🧠 Classification Algorithms - Machine Learning Project

This project demonstrates the implementation of various classification algorithms using Python and essential machine learning libraries such as scikit-learn, pandas, matplotlib, and more.

---

📂 Project Structure

• Notebook Name: Classification Algorithms.ipynb
• Purpose: To explore, compare, and evaluate popular classification techniques using real-world datasets.

---

✅ Algorithms Covered

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest
• Naive Bayes
• Gradient Boosting (if included)

---

📊 Evaluation Metrics

• Accuracy Score
• Confusion Matrix
• Precision, Recall, F1 Score
• ROC Curve / AUC (if implemented)

---

📚 Libraries Used

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix