This document describes building a logistic regression model to analyze Universal Bank customer data. Key steps include:
1) Exploring the data and removing variables with high correlation or missing values.
2) Dividing the data randomly into training (60%) and validation (40%) sets.
3) Performing principal component analysis to reduce dimensions.
4) Building a logistic regression model on the training set and selecting the best subset of variables.
5) Evaluating model performance on the validation set, achieving an error rate of 4.75%.
Using data from Google Analytics, we look into factors that are associated with article and Ask an Expert (AaE) page views. Specifically, we utilize Google Analytics data from April 2009 – April 2012 and available Page Analytics (http://pageanalytics.extension.org/) in the personal finance or Financial Security for All (FSA) Community of Practice (CoP) areas. Top 25 articles and AaE with the highest page views per month will be selected to look for similar characteristics among these webpages. At the macro level, we will be exploring relevant statistics to our CoP such as search engine utilized, top search keywords, traffic sources, traffic referral, and mobile device access. We will be also be looking into the tendency of page hits associated with the following factors: newsletter, curriculum, number of words, topic (tag) , average Google search entrances per week, age of the article, and its seasonal nature (month, quarter). We share result implications and limitations based on the results of this study.
Using data from Google Analytics, we look into factors that are associated with article and Ask an Expert (AaE) page views. Specifically, we utilize Google Analytics data from April 2009 – April 2012 and available Page Analytics (http://pageanalytics.extension.org/) in the personal finance or Financial Security for All (FSA) Community of Practice (CoP) areas. Top 25 articles and AaE with the highest page views per month will be selected to look for similar characteristics among these webpages. At the macro level, we will be exploring relevant statistics to our CoP such as search engine utilized, top search keywords, traffic sources, traffic referral, and mobile device access. We will be also be looking into the tendency of page hits associated with the following factors: newsletter, curriculum, number of words, topic (tag) , average Google search entrances per week, age of the article, and its seasonal nature (month, quarter). We share result implications and limitations based on the results of this study.
Presentation on Private Equity Valuation of Bkash. This presentation was performed for a National Financial Modeling Competition called " Blueprints," organized by NSU Finance Club
Aegis Data BUS 221EMP IDGENDERYEARS SENIORITYBase SalaryCommission.docxgalerussel59292
Aegis Data BUS 221EMP IDGENDERYEARS SENIORITYBase SalaryCommissionTotal CompensationRegionContracts writtenQ1Q2Q3Q4Q5Q61100115$21,059.04$27,402.41$48,461.45313123452611002113$26,073.00$104,683.28$130,756.28446013125411003111$24,854.42$59,113.15$83,967.57332245531911004126$38,296.80$215,905.69$254,202.4964674355171100505$20,348.10$32,036.09$52,384.193714512171100611$17,511.06$19,491.42$37,002.48215522141100701$17,617.00$22,434.39$40,051.395054151101100804$20,304.01$28,212.55$48,516.5732234545161100912$19,356.08$18,844.77$38,200.86555155251101001$17,767.65$13,664.26$31,431.91335514181101111$18,154.03$17,502.49$35,656.5231401322131101219$22,895.49$74,395.67$97,291.15140254451411013110$23,775.44$90,762.04$114,537.4951702155151101400$17,095.89$10,531.46$27,627.3653433253101101512$20,314.42$15,078.80$35,393.2221565415191101605$20,648.15$36,094.16$56,742.31684412181101701$17,819.08$16,597.44$34,416.5152134315281101800$18,584.52$11,251.59$29,836.112363355281101903$19,199.32$25,395.12$44,594.455252211811020112$26,134.84$117,998.31$144,133.1461005523431102116$21,057.21$53,584.82$74,642.0352173224171102211$18,329.78$12,825.34$31,155.12114523261102314$19,581.43$38,541.13$58,122.56233444532611024111$26,195.47$82,855.33$109,050.8031605515371102512$17,717.43$22,937.13$40,654.56331552191102612$19,624.30$20,015.59$39,639.893105522171102712$18,876.61$22,160.06$41,036.6721322341371102803$18,677.60$30,688.91$49,366.524594342321102904$20,611.41$27,041.12$47,652.5341484323521103013$18,932.96$30,829.76$49,762.715853244181103113$19,384.87$20,195.62$39,580.4931144545581103201$17,281.44$15,474.38$32,755.8131605415341103316$21,221.32$53,022.07$74,243.3962644345241103410$17,910.93$9,638.65$27,549.5824525531711035012$27,110.32$118,241.41$145,351.7342284241541103614$20,935.96$31,330.05$52,266.012783554291103701$18,899.89$13,765.29$32,665.18281532361103811$17,512.90$16,083.06$33,595.9652913553151103902$19,678.19$26,083.41$45,761.6022002542241104016$21,751.62$58,155.43$79,907.053174115231104110$16,837.47$13,565.67$30,403.141425513251104215$20,240.94$41,365.91$61,606.8558022153101104314$19,231.78$39,975.26$59,207.045274455391104406$21,055.99$50,032.56$71,088.5542673452111104500$18,108.11$9,059.36$27,167.483383515161104606$22,379.28$40,598.03$62,977.3052534342391104710$17,895.01$11,457.95$29,352.962645444151104805$20,225.63$30,401.72$50,627.3551244452351104914$19,821.47$32,954.02$52,775.496204115181105010$16,693.58$11,533.27$28,226.8522954452101105119$22,754.04$90,168.67$112,922.7151761545231105203$19,180.95$33,900.10$53,081.05543415171105316$21,190.09$48,546.98$69,737.0711103515151105413$19,255.05$28,596.50$47,851.55310655521101105519$23,569.69$54,831.57$78,401.2644454122241105614$20,601.61$26,075.44$46,677.054601445221105705$21,358.48$39,764.61$61,123.0943403441301105810$18,783.54$7,733.01$26,516.556205554181105911$18,855.19$13,139.47$31,994.66205513171106005$20,834.31$39,429.04$60,263.3541373255531106111$19,508.57$13,906.75$33,415.3252853142511062119$30,585.43$231,130.01$2.
Take lic policy at age 25 get pension started at 45 upto age 60 risk cover ru...Nandini Bhatnagar
HERE IS A WONDERFUL INVESTMENT OPPORTUNITY FOR YOUNGSTERS AT AGE 25 AND EARNING GOOD INCOME TO START INVESTING RS 1,30,000/- YEARLY FOR 20 YEARS UPTO AGE 45 ONLY AND GET EXCELLENT RETURNS FROM AGE 45 YEARS............CALL AT NUMBER 09873927723 FOR INVESTMENT METHOD
Presentation on Private Equity Valuation of Bkash. This presentation was performed for a National Financial Modeling Competition called " Blueprints," organized by NSU Finance Club
Aegis Data BUS 221EMP IDGENDERYEARS SENIORITYBase SalaryCommission.docxgalerussel59292
Aegis Data BUS 221EMP IDGENDERYEARS SENIORITYBase SalaryCommissionTotal CompensationRegionContracts writtenQ1Q2Q3Q4Q5Q61100115$21,059.04$27,402.41$48,461.45313123452611002113$26,073.00$104,683.28$130,756.28446013125411003111$24,854.42$59,113.15$83,967.57332245531911004126$38,296.80$215,905.69$254,202.4964674355171100505$20,348.10$32,036.09$52,384.193714512171100611$17,511.06$19,491.42$37,002.48215522141100701$17,617.00$22,434.39$40,051.395054151101100804$20,304.01$28,212.55$48,516.5732234545161100912$19,356.08$18,844.77$38,200.86555155251101001$17,767.65$13,664.26$31,431.91335514181101111$18,154.03$17,502.49$35,656.5231401322131101219$22,895.49$74,395.67$97,291.15140254451411013110$23,775.44$90,762.04$114,537.4951702155151101400$17,095.89$10,531.46$27,627.3653433253101101512$20,314.42$15,078.80$35,393.2221565415191101605$20,648.15$36,094.16$56,742.31684412181101701$17,819.08$16,597.44$34,416.5152134315281101800$18,584.52$11,251.59$29,836.112363355281101903$19,199.32$25,395.12$44,594.455252211811020112$26,134.84$117,998.31$144,133.1461005523431102116$21,057.21$53,584.82$74,642.0352173224171102211$18,329.78$12,825.34$31,155.12114523261102314$19,581.43$38,541.13$58,122.56233444532611024111$26,195.47$82,855.33$109,050.8031605515371102512$17,717.43$22,937.13$40,654.56331552191102612$19,624.30$20,015.59$39,639.893105522171102712$18,876.61$22,160.06$41,036.6721322341371102803$18,677.60$30,688.91$49,366.524594342321102904$20,611.41$27,041.12$47,652.5341484323521103013$18,932.96$30,829.76$49,762.715853244181103113$19,384.87$20,195.62$39,580.4931144545581103201$17,281.44$15,474.38$32,755.8131605415341103316$21,221.32$53,022.07$74,243.3962644345241103410$17,910.93$9,638.65$27,549.5824525531711035012$27,110.32$118,241.41$145,351.7342284241541103614$20,935.96$31,330.05$52,266.012783554291103701$18,899.89$13,765.29$32,665.18281532361103811$17,512.90$16,083.06$33,595.9652913553151103902$19,678.19$26,083.41$45,761.6022002542241104016$21,751.62$58,155.43$79,907.053174115231104110$16,837.47$13,565.67$30,403.141425513251104215$20,240.94$41,365.91$61,606.8558022153101104314$19,231.78$39,975.26$59,207.045274455391104406$21,055.99$50,032.56$71,088.5542673452111104500$18,108.11$9,059.36$27,167.483383515161104606$22,379.28$40,598.03$62,977.3052534342391104710$17,895.01$11,457.95$29,352.962645444151104805$20,225.63$30,401.72$50,627.3551244452351104914$19,821.47$32,954.02$52,775.496204115181105010$16,693.58$11,533.27$28,226.8522954452101105119$22,754.04$90,168.67$112,922.7151761545231105203$19,180.95$33,900.10$53,081.05543415171105316$21,190.09$48,546.98$69,737.0711103515151105413$19,255.05$28,596.50$47,851.55310655521101105519$23,569.69$54,831.57$78,401.2644454122241105614$20,601.61$26,075.44$46,677.054601445221105705$21,358.48$39,764.61$61,123.0943403441301105810$18,783.54$7,733.01$26,516.556205554181105911$18,855.19$13,139.47$31,994.66205513171106005$20,834.31$39,429.04$60,263.3541373255531106111$19,508.57$13,906.75$33,415.3252853142511062119$30,585.43$231,130.01$2.
Take lic policy at age 25 get pension started at 45 upto age 60 risk cover ru...Nandini Bhatnagar
HERE IS A WONDERFUL INVESTMENT OPPORTUNITY FOR YOUNGSTERS AT AGE 25 AND EARNING GOOD INCOME TO START INVESTING RS 1,30,000/- YEARLY FOR 20 YEARS UPTO AGE 45 ONLY AND GET EXCELLENT RETURNS FROM AGE 45 YEARS............CALL AT NUMBER 09873927723 FOR INVESTMENT METHOD
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
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15. Data Processing
출발 시간이 10, 109로 600 ~ 2200 범위 를 벗어나는 아웃라이어로
판단하고 데이터 삭제
Scheduled departure time 데이터를 16개의 time block으로 재구성
예측 상황에서 미리 주어 질 수 없는 실제 비행기 출발 시간, 워싱턴 DC와
뉴욕 구간이기 때문에 모두 비슷한 수준 (평균 211.87, 중앙값 214,
최빈값 214, 표준 편차 13.31)이기 때문에 분석 변수에서 제외
명목형 변수인 tail number와 flight number 분석 변수에서 제외
비행 날짜는 요일에 비해 추후 예측에 활용할 여지가 적기 때문에 분석
변수에서 제외
데이터 세트를 60:40 비율로 Training set와 Validation set로 임의 분할
16. Naï Bayes
ve
Conditional probabilities
Classes-->
ontime
Value
Prob
CO 0.036312849
DH 0.231843575
DL 0.188081937
MQ 0.118249534
CARRIER
OH 0.013035382
RU 0.174115456
UA 0.016759777
US 0.22160149
EWR 0.273743017
DEST
JFK 0.176908752
LGA 0.549348231
BWI 0.057728119
ORIGIN
DCA 0.645251397
IAD 0.297020484
0
1
Weather
1
0
Mon 0.131284916
Tue 0.14990689
Wed 0.148044693
DAY_WEEK
Thur 0.181564246
Fri 0.170391061
Sat 0.111731844
Sun 0.10707635
600-700 0.058659218
700-800 0.055865922
800-900 0.082867784
900-1000 0.047486034
1000-1100 0.044692737
1100-1200 0.040968343
1200-1300
0.0716946
Binned_CRS_
1300-1400 0.083798883
DEP_TIME
1400-1500 0.090316574
1500-1600 0.067970205
1600-1700 0.081005587
1700-1800 0.104283054
1800-1900 0.044692737
1900-2000 0.047486034
2000-2100 0.019553073
2100-2200 0.058659218
Input
Variables
delayed
Value
Prob
CO 0.06122449
DH 0.306122449
DL 0.118367347
MQ 0.163265306
OH 0.012244898
RU 0.244897959
UA 0.004081633
US 0.089795918
EWR 0.387755102
JFK 0.187755102
LGA 0.424489796
BWI 0.102040816
DCA 0.502040816
IAD 0.395918367
0 0.930612245
1 0.069387755
Mon 0.220408163
Tue 0.130612245
Wed 0.151020408
Thur 0.130612245
Fri 0.159183673
Sat 0.069387755
Sun 0.13877551
600-700 0.032653061
700-800 0.053061224
800-900 0.06122449
900-1000 0.016326531
1000-1100 0.032653061
1100-1200 0.016326531
1200-1300 0.065306122
1300-1400 0.048979592
1400-1500 0.146938776
1500-1600 0.085714286
1600-1700 0.07755102
1700-1800 0.13877551
1800-1900 0.028571429
1900-2000 0.089795918
2000-2100 0.024489796
2100-2200 0.081632653
Prior class probabilities
According to relative occurrences in training data
Class
ontime
delayed
Prob.
0.814253222 <-- Success Class
0.185746778
RU (Continental Express Airline)를 타고 수요일
15:00 ~ 16:00 출발 IAD에서 LGA로 갈 경우 (기상은
양호함)
Ontime = 0.81*0.174 * 0.148 * 0.068 * 0.297 * 0.549 *1
0.00022971
Delay = 0.186* 0.245* 0.424 * 0.396 * 0.151* 0.0857 *0.931
0.0000092
Ontime 확률 = 0.00022971 / (0.00022971 + 0.0000092)
96% (Cutoff value 50%를 넘으므로 ontime으로 분류)
17. Performance Evaluation
Training Data scoring - Summary Report
Cut off Prob.Val. for Success (Updatable)
Validation Data scoring - Summary Report
0.5
Cut off Prob.Val. for Success (Updatable)
Classification Confusion Matrix
Predicted Class
Actual Class
ontime
delayed
ontime
1049
25
delayed
205
40
Classification Confusion Matrix
Predicted Class
Actual Class
ontime
delayed
ontime
685
14
delayed
155
26
Error Report
# Cases
# Errors
1074
25
245
205
1319
230
Error Report
# Cases
# Errors
699
14
181
155
880
169
0.5
Class
ontime
delayed
Overall
% Error
2.33
83.67
17.44
Training Data scoring - Summary Report
Cut off Prob.Val. for Success (Updatable)
Error Report
# Cases
# Errors
1074
0
245
228
1319
228
% Error
2.00
85.64
19.20
Training Data scoring - Summary Report
0.3
Classification Confusion Matrix
Predicted Class
Actual Class
ontime
delayed
ontime
1074
0
delayed
228
17
Class
ontime
delayed
Overall
Class
ontime
delayed
Overall
Cut off Prob.Val. for Success (Updatable)
0.8
Classification Confusion Matrix
Predicted Class
Actual Class
ontime
delayed
ontime
672
402
delayed
83
162
% Error
0.00
93.06
17.29
Class
ontime
delayed
Overall
Error Report
# Cases
# Errors
1074
402
245
83
1319
485
% Error
37.43
33.88
36.77
18. Performance Evaluation
Decile-wise lift chart (training dataset)
1200
Cumulative
1000
Cumulative Flight
Status when
sorted using
predicted values
800
600
400
Cumulative Flight
Status using
average
200
0
0
500
1000
Decile mean / Global mean
Lift chart (training dataset)
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1500
1
2
3
4
# cases
Cumulative Flight
Status when
sorted using
predicted values
Cumulative Flight
Status using
average
500
7
8
9
10
Decile-wise lift chart (validation dataset)
1000
Decile mean / Global mean
Cumulative
800
700
600
500
400
300
200
100
0
# cases
6
Deciles
Lift chart (validation dataset)
0
5
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
Deciles
7
8
9
10