Sales Analysis for AAL

Introduction

AAL, a renowned clothing business established in 2000, dominates the Australian market, catering to diverse groups: kids, women, men, and seniors. With branches spread across metropolises and tier-1 & tier-2 cities, the company is in a growth phase. This project aims to dissect AAL's sales data for the fourth quarter, offering insights for data-driven decisions in the coming year.

Project Objective

• Identify states yielding the highest revenues.
• Devise sales strategies for states with dwindling revenues.

Steps Involved:

1. Data Wrangling:

• Inspect Data: Understand the data's structure.
• Handle Missing Data: Identify and address missing values.
• Data Normalization: Scale the data for uniformity.
• Group Data: Aggregate data for pattern recognition.

2. Data Analysis:

• Descriptive Statistics: Gain insights using measures like mean, median, mode, and standard deviation.
• Group & State-wise Sales Analysis: Recognize sales patterns across categories and geographical locations.
• Time-based Analysis: Extract sales metrics on weekly, monthly, and quarterly bases.

3. Data Visualization:

• State-wise Sales Analysis: Visualize sales trends across states for different groups.
• Group-wise Sales Analysis: Understand sales distribution for various groups across states.
• Time-of-the-day Analysis: Recognize sales peaks and troughs during the day.

4. Report Generation:

• JupyterLab Notebook: Consolidate findings in an interactive report, combining code, visualizations, and narratives.
• Incorporate Markdown: Use Markdown for comprehensive explanations and interpretations.
• Visualizations: Implement box plots for statistical insights and distribution plots for data distributions.
• Conclude & Recommend: Offer summarized findings and actionable strategies.

Tools & Libraries Used:

• Python: The primary language for data manipulation and analysis.
• Pandas: For data handling and manipulation.
• Seaborn: For statistical data visualization.
• JupyterLab: For combining code execution with data visualization and markdown explanations.

How to Run the Project:

1. Clone the repository: git clone [repository-link]
2. Navigate to the project directory.
3. Ensure you have the required libraries installed: pip install -r requirements.txt
4. Launch JupyterLab: jupyter lab
5. Open the notebook and execute the cells sequentially.

Contributions:

Feel free to fork this repository, make your changes, and submit pull requests. For major changes, please open an issue to discuss the proposed change.

Acknowledgements:

• AAL for providing the data.
• OpenAI and the community for continuous support.