�ݺ�ߣshows by User: AkankshaJain20

�ݺ�ߣshows by User: AkankshaJain20 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: AkankshaJain20 / Tue, 17 Dec 2013 19:30:43 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: AkankshaJain20 Prospect Identification from a Credit Database using Regression, Decision Trees, And Neural Network /slideshow/data-mining-finalprojectsmallcleandataset/29304157 dataminingfinalprojectsmallcleandataset-131217193043-phpapp02
Identify prospects from a credit data set SMALL using data mining techniques Data set: SMALL data set • 145 Variables • 8,000 observations Tools Used: • SAS Enterprise Miner Workstation 7.1 • SAS 9.3_M1 Steps involved: • Data Quality Check • Data Partition - TRAIN/ VALIDATE/ TEST • Mining using Decision Trees - CHAID/ Pruned CHAID/ CART/ C4.5 • Data Mining using Regression - Forward/ Backward/ Stepwise • Data Mining using Regression with Interaction terms included • Data Mining using Neural Network • Model Comparison and Scoring Final Model Selection Analysis based on: • LIFT Chart • ROC Curve]]>
Identify prospects from a credit data set SMALL using data mining techniques Data set: SMALL data set • 145 Variables • 8,000 observations Tools Used: • SAS Enterprise Miner Workstation 7.1 • SAS 9.3_M1 Steps involved: • Data Quality Check • Data Partition - TRAIN/ VALIDATE/ TEST • Mining using Decision Trees - CHAID/ Pruned CHAID/ CART/ C4.5 • Data Mining using Regression - Forward/ Backward/ Stepwise • Data Mining using Regression with Interaction terms included • Data Mining using Neural Network • Model Comparison and Scoring Final Model Selection Analysis based on: • LIFT Chart • ROC Curve]]> Tue, 17 Dec 2013 19:30:43 GMT /slideshow/data-mining-finalprojectsmallcleandataset/29304157 AkankshaJain20@slideshare.net(AkankshaJain20) Prospect Identification from a Credit Database using Regression, Decision Trees, And Neural Network AkankshaJain20 Identify prospects from a credit data set SMALL using data mining techniques Data set: SMALL data set • 145 Variables • 8,000 observations Tools Used: • SAS Enterprise Miner Workstation 7.1 • SAS 9.3_M1 Steps involved: • Data Quality Check • Data Partition - TRAIN/ VALIDATE/ TEST • Mining using Decision Trees - CHAID/ Pruned CHAID/ CART/ C4.5 • Data Mining using Regression - Forward/ Backward/ Stepwise • Data Mining using Regression with Interaction terms included • Data Mining using Neural Network • Model Comparison and Scoring Final Model Selection Analysis based on: • LIFT Chart • ROC Curve <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataminingfinalprojectsmallcleandataset-131217193043-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Identify prospects from a credit data set SMALL using data mining techniques Data set: SMALL data set • 145 Variables • 8,000 observations Tools Used: • SAS Enterprise Miner Workstation 7.1 • SAS 9.3_M1 Steps involved: • Data Quality Check • Data Partition - TRAIN/ VALIDATE/ TEST • Mining using Decision Trees - CHAID/ Pruned CHAID/ CART/ C4.5 • Data Mining using Regression - Forward/ Backward/ Stepwise • Data Mining using Regression with Interaction terms included • Data Mining using Neural Network • Model Comparison and Scoring Final Model Selection Analysis based on: • LIFT Chart • ROC Curve

Prospect Identification from a Credit Database using Regression, Decision Trees, And Neural Network from Akanksha Jain

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0 Market Research to determine the need of an online integrated fitness resource /slideshow/market-res/29203751 fitopiamarketresearchproject2013linkedin-131214065803-phpapp02
This project was to understand if there is a need for an online community website, that integrates all the resources in which fitness enthusiasts are interested, into one online resource. The key objectives of market research were: - Understand and fine tune our target market - Determine the services most important to our target market - Determine if there is a need for a fitness website that integrates fitness resources Methodology: - Secondary Research - Primary Research - Qualitative and Quantitative Online survey link: http://www.instant.ly/s/FgF7s Findings and Recommendations: - Redefine the target market - Develop an app for instant sharing - Exclude features such as Forums from the website - Emphasize on local fitness information ]]>
This project was to understand if there is a need for an online community website, that integrates all the resources in which fitness enthusiasts are interested, into one online resource. The key objectives of market research were: - Understand and fine tune our target market - Determine the services most important to our target market - Determine if there is a need for a fitness website that integrates fitness resources Methodology: - Secondary Research - Primary Research - Qualitative and Quantitative Online survey link: http://www.instant.ly/s/FgF7s Findings and Recommendations: - Redefine the target market - Develop an app for instant sharing - Exclude features such as Forums from the website - Emphasize on local fitness information ]]> Sat, 14 Dec 2013 06:58:03 GMT /slideshow/market-res/29203751 AkankshaJain20@slideshare.net(AkankshaJain20) Market Research to determine the need of an online integrated fitness resource AkankshaJain20 This project was to understand if there is a need for an online community website, that integrates all the resources in which fitness enthusiasts are interested, into one online resource. The key objectives of market research were: - Understand and fine tune our target market - Determine the services most important to our target market - Determine if there is a need for a fitness website that integrates fitness resources Methodology: - Secondary Research - Primary Research - Qualitative and Quantitative Online survey link: http://www.instant.ly/s/FgF7s Findings and Recommendations: - Redefine the target market - Develop an app for instant sharing - Exclude features such as Forums from the website - Emphasize on local fitness information <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fitopiamarketresearchproject2013linkedin-131214065803-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This project was to understand if there is a need for an online community website, that integrates all the resources in which fitness enthusiasts are interested, into one online resource. The key objectives of market research were: - Understand and fine tune our target market - Determine the services most important to our target market - Determine if there is a need for a fitness website that integrates fitness resources Methodology: - Secondary Research - Primary Research - Qualitative and Quantitative Online survey link: http://www.instant.ly/s/FgF7s Findings and Recommendations: - Redefine the target market - Develop an app for instant sharing - Exclude features such as Forums from the website - Emphasize on local fitness information

Market Research to determine the need of an online integrated fitness resource from Akanksha Jain

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0 Social Media Strategy for Toffee Talk /AkankshaJain20/social-media-strategy-for-toffee-talk toffeetalklinkedin-131214025419-phpapp01
This project involves an in-depth analysis of a startup firm called Toffee Talk. It includes an analysis of their target segment, current marketing strategies and metrics. Based on their vision and budget considerations, the project highlights recommendations which includes: - Redefining the target segment - Proposed Social Media Strategy - Content Narrative - Tools and Techniques ]]>
This project involves an in-depth analysis of a startup firm called Toffee Talk. It includes an analysis of their target segment, current marketing strategies and metrics. Based on their vision and budget considerations, the project highlights recommendations which includes: - Redefining the target segment - Proposed Social Media Strategy - Content Narrative - Tools and Techniques ]]> Sat, 14 Dec 2013 02:54:19 GMT /AkankshaJain20/social-media-strategy-for-toffee-talk AkankshaJain20@slideshare.net(AkankshaJain20) Social Media Strategy for Toffee Talk AkankshaJain20 This project involves an in-depth analysis of a startup firm called Toffee Talk. It includes an analysis of their target segment, current marketing strategies and metrics. Based on their vision and budget considerations, the project highlights recommendations which includes: - Redefining the target segment - Proposed Social Media Strategy - Content Narrative - Tools and Techniques <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/toffeetalklinkedin-131214025419-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This project involves an in-depth analysis of a startup firm called Toffee Talk. It includes an analysis of their target segment, current marketing strategies and metrics. Based on their vision and budget considerations, the project highlights recommendations which includes: - Redefining the target segment - Proposed Social Media Strategy - Content Narrative - Tools and Techniques

Social Media Strategy for Toffee Talk from Akanksha Jain

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0 Predictive Model for Customer Segmentation using Database Marketing Techniques /slideshow/database-make/29193150 crmprojectdonorakanksha-131213181231-phpapp02
Develop a predictive model using historical data set DONOR_RAW, which can predict whether the prospect will donate/ not donate. Data set: DONOR_RAW data set • 50 Variables • 19,372 observations Tools Used: • SAS Enterprise Miner 4.3 • SAS 9.3_M1 Techniques Used: • Logistic Regression • Decision Trees - CHAID Also introduced Interaction Terms to have a better understanding of the data. Final Model Selection Analysis based on: • LIFT Chart]]>
Develop a predictive model using historical data set DONOR_RAW, which can predict whether the prospect will donate/ not donate. Data set: DONOR_RAW data set • 50 Variables • 19,372 observations Tools Used: • SAS Enterprise Miner 4.3 • SAS 9.3_M1 Techniques Used: • Logistic Regression • Decision Trees - CHAID Also introduced Interaction Terms to have a better understanding of the data. Final Model Selection Analysis based on: • LIFT Chart]]> Fri, 13 Dec 2013 18:12:31 GMT /slideshow/database-make/29193150 AkankshaJain20@slideshare.net(AkankshaJain20) Predictive Model for Customer Segmentation using Database Marketing Techniques AkankshaJain20 Develop a predictive model using historical data set DONOR_RAW, which can predict whether the prospect will donate/ not donate. Data set: DONOR_RAW data set • 50 Variables • 19,372 observations Tools Used: • SAS Enterprise Miner 4.3 • SAS 9.3_M1 Techniques Used: • Logistic Regression • Decision Trees - CHAID Also introduced Interaction Terms to have a better understanding of the data. Final Model Selection Analysis based on: • LIFT Chart <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/crmprojectdonorakanksha-131213181231-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Develop a predictive model using historical data set DONOR_RAW, which can predict whether the prospect will donate/ not donate. Data set: DONOR_RAW data set • 50 Variables • 19,372 observations Tools Used: • SAS Enterprise Miner 4.3 • SAS 9.3_M1 Techniques Used: • Logistic Regression • Decision Trees - CHAID Also introduced Interaction Terms to have a better understanding of the data. Final Model Selection Analysis based on: • LIFT Chart

Predictive Model for Customer Segmentation using Database Marketing Techniques from Akanksha Jain

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0 Managing Marketing Communications for Francesca's /slideshow/managing-marketing-communications-for-francescas/29192778 marcommprojectfrancescasakankshajainlinkedin-131213174430-phpapp02
This plan deals with an in-depth analysis of Francesca's present marketing efforts, and suggests pointers to help make the marketing communication efforts more integrated. Topics covered are: 1. Background/Situation Analysis 2. Customer Groups 3. Program Objectives 4. Channels & Contact Points 5. Content & Message Strategy 6. Measurement and ROI 7. Budgeting & Organization Considerations ]]>
This plan deals with an in-depth analysis of Francesca's present marketing efforts, and suggests pointers to help make the marketing communication efforts more integrated. Topics covered are: 1. Background/Situation Analysis 2. Customer Groups 3. Program Objectives 4. Channels & Contact Points 5. Content & Message Strategy 6. Measurement and ROI 7. Budgeting & Organization Considerations ]]> Fri, 13 Dec 2013 17:44:30 GMT /slideshow/managing-marketing-communications-for-francescas/29192778 AkankshaJain20@slideshare.net(AkankshaJain20) Managing Marketing Communications for Francesca's AkankshaJain20 This plan deals with an in-depth analysis of Francesca's present marketing efforts, and suggests pointers to help make the marketing communication efforts more integrated. Topics covered are: 1. Background/Situation Analysis 2. Customer Groups 3. Program Objectives 4. Channels & Contact Points 5. Content & Message Strategy 6. Measurement and ROI 7. Budgeting & Organization Considerations <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/marcommprojectfrancescasakankshajainlinkedin-131213174430-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This plan deals with an in-depth analysis of Francesca's present marketing efforts, and suggests pointers to help make the marketing communication efforts more integrated. Topics covered are: 1. Background/Situation Analysis 2. Customer Groups 3. Program Objectives 4. Channels & Contact Points 5. Content & Message Strategy 6. Measurement and ROI 7. Budgeting & Organization Considerations

Managing Marketing Communications for Francesca's from Akanksha Jain

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0 Predictive Model for Loan Approval Process using SAS 9.3_M1 /slideshow/german-bank-loanapprovaldecisionpredictivemodel/29189306 germanbankloanapprovaldecisionpredictivemodel-131213142510-phpapp01
This is a Predictive Model which uses Logistic Regression to statistically help make better loan approval decisions in future for a German Bank. It uses an historical credit data set with 1000 data points and 20 variables. Tool used: SAS 9.3_M1 Steps Involved are: - Data Quality check using Correlations and VIF Tests - Analysis of different Variable Selection Methods such as Forward, Backward and Stepwise - Variable Selection on the basis of Parameter Estimates and Odds Ratio - Outlier Analysis to identify the outliers and improve the model - Final Model Selection Decision based on ROC curve, Percent Concordant, PROC Rank and Hosmer Lemeshow Test]]>
This is a Predictive Model which uses Logistic Regression to statistically help make better loan approval decisions in future for a German Bank. It uses an historical credit data set with 1000 data points and 20 variables. Tool used: SAS 9.3_M1 Steps Involved are: - Data Quality check using Correlations and VIF Tests - Analysis of different Variable Selection Methods such as Forward, Backward and Stepwise - Variable Selection on the basis of Parameter Estimates and Odds Ratio - Outlier Analysis to identify the outliers and improve the model - Final Model Selection Decision based on ROC curve, Percent Concordant, PROC Rank and Hosmer Lemeshow Test]]> Fri, 13 Dec 2013 14:25:10 GMT /slideshow/german-bank-loanapprovaldecisionpredictivemodel/29189306 AkankshaJain20@slideshare.net(AkankshaJain20) Predictive Model for Loan Approval Process using SAS 9.3_M1 AkankshaJain20 This is a Predictive Model which uses Logistic Regression to statistically help make better loan approval decisions in future for a German Bank. It uses an historical credit data set with 1000 data points and 20 variables. Tool used: SAS 9.3_M1 Steps Involved are: - Data Quality check using Correlations and VIF Tests - Analysis of different Variable Selection Methods such as Forward, Backward and Stepwise - Variable Selection on the basis of Parameter Estimates and Odds Ratio - Outlier Analysis to identify the outliers and improve the model - Final Model Selection Decision based on ROC curve, Percent Concordant, PROC Rank and Hosmer Lemeshow Test <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/germanbankloanapprovaldecisionpredictivemodel-131213142510-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a Predictive Model which uses Logistic Regression to statistically help make better loan approval decisions in future for a German Bank. It uses an historical credit data set with 1000 data points and 20 variables. Tool used: SAS 9.3_M1 Steps Involved are: - Data Quality check using Correlations and VIF Tests - Analysis of different Variable Selection Methods such as Forward, Backward and Stepwise - Variable Selection on the basis of Parameter Estimates and Odds Ratio - Outlier Analysis to identify the outliers and improve the model - Final Model Selection Decision based on ROC curve, Percent Concordant, PROC Rank and Hosmer Lemeshow Test

Predictive Model for Loan Approval Process using SAS 9.3_M1 from Akanksha Jain

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0 https://cdn.slidesharecdn.com/profile-photo-AkankshaJain20-48x48.jpg?cb=1523734761 Marketing Analytics professional and a SAS certified advanced programmer, with over 6 years of experience in the field of Marketing, Analytics and Strategy, in Technology and Financial Services industries. Qualities: * Build higher-level business insights on cause-and-effect, and measure/forecast progress of various efforts against goals to drive social media strategy * Experienced with and passionate about using data to drive strategy and product recommendations * Present insights and recommendations for action to marketing/product leadership teams by creating dashboards that explain what happened and why * Marketing Automation, Marketing Strategy, Segmentation, Budgeting, A/B Testing *... https://cdn.slidesharecdn.com/ss_thumbnails/dataminingfinalprojectsmallcleandataset-131217193043-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-mining-finalprojectsmallcleandataset/29304157 Prospect Identificatio... https://cdn.slidesharecdn.com/ss_thumbnails/fitopiamarketresearchproject2013linkedin-131214065803-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/market-res/29203751 Market Research to det... https://cdn.slidesharecdn.com/ss_thumbnails/toffeetalklinkedin-131214025419-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds AkankshaJain20/social-media-strategy-for-toffee-talk Social Media Strategy ...