Upload The Logistic_Regression_Algorithm.py

pages/The Logistic_Regression_Algorithm.py

@@ -0,0 +1,84 @@
+import streamlit as st
+import pandas as pd
+
+st.set_page_config(page_title="Logistic Regression Explorer")
+
+st.title("📊 Logistic Regression - Classifier")
+
+st.header("🔍 Logistic Regression - In Depth")
+
+st.markdown("""
+## 📘 What is Logistic Regression?
+Logistic Regression is a **supervised learning** algorithm used for **binary or multi-class classification** tasks. Despite the name, it's a classification algorithm (not regression).
+
+It predicts the **probability** of a class label using the **logistic (sigmoid)** function.
+
+---
+
+## 📈 Mathematical Formulation
+The model predicts:
+
+\[ P(y=1|x) = \frac{1}{1 + e^{-z}} \quad \text{where} \quad z = w^Tx + b \]
+
+- `x`: input features
+- `w`: weights
+- `b`: bias
+- `sigmoid(z)`: squashes values between 0 and 1
+
+---
+
+## 🔑 Key Concepts
+- **Linear Decision Boundary**: Separates classes using a straight line/hyperplane.
+- **Sigmoid Activation**: Converts linear output into probability.
+- **Cross Entropy Loss**: Cost function to optimize.
+- **Maximum Likelihood Estimation (MLE)**: Estimates the best-fit parameters.
+
+---
+
+## 🔧 Hyperparameters Explained
+
+| Parameter | Description |
+|-----------|-------------|
+| `penalty` | Regularization method (`l1`, `l2`, `elasticnet`, `none`). |
+| `C` | Inverse of regularization strength. Smaller values mean stronger regularization. |
+| `solver` | Algorithm to optimize weights (e.g., `liblinear`, `saga`, `lbfgs`). |
+| `max_iter` | Maximum number of iterations for convergence. |
+| `fit_intercept` | Whether to include the bias term. |
+| `class_weight` | Assigns weights to classes (e.g., `balanced` handles imbalanced data). |
+| `multi_class` | Type of classification problem (`ovr`, `multinomial`). |
+
+---
+
+## ✅ Advantages
+- Simple and efficient for binary classification.
+- Outputs probability values.
+- Works well when the relationship is approximately linear.
+
+## ❌ Disadvantages
+- Not suitable for non-linear problems (unless using feature engineering).
+- Assumes no multicollinearity and linear separability.
+
+---
+
+## 🧪 Optuna for Hyperparameter Tuning (Explanation Only)
+- `Optuna` is a powerful library for **automated hyperparameter optimization**.
+- For Logistic Regression, Optuna can help optimize:
+    - `C` (regularization strength)
+    - `solver`
+    - `penalty`
+- Objective: **maximize validation accuracy** or **F1-score**.
+
+It performs trials to find the best parameter combination, using techniques like TPE (Tree Parzen Estimator).
+
+---
+
+## 📌 Use Cases
+- Spam detection
+- Medical diagnosis (disease prediction)
+- Customer churn prediction
+- Credit risk assessment
+
+---
+
+📎 **Tip**: Scale your data before applying Logistic Regression (especially with `l1` or `l2` regularization).
+""")