Notebook – Metric Dashboard

Notebook – Metric Dashboard#

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Where Your Metrics Learn to Dance 💃 (and Impress the CFO)

“Spreadsheets make people yawn. Dashboards make them clap.” 👏

Welcome to the Metric Dashboard Lab — your mission: Combine your model performance metrics, visuals, and business storytelling into one interactive dashboard that would make even your manager say,

“Wait, you built this in Python?” 🤯

🎬 Business Hook: The Boardroom Moment#

You’ve trained a sales forecast model. You have metrics. You have visuals. But in your meeting, someone asks:

“Can you show how this changes by product line and region?”

You gulp. Then smile. Because you built this lab. 😎

🧩 Your Objectives#

By the end of this lab, you’ll be able to: ✅ Build a dashboard-like notebook to compare models ✅ Display interactive visuals using Plotly ✅ Present key metrics with a business narrative ✅ Let users filter and explore model results

⚙️ Step 1. Load the Sample Data#

🧠 Pro Tip: Replace this with your own metrics! Try loading data from your previous chapters — regression, classification, etc.

🧮 Step 2. Compute Derived KPIs#

You can turn your metrics into business-friendly indicators.

💬 “Because no one gets excited about RMSE = 190, but they do get excited about ‘error reduced by 35%.’” 📉

📈 Step 3. Visualize the Metrics (Plotly Style)#

🎨 Plotly Tip: The fewer colors, the more “executive-friendly” your chart looks.

💼 Step 4. Create KPI Cards#

This is the “wow” part of your dashboard — mini metric boxes that show results at a glance.

💬 “Congratulations — you just built mini KPI cards like a pro dashboard designer.”

🧠 Step 5. Add Interactivity (Optional but Awesome)#

Use widgets to filter your dashboard — because executives love clicking buttons.

🧩 “Interactivity turns a static notebook into a Netflix for data nerds.”

🧪 Step 6. Create the Business Summary#

Use Markdown, visuals, and plain language to summarize:

“The Random Forest model achieved the best tradeoff between accuracy and interpretability, improving forecast precision by 25% over the baseline. This translates into an estimated $420K reduction in overstock costs.” 💰

💬 “A good dashboard answers questions. A great one creates budget justification.” 😏

🧩 Step 7. Export or Share#

✅ Download Notebook (via toolbar) ✅ Open in Colab for team collaboration ✅ Deploy with Streamlit for interactive business apps

Remember: A dashboard is not the end — it’s the conversation starter.

🧠 Practice Tasks#

🎯 Level 1 – Warm-Up:

  • Load your own dataset from a previous exercise (e.g. regression or classification).

  • Compute RMSE, MAE, R², and display them in a Plotly bar chart.

🎯 Level 2 – Business Twist:

  • Add calculated “error reduction %” or “accuracy gain %”.

  • Annotate results with business insights.

🎯 Level 3 – Dashboard Hero:

  • Add filters for business units or time periods.

  • Include a “summary text” explaining the best model choice.

  • Style it with emojis, colors, or icons (CFOs love polish).

🧭 Recap#

Step

Skill

Output

1

Load performance data

Pandas DataFrame

2

Compute KPIs

Derived metrics

3

Visualize

Plotly charts

4

Summarize

KPI cards

5

Interact

Filters & widgets

6

Communicate

Business insights

7

Share

Dashboard ready

💬 Final Thought#

“Your dashboard isn’t just about showing numbers — it’s about telling the story of your model’s impact on business.” 📈

🔜 Next Up#

👉 In the next chapter — Core ML Models — we’ll go from measuring models to building them. Get ready to meet Regression, Classification, and their weird cousin, Clustering — and find out why every ML algorithm has trust issues with your data. 😜

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