Logistic Regression Implementation
├── Introduction
│   └── Overview of Logistic Regression
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing Logistic Regression
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── Model Prediction Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of Logistic Regression

• Logistic Regression is a statistical method used for binary classification. It models the probability of a binary outcome based on one or more predictor variables.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.datasets import load_iris

Loading the Dataset

# Python code to load a dataset for binary classification
iris = load_iris()
X = iris.data[:, :2]  # Using only the first two features
y = (iris.target != 0) * 1  # Modifying the target for binary classification

3. Implementing Logistic Regression

Data Preparation

# Python code to split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Model Training

# Python code to train the Logistic Regression model
model = LogisticRegression()
model.fit(X_train, y_train)

Model Evaluation

# Python code to evaluate the model
y_pred = model.predict(X_test)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)

4. Visualization

Model Prediction Visualization