Esaminare i valori Tf-idf

Dopo aver creato le caratteristiche Tf-idf, spesso vorrai capire quali sono le parole con il punteggio più alto per ciascun corpus. Questo si può fare isolando la riga che vuoi analizzare e poi ordinando i punteggi dal più alto al più basso.

Il DataFrame dell’esercizio precedente (tv_df) è disponibile nel tuo workspace.

Questo esercizio fa parte del corso

Feature Engineering per il Machine Learning in Python

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Istruzioni dell'esercizio

Assegna la prima riga di tv_df a sample_row.
sample_row è ora una serie di pesi associati alle parole. Ordina questi valori per stampare le 5 parole con punteggio più alto.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Isolate the row to be examined
sample_row = tv_df.____

# Print the top 5 words of the sorted output
print(sample_row.____(ascending=____).____())

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Feature Engineering per il Machine Learning in Python

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In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.

Exercise 1: Why generate features?Exercise 2: Getting to know your data Exercise 3: Selecting specific data types Exercise 4: Dealing with categorical features Exercise 5: One-hot encoding and dummy variables Exercise 6: Dealing with uncommon categories Exercise 7: Numeric variables Exercise 8: Binarizing columns Exercise 9: Binning values

This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.

Exercise 1: Why do missing values exist?Exercise 2: How sparse is my data?Exercise 3: Finding the missing values Exercise 4: Dealing with missing values (I)Exercise 5: Listwise deletion Exercise 6: Replacing missing values with constants Exercise 7: Dealing with missing values (II)Exercise 8: Filling continuous missing values Exercise 9: Imputing values in predictive models Exercise 10: Dealing with other data issues Exercise 11: Dealing with stray characters (I)Exercise 12: Dealing with stray characters (II)Exercise 13: Method chaining

In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.

Exercise 1: Data distributions Exercise 2: What does your data look like? (I)Exercise 3: What does your data look like? (II)Exercise 4: When don't you have to transform your data?Exercise 5: Scaling and transformations Exercise 6: Normalization Exercise 7: Standardization Exercise 8: Log transformation Exercise 9: When can you use normalization?Exercise 10: Removing outliers Exercise 11: Percentage based outlier removal Exercise 12: Statistical outlier removal Exercise 13: Scaling and transforming new data Exercise 14: Train and testing transformations (I)Exercise 15: Train and testing transformations (II)

Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.

Exercise 1: Codificare il testo Exercise 2: Pulizia del testo Exercise 3: Feature testuali di alto livello Exercise 4: Conteggi di parole Exercise 5: Contare le parole (I)Exercise 6: Conteggio delle parole (II)Exercise 7: Limitare le tue feature Exercise 8: Da testo a DataFrame Exercise 9: Term frequency-inverse document frequency Exercise 10: Tf-idf Exercise 11: Esaminare i valori Tf-idf

Esercizio in corso

Exercise 12: Trasformare dati mai visti Exercise 13: N-grammi Exercise 14: Usare n-gram più lunghi Exercise 15: Trovare le parole più comuni Exercise 16: Riepilogo