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https://github.com/priyanka7411/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/priyanka7411/flight-price-prediction-and-customer-satisfaction-ml

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

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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.

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README 

        
# Flight Price and Customer Satisfaction Prediction



This notebook contains two machine learning projects: **Flight Price Prediction** (Regression) and **Customer Satisfaction Prediction** (Classification). Both projects are designed to predict flight prices and customer satisfaction levels using various machine learning algorithms. The models are deployed using **Streamlit** and experiment tracking is managed using **MLflow**.



---



## Project Overview



### Project 1: **Flight Price Prediction (Regression)**



#### **Objective**:

Predict flight ticket prices based on multiple factors such as departure time, source, destination, and airline type.



#### **Tech Stack**:

- Python, Streamlit, Machine Learning, MLflow, Data Analysis



#### **Domain**: 

Travel and Tourism



#### **Key Features**:

- Load and preprocess flight price data.

- Perform exploratory data analysis (EDA) to identify trends and correlations.

- Train regression models like **Linear Regression**, **Random Forest**, and **XGBoost**.

- Develop a **Streamlit** app to predict flight prices based on user input (e.g., route, time, and date).

- Use **MLflow** for tracking experiments and saving models.



---



### Project 2: **Customer Satisfaction Prediction (Classification)**



#### **Objective**:

Predict customer satisfaction levels based on features such as customer feedback, demographics, and service ratings.



#### **Tech Stack**:

- Python, Streamlit, Machine Learning, MLflow, Data Analysis



#### **Domain**:

Customer Experience



#### **Key Features**:

- Load and preprocess customer satisfaction data.

- Perform exploratory data analysis (EDA) to understand relationships between features.

- Train classification models like **Logistic Regression**, **Random Forest**, and **Gradient Boosting**.

- Develop a **Streamlit** app to predict customer satisfaction levels based on input features.

- Use **MLflow** for tracking experiments and saving models.



---



## Business Use Cases



### Flight Price Prediction:

- **For Travelers**: Help travelers plan trips by predicting flight prices based on preferences (route, time, etc.).

- **For Travel Agencies**: Assist in price optimization and marketing strategies.

- **For Businesses**: Enable businesses to budget for employee travel by forecasting ticket prices.

- **For Airlines**: Support airlines in identifying trends and optimizing pricing strategies.



---



### Customer Satisfaction Prediction:

- **For Airlines**: Enhance customer experience by predicting and addressing dissatisfaction.

- **For Businesses**: Provide actionable insights to improve services.

- **For Marketing**: Identify target customer groups for specific promotions.

- **For Management**: Support decision-making for customer retention strategies.



---



# How to Run the Streamlit App



### Install Dependencies:

First, install the required dependencies using the following command:



```bash

pip install -r requirements.txt



### Run the Streamlit App:

To run the Streamlit app, use the command below. This will launch a web interface where you can interact with both projects.



```bash

streamlit run app.py

```



# Key Features of the Streamlit App



## **Flight Price Prediction:**

- Users can input parameters like route, time, and date to get predicted flight prices.

- Displays visualizations of flight price trends.

- Uses regression models for price predictions.



## **Customer Satisfaction Prediction:**

- Users can input passenger features such as age, type of travel, and service ratings to predict satisfaction levels.

- Visualizes customer satisfaction trends.

- Uses classification models for satisfaction predictions.



---



## **MLflow Integration**

Both projects utilize **MLflow** to track and manage experiments, models, and metrics:



### **Experiment Tracking:**

- MLflow logs parameters, metrics (e.g., accuracy, RMSE), and artifacts (e.g., model files, visualizations).



### **Model Registry:**

- All trained models are saved and organized using **MLflow's model registry**.



---



# Project Deliverables



### **Python Scripts:**

- For data preprocessing, model training, and MLflow integration.



### **Cleaned Datasets:**

- Cleaned and processed CSV files containing flight price and customer satisfaction data.



### **Trained Models:**

- Regression and classification models trained and logged with MLflow.



### **Streamlit App:**

- Interactive app for data visualization and predictions, integrated with MLflow metadata.



### **Documentation:**

- Comprehensive documentation covering methodology, analysis, and insights.



---



# Project Evaluation Metrics



### **Data Preprocessing:**

- Completeness and accuracy of data cleaning and feature engineering.



### **Model Performance:**

- **Flight Price Prediction**: RMSE, R-squared.

- **Customer Satisfaction Prediction**: Accuracy, F1-score, confusion matrix.



### **Streamlit App:**

- Functionality, usability, and visual appeal.



### **MLflow Integration:**

- Effectiveness in tracking experiments and managing models.



---



# Requirements



- **Python 3.8+**



# Dataset Information



## **Flight Price Dataset**

- **Columns:**

  - Airline, Date_of_Journey, Source, Destination, Route, Dep_Time, Arrival_Time, Duration, Total_Stops, Additional_Info.



## **Customer Satisfaction Dataset**

- **Columns:**

  - Gender, Customer Type, Age, Type of Travel, Class, Flight Distance, Inflight Wifi Service, Seat Comfort, On-board Service, Cleanliness, Departure Delay, Arrival Delay, Satisfaction.



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# License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.