**Sales Data Analysis Project**

**Overview:**

Welcome to the Sales Data Analysis Project! In this project, we will delve into a large sales dataset to extract valuable insights. 
Our main objectives include exploring sales trends over time, identifying the best-selling products, calculating revenue metrics such as total sales and profit margins, and creating visualizations to present our findings effectively. 
This project showcases our ability to manipulate and derive insights from large datasets, enabling us to make data-driven recommendations for optimizing sales strategies.

**Project Structure:**

1. **data_preprocessing.py:**
   - This file contains code for basic data cleaning, including handling missing values and duplicates in the dataset.
   
2. **eda.py:**
   - In this file, we conduct exploratory data analysis (EDA) by visualizing the data with various plots and calculating summary statistics.

3. **sales_analysis.py:**
   - This is the main script where we perform sales trends analysis, identify the best-selling products, calculate revenue metrics, create data visualizations, and generate a report with recommendations.

4. **Sales Data.csv:**
   - The raw sales data in CSV format. This is the dataset we'll analyze.

**Requirements:**

Make sure you have the following Python libraries installed to run the project:
- pandas
- matplotlib
- seaborn

**How to Run:**

1. Open a terminal or command prompt.

2. Navigate to the project directory:
3. Run the main script `sales_analysis.py`:


**Project Outcome:**

Upon running the project, you'll generate insights and findings based on the sales dataset. These insights, along with actionable recommendations, can be valuable for optimizing sales strategies. The project showcases your ability to analyze data, create data visualizations, and draw conclusions from the results.

Feel free to explore the code in the various Python files and adapt it to your specific needs. Enjoy the data analysis journey!

**Author:**
Ayesha Kalsoom