Data Multiple Predictor

Description

We're going to use some models to predict the Datasets and show the predictions results in a table.

Models to be implemented

We're going to use the following models:

1. KNN
2. Naive Bayes
   1. Gaussian
   2. Multinomial
   3. Bernoulli
   4. Complement
3. Decision Tree
4. Random Forest

Optimization of the models

We're going to use the following optimization methods:

1. Grid Search
2. Random Search

Datasets

We're going to use the following datasets:

1. Iris Sklearn
2. Wine Quality https://www.kaggle.com/datasets/nareshbhat/wine-quality-binary-classification
3. Titanic https://www.kaggle.com/datasets/heptapod/titanic (Modified)

How to run

1. Clone the repository
2. Install the requirements
3. Run the main.py file
4. Check the results in the terminal

Results

The results are shown in the following table: