{"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","execution":{"iopub.status.busy":"2022-08-02T10:39:48.072991Z","iopub.execute_input":"2022-08-02T10:39:48.073378Z","iopub.status.idle":"2022-08-02T10:39:48.082754Z","shell.execute_reply.started":"2022-08-02T10:39:48.073346Z","shell.execute_reply":"2022-08-02T10:39:48.081563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\ntrain_data.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-02T10:39:48.084440Z","iopub.execute_input":"2022-08-02T10:39:48.085009Z","iopub.status.idle":"2022-08-02T10:39:48.111997Z","shell.execute_reply.started":"2022-08-02T10:39:48.084975Z","shell.execute_reply":"2022-08-02T10:39:48.110988Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\ntest_data.head()","metadata":{"execution":{"iopub.status.busy":"2022-08-02T10:39:48.114131Z","iopub.execute_input":"2022-08-02T10:39:48.114471Z","iopub.status.idle":"2022-08-02T10:39:48.135569Z","shell.execute_reply.started":"2022-08-02T10:39:48.114440Z","shell.execute_reply":"2022-08-02T10:39:48.134105Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\nrate_women = sum(women)/len(women)\nprint(\"% of women who survived : \", rate_women)","metadata":{"execution":{"iopub.status.busy":"2022-08-02T10:39:48.136874Z","iopub.execute_input":"2022-08-02T10:39:48.137215Z","iopub.status.idle":"2022-08-02T10:39:48.144553Z","shell.execute_reply.started":"2022-08-02T10:39:48.137184Z","shell.execute_reply":"2022-08-02T10:39:48.143650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\nrate_men = sum(men)/len(men)\nprint(\"% of men who survived : \", rate_men)","metadata":{"execution":{"iopub.status.busy":"2022-08-02T10:39:48.146421Z","iopub.execute_input":"2022-08-02T10:39:48.146993Z","iopub.status.idle":"2022-08-02T10:39:48.158461Z","shell.execute_reply.started":"2022-08-02T10:39:48.146960Z","shell.execute_reply":"2022-08-02T10:39:48.157584Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\ny = train_data[\"Survived\"]\n\nfeatures = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\nX = pd.get_dummies(train_data[features])\nX_test = pd.get_dummies(test_data[features])\n\nmodel = RandomForestClassifier(n_estimators = 200, max_depth = 5, random_state = 1)\nmodel.fit(X,y)\npredictions = model.predict(X_test)\n\noutput = pd.DataFrame({'PassengerId' : test_data.PassengerId, 'Survived' : predictions})\noutput.to_csv('submission1.csv', index = False)\nprint('Your submission was successfully saved!')","metadata":{"execution":{"iopub.status.busy":"2022-08-02T10:39:48.159798Z","iopub.execute_input":"2022-08-02T10:39:48.160322Z","iopub.status.idle":"2022-08-02T10:39:48.518113Z","shell.execute_reply.started":"2022-08-02T10:39:48.160290Z","shell.execute_reply":"2022-08-02T10:39:48.516965Z"},"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":[]}]}