How to Build an ML Model

In the last post, we learned what Machine Learning is and what a model does. Now let's see how to actually build one.

Building an ML model has five main steps. Let's walk through each one.

1. Collect Data

Everything starts with data. Without data, there's nothing to learn.

What Kind of Data?

It depends on your goal:

Want to predict house prices? You need past sales data with prices.
Want to detect spam emails? You need emails labeled as spam or not spam.
Want to recognize faces? You need lots of face images.

Where Does Data Come From?

Data can come from many places:

Your company's database
Public datasets online
APIs from other services
Sensors and devices
User inputs

The better your data, the better your model. Garbage in, garbage out.

2. Prepare the Data

Raw data is messy. You need to clean it first.

Common Problems

Missing Values
Some rows have empty fields. You can fill them with averages or remove those rows.

Wrong Formats
Dates might be in different formats. Numbers might be stored as text.

Outliers
Some values are way off. A house price of $1 is probably a mistake.

Duplicates
The same record appears twice. Remove the extras.

Feature Engineering

This is where you create new useful columns from existing data.

Example: If you have a date of birth, you can calculate age. Age is often more useful than the raw date.

3. Choose a Model Type

Different problems need different types of models.

For Predicting Numbers

Use Regression models:

Linear Regression (simple, fast)
Decision Trees
Random Forest
Neural Networks

Example: Predicting house prices, stock prices, or temperatures.

For Classifying Things

Use Classification models:

Logistic Regression
Decision Trees
Random Forest
Neural Networks

Example: Is this email spam? Will this customer leave? Is this image a cat?

For Grouping Similar Items

Use Clustering models:

K-Means
DBSCAN

Example: Group customers by buying habits. Find similar movies.

4. Train the Model

Training is where the learning happens.

What Happens During Training?

You give the model your prepared data
The model looks at patterns
It adjusts itself to match those patterns
It repeats until it gets good at predicting

Training vs Testing Data

You split your data into two parts:

Training set (80%) - Used to teach the model
Testing set (20%) - Used to check if it learned well

Why split? If you test on the same data you trained on, you won't know if it can handle new data.

The Training Loop

For each round:
    1. Model makes predictions
    2. Compare predictions to actual answers
    3. Calculate how wrong it was (loss)
    4. Adjust the model to reduce errors
    5. Repeat

After many rounds, the model gets better and better.

5. Evaluate the Model

How do you know if your model is good?

For Regression (Predicting Numbers)

Mean Absolute Error (MAE) - Average difference between predicted and actual
Root Mean Square Error (RMSE) - Penalizes big mistakes more

Example: If predicting house prices, an MAE of $10,000 means predictions are off by $10K on average.

For Classification (Categories)

Accuracy - What percentage did it get right?
Precision - Of things it labeled positive, how many were actually positive?
Recall - Of all actual positives, how many did it find?

Example: A spam filter with 99% accuracy sounds great. But if only 1% of emails are spam, a model that says "nothing is spam" would also be 99% accurate. That's why we check precision and recall too.

The Complete Picture

Here's the full journey:

Raw Data
    ↓
Clean & Prepare
    ↓
Split (Train/Test)
    ↓
Choose Model Type
    ↓
Train on Training Data
    ↓
Evaluate on Test Data
    ↓
If good → Deploy
If not → Adjust and retry

A Simple Example

Let's say you want to predict if a student will pass or fail.

1. Collect Data
Get records of past students: hours studied, attendance, and whether they passed.

2. Prepare Data
Remove students with missing info. Convert "passed" to 1 and "failed" to 0.

3. Choose Model
This is classification (pass/fail), so pick Logistic Regression.

4. Train
Feed the model 80% of your data. Let it learn the patterns.

5. Evaluate
Test on the remaining 20%. See if it predicts correctly.

That's it. You've built a model.

What's Next?

Building a model is just the start. In real life, you face more questions:

How do you serve this model to users?
How do you update it when new data comes in?
How do you monitor if it's still working well?
How do you version your models?

This is where MLOps comes in. It's the set of practices that handle all these challenges.

Key Takeaways

Collect Data - Get relevant examples
Prepare Data - Clean and transform it
Choose Model - Pick the right type for your problem
Train - Let the model learn patterns
Evaluate - Check if it works on new data

Next: MLOps Introduction - What It Is and Why You Need It