Constructing the model and making predictions

prediction.py

+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeRegressor
+from sklearn import metrics
+
+# Load the training dataset
+train_data = pd.read_csv('train_V110.csv', sep=';', encoding=None)
+
+# Load the testing dataset
+test_data = pd.read_csv('test_V110.csv', sep=';', encoding=None)
+
+# Prepare features and target for training
+X_train = train_data.drop(['p1', 'p4', 'score_reel'], axis=1)
+y_train = train_data['score_reel']
+
+# Prepare features for testing
+X_test = test_data.drop(['p1', 'p4'], axis=1)
+
+# Build the Decision Tree Regressor model
+clf = DecisionTreeRegressor(random_state=42)
+clf.fit(X_train, y_train)
+
+# Make predictions
+y_pred = clf.predict(X_test)
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