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Stars1
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LanguageJupyter Notebook
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Created 4 months ago
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Updated 4 months ago
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TimeSeriesAnalysis
Here we have considered a data set where we are seeing the number of air Passengers per month. We have applied time series analysis technique to predict the future passenger details. We used both ARIMAX and SARIMAX technique to predict.StoreSales-Time-Series-Forecasting
The goal of this analysis is to predict the sales for the thousands of product families sold at Favorita stores located in Ecuador. Predictions arenโt just for meteorologists. Governments forecast economic growth. Scientists attempt to predict the future population. And businesses forecast product demandโa common task of professional data scientists. Forecasts are especially relevant to brick-and-mortar grocery stores, which must dance delicately with how much inventory to buy. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leading to lost revenue and upset customers. More accurate forecasting, thanks to machine learning, could help ensure retailers please customers by having just enough of the right products at the right time.H-M-Personalized-Fashion-Recommendations
In this competition, H&M Group invited us to develop product recommendations based on data from previous transactions, as well as from customer and product meta data. The available meta data spans from simple data, such as garment type and customer age, to text data from product descriptions, to image data from garment images.Love Open Source and this site? Check out how you can help us
