This notebook focuses on creating insightful visualizations to explore and present the Qatar Airlines reviews data.

This notebook conducts the initial steps for analyzing Qatar Airlines reviews data frame, focusing on cleaning and preparing the data for further enhancement, analysis and visualization, and forecasting.

This R Markdown document serves as a proposal for the improvement of my project, Resume Wizard, a Resume Ranking & Clustering Tool. Resume Wizard 2.0 aims to provide a solution that enhances the accuracy and effectiveness of resume screening process.

This R Markdown document serves as a proposal for the development of my project, Homework Truthweaver, an analysis tool for assessing the similarity of homework documents. Homework Truthweaver is designed to address the challenge of identifying academic dishonesty in homework grading through the application of cosine similarity with TF-IDF weighting.

This R Markdown document serves as a proposal for the development of my project, Resume Wizard, a Resume Ranking & Clustering Tool. Resume Wizard aims to provide a solution that enhances the accuracy and effectiveness of resume screening process.

This R Markdown serves as my project proposal for the development of a resume ranking tool. The primary objective of this project is to address the limitations posed by Applicant Tracking Systems (ATS) in the context of resume screening, utilizing Natural Language Processing (NLP) techniques. This project aims to offer a solution that enhances the accuracy and effectiveness of the hiring process while mitigating the constraints associated with ATS.

This Proposal is my Resume Ranker project proposal for Algoritma DCD 20

In this R Markdown, I conducted an analysis of Uber fare pricing and proceeded to construct predictive models that dynamically adjust pricing, using various machine learning algorithms.

In this R Markdown, I conducted an analysis focusing on the detection of hazardous asteroids. I proceeded to construct predictive models capable of detecting the presence of hazardous asteroids, leveraging machine learning algorithms.

This R Markdown explores the realm of customer churn prediction using Machine Learning techniques. The goal is to identify customers that are likely to leave the service, thereby refining business strategies by accurately anticipating potential attrition.

This R Markdown delves into the domain of credit risk assessment, leveraging Machine Learning algorithms. The aim is to pinpoint borrowers with elevated risk levels, enhancing lending strategies through precise anticipation of potential defaults.

In this R Markdown, I did an analysis of the trip duration of NYC Taxi and then built some predictive models that predict the taxi trip duration (estimated time arrival) with several machine learning algorithms including Feed-Forward Neural Network.

In this R Markdown, I did an analysis of the trip duration of NYC Taxi and then built a predictive model that predicts the taxi trip duration (estimated time arrival) using the most popular machine learning algorithm that is Linear Regression

In this R markdown. I did an analysis of fake and real news trend and then went through Natural Language Processing. I then build a predictive model that detects whether a news text is genuine or fake/hoax using the best machine learning algorithm for text classification that is Naive Bayes.

In this R markdown. I made a predictive model that predict whether a company is gonna go bankrupt or not. I used two popular machine learning algorithms which are Logistic Regression and K-Nearest Neighbour Classifier. I also implemented unsupervised learning statistical technique in feature engineering process.

My first Rpubs document: Algoritma Data Visualization > Interactive Ploting > Day 3