🛒 SmartCart Customer Analytics & Feature Engineering

A data analytics project focused on cleaning, transforming, and engineering customer data to generate meaningful business insights for SmartCart.

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📖 Project Overview

Dataset Preview

Customer data is one of the most valuable assets for modern businesses.

This project analyzes SmartCart customer data and performs:

• Data Cleaning
• Missing Value Treatment
• Feature Engineering
• Customer Profiling
• Data Transformation
• Exploratory Data Analysis (EDA)

The goal is to prepare customer data for future applications such as:

• Customer Segmentation
• Customer Lifetime Value Prediction
• Marketing Campaign Analysis
• Recommendation Systems

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📊 Dataset Information

Dataset Size:

• 2,240 Customers
• 22 Original Features

Features include:

• Customer Income
• Education Level
• Marital Status
• Purchase History
• Web Purchases
• Store Purchases
• Product Spending
• Campaign Response

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🛠️ Technologies Used

• Python
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

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🔄 Project Workflow

1. Data Loading

Imported customer dataset using Pandas.

2. Data Cleaning

Identified missing values.

Missing Values

Found missing values in:

• Income Handled using median imputation.

3. Feature Engineering

Created several business-focused features.

Customer Age

Customer Tenure

Calculated the number of days since customer registration.

Total Spending

Combined spending across all product categories. Includes:

• Wines
• Fruits
• Meat Products
• Fish Products
• Sweet Products
• Gold Products

Total Children

4. Data Transformation

Education Categories

Converted education levels into broader categories:

• Undergraduate
• Graduate
• Postgraduate

Living Status

Categories:

• Partner
• Alone

5. Feature Selection

Removed unnecessary columns:

• ID
• Year_Birth
• Marital_Status
• Kidhome
• Teenhome
• Dt_Customer Also removed raw spending columns after creating Total_spending.

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6. Exploratory Data Analysis

Performed relationship analysis using: Analyzed:

• Income
• Age
• Total Spending
• Customer Response
• Recency
• Total Children

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Feature Relationships

📂 Project Structure

smartcart-customer-analytics/
│
├── SmartCart_project.ipynb
├── smartcart_customers.csv
├── README.md
└── images/

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📈 Engineered Features

Feature	Description
Age	Customer Age
customer_tenure_days	Time since registration
Total_spending	Overall customer spending
Total_Children	Number of children
Living_with	Customer relationship status

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🎯 Business Applications

The processed dataset can be used for:

• Customer Segmentation
• Marketing Analytics
• Churn Prediction
• Spending Behavior Analysis
• Recommendation Systems
• Customer Lifetime Value Prediction

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📚 Skills Demonstrated

• Data Cleaning
• Missing Value Handling
• Feature Engineering
• Data Transformation
• Exploratory Data Analysis
• Business Analytics
• Customer Data Processing

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HeatMap

##3D projection

👨‍💻 Author

Kunal Singh

Aspiring AI & Machine Learning Developer

GitHub: https://github.com/Kunalthakur01

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