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

1. Introduction

Overview of Voting Ensemble

• Voting Ensemble is an ensemble machine learning model that combines the predictions from multiple other models. It can be used for both classification and regression tasks.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

Loading the Dataset

# Python code to load a sample dataset
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0, n_clusters_per_class=1, random_state=6)

3. Implementing Voting Ensemble

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=42)

Model Training

# Python code to create and train the Voting Ensemble model
log_clf = LogisticRegression()
dt_clf = DecisionTreeClassifier()
svm_clf = SVC()

voting_clf = VotingClassifier(
    estimators=[('lr', log_clf), ('dt', dt_clf), ('svm', svm_clf)],
    voting='hard')
voting_clf.fit(X_train, y_train)

Model Evaluation

# Python code to evaluate the model
y_pred = voting_clf.predict(X_test)

4. Visualization

Voting Ensemble Classification Visualization