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This is a capstone project for FTW Foundation Data Science program that predicts the price of second-hand cars, and created by Elyse Go, Nicole Lumagui, Bernadette Misa and Jero Santos.
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Machine Learning Exercise: Exploring categorical plots, LabelEncoder, pipelines and GridSearchCV using Telco Customer Churn data from Kaggle
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Machine Learning Exercise: Using Logistic Regression, Naive Bayes and Random Forest to classify people with and without diabetes based on Pima Indian data from Kaggle
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Machine Learning Exercise: Exploring the concept of Upsampling / Oversampling and using KNN, Decision Tree and Random Forest to predict Class on Lymphography data from UCI.
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Machine Learning Exercise: Using Linear Regression to predict Sales on Advertising data from Kaggle
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Machine Learning Exercise: Using Logistic Regression, KNN and Decision Tree to classify Biopsy on Cleaned Cervical Cancer data from Kaggle
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