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Intro Data Science Lifecycle Why DS Role of DS Applications Learning Portfolio

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Intro Data Science Lifecycle Why DS Role of DS Applications Learning Portfolio Benefits Low High

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Intro Data Science Lifecycle Why DS Role of DS Applications Learning Portfolio Data Science: What’s Happening Now!

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Data Source 1 Data Source 2 Organized Data Decision Tree Naïve Bayes Random Forest Logistic Regression Algorithms used to predict Recommend the model with highest accuracy 2006 2007 2008 . . . 2015 2016 2017 2018 Historical Data Model Building Model Testing Prediction Existing Customers Who is likely to buy? Model Results Case Study 1 – Who is likely to Buy? Intro Data Science Lifecycle Why DS Role of DS Applications Learning Portfolio

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Decision Tree Logistic Regression Random Forest Naïve Bayes 62.8% 37.2% 46.2% 53.8% 62.1% 37.9% 61.5% 38.5% Results Algorithms Four different algorithms for prediction – Lower accuracy in each model Expected accuracy level is around 75-80% Explore other methods to improve accuracy of predicted values ▪ Data until 2015 is used for model building and predicted for 2016 ▪ The results from model were validated using actual data of 2016 Model Building Issue of Low Prediction Accuracy Intro Data Science Lifecycle Why DS Role of DS Applications Learning Portfolio

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Intro Data Science Lifecycle Why DS Role of DS Applications Learning Portfolio