Airline Satisfaction Classification

This repository contains a Jupyter Notebook for classifying airline passenger satisfaction based on various features.

Overview

The goal of this project is to build a machine learning model that can predict whether a passenger is satisfied or not based on various features provided in the dataset.

Dataset

The dataset includes features such as:

• Flight Distance
• Inflight wifi service
• Departure/Arrival time convenient
• Ease of Online booking
• Gate location
• Food and drink
• Online boarding
• Seat comfort
• Inflight entertainment
• On-board service
• Leg room service
• Baggage handling
• Checkin service
• Inflight service
• Cleanliness
• Departure Delay in Minutes
• Arrival Delay in Minutes

Notebook Contents

The notebook includes the following sections:

1. Data Loading and Exploration

   • Importing necessary libraries
   • Loading the dataset
   • Initial data exploration and visualization

2. Data Preprocessing

   • Handling missing values
   • Feature encoding
   • Data normalization/standardization

3. Model Building

   • Splitting the data into training and testing sets
   • Training various classification models (e.g., Logistic Regression, Random Forest, etc.)
   • Model evaluation using appropriate metrics

4. Model Evaluation

   • Evaluating model performance on the test set
   • Visualizing results with confusion matrices and classification reports

5. Conclusion

   • Summary of findings
   • Potential improvements

Dependencies

The project requires the following Python packages:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• jupyter