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https://github.com/newking9088/marketing_campaign_ml_prediction_dashboard
Transform your marketing strategy with our intuitive ML Prediction Dashboard, providing real-time, data-driven insights to optimize campaign success.
https://github.com/newking9088/marketing_campaign_ml_prediction_dashboard
data-visualization finance-application streamlit-webapp
Last synced: 8 days ago
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Transform your marketing strategy with our intuitive ML Prediction Dashboard, providing real-time, data-driven insights to optimize campaign success.
- Host: GitHub
- URL: https://github.com/newking9088/marketing_campaign_ml_prediction_dashboard
- Owner: newking9088
- Created: 2024-09-28T01:56:34.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-10-24T17:59:01.000Z (24 days ago)
- Last Synced: 2024-10-26T02:25:35.909Z (22 days ago)
- Topics: data-visualization, finance-application, streamlit-webapp
- Language: PowerShell
- Homepage:
- Size: 5.16 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Streamlit Dashboard for Customer Subscription Prediction
[View Live Dashboard](https://testst-4neyqhlk2bjx6srapde5f9.streamlit.app/)
## Table of Contents
- [Overview](#overview)
- [Dashboard Features](#dashboard-features)
- [File Upload](#1-upload-a-csv-file)
- [Data Visualization](#2-view-clean-test-data)
- [Prediction Engine](#3-make-predictions)
- [Results Display](#4-view-result-table)
- [Executive Summary](#5-view-executive-summary)
- [Customer Reports](#6-generate-final-report-for-selected-customer)
- [Deployment Guide](#deployment-guide)
- [Project Structure](#1-project-structure)
- [Dependencies Management](#2-dependencies-management)
- [Path Configuration](#3-file-paths)
- [Configuration Setup](#4-configuration-files)
- [Testing & Optimization](#5-testing-and-optimization)
- [Monitoring](#6-cloud-monitoring)
## Overview
This dashboard implements CI/CD for model deployment and monitors performance to address model drift and related issues. It provides an interactive interface for predicting customer term deposit subscriptions based on various features.
## Dashboard Features
### 1. Upload a CSV File
- Upload custom dataset via CSV
- Default test dataset available
- Automatic data validation
### 2. View Clean Test Data
- Display processed dataset
- Column overview
- Data quality checks
### 3. Make Predictions
- Automated prediction pipeline
- Pre-trained model integration
- Real-time processing
### 4. View Result Table
- Complete dataset display
- Prediction labels
- Confidence scores
### 5. View Executive Summary
Key metrics displayed:
- Age distribution
- Job categories
- Marital status
- Education levels
- Subscription probabilities
### 6. Generate Final Report for Selected Customer
Detailed customer analysis including:
- **Tier Classification**: Based on subscription probability
- **Customer Profile**:
- Subscription probability score
- Demographic information
- Historical behavior
- **Prediction Details**:
- Subscription status
- Default history
- Previous interactions
## Deployment Guide
### 1. Project Structure
```markdown
marketing_campaign_ml_prediction_dashboard/
├── .streamlit/
│ └── config.toml
├── virtual_env/
├── README.md
├── app.py
├── best_xgb.pkl
├── clean_test_data.csv
└── requirements.txt
```
### 2. Dependencies Management
```bash
# Create virtual environment
python -m venv virtual_env
# Activate environment
source virtual_env/bin/activate # Unix
virtual_env\Scripts\activate # Windows
# Generate requirements
pip freeze > requirements.txt
```
### 3. File Paths
```python
# Best practices for file paths
import os
base_path = os.path.abspath(os.path.dirname(__file__))
data_path = os.path.join(base_path, 'data')
```
### 4. Configuration Files
```toml
# .streamlit/config.toml
[theme]
base="light"
primaryColor="#ad200f"
backgroundColor="#f0f7f3"
secondaryBackgroundColor="#f5970c"
textColor="#000000"
font="sans serif"
```
### 5. Testing and Optimization
```python
# Performance optimization example
@st.cache
def load_data():
# Expensive data loading operation
return data
# Local testing
streamlit run app.py
```
### 6. Cloud Monitoring
- Monitor application logs
- Track performance metrics
- Debug deployment issues
- Implement error handling