Lab – Sales Forecasting

Lab – Sales Forecasting#

⏳ Loading Pyodide…

Where data meets drama, and revenue meets regression! 😎

🎬 The Business Scenario#

Congratulations — you’ve just been promoted to Data Science Intern of the Year at Acme Retail Co. 🎉

Your boss (who once said “AI is just fancy Excel”) wants you to predict monthly sales using marketing spend, pricing, and seasonal factors.

Your mission:

Build a simple but effective regression model to forecast sales, explain it clearly, and make it look fancy on a dashboard so everyone thinks it’s magic. ✨

🧾 Step 1. Load the Data#

You’ve been handed a “beautifully messy” Excel file by the finance team (of course 🙃).

📊 Expected columns:

  • Month

  • TV_Spend

  • Social_Media_Spend

  • Discount_Percent

  • Season

  • Sales

🧹 Step 2. Clean the Data#

Finance swore the data was “clean.” You’ll find out soon enough.

🧽 Handle missing values or weird outliers:

✅ Tip: Don’t delete too much — remember, data is like gossip; even the noisy parts tell a story. 😏

🔍 Step 3. Feature Engineering#

Convert categorical Season into numerical features:

Normalize ad spend (optional but helps training):

⚙️ Step 4. Split Data#

Time to create our train-test split — because life is all about testing your assumptions.

🧠 Step 5. Train the Model#

Let’s start simple — just a Linear Regression:

Check the coefficients (aka “how much each feature drives sales”):

📈 Translation: For every $1 increase in TV spend, sales increase by X units (if your CFO is reading this, round the number dramatically). 😅

📉 Step 6. Evaluate Performance#

✅ Interpretation:

  • MAE = average error in prediction

  • RMSE = punishes big mistakes more

  • R² = model’s bragging rights (1.0 = perfection, 0.0 = chaos)

📊 Step 7. Visualize Predictions#

Let’s make the boss happy with a plot that looks “AI-powered.” 🤖

🎨 If points hug the orange line → your model is doing great!

💬 Step 8. Business Insights#

Feature

Effect

Business Advice

TV_Spend

+ve

Ads still work! Old-school isn’t dead 📺

Social_Media_Spend

+ve

Keep feeding the algorithm 💰

Discount_Percent

-ve (sometimes)

Too many discounts hurt profit margin 💸

Season_Summer

+ve

Ice creams sell better in heat 😎

💡 “A regression model is just a data-driven way to say ‘I told you so’ in a meeting.”

📈 Step 9. Bonus: Predict Future Sales#

You can predict next month’s sales like a forecasting wizard:

Now go make that dashboard and act mysterious when someone asks,

“So… how does it work?” 😏

💡 Optional Extension#

Try the same task using:

  • Ridge or Lasso Regression

  • PolynomialFeatures

  • or even a Random Forest (coming soon in later chapters 🌲)

Then compare R² — because competition keeps models humble.

🧩 Practice Exercises#

Challenge

Hint

1️⃣ Train a Ridge Regression model and compare its R²

from sklearn.linear_model import Ridge

2️⃣ Add a new feature Price_Per_Unit = Sales / Discount_Percent

Think creatively!

3️⃣ Create a seaborn plot of coefficients

Use sns.barplot()

4️⃣ Try predicting sales when Discount_Percent = 0

Does the model behave logically?

5️⃣ Deploy your model to Colab and make it interactive

Add widgets or sliders!

🧠 Recap#

  • You loaded, cleaned, and prepped real data

  • Built a regression model

  • Evaluated business KPIs with metrics

  • Made it explainable and visually appealing

You’re now a certified Sales Forecast Whisperer 📊🧙‍♂️

🐍 Python Help#

If you’re unsure about any pandas or sklearn syntax, 🪄 explore Programming for Business — it’s your friendly guide to all the Python basics behind the magic!

🚀 Next Up#

➡️ Chapter 6: Classification Models Because predicting “how much” is cool — but predicting “who buys” is where real marketing power begins. 🎯

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