astonglen
Pursuing Masters in Data Science. Aspiring Data Scientist passionate about leveraging data-driven insights to solve complex problems and drive business growth.
Stevens Institute of TechnologyUnion City, New Jersey, United States
Pinned Repositories
AirBnb-Price-Prediction
The ability to predict prices and features affecting the appraisal of property can be a powerful tool in such a cash intensive market for a lessor. Additionally, a predictor that forecasts the number of reviews a specific listing will get may be helpful in examining elements that affect a property's popularity.
BankApplication_SageMaker
End-to-End Project to build, train and deploy a XGBoost classification model using AWS SageMaker through code making it a streamlined process.
churn-prediction
To build a classification system to predict whether a customer will churn or not based on the IBM Telecom Data from Kaggle. Technically, it is a binary classifier that divides clients into two groups-those who leave and those who do not. The classifier will be built using bagging algorithms like Random Forest, boosting algorithms & Neural Networks
Employee-Attrition
This project analyzes employee attrition data to uncover key factors, predict turnover, and develop strategies for retention, ultimately enhancing organizational stability and performance.
Fake-News-Detection-Project
The scope of this project is to classify fake and true news. After performing an analysis on the dataset using two different vectorizers and two machine learning algorithms, the results are conveyed in the form of accuracy score and confusion matrices.
Malware-Detection
This project aims to develop a model using MLLib's classification models that predict whether a Windows operating system will be attacked by malware. The goal is to assess system vulnerability and identify and patch weak points before an attack happens. Model performance will be compared, and the project will be performed on PYSPARK on GCP.
Statistical-Methods-Project
Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.
Text-to-Image-Generation
In this research project a framework is proposed to formulate image generation conditioned on the text input. Converting regional language text descriptions into images using Stack Generative Adversarial Network(Stack-GAN) and Gated Recurrent Unit (GRU).
Time-Series-Analysis-Non-Seasonal
The goal of this project is to predict and forecast daily return values for a particular stock. Given the highly volatile nature of stock data, we will fit a univariate GARCH model to achieve our goal of predicting the daily returns value to allow you to make statistically informed trades!
Time-Series-Analysis-Seasonal
A seasonal time series of Hotel Hospitality Industry Employees. This dataset contains the number of employees in thousands of persons as monthly averages from 1990-01-01 to 2018-12-01. The goal of this project is to predict and forecast the monthly number of employees.
astonglen's Repositories
astonglen/AirBnb-Price-Prediction
The ability to predict prices and features affecting the appraisal of property can be a powerful tool in such a cash intensive market for a lessor. Additionally, a predictor that forecasts the number of reviews a specific listing will get may be helpful in examining elements that affect a property's popularity.
astonglen/BankApplication_SageMaker
End-to-End Project to build, train and deploy a XGBoost classification model using AWS SageMaker through code making it a streamlined process.
astonglen/churn-prediction
To build a classification system to predict whether a customer will churn or not based on the IBM Telecom Data from Kaggle. Technically, it is a binary classifier that divides clients into two groups-those who leave and those who do not. The classifier will be built using bagging algorithms like Random Forest, boosting algorithms & Neural Networks
astonglen/Employee-Attrition
This project analyzes employee attrition data to uncover key factors, predict turnover, and develop strategies for retention, ultimately enhancing organizational stability and performance.
astonglen/Fake-News-Detection-Project
The scope of this project is to classify fake and true news. After performing an analysis on the dataset using two different vectorizers and two machine learning algorithms, the results are conveyed in the form of accuracy score and confusion matrices.
astonglen/Malware-Detection
This project aims to develop a model using MLLib's classification models that predict whether a Windows operating system will be attacked by malware. The goal is to assess system vulnerability and identify and patch weak points before an attack happens. Model performance will be compared, and the project will be performed on PYSPARK on GCP.
astonglen/Statistical-Methods-Project
Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.
astonglen/Text-to-Image-Generation
In this research project a framework is proposed to formulate image generation conditioned on the text input. Converting regional language text descriptions into images using Stack Generative Adversarial Network(Stack-GAN) and Gated Recurrent Unit (GRU).
astonglen/Time-Series-Analysis-Non-Seasonal
The goal of this project is to predict and forecast daily return values for a particular stock. Given the highly volatile nature of stock data, we will fit a univariate GARCH model to achieve our goal of predicting the daily returns value to allow you to make statistically informed trades!
astonglen/Time-Series-Analysis-Seasonal
A seasonal time series of Hotel Hospitality Industry Employees. This dataset contains the number of employees in thousands of persons as monthly averages from 1990-01-01 to 2018-12-01. The goal of this project is to predict and forecast the monthly number of employees.
astonglen/Web-Mining-Project
In this data analysis project, we embarked on a comprehensive exploration of Oracle's interview review data scraped from Glassdoor. Our objective was to gain valuable insights into the interview experiences of candidates applying for specific job postings at Oracle.
astonglen/API
astonglen/sales_data_assessment
astonglen/Tableau-Projects