customer-satisfaction

Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.

Know exactly what to press or say to get a human operator

Call Centre Trends Visualising customer and agent behaviour.

User feedback survey widgets for React

Top Voice Of Customer (VOC) Platform 🌟 Star if you like it! 🌟

Analyzing and Monitoring Net Promoter Score (NPS) Performance for Healthcare Companies using SQL and Power BI

Self case study 1

Predicting customer satisfaction for purchases made in Brazilian e-commerce.

Internship project that dealt with Customer Satisfaction/Dissatisfaction Analysis for a major Technology giant. Converted Voice transcipts to text, performed Text and Sentiment analysis to produce results.

This project automates the analysis of large-scale customer feedback using Natural Language Processing (NLP) and Machine Learning. The core of this tool is a model that instantly translates raw review text into a corresponding star rating, providing rapid and actionable insights into product satisfaction.

Factors that lead to customer satisfaction for an Airline. Model built using Python and trained using Amazon SageMaker.

A rating system that can be integrated with any customer satisfaction and feedback system

Use Twitter text analysis to measure client satisfaction

Exploratory Data Analysis (EDA) project on airline passenger satisfaction data. Includes data preprocessing, outlier handling, correlation analysis, segmentation, and actionable business insights to improve service quality and reduce dissatisfaction.

Data analysis project optimizing customer support operations for SwiftConnect Telecom.

An open-source customer satisfaction dashboard

Capstone project: employee engagement vs customer satisfaction vs branch performance (R, regression, clustering, Shiny)

A comprehensive project predicting flight prices and customer satisfaction using machine learning models, deployed through interactive Streamlit apps.

Deep Neural Network for predicting airline passenger satisfaction based on customer experience and flight-related features.