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c-kosasih/README.md

👋 Hello, world! I'm Clarissa Beatrice Kosasih

🎓 I'm an undergraduate Data Science major at BINUS University (currently in my 5th semester)
🔍 Actively seeking opportunities in the Data Science and Machine Learning fields
📍 Based in Indonesia 🌏

👀 About Me

  • 💡 Deeply curious in Machine Learning, especially in unraveling patterns in the financial sector — like fraud detection and risk optimization 💳📊
  • 🌱 I love learning new things — whether it’s tools, techniques, or ideas — and I’m always up for growing through collaboration and new experiences!
  • 🤝 Open to collaborations, new connections, and fun challenges!

📓 Projects Notebook

🛠️ Skills & Tools

👩‍💻 Programming & Scripting

Python R SQL Markdown

📚 Data Science & Machine Learning

Pandas NumPy Scikit-learn GridSearchCV TensorFlow Keras

🧩 Models I Have Worked With

XGBoost Random Forest Decision Tree CatBoost SVM KNN Logistic Regression Linear Regression AdaBoost

🧰 Tools & Workflow

Git GitHub Google Colab Jupyter Notebook VS Code FastAPI Streamlit Kaggle Figma

🌐 Web Dev Basics

While not directly tied to Data Science, I learned these during my university coursework

HTML CSS JavaScript

📊 GitHub Stats

Clarissa's GitHub stats

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  1. c-kosasih c-kosasih Public

    Config files for my GitHub profile.

    1

  2. Hotel-Booking-Cancellation-Prediction-App Hotel-Booking-Cancellation-Prediction-App Public

    Streamlit app that predicts hotel booking cancellations using a Random Forest Classifier based on reservation and guest details.

    Python

  3. Predict-Abalone-Ring-FastApi Predict-Abalone-Ring-FastApi Public

    Predicting abalone age (ring count) using a Random Forest Regressor. The model is deployed via a FastAPI backend for efficient and scalable predictions.

    Jupyter Notebook

  4. Cirrhosis-Multiclass-Classification Cirrhosis-Multiclass-Classification Public

    Multiclass classification of liver cirrhosis stages using clinical data. Compares Random Forest vs. XGBoost with SMOTE for class imbalance and hyperparameter tuning for optimization. Aims to suppor…

    Jupyter Notebook

  5. Clustering-Clothing-Company-Customers Clustering-Clothing-Company-Customers Public

    Customer segmentation using K-Means clustering with PCA. Identifies product preferences to enable targeted promotions and personalized marketing strategies.

    Jupyter Notebook

  6. Game-Recommendation-System Game-Recommendation-System Public

    A content-based recommender that suggests games by combining features into a soup feature. Uses TF-IDF to compute similarity and deliver personalized recommendations.

    Jupyter Notebook