🔍 Logistic Regression using Supervised Machine Learning

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📌 Overview

This project demonstrates the implementation of Logistic Regression, a widely used Supervised Machine Learning algorithm for classification tasks.

The notebook covers the complete machine learning workflow including data preprocessing, model training, prediction, evaluation, and visualization using Python and Scikit-learn.

The primary goal of this project is to understand how classification models work and how machine learning can be applied to solve real-world prediction problems.

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🤖 About Logistic Regression

Logistic Regression is a supervised learning algorithm used to classify data into categories.
Unlike Linear Regression, it predicts probabilities instead of continuous values.

Logistic Function

[ P(Y=1)=\frac{1}{1+e^{-z}} ]

Where:

• P(Y=1) → Probability of belonging to a class
• e → Euler’s constant
• z → Linear combination of input features

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⚙️ Technologies Used

Technology	Purpose
Python	Programming Language
Pandas	Data Manipulation
NumPy	Numerical Computing
Matplotlib	Data Visualization
Seaborn	Statistical Visualization
Scikit-learn	Machine Learning

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📂 Project Workflow

• Import required libraries
• Load and explore the dataset
• Perform data preprocessing
• Split dataset into training and testing sets
• Train Logistic Regression model
• Make predictions
• Evaluate model performance
• Visualize results

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💻 Implementation

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

model = LogisticRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)