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handling-outlier

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Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.

  • Updated Jun 28, 2024
  • Jupyter Notebook
Diabetes-Predection-using-Ensemble-Learning

This research work summarized different machine learning algorithms to create models for predicting diabetes patients utilizing the Diabetes Dataset (PIDD) from the UCI repository. The classifiers were K-Nearest Neighbors, Naïve Bayes, Support Vector, Decision Tree, Random Forest, Logistic Regression and Ensemble Model using a voting classifier.

  • Updated Jan 8, 2023
  • Jupyter Notebook

* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering *Accuracy score *Confusion matrix *Classification report

  • Updated Jun 4, 2024
  • Jupyter Notebook

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