This project utilizes data from Kaggle to perform exploratory data analysis (EDA) and predictive modeling on customer satisfaction with British Airways. The aim is to analyze customer feedback and sentiments to gain insights into passenger preferences and behavior.

• pandas
• numpy
• matplotlib
• seaborn
• missingno
• nltk
• scikit-learn
• wordcloud

1. Data Collection: The dataset was downloaded from Kaggle.
2. Exploratory Data Analysis (EDA): Initial exploration involved visualizing data to understand passenger satisfaction across different categories such as seat type and traveler type.
3. Predictive Modeling: Logistic regression was used to predict whether a passenger would recommend British Airways based on their rating score.
4. Natural Language Processing (NLP): Basic NLP techniques were applied to analyze customer feedback. Sentiment analysis was performed using the NLTK library and pre-trained models from Hugging Face.

• Girish Joshi

This project is licensed under the MIT License.