{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Getting Started Notebook\n\nIn case you're stuck on how to get started with your first model, here's a baseline using SKLearn's implementation of RandomForest.\n\nIt doesn't do any hyperparam tuning, clever feature engineering or data prep beyond the basics...that's your job for the competition :). "},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Read in Data"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/caiis-dogfood-day-2020/train.csv')\ntest_df = pd.read_csv('/kaggle/input/caiis-dogfood-day-2020/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.get_dummies(train_df)\ntest_df = pd.get_dummies(test_df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df, test_df = train_df.align(test_df, join='left', axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df.drop('target', axis=1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Train your Random Forest Classifier (Note: no hyperparam optimization)"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nclf = RandomForestClassifier()\nX = train_df.loc[:, train_df.columns != 'target']\nY = train_df['target']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"clf.fit(X, Y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df.fillna(0, inplace=True)\ntest_df.drop('target', axis=1, inplace=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Predict and write out your submission file"},{"metadata":{"trusted":true},"cell_type":"code","source":"preds = clf.predict(test_df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame({'id': test_df['id'], 'target': preds}).set_index('id')\nsubmission.to_csv('RFbaseline.csv') ","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}