{"id":26013321,"url":"https://github.com/halyusa16/e-commerce-analysis","last_synced_at":"2026-06-09T14:31:40.343Z","repository":{"id":272263283,"uuid":"916009369","full_name":"halyusa16/e-commerce-analysis","owner":"halyusa16","description":"This project analyzes a public e-commerce dataset to uncover valuable insights and answer critical business questions. 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The dataset contains customer, product, order, and transaction details, providing a comprehensive view of the e-commerce platform's operations.\n\n\n\n## **Objectives**\nThe primary objectives of this project include:\n1. Analyzing **monthly sales trends**\n2. Identifying **best-selling and least-selling products**\n3. Understanding **customer payment preferences**\n4. Evaluating **customer review distributions**\n\n## **Process**\n### 1️⃣ **Data Wrangling**\n- Uploaded and inspected each dataset.\n- Cleaned missing values and handled duplicates.\n- Standardized data types for consistency.\n\n### 2️⃣ **Exploratory Data Analysis (EDA)** \n- Conducted analysis to uncover patterns in sales, customer behavior, and payment methods.\n- Merged datasets to derive meaningful insights.\n- Used visualizations for trend analysis and distribution comparisons.\n\n### 3️⃣ **Data Visualization**\n- Monthly sales trends\n- Payment method distributions\n- Product performance\n- Customer review scores\n\n## **Key Insights**\n1. **Sales Trends:**\n   - Monthly sales increased significantly from December 2016, peaking in November 2017 with sales exceeding $1M.\n2. **Top-Selling Products:**\n   - Categories like `bed_bath_table` and `health_beauty` were the most popular.\n   - Least sold categories included niche items like `fashion_bags` and `security`.\n3. **Payment Preferences:**\n   - Credit card usage dominated, accounting for 75.2% of all transactions.\n   - boleto (bank slips) was the second most popular payment method.\n4. **Customer Reviews:**\n   - Majority of customers rated their experience 5 stars, indicating high satisfaction levels.\n\n\n## Business Insights \u0026 Recommendations  \n\n### 1️⃣ Sales Trends \u0026 Seasonality   \n**Insight:** Sales peaked in **November 2017**, indicating a seasonal trend (likely due to Black Friday or holiday shopping).  \n**Recommendation:**  \n- Run **promotional campaigns** ahead of peak months to maximize revenue.  \n- **Optimize inventory management** to prevent stockouts.  \n\n### 2️⃣ Product Performance  \n**Insight:** Best-selling categories (`bed_bath_table`, `health_beauty`) drive most sales, while `fashion_bags` and `security` have low demand.  \n**Recommendation:**  \n- Increase **marketing campaigns** for best-sellers to sustain growth.  \n- Consider **discounts, bundling, or removal** of the low-performing products if not profitable.  \n\n### 3️⃣ Customer Payment Preferences \n**Insight:** **75.2% of transactions** were made using **credit cards**, while alternative payment methods were used less frequently.  \n**Recommendation:**  \n- Promote **alternative payment methods** to expand customer reach, especially in regions where bank slips or other payment methods are preferred.  \n\n### 4️⃣ Customer Satisfaction \u0026 Reviews   \n**Insight:** Most customers gave **5-star reviews**, but some low ratings exist.  \n**Recommendation:**  \n- Analyze **negative reviews** to identify common issues (e.g., delivery delays, product quality).  \n- Improve **customer support** and **refund processes** to enhance satisfaction.  \n\n\n## **Technologies Used**\n-  **Languages:** Python\n-  **Libraries:** Pandas, NumPy, Matplotlib, Seaborn\n-  **Tools:** Jupyter Notebook\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalyusa16%2Fe-commerce-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhalyusa16%2Fe-commerce-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalyusa16%2Fe-commerce-analysis/lists"}