JJRyan0/Machine Learning - Accelarating Claims Process.ipynb

## Machine Learning - Accelarating Claims Process.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Accelerating Insurance Claim Process with Machine Learning\n\n### Random Forests, Support Vector Machines, MLP Neural Networks and K- Nearest Neighbour Learning\n\nExtra Tree Classifiers, Isolation Forests\n\nCreated by John Ryan 22th April 2017\n\nData source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython.display import Image\nImage(\"C:\\\\data\\\\image.png\",  width=900, height=600)",
      "execution_count": 1,
      "outputs": [
        {
          "execution_count": 1,
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "image/png": {
              "width": 900,
              "height": 600
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "Image(\"C:\\\\data\\\\bnp.png\",  width=900, height=600)",
      "execution_count": 2,
      "outputs": [
        {
          "execution_count": 2,
          "output_type": "execute_result",
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Z4z8h9oG00atv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\np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niGhM6RpjkE9A8wANuNOxxrh9/kIXW35sHgEA8AAAAAAEkkJ/I/fuRyl9D521PuMP9w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\necw43TMZqn+zH0NHwv5LWG3fjjjf+B/HsXPF4hMjL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8XYCeHiVs8bzUnGiF/L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\n07T/Mhb5OKV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            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "image/png": {
              "width": 900,
              "height": 600
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython.display import Image\nImage(\"C:\\\\data\\\\Image2.png\",  width=900, height=600)",
      "execution_count": 3,
      "outputs": [
        {
          "execution_count": 3,
          "output_type": "execute_result",
          "data": {
            "image/png": 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/ZfkDX/UNCeojG/I95fcf0fc3vej+e6oY9J1PyPZsyBOwv6DyRTH1A8R8zJT9\nQS03BKqLTj4yzXzerbylXT6PNNn18oLIk40cGz7uI+30RePblm9768fdZ13q8fSX+C3HclTrKvny\n0T6YXrK86t35i0Wb49ryxS9Z5f6DcRfP/QH9bhk58p3t6mbN8Ievo1wXfHCevv4zR76uNuvmct3h\nB3F+U8fwP3REfnJfj2P37ZZX/rrmt3ljafx5qE1eZe3yHVYbfPzX8g8r0Rk8ruS6kGvpImLJvpRY\nE89VwsH5BUEQBEEQBMHFIy/wwBd0DMOPRarIv8PBj9TJ/ye3vwfl/z/X8d2Lfhb5Izf/+4H/3Wr4\nUXhz3HL7W5YH6fWIax9afhzMXw5uW37kvfy7+Wd/4yf6N8BuvWO5O9VUPuUlf9LCzo/Ca+uZ9wr8\n7u9fYOy+4Ditna8fEOvL1t930/LkOsi/28H9z3Pld2x/PlgTwZ/gwTcR+J0q/pNzLin+912EPL7k\nj6SrvZF3scx9FJ68Hgc/Ck+urz2/a18P5Pmlcy0Qt7zynfwJMeWxtO+xNf7IwPWaJ9v82mLzgnxB\nbgTp4+u4G0t7nvNHeYHHPYjPNvjjJA+7sbTz+S37Tj1fXELkGhr+DJA/gnZr5nXlT2DqfBSefN2D\n1GfXfgMa/yPqf2E3lp77Ez9e3WQhXvD/vBna7oFu4PyZ1//fjbbwtf/sZ5KNtf+an/5JaEt83T//\nZ5X9ofyxf/iPGm2Ki2zPhjwB+wsqXxSzP0DKi//mh5+M1Red/OdT7tLKl4a5J6deXhDRlGPk47+b\nR/P7ytd8KP3Flhz8l1tvWL735d+1fM3Xr7mVnPio1uXm/IPpxb8mX3pIx7Xl8Uffv9z5xn+xvOxb\nvmv5Kvpuo1JvPnoPxoSLtTz05sF/TN27s/KTU3Wzxr9jawTM09X/w29bvn5d1xj+q4atuhFVrnI9\nOj/53pxNnN8H70S5OdJf7QGtgtwM2di3Lcr3Iq3XxxP8S8f4o/A2HhvlXTnZzr+DCflAJuLJdSE/\n/C4iljzXlD0Fn0vc4+D8giAIgiAIguDikRd44As6lvx/4/aL3PXTEfiF0d4LVOYL++0L21nXx+ff\n1T6z3P1K+/uY+ah3+f1A/s/evFief5cEX4j/Zd+4/p+dcnyUvoc5v1iOvvP11p9d3kkvTl72jzkr\nX67+zPLIfe47kAnzh6FPPviG8okJsvdPrrWrvhu2fK/wteVjb80vdnf2qfsi74p/gbH7guO0dj7/\n0keXV9u9WvN97UP5tRi5DvLvdvC6znPld2x/PlgTwzdQnvnovctdj1ALPj7/MuJ/323Qxyj+KLyv\nW75y1aDHevt6wvrYvJd9y3c5v/Tu5WP0MsT6GCzf2ZR4yfr45utx9nXB64Y8v3SuhWe//G3Lh9JS\nri2P/ppea7seW3KTo6mT7Me15eF/m79qQa7re+t3Uep3iOvji59DQX2b613Ir5nI9ZHXvv2cr+tF\nr1s1j3sQn99x095YKjftqnV0Hp9Od9ceSF1nni8uI897w/K+tKQnl9+68wf4NVGL+a54/FF48gcK\n63X8jvra0j3Q+uzab0Djf0T9L+zG0lf8yRcs3/HJ9zc3W455lxBp/sV7fqXRJCjWH/ne74d+xFd+\nz99O75pCvvSupv/2j/9Z6DcDfbeS16Z3RP3B226H9mdDnoD9BZUvirkfIHLXfT3MDz98Y0m/UCz9\nEJR3MPmPY+vlBclPWOXo+dy2fO3r+e3+euhfrfz4g/lRSMcT/2X5fv8fPfnrmnxU6yr58lE/mOTt\nw/n4+NvqJ8yV8r0x+eg9GBPy1xByoP9cC9/4tqVSdp+tygf+yx3+y4d8yP6U64IPybOu3Y6P1hvW\njYE3llwN2u9mWnEfd8YfJVB/N9NpvjtHboZs7NuQ29Z6818XpP/83S6/dLQ/MGy80UcAtt+xJI+T\nPd9FREi8wWNRrgv/y8Q5Y8lzTblJNPoiVv3sYf5erUPzC4IgCIIgCIKLR35/gy/oVOiLzs888XvL\nB+SjkB7mT8945on3Lz+Sfs994fo7HL+A9fRnPl4+MumhT/3+8szTTy6P0w2dpx5bPpA/QunLvvPe\nJf096OP/lW1/5Z+kePICNn000kNW49pTy5OkIb8fyO995vUCQd9J8OTyyIdyvg98fPkkfRz9Onbf\nT/L/18sfgj79heVjxe4jy8eeoNWi35suH7e88u7lYS77uo6nlk88zOv4wCOfKx+Rb78bOXHrHctb\nH023+pann3g076mse1me/N279IXbXOf2Oum8yLviX2DsvuC4Q1uuV70Gc75PrdcFTch1kH+3g9d1\nniu/Y/vz8jqLfAzYry5/w/qvyF/wj39XvGTI77vyWHOkxxfN2xsczd68ZPmZB5/k54Evfbp+bNLY\nZ9fngfJHmetzxq89xh8paR/H9JUX1x5dPpYe+DOvC15H5PllXesHc/7Met19Tj556Nry6fe+zt28\n2PHYWnnxv/1E8xz0gUdzne1NvOf94vIAPf+tM5/Kj3F+Dr62PPkU3ZzRx9fRN5amn/P1cYlet2oe\n9/lj1b742O8kvf/wmr9unoP1OuE4zyyPP0HrWp/TPiQfXdp5fPp17dmD7Dv1fHFJueWV9y6P5Of6\nZ770uP4se/ix8jPgyd+9W68lv65v/MXlfU+kH5jL449+hH0/9HvL4+vQk488ml6zlvrs2m9A439E\n/S/sxhLxp177f1U3W4hv+9h7Dvp+o//xL7x4efGD/7nRI+g7k579zS+FfpaRBn0f0x96/rdAvxFf\n8Sf+/PKX3vHWRu+2N/1LaH9W5AnYX1D5opj6ASJ/SbQemx+FR7zo7uVjNPGl31ruyi+mN59128sL\nIi8Sy/G55T/+U/OOE/reoTfes9z3iPmiz3yUv9JYqW5gNDdH9IV/Oap1lXz5qN8e6/Jrvq/lJcvP\nP0xPDHqMnwxfuOZav+uK/tN/350/tbwwv7uGPneavv9KvviNj/YvmeT44sNvW77J/Of1lm+/h/9q\nJR/le4bKdcGH5HnLT37A3LBb/1N/74/Wd9/dx/KV/XZ5oHXDG0tNDT6zvPNHX2L8XrL8w/9i90u+\nf0c/giAd1z693P2D9TuS9K9Z6Oi9fdyiN3rG+9an1G/N5678137ymeeUw7132P8Ea7zlU78O3tFE\n6DVl/zJL/mON3wlF5M+iXg++AUNjO272mF8mzh6r/EdKdZ5ZH1v+pm1CPqfW6B6WXxAEQRAEQRBc\nPPICD3xBp+G25Zteff/yUPn+oWV5+qnPLR9791uWF9GnZYjdra9YXv2AvpiVbnT89j3LD9/+guUV\n/+lzyffpT8hHQr9k+ZkHHtePdjcvan7T6z+4fOIpfhFwufb7y2ceef/y6m+/o77ZIL/3gRtLxC3f\n/gvL2x/WmyuUy6dWnde+3P6udtvytT92z/LAJ5/UPJZnlic/8+hy35vAX39fVtKX339k+cQT5nuR\n1/V+/pMfWe76uc46bn3Z8sN3rT5f0BfJv/iFx5YH7nLvfMp1bq+Ti72xRNfL97/j48tn5Luq87X1\n/fSuLTo/yY0l+p35gxoDvXYlvwde9nezWeT33d5Raml84N68YHnRm95vvoeMXMHzQEIeW/qdZU9+\n8oPp8Zdej7ns9XOvK9nj6ae+sHzi4fuXN/7dl+Ebz7OPrUT7HJRuDrz7zdXracQt3/7m5d2lnqvm\n59bnqde/Yvk29/g6/sYSMfec39woMDSPe7rh87uqx9eWe75P18lHljt/7GXLs+941/IpMn760eXN\n38ka8PGJ1jW7B9l36vniMrP+DHjtfR83612P9DPvg8udP/03658BaF3k/+5HS23pGnzoHWutXsmP\nA3n9atd+Axr/I+p/oTeW6B1GL/3o/c1NF3o3D31U3sxH0H35H31e+pi53ruNvvk//fsUB/kiRu96\nopte9I6q2Y/G+9/+6nelm1peh3Ldk9PJKE/A7oLKF8X4xtILlq/6vrfozQv3zpnujSXzgi0f/f+0\nbF3ojL+xNHf4F5T1r1nokI+r++XlNW+8J/0nV55Q5ajW1fwgW59g0/ezvH/50Wa9zyyf+u13Lm8k\n7Tvfubwn/4WDPTafDMvb6OePer3uBst6PPOlJ9NfTD1Ufvjlw37XT7ku+NA87duk+eAf4KRn/+O/\nHvYt8a5uaN34xtKKvGW7HPxLxQc+pE+wctgbiG3taK8eWx56wD2xr8cXH3ozvlFRITdDJvYNUfLx\nf2VnavrUh5YfL39RpPHoePJ337nc8Vf0ueOWr//7y2sf/Hxeh/tYCnnr7Xp4vy977t9c7nhH/guc\n6rEs8QaPRbku7C8T545l/iN1yx3v4Xc+0uP2w29bXkofXZnnnv1yfY6qbjwdlF8QBEEQBEEQBEFw\nQ5BvLDV/yBxMkr8D57F74zuHg+CSw9/hdG1532vw/PXkQm8sEXTzhW4k+ZsvxO33/WqaRzdy/sBX\nfUO6ydN7hxFB37VEN5687xYU70+/7jVQk3jR/fcsf/hl3w19CfqIO/RuLOE532Xf4XKBbN5Ymjyu\nfX5518/ad4yMbiytF/y/+pi+gJ8+M7meP++NpWvL479zz/Jd/q80tm7WXLtW3XSo15W/ALE5+EaI\n/bxLeDjtmRsUt9z+C8t99PbkzYPW+876L1oSL1le9V65AdE5nv7ccp/d1+6NpZVb71h+6Xfam2TV\n8fRjy132HULH3FhaqT5KAB60dvM2UuP34fT20f7x9KPvbK8RSH2jZ+bQa0dvHsF3ztibHx+Uz5yV\neJ9bHnwwv2WdDvort/KXI+tx7cnlfa9v/yrHvvU2HcjvdfaxfOCNpZWzxvJ/ofNLD6e3/8pBNzbL\nXzyuxzOf/cDyKvcY2J9fEARBEARBEARBcCPAfzyMP/o/yORPFEKf/iGfohI35oLgcsDfc+U/1Wjl\n1vyd7dc+tvzc7HePXyAXfmOJoHccoRswAr2rid5F9Od++Y3p+45GN5MEeqcQfdzcrT/wQ7tuLtFN\nJbrxQ77o3UYW+t6mF77r7uX5d/5isqeWbjohW4HeiYXiXghH3lhK73JJb5d3F/XK6MbSl936uuX+\n/PF58HtiznBjafMtsCu33P7q5c7ffmx5orzjhd6CSW8hfdvy3bf/LP+1Rj6e+eAvVjqt73pc+9Dy\n43me3jlxn/ns5FUhvVPmgXe8aXnR7fX3AcGaQV6wfMM/eNvy9g89unyqercNaa95P/DO5adecXt3\nvemtvH/3bSmves2PLQ/d95bqnR+J0Y2lBOu9/WFfQ9Zr3nJ95I2lROejBODbSC23vmz57jvvr99R\nde33lyc++fHl7bs+SuHwG0vyZYr2I/A85XNsl99fHnw93eSwN190/+S64sfkuu/Vx0Y48ltn7TWT\nHh/wsXz4jaXEuWJVN5aYZ7/8TctdzeO39zb2zK78giAIgiAIgiAIgsvL/2f5t/S9JfK9I/bTSwKA\nfN8afWrPB5e77qRP7fnl5c53c/2q73IKguD68j338psW6Huu7rsnPVbpk7AeSG86uLzfNXhdbiwR\ndAOo986lY6GbUSgm4hv+9S9AjWOhtX31Hf8AxgyCIMBM3HwJgiAIgiAIgiAIgpuO/3t5d/qjy2eW\nzz/cfnpJgHjJ8p13vn/52Of0jy35u4Petnxn3FQKgksFfSch/UF19aYF+o6+V7/i0t5Ev243lgj6\n2Ltvfe/b4Y2ZY/mKP/HnYUwLfbwevQsJ+R8DvfPpD/+1vwFjBkEQ9IkbS0EQBEEQBEEQBEEQBEEQ\nXG6u640lgj6K7tYf+nvLX37gXniT5hDoo/NQLAR9tB3SOIS/8tv3pXcpHfI9T0EQBHFjKQiCIAiC\nIAiCIAiCIAiCy851v7Fk+V++9TuWr/vn/yx9lB19dxHdINoL+f6vL/5OqI+gmPI9Tnuhm1L0XVBf\n89M/mXSQfhAEwTxxYykIgiAIgiAIgiAIgiAIgsvNpbqxFARBcHMTN5aCIAiCIAiCIAiCIAiCILjc\nxI2lIAiCIAiCIAiCIAiCIAiCIAiCYIq4sRQEQRAEQRAEQRAEQRAEQRAEQRBMETeWgiAIgiAIgiAI\ngiAIgiAIgiAIginixlIQBEEQBEEQBEEQBEEQBEEQBEEwRdxYCoIgCIIgCIIgCIIgCIIgCIIgCKaI\nG0tBEARBEARBEARBEARBEARBEATBFHFjKQiCIAiCIAiCIAiCIAiCIAiCIJgibiwFQRAEQRAEQRAE\nQRAEQRAEQRAEU8SNpSAIgiAIgiAIgiAIgiAIgiAIgmCKuLEUBEEQBEEQBEEQBEEQBEEQBEEQTBE3\nloIgCIIgCIIgCIIgCIIgCIIgCIIp4sZSEARBEARBEARBEARBEARBEARBMEXcWAqCIAiCIAiCIAiC\nIAiCIAiCIAimiBtLQRAEQRAEQRAEQRAEQRAEQRAEwRRxYykIgiAIgiAIgiAIgiAIgiAIgiCYIm4s\nBUEQBEEQBEEQBEEQBEEQBEEQBFPEjaUgCIIgCIIgCIIgCIIgCIIgCIJgirixFARBEARBEARBEARB\nEARBEARBEEwRN5aCIAiCIAiCIAiCIAiCIAiCIAiCKeLGUhAEQRAEQRAEQRAEQRAEQRAEQTBF3FgK\ngiAIgiAIgiAIgiAIgiAIgiAIpogbS0EQBEEQBEEQBEEQBEEQBEEQBMEUcWMpCIIgCIIgCIIgCIIg\nCIIgCIIgmCJuLAVBEARBEARBEARBEARBEARBEARTxI2lIAiCIAiCIAiCIAiCIAiCIAiCYIq4sRQE\nQRAEQRAEQRAEQRAEQRAEQRBMETeWgiAIgiAIgiAIgiAIgiAIgiAIginixlIQBEEQBEEQBEEQBEEQ\nBEEQBEEwRdxYCoIgCIIgCIIgCIIgCIIgCIIgCKaIG0tBEARBEARBEARBEARBEARBEATBFHFjKQiC\nIAiCIAiCIAiCIAiCIAiCIJgibiwFQRAEQRAEQRAEQRAEQRAEQRAEU8SNpSAIgiAIgiAIgiAIgiAI\ngiAIgmCKuLEUBEEQBEEQBEEQBEEQBEEQBEEQTPGs/9dzvna5Efny//3rGOrn8//G8pU1xX4F6QXK\nbF17Na380/kMl39fynpzf6YujPFt1u25uDqUnNY+WovmTPg8e1zsPkr+M2tA/j2sLvtT2+O0ay6x\n19avx64J+RLFP/VHzOWtehTX+rdQrtwf53hKSn5r29t/5LcXibM3hvixHbWeffuwJz7vm8RBcE6k\nZ8/7jHNN/mu7t0YjRJM1JI8W5OsZ5Yfse5C91RjntqNmm1qaO9v1bDvXQ/atY83lJ7Y4HgbpWVS7\nR5tbz571iHp93hetfZSD9W3nVcfP6R7V4zJHOWztt7UTW8kn5ZR8kT+Pozr4cdtvdTA+n6TjxtL4\nCulDjTRu/Ann7+dkHul5n3qe/Xr+w9g5rrVRnI7RkHPro3219X4+tmj3aPLMsLY9b30lJvXFzp+z\nr4xjHZrzebSx6zkfx+oJMk62tV3Ph7XFlvZM4nhUT2htdF0erYfVkbbnR/n4MYRoJrIPzqNG4mp8\n9R/btfjYbMc5eVumzTf5+P835nFPZVPQWqqdjVPP03mjYebZBuBsekDfCpMb8BeSHcq1MJjv6Zma\nEN7GYu2tjx2nOCOdxj6DbAm4FvIBtkLRBzl19cgnI+fYFmF9NK5o+fy2gDHS+H4tAWpSfmmu1U1z\nK1UdxcePdzRYZwRrbmnMUOXkobkTxJihXWOG5pztZYX35XQ1bPSy1rn2BNbfQjbJ7jRxeX1ZuyGv\nMdshf4F1BrWC+lvMx7+Z4DpLjbaQfeHnqnLdZh2kf040n3yNwGsl55j6PQ1qR2SNjNc4JRTrhr2x\nhKiKt/bt5iTMxvFmMEgrUIZ1nahpGS99are4/PtS1rQyUxNG11evt8fF1KHkl/t+PWU+YfMbcbF7\nmHKjdsXn79eA/HtYXfanFoP8j6HEXttD1nPKvNMLBKkln7EeQbly/+Kug5TX2p5q7xFbMZAPIX5s\nR61nLjfR2RM/xSVgXIJzIj173gPFsByS4xaiOc7t8Bqy7nxuXkP8cV5Yw0K+s1pEnTe26cUpeZt9\nQbYWsuHc6hjbTGgTwFdqge3bXNIcja9tXR/V6M31ciCsbzuvOn5O8vfjMqf1b+cFayc5Sz4pp24M\nHrdrteuw49r3Ghiv2Ysjc0ytkXS8xnre05A5nq91GLO+tY/mR+vsxi0+3Jb+SqORxlSjjmf9OZ7v\n29jiL9o9rK3oiK6P2cIxrR2fa07iK+MIyaGOV/v7cx9HtCw0LtrWru/TzkucGp6nmkneyEZit3Nu\nvdKmmCPNOURrn57mK7C/s3M2zXyijs12OadOHj5frN/myDbbeYqd5tBqNRrOZjYOovEriKbJDfgL\nyQ7lURjM9/RMXDr3NpbK3pDGJvInKp9sT+fIloBryRrYvtbnXO2c1RFMPgVkh7A+Lm6O6XMcwTpW\nP48B21lavUyaa/PjHPJaqjr2x70G64zIWtQm2311EoqO5OQ5QYwZOA9qATTv7C8rTT2pn8cOqWFX\nj+byPPI7BI5FbQ+Ni/z3shmPbLId8hdYh/F14tpnvV3oWrfi30xwnaVGW6gt70Ouae4j/XMhMYly\njTTXic2vp8Hr6ZM1hPXc65wKzoeR9VypG0uesmDq53O7gXZji20G6QVMqhG1ub+nptVYOad2i8u9\nJ35NczXRtdXnPS6uBikfalf8WjRfwebY42L3L+VF7YrP368B+fewuuxPLQb5H0OJvbZ711N8pQ/y\nZebqUettQ7ly/2Kug5Lf2vZqhfz2cGiM4pf6GOTnEZ1dsYVkw7EQpMf9sR2KYRnliOxnEM1xbnP6\nVX4T9UN4DfE/JK9Ka6jD1HY92861kH0Tk+sWGxxnDNKz9PZU1tfYd3NhW7Qn1lfmZF582JbamuKb\nbDwaw89J/n5c5jS/dl6QPH2+JaeuP9sivzSe58SG7b0GxvomvRU/ZuMgbb9+5G81aM7ae7xPPc9+\nPf9h7BxXbWpfi9Wo49X+Sa/pt7GZOoZFbVVHtFLMct76EhKn9vNadRyPztU61t+eY3vVE6yttev7\n+LiyDzXtPNbqzXVzSrY4Jo6B0PxZu6fnYTsh6YCY3sbPl/hpTmug+Xh7M2982K+1kzm1AZrORuw0\nhzoOz3t8LiCO0+jR+gm65rJ+4C+ovdcRBvNAazYu0dgbZEzGkb9Q7CfX21tLLw7SF1uolWKwreTP\nIFuE+vTi7gHGSOP7tQjOzWhZTWdrfexabF2a8WyPNGDcRNbKLfKfoehITh6aI5tsizROAecha7Pk\n2M7+MgJrSeeyhrVFfj0avax1jv3gWNT2yDGzLdLYw554yF9gHQbWCWpvobGpj+LejOBaIWRPuF+u\n16Rx8fXUfPL10Xs8ZVvsT+0G7g+/vc6pkLUQvfVc6RtLiFQQanPfFqMq0ootINIKmNma2noSxVco\n59Rucbn3xK8J1aTYFHRt9Vp7XEwNSn65P16HzW/Exe1fyW3tb+0D8u8hmjPXLfI/Bol9yHrEl/Me\nMVeP2RoQlCf3+/mdGlnv3jrtYSsG8iG0dhjk45HY1Nr4o9jWB8UVSAuNe1AMi8RD9UH2M3Du4/yQ\nH4J0UG578vMa49zGuhKbtKSPdZiZmN04a1v7T+QmbUJjbIH0LFua0D61Hl6DXZusz/qiuaSZEC2l\n+CY7j/i1vrqP9bjMae3becLmqbb1PuGciNUn+1s/6RfdysZrYKyPaIqejEluZNvTUP9Ws2jkcdWv\ndRgTc+2j+ZE/j4O42U/8VbvVEe3iu55bn6KT5+q+xrX+oo2R2rGtxY5h316eei6+mlOLztU61t+e\ni72cq02N2Hqtvo/a8bz41/h5vga9HfYl2pxqPRgTxmgRzUTyEbA9o3E1PvBpbFokdr0Wm0/Hh+Yq\nbWdr5iyVTaK3FptDbdNqEFs2rQ6C7bwvj0suvbwsau91hME80KriunlPY2/wY8hfsPbig+wIuA7y\nAbZCo2/Ge3rtmpAdQmzZD8XdA459mJbQ6hF9zbIOt0dwPNsjDRyXyFqZQ9dWdCQnT57v5XgqOA9Z\nm+X8sU8Fr6Gu3aH1Ey2oZ+aR7yHg2hvIJtmdJiaMUchrTHb9eFIDwteI6y56e5iLfTPBNZb6bKG2\nvAe5nrmP9M+FxCTK9dFcIza/ngavp0/WKJxnnZwLs7mebH/T3VjylKLlvi1WwhSy2GaQXpBrSm3u\n76mnH2NofIvLvR9pHdSuzNYDr7PHxa0/5ZZbvxbNfU/+F5w7sfZ97n4fkH8P0ZS64HUSp12r5Npb\nj8wjX6LkTMB8GeTrkTxYi+KqP4Ly5P7F7H/Jb2331mkWiXHwPiQ7aj1zue2NL/Y8L7EOx+t7UH69\n3GaYzR/5ek6Rm9cQf5QTgTQErNVqCDMxURwCrRvZWciG49Uxthlrp9xT2wNcxx171strM4+F4kfj\ngpljf7y24lvsLKyBfLlWWNPGR/OEXYNfR8kJ+rOtX6PY2/GZPDxqzz4ojubGNh5rT63VtP62Bjxf\n6xBiLz71vGog/1HuElcQe6Qh/m085+/64qdxZbyOUVPXzeLj0bn3t3HEx2vRuc3Jo3O1j/brObGX\nc7Wpodq1ujZX76N2Ou/ttuYZXZPH5VTp6VzrN4Nq1XpbaE1Lbk0OrU09z2Mpdppr19na67zYq35t\nY+ewjdDa1jmM4mTMPLZpdRBs5315XHKxefU01d7rCIN5oJXsO/XwFFuXZ6WR7ZA/sd8ewRrY3ukn\nRloE29ZrQnYI9alqk3Px+W0B9dP4fi2i1cukuVYzxVuRdXBNeNyub7RG1qAWkfUzh9cp60hOFtHO\nIP9TwTFkbZbD13aRSI2Ipn55Hvn16GkdqjeCY1HbI8d0focyjqexRmtkDQbWCGpvMRf7ZoJrLPXZ\nQmzzHpB/1kDa54LzYMq10VwfOb/U72lQOyJrCOu51zkFnAuzuZ5s7zVu+htLnlJU6udzW9Cq0CvF\nfgXpBbmm1OZ+r562lmNIZwzK47JQ1pH7c/Wo17fNxVyPKTdqV/w6Dsv/4h5HJbe173P3e4D8e4im\n7C9eJ3HatUquvfXIPPIlTpmz5MF6YyRPPr+Y/Zf8ejU6Ng/Rp7YXY8vP12lPfURnNrbY87zEOhyv\n70H59XKbYTZ/5Os5RW6VhvFHOW3pWq2xDiN5740n2nvWnGxSux+kZxnrtnn17NPc2kpdZG3Fj9YA\n5tifzqVvyTbFzsIayHe0L3Xd23lCbASylXXYtbS+PN5bvx3XvtfAeE25VntxqB1pIE3VqGuAtKxP\nO88+6m/nmG7c7ONzRVgNicf2WSOdq6bt27gE16yNYeE4JsdMimfic9v6pjjGx+YpiJ33txr1+myf\n7eRc7OXc2nha3eyX8vT2akewb02ZX21F29vQWH+uzp3ORU/9DqPktkuPbSyjvNWmpcSt7DgfZC9z\nNlesPZmjsxE7lJfMbWk088mnzccDtbO/rNeuvaep9l5H6M2vY0iLYoKaI5C9UMaAn6XRADYCa1Pr\nyHM9H68vtlArxci2BWSHENuskWOKls9ti1Z/JY3v1yI0PwfNOVu19+vojGd7rEEtIutkDl1b0ZF9\nthypvQe8xhWaS/PnjX8sTR2pn8f25i9aUM/MI9+9sBa1PXLMbIs09jCONxeLNRhfH6656O1hLvbN\nBK4TwtY8703y5z7SPhcl/kq5Nvz1QXbZFvtTu8VY5xRwLmAtO9bjiRtLE5TC574tdsJsRLHNIL2b\nnVQbanN/tpYY0tjicu9DWge1K/O1sOvb4mLWX3LLfbuOMlew+fVBcc5BySv3R3uA/HsUzdQfcdo9\nsnH3rqXOmeyoRczlPKfFUJ7c7+d3SlKc3Po6nSoHibFHX3zYhlrPZO2zzmxssU+f1wvj7sPre1B+\nvdxmKPknDc2j5oDaTewZgmzbtdlcFORvsXUa6QhbdjAG2a+t5Cs5I1vLds0xSMvCui2yNmzf5pHm\nUr/ei+KXfNq5NN59LGSbom1hDaaeG+2LjY/mCbERyFbWYdfS+vI4XP+KHZe21cB4TdFo4tD4Sk9j\ny9/P8XytQ3j7en71yxrt3JoHiEn4+vRiEzIn8dV+ba1O6bOPjNu4dM7UMSxiK3pWi+brsY6vm7c+\nhNhZX0Hn1L7us52ci72cWxtPq8t+3o5RO6oF+9ZYHTTPiG8bp81HW5rra24jmlYP2dVoXLZf/VfG\nNgi0jpxPo8eUuUrb2Zq5rk2izlHsfD3qec/cOq0Ngmx6vlyLnFfu9zTV3usIvflWM9nmmBLXznuQ\nfRkzuSNfAWkgO4L1qbWsY8BW7b2+nbM6Qm3bt+thYkoNcjyf3wgcl7WQ/RZYbyV/bwa21zXU9XDj\n2R5rUIvIOplDakQUHam35UjtPeA1rtCcs71sSA2rOlKfxvI88uvR1SLyPPI7BFhzC9kku+Nj8rqy\nLoJssh3yJ1gD14drlLV2keua9VHcmwmur9RmC7HN9Sf/rIG0zwXnwZTrork2cn6p39OgdkTWENZz\nr3MsnAdTrWW4nn15xI2lA5BC2823G+I3q9ivIL2bndla2joq2b863+Ly7kNZR+7P1cKubYaLWX/K\nLbd+HZr7nvwvbt9SXrkd1R/59ijXOAHXJ5x2ncespfim/ojtnOe1GMqT+xez75Kfr1EazyC/Wbb0\nRz6jmiE/z97Y9RdBHo/X96D8ernNIHqsoXnUzGmTHaobskVILqLBOfXzQhqC1drSIdiO+j27/nUn\n+c7uhdjgOCM2dAmgK2tr7Dt5lLm1RevqzZXHgtNjsi+cNxpujnNvx2VO47fzhNgIZCvrL2uB/jyO\n/Oy4nW81MN7HrqPNi208W/4lzzxn7Wt8zHouaRR/O7cRN/v0fAXSEP/kl32pb3WSbR5XG41p41p9\nj9qynsDzes55YV8fv/hkbAyPzqm9Xych52Iv59ZGYQ2Jae2wfWsnvnhe98fbjOZkPOlIa/RQzDlU\ni/VmtDSmxkbr8TYtKW7y1bXbXFpkrs6hsjHjXZuC12Ftn5fOe2obPnc2xr8H9EtoHjYnoqfDNl5H\n6M23msl2IqaA7O2YjCNfwtvLObIl2jUQfZ+iLfp5jOd61LlgmxHsN1uDHlA7je/XIqAe5UZz6/89\naluzhrI3bpxaOs/21l91RsxpjKjy8RypPUv/D4JW0jz2uyw0NZS6EXke+SEarTPuA8eitkeO6fwO\n4RSxWIPxteF6W71ZNC71UdybCa6v1GYLseU9kRoSSPsclNgr6DHD14XNradB7YisIaznXudYOA+m\nWstwPcflETeWToRshmyM3bCE2cxim0F6NzOpLtTm/mwdGbMX5XyLy7sHZR25j2pRbAp2bVtczNpT\nXtSu+DUclvvF7VnJfW197X3+yB8htWDdEaddZ8ozt34tsoZeTM2XbKjFIF/PrBYhefL5+fc95ZTb\nXo2Q3yyH6mvNMMjH04t9TNw9IH2L5Deb2xaixxqahwX5eU6RV6VR/OtclLEuzUsuYx1my64Xw+Yr\nOSNbQXxQjC2QnsC59/P3efXzYLt2H9S/N9df12heNfwc70k9JtTxxzaC5FHW0fVd7Y2v+PlxzcH7\nY2zOUg8/ZuP0NDQf7F80KMeM1yG8fT3POfR8uzGzj8bF/oTVSL7F3vqzne2Lj8aV8Vq/po6jmoye\nez/2qePwufgINp+WNj6KK+fe1tpYW9XNvuu5+Hl7Gi92ztfi51mztqFxPFfnTmNJL9nheLNIXqIn\ncZCtwnZCyqvx8TYIjmttZQzVp56zPrWNjFtovLZb6dihGDznaXNvbIx/D+iX0DxsTkRPB9dN6M23\nmsl2IiYh894ejSF/orHPY8iWaNewksYHtcnaoi/jUCuPc+56Pg/ZtzFpzOc2AsbtvKNohkZLSHN1\nbtUa7DrA+Lj2I0gr1znZ7qsP0eRjofEjtGeRdbTk2M7+MiH1I05Rt64WkeeR3144DrU9crxsizT2\ngGMI27E4X8bXhusjWns47RpvdLi+UpsRYsf7kcj+SPdc2PjlmvDXBdllW+xP7RZjnVPAuWyshTC2\nSOdQ4sbSmZDNsheQ3dRqs1eK/QrSu5mZraOtoZL9q/MtLu8epDVQuzJfB7u2Lc6/9pJX7tt1lLmE\nzWvExe1Xyiu3W7VH/gjZT/ajtsdp15ni5davhXPB6yh+0oe5Etv5zmsxfI1Qn9rz77vkt6c+e9jS\nhz5CsqHWM5fT3tga93i8tgfl1strBtFDuQjIz3OKvMhW/MUX5UMgfyH5rS3r5HPnb9m261xv67jk\nO7tervU4HwTSEpJeant0rtmOXdlHc91bP7TefvzRvGq0c33q+B2bnLsgedh1ID+ys/rF3o1Tn1qs\n0eI1kwbIjTWxrsS0/ta3jOc5mW+1fLx6Lml0fP06qrg5puB9Betv/VI/n4td26/jck3aGJZ+frbf\n+kks6otdipn7dkzsWjrrNH2yk3NvyzZWj229rtXw9hzD6olvjZ1nWhuJieZ8DG51r1r7GVRTdLe1\n2MZCY8i2tvHkuMnX7U1Hr8xVuq2tzI1srIa1QzF4DlHXopk3cz1gbgmpQ85phW3b+otOr24Cnm81\nk5apQy8mIf7F3rBHQ9Yo9siOYG1qLX0ftjfa2Z/nelhbtt9Hjje5/h5IN2m5dxTN0F0HzTlbsbdr\nsPVA496fNUZwPsfVx+VjofEjtGeRdbScP/axcO6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xvk4j/Z6/+Ca7Er+fH9IRW7b3sD/H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3O0PunczLV+FwXnJUh+kuPIR2xl7aphbTG17b7YMqca1o/Hax+m2GS7Lbw26+s6cRzNydqKHqLW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\n6jmyF2q/Kha0J1iTbGt70Wntd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lGaBWN/ZF1fvipvm9rRn0N2Vithjs0FXabfOe9rK467JqOwmWvCVYSizBkrsES+kxW4KlR7ro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gB7kJMBwC5Z87JprNnlYGKJcCoozACg/GiWAcAu5GUAsAc5GQDskjUvm8aaXQ4mlgingsIM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\n",
            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "image/png": {
              "width": 900,
              "height": 600
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "Image(\"C:\\\\data\\\\image1.png\",  width=500, height=200)",
      "execution_count": 4,
      "outputs": [
        {
          "execution_count": 4,
          "output_type": "execute_result",
          "data": {
            "image/png": 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wI6GBmiu15km/rQAAAPAqnv1+iCc34An5CQtAm39lqYp7+DpTnqetH2YEAAB4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\neRCvz6o6V+XlZfB5GF2Gyop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            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "image/png": {
              "width": 500,
              "height": 200
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#import dependencies\nimport os\nimport warnings\nimport pandas as pd\nimport numpy as np\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning,\n                       module=\"pandas, lineno=570\")\nfrom __future__ import print_function\nimport os\nimport pandas as pd\nimport numpy as np\nimport io\nimport requests\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.set_style('whitegrid')\n%matplotlib inline\nfrom sklearn import preprocessing\nfrom sklearn.metrics import log_loss, auc, roc_auc_score\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn import metrics",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Data Cleaning, Preperation and Model Building**\n\n1. Read in the csv file.\n2. Describe and analysis the data\n3. Randomization of the data, separate out all variables to make sure they have the correct     data type i.e. numeric, nominal and categorical.\n3. Missing value treatment.\n4. Encode labels in the data set with \"one hot encoder\".\n5. Use cross Validation to create a test, training and validation data set.\n6. Remove columns from the training data set and select important features.\n7. Randomization of the data, separate out all variables to make sure they have the correct data type i.e. numeric, nominal and categorical.\n8. Build machine Learning algorithms using pythons sci-kit learn from on the training, test data set constructed and preprocessed.\n9. Evaluate the results.\n10.Improve Performance.\n"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Load the data and make dataset a Pandas DataFrame\ndf = pd.read_csv(\"C:\\\\data\\\\claims.csv\")\ndf.head()",
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   ID  target        v1        v2 v3        v4         v5        v6        v7  \\\n0   3       1  1.335739  8.727474  C  3.921026   7.915266  2.599278  3.176895   \n1   4       1       NaN       NaN  C       NaN   9.191265       NaN       NaN   \n2   5       1  0.943877  5.310079  C  4.410969   5.326159  3.979592  3.928571   \n3   6       1  0.797415  8.304757  C  4.225930  11.627438  2.097700  1.987549   \n4   8       1       NaN       NaN  C       NaN        NaN       NaN       NaN   \n\n         v8    ...         v122      v123      v124  v125      v126      v127  \\\n0  0.012941    ...     8.000000  1.989780  0.035754    AU  1.804126  3.113719   \n1  2.301630    ...          NaN       NaN  0.598896    AF       NaN       NaN   \n2  0.019645    ...     9.333333  2.477596  0.013452    AE  1.773709  3.922193   \n3  0.171947    ...     7.018256  1.812795  0.002267    CJ  1.415230  2.954381   \n4       NaN    ...          NaN       NaN       NaN     Z       NaN       NaN   \n\n       v128  v129      v130      v131  \n0  2.024285     0  0.636365  2.857144  \n1  1.957825     0       NaN       NaN  \n2  1.120468     2  0.883118  1.176472  \n3  1.990847     1  1.677108  1.034483  \n4       NaN     0       NaN       NaN  \n\n[5 rows x 133 columns]",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>target</th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v3</th>\n      <th>v4</th>\n      <th>v5</th>\n      <th>v6</th>\n      <th>v7</th>\n      <th>v8</th>\n      <th>...</th>\n      <th>v122</th>\n      <th>v123</th>\n      <th>v124</th>\n      <th>v125</th>\n      <th>v126</th>\n      <th>v127</th>\n      <th>v128</th>\n      <th>v129</th>\n      <th>v130</th>\n      <th>v131</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>1</td>\n      <td>1.335739</td>\n      <td>8.727474</td>\n      <td>C</td>\n      <td>3.921026</td>\n      <td>7.915266</td>\n      <td>2.599278</td>\n      <td>3.176895</td>\n      <td>0.012941</td>\n      <td>...</td>\n      <td>8.000000</td>\n      <td>1.989780</td>\n      <td>0.035754</td>\n      <td>AU</td>\n      <td>1.804126</td>\n      <td>3.113719</td>\n      <td>2.024285</td>\n      <td>0</td>\n      <td>0.636365</td>\n      <td>2.857144</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>9.191265</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.301630</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.598896</td>\n      <td>AF</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.957825</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>1</td>\n      <td>0.943877</td>\n      <td>5.310079</td>\n      <td>C</td>\n      <td>4.410969</td>\n      <td>5.326159</td>\n      <td>3.979592</td>\n      <td>3.928571</td>\n      <td>0.019645</td>\n      <td>...</td>\n      <td>9.333333</td>\n      <td>2.477596</td>\n      <td>0.013452</td>\n      <td>AE</td>\n      <td>1.773709</td>\n      <td>3.922193</td>\n      <td>1.120468</td>\n      <td>2</td>\n      <td>0.883118</td>\n      <td>1.176472</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6</td>\n      <td>1</td>\n      <td>0.797415</td>\n      <td>8.304757</td>\n      <td>C</td>\n      <td>4.225930</td>\n      <td>11.627438</td>\n      <td>2.097700</td>\n      <td>1.987549</td>\n      <td>0.171947</td>\n      <td>...</td>\n      <td>7.018256</td>\n      <td>1.812795</td>\n      <td>0.002267</td>\n      <td>CJ</td>\n      <td>1.415230</td>\n      <td>2.954381</td>\n      <td>1.990847</td>\n      <td>1</td>\n      <td>1.677108</td>\n      <td>1.034483</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Z</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 133 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#summerize the data to make some initle assessments\ndf.describe()",
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                  ID         target            v1            v2            v4  \\\ncount  114321.000000  114321.000000  6.448900e+04  6.452500e+04  6.452500e+04   \nmean   114228.928228       0.761199  1.630686e+00  7.464411e+00  4.145098e+00   \nstd     65934.487362       0.426353  1.082813e+00  2.961676e+00  1.148263e+00   \nmin         3.000000       0.000000 -9.996497e-07 -9.817614e-07 -6.475929e-07   \n25%     57280.000000       1.000000  9.135798e-01  5.316428e+00  3.487398e+00   \n50%    114189.000000       1.000000  1.469550e+00  7.023803e+00  4.205991e+00   \n75%    171206.000000       1.000000  2.136128e+00  9.465497e+00  4.833250e+00   \nmax    228713.000000       1.000000  2.000000e+01  2.000000e+01  2.000000e+01   \n\n                 v5            v6            v7            v8            v9  \\\ncount  6.569700e+04  6.448900e+04  6.448900e+04  6.570200e+04  6.447000e+04   \nmean   8.742359e+00  2.436402e+00  2.483921e+00  1.496569e+00  9.031859e+00   \nstd    2.036018e+00  5.999653e-01  5.894485e-01  2.783003e+00  1.930262e+00   \nmin   -5.287068e-07 -9.055091e-07 -9.468765e-07 -7.783778e-07 -9.828757e-07   \n25%    7.605918e+00  2.065064e+00  2.101477e+00  8.658986e-02  7.853659e+00   \n50%    8.670867e+00  2.412790e+00  2.452166e+00  3.860317e-01  9.059582e+00   \n75%    9.771353e+00  2.775285e+00  2.834285e+00  1.625246e+00  1.023256e+01   \nmax    2.000000e+01  2.000000e+01  2.000000e+01  2.000000e+01  2.000000e+01   \n\n           ...               v121          v122          v123          v124  \\\ncount      ...       6.448100e+04  6.447000e+04  63643.000000  6.570200e+04   \nmean       ...       2.737596e+00  6.822439e+00      3.549938  9.198120e-01   \nstd        ...       1.356294e+00  1.795978e+00      2.604704  2.099407e+00   \nmin        ...      -9.820642e-07 -9.978497e-07      0.019139 -9.994953e-07   \n25%        ...       1.786965e+00  5.647712e+00      1.963315  2.053777e-02   \n50%        ...       2.436195e+00  6.749117e+00      2.739239  1.398639e-01   \n75%        ...       3.379175e+00  7.911392e+00      4.075361  8.718333e-01   \nmax        ...       2.000000e+01  2.000000e+01     19.686069  2.000000e+01   \n\n               v126          v127          v128           v129          v130  \\\ncount  6.448900e+04  6.448900e+04  6.569700e+04  114321.000000  6.447800e+04   \nmean   1.672658e+00  3.239542e+00  2.030373e+00       0.310144  1.925763e+00   \nstd    5.031683e-01  1.625988e+00  1.074232e+00       0.693262  1.264497e+00   \nmin   -9.564174e-07 -9.223798e-07  8.197812e-07       0.000000 -9.901257e-07   \n25%    1.417600e+00  2.101900e+00  1.393830e+00       0.000000  1.106172e+00   \n50%    1.614802e+00  2.963620e+00  1.798436e+00       0.000000  1.560138e+00   \n75%    1.843886e+00  4.108146e+00  2.390158e+00       0.000000  2.332425e+00   \nmax    1.563161e+01  2.000000e+01  2.000000e+01      11.000000  2.000000e+01   \n\n               v131  \ncount  6.442600e+04  \nmean   1.739389e+00  \nstd    1.134702e+00  \nmin   -9.999134e-07  \n25%    1.012658e+00  \n50%    1.589403e+00  \n75%    2.261905e+00  \nmax    2.000000e+01  \n\n[8 rows x 114 columns]",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>target</th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v4</th>\n      <th>v5</th>\n      <th>v6</th>\n      <th>v7</th>\n      <th>v8</th>\n      <th>v9</th>\n      <th>...</th>\n      <th>v121</th>\n      <th>v122</th>\n      <th>v123</th>\n      <th>v124</th>\n      <th>v126</th>\n      <th>v127</th>\n      <th>v128</th>\n      <th>v129</th>\n      <th>v130</th>\n      <th>v131</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>114321.000000</td>\n      <td>114321.000000</td>\n      <td>6.448900e+04</td>\n      <td>6.452500e+04</td>\n      <td>6.452500e+04</td>\n      <td>6.569700e+04</td>\n      <td>6.448900e+04</td>\n      <td>6.448900e+04</td>\n      <td>6.570200e+04</td>\n      <td>6.447000e+04</td>\n      <td>...</td>\n      <td>6.448100e+04</td>\n      <td>6.447000e+04</td>\n      <td>63643.000000</td>\n      <td>6.570200e+04</td>\n      <td>6.448900e+04</td>\n      <td>6.448900e+04</td>\n      <td>6.569700e+04</td>\n      <td>114321.000000</td>\n      <td>6.447800e+04</td>\n      <td>6.442600e+04</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>114228.928228</td>\n      <td>0.761199</td>\n      <td>1.630686e+00</td>\n      <td>7.464411e+00</td>\n      <td>4.145098e+00</td>\n      <td>8.742359e+00</td>\n      <td>2.436402e+00</td>\n      <td>2.483921e+00</td>\n      <td>1.496569e+00</td>\n      <td>9.031859e+00</td>\n      <td>...</td>\n      <td>2.737596e+00</td>\n      <td>6.822439e+00</td>\n      <td>3.549938</td>\n      <td>9.198120e-01</td>\n      <td>1.672658e+00</td>\n      <td>3.239542e+00</td>\n      <td>2.030373e+00</td>\n      <td>0.310144</td>\n      <td>1.925763e+00</td>\n      <td>1.739389e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>65934.487362</td>\n      <td>0.426353</td>\n      <td>1.082813e+00</td>\n      <td>2.961676e+00</td>\n      <td>1.148263e+00</td>\n      <td>2.036018e+00</td>\n      <td>5.999653e-01</td>\n      <td>5.894485e-01</td>\n      <td>2.783003e+00</td>\n      <td>1.930262e+00</td>\n      <td>...</td>\n      <td>1.356294e+00</td>\n      <td>1.795978e+00</td>\n      <td>2.604704</td>\n      <td>2.099407e+00</td>\n      <td>5.031683e-01</td>\n      <td>1.625988e+00</td>\n      <td>1.074232e+00</td>\n      <td>0.693262</td>\n      <td>1.264497e+00</td>\n      <td>1.134702e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>-9.996497e-07</td>\n      <td>-9.817614e-07</td>\n      <td>-6.475929e-07</td>\n      <td>-5.287068e-07</td>\n      <td>-9.055091e-07</td>\n      <td>-9.468765e-07</td>\n      <td>-7.783778e-07</td>\n      <td>-9.828757e-07</td>\n      <td>...</td>\n      <td>-9.820642e-07</td>\n      <td>-9.978497e-07</td>\n      <td>0.019139</td>\n      <td>-9.994953e-07</td>\n      <td>-9.564174e-07</td>\n      <td>-9.223798e-07</td>\n      <td>8.197812e-07</td>\n      <td>0.000000</td>\n      <td>-9.901257e-07</td>\n      <td>-9.999134e-07</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>57280.000000</td>\n      <td>1.000000</td>\n      <td>9.135798e-01</td>\n      <td>5.316428e+00</td>\n      <td>3.487398e+00</td>\n      <td>7.605918e+00</td>\n      <td>2.065064e+00</td>\n      <td>2.101477e+00</td>\n      <td>8.658986e-02</td>\n      <td>7.853659e+00</td>\n      <td>...</td>\n      <td>1.786965e+00</td>\n      <td>5.647712e+00</td>\n      <td>1.963315</td>\n      <td>2.053777e-02</td>\n      <td>1.417600e+00</td>\n      <td>2.101900e+00</td>\n      <td>1.393830e+00</td>\n      <td>0.000000</td>\n      <td>1.106172e+00</td>\n      <td>1.012658e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>114189.000000</td>\n      <td>1.000000</td>\n      <td>1.469550e+00</td>\n      <td>7.023803e+00</td>\n      <td>4.205991e+00</td>\n      <td>8.670867e+00</td>\n      <td>2.412790e+00</td>\n      <td>2.452166e+00</td>\n      <td>3.860317e-01</td>\n      <td>9.059582e+00</td>\n      <td>...</td>\n      <td>2.436195e+00</td>\n      <td>6.749117e+00</td>\n      <td>2.739239</td>\n      <td>1.398639e-01</td>\n      <td>1.614802e+00</td>\n      <td>2.963620e+00</td>\n      <td>1.798436e+00</td>\n      <td>0.000000</td>\n      <td>1.560138e+00</td>\n      <td>1.589403e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>171206.000000</td>\n      <td>1.000000</td>\n      <td>2.136128e+00</td>\n      <td>9.465497e+00</td>\n      <td>4.833250e+00</td>\n      <td>9.771353e+00</td>\n      <td>2.775285e+00</td>\n      <td>2.834285e+00</td>\n      <td>1.625246e+00</td>\n      <td>1.023256e+01</td>\n      <td>...</td>\n      <td>3.379175e+00</td>\n      <td>7.911392e+00</td>\n      <td>4.075361</td>\n      <td>8.718333e-01</td>\n      <td>1.843886e+00</td>\n      <td>4.108146e+00</td>\n      <td>2.390158e+00</td>\n      <td>0.000000</td>\n      <td>2.332425e+00</td>\n      <td>2.261905e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>228713.000000</td>\n      <td>1.000000</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>...</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>19.686069</td>\n      <td>2.000000e+01</td>\n      <td>1.563161e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>11.000000</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 114 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#A glimpse at the target variable using a bar plot\nsns.countplot(x=\"target\", data=df)",
      "execution_count": 8,
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x11fd7128>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7299358>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#View of target varable by labels 0 = not eligible for acceleration,  \n#1 = claims suitable for an accelerated approval\nd = sns.countplot(x =\"target\", data=df)\nd.set(xticklabels=['not eligible for acceleration', 'claims suitable for an accelerated approval'])\nplt.xlabel(' ')\nd;",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AC1ADtlfKd5BuGV0tr1fKuhraGNPHPpiZ2SDo6zmakTzzlwB28ewf2Wx0GbAvIqaTRihf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\ngg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0xcc08e80>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Encode Labels \"One Hot Encoder\"**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Label encoder tranforms any label or attribute for input to the algorithim \n#we can also see some missing values in the top few rows of the data set these will also\n#need to be treated in a suitable mannor.\nfor feature in df.columns:\n    if df[feature].dtype=='object':\n        le = LabelEncoder()\n        df[feature] = le.fit_transform(df[feature])\ndf.tail(3)",
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "            ID  target        v1        v2  v3        v4         v5       v6  \\\n114318  228711       1       NaN       NaN   3       NaN  10.069277      NaN   \n114319  228712       1       NaN       NaN   3       NaN  10.106144      NaN   \n114320  228713       1  1.619763  7.932978   3  4.640085   8.473141  2.35147   \n\n              v7        v8    ...         v122      v123      v124  v125  \\\n114318       NaN  0.323324    ...          NaN       NaN  0.156764    81   \n114319       NaN  0.309226    ...          NaN       NaN  0.490658    51   \n114320  2.826766  3.479754    ...     7.936508  2.944285  3.135205    86   \n\n            v126      v127      v128  v129     v130      v131  \n114318       NaN       NaN  2.417606     2      NaN       NaN  \n114319       NaN       NaN  3.526650     0      NaN       NaN  \n114320  1.943149  4.385553  1.604493     0  1.78761  1.386138  \n\n[3 rows x 133 columns]",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>target</th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v3</th>\n      <th>v4</th>\n      <th>v5</th>\n      <th>v6</th>\n      <th>v7</th>\n      <th>v8</th>\n      <th>...</th>\n      <th>v122</th>\n      <th>v123</th>\n      <th>v124</th>\n      <th>v125</th>\n      <th>v126</th>\n      <th>v127</th>\n      <th>v128</th>\n      <th>v129</th>\n      <th>v130</th>\n      <th>v131</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>114318</th>\n      <td>228711</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>10.069277</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.323324</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.156764</td>\n      <td>81</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.417606</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>114319</th>\n      <td>228712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>10.106144</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.309226</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.490658</td>\n      <td>51</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.526650</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>114320</th>\n      <td>228713</td>\n      <td>1</td>\n      <td>1.619763</td>\n      <td>7.932978</td>\n      <td>3</td>\n      <td>4.640085</td>\n      <td>8.473141</td>\n      <td>2.35147</td>\n      <td>2.826766</td>\n      <td>3.479754</td>\n      <td>...</td>\n      <td>7.936508</td>\n      <td>2.944285</td>\n      <td>3.135205</td>\n      <td>86</td>\n      <td>1.943149</td>\n      <td>4.385553</td>\n      <td>1.604493</td>\n      <td>0</td>\n      <td>1.78761</td>\n      <td>1.386138</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 133 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Missing Value Treatment**\n\nFor this example we will use the PVI technique meaning Predictive Value Imputation. Before the model is created we will use this method when detecting missing values to estimate values that to replace NaN's with the attribute’s mean, median or mode value."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Print out the number of missing values\nprint(\"Number of NA values : {0}\".format((df.shape[0] * df.shape[1]) - df.count().sum()))",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Number of NA values : 4967293\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Missing Value PVI using mean value of attributes;\nx = df.fillna(df.mean())\nx.head(7)",
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   ID  target        v1        v2  v3        v4         v5        v6  \\\n0   3       1  1.335739  8.727474   3  3.921026   7.915266  2.599278   \n1   4       1  1.630686  7.464411   3  4.145098   9.191265  2.436402   \n2   5       1  0.943877  5.310079   3  4.410969   5.326159  3.979592   \n3   6       1  0.797415  8.304757   3  4.225930  11.627438  2.097700   \n4   8       1  1.630686  7.464411   3  4.145098   8.742359  2.436402   \n5   9       0  1.630686  7.464411   3  4.145098   8.856791  2.436402   \n6  12       0  0.899806  7.312995   3  3.494148   9.946200  1.926070   \n\n         v7        v8    ...         v122      v123      v124  v125      v126  \\\n0  3.176895  0.012941    ...     8.000000  1.989780  0.035754    22  1.804126   \n1  2.483921  2.301630    ...     6.822439  3.549938  0.598896     7  1.672658   \n2  3.928571  0.019645    ...     9.333333  2.477596  0.013452     6  1.773709   \n3  1.987549  0.171947    ...     7.018256  1.812795  0.002267    65  1.415230   \n4  2.483921  1.496569    ...     6.822439  3.549938  0.919812    90  1.672658   \n5  2.483921  0.359993    ...     6.822439  3.549938  0.049861    88  1.672658   \n6  1.770427  0.066251    ...     3.476299  1.992594  0.083758    38  3.276100   \n\n       v127      v128  v129      v130      v131  \n0  3.113719  2.024285     0  0.636365  2.857144  \n1  3.239542  1.957825     0  1.925763  1.739389  \n2  3.922193  1.120468     2  0.883118  1.176472  \n3  2.954381  1.990847     1  1.677108  1.034483  \n4  3.239542  2.030373     0  1.925763  1.739389  \n5  3.239542  1.536222     0  1.925763  1.739389  \n6  1.623298  2.266575     0  2.263736  0.970873  \n\n[7 rows x 133 columns]",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>target</th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v3</th>\n      <th>v4</th>\n      <th>v5</th>\n      <th>v6</th>\n      <th>v7</th>\n      <th>v8</th>\n      <th>...</th>\n      <th>v122</th>\n      <th>v123</th>\n      <th>v124</th>\n      <th>v125</th>\n      <th>v126</th>\n      <th>v127</th>\n      <th>v128</th>\n      <th>v129</th>\n      <th>v130</th>\n      <th>v131</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>1</td>\n      <td>1.335739</td>\n      <td>8.727474</td>\n      <td>3</td>\n      <td>3.921026</td>\n      <td>7.915266</td>\n      <td>2.599278</td>\n      <td>3.176895</td>\n      <td>0.012941</td>\n      <td>...</td>\n      <td>8.000000</td>\n      <td>1.989780</td>\n      <td>0.035754</td>\n      <td>22</td>\n      <td>1.804126</td>\n      <td>3.113719</td>\n      <td>2.024285</td>\n      <td>0</td>\n      <td>0.636365</td>\n      <td>2.857144</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1.630686</td>\n      <td>7.464411</td>\n      <td>3</td>\n      <td>4.145098</td>\n      <td>9.191265</td>\n      <td>2.436402</td>\n      <td>2.483921</td>\n      <td>2.301630</td>\n      <td>...</td>\n      <td>6.822439</td>\n      <td>3.549938</td>\n      <td>0.598896</td>\n      <td>7</td>\n      <td>1.672658</td>\n      <td>3.239542</td>\n      <td>1.957825</td>\n      <td>0</td>\n      <td>1.925763</td>\n      <td>1.739389</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>1</td>\n      <td>0.943877</td>\n      <td>5.310079</td>\n      <td>3</td>\n      <td>4.410969</td>\n      <td>5.326159</td>\n      <td>3.979592</td>\n      <td>3.928571</td>\n      <td>0.019645</td>\n      <td>...</td>\n      <td>9.333333</td>\n      <td>2.477596</td>\n      <td>0.013452</td>\n      <td>6</td>\n      <td>1.773709</td>\n      <td>3.922193</td>\n      <td>1.120468</td>\n      <td>2</td>\n      <td>0.883118</td>\n      <td>1.176472</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6</td>\n      <td>1</td>\n      <td>0.797415</td>\n      <td>8.304757</td>\n      <td>3</td>\n      <td>4.225930</td>\n      <td>11.627438</td>\n      <td>2.097700</td>\n      <td>1.987549</td>\n      <td>0.171947</td>\n      <td>...</td>\n      <td>7.018256</td>\n      <td>1.812795</td>\n      <td>0.002267</td>\n      <td>65</td>\n      <td>1.415230</td>\n      <td>2.954381</td>\n      <td>1.990847</td>\n      <td>1</td>\n      <td>1.677108</td>\n      <td>1.034483</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1</td>\n      <td>1.630686</td>\n      <td>7.464411</td>\n      <td>3</td>\n      <td>4.145098</td>\n      <td>8.742359</td>\n      <td>2.436402</td>\n      <td>2.483921</td>\n      <td>1.496569</td>\n      <td>...</td>\n      <td>6.822439</td>\n      <td>3.549938</td>\n      <td>0.919812</td>\n      <td>90</td>\n      <td>1.672658</td>\n      <td>3.239542</td>\n      <td>2.030373</td>\n      <td>0</td>\n      <td>1.925763</td>\n      <td>1.739389</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>9</td>\n      <td>0</td>\n      <td>1.630686</td>\n      <td>7.464411</td>\n      <td>3</td>\n      <td>4.145098</td>\n      <td>8.856791</td>\n      <td>2.436402</td>\n      <td>2.483921</td>\n      <td>0.359993</td>\n      <td>...</td>\n      <td>6.822439</td>\n      <td>3.549938</td>\n      <td>0.049861</td>\n      <td>88</td>\n      <td>1.672658</td>\n      <td>3.239542</td>\n      <td>1.536222</td>\n      <td>0</td>\n      <td>1.925763</td>\n      <td>1.739389</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>12</td>\n      <td>0</td>\n      <td>0.899806</td>\n      <td>7.312995</td>\n      <td>3</td>\n      <td>3.494148</td>\n      <td>9.946200</td>\n      <td>1.926070</td>\n      <td>1.770427</td>\n      <td>0.066251</td>\n      <td>...</td>\n      <td>3.476299</td>\n      <td>1.992594</td>\n      <td>0.083758</td>\n      <td>38</td>\n      <td>3.276100</td>\n      <td>1.623298</td>\n      <td>2.266575</td>\n      <td>0</td>\n      <td>2.263736</td>\n      <td>0.970873</td>\n    </tr>\n  </tbody>\n</table>\n<p>7 rows × 133 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Always good preactise to summerize the data after cleaning or transforming activities\nx.describe()",
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                  ID         target            v1            v2  \\\ncount  114321.000000  114321.000000  1.143210e+05  1.143210e+05   \nmean   114228.928228       0.761199  1.630686e+00  7.464411e+00   \nstd     65934.487362       0.426353  8.132649e-01  2.225036e+00   \nmin         3.000000       0.000000 -9.996497e-07 -9.817614e-07   \n25%     57280.000000       1.000000  1.346153e+00  6.575770e+00   \n50%    114189.000000       1.000000  1.630686e+00  7.464411e+00   \n75%    171206.000000       1.000000  1.630686e+00  7.551501e+00   \nmax    228713.000000       1.000000  2.000000e+01  2.000000e+01   \n\n                  v3            v4            v5            v6            v7  \\\ncount  114321.000000  1.143210e+05  1.143210e+05  1.143210e+05  1.143210e+05   \nmean        2.904847  4.145098e+00  8.742359e+00  2.436402e+00  2.483921e+00   \nstd         0.521065  8.626621e-01  1.543441e+00  4.506138e-01  4.427150e-01   \nmin         0.000000 -6.475929e-07 -5.287068e-07 -9.055091e-07 -9.468765e-07   \n25%         3.000000  4.068697e+00  8.394090e+00  2.340968e+00  2.376586e+00   \n50%         3.000000  4.145098e+00  8.742359e+00  2.436402e+00  2.483921e+00   \n75%         3.000000  4.340229e+00  8.924798e+00  2.484699e+00  2.528445e+00   \nmax         3.000000  2.000000e+01  2.000000e+01  2.000000e+01  2.000000e+01   \n\n                 v8      ...               v122           v123          v124  \\\ncount  1.143210e+05      ...       1.143210e+05  114321.000000  1.143210e+05   \nmean   1.496569e+00      ...       6.822439e+00       3.549938  9.198120e-01   \nstd    2.109786e+00      ...       1.348700e+00       1.943431  1.591555e+00   \nmin   -7.783778e-07      ...      -9.978497e-07       0.019139 -9.994953e-07   \n25%    2.653147e-01      ...       6.519607e+00       2.571053  8.471320e-02   \n50%    1.496569e+00      ...       6.822439e+00       3.549938  9.198120e-01   \n75%    1.496569e+00      ...       6.999999e+00       3.549938  9.198120e-01   \nmax    2.000000e+01      ...       2.000000e+01      19.686069  2.000000e+01   \n\n                v125          v126          v127          v128           v129  \\\ncount  114321.000000  1.143210e+05  1.143210e+05  1.143210e+05  114321.000000   \nmean       46.719964  1.672658e+00  3.239542e+00  2.030373e+00       0.310144   \nstd        25.405608  3.779128e-01  1.221225e+00  8.143413e-01       0.693262   \nmin         0.000000 -9.564174e-07 -9.223798e-07  8.197812e-07       0.000000   \n25%        26.000000  1.570974e+00  2.762497e+00  1.681261e+00       0.000000   \n50%        46.000000  1.672658e+00  3.239542e+00  2.030373e+00       0.000000   \n75%        69.000000  1.672658e+00  3.239542e+00  2.030373e+00       0.000000   \nmax        90.000000  1.563161e+01  2.000000e+01  2.000000e+01      11.000000   \n\n               v130          v131  \ncount  1.143210e+05  1.143210e+05  \nmean   1.925763e+00  1.739389e+00  \nstd    9.496402e-01  8.518204e-01  \nmin   -9.901257e-07 -9.999134e-07  \n25%    1.449477e+00  1.463414e+00  \n50%    1.925763e+00  1.739389e+00  \n75%    1.925763e+00  1.739389e+00  \nmax    2.000000e+01  2.000000e+01  \n\n[8 rows x 133 columns]",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>target</th>\n      <th>v1</th>\n      <th>v2</th>\n      <th>v3</th>\n      <th>v4</th>\n      <th>v5</th>\n      <th>v6</th>\n      <th>v7</th>\n      <th>v8</th>\n      <th>...</th>\n      <th>v122</th>\n      <th>v123</th>\n      <th>v124</th>\n      <th>v125</th>\n      <th>v126</th>\n      <th>v127</th>\n      <th>v128</th>\n      <th>v129</th>\n      <th>v130</th>\n      <th>v131</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>114321.000000</td>\n      <td>114321.000000</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>114321.000000</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>...</td>\n      <td>1.143210e+05</td>\n      <td>114321.000000</td>\n      <td>1.143210e+05</td>\n      <td>114321.000000</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n      <td>114321.000000</td>\n      <td>1.143210e+05</td>\n      <td>1.143210e+05</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>114228.928228</td>\n      <td>0.761199</td>\n      <td>1.630686e+00</td>\n      <td>7.464411e+00</td>\n      <td>2.904847</td>\n      <td>4.145098e+00</td>\n      <td>8.742359e+00</td>\n      <td>2.436402e+00</td>\n      <td>2.483921e+00</td>\n      <td>1.496569e+00</td>\n      <td>...</td>\n      <td>6.822439e+00</td>\n      <td>3.549938</td>\n      <td>9.198120e-01</td>\n      <td>46.719964</td>\n      <td>1.672658e+00</td>\n      <td>3.239542e+00</td>\n      <td>2.030373e+00</td>\n      <td>0.310144</td>\n      <td>1.925763e+00</td>\n      <td>1.739389e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>65934.487362</td>\n      <td>0.426353</td>\n      <td>8.132649e-01</td>\n      <td>2.225036e+00</td>\n      <td>0.521065</td>\n      <td>8.626621e-01</td>\n      <td>1.543441e+00</td>\n      <td>4.506138e-01</td>\n      <td>4.427150e-01</td>\n      <td>2.109786e+00</td>\n      <td>...</td>\n      <td>1.348700e+00</td>\n      <td>1.943431</td>\n      <td>1.591555e+00</td>\n      <td>25.405608</td>\n      <td>3.779128e-01</td>\n      <td>1.221225e+00</td>\n      <td>8.143413e-01</td>\n      <td>0.693262</td>\n      <td>9.496402e-01</td>\n      <td>8.518204e-01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>-9.996497e-07</td>\n      <td>-9.817614e-07</td>\n      <td>0.000000</td>\n      <td>-6.475929e-07</td>\n      <td>-5.287068e-07</td>\n      <td>-9.055091e-07</td>\n      <td>-9.468765e-07</td>\n      <td>-7.783778e-07</td>\n      <td>...</td>\n      <td>-9.978497e-07</td>\n      <td>0.019139</td>\n      <td>-9.994953e-07</td>\n      <td>0.000000</td>\n      <td>-9.564174e-07</td>\n      <td>-9.223798e-07</td>\n      <td>8.197812e-07</td>\n      <td>0.000000</td>\n      <td>-9.901257e-07</td>\n      <td>-9.999134e-07</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>57280.000000</td>\n      <td>1.000000</td>\n      <td>1.346153e+00</td>\n      <td>6.575770e+00</td>\n      <td>3.000000</td>\n      <td>4.068697e+00</td>\n      <td>8.394090e+00</td>\n      <td>2.340968e+00</td>\n      <td>2.376586e+00</td>\n      <td>2.653147e-01</td>\n      <td>...</td>\n      <td>6.519607e+00</td>\n      <td>2.571053</td>\n      <td>8.471320e-02</td>\n      <td>26.000000</td>\n      <td>1.570974e+00</td>\n      <td>2.762497e+00</td>\n      <td>1.681261e+00</td>\n      <td>0.000000</td>\n      <td>1.449477e+00</td>\n      <td>1.463414e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>114189.000000</td>\n      <td>1.000000</td>\n      <td>1.630686e+00</td>\n      <td>7.464411e+00</td>\n      <td>3.000000</td>\n      <td>4.145098e+00</td>\n      <td>8.742359e+00</td>\n      <td>2.436402e+00</td>\n      <td>2.483921e+00</td>\n      <td>1.496569e+00</td>\n      <td>...</td>\n      <td>6.822439e+00</td>\n      <td>3.549938</td>\n      <td>9.198120e-01</td>\n      <td>46.000000</td>\n      <td>1.672658e+00</td>\n      <td>3.239542e+00</td>\n      <td>2.030373e+00</td>\n      <td>0.000000</td>\n      <td>1.925763e+00</td>\n      <td>1.739389e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>171206.000000</td>\n      <td>1.000000</td>\n      <td>1.630686e+00</td>\n      <td>7.551501e+00</td>\n      <td>3.000000</td>\n      <td>4.340229e+00</td>\n      <td>8.924798e+00</td>\n      <td>2.484699e+00</td>\n      <td>2.528445e+00</td>\n      <td>1.496569e+00</td>\n      <td>...</td>\n      <td>6.999999e+00</td>\n      <td>3.549938</td>\n      <td>9.198120e-01</td>\n      <td>69.000000</td>\n      <td>1.672658e+00</td>\n      <td>3.239542e+00</td>\n      <td>2.030373e+00</td>\n      <td>0.000000</td>\n      <td>1.925763e+00</td>\n      <td>1.739389e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>228713.000000</td>\n      <td>1.000000</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>3.000000</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>...</td>\n      <td>2.000000e+01</td>\n      <td>19.686069</td>\n      <td>2.000000e+01</td>\n      <td>90.000000</td>\n      <td>1.563161e+01</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n      <td>11.000000</td>\n      <td>2.000000e+01</td>\n      <td>2.000000e+01</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 133 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Assign the target variable to Y for later processing and \n#Remove the ID Column that is not needed \ny = x.target.values\nx = x.drop(['ID'], axis = 1)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Feature Importance - Ensemble Random Tree Classifier**\n\nSometimes when we are faced with a large dataset with a large amount of features it may be useful to carry out an automatic variable selection on the columns. We can make use of machine learning to make an estimate on what are the most relevant feature for a predictive model scenario. To determine a list of predictive features a meta estimator can be used that fits a number of decision trees on sub-samples of the main dataset.\n\nThe n_estimators specifies the number of randomized trees to be used. It is also important to note that the max_features parameter can also be specified to look for the best split in the data. The log2 & square root of the number of features can be used, however for this example we have used the default \"auto\" which uses the square root of the number of features."
    },
    {
      "metadata": {
        "scrolled": true,
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Feature Importance - selecting only highly prdictive features using random forest Model\nfrom sklearn.ensemble import ExtraTreesClassifier\nx.shape\n# feature extraction\nmodel = ExtraTreesClassifier(n_estimators = 250, max_features = \"auto\", random_state=0)\nmodel.fit(x, y)\nprint(model.feature_importances_)",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "[  8.59747486e-01   6.49149406e-04   6.48240229e-04   2.35306696e-04\n   6.44989081e-04   6.54041387e-04   6.85861167e-04   6.45662100e-04\n   5.82026343e-04   6.33884435e-04   3.78767425e-03   5.99661664e-04\n   2.38747724e-03   6.02269023e-04   2.69156300e-03   6.22218504e-04\n   6.72080243e-04   6.14551541e-04   6.48057213e-04   5.98859933e-04\n   6.20538100e-04   2.06448005e-03   1.58215363e-03   7.21670390e-04\n   1.86642660e-03   5.87842171e-04   6.35629110e-04   6.40484732e-04\n   6.82761877e-04   5.66945889e-04   1.40852440e-03   4.22241328e-03\n   5.95350433e-04   6.20893277e-04   2.59562992e-03   6.28390446e-04\n   6.47273307e-04   6.51065347e-04   4.49284005e-04   6.42504466e-04\n   2.32761475e-03   5.90911164e-04   6.08185456e-04   6.07408830e-04\n   6.28350982e-04   6.33693113e-04   5.82212537e-04   5.74973320e-03\n   6.13149400e-04   5.82923395e-04   8.94366957e-03   6.58771934e-04\n   1.64379057e-03   6.05700059e-04   6.00073854e-04   6.44485140e-04\n   2.62486875e-03   6.87948704e-04   7.12451867e-04   6.27393960e-04\n   6.02550305e-04   6.55542193e-04   2.49141806e-03   5.97167734e-04\n   6.23145881e-04   6.10573145e-04   6.14221927e-03   5.93678540e-04\n   6.26523012e-04   6.79267707e-04   6.58716471e-04   9.26695201e-04\n   1.36560106e-03   6.29150397e-04   2.15942203e-04   8.19147258e-04\n   6.05335635e-04   5.99842402e-04   6.50437179e-04   2.02483933e-03\n   6.52552299e-04   6.51959803e-04   6.76534012e-04   6.13289963e-04\n   6.38002850e-04   6.84751296e-04   6.27876726e-04   6.55589221e-04\n   6.78538879e-04   6.16683445e-04   6.19057523e-04   1.41801404e-03\n   5.87724331e-04   6.14874384e-04   5.95500661e-04   6.18845108e-04\n   5.88122290e-04   6.17805929e-04   6.77774246e-04   6.66463523e-04\n   6.81253123e-04   6.74039855e-04   6.40397158e-04   6.17465745e-04\n   6.03949934e-04   6.00547181e-04   6.21316123e-04   1.41494538e-03\n   6.34662934e-04   6.49741679e-04   4.36367721e-03   6.32819311e-04\n   1.46391749e-03   3.39516438e-03   2.93169692e-03   6.63516174e-04\n   6.10565393e-04   6.59969155e-04   6.27320095e-04   8.11194472e-04\n   6.60139043e-04   6.44714066e-04   6.60805204e-04   7.01021040e-04\n   6.08050711e-04   1.55138864e-03   6.07439361e-04   6.30254314e-04\n   6.24697628e-04   1.70277232e-03   6.00015079e-04   6.38131230e-04]\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Ranking Important Variables**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Ranking the most imporatnt predictive variables potentially build model based on top ranked i.e 1 -16\nfeatureimportance = model.feature_importances_\nstd = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)\nindices = np.argsort(featureimportance)[::-1]\n#Print top ranked predictive featurescdata = pd.DataFrame(x, columns = [ 'target','v50','v66','v47','v110','v31','v10','v113','v114'..])\nprint(\"Feature ranking:\")\n\nfor feature in range(x.shape[1]):\n    print(\"%d. feature %d (%f)\" % (feature + 1, indices[feature], featureimportance[indices[feature]]))   ",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Feature ranking:\n1. feature 0 (0.859747)\n2. feature 50 (0.008944)\n3. feature 66 (0.006142)\n4. feature 47 (0.005750)\n5. feature 110 (0.004364)\n6. feature 31 (0.004222)\n7. feature 10 (0.003788)\n8. feature 113 (0.003395)\n9. feature 114 (0.002932)\n10. feature 14 (0.002692)\n11. feature 56 (0.002625)\n12. feature 34 (0.002596)\n13. feature 62 (0.002491)\n14. feature 12 (0.002387)\n15. feature 40 (0.002328)\n16. feature 21 (0.002064)\n17. feature 79 (0.002025)\n18. feature 24 (0.001866)\n19. feature 129 (0.001703)\n20. feature 52 (0.001644)\n21. feature 22 (0.001582)\n22. feature 125 (0.001551)\n23. feature 112 (0.001464)\n24. feature 91 (0.001418)\n25. feature 107 (0.001415)\n26. feature 30 (0.001409)\n27. feature 72 (0.001366)\n28. feature 71 (0.000927)\n29. feature 75 (0.000819)\n30. feature 119 (0.000811)\n31. feature 23 (0.000722)\n32. feature 58 (0.000712)\n33. feature 123 (0.000701)\n34. feature 57 (0.000688)\n35. feature 6 (0.000686)\n36. feature 85 (0.000685)\n37. feature 28 (0.000683)\n38. feature 100 (0.000681)\n39. feature 69 (0.000679)\n40. feature 88 (0.000679)\n41. feature 98 (0.000678)\n42. feature 82 (0.000677)\n43. feature 101 (0.000674)\n44. feature 16 (0.000672)\n45. feature 99 (0.000666)\n46. feature 115 (0.000664)\n47. feature 122 (0.000661)\n48. feature 120 (0.000660)\n49. feature 117 (0.000660)\n50. feature 51 (0.000659)\n51. feature 70 (0.000659)\n52. feature 87 (0.000656)\n53. feature 61 (0.000656)\n54. feature 5 (0.000654)\n55. feature 80 (0.000653)\n56. feature 81 (0.000652)\n57. feature 37 (0.000651)\n58. feature 78 (0.000650)\n59. feature 109 (0.000650)\n60. feature 1 (0.000649)\n61. feature 2 (0.000648)\n62. feature 18 (0.000648)\n63. feature 36 (0.000647)\n64. feature 7 (0.000646)\n65. feature 4 (0.000645)\n66. feature 121 (0.000645)\n67. feature 55 (0.000644)\n68. feature 39 (0.000643)\n69. feature 27 (0.000640)\n70. feature 102 (0.000640)\n71. feature 131 (0.000638)\n72. feature 84 (0.000638)\n73. feature 26 (0.000636)\n74. feature 108 (0.000635)\n75. feature 9 (0.000634)\n76. feature 45 (0.000634)\n77. feature 111 (0.000633)\n78. feature 127 (0.000630)\n79. feature 73 (0.000629)\n80. feature 35 (0.000628)\n81. feature 44 (0.000628)\n82. feature 86 (0.000628)\n83. feature 59 (0.000627)\n84. feature 118 (0.000627)\n85. feature 68 (0.000627)\n86. feature 128 (0.000625)\n87. feature 64 (0.000623)\n88. feature 15 (0.000622)\n89. feature 106 (0.000621)\n90. feature 33 (0.000621)\n91. feature 20 (0.000621)\n92. feature 90 (0.000619)\n93. feature 95 (0.000619)\n94. feature 97 (0.000618)\n95. feature 103 (0.000617)\n96. feature 89 (0.000617)\n97. feature 93 (0.000615)\n98. feature 17 (0.000615)\n99. feature 83 (0.000613)\n100. feature 48 (0.000613)\n101. feature 65 (0.000611)\n102. feature 116 (0.000611)\n103. feature 42 (0.000608)\n104. feature 124 (0.000608)\n105. feature 126 (0.000607)\n106. feature 43 (0.000607)\n107. feature 53 (0.000606)\n108. feature 76 (0.000605)\n109. feature 104 (0.000604)\n110. feature 60 (0.000603)\n111. feature 13 (0.000602)\n112. feature 105 (0.000601)\n113. feature 54 (0.000600)\n114. feature 130 (0.000600)\n115. feature 77 (0.000600)\n116. feature 11 (0.000600)\n117. feature 19 (0.000599)\n118. feature 63 (0.000597)\n119. feature 94 (0.000596)\n120. feature 32 (0.000595)\n121. feature 67 (0.000594)\n122. feature 41 (0.000591)\n123. feature 96 (0.000588)\n124. feature 25 (0.000588)\n125. feature 92 (0.000588)\n126. feature 49 (0.000583)\n127. feature 46 (0.000582)\n128. feature 8 (0.000582)\n129. feature 29 (0.000567)\n130. feature 38 (0.000449)\n131. feature 3 (0.000235)\n132. feature 74 (0.000216)\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Subsetting the data set using the top ranked variable produced by the algorithim\ncdata = pd.DataFrame(x, columns = [ 'target','v50','v66','v47','v110','v31','v10','v114'])\ncdata.tail(4)",
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        target       v50  v66  v47  v110  v31       v10       v114\n114317       1  3.269020    0    2     1    2  6.236324  11.248736\n114318       1  2.410681    0    2     1    2  2.078775   8.893134\n114319       1  0.821657    0    2     1    1  1.291029  12.381113\n114320       1  1.000661    2    2     1    1  0.853391  14.635298",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target</th>\n      <th>v50</th>\n      <th>v66</th>\n      <th>v47</th>\n      <th>v110</th>\n      <th>v31</th>\n      <th>v10</th>\n      <th>v114</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>114317</th>\n      <td>1</td>\n      <td>3.269020</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6.236324</td>\n      <td>11.248736</td>\n    </tr>\n    <tr>\n      <th>114318</th>\n      <td>1</td>\n      <td>2.410681</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2.078775</td>\n      <td>8.893134</td>\n    </tr>\n    <tr>\n      <th>114319</th>\n      <td>1</td>\n      <td>0.821657</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1.291029</td>\n      <td>12.381113</td>\n    </tr>\n    <tr>\n      <th>114320</th>\n      <td>1</td>\n      <td>1.000661</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.853391</td>\n      <td>14.635298</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#removing the target variable\nX = cdata.drop(['target'], axis = 1)\nY = cdata.target.values",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Machine Learning**"
    },
    {
      "metadata": {
        "collapsed": true
      },
      "cell_type": "markdown",
      "source": "** 1. Random Forest Algorithm**"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "There are many advantages to applying a tree model with bootstrap aggregation and building each tree from different random subset of features which encourages ensemble diversity and thus reduces training time significantly.\n\nScaling or transforming the data is not necessary for random forests.\nThis algorithim deals with convergence and numerical precision issues,\nwhich can sometimes trip up the algorithms used in logistic and linear regression, as well as neural networks, aren't so important. Because of this, you don't need to transform variables to a common scale like you might with a Neural Net."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Cross-Validation**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Cross - Validation - split the data into 70% training and the remainder for testing the model\nfrom sklearn.cross_validation import train_test_split\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=0)",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Building the Random Forest Model**\n\n"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Create the Random Forest Classification Model\nnp.random.seed(42)\nfrom sklearn.ensemble import RandomForestClassifier\nRFmodel = RandomForestClassifier(n_estimators=25, min_samples_split=25, max_depth=7, max_features='auto')\n#Prediction on held for testing\nRFpredict = RFmodel.fit(X_train, Y_train).predict(X_test)\nRFpredict",
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([0, 1, 1, ..., 1, 1, 1], dtype=int64)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#scale the data \n#from sklearn.preprocessing import StandardScaler\n#scaler = preprocessing.StandardScaler().fit(x)\n#X = scaler.transform(x)\n#Y = cdata.target.values",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Evaluate Model Performance - Classification Accuracy**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\ncmatrix = confusion_matrix(Y_test, RFpredict)\nprint (cmatrix)",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "[[ 1305  6840]\n [  680 25472]]\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Classification Report**\n\nThe f1-score is equal to the weighted average of the precision and recall. \n\n"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "creport1 = classification_report(Y_test, RFpredict)\nprint (creport1)",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "             precision    recall  f1-score   support\n\n          0       0.66      0.16      0.26      8145\n          1       0.79      0.97      0.87     26152\n\navg / total       0.76      0.78      0.73     34297\n\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true
      },
      "cell_type": "markdown",
      "source": "-------------------------------------------------------------------------------------------\n\n**2. K-Nearest Neighbour Classification**\n\nK-Nearest Neighbors often known as the lazy learning algorithm and one of the simple forms of classification. This method is most suitable for un-skewed data as it makes it easier to arrange data into groups. It is also suitable for establishing groups which differ but are not categorically distinct. It uses machine learning functions to group data into loosely knit categories. Each category is given a label. It does so by grouping data where there is a common pattern. It extracts sample data from the set as test data. This data is then classified. All future data is measured in relation to the test data. This is measured by way of Euclidean distance. Euclidean distance is the observed distance between two values.  A majority voting method is used to determine the closeness of the data to the test data.  The votes are weighted according to the distance between the values a neighboring value is always given a stronger weight than a value that is further away."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Euclidean distance is specified by the following formula\n\n$$ dist(p,q) = sqrt{(p1-q2)^2 +(p2-q2)^2+...+(pn-qn)^2} $$"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Methodolgy**\n\nIn relation to the nearest neighbor classification, the method most used utilized in banking & Insurance to determine the granting or rejecting of loan application will be adapted and attuned to the practice of granting insurance claims. An anonymous data set was provided by BNP Paribas which includes categorical and numerical data. This will determine a likelihood of a claim being successful or not. The train dataset had 133 columns. There is a target of 1 for claims with accelerated approval to meet.  This takes into account the insurers guidelines for the awarding of claims. This data will be used to predict the probability for each claim in the test data set.  An analysis of the normalized data will be created based on nearest neighbor classifications normalization formula using MinMaxScaler(). This model will determine the suitability of nearest neighbor classification for the task described."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Data Preperation**\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "__Outlier Detection- Ensemble unsupervised learning method - Isolation Forest__\n\nBack to ensemble trees i.e Random Forests for help!! this time we need isolation forests!\nThe isolation algorithm is an unsupervised machine learning method used to detect abnormal anomalies in data such as outliers. This is once again a randomized & recursive partition of the training data in a tree structure. The number of sub samples and tree size is specified and tuned appropriately. The distance to the outlier is averaged calculating an anomaly detection score: 1 = outlier 0 = close to zero are normal data. "
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#A quick look a the data for outliers using a boxplot using seaborn\nax = sns.boxplot(data=cdata, orient=\"t\", palette=\"Set1\")",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x17bc5f28>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Cross Validation -Train/test split method:** is by far the most optimal method for training and testing a classifier to unseen data the data into 70% training and the remainder for testing the model."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Cross Validation -Train Test split method is by far the most optimal method for training and testing a classifier to unseen data the data into 70% training and the remainder for testing the model \n#using the subsetted data determined from the feature importance stage\nX = cdata\nY = cdata.target.values\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=0)",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "##from sklearn.mixture import GaussianMixture\n##gmmodel = GuassianMixture(n_components = 1)\n##gmmodelfit = gmmodel.fit(X_train)#fit training data \n##log_dens = gmmmodel.score_samples(X_plot)\n##plot.fill(X_plot)\n",
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Isolation Forest**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Back to ensemble trees i.e Random Forests for help!! this time we need isolation forests!\nfrom sklearn.ensemble import IsolationForest\nrs = np.random.RandomState(30)\n#Build the ensemble model\nisomodel = IsolationForest(max_samples = 256, random_state=rs)\nisomodelfit = isomodel.fit(X_train)#fit the ensemble model and build the trees\ny_pred_train = isomodel.predict(X_train)\ny_pred_test = isomodel.predict(X_test)#find average depth\ny_pred_test",
      "execution_count": 27,
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([1, 1, 1, ..., 1, 1, 1])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#plot the results of the isolation forest\n#import plotly.plotly as py\n#import plotly.graph_objs as go\n#xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))\n#Z = isomodel.decision_function(np.c_[xx.ravel(), yy.ravel()])\n#Z = Z.reshape(xx.shape)\n\n#plt.title(\"Isolation Forest Outlier Detection\")\n#plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)\n\n#b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')\n#b2 = plt.scatter(X_train[:, 0], X_train[:, 1], c='green')\n#c = plt.scatter(y_pred_outliers[:, 0], y_pred_outliers[:, 1], c='red')\n\n#plt.axis('tight')\n#plt.xlim((-5, 5))\n#plt.ylim((-5, 5))\n#plt.legend([b1, b2, c],\n           #[\"training observations\",\n            #\"new regular observations\", \"new abnormal observations\"],\n           #loc=\"upper left\")\n#plt.show()\n",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Scaling Data - Min Max Scaler**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#subtracts the minimum of feature X from each value and divide by the range of X.\nfrom sklearn.preprocessing import MinMaxScaler\nX= np.array(X)#for min - max scaling in sklearn data needs to be an array\nmin_max_scaler = preprocessing.MinMaxScaler()\nrescaled_cdata = min_max_scaler.fit_transform(X)",
      "execution_count": 38,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "rescaled_cdata",
      "execution_count": 39,
      "outputs": [
        {
          "execution_count": 39,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[ 1.        ,  0.04497105,  1.        , ...,  0.33333333,\n         0.02715467,  0.78174538],\n       [ 1.        ,  0.06896055,  0.        , ...,  0.33333333,\n         0.07083828,  0.51540218],\n       [ 1.        ,  0.03022525,  0.        , ...,  0.33333333,\n         0.04132235,  0.56027808],\n       ..., \n       [ 1.        ,  0.12053412,  0.        , ...,  0.66666667,\n         0.11216063,  0.4446567 ],\n       [ 1.        ,  0.04108287,  0.        , ...,  0.33333333,\n         0.06965767,  0.61905567],\n       [ 1.        ,  0.05003309,  1.        , ...,  0.33333333,\n         0.0460449 ,  0.73176489]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "__Cross - Validation on rescaled data for KNN__\n\nFor this model we have we will used our sub sample date created during our feature importance stage of the analysis, the dataframe produced is \"cdata\". There is a fine balance between over-fitting and under-fitting the training data with this model which is sometimes reffered to as the \"bias-variance trade-off\". Assigning a large K will reduce this effect caused by noise."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Cross - Validation \nfrom sklearn import cross_validation\nx = rescaled_cdata\ny = cdata.target.values\nx_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.3, random_state=0)",
      "execution_count": 40,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Estimating the number of Neighbours**\n\nIn practice, choosing k depends on the difficulty of the concept to be learned and the number of records in the training data.\nTypically, k is set somewhere between 3 and 10.\nOne common practice is to set k equal to the square root of the number of training examples."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#get length of training data to determine the number of neighbours to select for KNN Model\nlen(x_train)",
      "execution_count": 41,
      "outputs": [
        {
          "execution_count": 41,
          "output_type": "execute_result",
          "data": {
            "text/plain": "80024"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#standard use square root of training data length for n_neighbours = 240\nimport cmath\ntrainlen = 57160\ntrain_sqrt = cmath.sqrt(trainlen)\nprint (train_sqrt)",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "(239.081576036+0j)\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "$$n = \\sqrt{57160} = 239.08157$$"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Build the KNN Model**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Model 1 - Training K Nearest Neighbour Classification\nfrom sklearn.neighbors import KNeighborsClassifier\nclf = KNeighborsClassifier(n_neighbors = 239, algorithm = 'auto', leaf_size = 20, metric='minkowski',\n      metric_params=None,n_jobs=3, p=1, weights='uniform')\ny_pred2 = clf.fit(x_train, y_train).predict(x_test)\ny_pred2",
      "execution_count": 50,
      "outputs": [
        {
          "execution_count": 50,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([1, 0, 1, ..., 0, 1, 1], dtype=int64)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Evaluating Model Performance - Classification Accuracy**"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Classification Report**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Evaluating KNN model performance\ncreport2 = classification_report(y_test, y_pred2)\nprint (creport2)",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "             precision    recall  f1-score   support\n\n          0       1.00      1.00      1.00      8145\n          1       1.00      1.00      1.00     26152\n\navg / total       1.00      1.00      1.00     34297\n\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**3. Artifical Neural Network - Multi Layer Perceptron Classifier**\n\nArtificial Neural Network (ANN) draws on a statistical technique known as Multi-Layer Perceptron (MLP). multilayer perceptron differs from many alternate “statistical techniques” as it does not formulate “prior assumptions” of how the data is distributed. It can model highly non-linear functions and can be trained to accurately generalize when presented with new, unseen data.\n\nArtificial Neural Networks produce an input & output on either side of one or more hidden layers. Starting with the input the data is multiplied by test weights which produce a sum that travels to a node in the next layer that forms a linear activation function producing an output which then becomes input to the hidden layers\n\n\nFor this type of problem we use a machine learning approach where the input variables are denoted by x and the output variable which is unknown is denoted as y. A correlation should exist between x and y in order for this approach to be successful. The target function asserts that y=f(x) therefore the objective of the ANN model is to learn and identify the overall hypothesis that closely estimates the unknown y=f(x). The model will come to a conclusion on what the hypothesis is denoting the function h(x) leading to a calculation of the unknown f (.)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "\n$$ f(.) becomes f(z) =f(.) -> f(z) =1/1 + exp(-z) $$\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "However, f (.) is unknown function so the ANN algorithms will come to an estimate to guess of a hypothesis that denotes function h(x) that offers a Hypothesis set."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "$$ H(x) = w1x1 +w2x2+w3x3+w4x4 = 0 $$"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Building the ANN Model**\n\nDuring the training of the artificial neural network 1 hidden layer was chosen with a number of neurons that correspond to the number of inputs. Lukasz Drag (2016) describes in recent research findings training Neural Networks that having a complex architecture of a Network is not conducive timely to results as the time to train the data set is increased as the size of the learning data set is increased as too many inputs to model can slow down the learning process. For this study the number of selected variables for input is not so many and complex therefore the aim is to lower the calculated function by reducing the number of hidden layers."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#import the MLP Classifier from sklearn and create the model \nfrom sklearn.neural_network import MLPClassifier\nANN1 = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(10, 5), random_state=1)\nANN1",
      "execution_count": 51,
      "outputs": [
        {
          "execution_count": 51,
          "output_type": "execute_result",
          "data": {
            "text/plain": "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n       hidden_layer_sizes=(10, 5), learning_rate='constant',\n       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n       warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Fit the model to make prediction on the test data\nAnnPredict1 = ANN1.fit(x_train, y_train).predict(x_test)\nAnnPredictProba = ANN1.predict_proba(x_test)[0:10, :]",
      "execution_count": 52,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Example of a Neural Networks with input layers, hidden layers, and output layer. \nImage(\"C:\\\\data\\\\ANNplot.png\",  width=500, height=200)",
      "execution_count": 53,
      "outputs": [
        {
          "execution_count": 53,
          "output_type": "execute_result",
          "data": {
            "image/png": 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61c5x86Rn4tc/fSz2WHfl2O/kG2PG1L7o7OyKqR0Px+d3XDe+ftndMbNfr4rn3s+x+tnP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JX2KRh33ojg9oODQX+1W9/1ybhoczf+wFvdBJk6SgHA5vqz+Ts5MddCmCh0a3fLe5OD8r4t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\nnC43io3QX6/YGAuy3Q8mMcC5MW45dcN5sk0WHA/vqXCri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i5UvaVigA3FnfgF/+GfMjNc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\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uuWUlPsiACnQJCzLt9ZXyE0h9++23r4hYQCz4d4wmyJiYJ08wyMqGerDK6eHEyMabFpCV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\nN34vzj//vLhu5hD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EKaddGjNnz+w6TY7NJqYKPJXiE+ZA4JlPtkl7bhrXaLOXp59+erWP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\nXvhlkqv9wD8pr6iy2roGm1dRVmwlZQ2rUiyrr7Wy4iIrXW0fl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            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {
            "image/png": {
              "width": 500,
              "height": 200
            }
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Evaluate Model Performance - Classification Accuracy**"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Classification Report**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "creport3 = classification_report(y_test, AnnPredict1)\nprint (creport3)",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "             precision    recall  f1-score   support\n\n          0       1.00      1.00      1.00      8145\n          1       1.00      1.00      1.00     26152\n\navg / total       1.00      1.00      1.00     34297\n\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**4. Support Vector Machine Classifier**\n\nThe Support Vector Machine is a linear learning system for binary classification.\n\nSupport Vector Machine utilizes both instance-based K nearest neighbor learning known as Lazy Learning as is previously described in the report, along with linear regression. It is described in the literature that the combination of both these techniques are impactful and accurate, ensuring that the model can map complex relationships in data. However positive results for this model are reliant on accurate sufficient labeling, which I feel is not represented in the dataset provided, which causes limitations. To create an SVM is also time expensive for an analyst to preprocess data and correctly train and evaluate the model results.\n\nSupport Vector Machines extend the idea associated with a Perceptron to find a certain hyperplane for classification purposes, but there is only 1 solution to a SVM:\n- A hyperplane that maximises the distance (margin) between the points of the training set\n\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Build SVC Model**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Model 2: train Support Vector Machine Classification model on top 6 ranked features- Further Tuning of the SVM\nfrom sklearn import svm\nsvc1 =svm.SVC(kernel='linear', C=1.0, gamma='auto', probability=True, cache_size=200, class_weight=None, verbose = False )\ny_pred = svc1.fit(x_train, y_train).predict(x_test)",
      "execution_count": 59,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Evaluate Model Performance - Classification Accuracy**\n\nFor the trained Support Vector Machine classification model, it is ideal to generate a confusion matrix to see well the model performs to unseen data and to identify correct separation maximized distance from hyper planes."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Classification Report**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "creport5 = classification_report(y_test, y_pred)\nprint (creport5)",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "             precision    recall  f1-score   support\n\n          0       1.00      1.00      1.00      8145\n          1       1.00      1.00      1.00     26152\n\navg / total       1.00      1.00      1.00     34297\n\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Data source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management"
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2",
      "language": "python"
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/26ee49f750948dae2b03870429d51a51"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.11",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 2,
        "name": "ipython"
      }
    },
    "gist": {
      "id": "26ee49f750948dae2b03870429d51a51",
      "data": {
        "description": "Machine Learning - Accelarating Claims Process.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}