Ecommerce Customer Insight Analysis: Mobile vs Website

This project analyzes customer behavior for an ecommerce company based in New York City. The company wants to decide whether to focus on improving the mobile app experience or the website. The analysis uses customer data to derive actionable insights.

Dataset

The dataset (Ecommerce Customer.csv) includes information such as:

• Customer demographics
• In-store session details
• Spending habits on the mobile app and website

Objective

To determine which platform (mobile app or website) contributes more to customer spending and provides better ROI for the business.

Steps Involved

1. Data Cleaning: Handle missing values and ensure data quality.
2. Exploratory Data Analysis (EDA): Analyze trends, correlations, and key metrics.
3. Modeling with Linear Regression:
   • Predict total spending based on usage of the mobile app and website.
   • Evaluate which platform has a stronger impact on spending.
4. Visualization: Use plots to represent insights.

Requirements

• Python 3.8+
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/Ankitaghavate/Ecommerce-Customer-Insight-Analysis.git

2. Navigate to the folder:

   cd Ecommerce-Insight-Analysis

3. Install dependencies:

   pip install -r requirements.txt

4. Run the analysis notebook or script.

Results

• Insights on customer behavior.
• Recommendations for prioritizing mobile app or website improvements.

Continue Learning.