Projects

Marketing Analytics

In this project, I worked with a Food Delivery Dataset. For this project, the technology used was Python (Matplotlib, Scikit-Learn, Pandas, Numpy and Seaborn.)

Was delivered a Machine Learning Model for predict clients segmentation by Food Delivery​

Santander Client Review

In this project, I worked with a Santander Client Review Dataset. Was used Python as the prior technology. The main library for this project (Matplotlib, Scikit-Learn, Pandas, Numpy and Seaborn.)

For this project jobs, was delivered a Machine Learning Model to rate Customer Review for Santander Bank

Inventory Demand

For this project, I worked with Inventory Demand Dataset. Was used technology as R (Caret, RandomForest, Corrplor, ROSE, and Data Table)

Was delivered a Machine Learning Model for predict the correct products number to be buy for a market.

Costumer Churn

In this project, I worked with a Company Dataset. Was used R as the prior technology. The main library for this project (Caret, RandomForest and C50.)

For this project jobs, was delivered a Machine Learning Model to rate Customer Churn Telecon.

Frauds Detection

For this project, I worked with Frauds Dataset. Was used technology as R (Caret, RandomForest, Corrplor, ROSE, and Data Table)

In this jobs was developed a Machine Learning Model for predict which application user could be a potential fraud.

Credit Rating

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