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Learn AI with Python · Lesson

Logistic Regression Implementation

Building classification models in Python.

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Implementing Logistic Regression in Python

In this lesson, we will implement logistic regression using the scikit-learn library. We’ll work with a binary classification dataset.

Logistic Regression Implementation — illustration 1

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Step 1: Importing Libraries and Dataset

We’ll start by importing the necessary libraries and loading a dataset. For simplicity, we will use scikit-learn's make_classification to generate a binary classification dataset.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Generate dataset
X, y = make_classification(n_samples=100, n_features=2, n_classes=2, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

All lessons in this course

  1. The Concept of Linear Regression
  2. Implementing Linear Regression in Python
  3. The Concept of Logistic Regression
  4. Logistic Regression Implementation
  5. Evaluating Model Performance
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