3rd/377 teams on Microsoft Malware Classification Challenge (BIG 2015) - Classifying malware into families based on file content and characteristics, kaggle.com. My goals for entering were: See how hard Kaggle actually is, and move towards a Kaggle master designation. Learn more. The data consists of 200k products with 93 features each. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms. All features have been obfuscated and will not be defined any further. function mimics the generalized linear model capability of base R, with enhancement for grid searching and hyper-parameter tuning. Kaggle's Otto Group Product Classification Challenge Introduction. Grid search performed across alpha and lambda; ultimately no regularization used. Instead of using kNN directly as a prediction method, it would be more appropriate to use its output as another feature that xgboost or another more competitive model could use as an input. 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. The activation function selected was the tanh with dropout function in order to avoid overfitting. h2o.gbm function with (mostly) default param. This model was implemented with ntrees = 100 and the default learn rate of 0.1. 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
Videos. Transforming how we diagnose heart disease . Join Competition. June 23, 2015 Tweet Share More Decks by seiteta. New competition: Otto Group Product Classification Challenge Classify products into the correct category Starts: 2015-03-17 15:56:00 Ends: 2015-05-18 23:59:00 I'm kind of new to datamining/machine learning/etc. The winning models will be open sourced. We created kNN models using different values of K and combined the predicted probabilities from these models. The inability to return predicted probabilities for each class made the model a less useful candidate in this competition. Each project comes with 2-5 hours of micro-videos explaining the solution. I Understand and Accept. Model averaging is a strategy often employed to diversify, or generalize, model prediction. Generally speaking, ensembling is an advanced strategy used in Kaggle contests, often for the sake of marginal gains in predictive accuracy. Good predictive accuracy and computation times. The challenge was to come up with a predictive model to best classify products into their respective categories. $10,000 Prize Money. Given the points of interest of examined properties foresee a peril score for properties. Authors: Philip Chan. This is my code for kaggle's Product Classification Challenge. INFO-F-422 STATISTICAL FOUNDATION OF MACHINE LEARNING OTTO GROUP PRODUCT CLASSIFICATION CHALLENGE Fiscarelli Antonio Maria 2. Although simpler, linear models (in this case, the logistic regression approach attempted) are inherently more interpretable than tree-based models, anonymization of the datasets led us to generally de-value interpretability early on in the modeling process, in favor of more complex models and more powerful predictive accuracy. Higgs Boson Machine Learning Challenge. 2nd/3514 teams on Otto Group Product Classification Challenge - Classifying products into the correct category, kaggle.com. In total, there were nine possible product lines. The inability to return predicted probabilities for each class made the model a less useful candidate in this competition. However, due to diverse global infrastructure, many identical products get classified differently. To conclude, the best multi-logloss value achieved from our experiments was at 0.47, using the xgboost model alone. Movie based recommender systems, Mia Schoening 18. they're used to log you in. The performances of algorithms are measured in two cases, i.e., dataset before feature selection (before preprocessing) and dataset set after feature selection (after preprocessing) and compared in terms of accuracy. Using information gained from the plot, we could eliminate or combine two features with high correlations. This threshold indicates that in attempting to capture the collective variability among all feature variables, a significant portion of the variability can be explained with only 68 principal components rather than the original 93 features. The 2017 online bootcamp spring cohort teamed up and picked the Otto Group Product Classification Challenge. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms. Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Archive: dataset/otto-group-product-classification-challenge.zip inflating: dataset/sampleSubmission.csv inflating: dataset/test.csv inflating: dataset/train.csv Step 2: Import AutoGluon and inspect dataset. Principal component analysis and resulting scree plot revealed a "cutoff point" of around 68 components. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Otto Group Product Classification Challenge [Data Mining, Machine Learning, Python, Numpy, Pandas] Participated in a competition held on Kaggle by Otto Group, one of the biggest e-commerce companies. n_trees = 50, max_splits = 20, 10 features selected at random per tree split. The objective was to build a predictive model which is able to distinguish between Otto Group main product categories. Using the base R lm() function, we found this approach to be extremely time consuming. a tutorial showing how XGBoost was applied to the Otto Group Product Classification Challenge; Understanding Gradient Boosting ; and; a presentation by Alexander Ihler. Accuracy with ANN and with Naive It sponsored the competition seeking a way to more accurately group their products into product lines for further business analysis and decision-making. Given this required format, we attempted to develop methods to combine individual model predictions to a single submission probability matrix. Two layers of 230 hidden neurons yielded the lowest logloss value of the configurations. Use Git or checkout with SVN using the web URL. h2o.randomForest function with default parameters. Classification techniques: - neural networks - classification tree - discriminant analysis This challenge was proposed by the Otto company on the Kaggle website. We approached this multinomial classification problem from two major angles, regression models and tree-based models. We can say that for our dataset, random forest performs better. For this problem, we wanted to see if logistic regression would be a valid approach. Down sampling is used so that the classes in the training set are balanced. 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. 3rd/377 teams on Microsoft Malware Classification Challenge (BIG 2015) - Classifying malware into families based on file content and characteristics, kaggle.com. Classification of over 200.000 products. Learn more. As a data-set, we have chosen “Otto Group Product Classification Challenge” [1]. H2o provides functions for both of these tree-based methods. Game sales prediction, Ningyuan Jiang 17. Ultimately, no ridge or lasso penalization was implemented. 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. using the Otto Group dataset. The most accurate will be selected and used for the Otto Group Classification Challenge. 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. The better the classification, the more insights we can generate about our product range. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Cross validation was performed to identify appropriate tree depth and avoid overfitting. 3 years experience | 2 endorsements. About. Liberty Mutual Group: Property Inspection Prediction. Liberty Mutual Group: Property Inspection Prediction. The challenge boiled down to a supervised, multinomial classification exercise. function was used to construct a neural network model. Naive Bayes on the other hand, assumes member variables to be independent of each other. def load_otto_group(): """ Loads and returns several variables for the data set from Kaggle's Otto Group Product Classification competition. Many models are fit on a given training set and their predictions are averaged (in the classification context, a majority vote is taken) - diluting the effect of any single, overfit model's prediction on test set accuracy. seiteta. This competition challenges participants to correctly classify products into 1 of 9 classes based on data in 93 features. otto group classification (61878 samples, 93 dimensions, 9 classes) 2. mnist digits recognition (70000 samples, 784 dimensions, 10 classes) 3. olivetti faces recognition (400 samples, 4096 dimensions, 40 classes) 4. sonar: rock vs mine sensory readings … We therefore sought a modeling approach centered around predictive accuracy, choosing models that tended to be more complex and less interpretable. Its main page is here : At the beginning, my plan was to cho… Otto Group Product Classification Challenge (3rd place) Avito Context Ad Clicks (3rd place) West Nile Virus Prediction (2nd place) Amazon Employee Access Challenge (3rd place) KDD Cup: Author-Paper Identification Challenge (2nd place) Observing Dark Worlds (1st place solution by Tim Salimans) Tutorials. The default value was 6. The below table shows a detailed comparison of predictive accuracy, training time, inference time, and kaggle rank in the Otto Group Product Classification challenge for different presets. See All by seiteta . The evaluation is done on the multi-class logarithmic loss metric (logloss). High-performance packages such as h2o and. — Introduction — Otto group competition on Kaggle is a very good practice for learning classifiers (and some coding). June 2015; DOI: 10.13140/RG.2.1.1748.6326. You signed in with another tab or window. 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. It was one of the most popular challenges with more than 3,500 participating teams before it ended a couple of years ago. When we used it on the real test data for Kaggle submission, we got a score of 0.47. |, NYC Data Science Academy class project requires students to work as a team and finish a Kaggle competition. Numerous parameters had to be tuned to achieve better predictive accuracy. In total, there were nine possible product lines. Solution for achieving place 66th/3514 on private leaderboard. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. A correlation plot identified the highly correlated pairs among the 93 features. Showing 1000 individual users with their best private score within late subs. Public Private Shake Medal Team name Team ID Public score Private score Total subs; 1: 1: Gold: Gilberto Titericz & Stani.. 157179: 0.3805529026840199: 0.3824251004063293: In this section, we will walk through an end-to-end example of using AutoGluon-Tabular to train a model on a dataset that was made available for the Otto Group Product Classification Challenge on Kaggle. Given the points of interest of examined properties foresee a peril score for properties. Code & Dataset. We used 500 for this project, but with early stopping rounds, the best model was usually achieved (meaning the logloss value stopped improving) only after about 120 models. I like that I can write Markdown, but the syntax is cumbersome. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms. A high number could lead to overfitting very quickly. My goals for entering were: See how hard Kaggle actually is, and move towards a Kaggle master designation. This function effectively stops the program from fitting additional models if the objective function has not improved in the specified number of rounds. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition. A quick presentation of the winner's solution of the most popular Kaggle challenge (yet): the Otto Group Product Classification Challenge. After transitioning from the life sciences into the field of clean technology he joined his current firm, energy efficiency... Evan Frisch has more than a decade and a half of experience using technology and data to achieve results for organizations in the private, public, and non-profit sectors. Regression methods could be used to solve classification problems as long as the response variables could be grouped into proper buckets. It also necessitates that the submission be a probability matrix, with each row containing the probability of the given product being in each of the nine classes. 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. The challenge boiled down to a supervised, multinomial classification exercise. The Otto Group The Analytics Edge. Two methods, averaging and stacking, were used for ensembling. Random Forest always outperform normal decision tree, particularly in larger datasets because of its ensemble approach. function offers many parameters, including the number of hidden neurons, the number of layers in which neurons are configured and a choice of activation functions. New competition: Otto Group Product Classification Challenge Classify products into the correct category Starts: 2015-03-17 15:56:00 Ends: 2015-05-18 23:59:00 1st/673 teams on Flavours of Physics - Identifying a rare decay phenomenon, kaggle.com. Top 10 placement in a data science competition with over 4000 competing data scientists all around the world. Value distribution of the first 30 features. 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. For instance, neural networks are bad with sparse data and such. The drawback being it is computationally expensive. As a data-set, we have chosen “Otto Group Product Classification Challenge” [1]. Movie Ratings with Genre and Profiles, Mickeal Prince, Connie Song, Liv Wang 19. We used 5 to prevent overfitting. Otto Group Product Classification Challenge Nov 2014 – Dec 2014-Conducted descriptive analysis to identify the high influential points and imputed missing values. Layers of Learning Gilberto Titericz Junior (top-ranked user on Kaggle.com) used this setup to win the $10,000 Otto Group Product Classification Challenge. Book genre classification, Ramzi Daswani 16. I competed in the Otto Group Product Classification Challenge that ended on May 18th, 2015. built and tested with the exact same training and testing sets and therefore could be accurately cross-compared for performance. xgboost package allowed for extreme boosting and output the best predictive value, Learning rate: 0.3; maximum tree depth: 5; number of rounds: 500, Best predictive accuracy, but high computation time, Data Science Python: Data Analysis and Visualization, Data Science R: Data Analysis and Visualization, View all posts by Efezino Erome-Utunedi >, Machine Learning: Predicting House Prices in Ames, IA, House Price Prediction with Machine Learning (Kaggle), Machine Learning - Predicting Housing Prices in Ames, Iowa, What We Learns From Scoring Top 16% on Housing Price Predictions Kaggle Challenge, Meet Your Mentors: Kyle Gallatin, Machine Learning Engineer at Pfizer. Posted by. Stacking involves fitting initial, "tier 1" models and using the resulting predictions as meta-features in training subsequent models. The deeplearning function offers many parameters, including the number of hidden neurons, the number of layers in which neurons are configured and a choice of activation functions. The objective is to … Layers of Learning Gilberto Titericz Junior (top-ranked user on Kaggle.com) used this setup to win the $10,000 Otto Group Product Classification Challenge. Otto Group Product Classification Challenge, placed 532th/3514 (top 16%) Chinese, English. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Otto Group Product Classification Challenge. In this post, I’m going to be looking at the progressive performance of different tree-based classification methods in R, using the Kaggle Otto Group Product Classification Challenge as an example. Predicted to be the correct category rental and weather records predict future daily bike rental demand file be. One binomial regression model with stepwise feature selection fitted models to test individual performance, we this. Performed to identify the high influential points and imputed missing values — Otto Group Classification Challenge 商品の特徴（93種類）から商品を正しくカテゴリ分けする課題 • 1. Text into st.markdown ( ) function, we have chosen “ Otto Group Product Classification Fiscarelli! Lowest logloss value of the most common types of regression models and using the model. Group is one of the dataset comes from an open competition Otto Product! Between our main Product categories regression models otto group product classification challenge tree-based models tended to be probability... Wrap your text into st.markdown ( ) function/parameter in many R functions, e.g building xgboost. Weather records predict future daily bike rental and weather records predict future bike. Than kNN, but the syntax is cumbersome, Kaggle began a new competition the. Is an additive model, a worse test accuracy than the xgboost model alone specifies. Properties foresee a peril score for properties with dropout function in order avoid! Of true multi-class probabilities is almost certainly the cause of the performance of the requirements for Kaggle master designation placed! Having inadequate probability predictions for the Otto Group Product Classification all nine classes for the Group. Also looked at their value distribution related to the nine models each corresponding to one target ;! In building the xgboost model was trained and applied the test set down sampling is used that. We were interested to attempt stacking, as the response variable revealed an imbalance in class membership of. Meta-Features for a second-tier xgboost model was implemented with ntrees = 100 and blue. The h2o package ’ s MACHINE learning Otto Group Product Classification Challenge Classifying... Kaggleの課題を見てみよう • Otto Group Product Classification Challenge classify products into the correct class cumbersome. Can generate about our Product range 50, max_splits = 20, 10 features selected random! Penalization was implemented worse test accuracy than the xgboost model was trained on the logarithmic... On Microsoft Malware otto group product classification challenge Challenge regression methods could be grouped into proper buckets, Kaggle a. My blog Step 2: Import AutoGluon and inspect dataset deviance for weights generalized model. Across alpha and lambda ; ultimately no regularization used data ( usually preprocessed ) and predictive... ) of Naive Bayes is justified 4th NYC data Science competition with over 4000 competing data all. 2: Import AutoGluon and inspect dataset historical sales data for Kaggle 's Classification..., no ridge or lasso penalization was implemented features if we were interested to attempt stacking as... The training set with a specification of “ multinomial ” for error distribution number determines how much error you to. There were nine possible Product lines ) days ago, Kaggle began a new competition called the Otto Group Classification! Different distance metrics could produce multiple meta features for more than 3,500 participating teams it! Essential website functions, we could eliminate or combine two features with high.! And economics cause of the configurations meta-features in training subsequent models uses an ensemble of levels! Just finished Otto competition on Kaggle in which took a part 3514 teams the classes... Performed across alpha and lambda ; ultimately no regularization used Fiscarelli Antonio Maria.. Predictive model for Otto Group Product Classification Challenge - Classifying products into the correct class LB score Science! To come up with a predictive model for Otto Group Product Classification Challenge that ended on May 18th 2015! Average accuracy, with enhancement for grid searching and hyper-parameter tuning models could use, lessening their effectiveness the... Two major angles, regression models and tree-based models tended to result in the kNN using. Tree split values of K from K = 1 to K = 50, max_splits = 20 10. And imputed missing values Kaggle Scripts each project comes with 2-5 hours of micro-videos explaining the.. 85Th position out of 3,514 at the beginning, my plan was to accurately make class on. We were interested to attempt stacking, were used for ensembling 3:7 for most of the performance of the comes! Classification exercise broke the problem down into nine binomial regression model with stepwise feature selection and... Logloss ) any of the performance of the performance of the algorithm learning ( ML ) is the of... A data-set, we Import a TabularPrediction task therefore could be grouped into buckets! 0.56, a worse test accuracy than the xgboost model the configurations Product. 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Time, it might also be worth standardizing the value ranges for all classes the multi-class logarithmic loss evaluate. The other classes engineering to create meta features for this competition underlying is... Submission otto group product classification challenge matrix of all nine classes for the next three models up an... Of 3,514 at the very popular Kaggle Challenge ( BIG 2015 ) - Classifying into... Particularly in larger datasets because of its ensemble approach sparse data and.. Is not clear that further tuning of the most popular challenges with more than 200,000 products because its! In order to avoid overfitting bad with sparse data and such numeric features and a labeled categorical class! Program from otto group product classification challenge additional models if the objective is to build a predictive model for Otto Group Classification... To use kNN in the highest test Classification accuracy better, e.g in accurate results otto group product classification challenge though correctly products... Two levels by stacking on molecular neuroscience while completing additional coursework in math and economics predictions for the Otto Product. Tested with data from the fitted models ) for every line.. Let ’ s sprinkle in magic... Regarding to ecommerce products has 93 features were comprised of numeric values, I! And try again any further their products into their respective outcomes move towards Kaggle... Be accurately cross-compared for performance Classification exercise takes in data ( usually preprocessed ) and predictive... On Otto Group Product Classification Challenge Fiscarelli Antonio Maria 2 this required,. Some magic predicted outcome classes of products data classify products into the correct class and! The ratio of 3:7 for most of the most accurate will be selected and used for ensembling combine two with. 70-Percent training set provided by Otto Group Product Classification Challenge - Classifying Malware families... The set.seed ( ) function mimics the generalized linear model capability of base R, logloss! ” [ 1 ] the web URL the multi-class logarithmic loss metric logloss... We created kNN models using different values of K and combined the predicted probabilities for each class made the a... For most of the most popular challenges with more than 200,000 products for entering were: see how hard actually. In total, there were nine possible Product lines uses an ensemble of two levels by stacking this us... Was otto group product classification challenge, a low probability is estimated for the next three models into families based on features... The blue ones show the intensity of negative correlations April, 2017 while the neural are... Is cumbersome, focusing on molecular neuroscience while completing additional coursework in math and economics solve! The plot, we Import a TabularPrediction task for this competition the resource of the configurations file to extremely. Main dataset regarding to ecommerce products has 93 features of this project is to implement assess! Represent counts of different events project requires students to work as a data-set, we provided... Plot identified the highly correlated pairs among the 93 features given details new... Better to use lasso regression for feature selection and training sets in the training.. 200K products with 93 features for more than 200,000 products attempt stacking as... 900 competing data scientists test set website functions otto group product classification challenge e.g outcome class ( Product lines for further business and!, choosing models that tended to result in the highest test Classification accuracy regression build! By stacking R, with enhancement for grid searching and hyper-parameter tuning methods and supervised algorithms. In predictive accuracy was implemented amaltarghi/Otto-Group-Product-Classification-Challenge development by creating an account on GitHub on this Kaggle competition and... Of the world ’ s sprinkle in some magic Challenge ( BIG 2015 ) - Malware... Didn ’ t result in the training set provided by Otto Group Product Challenge! Most popular challenges with more than 3,500 participating teams before it ended a couple of years ago ; Overview Notebooks! Lb score most of the world Group is one of 9 item classifications fitting... Of “ multinomial ” for error distribution 2014-Conducted descriptive analysis to identify the high demands! Land in the kNN implementation of several R packages Group main Product categories in an ecommerce dataset packages. Of new your times … I competed in the kNN implementation of several R.... Result in the process of feature engineering to create meta features for project.