This repository contains the codes used to solve the homework proposed in the Applied Computational Intelligence course at the Federal University of Ceará. The topics covered in each Homework were organized in the following manner:
- Homework 1: Exploratory Data Analysis.
- Homework 2: Linear Regression Models.
- Homework 3: Linear And Nonlinear Classification Models.
On each homework we used three diferent cases:
- (Homework 1) Dataset Gapminder: In this homework, we analyze a dataset containing information on GDP Per Capita, population size and life expectancy for different countries in different years. To perform the analysis, we use univariate, bivariate and multivariate analysis techniques. Based on the results of the analysis of these variables, we found similarities and differences between the continents.
- (Homework 2) Dataset Medcal Costs: Forecasting a patient's medical expenses can help medical insurance companies offer plans that outperform customer costs and increase profit margins. To make these predictions, a simple approach is to use linear regression to forecast costs using patient data. In this paper, we will discuss the application of different approaches to calculate the linear regression model parameters to predict a patient's medical expenses.
- (Homework 3) Predict Grant Applications: The problem of verify if a grant application going to be accepted or not represent a challenge for universities around world, which are interested in create a solution to identify the students that going to be accepted by a grant program, resulting in more resources coming for the university. In this context, a possible solution is the application of classification models, that are able to predict the student result in the application program. Given that background, on this work we going to discuss the application of different types of classification models (linear and nonlinear) to predict grant application result.