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معرفی کتاب «تحلیل داده های مالی با پایتون» به انگلیسی «Data Analytics for Finance Using Python»
یک کتاب جمع و جور برای علاقه مندان به تحلیل داده های مالی
عناوین فصل های کتاب عبارتند از :
Chapter 1: Portfolio management by applying
K-means clustering
Chapter 2: Predicting stock price using the ARIMA model
Chapter 3: Stock investment strategy using a logistic regression
Chapter 4: Predicting the stock buying and selling decisions
Chapter 5: The random forest technique
Chapter 7: Descriptive statistics for stock risk analysis
Chapter 8: Stock prediction using a multiple regression model
Chapter 9: F-test for stock risk assessment
Chapter 10: T-test for stock risk assessment
Chapter 11: Z-test for stock risk assessment
Chapter 12: Support vector machine for stock
prediction
Chapter 13: Stock risk analysis by visualization
Chapter 14: NLP for sentiment analysis for stock
Chapter 15: LSTM for stock price prediction

#معرفی_کتاب
#پایتون_مالی

پایتون برای مالی

🆔 www.tgoop.com/python4finance
🆔 ble.ir/python4finance



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معرفی کتاب «تحلیل داده های مالی با پایتون» به انگلیسی «Data Analytics for Finance Using Python»
یک کتاب جمع و جور برای علاقه مندان به تحلیل داده های مالی
عناوین فصل های کتاب عبارتند از :
Chapter 1: Portfolio management by applying
K-means clustering
Chapter 2: Predicting stock price using the ARIMA model
Chapter 3: Stock investment strategy using a logistic regression
Chapter 4: Predicting the stock buying and selling decisions
Chapter 5: The random forest technique
Chapter 7: Descriptive statistics for stock risk analysis
Chapter 8: Stock prediction using a multiple regression model
Chapter 9: F-test for stock risk assessment
Chapter 10: T-test for stock risk assessment
Chapter 11: Z-test for stock risk assessment
Chapter 12: Support vector machine for stock
prediction
Chapter 13: Stock risk analysis by visualization
Chapter 14: NLP for sentiment analysis for stock
Chapter 15: LSTM for stock price prediction

#معرفی_کتاب
#پایتون_مالی

پایتون برای مالی

🆔 www.tgoop.com/python4finance
🆔 ble.ir/python4finance

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