Cyberoctane29
Passionate about SQL, data analytics, and ML. Focused on turning raw data into insights using advanced analytics, predictive modeling, and problem solving.
India
Pinned Repositories
CUO-On-Site-Internship--Retail-Store-Database-Evaluation-and-Optimization
This repository showcases my internship project at CUO, focused on querying and analysing a music retail store database. Key tasks included analyzing the schema, improving query performance, ensuring data integrity, and implementing security measures. This project enhanced my understanding of database management in a business setting.
Cyclistic-Bike-Share--Analyzing-Rider-Behavior
Analyzed Cyclistic's bike-share data to uncover usage differences between casual riders and annual members. Utilized SQL and MySQL for data processing, R for visualisation, and Kaggle for collaboration. Insights will guide marketing strategies to convert casual riders into annual members.
Deutsche-Bank-Customer-Churn-Prediction-End-to-End-Analysis-and-Modeling
In this project, I aim to predict customer churn for Deutsche Bank using supervised machine learning. It involves data exploration, feature engineering, and building Naive Bayes, Decision Tree, Random Forest, and XGBoost models. Models are tuned, evaluated, and compared to identify the best approach for churn prediction.
EPA-Air-Quality-AQI-Analysis
This project involved analyzing air quality data from the EPA, focusing on the Air Quality Index (AQI). I used Python data structures like dictionaries and sets to manage and process the data, simulating real-world data analysis to assess pollution levels and their health implications.
HackerRank-SQL-Problem-Solving
This repository includes my SQL problem-solving approach and solutions to HackerRank challenges, organized by difficulty level: Easy, Medium, and Hard. Each challenge has its own solution file with clear explanations to make the reasoning easy to follow. I aim to share my thought process, logic, and approach behind each answer.
Invistico-Airlines-Customer-Satisfaction-Prediction-End-to-End-Analysis-and-Modeling
This project presents an end-to-end workflow for predicting airline customer satisfaction using survey data. It involves building and evaluating classification models (Logistic Regression, Decision Tree, Random Forest, XGBoost), covering data cleaning, exploratory analysis, model training, tuning, evaluation, and feature importance analysis.
Language-Translation-System-Using-Neural-Networks
Led development of a multilingual Language Translation System using Neural Networks. Implemented advanced NMT techniques with the mT5-small model for precise and versatile translation. Explore the future of cross-cultural communication with our innovative solution.
LeetCode-Python-Problem-Solving
This repository contains my Python problem-solving approach and solutions to LeetCode problems, categorized by difficulty: Easy, Medium, and Hard. Some problems include multiple approaches to showcase different techniques and optimizations. All solutions are well-commented, making this a helpful resource for improving Python problem-solving skills.
Salifort-Motors-Predicting-Employee-Turnover-and-Improving-Retention-Analysis-and-Modeling
In this project, I work as a data analytics professional at Salifort Motors, a fictional leader in alternative energy vehicles. I analyze employee survey data to identify turnover drivers and build predictive models, including multiple logistic regression, decision trees, and random forests, to forecast attrition and support retention strategies.
TikTok-Claims-Classification-End-to-End-Analysis-and-Modeling
This project involves analyzing TikTok videos to classify claims vs. opinions using Python. It includes EDA, statistical tests, logistic regression, and ML models (Random Forest, XGBoost) to support content moderation. Built with pandas, scikit-learn, and Tableau, the solution helps TikTok automate content review and enhance moderation efficiency.
Cyberoctane29's Repositories
Cyberoctane29/TikTok-Claims-Classification-End-to-End-Analysis-and-Modeling
This project involves analyzing TikTok videos to classify claims vs. opinions using Python. It includes EDA, statistical tests, logistic regression, and ML models (Random Forest, XGBoost) to support content moderation. Built with pandas, scikit-learn, and Tableau, the solution helps TikTok automate content review and enhance moderation efficiency.
Cyberoctane29/CUO-On-Site-Internship--Retail-Store-Database-Evaluation-and-Optimization
This repository showcases my internship project at CUO, focused on querying and analysing a music retail store database. Key tasks included analyzing the schema, improving query performance, ensuring data integrity, and implementing security measures. This project enhanced my understanding of database management in a business setting.
Cyberoctane29/LeetCode-Python-Problem-Solving
This repository contains my Python problem-solving approach and solutions to LeetCode problems, categorized by difficulty: Easy, Medium, and Hard. Some problems include multiple approaches to showcase different techniques and optimizations. All solutions are well-commented, making this a helpful resource for improving Python problem-solving skills.
Cyberoctane29/Cyberoctane29
Config files for my GitHub profile.
Cyberoctane29/Cyclistic-Bike-Share--Analyzing-Rider-Behavior
Analyzed Cyclistic's bike-share data to uncover usage differences between casual riders and annual members. Utilized SQL and MySQL for data processing, R for visualisation, and Kaggle for collaboration. Insights will guide marketing strategies to convert casual riders into annual members.
Cyberoctane29/EPA-Air-Quality-AQI-Analysis
This project involved analyzing air quality data from the EPA, focusing on the Air Quality Index (AQI). I used Python data structures like dictionaries and sets to manage and process the data, simulating real-world data analysis to assess pollution levels and their health implications.
Cyberoctane29/HackerRank-SQL-Problem-Solving
This repository includes my SQL problem-solving approach and solutions to HackerRank challenges, organized by difficulty level: Easy, Medium, and Hard. Each challenge has its own solution file with clear explanations to make the reasoning easy to follow. I aim to share my thought process, logic, and approach behind each answer.
Cyberoctane29/Language-Translation-System-Using-Neural-Networks
Led development of a multilingual Language Translation System using Neural Networks. Implemented advanced NMT techniques with the mT5-small model for precise and versatile translation. Explore the future of cross-cultural communication with our innovative solution.
Cyberoctane29/LeetCode-SQL-Problem-Solving
This repository features my SQL problem-solving approach and solutions to LeetCode problems, categorized by difficulty: Easy, Medium, and Hard. Each file may include multiple solutions to a problem, showcasing different approaches or optimizations. All solutions are explained clearly, making this a valuable resource for mastering SQL techniques.
Cyberoctane29/NOAA-Lightning-Analysis
This project explores lightning strike data from the National Oceanic and Atmospheric Administration (NOAA) to identify seasonal trends and analyze strike frequency across months. It demonstrates data manipulation, aggregation, and visualization using Python, providing insights into lightning activity patterns.
Cyberoctane29/Python-For-Data-Analysis
A repository dedicated to learning Python for data analysis, data science, and data analytics. This collection of Jupyter notebooks covers practical exercises and concepts from the Google Advanced Data Analytics Professional Certificate program.
Cyberoctane29/Train-Dataset-Insights
This project explores the Train dataset to gain basic insights using Pandas. It focuses on data manipulation, handling missing values, and summarizing key features to understand the dataset better.
Cyberoctane29/Unicorn-Companies-Analysis
This project explores unicorn companies, private startups valued at over $1 billion, using Python for data analysis. It covers industry trends, geographic distribution, and investment patterns through EDA, including data cleaning, handling missing values, datetime transformations, and visualizations to uncover key insights.
Cyberoctane29/Deutsche-Bank-Customer-Churn-Prediction-End-to-End-Analysis-and-Modeling
In this project, I aim to predict customer churn for Deutsche Bank using supervised machine learning. It involves data exploration, feature engineering, and building Naive Bayes, Decision Tree, Random Forest, and XGBoost models. Models are tuned, evaluated, and compared to identify the best approach for churn prediction.
Cyberoctane29/Fetch-user-data
This React project fetches data from the Random User API using Axios and displays user profiles with React Bootstrap cards. It includes infinite scrolling for loading more profiles and showcases core React concepts like hooks (useState, useEffect) and component-based architecture with Bootstrap CSS styling.
Cyberoctane29/Influencer-Impact-and-Marketing-Sales-Modeling-and-Analysis
This project analyzes influencer marketing and promotional budgets' impact on sales using ML and statistical modeling in Python. It includes EDA, data cleaning, and simple and multiple linear regression. Key libraries include pandas, NumPy, statsmodels, Seaborn, and Matplotlib. Insights help optimize marketing strategies and resource allocation.
Cyberoctane29/Invistico-Airlines-Customer-Satisfaction-Prediction-End-to-End-Analysis-and-Modeling
This project presents an end-to-end workflow for predicting airline customer satisfaction using survey data. It involves building and evaluating classification models (Logistic Regression, Decision Tree, Random Forest, XGBoost), covering data cleaning, exploratory analysis, model training, tuning, evaluation, and feature importance analysis.
Cyberoctane29/Mapty
An interactive workout tracker leveraging JavaScript, HTML, and CSS, integrated with Leaflet API and Chrome's local storage. Effortlessly log running or cycling sessions with geolocation-based mapping. Create markers for workouts, view them on the map, and track progress in a user-friendly side list.
Cyberoctane29/Penguins-Data-Analysis-and-Modeling
This project applies statistical modeling, including single and multiple linear regression, using Python. It covers exploratory data analysis, data cleaning, and modeling with pandas, NumPy, statsmodels, and scikit-learn. Regression analyzes relationships, while clustering identifies patterns. Seaborn visualizations enhance interpretability.
Cyberoctane29/Salifort-Motors-Predicting-Employee-Turnover-and-Improving-Retention-Analysis-and-Modeling
In this project, I work as a data analytics professional at Salifort Motors, a fictional leader in alternative energy vehicles. I analyze employee survey data to identify turnover drivers and build predictive models, including multiple logistic regression, decision trees, and random forests, to forecast attrition and support retention strategies.
Cyberoctane29/Diamonds-ANOVA-Analysis
This project uses ANOVA in Python to analyze how diamond color and cut affect pricing. By testing for statistical significance and running post hoc comparisons, it reveals key pricing patterns. Built with pandas, statsmodels, and Seaborn, the findings help inform diamond valuation and purchasing decisions.
Cyberoctane29/District-Literacy-Analysis
This project analyzes district literacy rates using descriptive statistics, probability distributions, confidence intervals, and hypothesis testing. It explores data patterns, detects outliers with z-scores, and conducts point estimation. The analysis is performed using Python with NumPy, Pandas, SciPy Stats, Statsmodels, and Matplotlib.
Cyberoctane29/Elderly-Motion-Analytics-for-Fall-Prevention
This project implements binomial logistic regression in Python to predict whether an individual is lying down based on vertical acceleration data. Designed for healthcare and fall detection applications, the model helps identify patterns in motion that correlate with lying down, a critical factor in elderly care and injury prevention.
Cyberoctane29/EPA-Carbon-Monoxide-AQI-Analysis
This project continues my EPA Air Quality AQI Analysis, focusing on carbon monoxide levels in EPA data. Using Python, I applied statistics, probability analysis, outlier detection, sampling, and hypothesis testing to assess pollution and health impacts. Leveraging Pandas, NumPy, SciPy, and Matplotlib, it supports environmental policy decisions.
Cyberoctane29/Excel-Dashboard-Suite-Customer-Service-Finance-and-Orders
This Excel dashboard project is a collection of three interactive dashboards designed to demonstrate how spreadsheet tools can drive data storytelling and deliver operational insights. Each dashboard focuses on a different business function, using clean design principles and interactivity to make insights accessible for non-technical users.
Cyberoctane29/NBA-Player-Career-Longevity-Prediction-Using-Naive-Bayes-Classifier
This project analyzes NBA rookie performance data to predict whether a player will stay in the league for at least five years. Using feature engineering and a Naive Bayes model, it identifies key indicators of career longevity, supporting managers and coaches in player retention decisions.
Cyberoctane29/Optimizing-K-in-K-means-A-Visual-and-Quantitative-Exploration
Exploring K-means clustering through image color compression and high-dimensional data analysis. Learn how pixel grouping in RGB space builds intuition, while inertia/silhouette scores optimize clusters. Demonstrates K-means' power to reveal patterns in both visual and abstract data by optimizing groupings and selecting ideal k-values.
Cyberoctane29/Python-Learning
This repository tracks my journey in mastering Python. It includes Python files on topics from basics to advanced concepts, with code, explanations, and visualizations demonstrating my progress. The collection spans data analysis, statistical modeling, and more, showcasing my development and achievements in Python programming.
Cyberoctane29/R-Learning
This repository showcases my R programming journey, featuring R Markdown files from basic to advanced topics. Each file includes code, explanations, and visualizations to demonstrate my progress and understanding in data analysis and statistical modeling.
Cyberoctane29/Superstore-Profit-and-Sales-Insights-Dashboard-in-Power-BI
This Power BI project is an interactive dashboard analyzing Superstore sales and profitability. It combines KPI tracking, trend analysis, and comparative insights across categories, regions, and segments. With drill-through, bookmarks, and clean design, it delivers actionable insights through modern data storytelling.