Balaji-Fast-Food-Sales-Analysis

This dataset was gotten off kaggle.com though not much information is given on the site the name of the dataset is "Balaji Fast Food Sales" which means the dataset is for a proposed fictional or non fictional fast food restaurant 'Balaji'.

Data Description

Columns: This dataset includes columns such as order_id, date, item_name, item_type, item_price, quantity, transaction_amount, transaction_type, received_by, and time_of_sale.

Data Size: This file contains 1000 rows and 10 columns.

Data Structure: The dataset is organized as a single CSV file, providing information on transactions at a local restaurant.

Data Cleaning and Preprocessing: The data has been carefully reviewed and cleaned to address duplicates and missing values, ensuring data quality for analysis.

Date of Last Update: The dataset was last updated on March 31, 2023.

Special Notes: Please note that the 'transaction_amount' column represents the total transaction amount, derived from the multiplication of 'item_price' and 'quantity' for each item in the order.

Data Source: This data was collected from a local restaurant situated near my home and is made available for analysis and educational purposes.

Aim

This dataset was gotten of kaggle.com though not much informattion is given on the site the name of the dataset is "Balaji Fast Food Sales" which means the dataset is for a proposed fictional or non fictional fast food restaurant 'Balaji'. From here we will go on to carry out analyze in order to;

• Understand customer preferences for different items.
• Evaluate the impact of payment methods on revenue.
• Investigate the performance of staff members based on gender.
• Explore the popularity of items at different times of the day.

Columns in the dataset ;

1. order_id: a unique identifier for each order.
2. date: date of the transaction.
3. item_name: name of the food.
4. item_type: category of item (Fastfood or Beverages).
5. item_price: price of the item for 1 quantity.
6. Quantity: how much quantity the customer orders.
7. transaction_amount: the total amount paid by customers.
8. transaction_type: payment method (cash, online, others).
9. received_by: gender of the person handling the transaction.
10. time_of_sale: different times of the day (Morning, Evening, Afternoon, Night, Midnight).