👉 Features • 👉 Prerequisites • 👉 Technologies • 👉 Author • 👉 Learn more
- Database import
- Identification and treatment of missing and inconsistent values
- Selection of the most relevant variables for score analysis
- Standardization and normalization of data
- Training of 2 distinct machine learning models
- Evaluation and comparison of accuracy metrics
- Presentation of results in a clear and concise manner
$ pip install pandas
$ pip install plotly
The following tools were used in the construction of the project:
Árvore de Decisão (Decision Tree):
- Divides the data space into regions based on questions about attributes.
- Classifies new instances by following the branches of the tree.
- Advantages: Easy to interpret, efficient to train.
- Disadvantages: Prone to overfitting, can be sensitive to small changes in the data.
Floresta Aleatória (Random Forest):
- Combines multiple decision trees to improve generalization and reduce overfitting.
- Trains each decision tree on a random subset of features and data.
- Classifies new instances by majority vote among the trees.
- Advantages: Usually more robust than individual decision trees, good performance in various tasks.
- Disadvantages: Can be computationally expensive to train, interpretability is reduced compared to decision trees.
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