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The dataset has 93 features for more than 200,000 products with the Test Data containing 144K rows and Training Data containing 61K rows. This data science project is a supervised , multinomial classification problem. The dataset has a total of 9 possible product lines and the objective is accurately make class predictions on 144,000 unlabeled products based on the 93 features for the products provided in the dataset.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
In this project, we are going to talk about insurance forecast by using regression techniques.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.