Introduction to Machine Learning with Python

1. What is Machine Learning?

Machine learning is a subset of AI that enables systems to learn from data. This post covers supervised learning, where models predict outcomes based on labeled data, using Python and scikit-learn.

2. Setting Up Python for ML

Install necessary libraries:

pip install scikit-learn numpy pandas matplotlib

3. Loading and Exploring Data

Use the Iris dataset:

from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
print(df.head())

4. Preprocessing Data

Split into training and testing sets:

from sklearn.model_selection import train_test_split

X = df
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

5. Building and Training a Model

Use a K-Nearest Neighbors classifier:

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

6. Visualizing Results

Plot a confusion matrix:

from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns

cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True)
plt.show()

7. Conclusion

Machine learning with Python is accessible and powerful. Experiment with different algorithms and datasets to deepen your understanding.