Data Science in making Smart Lemonade Stand

 

🧃 “Design a Smart Lemonade Stand Using Data Science”

🏡 Scenario:

You run a small lemonade stand in your neighborhood. You want to use data science to boost your sales, plan better, and understand your customers.

Your mission? Make smarter decisions using simple data science tools.

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🧩 Step-by-Step Tasks (Concept-by-Concept)

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🔢 Step 1: Collect the Data (Data Collection & Entry)

Manually create a small dataset with:

• Date

• Weather (sunny/cloudy/rainy)

• Temperature

• Number of cups sold

• Price per cup

• Special events (holiday/sports day)

🛠 Tool: Google Sheets or a CSV file

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🧼 Step 2: Clean the Data (Data Cleaning)

• Fix any typos (e.g., “sunyy” → “sunny”)

• Fill in missing values

• Convert text to lowercase

🛠 Tool: Python with pandas
📘 Concept: .fillna(), .lower(), .dropna()

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📊 Step 3: Explore the Data (EDA – Exploratory Data Analysis)

• Plot sales over time

• Group by weather to see average sales

• Create a bar chart for average sales by temperature range

🛠 Tool: matplotlib or seaborn
📘 Concept: groupby(), plot(), mean()

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📈 Step 4: Find Patterns (Basic Statistics)

• When do you sell the most?

• Does sunny weather increase sales?

• Is price affecting your sales?

📘 Concepts: Mean, median, mode, correlation
🛠 Tool: pandas, corr(), describe()

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🤖 Step 5: Predict Sales (Intro to Modeling)

Use a simple linear regression model to predict:

“How many cups will I sell tomorrow based on the weather and temperature?”

🛠 Tool: scikit-learn (LinearRegression)
📘 Concepts: Features, targets, training/testing split

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🎨 Step 6: Tell the Story (Data Communication)

• Create a small report (slides or notebook)

• Use graphs to explain findings to a friend who doesn't know data science

🛠 Tool: Jupyter Notebook or Google Slides
📘 Concept: Data storytelling

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💡 Bonus Challenge:

Use your model to decide:

“Should I make 20, 50, or 100 cups tomorrow?”

Column	Description
date	Day of the lemonade stand
weather	Weather condition (sunny, cloudy, rainy)
temperature	Daily temperature (°C)
price_per_cup	Price of a single lemonade cup
event	Local event (none, holiday, sports day)
cups_sold	Number of cups sold that day