Overview This repository contains data and Python scripts for performing sales performance analysis using Pandas, a powerful data manipulation and analysis library in Python. The dataset provided includes information about sales transactions such as date, customer demographics, product details, quantity, unit cost, unit price, cost, revenue, and other relevant information.

Repository Structure data: Contains the dataset used for analysis. The dataset is provided in CSV format. scripts: Contains Python scripts for performing various analyses on the sales data. README.md: This file provides an overview of the repository, including instructions for running the scripts and interpreting the results.

Analysis Tasks The scripts provided in the repository perform the following analysis tasks:

Data Cleaning: Preprocessing the dataset to handle missing values, data inconsistencies, and other data cleaning tasks. Exploratory Data Analysis (EDA): Exploring the dataset to gain insights into the sales trends, customer behavior, product performance, etc. Sales Performance Metrics Calculation: Calculating various sales performance metrics such as total revenue, average revenue per customer, etc. Visualization: Visualizing the analysis results using charts and graphs for better interpretation and presentation. Forecasting: Using time series analysis techniques to forecast future sales trends based on historical data.