Edna Fernandes Email & Phone Number | Data scientist with a passion for finding meaning in raw data through analytics. Experience in statistical analysis, machine learning, and 5 years of experience analyzing oil and gas data. I bring strong skills in analytics, cross-functional collaboration, and effective technical/non-technical communication to help organizations achieve their business goals. I am able to communicate in three different languages: English, Portuguese and Spanish. Below are some of the projects that I have worked on. They can also be found on my GitHub; the link is going to be below the project descriptions. Pump it Up: Data Mining the Water Table Classify water well pumps into functional, non-functional and need-repair to help Tanzanian maintenance operations ensure that clean, potable water is available to communities across Tanzania. - Used Pandas for data cleaning and Power BI for data analysis which helped obtain better features to train the model. - Trained an XGBoost machine learning model for pump classification that scored in the top 15% on the leaderboard. - Identified pump functionality and proposed restoration plan to improve the water system in Tanzania. Fraud Detection Used IEEE data for fraud detection to help businesses reduce their losses and improve their customers’ experience. - Used NumPy and Pandas for exploratory data analysis (EDA) resulting in a better understanding of the data and in the creation of new features to help improve the model performance. - Performed visualizations on Power BI to identify commonalities between fraudulent transactions. - Created a machine learning model using imbalanced XGBoost that scored 90% in identifying fraud. Santander Bank Product Recommendation Based on the customer’s history and profile, recommended the top products that the customer is more likely to purchase. - Used pandas for data cleaning and Power BI for data visualization and analysis. - Determined the probability of each customer purchasing the products using logistic regression. - Recommended the 7 products that each customer is more likely to buy. Predicting Home Prices in Seattle, WA Identify properties in the King County real estate market that are ideal for flipping. - Cleaned the dataset using Pandas. - Performed data visualization using seaborn, Matplotlib and Tableau. - Built a machine learning model by zip code using a generalized additive model (GAM) to predict house prices. The model had an average accuracy higher than 90%.

Edna Fernandes

Data scientist with a passion for finding meaning in raw data through analytics. Experience in stati...
Houston, Texas, United States

*50 free lookup(s) per month.

No credit card required.

Edna Fernandes’s Email edna_fernandes@boomi.com

Edna Fernandes’s Phone Numbers

Get EDNA’s Phone Number (For Free)

Social Media

Edna Fernandes’s Location Houston, Texas, United States

Edna Fernandes’s Current Industry Jpmorgan Chase

Edna Fernandes’s Prior Industry Boomi | Sonangol Pesquisa And Producao

Not the Edna Fernandes you were looking for?

Find accurate emails & phone numbers for over 700M professionals.

Work Experience

Jpmorgan Chase

Senior Associate Software Engineer

2023-10-01 00:00:00 - Present

Boomi

Data Scientist

2022-05-01 00:00:00 - 2022-12-01 00:00:00

Sonangol Pesquisa And Producao

Reservoir Engineer

2013-09-01 00:00:00 - 2018-09-01 00:00:00

Skills

No Skills Available

Languages

No Languages Available

FAQ About Edna Fernandes

Edna Fernandes is located at
Houston, Texas, United States

Edna Fernandes is a Data scientist with a passion for finding meaning in raw data through analytics. Experience in statistical analysis, machine learning, and 5 years of experience analyzing oil and gas data. I bring strong skills in analytics, cross-functional collaboration, and effective technical/non-technical communication to help organizations achieve their business goals. I am able to communicate in three different languages: English, Portuguese and Spanish. Below are some of the projects that I have worked on. They can also be found on my GitHub; the link is going to be below the project descriptions. Pump it Up: Data Mining the Water Table Classify water well pumps into functional, non-functional and need-repair to help Tanzanian maintenance operations ensure that clean, potable water is available to communities across Tanzania. - Used Pandas for data cleaning and Power BI for data analysis which helped obtain better features to train the model. - Trained an XGBoost machine learning model for pump classification that scored in the top 15% on the leaderboard. - Identified pump functionality and proposed restoration plan to improve the water system in Tanzania. Fraud Detection Used IEEE data for fraud detection to help businesses reduce their losses and improve their customers’ experience. - Used NumPy and Pandas for exploratory data analysis (EDA) resulting in a better understanding of the data and in the creation of new features to help improve the model performance. - Performed visualizations on Power BI to identify commonalities between fraudulent transactions. - Created a machine learning model using imbalanced XGBoost that scored 90% in identifying fraud. Santander Bank Product Recommendation Based on the customer’s history and profile, recommended the top products that the customer is more likely to purchase. - Used pandas for data cleaning and Power BI for data visualization and analysis. - Determined the probability of each customer purchasing the products using logistic regression. - Recommended the 7 products that each customer is more likely to buy. Predicting Home Prices in Seattle, WA Identify properties in the King County real estate market that are ideal for flipping. - Cleaned the dataset using Pandas. - Performed data visualization using seaborn, Matplotlib and Tableau. - Built a machine learning model by zip code using a generalized additive model (GAM) to predict house prices. The model had an average accuracy higher than 90%.