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This repository houses a machine learning project. Its goal is to predict airline customer satisfaction based on factors like flight distance, in-flight amenities, service quality, and travel class. This aims to assist airlines in understanding the main factors affecting customer satisfaction, enabling them to make data-driven decisions.
IT contains data analysis and visualizations aimed at improving a landscaping company's revenue and customer satisfaction. The project addresses two sub-goals: increasing revenue and improving customer satisfaction. Four visualizations are provided, each contributing insights toward achieving these objectives.
🚀 blah is audio analyzer api to allow call centers to extract information about their customers easily. it can be easily extended to more phone providers and more analyzers can be implemented if needed.
Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.
Using the dataset from Kaggle (https://www.kaggle.com/mohaimenalrashid/invistico-airline), implementing data scientist methodology to find useful insights that can be used for business purposes, applying data cleansing, EDA, data visualization, and machine learning modeling. We use Python Language Programming to solve and implement the Data Scie…