Predictive Analytics: Predicting Sales & Customer Lifetime Value

Predictive Analytics    |    Intermediate
  • 14 videos | 1h 41m 53s
  • Includes Assessment
  • Earns a Badge
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In recent years, retailing has changed from a fragmented space into a winner-takes-all sector, in which a key differentiating factor is the ability to tightly predict demand and measure customer lifetime value. Begin this course by attempting to predict the sales for each week in a Walmart store. You will explore and visualize your data, creating an Azure machine learning workspace and a hosted Python notebook to write code. Then, perform regression analysis to predict the sales after one-hot encoding the requisite explanatory variables. You will apply different models as well, including ridge regression, K-nearest neighbors, decision trees, random forests, and extra tree regressors. Next, predict the customer lifetime value using regression analysis, and perform cross-validation and feature selection on the model in order to improve its performance. Finally, experiment with feature selection, including recursive feature elimination, lasso regularization, and linear SVR.


  • Discover the key concepts covered in this course
    Read in walmart data to a pandas data frame
    Perform preprocessing on data
    Visualize data and remove outliers
    One-hot encode walmart sales data
    Predict walmart sales using a regression model
    Compare and contrast different regression models to predict walmart sales
  • Use ridge regression, knn, decision trees, extra tree regressors, and random forests to predict walmart sales
    Visualize clv data
    Predict clv using linear regression
    Perform cross-validation and feature selection on a clv prediction model
    Perform feature selection on clv prediction model
    Perform feature selection using recursive feature elimination (rfe), lasso regression, and support vector regression (svr) on a clv prediction model
    Summarize the key concepts covered in this course


  • 1m 37s
  • 6m 46s
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    3.  Preprocessing and Visualizing Data
    7m 21s
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    4.  Visualizing Data Using Charts
    7m 26s
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    5.  Performing One-hot Encoding on Walmart Data
    6m 57s
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    6.  Performing Linear Regression to Predict Sales
    9m 56s
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    7.  Applying Various Regression Models to Predict Sales
    6m 46s
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    8.  Predicting Sales Using Alternate Regression Models
    6m 49s
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    9.  Visualizing Customer Lifetime Value (CLV) Data
    11m 16s
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    10.  Predicting CLV Using Linear Regression
    8m 43s
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    11.  Cross-validating and Selecting Feature for CLV Model
    7m 59s
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    12.  Selecting Features for a CLV Prediction Model
    7m 16s
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    13.  Predicting CLV Using RFE, Lasso Regression, and SVR
    9m 54s
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    14.  Course Summary
    3m 10s


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