![]() add_argument( '-category_names', type = str, default = None) add_argument( '-topk', type = int, default = 5) ArgumentParser( description = 'Image prediction') model/checkpoint.pth -topk 5 -category_names. Step 3: Sum the forecasts of the existing and new stores together for the total produce sales forecast.# Example: python predict.py. Sum the new stores produce sales forecasts for each of the segments to get the forecast for all new stores.For example, if the forecasted average store produce sales for segment 1 for March is 10,000, and there are 4 new stores in segment 1, the forecast for the new stores in segment 1 would be 40,000.Multiply the average store produce sales forecast by the number of new stores in that segment.Forecast produce sales (not total sales) for the average store (rather than the aggregate) for each segment.Step 2: To forecast produce sales for new stores: Step 1: To forecast produce sales for existing stores you should aggregate produce sales across all stores by month and create a forecast. Note: Use a 6 month holdout sample for the TS Compare tool (this is because we do not have that much data so using a 12 month holdout would remove too much of the data) You’ve been asked to prepare a monthly forecast for produce sales for the full year of 2016 for both existing and new stores. Use the StoreDemographicData.csv file, which contains the information for the area around each store.įresh produce has a short life span, and due to increasing costs, the company wants to have an accurate monthly sales forecast.Use the model to predict the best store format for each of the 10 new stores.Make sure to compare a decision tree, forest, and boosted model. Use a 20% validation sample with Random Seed = 3 when creating samples with which to compare the accuracy of the models.Develop a model that predicts which segment a store falls into based on the demographic and socioeconomic characteristics of the population that resides in the area around each new store.However, we don’t have sales data for these new stores yet, so we’ll have to determine the format using each of the new store’s demographic data. The company wants to determine which store format each of the new stores should have. The grocery store chain has 10 new stores opening up at the beginning of the year. Use the StoreSalesData.csv and StoreInformation.csv files. Segment the 85 current stores into the different store formats. Use percentage sales per category per store for clustering (category sales as a percentage of total store sales). ![]() You’ve been asked to:ĭetermine the optimal number of store formats based on sales data. The terms "formats" and "segments" will be used interchangeably throughout this project. ![]() The actual building sizes will not change, just the product selection and internal layouts. Each store format will have a different product selection in order to better match local demand. To remedy the product surplus and shortages, the company wants to introduce different store formats. You've been asked to provide analytical support to make decisions about store formats and inventory planning. ![]() This is beginning to cause problems as stores are suffering from product surpluses in some product categories and shortages in others. Up until now, the company has treated all stores similarly, shipping the same amount of product to each store. ![]() Currently, all stores use the same store format for selling their products. Your company currently has 85 grocery stores and is planning to open 10 new stores at the beginning of the year. This project is the part of Udacity's Nanodegree program (Predictive Analytics for Business Nanodgeree) Task 1: Store Format for Existing Stores ![]()
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