denko5/Sales-Analysis

A complete SQL-based sales analysis project covering Africa, showcasing data cleaning, exploratory analysis, insights, and lessons learned. The project highlights sales trends, regional performances, and marketing effectiveness across multiple platforms. # Sales Analysis - This repository contains the **Sales Analysis** project, which focuses on cleaning and analyzing sales data using SQL queries to derive actionable insights. The project is structured to showcase the complete data analysis process from cleaning to exploratory analysis. - This dataset contains detailed sales records for various products sold across multiple regions in Africa from January 1, 2022, to December 24, 2022. It includes columns such as CustomerID, OrderDate, Salesperson, ProductName, ProductCategory, Quantity, UnitPrice_In_Dollars, TotalPrice_In_Dollars, City, Country, and Sales Channel. Additionally, the dataset captures marketing spend, leads generated, and conversion rates for different sales channels, including TikTok, Facebook, YouTube, Instagram, and X_Twitter. The diverse product categories include items like Maasai Shuka, Kitenge Fabric, Aloe Vera, and African Hair Care Products. With its rich information, this dataset is ideal for analyzing sales performance, regional trends, marketing effectiveness, and customer behavior to derive actionable insights for decision-making and strategy. ## Repository Contents - `data/`: Contains the raw and cleaned datasets in CSV format. - `sales_data3.csv`: The original dataset before cleaning. - `Final Cleaned_Sales_Datac1.csv`: The cleaned and processed dataset. - `queries/`: Includes SQL scripts for analysis. - `Data Cleaning on sales data.sql`: SQL queries used for data cleaning. - `EDA.sql`: SQL queries used for exploratory data analysis. - `documentation/`: Comprehensive PDF documents explaining the process and results. - `DATA CLEANING FINAL DOCUMENTATION.pdf`: Details the cleaning process with SQL queries. - `EDA Documentation..pdf`: Details the exploratory analysis process with SQL queries. - `THINGS_I_LEARNED_FROM_DATA_CLEANING_AND_EXPLORATORY_DATA_ANALYSIS[1].pdf`: Key insights and lessons learned from the analysis. ## Objectives 1. To clean and preprocess the dataset to ensure data …