Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shibbir-ahmad24/amazon-product-sales-data-analysis-trends-and-insights
Amazon Product Sales Data Analysis: Trends and Insights
https://github.com/shibbir-ahmad24/amazon-product-sales-data-analysis-trends-and-insights
amazon-dataset data-analysis matplotlib numpy pandas seaborn
Last synced: 18 days ago
JSON representation
Amazon Product Sales Data Analysis: Trends and Insights
- Host: GitHub
- URL: https://github.com/shibbir-ahmad24/amazon-product-sales-data-analysis-trends-and-insights
- Owner: shibbir-ahmad24
- Created: 2024-06-10T23:23:10.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-09-21T05:53:51.000Z (4 months ago)
- Last Synced: 2024-10-26T21:57:06.705Z (2 months ago)
- Topics: amazon-dataset, data-analysis, matplotlib, numpy, pandas, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Amazon Product Sales Data Analysis: Trends and Insights
### **Problem:**
- Difficulty in understanding sales performance across Amazon's vast product catalog.
- Challenges in identifying popular and underperforming products.
- Lack of insights into factors influencing product sales, affecting decision-making for inventory, pricing, and marketing.
### **Solution:**
**Data Exploration & Cleaning:**
- Analyzed dataset structure.
- Identified and addressed missing values, outliers, and inconsistencies.
- Ensured data integrity.
**Data Wrangling:**
- Manipulated and prepared the dataset for analysis.
- Extracted relevant features and created new variables.
- Transformed data as required for deeper insights.
**Data Mining:**
- Extracted insights on top product categories and sub-categories.
- Identified popular products based on ratings and discount pricing.
- Determined top-selling and least-selling products.
**Data Visualization:**
- Visualized popularity of product categories and sub-categories.
- Explored total sales per rating.
- Analyzed the correlation between ratings and number of ratings for popular products.
**Reporting:**
- Compiled findings in a comprehensive report.
- Summarized insights and highlighted key trends and factors affecting sales.
### **Impact:**
- Provided a detailed understanding of Amazon’s product sales landscape.
- Enabled better decision-making for product listings, pricing strategies, and marketing.
- Improved inventory management by identifying top-selling and least-selling products.
- Contributed to optimized sales outcomes and enhanced customer satisfaction.
### **Dataset Source:**
- [Amazon Products Dataset on Kaggle](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset/)
### **Data:**
- Amazon-Products.csv
### **Programming Language & Libraries:**
- Programming Language: Python
- Libraries:
- Numpy
- Pandas
- Matplotlib
- Seaborn
### **Analysis Visuals**
### **Summary of Insights**
**Popular Product Categories:**
- TV, Audio & Cameras
- Stores
- Appliances
- Women's Clothing
- Toys & Baby Products
- Accessories
- Beauty & Health
- Home & Kitchen
**Popular Product Sub-categories:**
- All Electronics
- Headphones
- Cameras
- Camera Accessories
- Home Entertainment Systems
- Men's Fashion
- All Appliances
- Kitchen & Home Appliances
- Amazon Fashion
- Bags & Luggage
**Top-Selling Products:**
- SanDisk products, especially Memory Cards
- Redmi products, especially Smartphones
- AmazonBasics Alkaline Batteries and HDMI cables
- boAt Bassheads and Eardopes products
- Huggies Wonderpants Diapers
- Products with Low Sales:
- SOOTRA BRANDED Carbon Film Resistor
- Okos Gold Plated Blue and Green Bracelet
- SBWC Stylish Slim Lice Narrow Comb
- Moti Chain Gold Plated Necklace
- Shelfkey.com ALUMINIUM ELECTROLYTIC CAPACITOR
**Trend in Product Sales:**
- Buyers tend to purchase products from the same brand as top-selling items. There is a preference for well-rated products with higher numbers of ratings.
**Important Factors Affecting Sales:**
- Product Categories: Well-established categories like Electronics, Appliances, and Clothing attract the most sales.
- Discounts: Discounts do not appear to encourage higher sales; well-discounted products often have lower sales.
- Price: The actual price of items has no significant effect on the volume of sales.