{"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)\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":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport pandas as pd \nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn.linear_model import SGDClassifier","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_folder = \"/kaggle/input/2021-ml-p5/pca_train.csv\"\ntest_folder = \"/kaggle/input/2021-ml-p5/pca_test.csv\"\ntrain_label_path = \"/kaggle/input/2021-ml-p5/train_label.csv\"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## 오래걸림 \n\ntrain = pd.read_csv(train_folder)\ntest = pd.read_csv(test_folder)\ntrain_label = pd.read_csv(train_label_path, index_col=0)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = train\ny_train = train_label","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_test=test","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\nqda=QuadraticDiscriminantAnalysis()\nqda.fit(X_train,y_train)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nlda=LinearDiscriminantAnalysis()\nlda.fit(X_train,y_train)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"qda_pred=qda.predict(X_test)\nlda_pred=lda.predict(X_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_={\"label\":qda_pred}\nsubmission = pd.DataFrame(data=pred_)\nsubmission.index = [i for i in range(X_test.shape[0])]\nsubmission.index.name = 'id'\n\n\nsubmission.to_csv('QDA_Pred.csv', index=True) ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_={\"label\":lda_pred}\nsubmission = pd.DataFrame(data=pred_)\nsubmission.index = [i for i in range(X_test.shape[0])]\nsubmission.index.name = 'id'\n\n\nsubmission.to_csv('LDA_Pred.csv', index=True) ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}