Amazon Sales Data Analysis Project Guide

E-commerce platforms like Amazon generate massive datasets related to product sales, customer reviews, pricing, and inventory. Understanding sales patterns is crucial for making informed decisions about pricing, marketing, inventory management, and customer targeting. Exploratory Data Analysis (EDA) helps to extract meaningful business insights from raw, unstructured sales data, driving data-driven decision-making for online businesses.

Performing EDA on Amazon sales data involves cleaning, transforming, and analyzing key features like order dates, product categories, sales volume, profit margins, and customer ratings. Using Python libraries like Pandas, Matplotlib, and Seaborn, you can visualize top-performing categories, seasonality in sales, peak shopping periods, and customer buying behavior. These insights enable businesses to optimize marketing strategies and inventory planning.

You start by collecting or using a sample Amazon sales dataset, cleaning missing data, and standardizing formats. You analyze metrics like total sales, top-selling products, category performance, and regional sales differences. Correlation heatmaps, bar charts, and trend lines are created to uncover relationships between variables like price, quantity sold, and customer ratings. EDA helps discover hidden opportunities and bottlenecks within the sales process.

• Collect or simulate an Amazon sales dataset with columns like Product Name, Order Date, Sales Amount, Category, and Ratings.
• Preprocess: handle missing values, fix date formats, standardize category names, and derive features like profit margin or sale discounts.
• Visualize important KPIs: total sales per month, revenue by category, most sold products, and customer sentiment distributions.
• Analyze seasonal trends, holiday effects, and promotional impacts on overall sales performance.
• Present insights using dashboards, reports, or infographics showing key findings and business recommendations.

• Amazon Sales Dataset (Kaggle)
• Exploratory Data Analysis Techniques
• Streamlit Documentation (EDA Dashboards)