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サバイバル分析によるユーザーの リテンション(継続率)分析
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Yosuke Yasuda
May 20, 2017
Technology
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320
サバイバル分析によるユーザーの リテンション(継続率)分析
サバイバル分析によるユーザーの リテンション(継続率)分析
Yosuke Yasuda
May 20, 2017
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Transcript
αόΠόϧੳʹΑΔϢʔβʔͷ Ϧςϯγϣϯ(ܧଓ)ੳ 2017/05/20@TokyoR ҆ా༸հɹ@yasu_919 1
ࣗݾհ 2 ҆ా༸հ Data Scientist @Exploratory ౦ژେֶͰػցֶशɾࣗવݴޠॲཧΛઐ߈ ओʹexploratoryͷRύοέʔδΛ։ൃΛओಋ email:
[email protected]
twitter: @yasu_919
Exploratory dplyr, tidyrͷؔΛϕʔεʹσʔλΛՃ ࣗͰRͷؔΛఆٛͯ͠͏͜ͱग़དྷΔ %>% Ͱͭͳ͕Ε͍ͯ͘ εςοϓ 3 ΧϥϜ͔ΒॲཧΛݺͼ ग़ͤΔUI
؆୯ͰײతͳՄࢹԽػೳ
4 survival ύοέʔδ ཁ͢ΔʹαόΠόϧʢੜଘʣ ੳΛ͢ΔͨΊͷΞϧΰϦ ζϜ͕ͦΖͬͨύοέʔδ Terry Therneau ࡞ऀ Contains
the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen- Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. આ໌
5 ੜଘੳ ͱͱɺੜ͕Ͳ Ε͘Β͍ͷ࣌ؒܦաͰ ࢮΜͰ͍͔͘ΛϞσϧԽ ͨ͠ͷ
6 Ԡ༻ Πϕϯτ͕࣌ؒܦաʹΑͬͯͲͷఔੜ ͡Δ͔ͱ͍͏ͷΛ֬ϞσϧԽͨ͠ͷ Ͱ • ϢʔβʔͷΩϟϯηϧͷਪҠ • ैۀһͷ৬ͷਪҠ •
ػցͷނোͷਪҠ ͳͲͷ෯͍Ԡ༻ઌ͕͋Δ
7 DEMO DEMO
8 Boot Camp σʔλαΠΤϯεɾϒʔτΩϟϯϓ։࠵ ୈ1ճ: 6/17(), 18() جૅฤ ୈ2ճ: 6/20(Ր),
21(ਫ) ि൛: 6/17(), 18(), 24(), 25() جૅ+࣮ફฤ ฏ൛: 6/27(Ր), 28(ਫ), 29() Exploratory͕ࣾɺγϦίϯόϨʔ ͷFacebookຊࣾɺUCόʔΫϨʔ Ͱߦ͖ͬͯͨτϨʔχϯάɾϓ ϩάϥϜΛຊʹͯެ։ ༰ https://exploratory.io/training-jp ৄࡉɾ͓ਃࠐΈ