Analisis KNN, Random Forest dan Boosting Algorithm.
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Updated
Jul 15, 2024 - Jupyter Notebook
Analisis KNN, Random Forest dan Boosting Algorithm.
📃This repository contains a data analysis and modelling for final project that explores employee attrition within an company. We analyze multiple method to classify attrition employee.
Developed a multi-class classification system to detect malicious URLs using lexical features and boosting algorithms (XGBoost, Light GBM, Gradient Boosting Machines), leveraging Pandas and Sklearn for data processing and model training.
ML-algorithms from scratch using Python. Classic Machine Learning course.
"This repository contains implementations of Boosting method, popular techniques in Model Ensembles, aimed at improving predictive performance by combining multiple models. by using titanic database."
Containing all the coding projects and assignments I've completed in COS 226 @ Princeton (Data Structures & Algorithms)
This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.
Practice Assignments for Data Science Coursework
Using R Markdown for Data Analysis, Machine Learning
This repository contains my coursework (assignments, semester exams & project) for the Statistical Machine Learning course at IIIT Delhi in Winter 2024.
This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.
Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).
A project to predict scores of students based on 7 parameters. Created a Flask App and then converted to a Docker Image. Hostel Online to demonstrate implementation of CI/CD pipelines on AWS EC2
Machine Learning for High Energy Physics.
Forward stagewise sparse regression estimation implemented for gretl.
A simulation study completed during a visit at the Microsoft Research, Cambridge (May-Sept, 2022)
Machine Learning course, Python.
Flight Price Prediction using Advance Machine learning
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