Skip to content

Latest commit

 

History

History
47 lines (42 loc) · 2.38 KB

README.md

File metadata and controls

47 lines (42 loc) · 2.38 KB

Group-3---Analysis-On-Customer-Purchase

We have been provided with information about the customers' historical purchase as the amount of money spent, Number of inactive months and so on. Two columns Potential_Customer and Cust_Last_Purchase represent the customers' respond to the latest promotion. The column Potential_Customer represents if the customer purchased any product, and the column Cust_Last_Purchase represents the amount of this purchase and it is Nan if there has been no purchase.

Objective

Design a predictive model to determine the potential customers. The target variable is Potential_Customer.

Attention: Because the column Cust_Last_Purchase relates to the target variable Potential_Customer, need to exclude it from model.

Table of Content

  • JDS Capstone
  • Data description
  • Objective
  • Submission Guideline
  • Presentation Guideline
  • Table of Content
    1. Import necessary Packages
    1. Load the Data into Pandas Dataframe
    1. Data Cleaning
    • 2.1 How big is the dataset? (number of rows, features and total datapoints)
    • 2.2 What is the type of each column?
      • 2.2.1 Why columns such as Cust_Last_Purchase are object while they should be float64? Fix the type of the columns as it should be.
    • 2.3 Check data for duplicate rows and remove the duplicates
    • 2.4 Do we need C_ID in our analysis? Drop the columns you will not use in your analysis, if there is any.
    1. Exploratory Data Analysis (EDA)
    • 3.1 Explore Categorical Variables
      • 3.1.1 Insight
      • 3.1.2 Solution
    • 3.2 Explore Relationship Between Categorical & Target Variable. Interpret the observation
      • 3.2.1. Insight
    • 3.3 Explore Numerical Variables
      • 3.3.1 Insight
      • 3.3.2 Check the Data for Missing Values
    • 3.4 Explore the Relationship between Numerical Variables & Target Variable. Interpret your observation
    • 3.5 Explore the Relationship between the columns and try to answer the following questions:
    1. Feature Enginearing
    • 4.1 Add Some High Level Features and explore their relationship with the target variable
    • 4.2 Check Correlation between Numerical Variables
    1. Feature Selection
    1. Data PreProcessing
    • 6.1 Separate X (features) and y (target)
    • 6.2 Dummy Variables
    • 6.3 Split data to train/test
    • 6.4 Feature Scaling
    • 6.5 PCA on Numerical Columns only
    1. Machine Learning