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
• 0. Import necessary Packages
• 1. Load the Data into Pandas Dataframe
• 2. 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.
• 3. 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:
• 4. Feature Enginearing
  • 4.1 Add Some High Level Features and explore their relationship with the target variable
  • 4.2 Check Correlation between Numerical Variables
• 5. Feature Selection
• 6. 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
• 7. Machine Learning