進階軟體開發專題

• R06631009

Python_進階軟體開發專題_Week1

• week1 Week1 html

  • 01 - 建立Github帳號
  • 02 - 熟悉91app資料集

notebook : Week1_HW html

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week2

Week2 html

• crawler_exercise

  • 股票網站爬蟲

• week_2任務

  • 股票漲幅與91app購買量做比較
  • 完成一支網站爬蟲上傳至資料夾中

notebook : Week2_HW html

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week3

Week3 html

weekend V.S sales volumn

This is a weekend V.S sales volumn case.

trading hours V.S sales volumn

This is a trading hours V.S sales volumn case.

notebook : Week3_HW html

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week4

Week4 html

weekend V.S sales volumn

This is a wordcloud for Trump case.

trading hours V.S sales volumn

This is a wordcloud for WithGaLove case.

notebook_TrumpFB : Week4_HW1 html

notebook_WithGaLoveFB : Week4_HW2 html

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week5

Week5 html

• TF-IDF
• use task_5 data
• use Jieba
• use sklearn to get word vector

notebook : Week5_HW html

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week7

Week7 html

• use SVM to classification
• use Wholesale customers data data source : Week7_HW_data html

notebook : Week7_HW html

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Project_1

Project_1 html

• TF-IDF
• use Jieba
• use sklearn to get word vector

notebook : Project_1 html

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Project_2

Project_2 html

• PCA
• use Titanic data
• Try to use data to predict survived

notebook : Project_2 html

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Project_3

Project_3 html

• Linear Regression & ANOVA
• use Teacher.csv data
• Try to use data to analysis

notebook : Project_3 html

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Project_4

Project_4 html

• Apriori
• use 91App data
• Try to use data to find some related items .

notebook : Project_4 html

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Project_5

Project_5 html

• NN
• use Titanic data
• Try to use data to predict survived .

notebook : Project_5 html

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Final_Project

Final_Project html

• Target : We want use climate information to RNN for recommender Item

• step1 : Use Web-crawler to get climate information(temperature & rainfull) notebook : climate1_getStationInfo html notebook : climate2_getClimate html

• step2 : use Order.csv data to get Itemid , quantity , area , date

• step3 : Combine step 1 & step 2 information notebook : Final_project html

• step4 : Organize the step3'data to use RNN type notebook : RNN_Final_Project html

• step5 : We get recommender accuracy 21% , so we maybe can use more features to learning.

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