Coffee Sales Dashboard with Power BI: Daily Trends, Top Flavors, and Peak Hours Analysis

Data Analytics On Tips Dataset 

In this project, I analyzed the popular “Tips” dataset from the Seaborn library. This dataset contains information about restaurant bills, tips, and customer details.

The goal was to:

1. Explore and visualize tipping patterns.

2. Discover factors that influence tip amounts.

3. Build a simple predictive model to estimate tips based on bill size and other factors

1. Dataset & Tools Dataset: 

    tips (Seaborn built-in dataset) 

    Tools Used: Python, Pandas, Seaborn, Matplotlib, Scikit-learn 

    Skills Demonstrated: Data Cleaning, EDA, Data Visualization, Linear Regression

2. Load and Explore the Dataset

tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

3.Apply Lamda Function

tips['total_bill'].apply(lambda bill: bill * 0.1)

0      1.699
1      1.034
2      2.101
3      2.368
4      2.459
       ...  
239    2.903
240    2.718
241    2.267
242    1.782
243    1.878
Name: total_bill, Length: 244, dtype: float64

Applying Discounts

def discount(tot_bill):

    discount = 0

    if tot_bill > 10:

        discount = 0.1

    else:

        discount = 0.05

    return discount    

tips['total_bill'].apply(discount)

0      0.1
1      0.1
2      0.1
3      0.1
4      0.1
      ... 
239    0.1
240    0.1
241    0.1
242    0.1
243    0.1
Name: total_bill, Length: 244, dtype: float64

Processing a Data Frame through a Loop

for i,r in tips.iterrows():

    total_paid,discount = 0,0

   

    total_paid = r.total_bill + r.tip

   

    if total_paid > 20 and r.day =='Sun' and r.smoker =='No':

        discount = total_paid * 0.20

    else:

        discount = total_paid * 0.005

       

    print(total_paid,discount)

       

for i,r in tips.iterrows():

    total_paid = 0

   

    total_paid = r.total_bill + r.tip

       

    if r.sex == 'Female' and total_paid > 25:

        print(i,r.day,r.time,total_paid)

Changing Data types to Data Frame

tips3_df['discount'] =  tips3_df['discount'].astype('int')

total_bill    float64
tip           float64
sex            object
smoker         object
day            object
time           object
size            int64
discount        int32
dtype: object