Text Mining Applications Using Real - World Data i

Stok Kodu:
9786254391736
Boyut:
13,5x21
Sayfa Sayısı:
124
Baskı Sayısı:
1
Basım Tarihi:
2021
Kapak Türü:
Ciltsiz
Kağıt Türü:
1. Hamur
%23 indirimli
130,00
100,10
Taksitli fiyat: 3 x 34,70
Temin süresi 4 gündür.
9786254391736
873909
Text Mining Applications Using Real - World Data i
Text Mining Applications Using Real - World Data i
100.10

Over the last two decades, the amount of existing data sources in the world have dramatically increased due largely to digitalization. In parallel, data analysis has become a crucial topic for researchers in many areas. One of the essential perspectives in data analysis is text mining. In various forms, textual data is the most generated data element compared to multimedia data. Since the available data sizes are exponentially increasing, we need intelligent computational methodologies to handle massive datasets. Data mining approaches, specifically text mining techniques, come into prominence. The application of both text mining and machine learning techniques together on data analysis provides decent solutions. For that purpose, this book is prepared with four major chapters discussing various aspects of data analysis with text mining methods, such as clustering, classification, sentiment analysis, and prediction tasks implemented in the Python programming language.

Over the last two decades, the amount of existing data sources in the world have dramatically increased due largely to digitalization. In parallel, data analysis has become a crucial topic for researchers in many areas. One of the essential perspectives in data analysis is text mining. In various forms, textual data is the most generated data element compared to multimedia data. Since the available data sizes are exponentially increasing, we need intelligent computational methodologies to handle massive datasets. Data mining approaches, specifically text mining techniques, come into prominence. The application of both text mining and machine learning techniques together on data analysis provides decent solutions. For that purpose, this book is prepared with four major chapters discussing various aspects of data analysis with text mining methods, such as clustering, classification, sentiment analysis, and prediction tasks implemented in the Python programming language.

AKBANK
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 100,10    100,10   
2 51,05    102,10   
3 34,70    104,10   
ZİRAAT BANKASI
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 100,10    100,10   
2 51,05    102,10   
3 34,70    104,10   
İŞ BANKASI
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 100,10    100,10   
2 51,05    102,10   
3 34,70    104,10   
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