Ai training model

## Objective:

This notebook focuses on applying analytical or machine learning methods to a cleaned dataset.

---

## Dataset:

The dataset used here is the cleaned version produced previously. All preprocessing steps have already been applied.

---

## Feature Selection

Relevant features are selected based on:

- Domain knowledge

- Correlation analysis

- Model requirements

---

## Data Splitting

The dataset is split into:

- Training set

- Test set

This allows proper evaluation of model performance.

---

## Model Selection

One or more models are applied depending on the problem, such as:

- Regression models

- Classification models

---

## Model Training

The selected model is trained using the training data.

---

## Model Evaluation

Performance is evaluated using appropriate metrics, such as:

- Accuracy

- Precision / Recall

- Mean Squared Error (MSE)

Results are interpreted and discussed.

---

## Results and Discussion

This section analyzes the obtained results and highlights strengths and limitations of the model.

---

## Conclusion

We summarize the main findings and suggest possible improvements or future work.

It was developed using python