Airline Passenger Satisfaction Prediction (XGBoost)
Summary
Developed a classification model using XGBoost to predict airline passenger satisfaction based on key features.
Aspiring AI/ML Engineer and Software Developer with a robust foundation in Python, machine learning, NLP, computer vision, and recommendation systems. Leveraged data-driven solutions to develop real-world projects in transportation, marketing analytics, and intelligent systems, demonstrating strong interdisciplinary technical skills through comprehensive AI/ML and software development training programs.
Training Program
Artificial Intelligence & Machine Learning
Training Program
Artificial Intelligence and Machine Learning
Training Program
Software Development
Courses
HTML
CSS
Javascript
Node.js
React
mongoDB
→
Engineering
Object Detection, Traffic Density Estimation (YOLO), OpenCV (conceptual exposure).
Python, HTML, CSS, Javascript, React, Node.js.
XGBoost, Linear Regression, Collaborative Filtering, Model Evaluation, Accuracy, Precision, Recall, F1-Score, MSE.
Data Cleaning, Feature Engineering, Exploratory Data Analysis, Model Tuning.
Text Preprocessing, TF-IDF, Bag of Words, News Classification.
Scikit-learn, Pandas, NumPy, Matplotlib, NLTK, spaCy (learning stage).
REST APIs, Software Development, Problem Solving, Analytical Thinking.
Data Science, Intelligent Systems.
Data-driven approaches, Real-world problems.
Summary
Developed a classification model using XGBoost to predict airline passenger satisfaction based on key features.
Summary
Developed a multiple linear regression model to analyze the relationship between sales and marketing channels.
Summary
Implemented a collaborative filtering recommendation system to suggest books based on user preferences.
Summary
Designed an NLP-based text classification model to automatically categorize news articles.
Summary
Conceptualized a computer vision-powered system for real-time traffic monitoring and analysis.