{"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":"2022-05-18T15:25:15.206763Z","iopub.execute_input":"2022-05-18T15:25:15.207076Z","iopub.status.idle":"2022-05-18T15:25:15.219054Z","shell.execute_reply.started":"2022-05-18T15:25:15.207042Z","shell.execute_reply":"2022-05-18T15:25:15.218404Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!mkdir data\n!ls -a\n!pwd","metadata":{"execution":{"iopub.status.busy":"2022-05-18T15:28:10.776131Z","iopub.execute_input":"2022-05-18T15:28:10.776536Z","iopub.status.idle":"2022-05-18T15:28:13.07093Z","shell.execute_reply.started":"2022-05-18T15:28:10.776495Z","shell.execute_reply":"2022-05-18T15:28:13.070088Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!unzip /kaggle/input/facebook-v-predicting-check-ins/train.csv.zip -d /kaggle/working/data","metadata":{"execution":{"iopub.status.busy":"2022-05-18T15:29:18.903568Z","iopub.execute_input":"2022-05-18T15:29:18.903921Z","iopub.status.idle":"2022-05-18T15:29:33.636368Z","shell.execute_reply.started":"2022-05-18T15:29:18.903887Z","shell.execute_reply":"2022-05-18T15:29:33.635247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.datasets import load_iris, fetch_20newsgroups, load_boston\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import classification_report\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn.ensemble import RandomForestClassifier\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score","metadata":{"execution":{"iopub.status.busy":"2022-05-18T15:19:50.8319Z","iopub.execute_input":"2022-05-18T15:19:50.832886Z","iopub.status.idle":"2022-05-18T15:19:52.152289Z","shell.execute_reply.started":"2022-05-18T15:19:50.832838Z","shell.execute_reply":"2022-05-18T15:19:52.15164Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv(\"/kaggle/working/data/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-05-18T15:29:49.079637Z","iopub.execute_input":"2022-05-18T15:29:49.080594Z","iopub.status.idle":"2022-05-18T15:30:08.301706Z","shell.execute_reply.started":"2022-05-18T15:29:49.080544Z","shell.execute_reply":"2022-05-18T15:30:08.300838Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-18T15:30:25.651299Z","iopub.execute_input":"2022-05-18T15:30:25.651679Z","iopub.status.idle":"2022-05-18T15:30:25.680596Z","shell.execute_reply.started":"2022-05-18T15:30:25.651641Z","shell.execute_reply":"2022-05-18T15:30:25.67956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}