{"cells":[{"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":{"trusted":true},"cell_type":"code","source":"from catboost import CatBoostRegressor, Pool\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import NuSVR, SVR\nfrom sklearn.kernel_ridge import KernelRidge\nimport matplotlib.pyplot as plt\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/LANL-Earthquake-Prediction/train.csv', nrows = 6000000, dtype={'acoustic_data':np.int16, 'time_to_failure':np.float64})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#visualise samples\ntrain_ad_sample_df = train['acoustic_data'].values\ntrain_ttf_sample_df = train['time_to_failure'].values\ndef plot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df, title):\n    fig,ax1 = plt.subplots(figsize=(12,8))\n    plt.title(title)\n    plt.plot(train_ad_sample_df, color ='r')\n    ax1.set_ylabel('acoustic_data', color='r')\n    plt.legend(['acoustic_data'], loc=(0.01,0.95))\n    ax2 = ax1.twinx()\n    plt.plot(train_ttf_sample_df, color='g')\n    ax2.set_ylabel('time to failure', color ='g')\n    plt.legend(['time to failure'], loc=(0.01,0.9))\n    plt.grid(True)\n\nplot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df,'graph')\ndel train_ad_sample_df\ndel train_ttf_sample_df","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}