Eliminazione dei dati mancanti

Nelle prossime tre esercitazioni, riordinerai il dataset music_df. Creerai una pipeline per imputare i valori mancanti e costruire un modello di classificazione KNN, quindi lo utilizzerai per prevedere se una canzone appartiene al genere "Rock".

In questo esercizio, in particolare, eliminerai i valori mancanti che rappresentano meno del 5% del set di dati e convertirai la colonna "genre" in una caratteristica binaria.

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Apprendimento supervisionato con scikit-learn

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# Print missing values for each column
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Apprendimento supervisionato con scikit-learn

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Pre-elaborazione dei dati Exercise 2: Creazione di variabili dummy Exercise 3: Regressione con caratteristiche categoriche Exercise 4: Gestione dei dati mancanti Exercise 5: Eliminazione dei dati mancanti

Esercizio in corso

Exercise 6: Pipeline per la previsione del genere di canzone: I Exercise 7: Pipeline per la previsione del genere di canzone: II Exercise 8: Centratura e scalatura Exercise 9: Centratura e scalatura per la regressione Exercise 10: Centratura e scalatura per la classificazione Exercise 11: Valutazione di più modelli Exercise 12: Visualizzazione delle prestazioni del modello di regressione Exercise 13: Previsione sul set di prova Exercise 14: Visualizzazione delle prestazioni del modello di classificazione Exercise 15: Pipeline per la previsione della popolarità delle canzoni Exercise 16: Congratulazioni