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Devaaldo/README.md

Hello World! I'm Akbar Pradana

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About Me

class Developer:
    def __init__(self):
        self.name = "Muhammad Akbar Pradana"
        self.alias = ["Akbar", "Devaaldo"]
        self.location = "Yogyakarta, Indonesia"
        self.education = "Undergraduate Informatics Student"
        self.university = "Jenderal Achmad Yani University of Yogyakarta"
        self.interests = ["AI/ML Engineer", "Data Scientist"]
        self.learning_philosophy = "Always curious, always growing"

    def current_focus(self):
        return "Machine Learning Engineer"

    def life_motto(self):
        return "Don't wait to be chosen, be the chosen one."

me = Developer()
print(me.life_motto())

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    A machine learning solution for predicting flood probability based on environmental and geographical factors, with multiple regression models compared on 1.1M+ samples.

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  5. Customer-Churn-Prediction-Model Customer-Churn-Prediction-Model Public

    Machine learning project comparing 5 classification algorithms (KNN, Decision Tree, Random Forest, SVM, Naive Bayes) to predict bank customer churn. Includes comprehensive EDA, data preprocessing, …

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    A deep learning project for classifying iris flower species using a neural network trained on the classic Iris dataset.

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