Sales Analysis of Electronic Items

This project involves analyzing sales data of electronic items to gain insights that can help improve business strategies. The data was cleaned using Pandas, and visualizations were created using Matplotlib to answer key business questions.

Project Overview

This project addresses five high-level business questions:

1. What was the best month for sales? How much was earned that month?
2. What city sold the most products?
3. What time should we display advertisements to maximize the likelihood of customer purchases?
4. What products are most often sold together?
5. What product sold the most? Why do you think it sold the most?

Tools and Libraries

• Python: For scripting and data analysis.
• Pandas: For data cleaning and manipulation.
• Matplotlib: For data visualization.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sales-analysis.git

2. Install the required dependencies:

   pip install -r requirements.txt

Data Cleaning and Analysis

The dataset was cleaned by handling missing values, correcting data types, and removing duplicates. The cleaned data was then used to perform analysis that answered the above business questions.

Key Findings

1. Best Month for Sales: December was the best month for sales, driven by holiday shopping.
2. Top Selling City: San Francisco had the highest sales, highlighting its strong market presence.
3. Optimal Advertisement Timing: The best times to display advertisements are around 11 AM and 7 PM, aligning with peak customer activity times.
4. Frequently Sold Together Products: Commonly bundled items included ('iPhone', 'Lightning Charging Cable') and ('Google Phone', 'USB-C Charging Cable'), presenting opportunities for marketing bundles.
5. Top Selling Product: AAA Batteries (4-pack), AA Batteries (4-pack), and USB-C Charging Cable were the top-selling products, likely due to high demand and frequent replacement needs.

Visualizations

The project includes various visualizations, such as bar charts and line graphs, to illustrate the findings clearly and effectively.

How to Use the Code

• Before running the Jupyter Notebook Sales_Analysis.ipynb, ensure that you remove final_data.csv to avoid using outdated data.
• Run the notebook to see the data cleaning process, analysis, and visualizations.
• Modify the code or input data to perform your custom analysis.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request.

License

This project is licensed under the MIT License.

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