Freelancer logo How It Works Browse Jobs Log In Sign Up Post a Project
EXPLORE
C Programming Machine Learning (ML) Matlab and Mathematica Microcontroller Python
Profile cover photoundefined
You're now following .
Error following user.
This user does not allow users to follow them.
You are already following this user.
Your membership plan only allows 0 follows. Upgrade here.
Successfully unfollowed user.
Error unfollowing user.
You have successfully recommended
Error recommending user.
Email successfully verified.
User Avatar
$26 USD / hour
Flag of ECUADOR
cuenca, ecuador
$26 USD / hour
It's currently 10:03 PM here
Joined July 21, 2016
2 Recommendations

Andrés G.

@and88x

annual-level-two.svgpreferred-freelancer-v2.svgverified.svg
0.0 (24 reviews)
5.1
5.1
$26 USD / hour
Flag of ECUADOR
cuenca, ecuador
$26 USD / hour
96%
Jobs Completed
91%
On Budget
95%
On Time
17%
Repeat Hire Rate

Data Science | Machine Learning Engineering

In my first steps as a freelancer, I worked on projects related to automatic control, embedded systems, IoT, and intelligent systems. From the beginning of 2021, I decided to focus on Data Science. This subfield of artificial intelligence allows me to learn a lot about other fields that are not related to engineering. I'm glad to know that all my analytical skills, statistical knowledge, and programming skills are really helpful in achieving my goal of becoming an expert data scientist. As a data scientist, I have experience in tasks such as: ✔️ Exploratory data analysis (EDA) and visualization (Dash/Plotly, Shiny, Tableau) ✔️ Getting and cleaning data ✔️ Clustering (k-means, DBSCAN, hierarchical, spectral clustering) ✔️ Neural Networks (ADALINE, perceptron, feed-forward NN, deep NN, convolutional NN, recurrent NN, Hopfield NN) ✔️ Classification algorithms (desition trees, logistic regression, gradient boosting, SVC, random forest, k-nearest neighbors, and so on) ✔️ Regression algorithms (DT, LR, GB, SVR, RF, k-NN, and so on) ✔️ Dimensionality reduction (PCA, SVD) ✔️ A/B testing ✔️ Time series forecasting (ML, DL) ✔️ SQL and NoSQL databases (MySQL, PostgreSQL, Cassandra, Neo4j) ✔️ Statistical inference ✔️ Reproducible research I can work in other fields that are not directly related to data science such as: ✔️ Fuzzy Logic (Mandani, Sugeno models) ✔️ Bioinspired optimization (PSO, Ant Colony Optimization, Artificial Bee Colony, Firefly Optimization, Dragonfly Optimization, Grey Wolf Optimizer) and Linear Programming ✔️ Autopilots for Unmanned Aerial Vehicles ✔️ C/C++ programming for Arduino and Microcontrollers ✔️ Python, R, MATLAB, Java, and Bash programming languages I prefer to use open-source tools like R or python to work. These programming languages remain on the cutting edge thanks to their active and supportive communities. I also use MATLAB a lot due to its power in math. Mostly available from 14:00 to 20:00 GMT-5 from Monday to Friday.
Freelancer Python Developers Ecuador

Contact Andrés G. about your job

Log in to discuss any details over chat.

Portfolio Items

The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website.

What is Dognition looking for?

- They mainly need is: 
Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests.

- So, the SMART objective is:
Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction

Reviews

Changes saved
Showing 1 - 5 out of 24 reviews
Filter reviews by: 5.0
$83.00 USD
Excelente! Terminó el proyecto mucho antes de lo planeado. Altamente recomendado!
Python R Programming Language
M
Flag of Carlos E M. @mroder75
1 month ago
4.8
$90.00 USD
La comunicación con Andrés fue excelente. Al final entregó satisfactoriamente el trabajo con todos los requisitos solicitados y de manera autónoma resolvió los conflictos que iban surgiendo al momento del desarrollo. Lo recomiendo para trabajar con él, esta muy atento a responder a las inquietudes aun después de finalizar el trabajo.
Python Web Scraping Google App Engine Google Slides Google Apps Scripts
User Avatar
Flag of Luisa Z. @LuisaZapata2
2 months ago
5.0
$76.00 USD
Se logró el objetivo sin problema alguno. Excelente trabajo!!
Python Machine Learning (ML) Data Science Scikit Learn Pandas
N
Flag of Naldo C. @Naldo91
2 months ago
5.0
$2,200.00 CAD
Excellent communication and high quality work.
Python C# Programming Cartography & Maps Machine Learning (ML) Radio Frequency Engineering
User Avatar
Flag of John S. @ikailo1
2 months ago
5.0
$350.00 USD
Highly recommended, went extra mile to help and support
Python Machine Learning (ML) Tensorflow Pytorch Deep Learning
User Avatar
Flag of Belal M. @belalmc
3 months ago

Experience

Research Assistant

ITESM
May 2018 - Dec 2019 (1 year, 7 months)
- Measurement, storage, and analysis of atmospheric pollutant concentrations, to track the sources of air pollutants - Develop an environmental monitoring platform based on 2 UAVs (quadcopters) - Teach courses on tools for data analysis and dimensionality reduction

Education

Master in Sciences of Engineering

Tecnológico de Monterrey, Mexico 2017 - 2019
(2 years)

Electronics and Telecommunications Engineering (Bachelor's degree)

Universidad de Cuenca, Ecuador 2011 - 2017
(6 years)

Qualifications

Certified LabVIEW Associate Developer

National Instruments
2018

Publications

Equidistributed Search+Probability Based Tracking Strategy to Locate an Air Pollutant Source

IEEE Access
A probability-based tracking strategy is proposed for guiding two cooperative unmanned aerial vehicles (UAVs) within a quest area to find an atmospheric pollutant source. This implies deploying algorithmically two phases: exploration and exploitation. During the exploration phase, each vehicle follows a trajectory based on plane coordinates generated from a Hammersley sequence. The navigation trajectories are smoothed by an TSP solver and a cubic spline planning algorithm.

Evaluating the Mindwave Head-set for Automatic Upper Body Motion Classification

INCISCOS - IEEE

Contact Andrés G. about your job

Log in to discuss any details over chat.

Verifications

Preferred Freelancer
Identity Verified
Payment Verified
Phone Verified
Email Verified
Facebook Connected

Certifications

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 97%

Top Skills

Python 16 Machine Learning (ML) 10 Matlab and Mathematica 7 C Programming 6 Microcontroller 5

Browse Similar Freelancers

Python Developers in Ecuador
Python Developers
Machine Learning Experts
Matlab and Mathematica Engineers

Browse Similar Showcases

Python
Machine Learning (ML)
Matlab and Mathematica
C Programming
Previous User
Next User
Invite sent successfully!
Registered Users Total Jobs Posted
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2022 Freelancer Technology Pty Limited (ACN 142 189 759)
There is no internet connection
Loading preview