-Logistic-Regression-Classification-Machine-Learning-Deployment-with-streamlit

I developed an end-to-end Machine Learning web app that visualizes actual data vs future predictions using Logistic Regression, all wrapped in an interactive Streamlit dashboard.

📊 Logistic Regression Classification Dashboard

An interactive Streamlit-based Machine Learning dashboard that demonstrates Logistic Regression classification, model evaluation, and visualization of actual vs future data.

This project is designed to showcase end-to-end ML workflow — from data loading and preprocessing to model training, evaluation, and visualization — all inside a clean web interface.

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🚀 Features

• 📁 Load and preview actual dataset and future dataset
• 🔢 Automatic categorical encoding using LabelEncoder
• ⚖️ Feature scaling with StandardScaler
• 🤖 Logistic Regression model training
• 📈 Model performance evaluation:
  • Accuracy score
  • Confusion Matrix
• 📉 ROC Curve visualization with AUC score
• 🎨 Custom UI styling using CSS
• ⚡ Cached data loading for better performance

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🛠️ Tech Stack

• Python
• Streamlit
• Pandas & NumPy
• Scikit-learn
• Matplotlib

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📊 Machine Learning Workflow

1. Load actual and future datasets
2. Encode categorical variables
3. Select features and target variable
4. Split data into training and testing sets
5. Apply feature scaling
6. Train Logistic Regression model
7. Evaluate model using accuracy & confusion matrix
8. Visualize performance using ROC curve

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