spaceship-titanic-sklearn-Perceptron.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/behnambaloochy/spaceship-titanic?scriptVersionId=140749741\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import Perceptron\n    \n# Data Preprocessing\ndf_train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv', header=None, encoding='utf-8')\ndf_train = df.dropna(how=\"any\")\ndf_test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv', encoding='utf-8')\ndf_test = df_test.dropna(how=\"any\")\n# print(df_train.head(10))\n# print(df_test.head(10))\n\ny_train = df_train.iloc[1:, 13].values\ny_train = np.where(y_train == 'True', 1, 0)\n# print(y)\n\nX_train = df_train.iloc[1:, [2, 5, 6, 7, 8, 9, 10, 11]].values\nX_train[:,0] = np.where(X_train[:,0] == 'True', 1, 0)\nX_train[:,2] = np.where(X_train[:,2] == 'True', 1, 0)\nX_train = np.float_(X_train)\n# print(X_train)\n# print('Class labels:', np.unique(y_train))\n\nX_test = df_test.iloc[0:, [2, 5, 6, 7, 8, 9, 10, 11]].values\nX_test[:,0] = np.where(X_test[:,0] == 'True', 1, 0)\nX_test[:,2] = np.where(X_test[:,2] == 'True', 1, 0)\nX_test = np.float_(X_test)\n# print(X_test)\n\n# Feature Scaling\nsc = StandardScaler()\nsc.fit(X_train)\nX_train_std = sc.transform(X_train)\nX_test_std = sc.transform(X_test)\n\n# sklearn-Perceptron Training\nppn = Perceptron(eta0=0.1, random_state=1)\nppn.fit(X_train_std, y_train)\n\n# sklearn-Perceptron Prediction\ny_pred = ppn.predict(X_test_std)\n# print(result)\n\ntrans = []\nfor i in y_pred:\n    trans.append(i==1)\nsubmission = np.column_stack((df_test.PassengerId, trans))\nprint(submission)\n\ndf_result = pd.DataFrame(submission)\ndf_result.to_csv(\"/kaggle/working/submission.csv\", header=['PassengerId', 'Transported'], index=False)\n","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2023-08-23T06:29:57.868115Z","iopub.execute_input":"2023-08-23T06:29:57.868537Z","iopub.status.idle":"2023-08-23T06:29:58.050864Z","shell.execute_reply.started":"2023-08-23T06:29:57.868489Z","shell.execute_reply":"2023-08-23T06:29:58.049142Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"[['0013_01' True]\n ['0018_01' False]\n ['0019_01' True]\n ...\n ['9266_01' True]\n ['9266_02' True]\n ['9277_01' True]]\n","output_type":"stream"}]}]}