Setup and First ML Model

Before writing any ML code, set up your environment. This chapter shows you how to create a clean workspace and run your first scikit‑learn example.

Step 1: Install Python and Create a Virtual Environment (Recommended)

# Create and activate a virtual environment
python -m venv ml-env
# On Windows:
ml-envScriptsactivate
# On Mac/Linux:
source ml-env/bin/activate

Step 2: Install Required Libraries

pip install numpy pandas scikit-learn matplotlib

Step 3: Run Your First ML Model (Linear Regression)

Create a file `first_model.py`:

import numpy as np
from sklearn.linear_model import LinearRegression

# Sample data: years of experience → salary
X = np.array([[1], [2], [3], [4]])   # features (must be 2D)
y = np.array([30000, 35000, 40000, 45000])

model = LinearRegression()
model.fit(X, y)

pred = model.predict([[5]])
print(f"Predicted salary for 5 years: {pred[0]:.0f}")

Run it:

python first_model.py

You should see a predicted salary (e.g., 50000).

What Just Happened?

X is input (years of experience).
y is target (salary).
LinearRegression().fit(X, y) learns a line that best fits the data.
predict uses that line to estimate salary for 5 years.

This is a tiny ML pipeline – you have just trained your first model!

Two Minute Drill

Use a virtual environment to keep dependencies isolated.
Install numpy, pandas, scikit‑learn, matplotlib.
First model: linear regression on toy data.
Congratulations – you have run your first ML code!

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