Welcome to the Superstore Sales Data Analysis project!
This repository contains a detailed Exploratory Data Analysis (EDA) using Python ,time series analysis to get the sales for forecasting for next 7 days and a Power BI Dashboard built on the Superstore Sales dataset.

├── data/ 
          ├── superstore_sales.csv 
├── notebooks/ 
          ├── Superstore Sales EDA.ipynb 
          ├── Superstore Sale Time Series Analysis.ipynb 
├── powerbi/ 
          ├── Superstore Sale BI.pbix` 
          ├── Superstore Sale Dashboard.png 
          ├── Superstore Sale Dashboard video.mp4
└── README.md

The dataset contains retail sales transactions including:

• Order IDs, Dates
• Product Categories & Sub-categories
• Customer Details
• Sales
• Geographic data (City, State, Region)
• Shipping Mode

• pandas
• numpy
• matplotlib
• seaborn
• plotly

• Most Valuable Customers based on revenue
• States & Cities with the highest revenue
• Revenue by Category and Sub-Category
• Top-performing Products by revenue
• Best performing Customer Segments
• Region and Shipping Mode performance

• Identified loyal customers and repeat buyers
• Calculated Average Revenue per Customer (ARPC) and Customer Lifetime Value (CLV)
• Analyzed Churn Rate and Order Frequency Distribution
• Pareto 80/20 Rule Analysis on revenue contribution

• Top States and Cities by the number of orders
• Identified low-performing states/cities

• Monthly and yearly sales trends
• Seasonal fluctuations in sales
• Identified peak and low revenue months

This project focuses on building a SARIMA model to forecast future sales based on the Superstore dataset.

• Data Cleaning & Preprocessing
• Resampling and Interpolating missing values
• ADF Test for stationarity check
• Hyperparameter tuning using AIC
• SARIMA Model Training & 7-day Sales Forecasting

• Python, Pandas, NumPy
• Statsmodels (SARIMAX)
• Matplotlib / Seaborn (Optional for visualization)

• Top Revenue States: California, New York, and Texas
• Top Revenue Cities: Los Angeles, New York City, and Seattle
• High Revenue Categories: Technology leads followed by Furniture
• Top Sub-Categories: Phones, Chairs, and Copiers
• Segment & Region Performance: Consumer Segment & West Region dominate sales
• Shipping Mode Preference: Standard Class leads significantly
• Monthly Sales Trend: Peaks during November & December, indicating strong year-end sales

This project helps uncover meaningful business insights into:

• Customer behavior and segmentation
• Product selling
• Geographic and regional sales performance
• Seasonal and yearly sales trends