The aim of this project is to develop a predictive model to solve a real problem in the area of Data Science and Machine Learning, in the context presented by the selected Market Partner.
APPLICATION EXAMPLES:
- Prediction of churn and turnover in consumer or employee retention contexts;
- Predicting the success/failure of new products in consolidated markets;
- Recommending products according to consumption patterns.
MANDATORY INFRASTRUCTURE RESOURCES:
- Python programming language, Numpy, Pandas and Scikit-learn libraries;
- Classic Machine Learning models: kNN, k-Means, Naive Bayes, Random Forest, Decision Tree, Linear Regression, Logistic Regression, SVM, among others. Neural networks and deep learning techniques will not be used;
- Database: anonymized CSV or Excel files provided by the partner (with target columns filled in, in the case of supervised learning);
- Version control system: GITHUB platform;
- System for developing predictive models and analyzing results: Google Collaboratory.
COMPLETION AND DELIVERY OF THE PROJECT:
All the prototypes generated during the project are delivered at the end of the 10th week.
