狠狠撸shows by User: ShantanuDeshpande6
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Tue, 19 May 2020 14:17:22 GMT狠狠撸Share feed for 狠狠撸shows by User: ShantanuDeshpande6Prediction of Corporate Bankruptcy using Machine Learning Techniques
/slideshow/prediction-of-corporate-bankruptcy-using-machine-learning-techniques/234274161
x18125514-thesis-report-200519141722 Aim is to build a classification model to predict whether company will become bankrupt or not using financial ratios of Polish companies. Applied various machine learning models like Random Forest, KNN, AdaBoost & Decision Tree with pre-processing techniques like SMOTE-ENN (to deal with class imbalance) & feature selection (for identifying ) and trained on Polish Bankruptcy dataset with prediction accuracy of 89%.]]>
Aim is to build a classification model to predict whether company will become bankrupt or not using financial ratios of Polish companies. Applied various machine learning models like Random Forest, KNN, AdaBoost & Decision Tree with pre-processing techniques like SMOTE-ENN (to deal with class imbalance) & feature selection (for identifying ) and trained on Polish Bankruptcy dataset with prediction accuracy of 89%.]]>
Tue, 19 May 2020 14:17:22 GMT/slideshow/prediction-of-corporate-bankruptcy-using-machine-learning-techniques/234274161ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Prediction of Corporate Bankruptcy using Machine Learning Techniques ShantanuDeshpande6Aim is to build a classification model to predict whether company will become bankrupt or not using financial ratios of Polish companies. Applied various machine learning models like Random Forest, KNN, AdaBoost & Decision Tree with pre-processing techniques like SMOTE-ENN (to deal with class imbalance) & feature selection (for identifying ) and trained on Polish Bankruptcy dataset with prediction accuracy of 89%.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-thesis-report-200519141722-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Aim is to build a classification model to predict whether company will become bankrupt or not using financial ratios of Polish companies. Applied various machine learning models like Random Forest, KNN, AdaBoost & Decision Tree with pre-processing techniques like SMOTE-ENN (to deal with class imbalance) & feature selection (for identifying ) and trained on Polish Bankruptcy dataset with prediction accuracy of 89%.
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7800https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-thesis-report-200519141722-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Corporate bankruptcy prediction using Deep learning techniques
/slideshow/corporate-bankruptcy-prediction-using-deep-learning-techniques/185749606
x18125514-ric-proposal-191023105106 Corporate Bankruptcy prediction using Recurrent neural networks 鈥� Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
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Corporate Bankruptcy prediction using Recurrent neural networks 鈥� Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
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Wed, 23 Oct 2019 10:51:06 GMT/slideshow/corporate-bankruptcy-prediction-using-deep-learning-techniques/185749606ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Corporate bankruptcy prediction using Deep learning techniquesShantanuDeshpande6Corporate Bankruptcy prediction using Recurrent neural networks 鈥� Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-ric-proposal-191023105106-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Corporate Bankruptcy prediction using Recurrent neural networks 鈥� Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
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8310https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-ric-proposal-191023105106-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Analyzing financial behavior of a person based on financial literacy
/slideshow/analyzing-financial-behavior-of-a-person-based-on-financial-literacy/185744861
x18125514-crm-ca2-191023102525 6. Analysed consumer behaviour and relationship using Financial literacy dataset. Identified patterns and predictor variables using logistic regression.
Methodologies & Tools: IBM SPSS, RapidMiner, PowerBI]]>
6. Analysed consumer behaviour and relationship using Financial literacy dataset. Identified patterns and predictor variables using logistic regression.
Methodologies & Tools: IBM SPSS, RapidMiner, PowerBI]]>
Wed, 23 Oct 2019 10:25:25 GMT/slideshow/analyzing-financial-behavior-of-a-person-based-on-financial-literacy/185744861ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Analyzing financial behavior of a person based on financial literacyShantanuDeshpande66. Analysed consumer behaviour and relationship using Financial literacy dataset. Identified patterns and predictor variables using logistic regression.
Methodologies & Tools: IBM SPSS, RapidMiner, PowerBI<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-crm-ca2-191023102525-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 6. Analysed consumer behaviour and relationship using Financial literacy dataset. Identified patterns and predictor variables using logistic regression.
Methodologies & Tools: IBM SPSS, RapidMiner, PowerBI
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610https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-crm-ca2-191023102525-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Pneumonia detection using CNN
/slideshow/pneumonia-detection-using-cnn-185739873/185739873
pneumoniadetectionreport-191023100300 Built a CNN based machine learning model to diagnose Pneumonia disease using chest x-rays.
Methodologies & Tools: KDD, Python, VGG19 model, Convolutional Neural Network.]]>
Built a CNN based machine learning model to diagnose Pneumonia disease using chest x-rays.
Methodologies & Tools: KDD, Python, VGG19 model, Convolutional Neural Network.]]>
Wed, 23 Oct 2019 10:03:00 GMT/slideshow/pneumonia-detection-using-cnn-185739873/185739873ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Pneumonia detection using CNNShantanuDeshpande6Built a CNN based machine learning model to diagnose Pneumonia disease using chest x-rays.
Methodologies & Tools: KDD, Python, VGG19 model, Convolutional Neural Network.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pneumoniadetectionreport-191023100300-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Built a CNN based machine learning model to diagnose Pneumonia disease using chest x-rays.
Methodologies & Tools: KDD, Python, VGG19 model, Convolutional Neural Network.
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2320https://cdn.slidesharecdn.com/ss_thumbnails/pneumoniadetectionreport-191023100300-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0X18125514 ca2-statisticsfor dataanalytics
/ShantanuDeshpande6/x18125514-ca2statisticsfor-dataanalytics
x18125514-ca2-statisticsfordataanalytics-191023095105 4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS]]>
4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS]]>
Wed, 23 Oct 2019 09:51:05 GMT/ShantanuDeshpande6/x18125514-ca2statisticsfor-dataanalyticsShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)X18125514 ca2-statisticsfor dataanalyticsShantanuDeshpande64. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-ca2-statisticsfordataanalytics-191023095105-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS
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1861https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-ca2-statisticsfordataanalytics-191023095105-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Pharmaceutical store management system
/slideshow/pharmaceutical-store-management-system-185736164/185736164
pharmaceuticalstoremanagementsystem-191023094344 2. Developed a strategic management information system for a virtual organization while considering the analytical requirements for management dashboards.
Tools: Salesforce Developer platform]]>
2. Developed a strategic management information system for a virtual organization while considering the analytical requirements for management dashboards.
Tools: Salesforce Developer platform]]>
Wed, 23 Oct 2019 09:43:43 GMT/slideshow/pharmaceutical-store-management-system-185736164/185736164ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Pharmaceutical store management systemShantanuDeshpande62. Developed a strategic management information system for a virtual organization while considering the analytical requirements for management dashboards.
Tools: Salesforce Developer platform<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pharmaceuticalstoremanagementsystem-191023094344-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 2. Developed a strategic management information system for a virtual organization while considering the analytical requirements for management dashboards.
Tools: Salesforce Developer platform
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78991https://cdn.slidesharecdn.com/ss_thumbnails/pharmaceuticalstoremanagementsystem-191023094344-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Data-Warehouse-and-Business-Intelligence
/slideshow/datawarehouseandbusinessintelligence/185733502
x18125514-dwbi-shantanu-deshpande-191023093326 Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau]]>
Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau]]>
Wed, 23 Oct 2019 09:33:26 GMT/slideshow/datawarehouseandbusinessintelligence/185733502ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Data-Warehouse-and-Business-IntelligenceShantanuDeshpande6Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-dwbi-shantanu-deshpande-191023093326-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau
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1412https://cdn.slidesharecdn.com/ss_thumbnails/x18125514-dwbi-shantanu-deshpande-191023093326-thumbnail.jpg?width=120&height=120&fit=boundsdocumentBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Dsm project-h base-cassandra
/slideshow/dsm-projecth-basecassandra/184362224
dsm-project-hbase-cassandra-191020120827 I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.]]>
I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.]]>
Sun, 20 Oct 2019 12:08:27 GMT/slideshow/dsm-projecth-basecassandra/184362224ShantanuDeshpande6@slideshare.net(ShantanuDeshpande6)Dsm project-h base-cassandraShantanuDeshpande6I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dsm-project-hbase-cassandra-191020120827-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> I have examined the performance of two databases - HBase and Cassandra in terms of their scalability, security, performance and compared the results thus obtained through different operations on the Ubuntu interface.