Add processing module

src/processing.py

@@ -0,0 +1,51 @@
+from numpy import mean
+from pandas import DataFrame
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import cross_val_score
+from sklearn.ensemble import GradientBoostingClassifier
+
+from preprocessing import parse_data, split_k_sets
+
+
+def predict_data(train_data, train_target, test_data, test_ids):
+    model = GradientBoostingClassifier(random_state=42)
+    accuracy_scores = []
+    for train_index, test_index in split_k_sets(train_data):
+        model.fit(train_data.iloc[train_index], train_target.iloc[train_index])
+        prediction = model.predict(train_data.iloc[test_index])
+        accuracy_scores.append(
+            accuracy_score(train_target.iloc[test_index], prediction)
+        )
+    cv_score = cross_val_score(model, train_data, train_target)
+    evaluate_performance(
+        accuracy=mean(accuracy_scores),
+        cv_score=mean(cv_score),
+    )
+    predictions = model.predict(test_data)
+    export_results(ids=test_ids, prediction=predictions)
+
+
+def evaluate_performance(accuracy, cv_score):
+    print("Accuracy Score: " + str(accuracy))
+    print("Cross validation score: " + str(cv_score))
+
+
+def export_results(ids, prediction):
+    result_df = DataFrame({"id": ids, "Precio_cat": prediction})
+    result_df.to_csv(path_or_buf="data/results.csv", index=False)
+
+
+def main():
+    train_data, train_target, test_data, test_ids = parse_data(
+        train="data/train.csv", test="data/test.csv"
+    )
+    predict_data(
+        train_data=train_data,
+        train_target=train_target,
+        test_data=test_data,
+        test_ids=test_ids,
+    )
+
+
+if __name__ == "__main__":
+    main()