Intro
Hello there! My name is Eko Setiawan, a final year college student who pursuing an Information Systems degree. As a student and self-claimed programmer, I have a huge interest in Data Science and Backend Development. I have been studying Data Science's fundamentals like Statistics, Probability, Statistical Learning, and some more advanced concept like neural networks since my first semester of college.
Well, I think you already guess it, I have learned and used Python Language a lot since then. Other than Data Science, I am studying Backend Development too. I am always curious about how a very complex backend system is built.
Not just studying through courses and tutorials, I spend a lot of time working on some projects, ranging from a well-documented project (look at my projects section) to some failed projects that I abandoned.
I am always looking for opportunities to apply my knowledge in real-world settings, if you have some challenges or jobs that you think suit my profile, please consider contacting me. Thank you.
Highlighted Projects
Tourist Forecasting: A Time Series Study Case
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I built this project while I was learning Time Series Forecasting, which is about tourist visit forecasting based on historical data.
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There are lots of Time Series forecasting algorithms, in this project I compare the XGBoost and Meta's Prophet models.
projects
Machine Learning Model in Production using Fast API & Docker Container
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In this project I built a website using Fast API framework for accessing a regression Machine Learning Model.
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I built the machine learning model using Scikit Learn MLP Regressor algorithm for completing one of my college project. It's a model for predicting abalone's age based on their physical features.
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In this project, I learned several things other than Modeling itself. I learn about how to write a backend function for ML model inference, run it locally and run on a Docker container.
Analyzing Airline’s Reviews (NLP Project)
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This is the project I made when participating in an online intern program on The Forage platform. I perform some sentiment analysis on thousands of reviews for an airline. I learn how to utilize topic modeling to gain insights from large chunks of text.
Sentiment Analysis on Product's Review
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Analisis sentimen terhadap review produk dapat memberikan informasi bagaimana respon konsumen terhadap suatu produk. Data untuk proyek ini saya peroleh melalui Web Scraping, proses pengumpulan datanya saya tulis pada artikel ini.
- Pelatihan model klasifikasi sentimen saya tulis di notebook ini.
Indonesian Language Words Embedding Using GENSIM and FASTTEXT
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Word Embedding adalah sebuah teknik mengubah data teks menjadi angka atau vektor. Artikel ini menjelaskan bagaimana langkah-langkah yang saya lakukan untuk membuat representasi vektor dari kata-kata bahasa Indonesia menggunakan algoritma Word2Vec.
- Data yang digunakan adalah data dari WikiHow bahasa Indonesia sebanyak 16.308 artikel.
Flowers Classifier
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This project I built based on Fast AI's "Practical Deep Learning for coders" course. It is just a begginer project that build using Transfer Learning. I made a flower classifier model that can distinguish lily flower, tullips and sunflower. You can try it here. I hope you like it, can't wait to create more projects in the future.