Sales Data Analysis

Overview

This repository contains Python code for conducting exploratory data analysis (EDA) on sales data. The code covers tasks such as merging multiple months of sales data into a single CSV file, data cleaning and formatting, visualizing sales trends, identifying the best-selling products, determining optimal advertisement times, and discovering frequently co-purchased items.

Requirements

• Python 3.x
• Pandas
• Matplotlib
• Seaborn
• Plotly

Usage

1. Clone the repository to your local machine:

   git clone https://github.com/shreeramdrao/Exploratory-Data-Analysis-On-Sales.git
   

2. Navigate to the project directory:

   cd Exploratory-Data-Analysis-On-Sales
   

3. Install the required libraries (if not already installed):

   pip install -r requirements.txt
   

4. Run the Python scripts in a Jupyter Notebook or any Python IDE to perform the following tasks:

   1. Merging Sales Data: Combine sales data from multiple CSV files into a single file for analysis.

   2. Data Cleaning and Formatting: Handle missing values, format columns, and prepare data for analysis.

   3. Visualizing Sales Trends: Generate plots and charts to visualize sales trends over time.

   4. Identifying Best-Selling Products: Determine which products sold the most and analyze their prices.

   5. Optimal Advertisement Time: Analyze order timestamps to identify the best time for product advertisements.

   6. Frequently Co-Purchased Items: Explore patterns of products frequently purchased together.

Files

merge_sales_data.py: Python script to merge 12 months of sales data into a single CSV file.

clean_and_format_data.py: Python script for data cleaning and formatting tasks.

visualize_sales_trends.py: Python script to visualize sales trends using Matplotlib, Seaborn, and Plotly.

best_selling_products.py: Python script to identify and analyze best-selling products.

optimal_advertisement_time.py: Python script to determine the optimal time for product advertisements.

frequently_purchased_items.py: Python script to discover frequently co-purchased items.

License

This project is licensed under the MIT License - see the LICENSE file for details.