Machine Learning - Python
Developed ML models with a predictive accuracy of 85%.
This project consisted of developing multiple Machine Learning models with Sklearn, evaluating them and tuning the winner´s parameters. I also performed a series of actions to clean and transform the data, and performed feature engineering to the variables.
Some of the models I created were Linear Regression, KNN, Decision Tree Classifier, Random Forest, SVM, and others. I built pipelines equiped with Column Transformers, Scalers, fixed seed and cross validation to refine our model´s predictive capabilities.
Results
+8
Machine Learning Models Built and Evaluated
1
Machine Learning Model Tuned
95%
Grade on Deep Learning Class
Goals
The objective of the project was to build multiple Machine Learning models, in different ways using the Sklearn library, to solve the problems given by our professor in the Masters in Big Data and Business Analytics I successfully completed in February 2022. The main goal was to train and test effectively different ML models, clean data, transform it, and create pipelines to build into the models to improve predictive results overall.