Machine Learning with Scikit-Learn

1. Introduction

Scikit-Learn is a powerful Python library for machine learning. It offers simple and efficient tools for data mining and data analysis.

2. Installation

pip install scikit-learn

3. Key Concepts

• Supervised Learning: Learning from labeled data.
• Unsupervised Learning: Learning from unlabeled data.
• Model Evaluation: Assessing the performance of a model.
• Feature Engineering: Selecting and transforming variables.

4. Step-by-Step Guide

4.1 Load Data

import pandas as pd

data = pd.read_csv('data.csv')

4.2 Preprocess Data

from sklearn.model_selection import train_test_split

X = data.drop('target', axis=1)
y = data['target']

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

4.3 Choose a Model

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()

4.4 Train the Model

model.fit(X_train, y_train)

4.5 Evaluate the Model

from sklearn.metrics import accuracy_score

predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print('Accuracy:', accuracy)

5. Best Practices

• Always split your data into training and testing sets.
• Use cross-validation to validate the model.
• Standardize your data if necessary.
• Regularly update the model with new data.

6. FAQ