https://github.com/aniketdash7/business_data_management

https://github.com/aniketdash7/business_data_management

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/aniketdash7/business_data_management
• Owner: aniketDash7
• Created: 2024-02-22T04:51:54.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-06T07:41:43.000Z (about 1 year ago)
• Last Synced: 2025-01-28T23:09:16.265Z (4 months ago)
• Language: Jupyter Notebook
• Size: 15.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
The report titled "Enhancing Business Efficiency and Customer Experience for Ali's Shawarma Truck near KIIT University" provides a comprehensive analysis of the financial performance, sales trends, temperature variations, and customer base of a food truck called 'Dopamine Dose.' Here's a detailed overview of the report:

# 1. **Executive Summary and Title**:

   - The title clearly identifies the focus of the report, which is to enhance business efficiency and customer experience for Ali's Shawarma Truck.

   - Financial performance analysis highlights the total revenue, profit, and profit margin, demonstrating robust financial health.

   - Performance analysis identifies the top-performing and underperforming shawarma products.

   - Temperature variation analysis explores potential correlations with sales trends, particularly for spicier products.

   - ![Feature Correlation Matrix](/feature_correlation_matrix.png)

# 2. **Detailed Explanation of Analysis Process**:

   - Data Preparation and Cleaning: The data from the midterm submission is split into two datasets and cleaned. Daily expenses are established, including chicken, diesel, staff, and gas cylinder costs.

   - Feature Engineering and Dataset Enhancements: Additional columns are created to calculate total revenue, profit, and profit margin. Correlation matrix and trend analysis are performed.

   - Identifying Maximum Revenue and Units Sold: Maximum revenue-generating product and maximum units sold are identified.

   - Trend Analysis: Profit trend and individual product trends are plotted against dates.

   - Temperature Data Processing: Temperature data is scraped and merged with sales data for correlation analysis.

   - ![Feature Correlation Matrix](/temp_correlation_matrix.png)

# 3. **Results and Findings**:

   - Financial Metrics: Tables present statistical parameters of revenue, profit, profit margin, and sales volume.

   - Performance Analysis: Top and least performing products are identified based on revenue and units sold.

   - Temperature Variation Analysis: Temperature data is analyzed for October, November, and December, showing variations and potential impacts on sales.

   - Sales of Spicier Products Analysis: Sales trends for spicier products are examined, showing intermittent spikes correlated with temperature drops.

   - Correlation Analysis with Temperature: Heatmaps illustrate weak correlations between menu items and temperature, with notable negative correlations for spicier products.

# 4. **Interpretation of Results and Recommendations**:

   - Analyzing Customer Base: Customer base analysis suggests a significant portion comprises KIIT University students.

   - Menu Item Correlation Analysis: No strong correlations between menu items are found, indicating independent customer preferences.

   - Addressing Chicken Quality Concerns: Recommendations are provided to optimize chicken quantity estimations and address concerns about chicken quality.

   - Recommendations: Suggestions include optimizing location for increased visibility, menu optimization for cost efficiency, and investing in online delivery partnerships.

The report concludes that strategic adjustments, informed by data-driven insights, can enhance profitability and operational efficiency for Dopamine Dose. It also provides detailed recommendations for improving business performance and customer experience. Additionally, attachments are provided for further reference, including the analysis Jupyter notebook, Google Sheets, and datasets.

### Check the [full report](Final_submission_BDM_21f1006102.pdf)