<jupyter_start><jupyter_text>Evaluation of Test<jupyter_code>X_test.head(1)
y_pred = treeModel.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred ))
#Close to what we have on our trained dataset
treeModel.score(X_train, y_train)<jupyter_output><empty_output><jupyter_text>Prediction of Test<jupyter_code>sub_test = test.drop(['PassengerId'], axis = 1)
sub_test_pred = treeModel.predict(sub_test).astype(int)
AllSub = pd.DataFrame({ 'PassengerId': test['PassengerId'],
                       'Survived' : sub_test_pred
    
})

AllSub.to_csv("All_Var_Video_DT1_basic.csv", index = False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11589895988112928, 'fraction_numerical': 0.004457652303120356, 'mean_word_length': 6.929411764705883, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '184748', 'license': 'MIT', 'n_tokens_mistral': 246, 'n_tokens_neox': 239, 'n_words': 37, 'path': 'Titanic Solution using Excel and Python/4 Titanic_Solution_DT_test_train_split_3Sep2019.ipynb', 'repo_name': 'KunaalNaik/YT_Kaggle_Titanic_Solution', 'size': '673'}