DEADLINE IN ONE HOUR Python analysis dataset activity -- 2
Budget: €8 – €30 EUR
STEP 1
Import the libraries you think you will need to import and clean the data, performance the analysis and the RFM segmentation.
STEP 2
Import the dataset called "dataset_transaction" and perform an EDA using at least 3 functions and 2 graphic to explore the data using matplotlib or seaborn (sweetviz is not allowed).
What can you tell about the dataset you are working with?
Express your ideas in more than 5 lines .
STEP 3
Create a new data frame transforming the original dataset to calculate the Recency, Frequency and the Monetary value taking into account all the information for each customer.
The Monetary Value must be expressed as the AVG spend ticket for each customer
STEP 4
Perform a new EDA and explain the highlights about the RFM metrics in more than 5 lines.
Include a scatter plot displaying the Monetary Value & the Frequency to highlight purchase behaviour
STEP 5
Use the K-means algorithm to define a segmentation based on RFM.
Use 5 clusters to perform a traditional customer segmentation: assign the right label to each clusters: identify the Champions, the Loyals, the Promising, the At risk and the Churn.
#Why you consider the Promising are within the cluster you have selected as Promising?
Import the libraries you think you will need to import and clean the data, performance the analysis and the RFM segmentation.
STEP 2
Import the dataset called "dataset_transaction" and perform an EDA using at least 3 functions and 2 graphic to explore the data using matplotlib or seaborn (sweetviz is not allowed).
What can you tell about the dataset you are working with?
Express your ideas in more than 5 lines .
STEP 3
Create a new data frame transforming the original dataset to calculate the Recency, Frequency and the Monetary value taking into account all the information for each customer.
The Monetary Value must be expressed as the AVG spend ticket for each customer
STEP 4
Perform a new EDA and explain the highlights about the RFM metrics in more than 5 lines.
Include a scatter plot displaying the Monetary Value & the Frequency to highlight purchase behaviour
STEP 5
Use the K-means algorithm to define a segmentation based on RFM.
Use 5 clusters to perform a traditional customer segmentation: assign the right label to each clusters: identify the Champions, the Loyals, the Promising, the At risk and the Churn.
#Why you consider the Promising are within the cluster you have selected as Promising?