https://github.com/halyusa16/e-commerce-analysis

This project analyzes a public e-commerce dataset to uncover valuable insights and answer critical business questions. The dataset contains customer, product, order, and transaction details, providing a comprehensive view of the e-commerce platform's operations.
https://github.com/halyusa16/e-commerce-analysis

data-analysis data-cleaning data-exploration data-visualization self-project

Last synced: 8 days ago
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This project analyzes a public e-commerce dataset to uncover valuable insights and answer critical business questions. The dataset contains customer, product, order, and transaction details, providing a comprehensive view of the e-commerce platform's operations.

• Host: GitHub
• URL: https://github.com/halyusa16/e-commerce-analysis
• Owner: halyusa16
• Created: 2025-01-13T09:31:18.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-10-22T06:21:35.000Z (8 months ago)
• Last Synced: 2025-12-07T19:57:57.677Z (6 months ago)
• Topics: data-analysis, data-cleaning, data-exploration, data-visualization, self-project
• Language: Jupyter Notebook
• Homepage:
• Size: 371 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# e-commerce-analysis | Ptyhon Project

## **Overview**

This project analyzes a public e-commerce dataset to uncover valuable insights and answer critical business questions. The dataset contains customer, product, order, and transaction details, providing a comprehensive view of the e-commerce platform's operations.

## **Objectives**

The primary objectives of this project include:

1. Analyzing **monthly sales trends**

2. Identifying **best-selling and least-selling products**

3. Understanding **customer payment preferences**

4. Evaluating **customer review distributions**

## **Process**

### 1️⃣ **Data Wrangling**

- Uploaded and inspected each dataset.

- Cleaned missing values and handled duplicates.

- Standardized data types for consistency.

### 2️⃣ **Exploratory Data Analysis (EDA)** 

- Conducted analysis to uncover patterns in sales, customer behavior, and payment methods.

- Merged datasets to derive meaningful insights.

- Used visualizations for trend analysis and distribution comparisons.

### 3️⃣ **Data Visualization**

- Monthly sales trends

- Payment method distributions

- Product performance

- Customer review scores

## **Key Insights**

1. **Sales Trends:**

   - Monthly sales increased significantly from December 2016, peaking in November 2017 with sales exceeding $1M.

2. **Top-Selling Products:**

   - Categories like `bed_bath_table` and `health_beauty` were the most popular.

   - Least sold categories included niche items like `fashion_bags` and `security`.

3. **Payment Preferences:**

   - Credit card usage dominated, accounting for 75.2% of all transactions.

   - boleto (bank slips) was the second most popular payment method.

4. **Customer Reviews:**

   - Majority of customers rated their experience 5 stars, indicating high satisfaction levels.

## Business Insights & Recommendations  

### 1️⃣ Sales Trends & Seasonality   

**Insight:** Sales peaked in **November 2017**, indicating a seasonal trend (likely due to Black Friday or holiday shopping).  

**Recommendation:**  

- Run **promotional campaigns** ahead of peak months to maximize revenue.  

- **Optimize inventory management** to prevent stockouts.  

### 2️⃣ Product Performance  

**Insight:** Best-selling categories (`bed_bath_table`, `health_beauty`) drive most sales, while `fashion_bags` and `security` have low demand.  

**Recommendation:**  

- Increase **marketing campaigns** for best-sellers to sustain growth.  

- Consider **discounts, bundling, or removal** of the low-performing products if not profitable.  

### 3️⃣ Customer Payment Preferences 

**Insight:** **75.2% of transactions** were made using **credit cards**, while alternative payment methods were used less frequently.  

**Recommendation:**  

- Promote **alternative payment methods** to expand customer reach, especially in regions where bank slips or other payment methods are preferred.  

### 4️⃣ Customer Satisfaction & Reviews   

**Insight:** Most customers gave **5-star reviews**, but some low ratings exist.  

**Recommendation:**  

- Analyze **negative reviews** to identify common issues (e.g., delivery delays, product quality).  

- Improve **customer support** and **refund processes** to enhance satisfaction.  

## **Technologies Used**

-  **Languages:** Python

-  **Libraries:** Pandas, NumPy, Matplotlib, Seaborn

-  **Tools:** Jupyter Notebook

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