Which Predictive Model Should You Use?
A practical guide for business managers. No complex stats, just business problems and the right tools to solve them.
The Most Important Rule
The best model isn't the most accurate one. It's the one that gets **used** to make a decision and create value. An 80% accurate model that your team trusts and understands is better than an 85% accurate model that no one can explain.
The 30-Second Model Picker
Start with: Regression Models
These models are perfect for predicting a number.
- Simple & Fast: Linear Regression
- More Complex: Decision Tree Regression
Real-World Example:
A real estate company uses regression to predict a house price based on its size, location, and number of bedrooms.
Start with: Classification Models
Use these to predict a "yes/no" or category answer.
- Easy to Explain: Logistic Regression, Decision Trees
- More Accurate (but less clear): Random Forest
Real-World Example:
Spotify predicts if a user is likely to cancel their subscription (churn) based on their listening habits.
Start with: Clustering
Use this to find hidden groups or segments in your customers or products.
- Most Common: K-Means Clustering
Real-World Example:
A retail company uses clustering to identify different types of shoppers (e.g., "Bargain Hunters," "Loyal Regulars") for targeted marketing campaigns.
Start with: Market Basket Analysis
Use this to discover which items are frequently purchased together.
Real-World Example:
Amazon's "Frequently Bought Together" feature is a classic example of market basket analysis.
Start with: Time Series Forecasting
Use this when you need to predict future values based on past data over time.
- Standard Models: ARIMA, Prophet
Real-World Example:
Walmart forecasts the demand for ice cream during the summer to ensure its stores are always in stock.
Common Managerial Pitfalls (and How to Avoid Them)
⚠️ Using a Sledgehammer to Crack a Nut
Don't use a highly complex model (like "Deep Learning") on a small dataset (e.g., 1,000 customers). A simple model will be faster, cheaper, and easier to explain.
⚠️ Chasing the Wrong Goal
A fraud model that is "99% accurate" might catch no fraud if fraud is rare. Focus on the business goal (e.g., "how much fraud did we catch?") not just the accuracy score.
⚠️ The "Black Box" Problem
In regulated industries like banking or healthcare, you must be able to explain why a model made a certain decision (e.g., why a loan was denied). Don't use a model you can't interpret.
⚠️ Forgetting Reality
A model that takes 10 hours to make a prediction is useless for a real-time website recommendation. The best model is one that is fast enough and simple enough to actually be deployed.