In this repository we present an application of the main types of classification models, providing a brief explanation of each one of them. Machine learning classification models are algorithms that automate the process of assigning categories to data or instances. They are widely used in a variety of fields such as pattern recognition, natural language processing, medical diagnosis, and fraud detection, among others. We study the Logistic regression, K-Nearest Neighbors (KNN), Decision Tree model, Support Vector Machines (SVM), and Artificial neural network.
I emphasize the importance of selecting the appropriate classification model for each project, considering the characteristics of the data and the requirements of the problem in question. I also highlight the need for techniques such as selection of relevant attributes and hyperparameter tuning to optimize model performance. Furthermore, I mention importance of evaluation metrics, such as accuracy, precision, recall and F1-score, to measure the quality of predictions and evaluate the performance of models.
| Project Name | Description |
|---|---|
| 📡 KNN models | In this notebook, we look at whether it is possible to predict whether or not a given machine has a proper operating process based on temperature and pressure. For this, we use the KNN model to be trained by these data to be able to predict whether or not there will be combustion for a given input condition. |
| 💡 Logistic regression | |
| 📯 Naive Bayes model | |
| ✂️ Decision Tree model | |
| 🚂 Support Vector Machines (SVM) |