�ݺ�ߣshows by User: sotbar7

�ݺ�ߣshows by User: sotbar7 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: sotbar7 / Wed, 24 Jul 2019 19:33:20 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: sotbar7 Twitter Mention Graph - Analytics Project /slideshow/twitter-mention-graph-analytics-project/157617712 twitter-mention-graph-sna-190724193320
In this study, we attempted to study the network of Twitter users and the mentions between them. Starting with a very large and incorrectly structured dataset, we used the Unix terminal (sed) and regular expressions to efficiently perform filtering and various transformations to end up with a lighter dataset. Then, using Python, we completely transformed the dataset from a linear (line by line) to a tabular format (columns), in order to load the data in iGraph. Using iGraph, we created a weighted directed graph and performed various tasks to explore the network: - Identifying basic properties of the network, such as the Number of vertices, Number of edges, Diameter of the graph, Average in-degree and Average out-degree. - Visualising the 5-day evolution of these metrics and commenting on observed fluctuations. - Identifying the important nodes of the graph, based on In-degree, Out-degree and PageRank - Performing community detections on the mention graphs, by applying fast greedy clustering, infomap clustering, and louvain clustering on the undirected versions of the 5 mention graphs. - Visualising the different communities in the mention graph.]]>
In this study, we attempted to study the network of Twitter users and the mentions between them. Starting with a very large and incorrectly structured dataset, we used the Unix terminal (sed) and regular expressions to efficiently perform filtering and various transformations to end up with a lighter dataset. Then, using Python, we completely transformed the dataset from a linear (line by line) to a tabular format (columns), in order to load the data in iGraph. Using iGraph, we created a weighted directed graph and performed various tasks to explore the network: - Identifying basic properties of the network, such as the Number of vertices, Number of edges, Diameter of the graph, Average in-degree and Average out-degree. - Visualising the 5-day evolution of these metrics and commenting on observed fluctuations. - Identifying the important nodes of the graph, based on In-degree, Out-degree and PageRank - Performing community detections on the mention graphs, by applying fast greedy clustering, infomap clustering, and louvain clustering on the undirected versions of the 5 mention graphs. - Visualising the different communities in the mention graph.]]> Wed, 24 Jul 2019 19:33:20 GMT /slideshow/twitter-mention-graph-analytics-project/157617712 sotbar7@slideshare.net(sotbar7) Twitter Mention Graph - Analytics Project sotbar7 In this study, we attempted to study the network of Twitter users and the mentions between them. Starting with a very large and incorrectly structured dataset, we used the Unix terminal (sed) and regular expressions to efficiently perform filtering and various transformations to end up with a lighter dataset. Then, using Python, we completely transformed the dataset from a linear (line by line) to a tabular format (columns), in order to load the data in iGraph. Using iGraph, we created a weighted directed graph and performed various tasks to explore the network: - Identifying basic properties of the network, such as the Number of vertices, Number of edges, Diameter of the graph, Average in-degree and Average out-degree. - Visualising the 5-day evolution of these metrics and commenting on observed fluctuations. - Identifying the important nodes of the graph, based on In-degree, Out-degree and PageRank - Performing community detections on the mention graphs, by applying fast greedy clustering, infomap clustering, and louvain clustering on the undirected versions of the 5 mention graphs. - Visualising the different communities in the mention graph. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/twitter-mention-graph-sna-190724193320-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this study, we attempted to study the network of Twitter users and the mentions between them. Starting with a very large and incorrectly structured dataset, we used the Unix terminal (sed) and regular expressions to efficiently perform filtering and various transformations to end up with a lighter dataset. Then, using Python, we completely transformed the dataset from a linear (line by line) to a tabular format (columns), in order to load the data in iGraph. Using iGraph, we created a weighted directed graph and performed various tasks to explore the network: - Identifying basic properties of the network, such as the Number of vertices, Number of edges, Diameter of the graph, Average in-degree and Average out-degree. - Visualising the 5-day evolution of these metrics and commenting on observed fluctuations. - Identifying the important nodes of the graph, based on In-degree, Out-degree and PageRank - Performing community detections on the mention graphs, by applying fast greedy clustering, infomap clustering, and louvain clustering on the undirected versions of the 5 mention graphs. - Visualising the different communities in the mention graph.

Twitter Mention Graph - Analytics Project from Sotiris Baratsas

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0 Suicides in Greece (vs rest of Europe) /slideshow/suicides-in-greece-vs-rest-of-europe/157596242 suicidesreport-grvseu-190724180847
The scope of this project is to use the data concerning suicides in Greece and the EU, to study whether there is an increasing trend of suicides in Greece, or better whether the behaviour of Greece is different than the rest of Europe. The output should be a report with at least 10 visualisations to study the data and create an informed opinion about the topic in a concrete and convincing manner. The key findings of the project are: * Greece has lower suicides than the EU as a whole. * Countries in the Mediterranean have fewer suicides, while Nothern Europe has higher suicide rates. * Men are significantly more likely to commit suicide compared to women. * The over-representation of men is universal across all countries in the EU. * The likelihood of suicide increases with age. * While not true for the whole EU, in Greece the economic slowdown might relate to an increase in suicides.]]>
The scope of this project is to use the data concerning suicides in Greece and the EU, to study whether there is an increasing trend of suicides in Greece, or better whether the behaviour of Greece is different than the rest of Europe. The output should be a report with at least 10 visualisations to study the data and create an informed opinion about the topic in a concrete and convincing manner. The key findings of the project are: * Greece has lower suicides than the EU as a whole. * Countries in the Mediterranean have fewer suicides, while Nothern Europe has higher suicide rates. * Men are significantly more likely to commit suicide compared to women. * The over-representation of men is universal across all countries in the EU. * The likelihood of suicide increases with age. * While not true for the whole EU, in Greece the economic slowdown might relate to an increase in suicides.]]> Wed, 24 Jul 2019 18:08:47 GMT /slideshow/suicides-in-greece-vs-rest-of-europe/157596242 sotbar7@slideshare.net(sotbar7) Suicides in Greece (vs rest of Europe) sotbar7 The scope of this project is to use the data concerning suicides in Greece and the EU, to study whether there is an increasing trend of suicides in Greece, or better whether the behaviour of Greece is different than the rest of Europe. The output should be a report with at least 10 visualisations to study the data and create an informed opinion about the topic in a concrete and convincing manner. The key findings of the project are: * Greece has lower suicides than the EU as a whole. * Countries in the Mediterranean have fewer suicides, while Nothern Europe has higher suicide rates. * Men are significantly more likely to commit suicide compared to women. * The over-representation of men is universal across all countries in the EU. * The likelihood of suicide increases with age. * While not true for the whole EU, in Greece the economic slowdown might relate to an increase in suicides. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/suicidesreport-grvseu-190724180847-thumbnail.jpg?width=120&height=120&fit=bounds" /> The scope of this project is to use the data concerning suicides in Greece and the EU, to study whether there is an increasing trend of suicides in Greece, or better whether the behaviour of Greece is different than the rest of Europe. The output should be a report with at least 10 visualisations to study the data and create an informed opinion about the topic in a concrete and convincing manner. The key findings of the project are: * Greece has lower suicides than the EU as a whole. * Countries in the Mediterranean have fewer suicides, while Nothern Europe has higher suicide rates. * Men are significantly more likely to commit suicide compared to women. * The over-representation of men is universal across all countries in the EU. * The likelihood of suicide increases with age. * While not true for the whole EU, in Greece the economic slowdown might relate to an increase in suicides.

Suicides in Greece (vs rest of Europe) from Sotiris Baratsas

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0 Predicting US house prices using Multiple Linear Regression in R /slideshow/predicting-us-house-prices-using-multiple-linear-regression-in-r/156253047 predictingushouseprices-mlrinr-190718101111
In this study, we attempted to formulate a Multiple Linear Regression model, to predict US house prices. Steps involved: Perform descriptive analysis and visualisation for each variable to get an initial insight of what the data looks like. Conduct pairwise comparisons between the variables in the dataset to investigate if there are any associations implied by the dataset. Construct a model for the expected selling prices according to the remaining features. Check whether this linear model fits well to the data. Find the best model for predicting the selling prices and select the appropriate features using stepwise methods (used Forward, Backward and Stepwise procedures according to AIC or BIC to choose which variables appear to be more significant for predicting selling prices). Get the summary of our final model, interpret the coefficients. Comment on the significance of each coefficient and write down the mathematical formulation of the model. Consider whether the intercept should be excluded from our model. Check the assumptions of your final model. Are the assumptions satisfied? If not, what is the impact of the violation of the assumption not satisfied in terms of inference? What could someone do about it? Conduct LASSO as a variable selection technique and compare the variables that we end up having using LASSO to the variables that you ended up having using stepwise methods.]]>
In this study, we attempted to formulate a Multiple Linear Regression model, to predict US house prices. Steps involved: Perform descriptive analysis and visualisation for each variable to get an initial insight of what the data looks like. Conduct pairwise comparisons between the variables in the dataset to investigate if there are any associations implied by the dataset. Construct a model for the expected selling prices according to the remaining features. Check whether this linear model fits well to the data. Find the best model for predicting the selling prices and select the appropriate features using stepwise methods (used Forward, Backward and Stepwise procedures according to AIC or BIC to choose which variables appear to be more significant for predicting selling prices). Get the summary of our final model, interpret the coefficients. Comment on the significance of each coefficient and write down the mathematical formulation of the model. Consider whether the intercept should be excluded from our model. Check the assumptions of your final model. Are the assumptions satisfied? If not, what is the impact of the violation of the assumption not satisfied in terms of inference? What could someone do about it? Conduct LASSO as a variable selection technique and compare the variables that we end up having using LASSO to the variables that you ended up having using stepwise methods.]]> Thu, 18 Jul 2019 10:11:10 GMT /slideshow/predicting-us-house-prices-using-multiple-linear-regression-in-r/156253047 sotbar7@slideshare.net(sotbar7) Predicting US house prices using Multiple Linear Regression in R sotbar7 In this study, we attempted to formulate a Multiple Linear Regression model, to predict US house prices. Steps involved: Perform descriptive analysis and visualisation for each variable to get an initial insight of what the data looks like. Conduct pairwise comparisons between the variables in the dataset to investigate if there are any associations implied by the dataset. Construct a model for the expected selling prices according to the remaining features. Check whether this linear model fits well to the data. Find the best model for predicting the selling prices and select the appropriate features using stepwise methods (used Forward, Backward and Stepwise procedures according to AIC or BIC to choose which variables appear to be more significant for predicting selling prices). Get the summary of our final model, interpret the coefficients. Comment on the significance of each coefficient and write down the mathematical formulation of the model. Consider whether the intercept should be excluded from our model. Check the assumptions of your final model. Are the assumptions satisfied? If not, what is the impact of the violation of the assumption not satisfied in terms of inference? What could someone do about it? Conduct LASSO as a variable selection technique and compare the variables that we end up having using LASSO to the variables that you ended up having using stepwise methods. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/predictingushouseprices-mlrinr-190718101111-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this study, we attempted to formulate a Multiple Linear Regression model, to predict US house prices. Steps involved: Perform descriptive analysis and visualisation for each variable to get an initial insight of what the data looks like. Conduct pairwise comparisons between the variables in the dataset to investigate if there are any associations implied by the dataset. Construct a model for the expected selling prices according to the remaining features. Check whether this linear model fits well to the data. Find the best model for predicting the selling prices and select the appropriate features using stepwise methods (used Forward, Backward and Stepwise procedures according to AIC or BIC to choose which variables appear to be more significant for predicting selling prices). Get the summary of our final model, interpret the coefficients. Comment on the significance of each coefficient and write down the mathematical formulation of the model. Consider whether the intercept should be excluded from our model. Check the assumptions of your final model. Are the assumptions satisfied? If not, what is the impact of the violation of the assumption not satisfied in terms of inference? What could someone do about it? Conduct LASSO as a variable selection technique and compare the variables that we end up having using LASSO to the variables that you ended up having using stepwise methods.

Predicting US house prices using Multiple Linear Regression in R from Sotiris Baratsas

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0 Azure Stream Analytics Report - Toll Booth Stream /slideshow/azure-stream-analytics-report-toll-booth-stream/156084601 report-190717102145
We have access to a data stream that’s generated from sensors placed in some checkpoints (toll stations and speed cameras). Each time a car passes by one checkpoint, an event is generated. All cars are equipped with tags that provide the vehicleTypeID and colorID of each car. Tag readers are capable of reading this information. In addition, a camera reads the license plate and completed the event’s data. We are asked to create an Azure Analytics solution for the tasks listed in the “QUERIES” section.]]>
We have access to a data stream that’s generated from sensors placed in some checkpoints (toll stations and speed cameras). Each time a car passes by one checkpoint, an event is generated. All cars are equipped with tags that provide the vehicleTypeID and colorID of each car. Tag readers are capable of reading this information. In addition, a camera reads the license plate and completed the event’s data. We are asked to create an Azure Analytics solution for the tasks listed in the “QUERIES” section.]]> Wed, 17 Jul 2019 10:21:45 GMT /slideshow/azure-stream-analytics-report-toll-booth-stream/156084601 sotbar7@slideshare.net(sotbar7) Azure Stream Analytics Report - Toll Booth Stream sotbar7 We have access to a data stream that’s generated from sensors placed in some checkpoints (toll stations and speed cameras). Each time a car passes by one checkpoint, an event is generated. All cars are equipped with tags that provide the vehicleTypeID and colorID of each car. Tag readers are capable of reading this information. In addition, a camera reads the license plate and completed the event’s data. We are asked to create an Azure Analytics solution for the tasks listed in the “QUERIES” section. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/report-190717102145-thumbnail.jpg?width=120&height=120&fit=bounds" /> We have access to a data stream that’s generated from sensors placed in some checkpoints (toll stations and speed cameras). Each time a car passes by one checkpoint, an event is generated. All cars are equipped with tags that provide the vehicleTypeID and colorID of each car. Tag readers are capable of reading this information. In addition, a camera reads the license plate and completed the event’s data. We are asked to create an Azure Analytics solution for the tasks listed in the “QUERIES” section.

Azure Stream Analytics Report - Toll Booth Stream from Sotiris Baratsas

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0 Brooklyn Property Sales - DATA WAREHOUSE (DW) /slideshow/brooklyn-property-sales-data-warehouse/155951003 dw-brooklyn-property-sales-190716174931
SCOPE OF THE PROJECT: The project is focused on the creation of a Data Warehouse application, for the analysis of property sales in Brooklyn, one of the five boroughs of New York CIty. The project is split into 5 main phases: Phase 1: Finding the dataset, understanding its structure and what are the meaningful business questions, this dataset could answer. Phase 2: Extract-Transform-Load processes for the data warehouse, using R Studio. Phase 3: Building of the Data Warehouse using Microsoft SQL Server. Phase 4: Building the Multidimensional Cube using Microsoft Analysis Services and Visual Studio. Phase 5: OLAP Report and Data Visualization (using Tableau). ]]>
SCOPE OF THE PROJECT: The project is focused on the creation of a Data Warehouse application, for the analysis of property sales in Brooklyn, one of the five boroughs of New York CIty. The project is split into 5 main phases: Phase 1: Finding the dataset, understanding its structure and what are the meaningful business questions, this dataset could answer. Phase 2: Extract-Transform-Load processes for the data warehouse, using R Studio. Phase 3: Building of the Data Warehouse using Microsoft SQL Server. Phase 4: Building the Multidimensional Cube using Microsoft Analysis Services and Visual Studio. Phase 5: OLAP Report and Data Visualization (using Tableau). ]]> Tue, 16 Jul 2019 17:49:31 GMT /slideshow/brooklyn-property-sales-data-warehouse/155951003 sotbar7@slideshare.net(sotbar7) Brooklyn Property Sales - DATA WAREHOUSE (DW) sotbar7 SCOPE OF THE PROJECT: The project is focused on the creation of a Data Warehouse application, for the analysis of property sales in Brooklyn, one of the five boroughs of New York CIty. The project is split into 5 main phases: Phase 1: Finding the dataset, understanding its structure and what are the meaningful business questions, this dataset could answer. Phase 2: Extract-Transform-Load processes for the data warehouse, using R Studio. Phase 3: Building of the Data Warehouse using Microsoft SQL Server. Phase 4: Building the Multidimensional Cube using Microsoft Analysis Services and Visual Studio. Phase 5: OLAP Report and Data Visualization (using Tableau). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dw-brooklyn-property-sales-190716174931-thumbnail.jpg?width=120&height=120&fit=bounds" /> SCOPE OF THE PROJECT: The project is focused on the creation of a Data Warehouse application, for the analysis of property sales in Brooklyn, one of the five boroughs of New York CIty. The project is split into 5 main phases: Phase 1: Finding the dataset, understanding its structure and what are the meaningful business questions, this dataset could answer. Phase 2: Extract-Transform-Load processes for the data warehouse, using R Studio. Phase 3: Building of the Data Warehouse using Microsoft SQL Server. Phase 4: Building the Multidimensional Cube using Microsoft Analysis Services and Visual Studio. Phase 5: OLAP Report and Data Visualization (using Tableau).

Brooklyn Property Sales - DATA WAREHOUSE (DW) from Sotiris Baratsas

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0 Car Rental Agency - Database - MySQL /sotbar7/car-rental-agency-database-mysql report-190716105656
A car rental company wants to develop a relational database to monitor customers, rentals, fleet and locations. The company's fleet consists of cars of different types. A car is described via a unique code (VIN), a description, color, brand, model, and date of purchase. A car may belong to one (exactly one) vehicle category (compact, economy, convertible, etc.). Each category is described by a unique ID, a label and a detailed description. The company has several locations around the globe. Each location has a unique ID, an address (street, number, city, state, country) and one or more telephone numbers. The company should also store in this database its customers. A customer is described by a unique ID, SSN, Name (First, Last), email, mobile phone number and lives in a state and country. Customers rent a car, which they pickup from a location and return it another location (not necessarily the same.) A rental is described by a unique reservation number, it has an amount and contains the pickup date and the return date. Entity-Relationship Diagram (ERD) Use the Entity-Relationship Diagram (ERD) to model entities, relationships, attributes, cardinalities, and all necessary constraints. Use any tool you like to draw the ERD.]]>
A car rental company wants to develop a relational database to monitor customers, rentals, fleet and locations. The company's fleet consists of cars of different types. A car is described via a unique code (VIN), a description, color, brand, model, and date of purchase. A car may belong to one (exactly one) vehicle category (compact, economy, convertible, etc.). Each category is described by a unique ID, a label and a detailed description. The company has several locations around the globe. Each location has a unique ID, an address (street, number, city, state, country) and one or more telephone numbers. The company should also store in this database its customers. A customer is described by a unique ID, SSN, Name (First, Last), email, mobile phone number and lives in a state and country. Customers rent a car, which they pickup from a location and return it another location (not necessarily the same.) A rental is described by a unique reservation number, it has an amount and contains the pickup date and the return date. Entity-Relationship Diagram (ERD) Use the Entity-Relationship Diagram (ERD) to model entities, relationships, attributes, cardinalities, and all necessary constraints. Use any tool you like to draw the ERD.]]> Tue, 16 Jul 2019 10:56:56 GMT /sotbar7/car-rental-agency-database-mysql sotbar7@slideshare.net(sotbar7) Car Rental Agency - Database - MySQL sotbar7 A car rental company wants to develop a relational database to monitor customers, rentals, fleet and locations. The company's fleet consists of cars of different types. A car is described via a unique code (VIN), a description, color, brand, model, and date of purchase. A car may belong to one (exactly one) vehicle category (compact, economy, convertible, etc.). Each category is described by a unique ID, a label and a detailed description. The company has several locations around the globe. Each location has a unique ID, an address (street, number, city, state, country) and one or more telephone numbers. The company should also store in this database its customers. A customer is described by a unique ID, SSN, Name (First, Last), email, mobile phone number and lives in a state and country. Customers rent a car, which they pickup from a location and return it another location (not necessarily the same.) A rental is described by a unique reservation number, it has an amount and contains the pickup date and the return date. Entity-Relationship Diagram (ERD) Use the Entity-Relationship Diagram (ERD) to model entities, relationships, attributes, cardinalities, and all necessary constraints. Use any tool you like to draw the ERD. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/report-190716105656-thumbnail.jpg?width=120&height=120&fit=bounds" /> A car rental company wants to develop a relational database to monitor customers, rentals, fleet and locations. The company's fleet consists of cars of different types. A car is described via a unique code (VIN), a description, color, brand, model, and date of purchase. A car may belong to one (exactly one) vehicle category (compact, economy, convertible, etc.). Each category is described by a unique ID, a label and a detailed description. The company has several locations around the globe. Each location has a unique ID, an address (street, number, city, state, country) and one or more telephone numbers. The company should also store in this database its customers. A customer is described by a unique ID, SSN, Name (First, Last), email, mobile phone number and lives in a state and country. Customers rent a car, which they pickup from a location and return it another location (not necessarily the same.) A rental is described by a unique reservation number, it has an amount and contains the pickup date and the return date. Entity-Relationship Diagram (ERD) Use the Entity-Relationship Diagram (ERD) to model entities, relationships, attributes, cardinalities, and all necessary constraints. Use any tool you like to draw the ERD.

Car Rental Agency - Database - MySQL from Sotiris Baratsas

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0 Predicting Customer Churn in Telecom (Corporate Presentation) /slideshow/predicting-customer-churn-in-telecom-corporate-presentation/155423927 presentationforwebsite-190714115350
This study attempted to formulate a predictive model that identifies whether a customer is probable to switch telecommunications providers (Churn) or stay with the company. We started with a Logistic Regression classifier, and moved on to methods such as Decision Tree, Random Forest, XGBoost, Adaboost, SVM, KNN and Naive- Bayes. We concluded that the best predictive model we could find was XGBoost, which manages to identify correctly almost all the non-churners and the vast majority of the churners. Closely trailing was the Decision Tree model, which is more easily interpretable and applicable in real business problems. On the other hand, Cluster Analysis was a bit more challenging. The Hierarchical Clustering methods we used weren’t very effective. Using the Mahalanobis distance and the Gower distance, we managed to produce 2 clustering methods with Silhouette values equal to 0.2. Using the K-Means method, the results became a little bit better, especially using Principal Components and creating 4 clusters.]]>
This study attempted to formulate a predictive model that identifies whether a customer is probable to switch telecommunications providers (Churn) or stay with the company. We started with a Logistic Regression classifier, and moved on to methods such as Decision Tree, Random Forest, XGBoost, Adaboost, SVM, KNN and Naive- Bayes. We concluded that the best predictive model we could find was XGBoost, which manages to identify correctly almost all the non-churners and the vast majority of the churners. Closely trailing was the Decision Tree model, which is more easily interpretable and applicable in real business problems. On the other hand, Cluster Analysis was a bit more challenging. The Hierarchical Clustering methods we used weren’t very effective. Using the Mahalanobis distance and the Gower distance, we managed to produce 2 clustering methods with Silhouette values equal to 0.2. Using the K-Means method, the results became a little bit better, especially using Principal Components and creating 4 clusters.]]> Sun, 14 Jul 2019 11:53:50 GMT /slideshow/predicting-customer-churn-in-telecom-corporate-presentation/155423927 sotbar7@slideshare.net(sotbar7) Predicting Customer Churn in Telecom (Corporate Presentation) sotbar7 This study attempted to formulate a predictive model that identifies whether a customer is probable to switch telecommunications providers (Churn) or stay with the company. We started with a Logistic Regression classifier, and moved on to methods such as Decision Tree, Random Forest, XGBoost, Adaboost, SVM, KNN and Naive- Bayes. We concluded that the best predictive model we could find was XGBoost, which manages to identify correctly almost all the non-churners and the vast majority of the churners. Closely trailing was the Decision Tree model, which is more easily interpretable and applicable in real business problems. On the other hand, Cluster Analysis was a bit more challenging. The Hierarchical Clustering methods we used weren’t very effective. Using the Mahalanobis distance and the Gower distance, we managed to produce 2 clustering methods with Silhouette values equal to 0.2. Using the K-Means method, the results became a little bit better, especially using Principal Components and creating 4 clusters. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationforwebsite-190714115350-thumbnail.jpg?width=120&height=120&fit=bounds" /> This study attempted to formulate a predictive model that identifies whether a customer is probable to switch telecommunications providers (Churn) or stay with the company. We started with a Logistic Regression classifier, and moved on to methods such as Decision Tree, Random Forest, XGBoost, Adaboost, SVM, KNN and Naive- Bayes. We concluded that the best predictive model we could find was XGBoost, which manages to identify correctly almost all the non-churners and the vast majority of the churners. Closely trailing was the Decision Tree model, which is more easily interpretable and applicable in real business problems. On the other hand, Cluster Analysis was a bit more challenging. The Hierarchical Clustering methods we used weren’t very effective. Using the Mahalanobis distance and the Gower distance, we managed to produce 2 clustering methods with Silhouette values equal to 0.2. Using the K-Means method, the results became a little bit better, especially using Principal Components and creating 4 clusters.

Predicting Customer Churn in Telecom (Corporate Presentation) from Sotiris Baratsas

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0 Understanding Customer Churn in Telecom - Corporate Presentation /slideshow/understanding-customer-churn-in-telecom-corporate-presentation/155422717 churnproject-presentationforwebsite-190714112530
This study attempted to formulate a regression model that identifies the characteristics that influence whether a customer is probable to switch telecommunications providers (Churn). We started with a full model, performed variable selection using AIC and BIC through the stepwise method and moved on to other models, using LASSO, or a few simple (aggregate) transformation of certain predictor variables. We concluded that the best logistic regression model we could find was produced with an aggregate transformation of the variables that concern domestic charges for various times of the day (Day Charges, Evening Charges, Night Charges).]]>
This study attempted to formulate a regression model that identifies the characteristics that influence whether a customer is probable to switch telecommunications providers (Churn). We started with a full model, performed variable selection using AIC and BIC through the stepwise method and moved on to other models, using LASSO, or a few simple (aggregate) transformation of certain predictor variables. We concluded that the best logistic regression model we could find was produced with an aggregate transformation of the variables that concern domestic charges for various times of the day (Day Charges, Evening Charges, Night Charges).]]> Sun, 14 Jul 2019 11:25:30 GMT /slideshow/understanding-customer-churn-in-telecom-corporate-presentation/155422717 sotbar7@slideshare.net(sotbar7) Understanding Customer Churn in Telecom - Corporate Presentation sotbar7 This study attempted to formulate a regression model that identifies the characteristics that influence whether a customer is probable to switch telecommunications providers (Churn). We started with a full model, performed variable selection using AIC and BIC through the stepwise method and moved on to other models, using LASSO, or a few simple (aggregate) transformation of certain predictor variables. We concluded that the best logistic regression model we could find was produced with an aggregate transformation of the variables that concern domestic charges for various times of the day (Day Charges, Evening Charges, Night Charges). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/churnproject-presentationforwebsite-190714112530-thumbnail.jpg?width=120&height=120&fit=bounds" /> This study attempted to formulate a regression model that identifies the characteristics that influence whether a customer is probable to switch telecommunications providers (Churn). We started with a full model, performed variable selection using AIC and BIC through the stepwise method and moved on to other models, using LASSO, or a few simple (aggregate) transformation of certain predictor variables. We concluded that the best logistic regression model we could find was produced with an aggregate transformation of the variables that concern domestic charges for various times of the day (Day Charges, Evening Charges, Night Charges).

Understanding Customer Churn in Telecom - Corporate Presentation from Sotiris Baratsas

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0 How to Avoid (causing) Death by Powerpoint! /slideshow/how-to-avoid-causing-death-by-powerpoint/45267746 howtoavoiddeathbypowerpoint-150228164624-conversion-gate01
Simple tips and tricks you can use to avoid making the audience pull their eyes out with your horrible Powerpoint Presentation! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me!]]>
Simple tips and tricks you can use to avoid making the audience pull their eyes out with your horrible Powerpoint Presentation! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me!]]> Sat, 28 Feb 2015 16:46:24 GMT /slideshow/how-to-avoid-causing-death-by-powerpoint/45267746 sotbar7@slideshare.net(sotbar7) How to Avoid (causing) Death by Powerpoint! sotbar7 Simple tips and tricks you can use to avoid making the audience pull their eyes out with your horrible Powerpoint Presentation! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtoavoiddeathbypowerpoint-150228164624-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Simple tips and tricks you can use to avoid making the audience pull their eyes out with your horrible Powerpoint Presentation! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me!

How to Avoid (causing) Death by Powerpoint! from Sotiris Baratsas

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0 The Secrets of the World's Best Presenters /slideshow/the-secrets-of-the-worlds-best-presenters/45267672 secretsofthebestpresenter-150228164228-conversion-gate01
How to use the techniques of the world's most captivating speakers to take your presentations to the next level! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me!]]>
How to use the techniques of the world's most captivating speakers to take your presentations to the next level! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me!]]> Sat, 28 Feb 2015 16:42:28 GMT /slideshow/the-secrets-of-the-worlds-best-presenters/45267672 sotbar7@slideshare.net(sotbar7) The Secrets of the World's Best Presenters sotbar7 How to use the techniques of the world's most captivating speakers to take your presentations to the next level! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/secretsofthebestpresenter-150228164228-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> How to use the techniques of the world's most captivating speakers to take your presentations to the next level! This is part of a training for "Presentation skills" delivered to the members of AIESEC in Athens in February 2015. There are some videos inside the presentation. Since they don't work on this format, if you want the links for them, feel free to contact me!

The Secrets of the World's Best Presenters from Sotiris Baratsas

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0 The Capitalist's Dilemma - Presentation /slideshow/the-capitalists-dilemma-presentation/43872201 thecapitalistsdilemma-englishv-baratsas-bariamispresentation-150125073646-conversion-gate02
This is a presentation accompanying an assignment for the course "Macroeconomics in English" of the Athens University of Economics and Business. The purpose of the presentation is to present the key points of the assignments and accompany the public presentation of the assignment.]]>
This is a presentation accompanying an assignment for the course "Macroeconomics in English" of the Athens University of Economics and Business. The purpose of the presentation is to present the key points of the assignments and accompany the public presentation of the assignment.]]> Sun, 25 Jan 2015 07:36:46 GMT /slideshow/the-capitalists-dilemma-presentation/43872201 sotbar7@slideshare.net(sotbar7) The Capitalist's Dilemma - Presentation sotbar7 This is a presentation accompanying an assignment for the course "Macroeconomics in English" of the Athens University of Economics and Business. The purpose of the presentation is to present the key points of the assignments and accompany the public presentation of the assignment. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thecapitalistsdilemma-englishv-baratsas-bariamispresentation-150125073646-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation accompanying an assignment for the course "Macroeconomics in English" of the Athens University of Economics and Business. The purpose of the presentation is to present the key points of the assignments and accompany the public presentation of the assignment.

The Capitalist's Dilemma - Presentation from Sotiris Baratsas

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0 Why Global Talent /slideshow/why-global-talent/43872141 whyglobaltalent-150125073149-conversion-gate01
This is a presentation for Global Talent, which is the Global Internship Program of AIESEC. The purpose of the presentation was to deliver the value proposition of the program and convince the attendees of the event to participate in it.]]>
This is a presentation for Global Talent, which is the Global Internship Program of AIESEC. The purpose of the presentation was to deliver the value proposition of the program and convince the attendees of the event to participate in it.]]> Sun, 25 Jan 2015 07:31:49 GMT /slideshow/why-global-talent/43872141 sotbar7@slideshare.net(sotbar7) Why Global Talent sotbar7 This is a presentation for Global Talent, which is the Global Internship Program of AIESEC. The purpose of the presentation was to deliver the value proposition of the program and convince the attendees of the event to participate in it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whyglobaltalent-150125073149-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation for Global Talent, which is the Global Internship Program of AIESEC. The purpose of the presentation was to deliver the value proposition of the program and convince the attendees of the event to participate in it.

Why Global Talent from Sotiris Baratsas

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0 A behavioral explanation of the DOT COM bubble /slideshow/a-behavioral-explanation-of-the-dot-com-bubble/43872081 dotcombubble-baratsasbarjamisclassroompresentationfinal-150125072751-conversion-gate01
This is a presentation accompanying an assignment for the course of Behavioral Finance in Athens University of Economics and Business.]]>
This is a presentation accompanying an assignment for the course of Behavioral Finance in Athens University of Economics and Business.]]> Sun, 25 Jan 2015 07:27:51 GMT /slideshow/a-behavioral-explanation-of-the-dot-com-bubble/43872081 sotbar7@slideshare.net(sotbar7) A behavioral explanation of the DOT COM bubble sotbar7 This is a presentation accompanying an assignment for the course of Behavioral Finance in Athens University of Economics and Business. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dotcombubble-baratsasbarjamisclassroompresentationfinal-150125072751-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a presentation accompanying an assignment for the course of Behavioral Finance in Athens University of Economics and Business.

A behavioral explanation of the DOT COM bubble from Sotiris Baratsas

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0 [AIESEC] Welcome Week Presentation /slideshow/aiesec-welcome-week-presentation/43872046 aiesecwelcomeweekpresentation-150125072516-conversion-gate01
This was a presentation delivered to the participants of the Welcome Week for Freshmen of Athens University of Economics and Business in 2014. The purpose of the presentation was for the audience to learn about AIESEC and the opportunities they have while being students.]]>
This was a presentation delivered to the participants of the Welcome Week for Freshmen of Athens University of Economics and Business in 2014. The purpose of the presentation was for the audience to learn about AIESEC and the opportunities they have while being students.]]> Sun, 25 Jan 2015 07:25:16 GMT /slideshow/aiesec-welcome-week-presentation/43872046 sotbar7@slideshare.net(sotbar7) [AIESEC] Welcome Week Presentation sotbar7 This was a presentation delivered to the participants of the Welcome Week for Freshmen of Athens University of Economics and Business in 2014. The purpose of the presentation was for the audience to learn about AIESEC and the opportunities they have while being students. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiesecwelcomeweekpresentation-150125072516-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This was a presentation delivered to the participants of the Welcome Week for Freshmen of Athens University of Economics and Business in 2014. The purpose of the presentation was for the audience to learn about AIESEC and the opportunities they have while being students.

[AIESEC] Welcome Week Presentation from Sotiris Baratsas

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0 Advanced Feedback Methodologies /slideshow/advanced-feedback-for-tsil/43871791 advancedfeedbackfortsil-150125070914-conversion-gate01
This is a training created and delivered at The Solution Is Leadership (TSIL) Conference 2014. The audience was consisted by Team Leaders and Team Members of AIESEC in Athens. The audience understood the importance of feedback, the basic rules of giving and receiving feedback and had the chance to experience some advanced methods for feedback giving and team assessment.]]>
This is a training created and delivered at The Solution Is Leadership (TSIL) Conference 2014. The audience was consisted by Team Leaders and Team Members of AIESEC in Athens. The audience understood the importance of feedback, the basic rules of giving and receiving feedback and had the chance to experience some advanced methods for feedback giving and team assessment.]]> Sun, 25 Jan 2015 07:09:13 GMT /slideshow/advanced-feedback-for-tsil/43871791 sotbar7@slideshare.net(sotbar7) Advanced Feedback Methodologies sotbar7 This is a training created and delivered at The Solution Is Leadership (TSIL) Conference 2014. The audience was consisted by Team Leaders and Team Members of AIESEC in Athens. The audience understood the importance of feedback, the basic rules of giving and receiving feedback and had the chance to experience some advanced methods for feedback giving and team assessment. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/advancedfeedbackfortsil-150125070914-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a training created and delivered at The Solution Is Leadership (TSIL) Conference 2014. The audience was consisted by Team Leaders and Team Members of AIESEC in Athens. The audience understood the importance of feedback, the basic rules of giving and receiving feedback and had the chance to experience some advanced methods for feedback giving and team assessment.

Advanced Feedback Methodologies from Sotiris Baratsas

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0 Advanced University Relations [AIESEC Training] /slideshow/advanced-university-relations-aiesec-training/43055042 universityrelationstransition-141228161723-conversion-gate02
Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.]]>
Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.]]> Sun, 28 Dec 2014 16:17:23 GMT /slideshow/advanced-university-relations-aiesec-training/43055042 sotbar7@slideshare.net(sotbar7) Advanced University Relations [AIESEC Training] sotbar7 Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/universityrelationstransition-141228161723-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.

Advanced University Relations [AIESEC Training] from Sotiris Baratsas

]]> 2426 5 https://cdn.slidesharecdn.com/ss_thumbnails/universityrelationstransition-141228161723-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 How to organize massive EwA Events [AIESEC Training] /sotbar7/how-to-organize-massive-ewa-events-aiesec-training ewaeventorganizing-141228161527-conversion-gate02
Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.]]>
Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.]]> Sun, 28 Dec 2014 16:15:27 GMT /sotbar7/how-to-organize-massive-ewa-events-aiesec-training sotbar7@slideshare.net(sotbar7) How to organize massive EwA Events [AIESEC Training] sotbar7 Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ewaeventorganizing-141228161527-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.

How to organize massive EwA Events [AIESEC Training] from Sotiris Baratsas

]]> 1325 5 https://cdn.slidesharecdn.com/ss_thumbnails/ewaeventorganizing-141228161527-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 How to run effective Social Media Campaigns [AIESEC Training] /slideshow/how-to-run-effective-social-media-campaigns-aiesec-training/43054988 socialmediacampaigns-141228161154-conversion-gate02
Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.]]>
Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.]]> Sun, 28 Dec 2014 16:11:54 GMT /slideshow/how-to-run-effective-social-media-campaigns-aiesec-training/43054988 sotbar7@slideshare.net(sotbar7) How to run effective Social Media Campaigns [AIESEC Training] sotbar7 Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/socialmediacampaigns-141228161154-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Created and Delivered by Sotiris Baratsas. This was part of the Transition for the new VPs Marketing for 2015-2016 of AIESEC in Greece.

How to run effective Social Media Campaigns [AIESEC Training] from Sotiris Baratsas

]]> 1450 2 https://cdn.slidesharecdn.com/ss_thumbnails/socialmediacampaigns-141228161154-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Global Youth to Business Forum Sponsorship Package /slideshow/global-youth-to-business-forum-sponsorship-package/42581216 globaly2bsponsorshippackage-141210153400-conversion-gate02
I was assigned to create the new sponsorship package for the Global Youth to Business Forum of 2015, Powered by AIESEC. I spent a week in Rotterdam working with AIESEC International, analyzing data, creating outlines and bringing it all together in one booklet! This is it!]]>
I was assigned to create the new sponsorship package for the Global Youth to Business Forum of 2015, Powered by AIESEC. I spent a week in Rotterdam working with AIESEC International, analyzing data, creating outlines and bringing it all together in one booklet! This is it!]]> Wed, 10 Dec 2014 15:34:00 GMT /slideshow/global-youth-to-business-forum-sponsorship-package/42581216 sotbar7@slideshare.net(sotbar7) Global Youth to Business Forum Sponsorship Package sotbar7 I was assigned to create the new sponsorship package for the Global Youth to Business Forum of 2015, Powered by AIESEC. I spent a week in Rotterdam working with AIESEC International, analyzing data, creating outlines and bringing it all together in one booklet! This is it! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/globaly2bsponsorshippackage-141210153400-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> I was assigned to create the new sponsorship package for the Global Youth to Business Forum of 2015, Powered by AIESEC. I spent a week in Rotterdam working with AIESEC International, analyzing data, creating outlines and bringing it all together in one booklet! This is it!

Global Youth to Business Forum Sponsorship Package from Sotiris Baratsas

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0 Online Marketing - STEP IT UP /slideshow/online-marketing-step-it-up/35080384 marketing-stepitup-140524130235-phpapp02
6+1 Tools to step up your Online Marketing! Ideal for Organizations and Start-ups. They can make a huge difference in your performance.]]>
6+1 Tools to step up your Online Marketing! Ideal for Organizations and Start-ups. They can make a huge difference in your performance.]]> Sat, 24 May 2014 13:02:35 GMT /slideshow/online-marketing-step-it-up/35080384 sotbar7@slideshare.net(sotbar7) Online Marketing - STEP IT UP sotbar7 6+1 Tools to step up your Online Marketing! Ideal for Organizations and Start-ups. They can make a huge difference in your performance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/marketing-stepitup-140524130235-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> 6+1 Tools to step up your Online Marketing! Ideal for Organizations and Start-ups. They can make a huge difference in your performance.

Online Marketing - STEP IT UP from Sotiris Baratsas

]]> 1028 5 https://cdn.slidesharecdn.com/ss_thumbnails/marketing-stepitup-140524130235-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-sotbar7-48x48.jpg?cb=1666667135 I’m on a mission to become a world-class consultant to help organisations solve critical challenges and outperform their competitors. Embracing challenging work, being solution-oriented, working well with others, accepting feedback and continuous learning have been the North Star of my work so far. Pairing my extensive experience in Project Management, Operations Strategy and Marketing with technical competences in Data Science and Business Intelligence, I aim to bring a unique contribution in every client project in order to consistently deliver more than expected. www.baratsas.com https://cdn.slidesharecdn.com/ss_thumbnails/twitter-mention-graph-sna-190724193320-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/twitter-mention-graph-analytics-project/157617712 Twitter Mention Graph ... https://cdn.slidesharecdn.com/ss_thumbnails/suicidesreport-grvseu-190724180847-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/suicides-in-greece-vs-rest-of-europe/157596242 Suicides in Greece (vs... https://cdn.slidesharecdn.com/ss_thumbnails/predictingushouseprices-mlrinr-190718101111-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/predicting-us-house-prices-using-multiple-linear-regression-in-r/156253047 Predicting US house pr...