Machine learning problem with only original data without test and validation data
Usually, with a predictive model, you generate a train and test sample from the original dataset, e.g. by using sklearn.
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
Train some model on one set and test on the other by comparing predictions to actual (true) outcomes.
You could also use cross validation, in which case a model is trained on one part of the data (e.g. 4/5 of the data with 5-fold cv) and tested on the remaining 1/5 of the data. This is done for all of the „folds“. See Ch. 5.1. of „Introduction to Statistical Learning“ for more details on that. https://www.statlearning.com/
Simply make sure you obtain a test score based on data not used for training.