Text Mining Applications ;Using Real-World Data in Python

Stok Kodu:
9786254391736
Boyut:
135-215-
Sayfa Sayısı:
124
Basım Yeri:
Ankara
Baskı:
1
Basım Tarihi:
2022-02-15
Kapak Türü:
Karton
Kağıt Türü:
Kitap Kağıdı
Dili:
İngilizce
%18 indirimli
532,00TL
436,24TL
Taksitli fiyat: 6 x 78,52TL
9786254391736
1250034
Text Mining Applications ;Using Real-World Data in Python
Text Mining Applications ;Using Real-World Data in Python
436.24
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.
Axess Kartlar
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Tek Çekim 436,24    436,24   
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3 154,14    462,41   
6 78,52    471,14   
QNB Finansbank Kartları
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 436,24    436,24   
2 226,84    453,69   
3 154,14    462,41   
6 78,52    471,14   
Bonus Kartlar
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 436,24    436,24   
2 226,84    453,69   
3 154,14    462,41   
6 78,52    471,14   
Paraf Kartlar
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 436,24    436,24   
2 226,84    453,69   
3 154,14    462,41   
6 78,52    471,14   
Maximum Kartlar
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 436,24    436,24   
2 226,84    453,69   
3 154,14    462,41   
6 78,52    471,14   
World Kartlar
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 436,24    436,24   
2 226,84    453,69   
3 154,14    462,41   
6 78,52    471,14   
Diğer Kartlar
Taksit Sayısı Taksit tutarı Genel Toplam
Tek Çekim 436,24    436,24   
2 -    -   
3 -    -   
6 -    -   
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