Data analysis of Amazon sales to uncover trends, top products, and business insights using Python.
📌 Overview
This project analyzes Amazon sales data to uncover insights into sales trends, product performance, customer behavior, and profitability. The analysis is done using Python (Pandas, NumPy, Matplotlib, Seaborn) with a focus on business intelligence and data-driven decision-making.
📂 Dataset
🔹Contains transactional sales data from Amazon (orders, categories, sales, profit, region, etc.)
🔹Includes information such as order date, product category, sales, profit, quantity, and region.
🔹Dataset was cleaned and preprocessed to handle missing values, duplicates, and inconsistencies.
🔑 Objectives
🔹Identify top-selling products and categories.
🔹Analyze revenue and profit trends over time.
🔹Detect seasonal sales patterns and regional variations.
🔹Explore customer purchasing behavior through data segmentation.
🛠️ Steps Performed
1.Data Cleaning & Preprocessing
🔹Removed duplicates and missing values.
🔹Standardized column formats (dates, numeric values).
2.Exploratory Data Analysis (EDA)
🔹Sales & profit distribution across categories.
🔹Regional sales analysis with visualizations.
🔹Time-series analysis for seasonal trends.
3.Visualization
🔹Created charts (bar, line, heatmaps) using Matplotlib & Seaborn.
🔹Highlighted key business insights with plots.
📈 Results & Insights
🔹Identified the most profitable product categories and least performing ones.
🔹Observed regional differences in sales performance.
🔹Found strong seasonal trends impacting customer purchases.
🔹Provided actionable insights to optimize sales strategy.
🚀 Future Work
🔹Develop an interactive dashboard (Power BI / Tableau / Plotly Dash).
🔹Incorporate customer segmentation for targeted marketing strategies.
💻 Technologies Used
🔹Python (Pandas, NumPy)
🔹Matplotlib & Seaborn for visualization
🔹Jupyter Notebook for analysis