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$25 USD / hour
Flag of ECUADOR
cuenca, ecuador
$25 USD / hour
It's currently 11:53 PM here
Joined October 7, 2021
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Wilmer H.

@MateoHeras7

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$25 USD / hour
Flag of ECUADOR
cuenca, ecuador
$25 USD / hour
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Data Analiyst Junior.

Hi, you can see my last Jobs of Machine Learning in my Web Site [login to view URL] and feel free to contact me. Currently my skills are directed towards data analysis, supervised learning (regression and classification algorithms), unsupervised learning (clustering), ensemble algorithms, time series and variable forecasting. EXPLORATORY DATA ANALYSIS The EDA allows us to know the general situation of the variables necessary to make a decision (customers, accounts, products, markets, etc). It allows us to find points of improvement in the institution and opportunities in the market. ECONOMETRIC MODELS They allow us to know the relationships, probabilities and level of influence of a certain variable on others. With this we enhance decision making. TIME SERIES AND FORECASTING They help to know how the variables of interest have fluctuated (they can be economic as well as business; GDP, CPI, sales, etc) over time and thus forecast the future values of these variables. EXTRA SKILLS I have also gained skills in Design Thinking, photo and video editing, graphic design and SCRUM. Not at great levels but I understand and manage the concepts.
Freelancer Copywriters Ecuador

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Portfolio Items

FORECAST OF THE PRICE OF THE STOCK OF THE NORTH AMERICAN COMPANY PFIZER INC. CONSIDERING THE PERIOD JANUARY 2010 TO FEBRUARY-2021, THROUGH THE ARIMA AND SARIMA MODELS.
This research presents an application of the methodology developed by Box and Jenkins to forecast the price of the shares of one of the leading companies in the manufacture of the SARS-CoV-2 vaccine, Pfizer INC. The analysis period was from January 2010 to February 2021 with monthly data. The ARIMA and SARIMA models were analyzed, the first being the one that best fits the nature of the data.
Info: https://mateoheras77.github.io/WEB/
Stock Prediction Pfizer Case
FORECAST OF THE PRICE OF THE STOCK OF THE NORTH AMERICAN COMPANY PFIZER INC. CONSIDERING THE PERIOD JANUARY 2010 TO FEBRUARY-2021, THROUGH THE ARIMA AND SARIMA MODELS.
This research presents an application of the methodology developed by Box and Jenkins to forecast the price of the shares of one of the leading companies in the manufacture of the SARS-CoV-2 vaccine, Pfizer INC. The analysis period was from January 2010 to February 2021 with monthly data. The ARIMA and SARIMA models were analyzed, the first being the one that best fits the nature of the data.
Info: https://mateoheras77.github.io/WEB/
Stock Prediction Pfizer Case
FORECAST OF THE PRICE OF THE STOCK OF THE NORTH AMERICAN COMPANY PFIZER INC. CONSIDERING THE PERIOD JANUARY 2010 TO FEBRUARY-2021, THROUGH THE ARIMA AND SARIMA MODELS.
This research presents an application of the methodology developed by Box and Jenkins to forecast the price of the shares of one of the leading companies in the manufacture of the SARS-CoV-2 vaccine, Pfizer INC. The analysis period was from January 2010 to February 2021 with monthly data. The ARIMA and SARIMA models were analyzed, the first being the one that best fits the nature of the data.
Info: https://mateoheras77.github.io/WEB/
Stock Prediction Pfizer Case
FORECAST OF THE PRICE OF THE STOCK OF THE NORTH AMERICAN COMPANY PFIZER INC. CONSIDERING THE PERIOD JANUARY 2010 TO FEBRUARY-2021, THROUGH THE ARIMA AND SARIMA MODELS.
This research presents an application of the methodology developed by Box and Jenkins to forecast the price of the shares of one of the leading companies in the manufacture of the SARS-CoV-2 vaccine, Pfizer INC. The analysis period was from January 2010 to February 2021 with monthly data. The ARIMA and SARIMA models were analyzed, the first being the one that best fits the nature of the data.
Info: https://mateoheras77.github.io/WEB/
Stock Prediction Pfizer Case
Task: Classify the users of a bank on the type of clients they belong to (good payers or defaulters) using the databases available in Kaggle.
Base 1: Contains socioeconomic information on clients (Unbalanced data).
Base 2: Contains the credit history of each client for 60 months (Unbalanced data). Techniques Used: Because no database had a class that classified clients, unsupervised learning techniques were used to find these two groups (in addition to a subjective analysis through fashion), after obtaining of the labels, the classification models were built for the respective analysis.
TECHNIQUES:
Unsupervised Learning (AnS): Agglomerative Grouping, Reduction and Balanced Iterative Grouping Through Hierarchies, K - Means, Gaussian Mixture Model.
Supervised Learning (AS): Linear, Nonlinear and Assembled Algorithms
Info: https://mateoheras77.github.io/WEB/
Credit Card Approval Prediction
Task: Classify the users of a bank on the type of clients they belong to (good payers or defaulters) using the databases available in Kaggle.
Base 1: Contains socioeconomic information on clients (Unbalanced data).
Base 2: Contains the credit history of each client for 60 months (Unbalanced data). Techniques Used: Because no database had a class that classified clients, unsupervised learning techniques were used to find these two groups (in addition to a subjective analysis through fashion), after obtaining of the labels, the classification models were built for the respective analysis.
TECHNIQUES:
Unsupervised Learning (AnS): Agglomerative Grouping, Reduction and Balanced Iterative Grouping Through Hierarchies, K - Means, Gaussian Mixture Model.
Supervised Learning (AS): Linear, Nonlinear and Assembled Algorithms
Info: https://mateoheras77.github.io/WEB/
Credit Card Approval Prediction
Task: Classify the users of a bank on the type of clients they belong to (good payers or defaulters) using the databases available in Kaggle.
Base 1: Contains socioeconomic information on clients (Unbalanced data).
Base 2: Contains the credit history of each client for 60 months (Unbalanced data). Techniques Used: Because no database had a class that classified clients, unsupervised learning techniques were used to find these two groups (in addition to a subjective analysis through fashion), after obtaining of the labels, the classification models were built for the respective analysis.
TECHNIQUES:
Unsupervised Learning (AnS): Agglomerative Grouping, Reduction and Balanced Iterative Grouping Through Hierarchies, K - Means, Gaussian Mixture Model.
Supervised Learning (AS): Linear, Nonlinear and Assembled Algorithms
Info: https://mateoheras77.github.io/WEB/
Credit Card Approval Prediction
Supermarket data analysis
With this information we will look for relationships / links between goods that people buy, in addition to making classification models and returning
https://mateoheras77.github.io/WEB/
Analysis of a Supermarket
Supermarket data analysis
With this information we will look for relationships / links between goods that people buy, in addition to making classification models and returning
https://mateoheras77.github.io/WEB/
Analysis of a Supermarket
Supermarket data analysis
With this information we will look for relationships / links between goods that people buy, in addition to making classification models and returning
https://mateoheras77.github.io/WEB/
Analysis of a Supermarket
Supermarket data analysis
With this information we will look for relationships / links between goods that people buy, in addition to making classification models and returning
https://mateoheras77.github.io/WEB/
Analysis of a Supermarket
A Gravitational Model is applied on the exports of Ecuador with its twenty main trading partners for the period 2005-2019, for which the econometric panel data tool is used through a robust Praiss-Winter estimation considering problems such as heteroscedasticity, autocorrelation, contemporary correlation and temporal effects. The main results are: the main countries that consume Ecuadorian products are the United States and China; The key variables of the model - Gross Domestic Product and distance - were highly significant and in accordance with the expected effects. On the one hand, Ecuadorian exports increase as the size of the foreign economy increases, but Ecuador's foreign trade with the rest of the countries decreases due to the physical distance between the two economies. Likewise, if the countries share the Spanish language, it benefits Ecuador since it increases the demand for its goods. Finally, a limitation of the work is the efficient correction of the endogeneity problem.
Gravitational Model - Data Panel Model
A Gravitational Model is applied on the exports of Ecuador with its twenty main trading partners for the period 2005-2019, for which the econometric panel data tool is used through a robust Praiss-Winter estimation considering problems such as heteroscedasticity, autocorrelation, contemporary correlation and temporal effects. The main results are: the main countries that consume Ecuadorian products are the United States and China; The key variables of the model - Gross Domestic Product and distance - were highly significant and in accordance with the expected effects. On the one hand, Ecuadorian exports increase as the size of the foreign economy increases, but Ecuador's foreign trade with the rest of the countries decreases due to the physical distance between the two economies. Likewise, if the countries share the Spanish language, it benefits Ecuador since it increases the demand for its goods. Finally, a limitation of the work is the efficient correction of the endogeneity problem.
Gravitational Model - Data Panel Model
A Gravitational Model is applied on the exports of Ecuador with its twenty main trading partners for the period 2005-2019, for which the econometric panel data tool is used through a robust Praiss-Winter estimation considering problems such as heteroscedasticity, autocorrelation, contemporary correlation and temporal effects. The main results are: the main countries that consume Ecuadorian products are the United States and China; The key variables of the model - Gross Domestic Product and distance - were highly significant and in accordance with the expected effects. On the one hand, Ecuadorian exports increase as the size of the foreign economy increases, but Ecuador's foreign trade with the rest of the countries decreases due to the physical distance between the two economies. Likewise, if the countries share the Spanish language, it benefits Ecuador since it increases the demand for its goods. Finally, a limitation of the work is the efficient correction of the endogeneity problem.
Gravitational Model - Data Panel Model
Stock Exchange
Creating an investment portfolio using the Yahoo Finance API
https://mateoheras77.github.io/WEB/
Creation of an Investment Portfolio With US Stocks.
Stock Exchange
Creating an investment portfolio using the Yahoo Finance API
https://mateoheras77.github.io/WEB/
Creation of an Investment Portfolio With US Stocks.
Stock Exchange
Creating an investment portfolio using the Yahoo Finance API
https://mateoheras77.github.io/WEB/
Creation of an Investment Portfolio With US Stocks.

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Experience

Trainee

Banco Pichincha
Jul 2021 - Sep 2021 (2 months, 1 day)
Part 1): I developed activities related to the digital transformation that the institution is going through, I supported in the implementation of agile methodologies and issues related to user experience. Part 2): I provided support to SME (PYMES) executives on issues related to the acquisition of products by known and new customers.

Education

Senior Grade

Universidad de Cuenca, Ecuador 2017 - 2021
(4 years)

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Copywriting Translation Internet Marketing Project Management Facebook Marketing

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