Business Case Study With ML Recommendation
Part of the Data Science Roadmap
In short
Hiring managers don't just want a model. They want someone who can say 'here's what we should do, and here's roughly what it's worth'. This project is your practice for exactly that.
A project framed entirely around a business decision, not just model accuracy — the problem, the ML approach, the results, and a specific, actionable recommendation for what to do next.
Think of it like this
Advising the kirana aunty
A kirana shop keeps throwing away expired bread. You don't hand the owner a model. You say: order less on Mondays, more before weekends, and you'll waste far less. The model is the evidence. The recommendation is the product.
Watch it happen
The case study, start to finish
1/6Describe the business situation in plain words: a telecom company losing customers each month.
The big ideas
What you need to know
Frame around a decision
The question is 'what should we do?', not 'which model scores best?'.
Compare real options
Always include 'do nothing' and a simple rule alongside the ML-based option.
Quantify with assumptions
Estimate impact in rupees and label every assumed number so a reader can change it.
One-page summary
Lead with the recommendation, expected impact, risks and the next step.
See it in code
Walk through the code
1import pandas as pd2 3scores = pd.read_csv("churn_scores.csv") # customer_id, churn_prob4offer_cost, saved_value, uplift = 100, 2000, 0.25 # assumptions, in rupees5 6for k in [500, 1000, 2000]:7 top = scores.nlargest(k, "churn_prob")8 expected = (top["churn_prob"] * uplift * saved_value).sum() - k * offer_cost9 print(f"Target top {k}: expected net Rs {expected:,.0f}")1/4The model's output: a churn probability for every customer.
Turning model scores into options with a rupee value. This table is what your recommendation rests on.
Save yourself the pain
Common mistakes
Oops
Ending the case study with 'the model achieved 0.84 AUC'.
Do this instead
End with a specific action, its expected impact and how to test it.
Oops
Presenting impact numbers without saying where they came from.
Do this instead
List each assumption. Show how the answer changes if a key one is off by half.
Your mission
First milestone: options table
Quick check
What belongs on the final slide of a business case study?
Go deeper
- 1Pick a business scenario and frame the ML problem explicitly around the decision it informs.
- 2Include a cost-benefit view: what does a false positive or false negative actually cost?
- 3End with a specific recommendation, not just a model performance summary.
More projects at this stop
End-to-End ML Project (Problem to Deployment)
Kaggle Competition Submission
NLP Project (Sentiment / Text Classification)
Time Series Forecasting Project
Computer Vision Project
Portfolio Website / GitHub Showcase
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