{"cells":[{"metadata":{},"cell_type":"markdown","source":"****This is just some fun stuff ****\n\nhttps://www.kaggle.com/c/LANL-Earthquake-Prediction"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-input":true,"_kg_hide-output":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 in \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 \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nimport matplotlib.pyplot as plt\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\ndf = pd.read_csv('../input/public-private/public_and_private.csv')\ndf.Delta_Abslute = df.Delta_Abslute.fillna(0)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**** Distribution of Number of Places Shake Up ****\n\nMax shake-up -  4024 places up (Lucky dude !!)\n\nMax shake-dowm -  3673 places down :( "},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true,"_kg_hide-input":true,"_kg_hide-output":false},"cell_type":"code","source":"plt.figure(figsize=(12,6))\nplt.xlabel(\"Places Shake-up\")\nplt.ylabel(\"Frequency\")\n\ndf.Delta.hist(bins = 20, edgecolor = 'black', color = 'green')\nprint(\"Mean shake-up       \" ,df.Delta.mean())\nprint(\"\\nMedian shake-up     \" ,df.Delta.median())\nprint(\"\\nMax shake-up        \" ,df.Delta.max())\nprint(\"\\nMin shake-down ;)   \" ,df.Delta.min())\nprint(\"\\nStd shake-up        \" ,df.Delta.std())\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**** Distribution of Absolute Number of Places Shake Up ****\n\nMean shake-up - 870 !!!! \n\nAvegare Guess - 571 (14 sample)      :P  https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/93949\n\nclosest guess - 666 Kain (@kainsama)\n\nNumber of No shake-up only 11 out of 4000 plus people !!!!!"},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(12,6))\nplt.xlabel(\"Absolute Shake-up\")\nplt.ylabel(\"Frequency\")\n\ndf.Delta_Abslute.hist(bins = 20, edgecolor = 'black', color = 'green')\nprint(\"Mean Absolute shake-up        \" ,df.Delta_Abslute.mean())\nprint(\"\\nMedian Absolute shake-up      \" ,df.Delta_Abslute.median())\nprint(\"\\nMax Absolute shake-up         \" ,df.Delta_Abslute.max())\nprint(\"\\nMin Absolute shake-up         \" ,df.Delta_Abslute.min())\nprint(\"\\nStd Absolute shake-up         \" ,df.Delta_Abslute.std())\nprint(\"\\nNumber of No shake-up         \",(df.Delta_Abslute == 0).sum())\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Distribution of Public Score****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(12,6))\nplt.xlabel(\"Best Public Score\")\nplt.ylabel(\"Frequency\")\ndf.Score_public[df.Score_public < 6].hist(bins = 20, edgecolor = 'black', color = 'green')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Distribution of Private Score****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(12,6))\nplt.xlabel(\"Best Private Score\")\nplt.ylabel(\"Frequency\")\ndf.Score_private[df.Score_private < 6].hist(bins = 20, edgecolor = 'black', color = 'green')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Distribution of Difference of Public and Private Score****"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"S = df.Score_public - df.Score_private \nplt.figure(figsize=(12,6))\nplt.xlabel(\"Difference : Public Score - Private Score\")\nplt.ylabel(\"Frequency\")\nS[(S<2) & (S>-4)].hist(bins = 20, edgecolor = 'black', color = 'green')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Public Score vs Private Score****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,7))\nplt.xlim(2.2,3)\nplt.ylim(1,2.5)\nplt.xlabel(\"Private Score\")\nplt.ylabel(\"Public Score\")\n\nplt.scatter(df.Score_private, df.Score_public, color = 'red')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Number of Entries vs Public Score****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,7))\n#plt.xlim(2.2,3)\nplt.ylim(1,5)\nplt.xlabel(\"Number of Entries\")\nplt.ylabel(\"Public Score\")\nplt.scatter(df.Entries, df.Score_public, color = 'red')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Number of Entries vs Private Score****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,7))\n#plt.xlim(2.2,3)\nplt.ylim(2.2,5)\nplt.xlabel(\"Number of Entries\")\nplt.ylabel(\"Private Score\")\nplt.scatter(df.Entries, df.Score_private, color = 'red')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Number of Entries vs Shake-up****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,7))\n#plt.xlim(2.2,3)\n#plt.ylim(2.2,5)\nplt.xlabel(\"Number of Entries\")\nplt.ylabel(\"Places Shake-up\")\nplt.scatter(df.Entries, df.Delta, color = 'red')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Number of Entries vs Public Rank****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,7))\n#plt.xlim(2.2,3)\n#plt.ylim(2.2,5)\nplt.xlabel(\"Number of Entries\")\nplt.ylabel(\"Public Rank\")\nplt.scatter(df.Entries, df.Rank_public, color = 'red')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****Number of Entries vs Private Rank****"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,7))\n#plt.xlim(2.2,3)\n#plt.ylim(2.2,5)\nplt.xlabel(\"Number of Entries\")\nplt.ylabel(\"Private Rank\")\nplt.scatter(df.Entries, df.Rank_private, color = 'red')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****;)****"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}