Scikit-Learn (sklearn) Example

Scikit-learn (sklearn) is a popular Python library for machine learning. It provides simple and efficient tools for data mining and analysis. Whether you're a beginner or an experienced data scientist, Scikit-learn makes it easy to implement machine learning algorithms.

In this blog post, we'll explore the basics of Scikit-learn using an example that classifies flowers using the famous Iris dataset and a Decision Tree model.

What is Scikit-Learn?

Scikit-learn is built on top of popular scientific computing libraries like NumPy and SciPy. It provides tools for:

• Classification

• Regression

• Clustering

• Dimensionality reduction

• Model selection

• Preprocessing

Key Features of Scikit-Learn:

• Simple and efficient API

• Well-documented and easy to use

• Supports various machine learning models

• Works well with other Python libraries

Now, let’s dive into an example using a Decision Tree classifier to predict flower species based on their features.

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Find below the sample code. You can run it on Google Colab.

 from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score

# Load dataset (Iris dataset)

iris = datasets.load_iris()

X = iris.data # Features (sepal & petal length/width)

y = iris.target # Labels (Flower types)

# Split into training and test sets (80% train, 20% test)

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

# Choose a model (Decision Tree)

model = DecisionTreeClassifier()

# Train the model

model.fit(X_train, y_train)

# Make predictions

y_pred = model.predict(X_test)

# Check accuracy

accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy:.2f}")

# Predict a new sample

new_sample = [[2.3, 2.8, 0.1, 1.5]] # Example flower

prediction = model.predict(new_sample)

print(f"Predicted class: {iris.target_names[prediction[0]]}")

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Understanding the Code Step-by-Step

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

1. Importing Required Libraries

• datasets: Provides access to various built-in datasets, including the Iris dataset.

• train_test_split: Splits data into training and testing sets.

• DecisionTreeClassifier: A machine learning model used for classification tasks.

• accuracy_score: Measures the accuracy of our model.

# Load dataset (Iris dataset)
iris = datasets.load_iris()
X = iris.data  # Features (sepal & petal length/width)
y = iris.target  # Labels (Flower types)

2. Loading the Dataset

• The Iris dataset is a well-known dataset in machine learning.

• It contains 150 samples of iris flowers categorized into three species: Setosa, Versicolor, and Virginica.

• X contains the features (sepal and petal dimensions).

• y contains the target labels (flower species).

# Split into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. Splitting the Data

• The dataset is split into training (80%) and testing (20%) sets.

• The random_state=42 ensures that the split is consistent each time the code runs.

# Choose a model (Decision Tree)
model = DecisionTreeClassifier()

4. Selecting a Model

• We choose the DecisionTreeClassifier, a simple yet effective model for classification tasks.

# Train the model
model.fit(X_train, y_train)

5. Training the Model

• The .fit() function trains the model using the training data.

# Make predictions
y_pred = model.predict(X_test)

6. Making Predictions

• The trained model makes predictions on the test set.

# Check accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")

7. Evaluating the Model

• accuracy_score() compares predicted values with actual values to measure accuracy.

• The accuracy score is printed as a percentage.

# Predict a new sample
new_sample = [[2.3, 2.8, 0.1, 1.5]]  # Example flower
prediction = model.predict(new_sample)
print(f"Predicted class: {iris.target_names[prediction[0]]}")

8. Predicting a New Sample

• A new flower sample is provided as input.

• The model predicts the class of the flower.

• The target_names array is used to display the predicted species name.

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Scikit-learn simplifies machine learning implementation with its user-friendly API. In this example, we used a Decision Tree classifier to predict flower species based on their features. This is just the beginning—Scikit-learn supports many other powerful models and techniques for machine learning tasks.

If you're new to machine learning, experimenting with different models and datasets in Scikit-learn is a great way to build your skills. Try modifying the dataset, adjusting model parameters, and testing different classifiers to see how they perform!

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