The third project carried out by our students was the construction of logic for prediction with artificial intelligence, with the aim of presenting innovative solutions to the project partners through the use of technology.
PROBLEM: Delivery drivers abandoning the platform.
OBJECTIVE: A predictive model that predicts which deliverers are most likely to abandon the platform, as well as creating strategies to keep them.
PUBLIC: RAPPI Operations Team.
PROBLEM: How to increase customer satisfaction with the personalized service model.
OBJECTIVE: A predictive model that analyzes the customer's current situation and offers an improved and personalized service, identifying in advance the reason for their contact, allowing for assertiveness and quality in the service.
PUBLIC:Banco Pan customers.
PROBLEM: Knowing the most relevant variables that impact on the experience of collaborators and that can lead to a break in the bond.
OBJECTIVE: A predictive model that helps to understand the journey of the company's employees, creating a better experience and increasingly valuing each person's talent.
PUBLIC: Everymind employees.
PROBLEM: Low precision of the possible results of new content.
OBJECTIVE: A predictive model that analyzes acceptance variables for new content and returns a forecast of the audience that will be reached.
AUDIENCE: Broadcaster viewers.
PROBLEM: Unpredictability of the causes and influences of breast cancer.
OBJECTIVE: To develop a predictive model that, based on clinical and laboratory data, supports the prediction of breast cancer variability;
PUBLIC: Doctors and health system employees.
PROBLEM: Supporting researchers and doctors in better prescribing treatment for breast cancer patients.
OBJECTIVE:Tocreate a predictive model based on the clinical and laboratory data of patients diagnosed with breast cancer, which can support the indication of the treatment regimen and follow-up practiced.
PUBLIC: Doctors in the oncology department at USP's medical hospital.
PROBLEM:There is no model for predicting customer churn
OBJECTIVE:A model trained to identify possible customer churn.
AUDIENCE:BCG's internal data analytics team
PROBLEM: There is no prediction model to suggest the best job or training for the candidate
OBJECTIVE: To recommend the best vacancy or training for the candidate user of the platform.
AUDIENCE: Bettha's internal data analytics team
PROBLEM:Improving the purchasing provisioning process and inventory control of SKUs
OBJECTIVE: A model capable of generating a sales forecast and purchase suggestion with quantity for each sku.
AUDIENCE: Mobly's internal data analytics team
PROBLEM: There is no risk prediction model for FIDC-type funds
OBJECTIVE: To predict the risk of the FIDC fund, assessing the chance of a problem occurring, whether due to future shortfalls or lack of provisions.
PUBLIC: CVM's internal data analytics team
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Location
An innovation and technology ecosystem located in São Paulo, within the university city.
Location
An innovation and technology ecosystem located in São Paulo, within the university city.
Instituto de Tecnologia e Liderança - CNPJ 35.078.331/0001-29 - Av. Professor Almeida Prado, 520 - Butantã, 05508-901 - São Paulo - SP
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