Food Order

purpose of the Sample Project:d profit.

Would an order with a discount be more profitable or actually red

The following notebook will elaborate on the ordered food information, taking into account some key figures about the profit and cost considerations and determining the profit margin. It includes loading, cleaning, and visualizing the data.

Principal Components and Justification:

Libraries Used:

pandas: Manipulation of data.

numpy: For math calculations.

seaborn and matplotlib: For visualization of data.

Dataset Management:

Reading the Data:

df = pd.read_csv('Food_Orders.csv')

Basic information is printed through df.info() and df.head() looking at the dataset.

Handling Missing Values:

Provide fill-in entries under "Discounts and Offers" to read "No Discount".

Feature Engineering:

Finding Profit:

python

df['Profit'] = df['Order Value'] - df['Commission Fee'] - df['Payment Processing Fee'] - df['Refunds/Chargebacks']

Cost in Total:

Python

df['Total_Cost'] = df['Commission Fee'] + df['Payment Processing Fee'] + df['Refunds/Chargebacks']

Computing Profit Margin:

python

Copy code

df['Profit_Margin'] = df['Profit'] / df['Order Value']

Data Visualization:

A scatter plot can be used to illustrate the relationship that exists between Order Value and Profit:

python

sns.scatterplot(x='Order Value', y='Profit', data=df)

plt.title('Order Value vs Profit')

Objective:

The notebook also searched for patterns in order value, discounting, and profit margins to improve the process of food ordering or maximize profitability.

Let me know if you want me to dive deeper into a section or if you'd like further analysis on the dataset!

You said,

You want to have a closer look at any part or do more detailed analysis on the dataset.

ChatGPT said:

ChatGPT

Sure, let me list here a few important areas we could investigate further to help you look closer into this dataset. Please tell me which of these interests you, or want me to look at all of them.

Possible Sections for Further Research:

Optimizing Profit:

Which kinds of order types bring in the most money?

Do certain categories, discounts, or delivery fees affect profits?

Discount Effectiveness:

Look how discounts affect the total order value anuce the profit margins?

Customer Behaviour Analysis:

Examine the high-value order patterns by time of day or day of the week.

Are there any associations between order size, delivery fees, and refunds/chargebacks? Refund/Chargeback Trends: Analyze whether there is the existence of refund-prone product types or customer profiles. Find out how much money is lost due to refunds or chargebacks. Seeing Things Properly Use the distribution of order value, profit margins, or delivery fees, assisted by histograms or bar charts. Time-series analysis if the dataset contains timestamps, such as finding sales trends over time. Outlier detection: Identify outlier orders that may mislead profit or loss estimations, such as very high or very low profits.