Machine Learning Model with Python

Build Your First Machine Learning Model with Python

Difficulty: Beginner – Intermediate

This tutorial shows you how to build your first supervised machine learning model using scikit-learn, one of the most popular Python libraries for ML. No advanced math required!

🔧 What You'll Learn

• What supervised learning is
• How to load and explore datasets
• How to train a model in Python
• How to test and evaluate it

🧰 Prerequisites

Ensure you have the following installed:

• Python 3.x
• scikit-learn
• pandas
• matplotlib (optional for visualization)

You can install them via pip:

pip install scikit-learn pandas matplotlib

📦 Step 1: Import Libraries

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

🌸 Step 2: Load the Dataset

We’ll use the built-in Iris dataset:

iris = load_iris()
X = iris.data  # Features
y = iris.target  # Labels

🔀 Step 3: Split the Data

Split into 80% training and 20% testing:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

🤖 Step 4: Train the Model

We’ll use the K-Nearest Neighbors algorithm:

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

📊 Step 5: Make Predictions

y_pred = model.predict(X_test)

📈 Step 6: Evaluate the Model

Check the accuracy:

accuracy = accuracy_score(y_test, y_pred)
print("Model Accuracy:", accuracy)

🎉 Done!

You’ve just built and evaluated your first ML model. You can now try other algorithms like Decision Trees, SVM, or even deep learning with TensorFlow.

📘 Challenge for Students

• Try changing n_neighbors to 5 or 7
• Visualize the dataset with matplotlib
• Try a different dataset (e.g., load_digits())

Subscribe to DarchumsTech for more tutorials and AI content tailored to students and young developers.