{"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":"import numpy as np\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport pandas as pd\nfrom sklearn.neural_network import MLPClassifier\nimport seaborn as sns\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"id":"HHHOVjQyLhSy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"/content/drive/MyDrive/train.csv\")\ntest = pd.read_csv(\"/content/drive/MyDrive/test.csv\")","metadata":{"id":"hSmGzBGThaU7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"categorical_cols = train.select_dtypes(include=['object']).columns\n\nfor col in categorical_cols:\n    le = LabelEncoder()\n    train[col] = le.fit_transform(train[col].astype(str))","metadata":{"id":"8QHeckLbWL1s"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = train.drop(['CONCEITO'], axis=1)\ny = train[['CONCEITO']]","metadata":{"id":"RHhxLb5Bhcii"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"scaling=StandardScaler()\n\nscaling.fit(train)\nScaled_data=scaling.transform(train)\n\nprincipal=PCA(n_components=5)\nprincipal.fit(Scaled_data)\nx=principal.transform(Scaled_data)\n \nprint(x.shape)","metadata":{"id":"Ml3zuIK_VuJC","outputId":"cec89557-bc5f-4a34-88f6-0fdac824d019"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train, X_test, Y_train, Y_test = train_test_split(X,y) \nscaler = StandardScaler()\nscaler.fit_transform(X,y)","metadata":{"id":"pH3kvXNXhdim","outputId":"4888d39a-7ddc-47b3-d321-139956a7b508"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MLPC = MLPClassifier(hidden_layer_sizes=30)\nMLPC.fit(X_train,Y_train.values.ravel())","metadata":{"id":"fcqGqTSrnNan","outputId":"c97fa5c8-4737-496f-eed2-fd2bf6378994"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_true = MLPC.predict(X_test)","metadata":{"id":"38-Pyvw2nQM5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import classification_report,confusion_matrix\nprint(classification_report(X_true, Y_test))\nprint(confusion_matrix(X_true, Y_test))","metadata":{"id":"yjclodwInR2B","outputId":"21884d5e-98bb-46cf-92df-b8ee687d8e79"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"categorical_cols = test.select_dtypes(include=['object']).columns\n\nfor col in categorical_cols:\n    le = LabelEncoder()\n    test[col] = le.fit_transform(test[col].astype(str))","metadata":{"id":"nzJ-6LaMkbBh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_r = MLPC.predict(test)\ntest_r = pd.Series(test_r)\ntest['RESULTADO'] = test_r","metadata":{"id":"p9bETKsukfix"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test","metadata":{"id":"WV5Y8X_6XLec","outputId":"78ac0fbe-47b4-4b94-81c3-d4e8a097e79c"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import csv\n\nwith open('Results.csv', 'w', newline='') as csvfile:\n    writer = csv.writer(csvfile)\n    for row in test:\n        writer.writerow(row)","metadata":{},"execution_count":null,"outputs":[]}]}