Moustafa Alzantot 1143rd place. Check train dataset:Avoid missing any information. Add to Wish List Add to Compare . Sort by. I ended up changing multiple parameters such as min_child_weight, max_depth to get a miniscule improvement. Finally, competition distance versus sales was rather difficult to interpret, where it would seem that having closer distance would encourage sales, a rather intriguing scenario. I used R and an average of two models: glmnet and xgboost with a lot of feature engineering. So, let’s comment out this line:Anyone knows a good way to change the order of the columns? Rossmann store sales quantity prediction @inproceedings{Sazontyev2015RossmannSS, title={Rossmann store sales quantity prediction}, author={Vladimir Sazontyev}, year={2015} } Vladimir Sazontyev; Published 2015; My project aimed to solve machine learning problem for Rossmann inc. Rossmann decided to apply a single solution across all stores in Germany which capture their each store characteristics, sales pattern and able to forecast sales of each store of 6 weeks in advance. Darker cells indicate high correlation.Seaborn specializes in static charts and makes making a heatmap from a Pandas DataFrame simple and a little bit nicer than matplotlib. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.

Link to Kaggle Competition.. A complete list of models developed for this problem can be found here and the final report with a detailed explanation of our approach and observations is included here.Additionally, each model-name.py file includes a description of the assumptions made & … Touchbar FPC connector J3300 J... $10.99. However, I see zeroes :/ Now, the question is “For train data, what is the mean of the sales for a store that is not open?”. posted in rossmann-store-sales 5 years ago. This FPC connector is for the touchbar on A1706 and A1707 USB-C Macbook Pros. Add to Wish List Add to Compare. We use Pandas for fetching and manipulating data, and also for plotting; Matplotlib and Seaborn for plotting; Numpy and Scipy for manipulating data.First, we download data from Kaggle competition page. Winning entry for ML class's sales prediction competition 16. 16 Jan 2016. I use Rossmann dataset that contains data since 2013 year till 2015. Introduction: Rossmann operates a chain of drug stores across 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Shared With You. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. 3,298 teams; 5 years ago ; Overview Data Notebooks Discussion Leaderboard Rules. I did these manually with for loops as I was not able to get the GridSearchCV to work properly. 3 > 2.A Rossmann shop is either open or close, as expected, but the following code is also for checking the NaN values:Compute pairwise correlation of columns using pandas.corr() function.Visualize correlation of the DataFrame using matplotlib.pcolor() function. Be careful for Date type, String type (factors in R).

June when data is Dec-Jan) “Rossmann operates over 3,000 drug stores in 7 European countries. I suspect this may be due to the importance of features being very skewed, and thus not hitting the more important features during selection would make scores worse.Average sales per month or per day of week did not give me an increase, most likely due to extrapolating data where there wasn’t any in test set, leading to overfitting. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality.

We will start with the exploration of data, try to simplify it and after that...# import data. Branson EC Electronic Solution... $23.75. Rossmann Store Sales. There’s like 1000+ possible combinations of them that you want to … Building Model (Lib: … Impact on Company: Reliable sales forecasts enable store managers to create effective…

Rossmann Store Sales Forecast sales using store, promotion, and competitor data 337. This is the first time I have participated in a machine learning competition and my result turned out to be quite good: 66th out of 3303. Thus I confirmed that there were no closed stores with sales, and that there were no open stores with no sales and could safely remove them from the data set.Next, my intuition was that sales would scale with the number of customers on that day. The same case goes with assortment types.Plot of Average Customer (X) vs Average Sales (Y) for different store typesFinally, competition distance versus sales was rather difficult to interpret, where it would seem that having closer distance would encourage sales, a rather intriguing scenario.Given that many of the data set had discrete components such as day, week, day of week, year, assortment, store type, whether they had a promotion or not, it would seem that decision trees would be a good step to begin on.A decision tree model would also enable me to realise the importance of certain features.

Rossmann Store Sales Forecast sales using store, promotion, and competitor data. (E.g.