{"repo":"Madhuarvind/Retail_sales_analysis","free":true,"listed":false,"github":"https://github.com/Madhuarvind/Retail_sales_analysis","clone":"git clone https://github.com/Madhuarvind/Retail_sales_analysis.git","description":"A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.","language":"Python","stars":32,"topics":["data-visualization","eda","forecasting","machine-learning","retail-analytics","sales-prediction","time-series"],"license":"MIT","category":"machine-learning","readme_excerpt":"🛒 Retail Sales Analysis A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level. --- 📊 Project Overview This project analyzes transactional sales data to: - Understand sales distribution across items and stores - Identify temporal trends and seasonality - Forecast future sales using machine learning and time series techniques - Help businesses optimize inventory and promotions --- 📈 Dashboards --- 📁 Data Description The dataset typically contains: Column Name Description ------------- ---------------------------------- date Date of transaction store id Unique ID for each store item id Unique ID for each product sales Number of items sold Note: The actual dataset used is Superstore.csv and cleaned superstore.csv . Key columns include: Order ID , Order Date , Ship Date , Ship Mode , Customer ID , Customer Name , Segment , Country , City , State , Postal Code , Region , Product ID , Category , Sub-Category , Product Name , Sales , Quantity , Discount , Profit . --- 🛠 Environment Setup To get started, clone the repository and install the required dependencies. 1. Clone the repository 2. Create a virtual environment and activate it 3. Install dependencies --- 🚀 Usage - To run the SQL queries: - If app.py is a web application (e.g., built with Flask or Dash), run it with: Further instructions depend","default_branch":null,"files":null,"tree":[],"storefront":"/r/Madhuarvind","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Madhuarvind/Retail_sales_analysis/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}