I am a computer science graduate with a deep interest in machine learning and artificial intelligence, particularly in the field of Language Models (LLM). I have solid experience in data management and analysis. I am highly interested in contributing to data-related work, and I am currently seeking opportunities in this field.
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Python
Spreadsheet
Tableau
Scikit Learn
Tensorflow
Pytorch
Sentiment analysis project for the game Clash of Clans, starting with independently scraping data from Play Store reviews, followed by data analysis based on the reviews. After that, building models to predict sentiment with three labels using traditional machine learning algorithms and deep learning.
The Indonesia Tour Recommendation System utilizes content-based filtering to recommend destinations tailored to the unique preferences of travelers. For users who have previously visited destinations, the system employs collaborative filtering to suggest new places they haven't explored yet.
Project focused on identifying features correlated with CO2 emissions and building a machine learning model for prediction. Five algorithms: Linear Regressor, Decision Tree Regressor, K-Nearest Neighbor, Random Forest Regressor, and Adaptive Boosting—were used to find the best predictive model. Hyperparameter tuning was applied to enhance the accuracy of the Decision Tree Regressor, K-Nearest Neighbor, Random Forest Regressor, and Adaptive Boosting models.
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This project use collaborative filtering techniques to recommend anime to users based on patterns of preferences from other users. It involves analyzing user interaction data with anime to predict which anime active users might like based on similarity in preference patterns.
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This project aims to analyze sentiment in hotel reviews using a GRU model. This model processes text sequences to understand whether each review expresses positive and negative sentiment, aiding in efficient evaluation of customer experiences.
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Heart Disease Prediction with Imbalanced Data Handling" develops a heart disease prediction model using oversampling and undersampling techniques to address imbalanced data. The dataset is processed to balance the classes, and the model is implemented in a web application using Flask, allowing users to input patient data and receive real-time risk predictions.
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The TFX Diabetes Classification project aims to develop a predictive system for diabetes risk using TFX components like CsvExampleGen, StatisticsGen, SchemaGen, Transform, Trainer, and Evaluator for automated model development, training, and evaluation based on patient health data.
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The TFX Machine Learning Pipeline for Human Stress Prediction aims to predict stress levels using components like CsvExampleGen, StatisticsGen, SchemaGen, Transform, Trainer, and Evaluator for automated model development, training, and evaluation based on diverse data inputs.
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The TFX Machine Learning Pipeline - Grade Multiclass aims to develop a system for classifying student grades using TFX components for data ingestion, preprocessing, hyperparameter tuning, model training, and automated performance evaluation.
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Create business questions based on data from bike rentals and answer them using a data analysis approach in Python. The process includes data wrangling (gathering, assessing, and cleaning data), exploratory data analysis (EDA), and visualization and explanatory analysis to address the posed business questions. A simple dashboard is created using Streamlit.
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Perform clustering on the Adult Income dataset using the K-Means algorithm, followed by several steps such as Exploratory Data Analysis (EDA), data preparation, feature selection, and model optimization. The clustering results will then be used for classification with various algorithms.
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