{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.feature_selection import f_regression, SelectKBest\n\nfrom sklearn.svm import SVR\nfrom sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\nfrom sklearn.linear_model import LinearRegression, LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder, LabelEncoder\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.feature_selection import chi2\n\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"0ebbabd2-ee6a-495e-81a7-881209a26d9a","_cell_guid":"9ce95fb6-a227-4053-bee8-88e79c29902c","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-03-29T14:33:45.291402Z","iopub.execute_input":"2023-03-29T14:33:45.292443Z","iopub.status.idle":"2023-03-29T14:33:47.776873Z","shell.execute_reply.started":"2023-03-29T14:33:45.292384Z","shell.execute_reply":"2023-03-29T14:33:47.775119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **1. Create an Function**","metadata":{}},{"cell_type":"markdown","source":"## **1.1. The function sets the 16:9 visualization size**","metadata":{}},{"cell_type":"code","source":"def wide(lebar):\n    tinggi = (lebar/16) * 9\n    return tinggi","metadata":{"execution":{"iopub.status.busy":"2023-03-29T14:33:47.779276Z","iopub.execute_input":"2023-03-29T14:33:47.779872Z","iopub.status.idle":"2023-03-29T14:33:47.786765Z","shell.execute_reply.started":"2023-03-29T14:33:47.779828Z","shell.execute_reply":"2023-03-29T14:33:47.785091Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **2. Read Data**","metadata":{}},{"cell_type":"code","source":"# df_train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')\ndf_test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/test.csv')\ndf_train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')\ndf_submision = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/sample_submission.csv')","metadata":{"_uuid":"939acbcc-de4f-48d3-8acc-b2b6bf41421d","_cell_guid":"0e73653d-ddd7-428b-96bd-0b66e05fc4f8","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-03-29T14:33:47.789390Z","iopub.execute_input":"2023-03-29T14:33:47.790015Z","iopub.status.idle":"2023-03-29T14:33:48.378471Z","shell.execute_reply.started":"2023-03-29T14:33:47.789969Z","shell.execute_reply":"2023-03-29T14:33:48.377091Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"762ceb71-52b7-45b0-be22-054e92d6b950","_cell_guid":"f3012e11-4aac-4017-b317-611641dea861","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"0999bc81-7af7-4d47-b04c-81cbb611732c","_cell_guid":"a26be924-606d-4cab-9bff-347417427387","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"4903d130-7ed4-446c-a5f4-0b1b51c33254","_cell_guid":"667b3a88-5dde-406a-88cf-02dbc1757299","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]}]}