Application Installation Predictions on the Google Playstore

This data science project aims to predict the number of installations (Installs) of an application on the Google Playstore based on available data columns, including App, Category, Rating, Reviews, Size, Installs, Type, Price, Content Rating, Genres, Last Updated , Current Ver, and Android Ver.

Tribunnews: News Trend Analysis with NLP

This data science project aims to analyze Indonesian news trends on a certain day from the Tribunnews.com website. The data used is taken from this source and processed using NLP (Natural Language Processing) techniques to identify the keywords that appear most often in the news on March 9, 2023

Predicting Housing Prices in the USA

This project aims to predict the price of houses in the USA using regression analysis. The data used in this project was sourced from the USA Housing dataset in CSV format. The dataset contains several columns, including Avg. Area Income, Area House Age, Area Number of Rooms, Area Num, Price.

Bike Sharing Demand Prediction in London

This project is to build an ML model that can accurately predict the number of bike users in London based on various parameters. The model will be trained using the train.csv dataset and tested using the test.csv dataset. The ultimate goal is to achieve a high accuracy score on the test dataset.

Predicting Ratings of Applications on Google Playstore using TensorFlow

this project aims to predict the rating of an application on Google Playstore using TensorFlow. I will build a model with three layers. The results of this project can provide valuable insights into predicting the ratings of applications on Google Playstore.

Uncovering the Skies: Analyzing British Airways Customer Reviews Through Web Scrapping

As a participant online internship in the role of a data scientist at BA, it will be my job to apply my analytical skills to influence real life multi-million-pound decisions from day one, making a tangible impact on the business as my recommendations, tools and models drive key business decisions.

Machine Learning for Diabetes Diagnosis: A Comparative Analysis of Classification Algorithms

This project aim to predict the outcome of diabetes using various machine learning models. The methodology for this project will be using classification methods. By the end of the project, we aim to have an accurate and reliable model for predicting the outcome of diabetes based on the given data.

Predictive Modeling of Airline Customer Bookings – British Airways

Customers are more empowered than ever because they have access to a wealth of information at their fingertips. Being reactive in this situation is not ideal; airlines must be proactive in order to acquire customers before they embark on their holiday. This is possible to use the predictive models.

Analyzing Customer Sentiment towards Indonesian Cellular Service Providers using NLP

In this project, we aim to perform sentiment analysis on tweets related to Indonesian cellular service providers. The goal is to predict positive sentiment using various methods such as web scraping, data preprocessing, natural language processing (NLP), and building an ML decision tree model.

Understanding Client Profiles and Forecasting Payment Difficulties using Data Science

The project is to determine the number of clients with payment difficulties and those without payment difficulties using data from Home Credit Indonesia. Additionally, we aim to explore various client backgrounds, including gender, number of children, total income, occupation, and more.

Data Analytics Project: Enhancing Marketing Strategy for Sprocket Central Ltd

In this project, we delve into the realm of data analytics to derive valuable insights and develop a strategic approach for optimizing the marketing efforts of Sprocket Central Ltd. The journey involves a comprehensive analysis of the provided dataset and encompasses key stages including RFM Model.