Store-Sales-Data-Analysis-project

Project Overview

This project involves a detailed analysis of store sales data to uncover key insights and trends. The dataset includes order information, customer details, product details, and sales data. By analyzing this dataset, we aim to identify key trends, areas for improvement, and support data-driven decision-making.

Project Description

Our final project involves a detailed analysis of store sales data to uncover key insights and trends. We aim to optimize store operations and enhance customer satisfaction through data visualization and analytics.

Dataset Summary

Number of Rows: 8399

Number of Columns: 23

Data Types:

Numeric: Row ID, Sales, Profit, etc.

Categorical: Ship Mode, Customer Segment, Product Category, etc.

Datetime: Order Date

Data Available: 2017-2020

Columns Available:

'Row ID', 'Order ID', 'Order Date', 'Order Priority', 'Order Quantity'

'Sales', 'Discount', 'Ship Mode', 'Profit', 'Unit Price', 'Shipping Cost'

'Customer Name', 'Province', 'Region', 'Customer Segment', 'Product Category'

'Product Sub-Category', 'Product Name', 'Product Container', 'Product Base Margin', 'Ship Date'

Additional Tables:

Users table:

'Region'

'Manager'

Returns table:

'Order Id'

'Status'

Techniques Used:

Data Quality:

Data Validation & Cleaning

Handling Missing Values

Correcting Data Types

Removing Duplicate Rows

Outlier Detection

Exploratory Data Analysis (EDA):

Univariate Analysis

Bi-variate Analysis

Multi-variate Analysis

Forecasting Visualization

Tools Used:

SQL

Pandas

NumPy

Matplotlib

Tableau