Predictive Analytics: Predicting Sales & Customer Lifetime Value

Predictive Analytics    |    Intermediate
  • 14 videos | 1h 41m 53s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 7 users Rating 4.3 of 7 users (7)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 1m 37s
  • 6m 46s
  • Locked
    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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