Hi, I'm Agus
Develover & Data Scientist
I have a keen interest in data and machine learning.
Contact MeAbout Me
My Introduction
I'am interest in data-driven solutions. I am enthusiastic about learning and growing in this field and am eager to contribute to addressing business challenges through the use of careful data analysis and effective machine learning solutions.
Completed
Written
Youtabe
Skills
My Technical LevelDevelopment
All About the CorePython
70%Java
50%PySpark
60%R
40%C++
40%JavaScript
50%Android
55%MS Excel
70%Photoshop
70%Indesign
90%Frameworks
Everyone Needs SupportNumPy
80%pandas
90%matplotlib
70%scikit-learn
85%Spark MLlib
70%Pytorch
85%Deep Graph Library
55%OpenCV and Roboflow
75%FastAPI
80%NLTK
60%streamlit
80%seaborn
70%Flask
65%Machine Learning
Theory, theory!Linear and Logistic Regression
95%Decision Trees
95%Ensemble Models
90%Clustering
65%Convolutional Neural Networks
80%Graph Neural Networks
60%Recommender Systems
75%Natural Language Processing
65%Exploratory Data Analysis
90%Multi-modal Learning
70%Time Series
55%LLMs
70%Cloud and Engineering
Fly Fast & High!AWS Sagemaker
65%AWS EMR
75%AWS Lambda
70%Big Query
40%Docker
60%Apache Airflow
40%Kafka
40%Databases and Viz
Wow! FactorMySQL
85%AWS Redshift
75%Amazon RDS
70%Tableau
50%Power BI
50%Looker
60%Qualification
My Personal JourneyB.Tech in Computer Engineering
Universitas GarutMachine Learning Engineer
Ragdalion Technology
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Conduct exploratory data analysis (EDA) to understand data characteristics, identify patterns, and extract meaningful insights.
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Monitored production ML models, identified and resolved issues, enhancing model performance by 8%.
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Implemented predictive machine learning for manufacturing client's QA Machine Test, focusing on real-time anomaly detection with Autoencoder. Achieved 99% accuracy, enhancing system dependability for reliable, high-quality machine testing.
Project Base Intern Data Scientist Intern
Home Credit
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Applied the CRISP-DM methodology in analyzing credit score data and created its presentation deck
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Pre-processed data, conducted Exploratory Data Analysis (EDA), and built Logistic Regression, K-Nearest Neighbor (KNN), and Random Forest model using Python.
Data Scientist Intern
LetGrowMore
Project Base Intern Data Engineer
BTPN Syariah
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Ensuring accurate and efficient data modeling by creating precise data models and optimizing database system performance.
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Performing data analysis by utilizing data analysis tools such as SQL, T-SQL, and Tableau to extract valuable information from
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Improving business process efficiency and accuracy by optimizing database systems and ETL processes.
Project Base Intern Backnd Developer
Evermos
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Developed and deployed an Android application for handsfree token printing for use in hospitals and clinics
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Currently in use in two hospitals across the city
Data Business Analysis
Ruangguru
Portfolio
My Projects
Codex ChatGPT AI
Web Developer
Build and Deploy Your Own ChatGPT AI App in JavaScript | OpenAI, Machine Learning
Tech Stack
Java Scirpt, HTML, MySQL, CSS, Git
View Code View Demo Aplication
Customer Segmentation Analysis and Product Recommender System
Data Scientist
I am using RFM (Recency, Frequency, Monetary) analysis with 5x5x5 dimensions. algorithm K-Means to perform a segmentation Gaussian method to form a cluster
Developed a Product Recommendation System to suggest products to customers based on their preferences and behavior.
Tech Stack
Research Papers Referred
View Repository
Credit Scoring Home Credit
Data Scientist
Build machine learning models to conduct credit assessments on Home Credit customers using customer data, including credit history, employment history, and social data.
Applying machine learning algorithms such as Random Forest, Gradient Boosting, and Logistic Regression to make credit risk predictions.
Evaluate model performance with metrics such as accuracy, precision, recall, and F1-score.
Tech Stack
Python, Algoritma machine learning: Random Forest, Framework: Scikit-Learn, Tools: Jupyter Notebook, Git
View Repository
Course Recommendation System
Data Science
Built a recommendation system that suggests courses to users based on their past preferences, search history, and ratings.
Developed a course recommendation system that could suggest relevant courses to users with high accuracy and precision.
Tech Stack
Programming language: Python, HTML, JavaScript, Machine learning algorithms: Collaborative Filtering and Content-Based Filtering Tools: Jupyter Notebook, VS Code
View Code
Customer Review Feedback
Data Science
Conducted sentiment analysis on customer reviews of British Airways to identify areas of improvement and inform business decisions.
Used Natural Language Processing (NLP) techniques such as text preprocessing, feature extraction, and topic modeling to analyze customer feedback.
Generated visualizations and reports to communicate insights to management and stakeholders.
Tech Stack
Python, Libraries: NLTK, Gensim, Scikit-Learn, Matplotlib, Tools: Jupyter Notebook
View Code
Car Prediction Model
Data Science
Built a machine learning model to predict the price of cars based on their features and specifications.
Used Random Forest Regressor, XGBoost Regressor, and Catboost Regressor algorithms to develop the prediction models.
XGBoost Regressor had the best performance in terms of accuracy, with the lowest MAE, MSE, and RMSE values.
Tech Stack
Python, Machine learning algorithms: Random Forest Regressor, XGBoost Regressor, and Catboost Regressor, Libraries: Pandas, NumPy, Scikit-Learn, Tools: Jupyter Notebook
View Code
Stroke Prediction
Data Science
Built a machine learning model to predict the risk of stroke in patients based on medical data such as age, gender, blood pressure, blood sugar levels, and others.
Developed a machine learning model PCA that could predict stroke risk with over 98% accuracy.
Tech Stack
Python, Algoritma machine learning: Random Forest,Decision tree, PCA, Naive Bayes Tools: Jupyter Notebook
View Code
Customer Churn Prediction
Data Science
Developed a machine learning model to predict customer churn for a telecommunications company.
Conducted exploratory data analysis (EDA) and feature engineering to identify relevant features for the model.
The Decision Tree model showed overfitting problem, while the Randomized Search CV on Random Forest Classifier gave us better accuracy of 87 %.
Tech Stack
Python, ML algorithms: Random Forest Classifier, Libraries: Pandas, NumPy, Scikit-Learn, Tools: Jupyter Notebook
View Code
Certifications
Extra Courses I have Undertaken
CCNA: Switching, Routing, and Wireless Essentials
Expiry Date: Does not expire
Software Engineering Specialization
Expiry Date: Does not expire
Data Engineering, Big Data, and Machine Learning on GCP by Google Cloud
Expiry Date: Does not expire
Building Cloud Computing Solution at Scale by Duke University
Expiry Date: Does not expire
Blog
My Technical Articles
The Art of Telling a Story with Data: A Guide to Effective Visualization
How to Use Data Visualization to Communicate Your Insights and Tell a Compelling Story
Read it!
2 Motivasi yang Selalu Kita Kejar
Motivasi merupakan proses yang memperjelaskan arah dan ketekunan seorang dalam mencapai cita-citanya
Read it!Why Visualization Matters: The Power of Data Visualization in Data Science
How Effective Data Visualization Can Drive Better Business Decisions
Read it!Achieve Your Goals with the Power of Atomic Habits
The Four Laws of Behavior Change: How to Develop Effective Atomic Habits
Read it!
A Step-by-Step Guide to Implementing Linear Regression in Python
Introduction to Linear Regression and Its Applications in Data Sciences
Read it!Tips and Best Practices for Creating Compelling Data Visualizations
Unlocking the Power of Your Data: A Guide to Creating Impactful and Effective Visualizations
Read it!5 Time Management Techniques to Boost Productivity
Learn practical time management strategies to increase your productivity and achieve your goals.
Read it!The Psychology of Visualization: Understanding How People Process Information
Exploring the Science behind Effective Data Visualization
Read it!
ChatGPT: Complete Usage Guide
ChatGPT (Generative Pretrained Transformer) is a machine learning model developed by OpenAI that aims to generate high-quality text. GPT can be used for various applications, including in chatbots.
Read it!