🤖 Machine Learning using Python

This repository contains a Jupyter Notebook (machine learning.ipynb) that demonstrates the application of Supervised and Unsupervised Machine Learning algorithms using Python.

The goal is to explore how different ML models can be applied to real-world datasets for classification, regression, and clustering tasks.

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📂 Repository Structure

• machine learning.ipynb → End-to-end notebook covering data preprocessing, feature engineering, model training, and evaluation.

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🛠️ Skills & Concepts Demonstrated

• Data Preprocessing

  • Handling missing values & outliers
  • Feature scaling (StandardScaler)
  • Balancing data using techniques like RandomOverSampler

• Supervised Learning Models

  • Logistic Regression
  • Support Vector Machine (SVC)
  • K-Nearest Neighbors (KNN)
  • Gaussian Naive Bayes
  • Random Forest Classifier

• Unsupervised Learning Models

  • K-Means Clustering
  • Principal Component Analysis (PCA) for dimensionality reduction
  • Neural Networks (basic implementation)

• Model Evaluation

  • Accuracy, Precision, Recall, F1 Score
  • Confusion Matrix & ROC Curve
  • Cross-validation for robust results

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⚡ Example Workflow

# Import libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

# Load dataset
df = pd.read_csv("dataset.csv")

# Preprocess
X = df.drop("Target", axis=1)
y = df["Target"]

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

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))