SQL_python_Ecommerce_analysis

📦 E-commerce Data Analysis Project This project is an end-to-end exploratory analysis of an E-commerce dataset using SQL, Python, and Data Visualization libraries like Matplotlib and Seaborn. The goal is to extract meaningful business insights from customer purchase data. 📊 Project Objective To analyze an E-commerce dataset by: Extracting and aggregating data with SQL Cleaning and processing data in Python (Pandas) Visualizing key metrics using Matplotlib and Seaborn Identifying trends in customer behavior, sales, and seasonality 🛠️ Tools & Technologies SQL (for data extraction) Python (Pandas, Matplotlib, Seaborn) Jupyter Notebook 🗃️ Dataset The dataset contains transaction-level data from an online E-commerce platform, including: order_id customer_id order_purchase_timestamp payment value and other related details 🔍 Key Analyses Performed 📅 Monthly Order Count: Extracted monthly order trends for the years 2017, 2018, and 2019 using SQL. 📈 Moving Average of Payment Value: Calculated and visualized moving average trends in customer payments. 📊 Subplots by Year: Created bar plots showing monthly order volume for each year in separate subplots. 📦 Customer Purchase Behavior: Explored how customers spent money over time and across months. 📌 Visualizations Bar plots for monthly order distribution (year-wise) Line plot of moving average of payment value over time Histograms to explore distribution of payment data