Predicting Customer Lifetime Values with different ways
Thinking about the important metrics for Customer Lifetime Value
Predict the customers that will bring the most profit to the company
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Connect to the database and extract data
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Customer segmentation with RFM
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Calculation Customer Lifetime Value in basic concept
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Predicting Customer Lifetime Value with BG-NBD & GammaGamma models by adding the concept of time
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Export tables and forecast outputs of all models to the database
Used Online Retail 2 dataset in this project.
This dataset contains the purchase values of a wholesale company's customers in UK between 2010-2011.
InvoiceNo: Unique invoice number. C means refundees.
StockCode: Unique item code
Description: Item description
Quantity: Item quantity number
InvoiceDate: Invoice date time
UnitPrice: Item price (Sterlin)
CustomerID: Unique Customer Number
Country: Country name. The country where the customer lives.
datetime
pandas
pymysql
sqlalchemy
sklearn
lifetimes
Oğuz Han Erdoğan - oguzerdo
VBO - Data Science and Machine Learning Bootcamp
www.veribilimiokulu.com