🚀 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:

🚨 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 repository.

🤖 Happy Predicting and Monitoring! ✈️🔮