<jupyter_start><jupyter_text>Straight from the Original Dataset<jupyter_code>from google.colab import drive
drive.mount('/content/gdrive') #mounting gdrive   
cd gdrive
cd My\ Drive
import pandas as pd
training_data = pd.read_csv("data-storm-10/training_data_modified.csv", index_col = 0)
test_data = pd.read_csv("data-storm-10/test_data_modified.csv", index_col = 0)
training_x = training_data.drop('NEXT_MONTH_DEFAULT', 1)
training_y = training_data['NEXT_MONTH_DEFAULT']
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(training_x, training_y, test_size = 0.2)
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

ada_clf = AdaBoostClassifier(
             DecisionTreeClassifier(max_depth=9), n_estimators=300,
             algorithm="SAMME.R", learning_rate=0.5
         )

ada_clf.fit(x_train, y_train)
y_pred_rf = ada_clf.predict(x_test)
accuracy_score(y_test,y_pred_rf)
confusion_matrix(y_test,y_pred_rf)
accuracy_score(y_test,y_pred_rf)
print(classification_report(y_test,y_pred_rf))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0912106135986733, 'fraction_numerical': 0.012437810945273632, 'mean_word_length': 7.100671140939597, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '133436', 'license': 'MIT', 'n_tokens_mistral': 444, 'n_tokens_neox': 428, 'n_words': 73, 'path': 'day1/Adaboost.ipynb', 'repo_name': 'Team-OptimizePrime/DataStorm-Initial-Round', 'size': '1206'}