diff --git a/glass/glass_classification.ipynb b/glass/glass_classification.ipynb
index 1f18afb..2d7889e 100644
--- a/glass/glass_classification.ipynb
+++ b/glass/glass_classification.ipynb
@@ -1,5 +1,59 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rk/anaconda/envs/py27/lib/python2.7/site-packages/matplotlib/__init__.py:913: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
+      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "from pandas import DataFrame, Series\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "\n",
+    "%matplotlib inline\n",
+    "from collections import defaultdict\n",
+    "import json\n",
+    "\n",
+    "import scipy as sp\n",
+    "\n",
+    "from matplotlib import rcParams\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "#colorbrewer2 Dark2 qualitative color table\n",
+    "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n",
+    "                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n",
+    "                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n",
+    "                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n",
+    "                (0.4, 0.6509803921568628, 0.11764705882352941),\n",
+    "                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n",
+    "                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843)]\n",
+    "\n",
+    "rcParams['figure.figsize'] = (10, 6)\n",
+    "rcParams['figure.dpi'] = 150\n",
+    "rcParams['axes.color_cycle'] = dark2_colors\n",
+    "rcParams['lines.linewidth'] = 2\n",
+    "rcParams['axes.facecolor'] = 'white'\n",
+    "rcParams['font.size'] = 14\n",
+    "rcParams['patch.edgecolor'] = 'white'\n",
+    "rcParams['patch.facecolor'] = dark2_colors[0]\n",
+    "rcParams['font.family'] = 'StixGeneral'"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -8,8 +62,7 @@
    },
    "outputs": [],
    "source": [
-    "#classification\n",
-    "import pandas as pd\n",
+    "#multiclass classification\n",
     "train=pd.read_csv(\"glass.csv\")"
    ]
   },
@@ -24,6 +77,19 @@
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -125,8 +191,7 @@
     }
    ],
    "source": [
-    "train.head()\n",
-    "\n"
+    "train.head()"
    ]
   },
   {
@@ -135,51 +200,219 @@
    "metadata": {
     "collapsed": false
    },
+   "outputs": [],
+   "source": [
+    "#Hypothesis testing - Silica and sand,Na, and Ca should be most important variable, refractive index of similar type should be near\n",
+    "#what really the value of columns Na, Mg , Al and Si, K, Ca, Ba , Fe\n",
+    "#My intuition says that for every type of glass refractive index will be quite similar so it can be converted into categorical variable\n",
+    "#The numerical values of various elements is not on same scale which can be a problem in some algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "RI      float64\n",
-       "Na      float64\n",
-       "Mg      float64\n",
-       "Al      float64\n",
-       "Si      float64\n",
-       "K       float64\n",
-       "Ca      float64\n",
-       "Ba      float64\n",
-       "Fe      float64\n",
-       "Type      int64\n",
-       "dtype: object"
+       "(214, 10)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#Hypothesis testing - Silica and sand,Na, and Ca should be most important variable, refractive index of similar type should be near\n",
-    "#what really the value of columns Na, Mg , Al and Si, K, Ca, Ba , Fe\n",
-    "#My intuition says that for every type of glass refractive index will be quite similar so it can be converted into categorical variable\n",
-    "#The numerical values of various elements is not on same scale which can be a problem in some algorithm\n",
-    "train.dtypes\n",
-    "\n"
+    "train.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>RI</th>\n",
+       "      <th>Na</th>\n",
+       "      <th>Mg</th>\n",
+       "      <th>Al</th>\n",
+       "      <th>Si</th>\n",
+       "      <th>K</th>\n",
+       "      <th>Ca</th>\n",
+       "      <th>Ba</th>\n",
+       "      <th>Fe</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.518365</td>\n",
+       "      <td>13.407850</td>\n",
+       "      <td>2.684533</td>\n",
+       "      <td>1.444907</td>\n",
+       "      <td>72.650935</td>\n",
+       "      <td>0.497056</td>\n",
+       "      <td>8.956963</td>\n",
+       "      <td>0.175047</td>\n",
+       "      <td>0.057009</td>\n",
+       "      <td>2.780374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.003037</td>\n",
+       "      <td>0.816604</td>\n",
+       "      <td>1.442408</td>\n",
+       "      <td>0.499270</td>\n",
+       "      <td>0.774546</td>\n",
+       "      <td>0.652192</td>\n",
+       "      <td>1.423153</td>\n",
+       "      <td>0.497219</td>\n",
+       "      <td>0.097439</td>\n",
+       "      <td>2.103739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.511150</td>\n",
+       "      <td>10.730000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.290000</td>\n",
+       "      <td>69.810000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.430000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.516523</td>\n",
+       "      <td>12.907500</td>\n",
+       "      <td>2.115000</td>\n",
+       "      <td>1.190000</td>\n",
+       "      <td>72.280000</td>\n",
+       "      <td>0.122500</td>\n",
+       "      <td>8.240000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.517680</td>\n",
+       "      <td>13.300000</td>\n",
+       "      <td>3.480000</td>\n",
+       "      <td>1.360000</td>\n",
+       "      <td>72.790000</td>\n",
+       "      <td>0.555000</td>\n",
+       "      <td>8.600000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.519157</td>\n",
+       "      <td>13.825000</td>\n",
+       "      <td>3.600000</td>\n",
+       "      <td>1.630000</td>\n",
+       "      <td>73.087500</td>\n",
+       "      <td>0.610000</td>\n",
+       "      <td>9.172500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.533930</td>\n",
+       "      <td>17.380000</td>\n",
+       "      <td>4.490000</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>75.410000</td>\n",
+       "      <td>6.210000</td>\n",
+       "      <td>16.190000</td>\n",
+       "      <td>3.150000</td>\n",
+       "      <td>0.510000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               RI          Na          Mg          Al          Si           K  \\\n",
+       "count  214.000000  214.000000  214.000000  214.000000  214.000000  214.000000   \n",
+       "mean     1.518365   13.407850    2.684533    1.444907   72.650935    0.497056   \n",
+       "std      0.003037    0.816604    1.442408    0.499270    0.774546    0.652192   \n",
+       "min      1.511150   10.730000    0.000000    0.290000   69.810000    0.000000   \n",
+       "25%      1.516523   12.907500    2.115000    1.190000   72.280000    0.122500   \n",
+       "50%      1.517680   13.300000    3.480000    1.360000   72.790000    0.555000   \n",
+       "75%      1.519157   13.825000    3.600000    1.630000   73.087500    0.610000   \n",
+       "max      1.533930   17.380000    4.490000    3.500000   75.410000    6.210000   \n",
+       "\n",
+       "               Ca          Ba          Fe        Type  \n",
+       "count  214.000000  214.000000  214.000000  214.000000  \n",
+       "mean     8.956963    0.175047    0.057009    2.780374  \n",
+       "std      1.423153    0.497219    0.097439    2.103739  \n",
+       "min      5.430000    0.000000    0.000000    1.000000  \n",
+       "25%      8.240000    0.000000    0.000000    1.000000  \n",
+       "50%      8.600000    0.000000    0.000000    2.000000  \n",
+       "75%      9.172500    0.000000    0.100000    3.000000  \n",
+       "max     16.190000    3.150000    0.510000    7.000000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "import numpy as np"
+    "train.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -187,22 +420,31 @@
     {
      "data": {
       "text/plain": [
-       "(175, 10)"
+       "RI      False\n",
+       "Na      False\n",
+       "Mg      False\n",
+       "Al      False\n",
+       "Si      False\n",
+       "K       False\n",
+       "Ca      False\n",
+       "Ba      False\n",
+       "Fe      False\n",
+       "Type    False\n",
+       "dtype: bool"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "train.describe()\n",
-    "train.shape"
+    "train.isnull().any()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -211,6 +453,19 @@
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -262,72 +517,32 @@
        "7         29"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#univariate analysis\n",
     "pd.crosstab(index=train['Type'],columns=\"count\")\n",
     "#classes 2 and 1 are dominating\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\python35.zip',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\DLLs',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages\\\\Sphinx-1.4.6-py3.5.egg',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages\\\\Pythonwin',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages\\\\setuptools-27.2.0-py3.5.egg',\n",
-       " 'C:\\\\Users\\\\hpp\\\\Anaconda3\\\\lib\\\\site-packages\\\\IPython\\\\extensions',\n",
-       " 'C:\\\\Users\\\\hpp\\\\.ipython']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# In univariate analysis going one by one from in each variable and looking at the frequency distribution and\n",
-    "# All independent variable is continuous variable.\n",
-    "import matplotlib.pyplot as plt\n",
-    "import sys\n",
-    "sys.path\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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Gr+zu/7zyeHe/uaqemuTVSV5dVQ/q7i8d7fODXbTn5+dW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\nIkwEAAAAAIb8Pyibxbp0tCA2AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf71fc4d0>"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -337,28 +552,25 @@
     "plt.xlabel(\"RI\")\n",
     "plt.ylabel(\"frq\")\n",
     "fig1=plt.gcf()\n",
-    "fig1\n",
-    "#Try different bin size\n",
-    "#the data RI is skewed and a lot of outliers are present "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AYHD7Vl0AALC+E885a9UlAOw627lt3X/m2dv2vQDATD4AAAAAGJyQ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\nAAAAgMH9f/aczckkhP+3AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3792890>"
       ]
      },
-     "execution_count": 13,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -367,29 +579,25 @@
     "plt.title(\"Si-freq\")\n",
     "plt.xlabel(\"si\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "#again lot of outliers \n",
-    "#here outliers may belong to a particular category which can be seen afterwords\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Zs0NKRFwFvGDOh12fmU9bXOmSJKmXlnLp5CnAp4GnAsfVr6cCO4BzMnNN/eooZNSOqY/5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\n9oPY7gU+gQ9iWym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5epJj1jkji2mXc2/Yc3S/JG9urf3zkudum8nVnd+f5LWz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\nAACAzin5AAAAAKBzSj4AAAAA6Nz/A0NA+XfXkFYLAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3b708d0>"
       ]
      },
-     "execution_count": 15,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -398,27 +606,25 @@
     "plt.title(\"Na-freq\")\n",
     "plt.xlabel(\"na\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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F/gh4LfBVYAOwOcnjqmr+Ma9lbvPmzfz+7//+Ar0GtUSxn9dpazmkQUO7m/sH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\nJElqjUFDkiS1xqAhSZJaY9CQJEmt+f94J1QecKPfqgAAAABJRU5ErkJggg==\n",
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AwMgJ+QAAAABg5IR8AAAAADByQj4AAAAAGDkhHwAAAACMnJAPAAAA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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3a16b50>"
       ]
      },
-     "execution_count": 16,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -427,27 +633,25 @@
     "plt.title(\"Ca-freq\")\n",
     "plt.xlabel(\"Ca\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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n3nmAfevud1TV3yc5LpPZfcB8PCvJDyZ5X1Utdvx20+Up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\n/wE5OsqGXaDleAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf38ba9d0>"
       ]
      },
-     "execution_count": 17,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -456,27 +660,25 @@
     "plt.title(\"Mg-freq\")\n",
     "plt.xlabel(\"Mg\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ABqfkAwAAAIDBKfkAAAAAYHBKPgAAAAAYnJIPAAAAAAa319QBYC1u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\nAAAAAGBwSj4AAAAAGNz/BwJ398eB+8s4AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3999f10>"
       ]
      },
-     "execution_count": 18,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -485,27 +687,25 @@
     "plt.title(\"K-freq\")\n",
     "plt.xlabel(\"K\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ool/ofZ/+4qJfAnbSjo0d2GeMHdgaY2cf22+fk3fh9Rg/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\nAMCghIMAAAAAMKj/B/pziG49l3joAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3c5b150>"
       ]
      },
-     "execution_count": 19,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -514,27 +714,25 @@
     "plt.title(\"Fe-freq\")\n",
     "plt.xlabel(\"Fe\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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uUVV9dZJzpo9P7e6zNqh7cZLbJrmou+9wgHbXCwfvmORz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf31fdbd0>"
       ]
      },
-     "execution_count": 20,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -543,27 +741,25 @@
     "plt.title(\"Ba-freq\")\n",
     "plt.xlabel(\"Ba\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ldBVwFUBEzL1yMmN3Sunudo8tSZKqt5QrHI+neMZvrp3A4ftVTXMnRcSOiLg1Ii6MiEdl\nPJckSeqgpQSOfwLeHxFrZzaUv38vcFOnCpvjK8ArgOcAbwJOBDY3uRoiSZJ6yFJuqbwW+BwwFRG3\nl9seC/wIOLNThc2WUrp81pf/EhH/DPwEOAm4Lsc5VV9TU1M0Go2mbVxGXJK6q+3AkVL6UUQ8FXgu\n8KRy8yTw1ZTSQhM+OyqltC0iGsBRNAkcGzZsYM2aNftsGx0dZXTU+ab9ampqivXrh5ie3lV1KZLU\n88bHxxkfH99n286dO7Ocq63AEREHUUzuPDuldDVwdZaqFq/jMcBhFB9QsaCNGzcyPLzQqo7qR41G\nowwbzZYQB5cRl6T5/xM+MTHByMhIx8/VVuBIKT1YXt3oqIg4mOJqxcycjCMj4mjgnvL1duCzwPay\n3buB24Atna5F/aLZEuLgMuKS1F1LmTS6iWIeRyc9HbgFuJliHY73Uazr8TfAHuCpwBXAVuBjFBNX\nT0gpPdjhOiRJUgZLmTR6IPCaiDiFIiA8MHtnSukN7R4wpfR1moef57d7TEmS1DtaChzlbZQfpJT2\nAk+huPoA8MQ5TbsyaVSSJNVLq1c4bqH4jJNfAI8DnpFS+mW2qiRJUl9pdQ7HvcAR5e8Pb+N9kiRJ\nLV/h+Czw9Yi4i+K2yXciYs98DVNKR3aqOEmS1B9aChwppddFxOcoHkn9IMWTIvfnLEySJPWPlp9S\nKT/RlYgYAT6QUjJwSJKklixlafNX5yhEkiT1Lyd/SpKk7AwckiQpOwOHJEnKzsAhSZKyM3BIkqTs\nDBySJCk7A4ckScrOwCFJkrIzcEiSpOwMHJIkKTsDhyRJys7AIUmSsjNwSJKk7AwckiQpOwOHJEnK\nzsAhSZKyM3BIkqTsDBySJCk7A4ckScrOwCFJkrIzcEiSpOwMHJIkKTsDhyRJys7AIUmSsjNwSJKk\n7AwckiQpOwOHJEnKzsAhSZKyO7DqAiS1b3JyctE2AwMDDA4OdqEaSVqcgUOqlbuAFYyNjS3actWq\n1WzdOmnokNQTDBxSrdwL7AU2AUNN2k0yPT1Go9EwcEjqCQYOqZaGgOGqi5CkljlpVJIkZWfgkCRJ\n2fVE4IiI4yPiyoj4eUTsjYgz5mnzjoi4MyJ2RcQ1EXFUFbVKkqT29UTgAA4GvgucA6S5OyPizcDr\ngdcBxwAPAFsi4mHdLFKSJC1NT0waTSldBVwFEBExT5NzgfNTSl8q27wC2AGcCVzerTolSdLS9MoV\njgVFxBHAOuBrM9tSSvcB3waOq6ouSZLUup4PHBRhI1Fc0ZhtR7lPkiT1uJ64pZLLhg0bWLNmzT7b\nRkdHGR0dragiSZJ6x/j4OOPj4/ts27lzZ5Zz1SFwbAcCWMu+VznWArc0e+PGjRsZHnZxJEmS5jPf\nf8InJiYYGRnp+Ll6/pZKSmkbReg4eWZbRBwCHAvcWFVdkiSpdT1xhSMiDgaOoriSAXBkRBwN3JNS\nuh24AHhrRPwY+BlwPnAHcEUF5UqSpDb1ROAAng5cRzE5NAHvK7dfCrwmpfSeiFgNXAQcCnwDOC2l\n9OsqipUkSe3picCRUvo6i9zeSSmdB5zXjXokSVJn9fwcDkmSVH8GDkmSlJ2BQ5IkZWfgkCRJ2fXE\npFFJ1ZiamqLRaDRtMzAwwODgYJcqktSvDBzSMjU1NcX69UNMT+9q2m7VqtVs3Tpp6JC0Xwwc0jLV\naDTKsLEJGFqg1STT02M0Gg0Dh6T9YuCQlr0hwM8ckpSXk0YlSVJ2Bg5JkpSdgUOSJGVn4JAkSdkZ\nOCRJUnYGDkmSlJ2BQ5IkZWfgkCRJ2Rk4JElSdgYOSZKUnYFDkiRlZ+CQJEnZGTgkSVJ2Bg5JkpSd\ngUOSJGVn4JAkSdkdWHUB6n9TU1M0Go1F2w0MDDA4ONiFiiRJ3WbgUFZTU1OsXz/E9PSuRduuWrWa\nrVsnDR2S1IcMHMqq0WiUYWMTMNSk5STT02M0Gg0DhyT1IQOHumQIGK66CElSRZw0KkmSsjNwSJKk\n7AwckiQpOwOHJEnKzsAhSZKyM3BIkqTsDBySJCk71+GQtKjJyclF23RqaXqXwpf6k4FDUhN3ASsY\nGxtbtGUnlqZ3KXypfxk4JDVxL7CXbi1N71L4Uv8ycEhqQbeXpncpfKnfOGlUkiRlV4vAERFvj4i9\nc14/rLouSZLUmjrdUvkBcDIQ5dcPVViLJElqQ50Cx0MppburLkKSJLWvFrdUSk+IiJ9HxE8iYlNE\nPLbqgiRJUmvqEji+BbwKOBU4GzgC+IeIOLjKoiRJUmtqcUslpbRl1pc/iIibgH8FXgxcXE1V/a+V\nFR93797NypUrF9zfygqVyqfZn38VY7PY95TfL1L/qkXgmCultDMibgOOatZuw4YNrFmzZp9to6Oj\njI6O5iyvL7S+4uMBwJ5ulKS2tL5CaLe0s4qopO4YHx9nfHx8n207d+7Mcq5aBo6IeARF2LisWbuN\nGzcyPOziQUvR2oqPm4G3tdhG3dXKCqHdHZv2vqckdcN8/wmfmJhgZGSk4+eqReCIiPcCX6S4jfIf\ngL8BHgTGm71PndBsxcfJNtqoGr04Nr1Yk6TcahE4gMcAnwIOA+4GbgCemVL6ZaVVSZKkltQicKSU\nnHQhSVKN1eWxWEmSVGMGDkmSlJ2BQ5IkZWfgkCRJ2dVi0qg0w5UqJameDByqDVeqlKT6MnCoNlyp\nUpLqy8ChGnKlSkmqGyeNSpKk7AwckiQpOwOHJEnKzsAhSZKyM3BIkqTsDBySJCk7A4ckScrOdThq\nZLFlvQF2797NypUr97uNS4SrH7Tyd2ZgYIDBwcEuVSQtXwaOmmh9We8DgD0daCPVW6t/Z1atWs3W\nrZOGDikzA0dNtLes9/62md1OqqfW/s5MMj09RqPRMHBImRk4aqeVZb33t83sdlLdLfa9LqkbnDQq\nSZKyM3BIkqTsDBySJCk7A4ckScrOwCFJkrIzcEiSpOx8LDajj33s45x//t8t2u7Zzz6JSy/9eBcq\n6n3NVjh19dPe18/j1+1VS+u6Smpd61Z+Bo6Mxsc/w+23HwSc2aTVLWzadImBg7uAFYyNjVVdiJak\nv8ev26uW1nWV1LrWre4wcGR3NPCuJvsvAr7apVp62b3AXlpbJVW9p7/Hr9urltZ1ldS61q3uMHCo\nx7SySqp6V7+PX7dXLa3rKql1rVs5OWlUkiRlZ+CQJEnZGTgkSVJ2Bg5JkpSdgUOSJGVn4JAkSdn5\nWKyk2urUyqaLte3ksXbv3s3KlSt76nyttIH+XyHUVVLzMnBIqqFOrWzayRVSWz3WAcCeHjtfazX1\n8wqhrpKan4FDUg11amXTVo7TyWPNHKeXztdqTf29QqirpOZn4JBUY51a2XSxlTE7dazJFtp0+3yt\n1rRc+OeQi5NGJUlSdgYOSZKUXa0CR0T8RURsi4hfRcS3IuIZVddUvfGqC+iiG6suoEuuqrqALlku\n37vLo5/j48ujn1q62gSOiHgJ8D7g7cDvA98DtkTEQKWFVW45/SX/ZtUFdMmWqgvokuXyvbs8+mng\n0GJqEziADcBFKaXLUkq3AmcDu4DXVFuWJElaTC0CR0QcBIwAX5vZllJKwFeB46qqS5IktaYuj8UO\nUKxMs2PO9h3A+u6X0457gIkm+6e6VYgkSZWpS+Bo1ypob3ngHB75yEcAXwCuadrusMPW8clPfrJp\nm23btpW/28y+z+jfAcy89x8XaDNbK206eaxOnu+eLp+vqj/PHfx2TLtxvqr+PGd/73bjfEtts7/H\navfv6P6eL0cbgOLfoM2bN8/7b+sdd9zB5s2bWzhW8+PMtmLFCvbu3duVNgv/G7tPK6D6ny25zerf\nqk4eN4o7E72tvKWyC3hRSunKWdsvAdaklF44p/3LWPhfbEmStLizUkqf6tTBanGFI6X0YETcDJwM\nXAkQEVF+/cF53rIFOAv4GTDdpTIlSeoHq4DD6fAjc7W4wgEQES8GLqF4OuUmiqdW/gR4Ukrp7gpL\nkyRJi6jFFQ6AlNLl5Zob7wDWAt8FTjVsSJLU+2pzhUOSJNVXLdbhkCRJ9WbgkCRJ2dU2cLT7QW4R\ncVJE3BwR0xFxW0S8slu17o92+hkRJ0bE3jmvPRHxu92suV0RcXxEXBkRPy9rPqOF99RuPNvtZ43H\n8y0RcVNE3BcROyLi8xHxxBbeV6sxXUo/6zimEXF2RHwvInaWrxsj4vmLvKdWYwnt97OOYzmfiPir\nsvb3L9Juv8e0loGj3Q9yi4jDgS9RLI1+NPAB4OMR8dxu1LtUS/zAugQ8AVhXvh6dUvpF7lr308EU\nk4DPoai/qbqOJ232s1TH8Twe+BBwLHAKcBBwdUQ8fKE31HRM2+5nqW5jejvwZmCY4iMmrgWuiIih\n+RrXdCyhzX6W6jaW+yj/A/s6ip8tzdodTifGNKVUuxfwLeADs74OiuX83rRA+3cD35+zbRzYXHVf\nOtzPE4E9wCFV174ffd4LnLFIm1qO5xL6WfvxLPsxUPb3D/t8TFvpZ7+M6S+BV/frWLbYz1qPJfAI\nYCvwHOA64P1N2nZkTGt3hWOJH+T2zHL/bFuatK/cEvsJRSj5bkTcGRFXR8Qf5K20ErUbz/3QD+N5\nKMX/BO9p0qYfxrSVfkKNxzQiVkTES4HVwDcXaFb7sWyxn1DjsQQ+AnwxpXRtC207Mqa1Cxw0/yC3\ndQu8Z90C7Q+JiJWdLa9jltLPu4A/A14E/CeKS4TXR8TTchVZkTqO51LUfjwjIoALgBtSSj9s0rTW\nY9pGP2s5phHxlIi4H9gNXAi8MKV06wLNazuWbfazlmMJUIappwFvafEtHRnT2iz8pcWllG4Dbpu1\n6VsR8XiKVVl7ftK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EfAAAAAAwckI+AAAAABg5IR8AAAAAjJyQDwAAAABGTsgHAAAAACMn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\nAAAjJ+QDAAAAgJH7/2RTPnsqHTPWAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3094410>"
       ]
      },
-     "execution_count": 22,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -572,31 +768,24 @@
     "plt.title(\"Al-freq\")\n",
     "plt.xlabel(\"Al\")\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "#The data is skewed and Fe, Ba , Ca and K are most skewed\n",
-    "#outliers K and Ba has most outliers\n",
-    "#various ways are there to tackle outliers one of them is tukey's HSD.\n",
-    "#as not all variables are on same scale, boxplot may give some intuition\n",
-    "\n",
-    "# multivariate analysis\n",
-    "\n",
-    "feat=train.columns[:-1].tolist()\n"
+    "feat=train.columns[:-1].tolist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -605,6 +794,19 @@
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -757,7 +959,7 @@
        "Fe -0.058692  1.000000  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -773,42 +975,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 214 entries, 0 to 213\n",
-      "Data columns (total 10 columns):\n",
-      "RI      214 non-null float64\n",
-      "Na      214 non-null float64\n",
-      "Mg      214 non-null float64\n",
-      "Al      214 non-null float64\n",
-      "Si      214 non-null float64\n",
-      "K       214 non-null float64\n",
-      "Ca      214 non-null float64\n",
-      "Ba      214 non-null float64\n",
-      "Fe      214 non-null float64\n",
-      "Type    214 non-null int64\n",
-      "dtypes: float64(9), int64(1)\n",
-      "memory usage: 16.8 KB\n"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7febf2ffb250>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AAJi2pVkHwGYZukfRIFV1/yTvjU2tAQAAAGZucEXRpKpqryQPTvLEJNfLKEmk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\n3UicOgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf7224fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "#missing value\n",
-    "train.info()\n",
-    "#No missing value yeah!\n"
+    "sns.heatmap(train.corr())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -828,7 +1027,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "collapsed": true
    },
@@ -858,7 +1057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -881,21 +1080,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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D9NCqukt3f2kN1T9nF/tO7m7hIQAAAAC7HeFhkqq6WpKTk3whyeOS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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf37b2a90>"
       ]
      },
-     "execution_count": 42,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -903,29 +1101,25 @@
     "plt.hist(train['RI'],bins=40)\n",
     "plt.xlabel('RI')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    \n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AAAAYJTxkpqpqj6q60xrGHVhVt5hFTcDGWOv5D2wN3gNg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\nMOr/A2kQ0HMNG6mnAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf2f65fd0>"
       ]
      },
-     "execution_count": 43,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -933,28 +1127,25 @@
     "plt.hist(train['Na'],bins=40)\n",
     "plt.xlabel('Na')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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iHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0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\nU/IBAAAAwMgp+QAAAABg5P4f3InO4lExsCUAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3a186d0>"
       ]
      },
-     "execution_count": 45,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -962,28 +1153,25 @@
     "plt.hist(train['Mg'],bins=40)\n",
     "plt.xlabel('Mg')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAA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\nAABGTsgHAAAAACP3/wCW+D2gS8u3GwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3c827d0>"
       ]
      },
-     "execution_count": 46,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -991,28 +1179,26 @@
     "plt.hist(train['Al'],bins=40)\n",
     "plt.xlabel('Al')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
+    "plt.show()\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf33095d0>"
       ]
      },
-     "execution_count": 47,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1020,28 +1206,26 @@
     "plt.hist(train['Si'],bins=40)\n",
     "plt.xlabel('Si')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
+    "plt.show()\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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m8zqqALJ/Xvf9zWWSJGnA1X5IZZ7rgJ8Dfr43q9sMrJ7XNtZ8SZK0vI2PjzM+Pj6nbXp6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\nRU5ErkJggg==\n",
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eleSXFxgPvqm7/2Rpv6o+m8RsKDaaf53ZbOhd3X333PFrq+q8zN5U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\nwMQp+QAAAABg4v4/3u+/6Kes9s4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3abb1d0>"
       ]
      },
-     "execution_count": 48,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1049,28 +1233,26 @@
     "plt.hist(train['K'],bins=40)\n",
     "plt.xlabel('K')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
+    "plt.show()\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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iHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMi\nHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAA\nQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IB\nAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0\nJ/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAA\nAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMiHwAAAAA0J/IBAAAAQHMi\nHwAAAAA0J/IBAAAAQHMiHwAAAAA0t7bsAQAAYBWceNF5e/Zeh59y/p69FwDQgzv5AAAAAKA5kQ8A\nAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5\nkQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAA\nAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKA5kQ8AAAAAmhP5AAAAAKC5tWUPAAAAHJkT\nLzpvT97n8FPO35P3AQB2z518AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcA\nAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCc\nyAcAAAAAzYl8R6Cq7lZVj1v2HAAAAAAwTeTboKruU1U3VtXY+Ejyf5N8ddkzAgAAAMA0ke+Wfjlb\n/10+NMa4dC+HAQAAAICdrC17gFuTqvqeJP8myb/O+l1700aSw3s9EwAAAADsROT7/52b5I1jjHcv\nexAAAAAAmJWP605U1QlJ/l2Sz1TVKcueBwAAAABmJfJ92y8kOS7JC5J8rKr+rqr+qKoevOS5AAAA\nAGBbIl+Sqjo2yTkbdt81ydlJ3jOJfQf3fjIAAAAA2JnIl2SMcX2SOyW5S5IHJjkvySemlpyd5E1V\ndWAJ4wEAAADAtkS+iTHGTWOML48x3jvGeHGS+yZ5UpIvT5Y8Isn5s1yrqq7a7JHk0GKmBwAAAGA/\n8+u6WxgwXsljAAAXHklEQVRjjCSvrar3Jnl3knsk+fdV9Z/GGF9b7nQAAMzqxIvOW/YIbe3l3+7w\nU2b6/9MBgC2IfDsYY3y6qs5J8tYk35HkB5JcusM5p222f3I336lzHxIAAACAfc3HdWcwxnhbkv8z\neXnCMmcBAAAAgI1Evtl9eLK9ZqlTAAAAAMAGIt/srptsDy91CgAAAADYQOSb3SlJvpjkimUPAgAA\nAADTRL4ZVNUPJDkjycvHGDctex4AAAAAmCbyJamq11TVZVX1/Kq67YZjt0/y6iTvSPKbSxkQAAAA\nALax7yNfVR1IclaSByU5P8mVVfWYqrpbVT08ybuTXJ7k0WOMG5c4KgAAAABsam3ZAyzbGONbVfXI\nJL+U5IeSHEryuiSfS/KeJD8zxvjQ8iYEAAAAgO3t+8iXJGOMy5Jctuw5AAAAAOBo7PuP6wIAAABA\ndyIfAAAAADQn8gEAAABAcyIfAAAAADQn8gEAAABAcyIfAAAAADQn8gEAAABAcyIfAAAAADQn8gEA\nAABAcyIfAAAAADQn8gEAAABAcyIfAAAAADQn8gEAAABAcyIfAAAAADS3tuwBAADYn0686LxljwAA\nsDLcyQcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8\nAAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAA\nzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAcAAAAAzYl8AAAAANCcyAfA\n/2vvzqNkvcs6gX+fLFwIl4wYAgTDcEk4DAeEsAwY2WIwEnYigZEDgRgJLiPDwCjbCLLpMQoyQYQZ\nFYkDMp5DGDZRQFlEIEwQQ8IyYcuAISxhywBZSCA880e9Pbdouvve232rKm/153POPe/71vurqqf7\nvqe66lu/BQAAgJET8gEAAADAyB206AIAAABg2R151jPn8jyXnHbGXJ5nXj9PMr+fCcZOTz4AAAAA\nGDkhHwAAAACMnJAPAAAAAEZOyAcAAAAAIyfkAwAAAICRE/IBAAAAwMgJ+QAAAABg5IR8AAAAADBy\nQj4AAAAAGDkhHwAAAACMnJAPAAAAAEZOyAcAAAAAIyfkAwAAAICRE/IBAAAAwMgJ+QAAAABg5IR8\nAAAAADByQj4AAAAAGDkhHwAAAACMnJAPAAAAAEZOyAcAAAAAIyfkAwAAAICRE/IBAAAAwMgJ+QAA\nAABg5IR8AAAAADByBy26AMbvyLOeObfnuuS0M+b2XFz3zevac90BwOzN8z3lvHgPsXne5wHsOz35\nAAAAAGDkhHwAAAAAMHJCPgAAAAAYOSEfAAAAAIyckA8AAAAARk7IBwAAAAAjJ+QDAAAAgJET8gEA\nAADAyAn5AAAAAGDkhHwAAAAAMHJCPgAAAAAYOSEfAAAAAIyckA8AAAAARk7IBwAAAAAjJ+QDAAAA\ngJET8gEAAADAyAn5AAAAAGDkhHwAAAAAMHJCPgAAAAAYOSEfAAAAAIyckA8AAAAARk7IBwAAAAAj\nJ+QDAAAAgJET8gEAAADAyB206AKA+TjyrGfO5XkuOe2MuTzPPM3rd5fM7/c3z58JAJgt7/M2bxnf\nEy3jz8Q4eC1aPD35AAAAAGDkhHwAAAAAMHJCPgAAAAAYOSEfAAAAAIyckA8AAAAARk7IBwAAAAAj\nJ+QDAAAAgJET8gEAAADAyAn5AAAAAGDkhHwAAAAAMHJCPgAAAAAYOSEfAAAAAIyckA8AAAAARk7I\nBwAAAAAjJ+QDAAAAgJET8q1SVfeqqjdU1Ser6qNV9amqenFVHb7o2gAAAABgLUK+KVX1tCTvS3Jx\nkjt1952SPCzJw5N8pKrusMj6AAAAAGAtQr5BVT02yR8k+VB3P6W7r0mS7v5UkkclOSLJW6vqxgss\nEwAAAAB+hJAvSVXdKMnLhsMXrz7f3ecn+fsku5I8e36VAQAAAMCeCfkmfjnJjZN8P8nfrtPmTcP2\n16pq51yqAgAAAIC9IOSbeNiw/Xh3X7lOmw8N2xtMtQcAAACAhdv2IV9VHZzk7sPhZzdoOn3up2dX\nEQAAAADsm20f8iW5RSa985Lky+s16u5vJ1np5WeVXQAAAACuM6q7F13DQlXVXZKcNxye0d3P2qDt\nV5McnuSj3X3MBu0+sc6p2+3YseOAo48+etP1Xhd9+v9eOrfnuu2P3Wxuz7Vs5vX/NM//o3lee/My\nr9/fMv7uAOC6Ztn+rnufx6L4HDgOy/haNA8XXXRRrr766u9096FbfSwhX9W9k7xvOHxBdz93g7YX\nJ7llkou6+zYbtFsv5LttkquSfGGT5bK2ldT0ooVWwXbjumPeXHMsguuOeXPNsQiuO+bNNce0Wya5\nsrtvvtUHOmg/FDN2V03tH7yHtivn11ucI0nS3YbzztFKqOr3zjy57pg31xyL4Lpj3lxzLILrjnlz\nzTEr5uRLvjq1f8ge2q6c//qMagEAAACAfSbkSy7J7p5563aNrKobJFkZH33hrIsCAAAAgL217UO+\nnkxKeM5weNQGTafPfWB2FQEAAADAvtn2Id/gzcP2TlV1vXXa3GPYXpPkbbMvCQAAAAD2jpBv4jVJ\nLk+yI8mJ67R5+LD9y+6+bC5VAQAAAMBeqMloVarqSUleluS93f0zq87dPslHk3wryTHdfcn8KwQA\nAACAtenJt9vLk5yZ5LiqevHKsN2qOirJ6zMJ+E4W8AEAAABwXaMn3ypVdWKS/5BkVybz7+1I8o4k\n/6W7v7DA0gAAAABgTUI+AAAAABg5w3UBAAAAYOSEfAAAAAAwckI+AAAAABg5IR9LpaoOqar7VNW/\nr6rnVdWORdcEAMAPq6oDqurOi66D7WOz11xVHV5Vt5xFTSw/r3XMm5CP0RteOB9VVe9I8vkkj0hy\nbpIXdvfVCy2OpVFVn6iq3ot/P6iqmyy6XpZHVR1cVb9aVe+pqo8N1+IlVfW6qrrboutj+VTVzqr6\nrar6cFWdV1UXVdUHhy/QDlx0fYxbVR1UVacmuTDJmXvR/uCqenJVnVtV51fVp6rqnOExYI/29Zqb\nut8RVfWSTD5f/OyMymNJbeK17p5VdXZVfamqrqmqr1fV26rqwbOvlmVy0KILgK2oqtsleXWSuyd5\nTZI7dPfXFlsVy6aqjk9y+71s/r+6++uzrIfto6pumuRtSW6Y5NHdff5w+02SnJXk3Ko6tbtfu8Ay\nWSJVdZskb09yaZIHd/elQ7B3epKXJ3lcVT2ouy9bZJ2MT1UdnOTUJM9KctRw85f3cJ+dSf4uyR2T\nnNTd76qqyuR6fFVVPTST18bvz65yxmoz19xwvyOTPCOT6+z6MyuQpbTJ17qnJnlxfrgT1mFJHpDk\nAVV1Znc/dQblsoT05GO0qurEJP+UScD31O5+vICPGXlSkr9Jcu8kRyY5Yo1/Txzanr2IAllar0hy\n1ySnrwR8STIEyY9J8s0kf6r3KPtDVR2aSah86ySP7e5Lk6S7r+3uP0nykiTHJnnTELTAvnhwkg8n\nuUeSr+yp8RAun53kp5M8vbvflSQ98WdJXprk5Ew+GMNa9umaS5KqulEmvfZ+J8lpsyuNJbavr3Un\nJPn9JH883OcnMnnv97tJrhmaPaWqHjuTalk61d2LrgH2WVXdM8m7k+xI8tvd/cIFl8SSGr7NfW2S\nE7r7exu0+6skv5DkVt39hXnVx/KqqkOSfDvJgUl2dvcVa7T56yQPSfLI7v6fcy6RJVNVz0vy3CTn\ndvexa5w/IskXk1SSJ3T3q+ZbIcuiqt6b5L5J3tvdP7NOm1MyGaVxWZKbd/c1q87fIsnK39u7dvcF\ns6uYsduba26N+9w6yf8ZDk/r7r+YTXUsq718rftIkpet9Te1qh6USUeDZJ2/zbCannyMzhC6vDGT\ngO+cTL7lgFn5XpLH7SHgOyTJQzMZqivgY3/ZmUnAlyS33UPbq2ZcC9vD44btR9Y62d1fTvKx4fDJ\nc6mIZbU3vQyeMWzftjrgS5Lu/lImIzoOSPKb+7E2ltNmerboDcNWbXgNVdUxSb6x3pdm3f23Sf5x\nOLzjfq6NJSXkY4x+L8lNh/1ndfcPFlkMy627L+3ui/fQ7CGZzJlmqC77TXd/NZPhuEny26vPV9X1\nk/xUkisy+aALmzYMUVuZO+gbGzS9cNgeU1U3m21VbFdDD6qfHA4/tEHTlXMnVdWO2VYFsN8dluQ5\ne2iz8jr3IyM6YC1CPkZlWGjjMcPhJ7v7HzdqD3PyC5l8U/f6RRfC0nn5sD2pql5WVdMLZr0okzeH\nTzYfKfvBoVP719ug3Zem9u80o1rgXlP7n92g3cq5ndkdCgKMQne/u7s/uIdmK6M6PjHrelgOQj7G\n5uTsvm4vrKqnVdUbq+q9VfWZqnp3Vf1GVd1wkUWyfQy9Xx6UyTwZhuqyv/1OkncO+09K8oGqul9V\nnZ3khCTHmxeN/eSbSVZ6xt96g3ZXTu0fNrty2OaOntrfaFXK6XN3mFEtAIu0a9i+epFFMB5CPsbm\nxKn9XUk+mOTk7j4ukyXGr8pklbXzqupW8y+PbehhSa4fQ3WZgWEeqocmWVlU4x5J3pVJj5Vj9WZm\nf+nuq7J72Pdxq3qNTvv+1P7Vs62KbezHp/Y3GqI2fU7oDCyVYRqC45N8LslfLbgcRkLIx9jsGrbf\nS/Lg7n7/ypx83X1RkpOSfDyTSepfV1W1kCrZTh6dyVBdIR8z0d3fTfL4JJ+auvl2Sf65qu66mKpY\nUr8/bA9P8sR12kx/gfaV2ZbDNjY9IuNHFt2Y8t117gOwDB6Z5F8lOX14Pwh7JORjbFYW3Pj4sMrf\nDxlWQH3pcHiPJPebV2FsP1X1Y0nuH0N1maGq+jeZTLr8z0nuneSjw6mjk7y/qh64qNpYLt39xuxe\nsf4lVfX4qjowSapqV1WdkeTnV5onuWgBZbI9TK8YfvAG7abPXbluK4CRqaqdmUzb8vzufvei62E8\nhHyMzbXDdqOhG++f2j9uhrXAz2cyQb1efMxEVd0tk2kJvpjklO7+QJJ/m+QPMglZbpDk9VV11PqP\nAnuvu5+d5GeTvCXJs5KcX1XvSPK0JG9O8vmh6YeHFaBhFqavrUM2aDd97uszqgVgEf5rkvd09/MX\nXQjjst58K3Bd9ZUkR2XjIRkXT+3fbLblsM09ethaVZf9rqoOSPKXSW6c5Bnd3cn/77H8jKr6dJJX\nZvIh99lJfmlRtbJchh4DP9JroKpun+SY4fANcy2K7ebTU/s336Dd9LkLZ1QLwFxV1XMymfP7Fxdc\nCiOkJx9jc96w3WhRjelJwa9atxVsQVXdJJPh4Od298V7ag+bcGwmc+9d3t3nrz7Z3X+e3QtyHD/P\nwti2njlsL03yx4sshKX3gan9jXoqr5y7PLunMgAYrap6SiajNh7T3dfuqT2sJuRjbN46bH+8qnat\n0+aIqf1PzrQatrOTM+kN/bpFF8LS+tfDdqOJlldCviM2aANbVlX3SXLKcPis7r58kfWw3Lr7kkzm\nIU2Su2/Q9B7D9u3dbbVnYNSq6tczme/73w0jN2CfCfkYm7Oze86Vk9Zpc/the00mcwrBLBiqy6xd\nMmxvMizyspaV3sqfm0M9bFNVdYskr0lSSV7c3WctuCS2h1cM2wetLAAzraoOT3LP4fCP5lYVwAxU\n1a9k8vn2Eet9aVFVD6yqh823MsZGyMeodPeV2T1c6D9V1aFrNFuZl+oPu/tL86mM7aSqjkhy3xiq\ny2ydk+Tjw/6vr9Pm/sP2lbMvh+1o6DX/95lMk/GiJE9fZD0sjRsP241Wzv3vSc5NctMkj1/j/FMz\n9Kjv7vft3/JYQntzza13n329H6zYq+uuqp6Q5ClJfiPJkVV1m1X/bl9Vpyd5aZK3zbZkxk7Ix+gM\n81D9YZJbJnlzVR25cq6qTstkGOWrkjxnMRWyDTwyk9dPq+oyM939gySPyGQ102dX1eOHxThSVQdU\n1ZOS/Eomi3OcubBCWUo1cUqSD2fyIeWk7n76ygIwsFlVdXySOwyHx1TVHddqN8xF9ahMvuw4c7jf\nymOcksmKz+9J8suzrZix29trbtV9Dkzya1M3PaKqNlrpGX7I3l53VXVqkj/NZB7mC5J8Zo1/n0jy\nZ0nONoyXPSnv1Rirqrp/Jn9875LkW0muzWR42590998ssjaWW1W9P8m9kuzq7n9ZdD0st6q6UZIn\nZfIFxuGZTFmwM5M5R1/Z3X+9wPJYMlV1qyQPziQ42ZHkz5O8YuhJD5s2TCb/H5PsWuP05zMZCv7y\nNe63M5MvNB6byZDxg5J8J8lZSc7q7u+vvg8kW7rm/iGTlcRXT5VxZZKLk/zcMG8k/Ih9ue6q6iFJ\n3py963z1gyRH+ezBngj5AABIVf1UJvMBXZDkgz5IAACMi5APAAAAAEbOnHwAAAAAMHJCPgAAAAAY\nOSEfAAAAAIyckA8AAAAARk7IBwAAAAAjJ+QDAAAAgJET8gEAAADAyAn5AAAAAGDkhHwAAAAAMHJC\nPgAAAAAYOSEfAAAAAIyckA8AAAAARu6gRRcAAMByqaq7JHlUkhOS3DTJ1Um+leTQJJcl+d9JLkhy\nYJIXdPeNFlQqAMDSqO5edA0AACyBqjoiyR8leWSSzyV5dpK3dPflw/kDkhyX5PlJ7rNyv+6u+VcL\nALBchHwAAGxZVR2T5K1JjkzyziQP7+4r12lbSV6Y5LcSIR8AwP4g5AMAYEuGHnznJbl5ks8kufN6\nAd+q+70mySlCPgCArRPyAQCwJVX19iQnDocP7O637+X9Dkvy5e6+3syKAwDYJqyuCwDAplXVfbM7\n4Ptsknfs7X27+xtJ/m4WdQEAbDdCPgAAtuK0qf239z4OE+nuh0wfV9Utq+r3quqfquqcqvpyVV1Q\nVb9ZVQful4oBAJaQkA8AgK34uan987byQFV1nySfSHJSkgd19z2T3CbJF5O8KMkrt/L4AADLTMgH\nAMCmVNWOJD8xddPXtviQZyS5UZLzu/trSdLdVyT53eH8qcMiHwAArCLkAwBgsw5bdfzdLT7eFcN2\n9cq8Xxy2lWTXFp8DAGApHbToAgAAGK3LVx0fusXHOznJ3ZJ8YOWGqqokd51qYyVeAIA1CPkAANiU\n7v52VV2Z5JDhpsO3+HjfSfIPSVJVN0tyepLbJvn0VLPaynMAACwrw3UBANiK90/t322rD1ZVN6uq\n/5bkDUne2d2nJnntVh8XAGDZCfkAANiKN0zt328YXrspVXX3JB9NcmySE7r73K0WBwCwXQj5AADY\nir9I8i/D/tFJ7r+ZB6mq6yd5U5KbJjmzu6/aL9UBAGwTQj4AADatu69O8oQk3xtuOrOqbri396+q\nR1fVsUnul+QWw82XrWp2xNT+gZutFQBgmQn5AADYku5+V5JfTHJ1ktsleUNVbbjSblUdXFUvyCQg\n/Fh+eEG4X62qQ4Z2RyV53tS5n6yqA4aFOQAAGAj5AADYsu7+H0nuleQjmQzZ/VhVPbGqDptuV1U7\nq+rRSd6X5NokD+nuK5J8MMm3hmYPSPLVqrp4uP0/J/nmcO5FSS5NcucZ/0gAAKNS3b3oGgAAWCJV\ndf8kD09y7yQ3T3J5JvP2XZtJWPfuJG/s7q+uut+9Mwnx7pTJkN23JHlBd3+lqn4pyUuG25/b3a+e\n048DADAKQj4AAAAAGDnDdQEAAABg5IR8AAAAADByQj4AAAAAGDkhHwAAAACMnJAPAAAAAEZOyAcA\nAAAAIyfkAwAAAICRE/IBAAAAwMgJ+QAAAABg5IR8AAAAADByQj4AAAAAGDkhHwAAAACMnJAPAAAA\nAEZOyAcAAAAAIyfkAwAAAICRE/IBAAAAwMgJ+QAAAABg5IR8AAAAADByQj4AAAAAGDkhHwAAAACM\n3P8DqIz2QeeyO90AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3a4abd0>"
       ]
      },
-     "execution_count": 49,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1078,28 +1260,25 @@
     "plt.hist(train['Ca'],bins=40)\n",
     "plt.xlabel('Ca')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DzOriXJ3sbzjoquqaSa477G41Zf78qe3NBvJ0mWn47DaL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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf2f4de50>"
       ]
      },
-     "execution_count": 50,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1107,28 +1286,25 @@
     "plt.hist(train['Fe'],bins=40)\n",
     "plt.xlabel('Fe')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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7k3wnyc26+wuLyvck+UKS6yZ5YHf/5Ua+g9uT2YjHZRKo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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf3010d10>"
       ]
      },
-     "execution_count": 51,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1136,14 +1312,12 @@
     "plt.hist(train['Ba'],bins=40)\n",
     "plt.xlabel('Ba')\n",
     "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -1151,19 +1325,19 @@
     {
      "data": {
       "text/plain": [
-       "RI    0.470849\n",
-       "Na    0.769483\n",
-       "Mg   -1.390967\n",
-       "Al    1.198670\n",
-       "Si   -0.560738\n",
-       "K     1.777861\n",
-       "Ca    1.137346\n",
-       "Ba    3.066943\n",
-       "Fe    1.855276\n",
+       "RI    0.610683\n",
+       "Na    0.360411\n",
+       "Mg   -1.328926\n",
+       "Al    1.043716\n",
+       "Si   -0.721044\n",
+       "K     6.615089\n",
+       "Ca    0.946308\n",
+       "Ba    3.336587\n",
+       "Fe    1.849913\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1176,7 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -1185,6 +1359,19 @@
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1204,51 +1391,51 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.00000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>197.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>1.517474</td>\n",
-       "      <td>13.406802</td>\n",
-       "      <td>2.791977</td>\n",
-       "      <td>1.516047</td>\n",
-       "      <td>72.813081</td>\n",
-       "      <td>0.484012</td>\n",
-       "      <td>8.619651</td>\n",
-       "      <td>0.182791</td>\n",
-       "      <td>0.053663</td>\n",
-       "      <td>2.773256</td>\n",
+       "      <td>1.517899</td>\n",
+       "      <td>13.422030</td>\n",
+       "      <td>2.787716</td>\n",
+       "      <td>1.473299</td>\n",
+       "      <td>72.712284</td>\n",
+       "      <td>0.51132</td>\n",
+       "      <td>8.752081</td>\n",
+       "      <td>0.159594</td>\n",
+       "      <td>0.054162</td>\n",
+       "      <td>2.751269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.001324</td>\n",
-       "      <td>0.672000</td>\n",
-       "      <td>1.369472</td>\n",
-       "      <td>0.442586</td>\n",
-       "      <td>0.557500</td>\n",
-       "      <td>0.333308</td>\n",
-       "      <td>0.813593</td>\n",
-       "      <td>0.479453</td>\n",
-       "      <td>0.093852</td>\n",
-       "      <td>2.116707</td>\n",
+       "      <td>0.001956</td>\n",
+       "      <td>0.700471</td>\n",
+       "      <td>1.377272</td>\n",
+       "      <td>0.489300</td>\n",
+       "      <td>0.670696</td>\n",
+       "      <td>0.66570</td>\n",
+       "      <td>0.940938</td>\n",
+       "      <td>0.451968</td>\n",
+       "      <td>0.094399</td>\n",
+       "      <td>2.093250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>1.514090</td>\n",
-       "      <td>11.950000</td>\n",
+       "      <td>1.512990</td>\n",
+       "      <td>11.030000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.290000</td>\n",
        "      <td>69.890000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>5.790000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -1256,51 +1443,51 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>1.516450</td>\n",
-       "      <td>12.877500</td>\n",
-       "      <td>2.717500</td>\n",
-       "      <td>1.250000</td>\n",
-       "      <td>72.537500</td>\n",
-       "      <td>0.365000</td>\n",
-       "      <td>8.210000</td>\n",
+       "      <td>1.516510</td>\n",
+       "      <td>12.930000</td>\n",
+       "      <td>2.410000</td>\n",
+       "      <td>1.210000</td>\n",
+       "      <td>72.370000</td>\n",
+       "      <td>0.15000</td>\n",
+       "      <td>8.220000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>1.517490</td>\n",
-       "      <td>13.260000</td>\n",
-       "      <td>3.480000</td>\n",
-       "      <td>1.430000</td>\n",
-       "      <td>72.865000</td>\n",
-       "      <td>0.570000</td>\n",
-       "      <td>8.530000</td>\n",
+       "      <td>1.517610</td>\n",
+       "      <td>13.290000</td>\n",
+       "      <td>3.490000</td>\n",
+       "      <td>1.400000</td>\n",
+       "      <td>72.830000</td>\n",
+       "      <td>0.56000</td>\n",
+       "      <td>8.570000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>1.518295</td>\n",
-       "      <td>13.735000</td>\n",
-       "      <td>3.582500</td>\n",
-       "      <td>1.662500</td>\n",
-       "      <td>73.110000</td>\n",
-       "      <td>0.610000</td>\n",
-       "      <td>8.845000</td>\n",
+       "      <td>1.518720</td>\n",
+       "      <td>13.870000</td>\n",
+       "      <td>3.610000</td>\n",
+       "      <td>1.630000</td>\n",
+       "      <td>73.100000</td>\n",
+       "      <td>0.61000</td>\n",
+       "      <td>9.060000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.090000</td>\n",
        "      <td>3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>1.521010</td>\n",
+       "      <td>1.523000</td>\n",
        "      <td>15.150000</td>\n",
        "      <td>4.490000</td>\n",
        "      <td>3.500000</td>\n",
        "      <td>75.180000</td>\n",
-       "      <td>2.700000</td>\n",
+       "      <td>6.21000</td>\n",
        "      <td>12.500000</td>\n",
        "      <td>2.880000</td>\n",
        "      <td>0.510000</td>\n",
@@ -1311,28 +1498,28 @@
        "</div>"
       ],
       "text/plain": [
-       "               RI          Na          Mg          Al          Si           K  \\\n",
-       "count  172.000000  172.000000  172.000000  172.000000  172.000000  172.000000   \n",
-       "mean     1.517474   13.406802    2.791977    1.516047   72.813081    0.484012   \n",
-       "std      0.001324    0.672000    1.369472    0.442586    0.557500    0.333308   \n",
-       "min      1.514090   11.950000    0.000000    0.290000   69.890000    0.000000   \n",
-       "25%      1.516450   12.877500    2.717500    1.250000   72.537500    0.365000   \n",
-       "50%      1.517490   13.260000    3.480000    1.430000   72.865000    0.570000   \n",
-       "75%      1.518295   13.735000    3.582500    1.662500   73.110000    0.610000   \n",
-       "max      1.521010   15.150000    4.490000    3.500000   75.180000    2.700000   \n",
+       "               RI          Na          Mg          Al          Si          K  \\\n",
+       "count  197.000000  197.000000  197.000000  197.000000  197.000000  197.00000   \n",
+       "mean     1.517899   13.422030    2.787716    1.473299   72.712284    0.51132   \n",
+       "std      0.001956    0.700471    1.377272    0.489300    0.670696    0.66570   \n",
+       "min      1.512990   11.030000    0.000000    0.290000   69.890000    0.00000   \n",
+       "25%      1.516510   12.930000    2.410000    1.210000   72.370000    0.15000   \n",
+       "50%      1.517610   13.290000    3.490000    1.400000   72.830000    0.56000   \n",
+       "75%      1.518720   13.870000    3.610000    1.630000   73.100000    0.61000   \n",
+       "max      1.523000   15.150000    4.490000    3.500000   75.180000    6.21000   \n",
        "\n",
        "               Ca          Ba          Fe        Type  \n",
-       "count  172.000000  172.000000  172.000000  172.000000  \n",
-       "mean     8.619651    0.182791    0.053663    2.773256  \n",
-       "std      0.813593    0.479453    0.093852    2.116707  \n",
+       "count  197.000000  197.000000  197.000000  197.000000  \n",
+       "mean     8.752081    0.159594    0.054162    2.751269  \n",
+       "std      0.940938    0.451968    0.094399    2.093250  \n",
        "min      5.790000    0.000000    0.000000    1.000000  \n",
-       "25%      8.210000    0.000000    0.000000    1.000000  \n",
-       "50%      8.530000    0.000000    0.000000    2.000000  \n",
-       "75%      8.845000    0.000000    0.090000    3.000000  \n",
+       "25%      8.220000    0.000000    0.000000    1.000000  \n",
+       "50%      8.570000    0.000000    0.000000    2.000000  \n",
+       "75%      9.060000    0.000000    0.090000    3.000000  \n",
        "max     12.500000    2.880000    0.510000    7.000000  "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1345,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -1354,6 +1541,19 @@
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1368,27 +1568,27 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>55</td>\n",
+       "      <td>67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>64</td>\n",
+       "      <td>67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>14</td>\n",
+       "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>7</td>\n",
+       "      <td>12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>7</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>25</td>\n",
+       "      <td>26</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1397,15 +1597,15 @@
       "text/plain": [
        "col_0  count\n",
        "Type        \n",
-       "1         55\n",
-       "2         64\n",
-       "3         14\n",
-       "5          7\n",
-       "6          7\n",
-       "7         25"
+       "1         67\n",
+       "2         67\n",
+       "3         17\n",
+       "5         12\n",
+       "6          8\n",
+       "7         26"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1416,504 +1616,414 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\hpp\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
-      "  RuntimeWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>RI</th>\n",
-       "      <th>Na</th>\n",
-       "      <th>Mg</th>\n",
-       "      <th>Al</th>\n",
-       "      <th>Si</th>\n",
-       "      <th>K</th>\n",
-       "      <th>Ca</th>\n",
-       "      <th>Ba</th>\n",
-       "      <th>Fe</th>\n",
-       "      <th>Type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1.720000e+02</td>\n",
-       "      <td>1.720000e+02</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>1.720000e+02</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>172.000000</td>\n",
-       "      <td>138.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.507953e-03</td>\n",
-       "      <td>1.697161e-01</td>\n",
-       "      <td>15.423279</td>\n",
-       "      <td>0.806488</td>\n",
-       "      <td>1.009147e+17</td>\n",
-       "      <td>0.376867</td>\n",
-       "      <td>1.554420</td>\n",
-       "      <td>0.023517</td>\n",
-       "      <td>0.016929</td>\n",
-       "      <td>1.811594</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.478820e-17</td>\n",
-       "      <td>6.588088e-09</td>\n",
-       "      <td>8.266208</td>\n",
-       "      <td>0.132788</td>\n",
-       "      <td>7.252737e+15</td>\n",
-       "      <td>0.220777</td>\n",
-       "      <td>0.037008</td>\n",
-       "      <td>0.047944</td>\n",
-       "      <td>0.025632</td>\n",
-       "      <td>0.892391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>3.507953e-03</td>\n",
-       "      <td>1.697161e-01</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.246425</td>\n",
-       "      <td>6.823150e+16</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.390330</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>3.507953e-03</td>\n",
-       "      <td>1.697161e-01</td>\n",
-       "      <td>11.654279</td>\n",
-       "      <td>0.731420</td>\n",
-       "      <td>9.711826e+16</td>\n",
-       "      <td>0.313961</td>\n",
-       "      <td>1.536844</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.507953e-03</td>\n",
-       "      <td>1.697161e-01</td>\n",
-       "      <td>19.282693</td>\n",
-       "      <td>0.793182</td>\n",
-       "      <td>1.013635e+17</td>\n",
-       "      <td>0.457031</td>\n",
-       "      <td>1.552295</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>3.507953e-03</td>\n",
-       "      <td>1.697161e-01</td>\n",
-       "      <td>20.488781</td>\n",
-       "      <td>0.864928</td>\n",
-       "      <td>1.046472e+17</td>\n",
-       "      <td>0.482876</td>\n",
-       "      <td>1.566831</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.046975</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>3.507953e-03</td>\n",
-       "      <td>1.697161e-01</td>\n",
-       "      <td>33.193077</td>\n",
-       "      <td>1.245689</td>\n",
-       "      <td>1.364329e+17</td>\n",
-       "      <td>1.359280</td>\n",
-       "      <td>1.699600</td>\n",
-       "      <td>0.130503</td>\n",
-       "      <td>0.062953</td>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 RI            Na          Mg          Al            Si  \\\n",
-       "count  1.720000e+02  1.720000e+02  172.000000  172.000000  1.720000e+02   \n",
-       "mean   3.507953e-03  1.697161e-01   15.423279    0.806488  1.009147e+17   \n",
-       "std    1.478820e-17  6.588088e-09    8.266208    0.132788  7.252737e+15   \n",
-       "min    3.507953e-03  1.697161e-01    0.000000    0.246425  6.823150e+16   \n",
-       "25%    3.507953e-03  1.697161e-01   11.654279    0.731420  9.711826e+16   \n",
-       "50%    3.507953e-03  1.697161e-01   19.282693    0.793182  1.013635e+17   \n",
-       "75%    3.507953e-03  1.697161e-01   20.488781    0.864928  1.046472e+17   \n",
-       "max    3.507953e-03  1.697161e-01   33.193077    1.245689  1.364329e+17   \n",
-       "\n",
-       "                K          Ca          Ba          Fe        Type  \n",
-       "count  172.000000  172.000000  172.000000  172.000000  138.000000  \n",
-       "mean     0.376867    1.554420    0.023517    0.016929    1.811594  \n",
-       "std      0.220777    0.037008    0.047944    0.025632    0.892391  \n",
-       "min      0.000000    1.390330    0.000000    0.000000    1.000000  \n",
-       "25%      0.313961    1.536844    0.000000    0.000000         NaN  \n",
-       "50%      0.457031    1.552295    0.000000    0.000000         NaN  \n",
-       "75%      0.482876    1.566831    0.000000    0.046975         NaN  \n",
-       "max      1.359280    1.699600    0.130503    0.062953    5.000000  "
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from scipy.stats import boxcox\n",
-    "features_bc = []\n",
+    "# from scipy.stats import boxcox\n",
+    "# features_bc = []\n",
     "\n",
-    "for feature in feat:\n",
-    "    bc_transformed, _ = boxcox(train[feature]+1)  # shift by 1 to avoid computing log of negative values\n",
-    "    features_bc.append(bc_transformed)\n",
+    "# for feature in feat:\n",
+    "#     bc_transformed, _ = boxcox(train[feature]+1)  # shift by 1 to avoid computing log of negative values\n",
+    "#     features_bc.append(bc_transformed)\n",
     "\n",
-    "features_boxcox = np.column_stack(features_bc)\n",
-    "df_bc = pd.DataFrame(data=features_boxcox, columns=feat)\n",
-    "df_bc['Type'] = train['Type']\n",
-    "df_bc.describe()"
+    "# features_boxcox = np.column_stack(features_bc)\n",
+    "# df_bc = pd.DataFrame(data=features_boxcox, columns=feat)\n",
+    "# df_bc['Type'] = train['Type']\n",
+    "# df_bc.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RI    0.000000\n",
-      "Na    0.000000\n",
-      "Mg   -1.002918\n",
-      "Al   -0.035515\n",
-      "Si    0.117081\n",
-      "K     0.007275\n",
-      "Ca   -0.055444\n",
-      "Ba    1.635453\n",
-      "Fe    0.904664\n",
-      "dtype: float64\n",
-      "\n",
-      "\n",
-      "RI    0.470849\n",
-      "Na    0.769483\n",
-      "Mg   -1.390967\n",
-      "Al    1.198670\n",
-      "Si   -0.560738\n",
-      "K     1.777861\n",
-      "Ca    1.137346\n",
-      "Ba    3.066943\n",
-      "Fe    1.855276\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "# print(df_bc[feat].skew())\n",
+    "# print(\"\\n\")\n",
+    "# print(train[feat].skew())\n",
+    "#boxcox helped a lot in reducing skewness and all the variables are on same scale now so no leverage to any one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# df_bc.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
-    "print(df_bc[feat].skew())\n",
-    "print(\"\\n\")\n",
-    "print(train[feat].skew())\n",
-    "#boxcox helped a lot in reducing skewness and all the variables are on same scale now so no leverage to any one.\n"
+    "# df_bc['Type'].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# df_bc.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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6JJuGi++95MAAAAAAsAKUjst3+krH+xfboKo2ZP4Kx99prR1cbLvWWkvyP4aL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\ndpJ7Wmsf6eiwpx+sM3uRbU4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hQ+cAAJg0VXVUVT196Byw3KrqsKo6uapeVFWv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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "<matplotlib.figure.Figure at 0x7febf39b4e50>"
       ]
      },
-     "execution_count": 66,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FixfvPYZJtb6+HhERTz/9dOEkAADQDiUfren3+zE/P186xljo9XoxMzNTOgZjam5urnQE\nKGowGMSVK1cipRRPPfWUwhsAgLFgqhDAhEkpRUqpdAwo5saNG3H79u144YUX4saNG6XjAABAK5R8\nAMDEuHPnTqyurt57vrq6Gi+++GLBRAAA0A4lHwAwMa5fvx6DweDe88FgENeuXSuYCAAA2mFNPlrT\nNE30er3SMfa0ubkZi4uLO8Zu3rwZ09PThRLdX9M0pSMAjJ1bt27F2trarvG1tbV45plnYnZ2tkAq\nAABoh5KP1nQ6nao2i5ienq4qLwAPL+ccly9f3vf4pUuX4rnnnrNeJQAA1XK7LgAw9tbX12NjY2Pf\n4xsbG7G+vn6MiQAAoF1KPgBgrA0Gg7hy5coDz7t69eqO9foAAKAmSj6ACZNzjpxz6RgAAAC0SMkH\nMGHW19fdlshE6Xa7cfHixQeed+HCheh2u8eQCAAA2qfkA5ggb9y26LZEJs3c3FycPXt23+Nnz56N\nubm5Y0wEAADtsrsuwAS5ceNG3L59+97jZ599tnAiOB4ppVhZWdn3ml9ZWbGzLlCl4XC4a+x1n+Nx\nBPa6rva6/oBylHwAE+LOnTuxurp67/nq6mq8733vi9OnTxdMBcdndnY2FhYWYm1tbcf4wsJCzM7O\nFkoFcDhbW1u7xj7+05Ye4HhsbW3FqVOnSscAtrldF2BCXL9+fcctuoPBIK5du1YwERy/c+fO7Vh3\nr9vtxvnz5wsmAgCAdij5ACbArVu3ds1eiohYW1uL559/vkAiKOPMmTOxvLx87/ny8rLZrAAAjAW3\n6wKMuZxzXL58ed/jly5diueee856ZEyMpaWl+MhHPhIppVhaWiodBwBo2ScH5jM9yDBHvPzKzv//\nf+zRHB1/JNhXDdeVkg9gzK2vr8fGxsa+xzc2NmJ9fT2efvrpY0wF5XS73bh48eK9xwA1m5qa2jX2\nRV89iBP+9UbLXh/sXu9xr+tvFHzfT5ilz2RS8gGMscFgEFeuXHngeVevXo2nnnpK4cHEmJubKx0B\noBWdzu6ZJSe6oeTjWOx1/QHlKPkAgInj9nQAAMaN2h1gjL35tsT7uXDhgll8AAAAFTOTD2DMzc3N\nxYc//OF91+U7e/asWxcBAKhS0zTR6/VKx6jO5uZmLC4u7hi7efNmTE9PF0pUp6ZpSkfYQckHMOZS\nSrGyshLPPvvsnsdXVlbcuggAQJU6nU7MzMyUjjEWpqen/S4r53ZdgAkwOzsbCwsLu8YXFhZidna2\nQCIAAADapOQDmBDnzp3bse5et9uN8+fPF0wEAABAW9yuCzAhzpw5E8vLy/Hcc89FRMTy8nKcPn26\ncCrG0XA4jH6/XzrGnobDYWxtbe0Ym5qaik5n9D73bJpmJHMBADCalHwAE2RpaSk+8pGPREoplpaW\nSsdhTPX7/Zifny8do3q9Xs+6OAAAHJiSD2CCdLvduHjx4r3HAAAAjAclH8CEmZubKx0BAACAlin5\nACZMSql0BAAAAFqm5AMAWtU0TfR6vdIx9rS5uRmLi4s7xm7evBnT09OFEu2vaZrSEQAAqIiSDwBo\nVafTqWrDiOnp6aryAgDAXjqlAwAAAAAAh6PkAwAAAIDKKfkAAAAAoHJKPgAAAAConJIPAAAAACqn\n5AMAAACAyin5AAAAAKBySj4AAAAAqJySDwAAAAAqp+QDAAAAgMop+QAAAACgcidKBwAAAKA9rw9S\nROTSMUZWzhGffmXn2NsejUipTJ5a3L2ugFGm5AMAABgjH//pR0tHAKAAt+sCAMCEyTlHzmZ6AcA4\nGfuSL6XUSSl9aekcAAAwKtbX12N9fb10DACgRWNb8qWUTqSUvi4ifiMiLj/kezydUvp0SunrWw0H\nAACFDAaDuHLlSly9ejUGg0HpOABAS8ZuTb6U0smI+LqI+PaI+ILt4d9/iPf57Ij4sRjjIhQAgMlz\n48aNuH379r3Hzz77bOFEHEbTNNHr9UrHqMrm5mYsLi7uGLt582ZMT08XSlSvpmlKRwDeZOxKvoj4\nqoj4lYh4b0T8ekQ8+VbfIKX0toi4GRF/rN1oAABQzp07d2J1dfXe89XV1Xjf+94Xp0+fLpiKw+h0\nOjEzM1M6RvWmp6f9HoHqjd0stZzzT+WcP5Zz/oOI+PhDvs33xN3bfH+vvWQAAFDW9evXd9yiOxgM\n4tq1awUTAQBtGbuS7zO85S3DUkpPR8RCRKy0HwcAAMq4detWrK2t7RpfW1uL559/vkAiAKBN417y\nvSXb6/Bdj4ivzTlbhRgAgLGQc47Ll/ffi+7SpUuR81v+fBwAGCFKvm3b6/DdiIjvyjk/7G2+AAAw\nctbX12NjY2Pf4xsbG7G+vn6MiQCAtin5/sh3R8Tv5Jx/rHQQAABoy2AwiCtXrjzwvKtXr+5Yrw8A\nqMs47q77lm2vw/dfRMSfK50FAAAAAN6qiZ/Jt70O33MRsZhzfrml97y111dEvKuN9wcAgIPqdrtx\n8eLFB5534cKF6Ha7x5AIADgKEz2T703r8H1vzvlW6TxA/YbDYfT7/dIx9jQcDmNra2vH2NTUVHQ6\no/l5T9M0I5sNoDZzc3Px4Q9/eN91+c6ePRtzc3PHnAoAaNNEl3xxdx2+38s5/3Cbb5pzfvde49uz\n+Wbb/FnAaOn3+zE/P186xljo9XoxMzNTOgbAWEgpxcrKSjz77LN7Hl9ZWYmU0jGnAgDaNOlTJL4j\nIr48pfSbe31FxL+3fd7fe9P4XyyYFwAAHsrs7GwsLCzsGl9YWIjZWZ9DA0DtJn0mXyci/v0DnPfk\n9ldERHN0cQAA4OicO3cuPvrRj97bRbfb7cb58+cLpwIA2jDRM/lyzul+XxHxL7ZP/cCbxj9UMDIA\nADy0M2fOxPLy8r3ny8vLcfr06YKJAIC2TPpMPoBWNU0TvV6vdIw9bW5uxuLi4o6xmzdvxvT0dKFE\n99c0Jk4DHIWlpaX4yEc+EimlWFpaKh0HAGjJuJd8b6zYfrJoCmBidDqdqjaLmJ6eriovAIfX7Xbj\n4sWL9x4DAONhbEu+lNJXRMQbu9y+J6V0Nue8UTITAACMgrm5udIRAICWjd2afCmlD6aUPhERH42I\nt20PPx4Rv5ZS+kRK6UK5dAAAUF5KKVJKpWMAAC0au5l8OefLEXG5pff6/DbeBwAAAACO0tjN5AMA\nAACASaPkAwAAAIDKKfkAAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyp0oHQAAAABg3AyHw+j3\n+6Vj7Glzc/NAY6OiaZrodMxTexAlHwAAAEDL+v1+zM/Pl45xYIuLi6Uj7KvX68XMzEzpGCNPDQoA\nAAAAlVPyAQAAAEDllHwAAAAAUDlr8gEAAAC0rGma6PV6pWPsaTgcxtbW1o6xqampkd3comma0hGq\noOQDAAAAaFmn0xnpzSJOnTpVOgItG82KFgAAAAA4MCUfAAAAAFROyQcAAAAAlVPyAQAAAEDllHwA\nAAAAUDm76wIAANCq4XAY/X6/dIw9bW5uHmhsVDRNE52O+TnAgyn5AAAAaFW/34/5+fnSMQ5scXGx\ndIR99Xq9mJmZKR0DqICPAwAAAACgcko+AAAAAKickg8AAAAAKmdNPgAAAFrVNE30er3SMfY0HA5j\na2trx9jU1NTIbm7RNE3pCEAllHwAAAC0qtPpjPRmEadOnSodAaB1o/lRBQAAAABwYEo+AAAAAKic\nkg8AAAAAKqfkAwAAAIDKKfkAAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyin5AAAAAKBySj4A\nAAAAqJySDwAAAAAqp+QDAAAAgMop+QAAAACgcko+AAAAAKickg8AAAAAKqfkAwAAAIDKnSgdAAB4\n64bDYfT7/dIxqrO5uXmgMe6vaZrodHxWDAAwSpR8AFChfr8f8/PzpWOMhcXFxdIRqtPr9WJmZqZ0\nDAAA3sRHsAAAAABQOSUfAAAAAFROyQcAAAAAlbMmHwCMiS977wfikZNvLx1jpOWc47XXBzvGTp7o\nRkqpUKLR9+prn4pf/OUfKR0DAIAHUPIBwJh45OTb45FHHisdY+Q9+ujjpSMAAEDr3K4LAAAAAJVT\n8gEAAABA5ZR8AAAAAFA5JR8AAAAAVE7JBwAAAACVU/IBAAAAQOWUfAAAAABQOSUfAAAAAFROyQcA\nAAAAlVPyAQAAAEDllHwAAAAAUDklHwAAAABUTskHAAAAAJVT8gEAAABA5U6UDsBbNxwOo9/vl45R\nlc3NzQON8WBN00Sn4/MBAAAAGCVKvgr1+/2Yn58vHaN6i4uLpSNUqdfrxczMTOkYAAAAwJuYjgMA\nAAAAlVPyAQAAAEDllHwAAAAAUDlr8o2JT86/J/IjJ0vHGF05R7z6+s6xR05EpFQmTyXSq6/F471f\nLR0DAAAAeAAl35jIj5yM6Cr57uvtj5ROUJ1cOgAAAABwIG7XBQAAAIDKKfkAAAAAoHJKPgAAAACo\nnJIPAAAAACqn5AMAAACAytldF6jOcDiMfr9fOkZ1Njc3DzTG/TVNE51O+c/IhsPhrrFXX/tUgSSM\nu72uq72uPwAAylLyAdXp9/sxPz9fOsZYWFxcLB2hOr1eL2ZmZkrHiK2trV1jv/jLP1IgCZNoa2sr\nTp06VToGAABvUn4qAgAAAABwKEo+AAAAAKickg8AAAAAKmdNPmAsrPzJO/HYCQvB30/OES9/Ou0Y\ne+xtOVLa5wXEy6934tJvnSkdY09TU1O7xr7svR+IR06+vUAaxtmrr31q13qPe11/AACUpeQDxsJj\nJ4bxDiXfA73zZOkEtGWvHX4fOfn2eOSRxwqkYdKMwg7To25Ud4IfDoe7Nu6Zmpoa2b+no7KjOQDU\nQMkHAAAtsxN8O0ZlR3MAqIGPxQAAAACgcko+AAAAAKickg8AAAAAKmdNPgAAaFnTNNHr9UrH2GVz\nczMWFxd3jN28eTOmp6cLJbq/pmlKRwCAaij5AACgZZ1Op5oNI6anp6vJCgDsz+26AAAAAFA5JR8A\nAAAAVE7JBwAAAACVU/IBAAAAQOWUfAAAAABQOSUfAAAAAFROyQcAAAAAlVPyAQAAAEDllHwAAAAA\nUDklHwAAAABUTskHAAAAAJVT8gEAAABA5ZR8AAAAAFA5JR8AAAAAVO5E6QAAQDtefe1TpSOMvJxz\nvPb6YMfYyRPdSCkVSjT6XFcAAHVQ8gHAmPjFX/6R0hEAAIBC3K4LAAAAAJVT8gEAAABA5ZR8AAAA\nAFA5a/IBQIWapoler1c6RnU2NzdjcXFxx9jNmzdjenq6UKI6NU1TOgIAAJ9ByQdUZzgc7hr75Osp\nTE6mbXevq532uv5K6HQ6MTMzUzrGWJienva7BACgekq+Cu35B8xXXjv+IIy/Pa6rUSg4tra2do1d\n/q0nCiRhEm1tbcWpU6dKxwAAANhByVehvQqOd/wfv1ogCZNIwQEAAACjx71tAAAAAFC5YiVfSumz\nU0r/NKV0ulQGAAAAABgHJW/X/YOI+OKI+MGIeH/BHEBlpqamdo198E/ejsdP5AJpGGeffD3tWu9x\nr+sPAACgtCMp+VJKfyoi/npE/ImIeCQiPnN7wrdFxBdGxOmI+MqjyDDO9voD5ktf+Z6IR08WSMNY\ne+W1Xes9jkLB0ensnoT8+Ikc7zhRflMQxs3ua22v6w8AAKC01ku+lNKXRMQvRMTjsbvc28vH2s4w\n7vb8A+ajJyO6Sj6OnoIDAAAARs9RzOT7roh4e0T8bET8XkR83vb4777pnM+OiPdGxHdHxIeOIAMA\nAAAATIyjKPm+PCK+Muf8sxERKaXPioi/lXP+b958Ukrpr0XEn8k5bx1BBgAAAACYGEdx392/faPg\ni4jIOf+biDiVUup+xnn/c0R8bUrpvzqCDAAAAAAwMY6i5Pt3e4x9OCK+5c0DOedPR0Q/Ir75CDIA\nAAAAwMQ4ipKvn1I6n1L6wpTSn4iIyDn/k4j4mpTSX3rjpJTSXER8TkR86RFkAAAAAICJcRRr8v1w\nRPyjiMgR8UpK6d05509ExPdFxE+klP5e3J3t96e3z7cmHwAAAAAcQusz+XLOPxkR/1tEpIjoRsQ7\ntsf/cUT8SER8ftydvfe27Zf8eNsZAAAAAGCSHMVMvoiIZyLiL0TEyznnjTcGc87/dUrpYxHxV7Z/\ndi8i/s5COmtuAAAgAElEQVQRZQAAAACAifDQJV9K6ULO+epex3LOOSLW9zn2DyPiHz7szwUAAAAA\ndjrMTL7vTSn94HahBwAAx2o4HEa/3y8doyqbm5sHGuPBmqaJTuco9jEEgIdzmJKviYi/GRH/oKUs\nAABwYP1+P+bn50vHqN7i4mLpCFXq9XoxMzNTOgYA3HPYNfm+P6X0rRHxOxHxqbi7o+5b9amc81cd\nMgcAAAAATKzDlnwvR8TPRcQn4m7J9yDviYj/Mu6WgSki7mw/BwAAAAAe0mFLvv885/xPD3JiSun9\nEfG34o8Kvl/ffv3vHjIDAAAAAEy0w5R8/+YtFHzfGxHf/sbTiFiLiK/NOb90iJ9/ICmlTkT86Zzz\nx476ZwEAUNZ3dZ+Od6RHS8cYWcOc4+V4dcfYY/FIdFIqlKgOL+VX4nsGP1M6BgDc12FKvq9/0Akp\npXdGxI2IWIi75V5ExP8YEX8j5zw8xM9+oJTSiYj4yxHxHRHx+xHxnz7g/C+Mu0Xk0xHxRERsRcTP\nR8TfzTn/s6PMCgBAO96RHo13Kvn2lyKa6JZOAQAcgYfe8z3nvHa/49ul2S/FHxV8r0fEN+WcP3iU\nBV9K6WRK6Rsi4v+JiA9FxBcd4DVfHhEfi4iv2x4aRMSpuLte4C+klGzbBgAAAMDIeuiS7362S7Ff\nioj/IO4WfP2IWMg5Xz+Kn/cZvioifiUi3hsRLzzo5JTS50bEP4qIH4iIz8o5f27OeSoi/vz2609E\nxJWjiwsAAAAAh9N6yZdS+raI+OmIaOJuwfc7EfFlOef1tn/WXnLOP5Vz/ljO+Q8i4uMHeMm3RMSF\nnPN35pz/3Zve5+ci4lu3n35BSunMEcQFAAAAgEM77O6696SU3h4RPxIR748/Wn/v5yPia7YLtxLy\n/Q6mlFJE3Mo5/+N9Tvnn29+HcXeNPgAAAAAYOa3M5EspfV5E/F+xs+D7XyJirmDB90D5rufuc8rp\n7e8/lXMeHEcmAAAAAHirDl3ypZT+k4j4ZxHxnrhb8OWI+I6c8wdyzq8d4PWPHTbDEfr6iPjtiPim\nwjkAAAAAYF+HKvlSShcj4p/E3RlvKSJejoj355z/+7fwNsexGcdbllL6GxHxH0bEn8053y6dBwAA\nAAD289AlX0rphyPif4iIk9tD/zoi/uOc80++hfd4Z0TMP2yGtqWUOiml/yyl9HNxd7fdd0fE/5lS\nenfhaAAAAACwr8NsvPGBuHtrbo6I1yLiByPiS1JKX3KA1z4SEZ8TEc9ExB87RIa2/bcRMRsRj8bd\nv6aTEfFnI+KXUkp/Luf86wd5k5TSrX0OvauVlAAAAADwJm3srns7Il6JiL/6Fl7zaER81vbPv+8O\nuMcp5/x33nicUvrjEfG9cbfMfDwivj8ivqpQNAAAAADY12FLvh+LiA/knIdv9YUppRMRsRgRHzpk\nhiORc/7XEfHs9sYgfyki5lJKj+ScXz3Aa/e8vXd7ht9su0kBAAAAmHSH2XjjDyPiGx+m4IuIyDm/\nnnP+0Yj4hUNkOA4/sP39kbg7+xAAAAAARsphSr7VnPMrLWT4X1t4j6P0G296vFUsBQAAAADs46FL\nvpzzX28jQM75ehvvc4Te2D34+ZzzS0WTAAAAAMAeDjOTb1L8+e3vV4qmAAAAAIB9tLG77iib2f5+\ncr8TUkoX4u5uvx/KOf/bzzj2zri7w+7/HhE/dFQhAQAAAOAwxrbkSyl9RUS8scvte1JKZ3POG59x\nzufFH83Q++9SSj8QET8eES9ExH8UEX8vIn42IlYedoMR4Hi8/LqJyQ+Sc8TLn047xh57W46U9nkB\nrisAAKAaY1fypZQ+GBHfHBGf/6bhxyPi11JK/19E/IOc89WIiJzz76aUvi0ino2Iz42Ivx0Rfy0i\nfjMifi4i3p9z/hfHlx54WJd+60zpCAAAAFDM2JV8OefLEXH5LZz//RHx/UeXCACAozAc7r7R4qX8\nSoEkjLu9rqu9rj8AKGnsSj4AACbD1tbWrrHvGfxMgSRMoq2trTh16lTpGABwj8WGAAAAAKByZvIB\n1WmaJnq9XukY1dnc3IzFxcUdYzdv3ozp6elCierUNE3pCAAAALso+YDqdDqdmJmZKR1jLExPT/td\nAgAAjAElHwAAVZqamto19l3dp+Md6dECaRhnL+VXdq33uNf1BwAlKfkAAKhSp7N7eel3pEfjnUo+\njsFe1x8AlOS/TAAAAABQOSUfAAAAAFTO7boAQKuGw2H0+/3SMfa0ubl5oLFR0DSN2wEBADgwJR8A\n0Kp+vx/z8/OlYxzY4uJi6Qh76vV6dr8GAODAfDwMAAAAAJVT8gEAAABA5ZR8AAAAAFA5a/IBAK1q\nmiZ6vV7pGHsaDoextbW1Y2xqamokN7homqZ0BAAAKqLkGxPp1dcilw4xynKOePX1nWOPnIhIqUye\nSqRXXysdAahQp9MZ6Q0jTp06VToCAAC0Tsk3Jh7v/WrpCAAAAAAUMnr3pgAAAAAAb4mSDwAAAAAq\np+QDAAAAgMpZk69Co7xr4aja3NyMxcXFHWM3b96M6enpQonqZbdHAAAAGD1KvgqN+q6FtZienvZ7\nBAAAAMaC23UBAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyin5AAAAAKBySj4AAAAAqJySDwAA\nAAAqp+QDAAAAgMop+QAAAACgcidKBwAAgLa8lF8pHWGkDXOOl+PVHWOPxSPRSalQojq4rgCogZIP\nAICx8T2DnykdAQCgCLfrAgAAAEDllHwAAAAAUDklHwAAAABUzpp8AABUqWma6PV6pWNUZXNzMxYX\nF3eM3bx5M6anpwslqlfTNKUjAMAOSj4AAKrU6XRiZmamdIzqTU9P+z0CwBhwuy4AAAAAVE7JBwAA\nAACVU/IBAAAAQOWUfAAAAABQORtvAABAy4bDYfT7/dIxdtnc3DzQ2KhomiY6HfMSAOAglHwAANCy\nfr8f8/PzpWMcyOLiYukI++r1enb+BYAD8rEYAAAAAFROyQcAAAAAlVPyAQAAAEDlrMkHAAAta5om\ner1e6Ri7DIfD2Nra2jE2NTU1sptbNE1TOgIAVEPJBwAALet0OiO7YcSpU6dKRwAAjsBofmQHAAAA\nAByYkg8AAAAAKqfkAwAAAIDKKfkAAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyin5AAAAAKBy\nSj4AAAAAqJySDwAAAAAqp+QDAAAAgMop+QAAAACgcko+AAAAAKickg8AAAAAKqfkAwAAAIDKKfkA\nAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyin5AAAAAKBySj4AAAAAqNyJ0gEYH8PhMPr9fukY\ne9rc3DzQ2KhomiY6HR08AAAAcDBKPlrT7/djfn6+dIwDW1xcLB1hX71eL2ZmZkrHAAAAACphqhAA\nAAAAVE7JBwAAAACVU/IBAAAAQOWsyUdrmqaJXq9XOsaehsNhbG1t7Ribmpoa2c0tmqYpHQEAAACo\niJKP1nQ6nZHeLOLUqVOlIwAAAAAcidGcxgQAAAAAHJiSDwAAAAAqp+QDAAAAgMop+QAAAACgcko+\nAAAAAKickg8AAAAAKqfkAwAAAIDKKfkAAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyin5AAAA\nAKBySj4AAAAAqJySDwAAAAAqp+QDAAAAgMop+QAAAACgcko+AAAAAKickg8AAAAAKqfkAwAAAIDK\nKfkAAAAAoHJKPgAAAAConJIPAAAAACqn5AMAAACAyin5AAAAAKBySj4AAAAAqJySDwAAAAAqp+QD\nAAAAgMop+QAAAACgcko+AAAAAKickg8AmDg558g5l44BAACtOVE6AMA4GQ6H0e/3S8fY0+bm5oHG\nRkXTNNHp+CyKo7G+vh4REU8//XThJAAA0A4lH0CL+v1+zM/Pl45xYIuLi6Uj7KvX68XMzEzpGIyh\nwWAQV65ciZRSPPXUU9HtdktHAgCAQzNFAgCYKDdu3Ijbt2/HCy+8EDdu3CgdBwAAWqHkAwAmxp07\nd2J1dfXe89XV1XjxxRcLJgIAgHYo+QCAiXH9+vUYDAb3ng8Gg7h27VrBRAAA0A5r8gG0qGma6PV6\npWPsaTgcxtbW1o6xqampkd3comma0hEYM7du3Yq1tbVd42tra/HMM8/E7OxsgVQAANAOJR9Aizqd\nzkhvFnHq1KnSEaCInHNcvnx53+OXLl2K5557LlJKx5gKAADaM5rTNwAAWrS+vh4bGxv7Ht/Y2Ij1\n9fVjTAQAAO1S8gEAY20wGMSVK1ceeN7Vq1d3rNcHAAA1UfIBAAAAQOWUfADAWOt2u3Hx4sUHnnfh\nwoXodrvHkAgAANqn5AMAxt7c3FycPXt23+Nnz56Nubm5Y0wEAADtUvIBAGMvpRQrKyv7Hl9ZWbGz\nLgAAVVPyAQATYXZ2NhYWFnaNLywsxOzsbIFEAADQHiUfADAxzp07t2PdvW63G+fPny+YCAAA2qHk\nAwAmxpkzZ2J5efne8+Xl5Th9+nTBRAAA0A4lHwAwUZaWluKJJ56IJ598MpaWlkrHAQCAVpwoHQAA\n4Dh1u924ePHivccAADAOlHwAwMSZm5srHQEAAFql5AMAJk5KqXQEAABolTX5AAAAAKBySj4AAAAA\nqJySD4D/v717j9brrssE/nxpSo/lolyahpsUKsqkE2EYRESjFA4VgjIMBcWgAVS0tVEbL4zMDAJe\nQOTSqqmtM4oYNY537II46KEiIgOKgMS2oKtcRZIWFiCCp5TynT/Om/aQ5p73vLv7nM9nrXft22+/\n5zldfZOT5+y9fwAAAIzcqi/5quoOVfXQoXMAAAAAwEpZtSVfVa2rqmcmuTbJpccw/lFV9ftV9S9V\n9bmq+lhV/WlVPXHl0wIAAADAiVt1JV9VnVpV35vkvUleneQrj+GcHUn+KslTk9wryalJ7pHk8Ule\nW1WXrFhgAAAAADhJq67kS/LEJG9P8ogk+442uKrmk7w0yc7JOfdJ8rAkP5vkc5NhF1fVM1YkLQAA\nAACcpHVDB5i27n7NgfWq+sckG45yysuSXNDdr1q271+SvLOq3pLkdZN9P5jkt6eZFQAAAACmYTVe\nybdcH+lgVT0kyccPKvhuPbl7T5I3TTY3TTkbAAAAAEzFai/5juYeSZ5/lDF/M1l+ZoWzAAAAAMAJ\nWXW36x6P7r7qGIadMllevZJZAAAAAOBErfUr+Y7FWZPlriFDAAAAAExTd6f7iE86Y0TW9JV8R1NV\npyU5N8n7k/zOcZx3uKv+zp5GLgAAAICTtbCwkCR53OMeN3ASpkHJd2RPTfKlSc7v7sWhwwAAAABM\nw+LiYnbu3JmqyubNmzM3Nzd0JE6S23UPo6runORnkrzoGJ/dd4vuPudQryTXrUhYAAAAgOOwe/fu\n7N+/P/v27cvu3buHjsMUKPkO7/Ikf9HdLxo6CAAAAMC0XH/99dm169apB3bt2pUbbrhhwERMg5Lv\nEKrq+Unmkjxn6CwAAAAA03TFFVdkcfHWp5ItLi7m8ssvHzAR06DkO0hVXZzk4Um2dvfNQ+cBAAAA\nmJarr746e/bsuc3+PXv25JprrhkgEdOi5Fumqi5Kcl6Sb+vum4bOAwAAADAt3Z1LL730sMcvueSS\ndPcMEzFNSr6Jqvr+JE9O8pTuvvEwY55QVU+abTIAAACAk7ewsJC9e/ce9vjevXuzsLAww0RM07qh\nA6ywu02Wpx5pUFV9T5KLk3x7kvtW1cFD7pjkUUmem+ScKWcEAAAAWFGLi4vZuXPnUcdddtll2bx5\nc+bm5maQimlatSVfVZ2bWwu5h1TVpu6+TV1dVc9M8r+ydFXj3x/lbV/sNl4AAAAAbm9W3e26VXVx\nVb0/yVVJTpnsvlOSd1fV+yfP3Tsw9luSvCrH9t/hC1kqAwEAAABGZW5uLtu3bz/quIsuushVfCO1\n6kq+7r60ux/Q3XWI1wO6+7JlY1/b3accZuzBr1O6+4NDfm8AAAAAJ2p+fj6bNm067PFNmzZlfn5+\nhomYplVX8gEAAABwW1WVHTt2HPb4jh07coh5ChgJJR8AAADAGrFx48Zs2bLlNvu3bNmSjRs3DpCI\naVHyAQAAAKwhF1xwwRc9d29ubi4XXnjhgImYBiUfAAAAwBqyfv36bNu27Zbtbdu25YwzzhgwEdOg\n5AMAAABYY7Zu3ZozzzwzGzZsyNatW4eOwxSsGzoAAAAAALM1NzeX7du337LO+Cn5AAAAANag+fn5\noSMwRUo+AAAAgDWoqoaOwBR5Jh8AAAAAjJySDwAAAABGTskHAAAAACOn5AMAAACAkVPyAQAAAMDI\nKfkAAAAAYOSUfAAAAAAwcko+AAAAABg5JR8AAAAAjJySDwAAAABGTskHAAAAACOn5AMAAACAkVPy\nAQAAAMDIKfkAAAAAYOSUfAAAAAAwcko+AAAAABg5JR8AAAAAjJySDwAAAABGTskHAAAAACOn5AMA\nAACAkVPyAQAAAMDIKfkAAAAAYOSUfAAAAAAwcko+AAAAABg5JR8AAAAAjJySDwAAAABGTskHAAAA\nACOn5AMAAACAkVPyAQAAAMDIKfkAAAAAYOSUfAAAAAAwcko+AAAAABg5JR8AAAAAjJySDwAAAABG\nTskHAAAAACOn5AMAAACAkVPyAQAAAMDIKfkAAGCN6e5099AxAIApUvIBAMAas7CwkIWFhaFjAABT\ntG7oAAAAwOwsLi5m586dqaps3rw5c3NzQ0cCAKbAlXwAALCG7N69O/v378++ffuye/fuoeMAAFOi\n5AMAgDXi+uuvz65du27Z3rVrV2644YYBEwEA06LkAwCANeKKK67I4uLiLduLi4u5/PLLB0wEAEyL\nkg8AANaAq6++Onv27LnN/j179uSaa64ZIBEAME1KPgAAWOW6O5deeulhj19yySXp7hkmAgCmTckH\nAACr3MLCQvbu3XvY43v37s3CwsIMEwEA06bkAwCAVWxxcTE7d+486rjLLrvsi57XBwCMi5IPAAAA\nAEZOyQcAAKvY3Nxctm/fftRxF110Uebm5maQCABYCUo+AABY5ebn57Np06bDHt+0aVPm5+dnmAgA\nmDYlHwAArHJVlR07dhz2+I4dO1JVM0wEAEybkg8AANaAjRs3ZsuWLbfZv2XLlmzcuHGARADANCn5\nAABgjbjgggu+6Ll7c3NzufDCCwdMBABMi5IPAADWiPXr12fbtm23bG/bti1nnHHGgIkAgGlR8gEA\nwBqydevWnHnmmdmwYUO2bt06dBwAYErWDR0AAACYnbm5uWzfvv2WdQBgdVDyAQDAGjM/Pz90BABg\nypR8AACwxlTV0BEAgCnzTD4AAAAAGDklHwAAAACMnJIPAAAAAEZOyQcAAAAAI6fkAwAAAICRU/IB\nAAAAwMgp+QAAAABg5JR8AAAAADBySj4AAAAAGDklHwAAAACMnJIPAAAAAEZOyQcAAAAAI6fkAwAA\nAICRU/IBAAAAwMgp+QAAAABg5JR8AAAAADBySj4AAAAAGDklHwAAAACMnJIPAAAAAEZOyQcAAAAA\nI6fkAwAAAICRU/IBAAAAwMgp+QAAAABg5JR8AAAAADBySj4AAAAAGDklHwAAAACMnJIPAAAAAEZO\nyQcAAAAAI6fkAwAAAICRU/IBAAAAwMgp+QAAAABg5JR8AAAAADBySj4AAAAAGDklHwAAAACMnJIP\nAAAAAEZOyQcAAAAAI6fkAwAAAICRU/IBAAAAwMgp+QAAAABg5JR8AAAAADBySj4AAAAAGDklHwAA\nAACMnJIPAAAAAEZOyQcAAAAAI6fkAwAAAICRU/IBAAAAwMgp+QAAAABg5JR8AAAAADBySj4AAAAA\nGDklHwAAAACMnJIPAAAAAEZOyQcAAAAAI6fkAwAAAICRU/IBAAAAwMgp+QAAAABg5JR8AAAAADBy\nSj4AAAAAGDklHwAAAACMnJIPAAAAAEZOyQcAAGtMd6e7h44BAEyRkg8AANaYhYWFLCwsDB0DAJii\ndUMHAAAAZmdxcTE7d+5MVWXz5s2Zm5sbOhIAMAWu5AMAgDVk9+7d2b9/f/bt25fdu3cPHQcAmJJV\nX/JV1R2q6qEncN4ZVXW/lcgEAABDuP7667Nr165btnft2pUbbrhhwEQAwLSs2pKvqtZV1TOTXJvk\n0uM4715V9cokH0jy2BWKBwAAM3fFFVdkcXHxlu3FxcVcfvnlAyYCAKZl1ZV8VXVqVX1vkvcmeXWS\nrzzG8+5bVb+U5H1JdiQ5fcVCAgDAjF199dXZs2fPbfbv2bMn11xzzQCJAIBpWnUlX5InJnl7kkck\n2XcsJ1TVXbJ01d7PJHn2ykUDAIDZ6+5ceunhb2655JJL0t0zTAQATNuqK/m6+zXd/a7u/niSfzzG\ncz7d3b/R3fuTvG1lEwIAwGwtLCxk7969hz2+d+/eLCwszDARADBtq67kO8iJ/DrSrzABAFg1FhcX\ns3PnzqOOu+yyy77oeX0AwLis9pIPAAAAAFY9JR8AAKxic3Nz2b59+1HHXXTRRZmbm5tBIgBgJSj5\nAABglZufn8+mTZsOe3zTpk2Zn5+fYSIAYNqUfCugqq4+1CvJ2UNnAwBg7amq7Nix47DHd+zYkaqa\nYSIAYNqUfAAAsAZs3LgxW7Zsuc3+LVu2ZOPGjQMkAgCmScm3Arr7nEO9klw3dDYAANauCy644Iue\nuzc3N5cLL7xwwEQAwLQo+QAAYI1Yv359tm3bdsv2tm3bcsYZZwyYCACYFiUfAACsIVu3bs2ZZ56Z\nDRs2ZOvWrUPHAQCmZN3QAQAAgNmZm5vL9u3bb1kHAFYHJR8AAKwx8/PzQ0cAAKZMyQcAAGtMVQ0d\nAQCYstX+TL67TZannsA5x3seAAAAAAxi1ZZ8VXVuknMmmw+pqk3HcM4pSS5ctuspVXX6SuQDAAAA\ngGlZdSVfVV1cVe9PclWSUya775Tk3VX1/qq66DDnvTHJx5I8Z9nuxye5oaqurar7rmBsAAAAADhh\nq+6ZfN19aZJLT+C8R08/DQAAAACsvFV3JR8AAAAArDVKPgAAAAAYOSUfAAAAAIyckg8AAAAARk7J\nBwAAAAAjp+QDAAAAgJFT8gEAAADAyCn5AAAAAGDklHwAAAAAMHJKPgAAAAAYOSUfAAAAAIyckg8A\nAAAARk7JBwAAAAAjp+QDAAAAgJFT8gEAAADAyFV3D51hzaiqfz3ttNPucvbZZw8dBQAAAICBXXfd\ndbnxxhs/3d13Pdn3UvLNUFXtS3J6kg8PnWUNO9CwXjdoChiWzwH4HIDPAPgcQOJzcHtwvySf7e4N\nJ/tGSj7WlKq6Okm6+5yhs8BQfA7A5wB8BsDnABKfg9XGM/kAAAAAYOSUfAAAAAAwcko+AAAAABg5\nJR8AAAAAjJySDwAAAABGzuy6AAAAADByruQDAAAAgJFT8gEAAADAyCn5AAAAAGDklHwAAAAAMHJK\nPgAAAAAYOSUfAAAAAIycko81p6ruUFUPHToHAMDtTVWtr6qnDJ0DZq2qTq+qzVX1A1X1wqo6behM\nAMdLyceaUVXrquqZSa5NcunQeWCWquqeVfWSqrq6qj5bVZ+pqndV1f+oqjsPnQ9moapOq6rnTv7f\n/+zk9XdVdWFV+ZmINaOqvqqqbq6qPviVZH+STw6dEWZh8sv/p1XV65N8IMlTkrwtyU93942DhoMV\nMvn3wG3+/D/E6wtVdc+h83J81g0dAFZaVZ2a5JlJnpfkgZPdHx0uEcxWVW1M8mdJ7nPQoYdMXs+o\nqsd0976Zh4MZqaovSXJVkkcm+USWSowNSR6W5JeTfH2S7xwsIMzW83L4X/a/o7uvmmUYGEJVPTjJ\nriRfk+Q3k5zT3TcMmwpWVlWdm2TjMQ5/a3d/bCXzMH1+a81a8MQkb0/yiCRKDNaUqppL8idJPpzk\nW5M8IMlXJNmW5J8mw/5Dln7IhdXs15P8e5KHdffdu/veWSq+/2Ry/BlV9Y2DpYMZqar7J/n2JI/O\n0p//y18PTuJzwKpXVd+c5G+zVPDt6O5tCj7WiO1JXpfkG5LcN8m9DvF6zmTs7w8RkJPjSj5Wve5+\nzYH1qvrHLF25AWvF9yf5UJJv7u7PL9t/XVVdmaUfcB+U5HFV9eDufs8QIWElVdXDkpya5Lzln4Pu\n/mhVPSNLV3ffJUtX+b1pmJQwM89N8ofd/ZdDB4EhVNWjsvQLntOS/GR3e4wPa0JV3TfJPZPMd/dN\nRxj32CSd5A9mlY3pcSUfa00PHQBm7DuSPOeggi9J0t2fSvKSZbs2zSwVzNZXJXn2YT4Hn0ny3snm\nJ2aaCmasqjYk+e4k75vcqghryqTk+OMsFXxvSfKzwyaCmbopyXcdpeA7PUt3/7y1uz88s2RMjZIP\nYJWqqnVJ/qi733eEYX+zbP0zKxwJBtHdv9Pd/3qEIWdk6VbeK2cUCYbyI0nmkjw/ybVVdX1Vvbqq\nNg+cC2blJUnWT9af191fGDIMzFJ37+/uDx1l2LckuVPcqjtaSj6AVaq7P9/dP3+UYacsW79mJfPA\n7VFVPTpLz6R5VnfvHzgOrJiqOi3J9x20+4wsTU72pknZZ7Z1Vq3J1atbJ5vv6W6PZ4Db+va4VXfU\nPJMPYG07a7J8U3d/YMAcMHNV9R+zNOnMt3b3nw6dB1ZSd99YVXdPcrcs3cK+OcmzJ+vJUtl376p6\n4pFu5YIROz+3XuRybVX9eJJHJbl7kntnaZKy1yW5YvIoB1hTquouSbYkeZtbdcfLlXwAa9sTJssX\nD5oCZqiqzq6qVyT5uyT3S/JbVXXBwLFgxXX3F7r74939lu5+aZZm1N2W5OOTIY9L8nODBYSV9c3L\n1s9K8v+SnN/d35Tk8Vl6bMPLk7xjMgs1rDVPytIjHdyqO2JKPoA1anJb1tOT7Oru1w+dB2ahqh6T\n5AVJNib51GT33ZNcXlUvHCoXDKGX/GaSr03ykcnuC6vqrgPGgpVy1mR5U5IndvebDzyTr7uvS/Lk\nJNA277MAAAmNSURBVP+Q5CuT/F5V1SApYThPz9Ktukq+EVPyAaxdL0jyoSQ/OHQQmJXuvqq7t3X3\nE5LcJ8mzcuukM/+9qu49WDgYyKTgOPC8vi9J8vAB48BKOTDhxj9090cPPji5Tf0XJpuPSPKYWQWD\noVXVlyU5L27VHT0lH8AaVFXnJfm2JFuOMusorFrdfVN3/0aSp012nZql2xVhzenuPUk+ONncMGQW\nWCE3T5ZHet7em5etf9MKZoHbm/+a5I5xFd/oKfkA1piq2pSl31Sf190fOdp4WO0mk24cmF36PkNm\ngYH9/WT5b4OmgJWxb7K80xHGfGjZ+pkrmAVub54+WZpVd+SUfABrSFV9VZLdSZ7a3e8dOg/cjlw7\nWbqylbXs3yfLfx40BayMd0yWR5pU4/PL1v/9sKNgFamqe2bp9vS3dfeHjjae2zclH8AaUVUPSvKH\nSb6ru68eOg/czpw6Wb5t0BQwrAcn+WiSdw0dBFbAayfLu1fVWYcZc69l6+9Z0TRw+3F+knVJfm/o\nIJw8JR/AGlBVZyf54yTP6u5D/uOtqu5cVS+bbTIYXlWdluRRSf6mu/926DwwhKp6eJKHJLnkwIyj\nsMr8fpKPTdaffJgxGyfLzyW5csUTwe2DW3VXESUfa83dJstTjzgKVpHJb6v/LMlLk3yyqr7ioNeD\nquqxSV6f5K8GjAoroqq+rqpeVFVfc5ghP5mkknz3DGPBTFXVq6rqr6rqv1XVHQ86dtckv5alvwde\nOUhAWGHd/dkkPzHZ/JHJ//cHO/D3wCu6+19mkwyGU1X3SvKNcavuqlHdPXQGmImqOjfJnyc5JUuz\nan1dd+8dNhWsrKr68iR/meSsYxj+4SQP6O6bjzoSRqSqrkpy7mTzj7JUYuxNsj7JxUm+Nku3sbs1\ni1Wpqk5N8qkkXzLZ9d4kz03y1iRfneRlSd6e5Ae6+6ZBQsKMVNXLk/xokjdm6c/+f57sf3aWyu5f\nT/J9fh5iLaiqH0zyi0l+rLtfMXQeTp6Sj1Wvqi5O8sM5dMnxgSQv7+7LZpkJZqGq7pLknUnOPsZT\nnt/dP7OCkWAQVfXIJD+XpVsR75zkk1maWODNSa7s7j8fMB7MRFVtTvLjWSq1756lSQU+kORNSV7V\n3e84/NmwulTVeUkuTPKfslSA35ylvxd+pbtfN2Q2mKWqenOSr09yVnd/cOg8nDwlHwAAAACMnGfy\nAQAAAMDIKfkAAAAAYOSUfAAAAAAwcko+AAAAABg5JR8AAAAAjJySDwAAAABGTskHAAAAACOn5AMA\nAACAkVPyAQAAAMDIKfkAAAAAYOSUfAAAAAAwcko+AAAAABg5JR8AAAAAjNy6oQMAADCsqro4ySWT\nzZuSfDDJp5N8IclZSe4xOfaBJB/P0s+Q90xyn2Vv84Du/sDKpwUA4FBcyQcAwAEvSnK37n5Qdz+s\nux+e5LXLj3f3w7v7od193ywVgAtDBAUA4Isp+QAASJI3d/cLu/szx3pCd38wyXcmuXHlYgEAcCyU\nfAAAJMlvn8hJ3b0/yZ9POQsAAMfJM/kAAHhrko+cxPmvTPLJKWUBAOAEKPkAANa47n7rSZ7/F9PK\nAgDAiXG7LgAAJ62q9lRVL3t9oareU1X3WjbmUVW1b9mYn1x27Muq6nur6qrJ9gOq6jeqan9Vfaaq\n3lpV5x/h6z+wqq6oqjdU1fuq6oaqel1VPWZlv3MAgNsHJR8AACetu7cked6yXb/c3Q/u7o8uG/OW\nJPdPcnOSn+7un6qqR1TV65Ncn+R/Jzm3qjYneWeSJySpJKcn+dokf1BVzz34a1fVk5K8LsmvdPdj\nu/uBSb4nyXlJ3lBVP7wC3zIAwO2Kkg8AgGl5RZIPTta/4TBjHpnkc5OxSfKOJOcnWVg25gVJ5rt7\nfXevT/Jfknx6cuzFVfXQAwOralOS303yQ939zgP7u/vKJH802XxZVX35CX9XAAAjoOQDAGAquvum\nJC+dbD6kqh57iGHPSvKH3f2pyTmf7+5/S/J3y8Zs7e63L3vfK5P8yGTzlCQ/tGzsS5N8qrsPNcPv\ngX2nJnnacX47AACjYuINAACm6VVJ/meSeyd5bpI3HDhQVXdK8tQkTzrEeTcfWOnu6w9x/DeTvDjJ\nGUkePXm/eyR5fJJ/q6pDTR4yl1tnDf7S4/w+AABGRckHAMDUdPeNVfXyJK9Mcl5VPbS73zU5fH6W\nnr33xhN8379O8uQsFYhJ8vAsPbPvH7r7UScdHgBgxNyuCwDAtP1Kkhsm68snynhWkld3d5/g+/7z\nZHnTZHmPyfKuJ/h+AACrhpIPAICp6u7PJrlksvm0qrp/Vd0/yeYkrz6Jt16cLA+UfTdOll9RVaed\nxPsCAIyekg8AgJWwM8knsvR4mB9Nsi3JG7r7wyfxngdmyH3jZPm+yfK0JN9xpBOr6qeqasNJfG0A\ngNs1JR8AAEdyQj8vdvenk/zCZPN7Jq9XnWiIqjolySOSdJIrJrvfnWTfZP2lVXW/w5z7zCQP6+59\nhzoOALAaKPkAADiSL1u2frzPvvvFJJ9OcnqSuyT5k2M5aTJr7sHOT3JWkl/q7r9Pku6+OcnPT46v\nT/LXVfVtVXV6Vd2hqh5YVZdMcuw4zuwAAKOi5AMA4JCqan2S5bPWPul4nn3X3Z9I8muTzd/u7huP\nNH6Z11TVOctyfEOSy5L8bpZu/V3uF5L8n8n6/SZjPpOl5/Vdl+S7kzylu//pWHMDAIyRkg8AgC9S\nVU+oqmuzNMHF8qvqHpvkY1X17slEGsfiqsnyeG7VfVGSX62qa6vqHUl+NskPdffTu/vzywd29xeS\nbE3yzCRvTvKvST6X5INJfinJOd39huP42gAAo1TdPXQGAABWqap6SZLzuvs/H2XcC5O8IEm6u2YQ\nDQBgVXElHwAAK6Kq7pilWXUvHzoLAMBqp+QDAGCl/ESSSvJbQwcBAFjt1g0dAACA8auqeyf50yRz\nSa5McnOSH09yQXcvHsNbnLrsveaO8RwAACaUfAAATMMjk3z1ZP3HJssrk/zq0U6c3Nb79ct2nZul\nwhAAgGPkdl0AAKbh/yZ5bZLFJB9J8uIkT+ujzPJWVduT7E/yTct2v7aq/qmqLl6psAAAq43ZdQEA\nAABg5FzJBwAAAAAjp+QDAAAAgJFT8gEAAADAyCn5AAAAAGDklHwAAAAAMHJKPgAAAAAYOSUfAAAA\nAIyckg8AAAAARk7JBwAAAAAjp+QDAAAAgJFT8gEAAADAyCn5AAAAAGDklHwAAAAAMHJKPgAAAAAY\nuf8P/Dm8cIP61jQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf2da3090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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p319K+ckkv5vkW5P8dinlTyf5A7fP/703P7mU8o1Jnkzy/+xBNoBOqrXm/Pnz\nu86fO3cub9ruEwAAgA7ai1Vrl5L0k3w8yZv/1liS/Gyt9d8rpbyQ5OTt+adLKR+vtf7l2+f9Z7fH\nP70H2QA6aTAYZG1tbdf5tbW1DAaDPPfcc/uYCgAYhc3NzWxsbDQdo3Nu3LjxQGPc2+TkZCYm9mIN\nD/B2jLyoq7XWUspHknxvku9K8pW5dXOIF5P84O3T5nPrMtg/nOTbknyilPJbuXW316+6fc7fHHU2\ngC4aDodZXFy873kXLlzIiRMn0uv19iEVADAqGxsbOXnyZNMxxsKpU6eajtA5q6urOXr0aNMxgNv2\nZB+4Wutmkv/69p+d5l9L8mfuHJdSziY5n1sFXknyE7XWf7QX2QAAAACgjd72+tadbhjxNl/nffWW\n70nyB5NM11r/g1G8NsA46PV6mZ+fv+95Z8+etZoOAAAOmFqr/arHyKNciL46ogw/fedBrfX3aq3r\nI3pdgLHR7/czPT296/z09HT6/f4+JgIAANpgMBhkMBg0HYMReZRLX7+qlPJltda3fXfWUspXJtn9\nb54AJElKKVlYWMjzzz+/4/zCwkJKKfucCgDYK1/97a/mUM8KmXupNfniq1vH3vFE4n+JdvfGsOQ3\nfuaJpmMwQnf2sy6l2K96TDxKUXcoya+UUn4+ycPeoqgkeU+SP5bkHY+QAeDAmJqayszMTFZWVraM\nz8zMZGpqqqFUAMBeONSrOeTv2/f12JNNJ+ga5e+4WV5eztWrV+8+3u2LfbrjUW8m8aVJ/vQjPL/E\nvykAHtjp06fzqU99KsPhMMmt/evOnDnTcCoAAGC/Xbt2LUtLS3ePl5aW8qEPfSjHjh1rMBWP6lH2\nqLujPMIfAB7C8ePHMzc3d/d4bm7Of4gBAOAAunTp0t0v8JNbl8FevHixwUSMwqOsqPtMkq9I8nqS\nF5P8wzx4+VaSHE3yp5KceIQMAAfO7OxsXnrppZRSMjs723QcAABgn62vr2/bEidJVlZW8uEPf9jW\nOB32KEXdVyb5s0n+kyTfkWQqyV9N8lO11s0HeYFSyo8nufYIGQAOnF6vl/n5+buPAQCAg6PWmvPn\nz+86f+7cubzwwgtuNtdRb/vS11rrF2uty7XWfzNJP8nVJMtJfrOUMl9Kue+2nrXWV5P81tvNAHBQ\n9fv99Pv9pmMAAAD7bDAYZG1tbdf5tbW1DAaDfUzEKI1ij7rUWj9Va51JMp3k55L8WJLPlFI+UUp5\nz32e/vUL5njJAAAgAElEQVSjyABwkJRSfEMGAAAHzHA4zOLi4n3Pu3Dhwpb96+iOkRR1d9Rar9Ra\n/0KS9yX5G0nOJvmdUsqPl1K+cpfnPNBlsgAAAAAwzkZa1N1Ra71aa/3BJO9N8n1JPpjk10sp/30p\n5Rv24ncCAAAAjLM371d9L2fPnrWfdUftSVF3R63187XWv57kq5KcSvJlSf5ZKeUfl1Jm9vJ3AwAA\nAIybfr+f6enpXeenp6ftZ91he1rU3VFrrUn+QZKfTfKFJCeS/A+llLVSysf2IwMAAABA15VSsrCw\nsOv8wsKC/aw7bM+LulLK8VLKjyb53SR/Ocmdu8GWJE/n1io7AAAAAB7A1NRUZma2X6g4MzOTqamp\nBhIxKntW1JVS3ldKuZDkt5L8pSSHc6ucK0l+L8n3JPmDtdYf2asMAOOq1ppbi5UBAICD6PTp01v2\noev1ejlz5kyDiRiFkRd1pZSvLaX8ZJLfSHI6t1bQ3Vlz+WtJvjPJs7XWv15r/cKofz/AQTAYDDIY\nDJqOAQAANOT48eOZm5u7ezw3N5djx441mIhRODSqFyqlfHOS70/yJ+8MvWn6F5P8aK31747q9wEc\nVMPhMIuLiyml5MSJE+7mBAAAB9Ts7GxeeumllFIyOzvbdBxG4JGLulLKn8itgu7EnaE3Tf+j3Cro\nPnWP5x+ttX72UXMAHBTLy8u5evXq3cfPP/98w4kAAIAm9Hq9zM/P331M973tS19LKR8tpfxSkpXc\nKunu7D9Xk/ydJN9Ya33uXiXdbT//djMAHDTXrl3L0tLS3eOlpaVcv369wUQAAECT+v1++v1+0zEY\nkUfZo+5vJfk38i8LuteTfDLJVK31z9Ra/5f7vUAp5ViSr3qEDAAHyqVLlzIcDu8eD4fDXLx4scFE\nAABAk0opKaXc/0Q6YRR71NUkV5NcSPKZJN9USvmm+zynl+S9ST6S5LERZAAYe+vr61lZWdk2vrKy\nkg9/+MNuww4AANBxj1rUlSSfTfJqku96iOf0krwnt1b01UfMADD2aq05f/78rvPnzp3LCy+84Js0\nAACADnvUou6DtdbB23liKeWJJN+b5EceMQPA2BsMBllbW9t1fm1tLYPBIM8999w+pgIAAGCUHmWP\nut95uyVdktRaX621/pUkdkEHuIfhcJjFxcX7nnfhwoUt+9cBAADQLY9S1P3kiDK8OKLXAQAAAIDO\nettFXa3146MIUGv9j0fxOgDjqtfrZX5+/r7nnT17Nr1ebx8SAQAAsBceZUUdAPuk3+9nenp61/np\n6en0+/19TAQAAMCoKeoAOqCUkoWFhV3nFxYW3PEVAACg48auqCulvKeU8qOllPVSyudLKZ8rpfxy\nKeUHSynvbjofwNs1NTWVmZmZbeMzMzOZmppqIBEAAACjNFZFXSllKskvJ/m+JFNJnkzyziRfl+S/\nTPLpUsozzSUEeDSnT5/esg9dr9fLmTNnGkwEAADAqIxNUVdK6SV5Kclnknwoyb+a5P1J5pL8n7dP\n+9eSLDUSEGAEjh8/nrm5ubvHc3NzOXbsWIOJAAAAGJWxKeqS/IdJfjfJiVrrz9Zaf7vW+nKt9b9L\n8g35l2Xdc6WUP9RYSoBHNDs7m6effjrPPPNMZmdnm44DAADAiBxqOsAInUoyW2t9460TtdaNUsqP\nJvmbt4emk/zafoYDGJVer5f5+fm7jwEAABgPY1HUlVIOJfnbtdb/6x6nffpNjz+3x5EA9lS/3286\nAgAAACM2FkXd7VV0f/U+p73jTY+v7GEcgD1XSmk6AgAAACM2TnvU3c/7bv/8+VrrbzeYAwAAAAC2\nGYsVdQ/o5O2fP/KgTyilrO8y9eyjxwEAAACAf+lArKgrpbw7yZ9LslRr/ftN5wEAAACAtzooK+p+\nKMnvJvmPHuZJtdYP7DR+e6Xd1AhyAQAAAECSA1DUlVI+mOSjSb6l1nqz6TwAAABvtrm5uW3sjWED\nQRh7O72vdnr/Ac0Z66KulDKd5L9J8sFa6//ddB4AAIC3unlz+3qC3/iZXgNJOIhu3ryZp556qukY\nwG1ju0ddKeVrkiwn+XCt9debzgMAAAAA9zKWK+pKKV+V5KeT/Pla6253bgUAAACA1hi7FXWllGeT\n/J0kH6u1/vIu57y7lPJj+5sMAAAAAHY3VivqSinvS/IPkvxwkhullPe/9ZQk703yXyT5K/uZDQAA\nYCeHDx/eNvbV3z7MIdvUMWJvDLfvf7jT+w9oztgUdaWU9yb5uSTvS7J0n9M/k+R/3OtMAAAA9zMx\nsf1Cp0O9KOrYFzu9/4DmjMUnspTyJUk+lVsl3YN4odb6xb1LBAAAAAAPZyxW1NVafz/JWy9zBQAA\nAIDOGIsVdQAAAADQdYo6AAAAAGgBRR0AAAAAtICiDgAAAABaQFEHAAAAAC2gqAMAAACAFlDUAQAA\nAEALKOoAAAAAoAUUdQAAAADQAoo6AAAAgI6qtabW2nQMRkRRBwAAANBRg8Egg8Gg6RiMyKGmAwAA\nAADw8IbDYRYXF1NKyYkTJ9Lr9ZqOxCOyog4AAACgg5aXl3P16tW88sorWV5ebjoOI6CoAwAAAOiY\na9euZWlp6e7x0tJSrl+/3mAiRkFRBwAAANAxly5dynA4vHs8HA5z8eLFBhMxCoo6AAAAgA5ZX1/P\nysrKtvGVlZVcuXKlgUSMiqIOAAAAoCNqrTl//vyu8+fOnUutdR8TMUqKOgAAAICOGAwGWVtb23V+\nbW0tg8FgHxMxSoo6AAAAgA4YDodZXFy873kXLlzYsn8d3aGoAwAAAIAWUNQBAAAAdECv18v8/Px9\nzzt79mx6vd4+JGLUDjUdAACAg2tzczMbGxtNx+icGzduPNAY9zY5OZmJCWsXgG7p9/t58cUXd92n\nbnp6Ov1+f59TMSqKOgAAGrOxsZGTJ082HWMsnDp1qukInbO6upqjR482HQPgoZRSsrCwkOeff37H\n+YWFhZRS9jkVo+LrIwAAAIAOmZqayszMzLbxmZmZTE1NNZCIUVHUAQAAAHTM6dOnt+xD1+v1cubM\nmQYTMQqKOgAAAICOOX78eObm5u4ez83N5dixYw0mYhTsUQcAQKv8UO+5vLs80XSMVtusNZ/Pa1vG\n3pnHM2FPol39i/pqPjH8h03HABip2dnZvPTSSymlZHZ2tuk4jICiDgCAVnl3eSJfoqi7t5JMpnf/\n8wAYa71eL/Pz83cf032KOgAAAICO6vf7TUdghBR1AAAAAB1VbHswVtxMAgAAAABaQFEHAAAAAC2g\nqAMAAACAFlDUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6AAAAAGgBRR0AAAAAtICiDgAAAABaQFEH\nAAAAAC2gqAMAAACAFlDUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6AAAAAGgBRR0AAAAAtICiDgAA\nAABaQFEHAAAAAC2gqAMAAACAFlDUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6AAAAAGgBRR0AAAAA\ntICiDgAAAABaQFEHAAAAAC2gqAMAAACAFlDUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6AAAAAGgB\nRR0AAAAAtICiDgAAAABaQFEHAAAAAC2gqAMAAACAFlDUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6\ngA6qtabW2nQMAAAARkhRB9BBg8Egg8Gg6RgAAACM0KGmAwDwcIbDYRYXF1NKyYkTJ9Lr9ZqOBAAA\nwAhYUQfQMcvLy7l69WpeeeWVLC8vNx0HAACAEVHUAXTItWvXsrS0dPd4aWkp169fbzARAAAAo6Ko\nA+iQS5cuZTgc3j0eDoe5ePFig4kAAAAYFUUdQEesr69nZWVl2/jKykquXLnSQCIAAABGSVEH0AG1\n1pw/f37X+XPnzqXWuo+JAAAAGDVFHUAHDAaDrK2t7Tq/traWwWCwj4kAAAAYNUUdQMsNh8MsLi7e\n97wLFy5s2b8OAACAblHUAQAAAEALKOoAWq7X62V+fv6+5509eza9Xm8fEgEAALAXFHUAHdDv9zM9\nPb3r/PT0dPr9/j4mAgAAYNQUdQAdUErJwsLCrvMLCwsppexjIgAAAEZNUQfQEVNTU5mZmdk2PjMz\nk6mpqQYSAQAAMEqKOoAOOX369JZ96Hq9Xs6cOdNgIgAAAEZFUQfQIcePH8/c3Nzd47m5uRw7dqzB\nRAAAAIyKog6gY2ZnZ/P000/nmWeeyezsbNNxAAAAGJFDTQcA4OH0er3Mz8/ffQwAAMB4UNQBdFC/\n3286AgAAACOmqAPooFJK0xEAAAAYMXvUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6AAAAAGgBRR0A\nAAAAtICiDgAAAABaQFEHAAAAAC2gqAMAAACAFlDUAQAAAEALKOoAAAAAoAUUdQAAAADQAoo6AAAA\nAGgBRR0AAAAAtICiDgAAAABaQFEHAAAAAC2gqAMAAACAFlDUAQAAAEALKOoAAPj/27v7cEvrulzg\n93cAZ0RheBFEyCOKFg4H9XjITKN8IQwo64SWYKG9HkkqkPRkWGqKZkGDhWnXKTMsPBWYkkDmSGXm\nUSM0kRfrYIpyAM0jg4ADyHzPH3sNboZ5Zfbez7PWfD7Xta7n7bfWvreuxd5z7+d5fgAAjICiDgAA\nAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAwAoo6AAAAABgBRR0AAAAA\njICiDmAKdXe6e+gYAAAALKCZL+qqallVPWnoHAALac2aNVmzZs3QMQAAAFhAuw4dYLFU1a5JXpjk\nV5LcmOQZgwYCWCDr1q3Lueeem6rKkUcemRUrVgwdCQAAgAUwc2fUVdVuVfXTST6T5B1JvnXYRAAL\n6/zzz8/NN9+cm266Keeff/7QcQAAAFggM1fUJTkuyeVJnpLkpoGzACyoL33pSznvvPPu3T7vvPPy\n5S9/ecBEAAAALJSZK+q6+z3d/cnu/kqSfx06D8BCetvb3pZ169bdu71u3bq89a1vHTARAAAAC2Xm\nirqNmBIRmBlXXXVVLrnkkvvtv+SSS3L11VcPkAgAAICFNOtFHcBM6O6cc845mz2+evXqdPvbBAAA\nwDRT1AFMgTVr1uTKK6/c7PErr7wya9asWcJEAAAALDRFHcDIrVu3Lueee+5Wx73lLW+5z/3rAAAA\nmC67Dh1gzKrqqs0cOmRJgwAAAAAw85xRBzByK1asyCmnnLLVcS996UuzYsWKJUgEAADAYlDUbUF3\nH7apR5Lrhs4G7FyOOuqoHH744Zs9fvjhh+eoo45awkQAAAAsNEUdwBSoqpx22mmbPX7aaaelqpYw\nEQAAAAtNUQcwJVatWpVjjz32fvuPPfbYrFq1aoBEAAAALCRFHcAUeclLXnKf+9CtWLEiJ5988oCJ\nAAAAWCiKOoApsv/+++ekk066d/ukk07KfvvtN2AiAAAAFoqiDmDKnHjiiXn4wx+eAw44ICeeeOLQ\ncQAAAFgguw4dYJHtPVnuNmgKgAW0YsWKnHLKKfeuAwAAMBtmtqirqmcmOWyy+cSqOry7rxwyE8BC\nOeqoo4aOAAAAwAKbuUtfq+rUqvr3JJcl2WWy+yFJPlVV/15VLx0uHcDCqKpU1dAxAAAAWEAzd0Zd\nd5+T5JyhcwAAAADA9pi5M+oAAAAAYBop6gAAAABgBBR1AAAAADACijoAAAAAGAFFHQAAAACMgKIO\nAAAAAEZAUQcAAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAAAABgBBR1AAAAADACijoAAAAAGAFFHQAA\nAACMgKIOAAAAAEZAUQcAAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAAAABgBBR1AAAAADACijoAAAAA\nGAFFHQAAAACMgKIOAAAAAEZAUQcAAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAAAABgBBR1AAAAADAC\nijoAAAAAGAFFHQAAAACMgKIOAAAAAEZAUQcAAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAAAABgBBR1\nAAAAADACijoAAAAAGAFFHQAAAACMgKIOAAAAAEZAUQcAAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAA\nAABgBBR1AAAAADACuw4dAAAAgPv6xrpK0kPHGLXu5J4777tvl+VJ1TB5psHc+woYM0UdAADAyPzr\nRcuHjgDAAFz6CgAAAAAjoKgDAAAAgBFQ1AEAAADACLhHHQAAwIBWrlyZSy+9dOgYU+eWW27JCSec\ncJ9973rXu7LXXnsNlGg6rVy5cugIwDyKOgAAgAEtW7Yse++999AxZsJee+3lf0tgqrn0FQAAAABG\nQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAwAoo6AAAAABgBRR0AAAAAjICi\nDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAwAoo6AAAAABgBRR0A\nAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAwAoo6AAAA\nABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAw\nAoo6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQU\ndQAAAAAwAoo6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoA\nAAAAYAQUdQAAAAAwAoo6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEdh16AAA\nAOy81q9ff799t/WdAyRh1m3qfbWp9x8ADElRBwDAYG699db77Xvtug8MkISd0a233pp999136BgA\ncC+XvgIAAADACCjqAAAAAGAEFHUAAAAAMALuUQcAwGD23HPP++179YrvzUNr+QBpmGW39Z33u//h\npt5/ADAkRR0AAINZtuz+F3g8tJZnD0UdS2BT7z8AGJKfTAAAAAAwAoo6AAAAABgBRR0AAAAAjICi\nDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAwAoo6gCm0fv36rF+/\nfugYAAAALKCZLOqq6ulV9e6quraqPlVVn6mqs6pqv6GzASyE1atX55xzzhk6BgAAAAto5oq6qnp5\nkn9Icn2SJ3T3E5I8N8kPJvlEVR02ZD6AHbV27dpceOGFueCCC7J27dqh4wAAALBAZqqoq6oXJvnN\nJB/v7lO7+64k6e7PJHl+kkckeV9V7T1gTIAdcsYZZ9x76esZZ5wxdBwAAAAWyMwUdVW1R5LfnWye\ntfHx7v5kkg8kOTjJq5YuGcDCueaaa3L55Zffu3355Zfn2muvHTARAAAAC2VmirokP5tk7yTfSHLJ\nZsa8Z7I8uaoeuiSpABbQq151/78zOKsOAABgNsxSUffcyfLT3X3HZsZ8fLJ88LzxAFPhr/7qr3LD\nDTfcb/8NN9yQiy++eIBEAAAALKRdhw6wEKpqtyTfPtn8P1sYOv/YdyY5f9FCASyg9evXZ/Xq1Zs9\nfvbZZ+eYY47JsmWz9PcXYGd1W985dIQkyfru3JG7ho4xE3bPg7KsatAMY3lfTZv169ePdvKqW265\nZZv2jcHKlSv9nrYV899rY/v/8Z577slNN900dIypd8ABB2SXXXYZOsZ97LXXXknG9RmdiaIuyYGZ\nO0suSW7c3KDuvrWq7kiyexKzvwJTY/Xq1bnjjs2dLJzccccdOeecc/Kyl71sCVMBLI7XrvvA0BGA\nibVr1+aYY44ZOsY2O+GEE4aOsEmXXnpp9t7bnIZbMm3vNWbLmD6j46gLd9w+89Zv38rYDcf33dqL\nVtVVm3okOeSBBgXYXmvXrs2FF1641XEXXHDBaP/iDQAAwNbNSlH3kHnrW7s+Yd0mngMAAAAAg5qV\nou7r89Z328rYDcc3fw3ZRHcftqlHkuseaFCA7bVy5cocf/zxWx33vOc9LytXrlyCRAAAACyG6u6h\nM+ywqnpkkusnm2/u7lO3MHZtkj2T/G13P+sBfr2rVq1ateqqq656IE8H2G7r16/PUUcdtdn71O2+\n++5Zs2bNaG6ACrCtxnyj+vXr1+fWW28dOsZM2HPPPUf5M2pMNw8fq2n7jHqvTS+TScy+WZ5M4rDD\nDsvVV1999eTkrh0yK5NJfDFzZ8jtnuSAzQ2qqgdnrqRLkmuWIBfAgli2bFlOP/30vO51r9vk8dNP\nP90vf8BUWrZs2Whu3rwp++671dsaw0zzGWWpzH+vjfE999jHPnboCOwkZuJfdT13WuBHJpuP2cLQ\n+cf+cfESASy84447LgcddND99h900EE57rjjBkgEAADAQpqJom7ivZPlE6rqQZsZ85TJ8q4kly5+\nJICF9frXv/5++84888wBkgAAALDQZqmoe2eS25IsT/KczYz5wcnyT7r7q0uSCmABPf7xj88RRxxx\n7/YRRxyRQw89dMBEAAAALJSZKeq6e22SV042T9/4eFWtSvL9Sf5fklcvYTSABXXmmWdm2bJlWbZs\nmbPpAAAAZsisTCaxwVuSHJLk1Ko6K8mvdPddVfWYJBckWZvk+O7+4pAhAXbEypUrc/zxx6eqsnLl\nyqHjAAAAsEBmqqibTCpxWlX9dZKfT3JFVd2Vucth/zrJ6u7+wpAZARbCaaedNnQEAAAAFthMFXUb\ndPf7k7x/6BwAi2XZspm5cwEAAAAT/qUHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAAYAQUdQAA\nAAAwAoo6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAIKOoAAAAA\nYAQUdQAAAAAwAoo6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACAEVDUAQAAAMAI\nVHcPnWHqVNWty5cv3+OQQw4ZOgoAAAAAA7ruuuty5513fq2799zR11LUPQBVdVOS3ZN8YegsO7EN\nLel1g6aA4fgMgM8BJD4HkPgcgM/A8B6Z5I7uPmBHX0hRx1SqqquSpLsPGzoLDMFnAHwOIPE5gMTn\nAHwGZot71AEAAADACCjqAAAAAGAEFHUAAAAAMAKKOgAAAAAYAUUdAAAAAIyAWV8BAAAAYAScUQcA\nAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAAAABgBBR1AAAAADACijoAAAAAGAFFHVOpqpZV1ZOGzgEA\nMDZVtX9V/fDQOWCpVdXuVXVkVf1cVb2mqpYPnQlgeynqmCpVtWtVvSjJNUnOGToPLLWqelhVvbGq\nrqqqO6rq9qr6ZFWdUVUPHTofLLaqWl5Vr5i87++YPP65qk6uKr/XsNOoqm+rqnuqqjd+JLk5yS1D\nZ4SlMPkD/vOr6v1JPpfkh5N8LMnruvvOQcPBIpr8e+B+PwM28VhfVQ8bOi/bbtehA8C2qKrdkrwo\nySuTPGay+8bhEsHSq6pVSf4myUEbHXri5PHCqnpWd9+05OFgCVTVg5NcluSpSb6auSLigCRPTvJ7\nSZ6e5McGCwhL65XZ/B/dr+juy5YyDAyhqg5Ncl6Sb0/yziSHdfeXh00Fi6+qnplk1TYO/2h3/8di\n5mFh+csz0+K4JJcneUoSJQQ7napakeS9Sb6Q5AeSPDrJY5OclOTfJsMen7lfVmFW/VGSryd5cnfv\n090HZq64fu/k+Aur6rsHSwdLpKoeleRHkzwjc//tn/84NInPATOvqp6T5J8yV9Kd1t0nKenYiZyS\n5OIk35XkW5I8YhOPn5mM/YshAvLAOaOOqdDd79mwXlX/mrkzKGBn8t+TXJ/kOd39jXn7r6uqizL3\ni+rjknxvVR3a3dcOERIWS1U9OcluSY6e/xno7hur6oWZO8t6j8ydbfehYVLCknlFkgu7+++HDgJD\nqKqnZe6PNMuT/Fp3uyUOO42q+pYkD0tyVHffvYVxz07SSS5YqmwsDGfUMY166AAwgBOS/MxGJV2S\npLvXJnnjvF2HL1kqWDrfluQnNvMZuD3JZyabX13SVLDEquqAJD+Z5LOTy/5gpzIpKf4ycyXdR5Kc\nOWwiWHJ3J/nxrZR0u2fuKpyPdvcXliwZC0JRBzByVbVrknd392e3MOzj89ZvX+RIsOS6+13dfesW\nhuyXuctiL1qiSDCUlyVZkeRXk1xTVV+qqndU1ZED54Kl8sYk+0/WX9nd64cMA0utu2/u7uu3Muz7\nkzwkLnudSoo6gJHr7m90929uZdgu89avXsw8MDZV9YzM3Z/lxd1988BxYNFU1fIkP7vR7v0yN+HW\nhyaFnRnAmVmTs0hPnGxe291udQCb9qNx2evUco86gNlw8GT5oe7+3IA5YElV1X/O3CQqP9Ddlw6d\nBxZTd99ZVfsk2Ttzl4MfmeQnJuvJXGF3YFUdt6VLomCKHZ9vnmxyTVW9PMnTkuyT5MDMTbp1cZK3\nTW6LADudqtojybFJPuay1+nkjDqA2XDMZPmGQVPAEqmqQ6rq7CT/nOSRSf6kql4ycCxYdN29vru/\n0t0f6e43ZW6m15OSfGUy5HuT/MZgAWFxPWfe+sFJ/neS47v7e5J8X+ZugXBWkismsyPDzui5mbtF\ngstep5SiDmDKTS5zekGS87r7/UPngcVWVc9K8uokq5KsnezeJ8lbq+o1Q+WCIfScdyb5jiQ3THaf\nXFV7DhgLFsvBk+XdSY7r7g9vuEddd1+X5IeSfDrJtyb586qqQVLCsF6QucteFXVTSlEHMP1eneT6\nJD8/dBBYCt19WXef1N3HJDkoyYvzzUlUfqWqDhwsHAxkUlJsuH/dg5McMWAcWCwbJpH4dHffuPHB\nySXfb55sPiXJs5YqGIxBVe2V5Oi47HWqKeoAplhVHZ3kR5Icu5UZMWEmdffd3f3HSZ4/2bVb5i79\ng7fGxcgAAAjwSURBVJ1Od1+S5POTzQOGzAKL5J7Jckv3n/vwvPXvWcQsMEb/LcmD4my6qaaoA5hS\nVXV45v5qfHR337C18TDLJhNJbJjx+KAhs8DA/mWyvG3QFLA4bposH7KFMdfPW3/4ImaBMXrBZGm2\n1ymmqAOYQlX1bUnOT/K87v7M0HlgJK6ZLJ1dys7s65PlFwdNAYvjislySxNFfGPe+tc3OwpmTFU9\nLHOXe3+su6/f2njGS1EHMGWq6nFJLkzy49191dB5YER2myw/NmgKGNahSW5M8smhg8AieN9kuU9V\nHbyZMY+Yt37toqaBcTk+ya5J/nzoIOwYRR3AFKmqQ5L8ZZIXd/cm/xFWVQ+tqt9a2mQwrKpanuRp\nST7e3f80dB4YQlUdkeSJSVZvmAkTZsxfJPmPyfoPbWbMqsnyriQXLXoiGA+Xvc4IRR3TaO/Jcrct\njoIZM/nL8d8keVOSW6rqsRs9HldVz07y/iT/MGBUWHBV9Z1V9dqq+vbNDPm1JJXkJ5cwFiypqnp7\nVf1DVf2PqnrQRsf2TPKHmfsZ8NuDBIRF1t13JPnlyebLJu/7jW34OXB2d//fpUkGw6qqRyT57rjs\ndSZUdw+dAbZZVT0zyQeS7JK52Z6+s7uvHDYVLL6q+k9J/j7Jwdsw/AtJHt3d92x1JEyJqrosyTMn\nm+/OXBFxZZL9k5ya5Dsydzm4y5yYSVW1W5K1SR482fWZJK9I8tEkT0jyW0kuT/Jz3X33ICFhiVTV\nWUlOT/J3mftv/xcn+38ic4X1HyX5Wb8LsbOoqp9P8jtJfqm7zx46DztGUcdUqKpTk/xiNl1SfC7J\nWd39lqXMBEulqvZI8okkh2zjU361u1+/iJFgyVXVU5P8RuYu63toklsyd7P8Dye5qLs/MGA8WBJV\ndWSSl2eumN4nczfK/1ySDyV5e3dfsflnw2ypqqOTnJzkv2SuxL4ncz8Xfr+7Lx4yGyy1qvpwkqcn\nObi7Pz90HnaMog4AAAAARsA96gAAAABgBBR1AAAAADACijoAAAAAGAFFHQAAAACMgKIOAAAAAEZA\nUQcAAAAAI6CoAwAAAIARUNQBAAAAwAgo6gAAAABgBBR1AAAAADACijoAAAAAGAFFHQAAAACMgKIO\nAAAAAEZg16EDAACwMKrq1CSrJ5t3J/l8kq8lWZ/k4CT7To59LslXMve74MOSHDTvZR7d3Z9b/LQA\nAGzMGXUAALPntUn27u7HdfeTu/uIJO+bf7y7j+juJ3X3t2SuxFszRFAAAL5JUQcAMFs+3N2v6e7b\nt/UJ3f35JD+W5M7FiwUAwNYo6gAAZsufPpAndffNST6wwFkAANgO7lEHADA7Pprkhh14/m8nuWWB\nsgAAsJ0UdQAAM6K7P7qDz//bhcoCAMD2c+krAAD3qqpLqqrnPdZX1bVV9Yh5Y55WVTfNG/Nr847t\nVVU/XVWXTbYfXVV/XFU3V9XtVfXRqjp+C1//MVX1tqr6YFV9tqq+XFUXV9WzFvc7BwAYnqIOAIB7\ndfexSV45b9fvdfeh3X3jvDEfSfKoJPckeV13/3pVPaWq3p/kS0n+Z5JnVtWRST6R5JgklWT3JN+R\n5IKqesXGX7uqnpvk4iS/393P7u7HJPmpJEcn+WBV/eIifMsAAKOhqAMAYGNnJ/n8ZP27NjPmqUnu\nmoxNkiuSHJ9kzbwxr05yVHfv3937J/nBJF+bHHtDVT1pw8CqOjzJnyX5he7+xIb93X1RkndPNn+r\nqv7TA/6uAABGTlEHAMB9dPfdSd402XxiVT17E8NenOTC7l47ec43uvu2JP88b8yJ3X35vNe9KMnL\nJpu7JPmFeWPflGRtd29q5tkN+3ZL8vzt/HYAAKaGySQAANiUtyd5VZIDk7wiyQc3HKiqhyR5XpLn\nbuJ592xY6e4vbeL4O5O8Icl+SZ4xeb19k3xfktuqalMTYqzIN2ezXbmd3wcAwNRQ1AEAcD/dfWdV\nnZXkt5McXVVP6u5PTg4fn7l70f3dA3zdf0zyQ5krAZPkiMzdw+7T3f20HQ4PADClXPoKAMDm/H6S\nL0/W50/+8OIk7+jufoCv+8XJ8u7Jct/Jcs8H+HoAADNBUQcAwCZ19x1JVk82n19Vj6qqRyU5Msk7\nduCl102WGwq7OyfLx1bV8h14XQCAqaaoAwBgS85N8tXM3TLl9CQnJflgd39hB15zw8ytfzdZfnay\nXJ7khC09sap+vaoO2IGvDQAwWoo6AICdwwP6va+7v5bkzZPNn5o83v5AQ1TVLkmekqSTvG2y+1NJ\nbpqsv6mqHrmZ574oyZO7+6ZNHQcAmHaKOgCAncNe89a3915wv5Pka0l2T7JHkvduy5Mms7lu7Pgk\nByf53e7+lyTp7nuS/Obk+P5J/rGqfqSqdq+qZVX1mKpaPclx2nZmBwCYGoo6AIAZV1X7J5k/m+pz\nt+decN391SR/ONn80+6+c0vj53lPVR02L8d3JXlLkj/L3GW08705yf+arD9yMub2zN2/7rokP5nk\nh7v737Y1NwDAtFHUAQDMqKo6pqquydykDfPPbnt2kv+oqk9NJofYFpdNlttz2etrk/xBVV1TVVck\nOTPJL3T3C7r7G/MHdvf6JCcmeVGSDye5NcldST6f5HeTHNbdH9yOrw0AMHWqu4fOAADAyFXVG5Mc\n3d3/dSvjXpPk1UnS3bUE0QAAZoYz6gAA2KKqelDmZnt969BZAABmmaIOAICt+eUkleRPhg4CADDL\ndh06AAAA41FVBya5NMmKJBcluSfJy5O8pLvXbcNL7DbvtVZs43MAAIiiDgCA+3pqkidM1n9psrwo\nyR9s7YmTS2SfPm/XMzNX+gEAsA1c+goAwHx/neR9SdYluSHJG5I8v7cyA1lVnZLk5iTfM2/3+6rq\n36rq1MUKCwAwS8z6CgAAAAAj4Iw6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOgqAMAAACA\nEVDUAQAAAMAIKOoAAAAAYAQUdQAAAAAwAoo6AAAAABgBRR0AAAAAjICiDgAAAABGQFEHAAAAACOg\nqAMAAACAEVDUAQAAAMAI/H9VH4ha3qvA1wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf38bb2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1jUajoSM8qLsCsKouS/KLSV7eWnv/Sba5IMnrWmuvPsV0b0lybZJLq2pPa+32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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf2e92350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Iwrjb7H212fsPAIB+KfkAoEHLy8sbxiypZL8sLy/n8OHDfccAAOAqFtADAAAA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\nr492KPkAAAAAoHFKPgAAAIA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tvG6vqsPHzsvu2W/sALDWqmr/JM9J8pIkDxh2f2q8RLC+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\n6jblHwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf2f85990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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AOiDUAQAAAEAH1g89AAAAerS0tJSFhYWhZ5zS0tJSHnnkkcccu/TSS7NuXZ//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\ngEIdAAAAAPSAQh0AAAAA9MD/B8sev1PNVmD/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf32eda90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kyZMNJgIAAKBfBr3kO7T+9d47OOfVngewrx05ciTHjx+/sX38+PEcPny4wUQAAAD0y8CW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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf3106a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JAFtrV+TGXXmfv2N7Va1L8oQk30vykrH7B1XVh6rq8qp6xtj9VVX1\nz1X1/qp6Trfab3y8w5K8Lslft9b+cQl+JAAAAADYa4MJADunJDkpyaOq6oTZR4Gr6h5J3p7kiiRH\nt9a+OfaZI5M8Msntkrxw7P4hGa0YfHSSNyX5aFU9pqoOqapfSvJvSd6c5DlL/DMBAAAAwB7bt+8C\nFlO3GcjmqnpvkucmOa+qrsnoseD3JjmxtfaNHT72gSSfTHLvJG8YG+sbVfXkJMcneUCSn0ryjox2\n/f3XJI9vrf3H0v5EAAAAALB3BhUAzmqtnZPknAX2/V5u3Bxkx7Z3JnnnIpYGAAAAABM1tEeAAQAA\nAIAxAkAAAAAAGDABIAAAAAAMmAAQAAAAAAZMAAgAAAAAAyYABAAAAIABEwACAAAAwIAJAAEAAABg\nwASAAAAAADBgAkAAAAAAGDABIAAAAAAMmAAQAAAAAAZMAAgAAAAAAyYABAAAAIABEwACAAAAwIAJ\nAAEAAABgwASAAAAAADBgAkAAAAAAGDABIAAAAAAMmAAQAAAAAAZMAAgAAAAAAyYABAAAAIABEwAC\nAAAAwIAJAAEAAABgwASAAAAAADBgAkAAAAAAGDABIAAAAAAMmAAQAAAAAAZMAAgAAAAAAyYABGC7\n1lpaa32XAQAAwCISAAKw3ZYtW7Jly5a+ywAAAGAR7dt3AQAsD9u2bcvJJ5+cqsoRRxyRVatW9V0S\nAAAAi8AKQACSJKeffnouueSSXHzxxTn99NP7LgcAAIBFIgAEIJdeemlOO+207dennXZaLrvssh4r\nAgAAYLEIAAHIqaeemm3btm2/3rZtW974xjf2WBEAAACLRQAIMOXOP//8nHXWWTe7f9ZZZ+ULX/hC\nDxUB0De7wgPAsAgAAaZYay0nnXTSnO0nnniiXwABppBd4QFgWASAAFNsy5Yt2bp165ztW7du9Qsg\nwJSZ3RX+lFNOucnrIQCAlUsACDClZn/Bm49fAAGmi13hAWB4BIAAAEASu8IDwFAJAAGm1KpVq3L8\n8cfP2++4447LqlWrJlARAH2zKzwADJMAEGCKzczMZP369XO2r1+/PjMzMxOsCIC+2BUeAIZLAAgw\nxaoqmzdvnrN98+bNqaoJVgRAH+wKDwDDJgAEmHLr1q3Lhg0bbnZ/w4YNWbduXQ8VATBpdoUHgGET\nAAKQY4455ibv+Vu1alWOPfbYHisCYFLsCg8AwycABCCrV6/Opk2btl9v2rQphxxySI8VAQAAsFgE\ngAAkSTZu3JhDDz00a9asycaNG/suB4AJsSs8AAzfvn0XAMDyMP4LoF/wAKbLzMxMzjjjjDnfA2hX\neABY2awABGC7mZkZv+ABTCG7wgPAsAkAAdiuqvyCBzCl7AoPAMMlAAQAAJLYFR4AhkoACAAAJLEr\nPAAMlQAQAADYzq7wADA8dgEGAAC2sys8AAyPABAAALgJO8IDwLAIAAEAxrTWksSO2Ew1f/4BYFi8\nAxAAYMyWLVuyZcuWvssAAIBFYwUgANtZ+cS027ZtW04++eRUVY444gjvPwMAYBCsAARgOyufmHan\nn356Lrnkklx88cU5/fTT+y4HAAAWhQAQgCQ3rnw65ZRTsm3btr7LgYm79NJLc9ppp22/Pu2003LZ\nZZf1WBEAACwOASAASax8glNPPfUm4fe2bdvyxje+sceKAABgcQgAAbDyial3/vnn56yzzrrZ/bPO\nOitf+MIXeqgIAAAWjwAQACufmGqttZx00klztp944onbN8gBAICVSAAIndaaX/CYSlY+Me22bNmS\nrVu3ztm+detWm+MAALCiCQChY/dTppGVT0y72c1v5mNzHAAAVjIBIMTup0wvK58AAACGTwAIsfsp\n08nKJ0hWrVqV448/ft5+xx13XFatWjWBigAAYPEJAJl6dj8FmG4zMzNZv379nO3r16/PzMzMBCsC\nAIDFJQBk6tn9lGll5ROMVFU2b948Z/vmzZtTVROsCAAAFpcAkKlm91Om3czMTNauXTtn+9q1a618\nYiqsW7cuGzZsuNn9DRs2ZN26dT1UBAAAi0cAyNSy+ynMz6onpskxxxxzk9Wuq1atyrHHHttjRQAA\nsDgEgEwtu5/CaB5ceOGFc7ZfcMEF5gFTY/Xq1dm0adP2602bNuWQQw7psSIAAFgcAkCmkt1PwTyA\nndm4cWMOPfTQrFmzJhs3buy7HAAAWBT79l0AAMByMb45js1vAAAYCisAmUp2PwXzAOYyMzNj8xsA\nAAZFAMjUOvLII3PAAQfM2X7AAQfkyCOPnGBFMHnmAdxcVdkABwCAQREAMrXOPffcXHXVVXO2X3XV\nVTn33HMnWBFMnnkAAAAwfAJAppLND8A8AAAAmBYCQAAAAAAYMAEgU8nmB2AeAAAATAsBIFNrZmYm\n69evn7N9/fr1doFk8GZmZrJ27do529euXWseAAAArHACQKZWVWXz5s1ztm/evNkukEw9cwAAAGDl\nEwAy1datW5cNGzbc7P6GDRuybt26HiqCydqyZUsuvPDCOdsvuOCCbNmyZYIVAQAAsNgEgEy9Y445\n5ibvN1u1alWOPfbYHiuCybALMAAAwHQQADL1Vq9enU2bNm2/3rRpUw455JAeKwIAAABYPAJASLJx\n48YceuihWbNmTTZu3Nh3OTARdgEGAACYDvv2XQAsB+NBiKCDaTIzM5MzzjgjW7du3Wm73bABAABW\nPisAoTMzMyPoYOrYDRsAAGD4BhkAVtUjquodVfWlqvpcVX25qk6oqj1+sVtVPaGq3ltVX6iqrVV1\nflX9SVXdejFrpz9VJehgKtkNGwAAYNgGFwBW1QuSfCjJRUnu11q7X5InJnlSks9U1X33YMzXJ/nf\nSc5N8pOttfVJfiPJc5N8oqoOW6z6AfpgN2wAAIDhGlQAWFVPT/KqJJ9orT2vtXZNkrTWvpzkKUnu\nlOTdVXX73RjzDzMK+s5orb26tXZDN+bHMgoB1yV5V1Xtt7g/DcDk2A0bAABguAYTAFbVbZO8obs8\nYcf21tpnk7wvyeFJ/miBYx6e5GXd5at3MuaZSb6U5EFJjtnNkgGWFbthAwAADNNgAsAkz0ly+yTX\nJTlrjj7v7I7HVtVtFjDm8zPaKflbrbVPzdHnXd3xBeUFcsAKNrsb9nHHHWc3bAAAgAHZt+8CFtET\nu+PnW2tXzdHnE93xVl3/0xc45id30Wd2zLsmeViSj84zJsCyZSdsAACA4RlEANi9f+/B3eUFu+g6\n3vaw7CIArKq7JPmJPRiz1wDwhhtuyBVXXJEk+f73v99nKTdz/fXX5+KLL+67jBVvzZo1ucUtbtF3\nGTdxu9vdLkly0EEHZZ99+l9YvFzngTmweMyDXVuucyAxDxbTcpsHy2kOJObBNFhucyAxD3aHebA4\nzIOVa3x+Ljc7+/tiuf0dMs6ftYUZRACY5LCMVvUlybfn6tRa+0FVXZXkgCTz7Qa8dux8zjF3aNvt\nHYYX2xVXXJHHPe5xfZfBlDr77LNz+9sveI+dJWMe0KflMA/MAfq0HOZAYh7QL/MAls88WK5W2vx8\n2tOe1ncJc/JnbWGGEgAePHZ+5Tx9r8woALzDIo053jbfmEmSqjp/jqa1c9wHAAAAgD0ylDWStx47\nv2aevtt28pm9GXPb2Pl8YwIAAADARA1lBeCPx873m6fvbPtcG4Xs7pjjbfONmSRpre30UeFuZeC6\nhYwBAAAAAAsxlADw0rHzA+bpO9v+nUUac7xtvjGX3EEHHZSzzz47yfJ7SacX/S6O5f6i3+Vguc4D\nc2DxmAe7tlznQGIeLKblNg+W0xxIzINpsNzmQGIe7A7zYHGYByvX+Pxcbm644Yb84Ac/uMm9Aw88\ncNlutOHP2sIMJQD8Zkar7w5IsmauTlV1qyQHdpdfnGfM/xg7n3PMHdrmG3PJ7bPPPttffrkcX4J5\nz3ves+8SmALLeR6YA0zCcp4DiXnAZJgHYB7AcjY+P5ejO9xhQVscsIIsz/h2N7XWWpKPdpf32EXX\n8baPzDPmJUkuWMwxAQAAAGDSBhEAdt7VHe9XVbeco89DuuM1SRay1nZ2zAfvos/smJck+dgCxgQA\nAACAiRlSAPjWJD9Ksn+Sx87R50nd8W2ttcsXMOapSW5IcnhVrZ9nzFNaazcstFgAAAAAmITBBICt\ntSuSvKi7fP6O7VW1LskTknwvyUvG7h9UVR+qqsur6hk7jHlBkhO7y9/fyZhHJbl/kguTnLQYPwcA\nAAAALKbBBICdUzIK4h5VVSfMPgpcVfdI8vYkVyQ5urX2zbHPHJnkkUlul+SFOxnzxUnOSLKpqjZX\n1T7dmA9M8ndJLkryS621Hy7RzwQAAAAAe2wouwAn2b4ZyOaqem+S5yY5r6quyeix4PcmObG19o0d\nPvaBJJ9Mcu8kb9jJmFdX1VOTvCPJbyV5dlVdm2S/JP8jyetba99doh8JAAAAAPbKoALAWa21c5Kc\ns8C+38uNG3nM1acl+YfuCwAAAABWjKE9AgwAAAAAjBEAAgAAAMCACQABAAAAYMAEgAAAAAAwYAJA\nAAAAABgwASAAAAAADJgAEAAAAAAGTAAIAAAAAAMmAAQAAACAARMAAgAAAMCACQABAAAAYMAEgAAA\nAAAwYAJAAAAAABgwASAAAAAADJgAEAAAAAAGTAAIAAAAAAMmAAQAAACAARMAAgAAAMCACQABAAAA\nYMCqtdZ3DXSq6gf777//bdeuXdt3KQAAAAD06MILL8zVV1/9w9bagXs7lgBwGamqi5MckOQbfdcy\npWaT1wt7rQL6ZR6AeQCJeQDmAJgHy8Fdk1zVWluztwMJAKFTVecnSWvtvn3XAn0xD8A8gMQ8AHMA\nzIOh8Q5AAAAAABgwASAAAAAADJgAEAAAAAAGTAAIAAAAAAMmAAQAAACAAbMLMAAAAAAMmBWAAAAA\nADBgAkAAAAAAGDABIAAAAAAMmAAQAAAAAAZMAAgAAAAAAyYABAAAAIABEwDCmKrap6oe0HcdAADL\nTVWtrqon910HTFpVHVBVR1TVb1fVS6tq/75rAthdAkBIUlX7VtUzk3wxyUl91wOTVlV3rKpXVNX5\nVXVVVV1ZVZ+tqhdX1W36rg+WWlXtX1Uv7P7cX9V9fbqqjq0q/15ialTVvavq+qpqO34luSTJ9/uu\nESahWxjwlKo6J8nXkjw5yceT/Flr7epei4Ml0v0ucLO//3fydUNV3bHvetk9+/ZdAPSpqvZL8swk\nL0pyj+72t/urCCavqtYl+Zckd96h6f7d19Or6jGttYsnXhxMQFXdKsn7kzw0yeUZBRxrkjwwyV8m\neUSSZ/RWIEzWizL3IoHzWmvvn2Qx0Iequk+S05I8OMlbk9y3tXZZv1XB0qqqRydZt8DuH2utfWcp\n62Hx+T/aTLvHJ/lUkockEW4wdapqVZJ3JflGkl9Icvck90yyKclXum7/OaN/BMNQ/W2SHyd5YGvt\n4NbaYRkF4u/q2p9eVT/TW3UwIVV1tyS/muRnM/q7f/zrPknMAwavqh6b5JMZhX+bW2ubhH9MieOT\nvCfJI5PcJcmddvL1m13ff+yjQPaOFYBMtdbaO2fPq+o/MlrxAdPkt5JclOSxrbXrxu5fWFVnZvQP\n4Hsl+bmquk9r7Ut9FAlLpaoemGS/JEeNz4HW2rer6ukZrQq/bUarAz/YT5UwMS9M8k+ttX/ruxDo\nQ1U9PKP/+bN/kj9prXk1EFOhqu6S5I5JZlpr1+6i35FJWpK3T6o2Fo8VgHCj1ncB0IOnJfnNHcK/\nJElr7Yokrxi7tX5iVcHk3DvJr88xB65M8uXu8vKJVgUTVlVrkjw7yVe7xx9hqnQByD9nFP59NMmf\n91sRTNS1SX5tnvDvgIyeGPpYa+0bE6uMRSMABJhSVbVvkne01r66i26fGDu/colLgolrrf3P1toP\ndtHlkIweDz5zQiVBX34vyaokf5zki1V1aVW9paqO6LkumJRXJFndnb+otXZDn8XAJLXWLmmtXTRP\ntyckuXU8/rtiCQABplRr7brW2qvm6XaLsfMvLGU9sNxU1c9m9A6cZ7XWLum5HFgyVbV/kufscPuQ\njDZK+2AXBNoRnsHqVr1u7C6/1Frzyge4uV+Nx39XNO8ABGBXDu+OH2ytfa3HOmCiquonM9r85hda\na2f3XQ8spdba1VV1cJLbZ/RY/BFJfr07T0ZB4GFV9fhdPR4GK9jRuXFxzBer6gVJHp7k4CSHZbRZ\n2nuSnNq9HgKmSlXdNsmGJB/3+O/KZQUgALvyuO748l6rgAmpqrVV9Zokn05y1yRvq6pjei4Lllxr\n7YbW2ndbax9trb0yo51/NyX5btfl55L8RW8FwtJ67Nj54Un+T5KjW2uPSvLzGb0K4oQk53W7ZcO0\neWJGr4nw+O8KJgAEYKe6x72emuS01to5fdcDS62qHpPkJUnWJbmiu31wkjdW1Uv7qgv60EbemuSn\nk3yru31sVR3YY1mwVA7vjtcmeXxr7cOz7wBsrV2Y5BeTfD7Jf0pyRlVVL1VCf56a0eO/AsAVTAAI\nwFxekuSiJM/tuxCYhNba+1trm1prj0ty5yTPyo2b3/xhVR3WW3HQky78mH0/4K2SPKjHcmCpzG7+\n8fnW2rd3bOwefX9dd/mQJI+ZVGHQt6q6XZKj4vHfFU8ACMDNVNVRSX4lyYZ5dkiFQWqtXdta+7sk\nT+lu7ZfRI5AwdVprZyX5ene5ps9aYIlc3x139X6/D4+dP2oJa4Hl5peS3DJW/614AkAAbqKq1mf0\nf7mPaq19a77+MGTdBiCzO2Dfuc9aoGf/3h1/1GsVsDQu7o633kWfi8bOD13CWmC5eWp3tPvvCicA\nBGC7qrp3ktOT/HJr7ct91wPLxBe7o9WwTLMfd8dv9loFLI3zuuOuNvi4buz8x3P2ggGpqjtm9Mj7\nx1trF83Xn+VNAAhAkqSq7pXkn5L8Wmvt/L7rgWVkv+748V6rgH7dJ8m3k3y270JgCby7Ox5cVYfP\n0edOY+dfWtJqYPk4Osm+Sc7ouxD2ngAQgFTV2iT/nORZrbWd/nJXVbepqldPtjLoV1Xtn+ThST7R\nWvtk3/VAH6rqQUnun+TE2Z1RYWD+Mcl3uvNfnKPPuu54TZIzl7wiWB48/jsgAkC40e2743677AUD\n0/2f7n9J8sok36+qe+7wda+qOjLJOUk+1GOpsOiq6mFV9bKqevAcXf4kSSV59gTLgomqqjdX1Yeq\n6r9V1S13aDswyd9k9N+A1/ZSICyx1tpVSf6gu/y97s/9jmb/O/Ca1tr/m0xl0J+qulOSn4nHfwej\nWmt91wC9q6pHJ3lfkltktPvXw1prW/utCpZeVf1Ekn9LcvgCun8jyd1ba9fP2xNWiKp6f5JHd5fv\nyENfwA4AAAcDSURBVCjg2JpkdZLnJfnpjB6L97gXg1RV+yW5IsmtultfTvLCJB9Lcr8kr07yqSS/\n3Vq7tpciYUKq6oQkz0/ygYz+7v9md//XMwrC/zbJc/xbiGlQVc9N8vokv99ae03f9bD3BIBMtap6\nXpLfzc7Dj68lOaG1dsoka4JJqarbJvlMkrUL/Mgft9b++xKWBBNXVQ9N8hcZPd54myTfz2iTgw8n\nObO19r4ey4OJqKojkrwgo8D74Iw2OPhakg8meXNr7by5Pw3DUlVHJTk2yX/JKBy/PqP/Lryptfae\nPmuDSaqqDyd5RJLDW2tf77se9p4AEAAAAAAGzDsAAQAAAGDABIAAAAAAMGACQAAAAAAYMAEgAAAA\nAAyYABAAAAAABkwACAAAAAADJgAEAAAAgAETAAIAAADAgAkAAQAAAGDABIAAAAAAMGACQAAAAAAY\nMAEgAAAAAAyYABAAAAAABmzfvgsAAGD5qqrnJTmxu7w2ydeT/DDJDUkOT3KHru1rSb6b0b8v75jk\nzmPD3L219rWlrxYAgJ2xAhAAgIV4WZLbt9bu1Vp7YGvtQUnePd7eWntQa+0BrbW7ZBQObumjUAAA\nbkoACADAfD7cWntpa+3KhX6gtfb1JM9IcvXSlQUAwEIIAAEAmM/f78mHWmuXJHnfItcCAMBu8g5A\nAAB25WNJvrUXn39tku8vUi0AAOwBASAAAHNqrX1sLz//r4tVCwAAe8YjwAAALKmqOquq2tjXDVX1\npaq601ifh1fVxWN9/mSs7XZV9V+r6v3d9d2r6u+q6pKqurKqPlZVR+/i+9+jqk6tqnOr6qtVdVlV\nvaeqHrO0PzkAwPIgAAQAYEm11jYkedHYrb9srd2ntfbtsT4fTXK3JNcn+bPW2p9W1UOq6pwklyb5\nqySPrqojknwmyeOSVJIDkvx0krdX1Qt3/N5V9cQk70nyptbaka21eyT5jSRHJTm3qn53CX5kAIBl\nRQAIAMAkvCbJ17vzR87R56FJrun6Jsl5SY5OsmWsz0uSzLTWVrfWVid5UpIfdm0vr6oHzHasqvVJ\n/iHJ77TWPjN7v7V2ZpJ3dJevrqqf2OOfCgBgBRAAAgCw5Fpr1yZ5ZXd5/6o6cifdnpXkn1prV3Sf\nua619qMknx7rs7G19qmxcc9M8nvd5S2S/M5Y31cmuaK1trOdiGfv7ZfkKbv54wAArCg2AQEAYFLe\nnOSPkhyW5IVJzp1tqKpbJ/nlJE/cyeeunz1prV26k/a3Jnl5kkOS/Gw33h2S/HySH1XVzjYyWZUb\ndzc+aDd/DgCAFUUACADARLTWrq6qE5K8NslRVfWA1tpnu+ajM3rX3wf2cNyPJPnFjMLFJHlQRu8I\n/Hxr7eF7XTwAwArmEWAAACbpTUku687HN+14VpK3tNbaHo77ze54bXe8Q3c8cA/HAwAYDAEgAAAT\n01q7KsmJ3eVTqupuVXW3JEckecteDL2tO84GgVd3x3tW1f57MS4AwIonAAQAYNJOTnJ5Rq+jeX6S\nTUnOba19Yy/GnN3J9wPd8avdcf8kT9vVB6vqT6tqzV58bwCAZU0ACADAntqjf0u21n6Y5HXd5W90\nX2/e0yKq6hZJHpKkJTm1u/25JBd356+sqrvO8dlnJnlga+3inbUDAAyBABAAgD11u7Hz3X3X3uuT\n/DDJAUlum+RdC/lQt7vvjo5OcniSN7TW/j1JWmvXJ3lV1746yUeq6leq6oCq2qeq7lFVJ3Z1bN7N\n2gEAVhQBIAAAu62qVicZ3133ibvzrr3W2uVJ/qa7/PvW2tW76j/mnVV137E6HpnklCT/kNHjxONe\nl+R/ded37fpcmdH7AS9M8uwkT26tfWWhdQMArEQCQAAAFqyqHldVX8xos43x1XhHJvlOVX2u29Rj\nId7fHXfn8d+XJfnrqvpiVZ2X5M+T/E5r7amttevGO7bWbkiyMckzk3w4yQ+SXJPk60nekOS+rbVz\nd+N7AwCsSNVa67sGAACmUFW9IslRrbWfmqffS5O8JElaazWB0gAABsUKQAAAJq6qbpnR7r9v7LsW\nAIChEwACANCHP0hSSd7WdyEAAEO3b98FAAAwbFV1WJKzk6xKcmaS65O8IMkxrbVtCxhiv7GxVi3w\nMwAAdASAAAAstYcmuV93/vvd8cwkfz3fB7tHhR8xduvRGYWJAAAskEeAAQBYau9N8u4k25J8K8nL\nkzylzbMbXVUdn+SSJI8au/3uqvpKVT1vqYoFABgauwADAAAAwIBZAQgAAAAAAyYABAAAAIABEwAC\nAAAAwIAJAAEAAABgwASAAAAAADBgAkAAAAAAGDABIAAAAAAMmAAQAAAAAAZMAAgAAAAAAyYABAAA\nAIABEwACAAAAwIAJAAEAAABgwASAAAAAADBgAkAAAAAAGLD/D/ah/DeFrl/GAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf2e3ded0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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AfqSqfvZCC6rqJ5O8Pae+Hvw7yzADLGp6ejqbNm1a8PimTZsyPe0ZNQAAAMDV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\n20z0vyYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febf3c11050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['RI'],bins=40)\n",
-    "plt.xlabel('RI')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "for i in feat:\n",
+    "    sns.boxplot(train['Type'],train[i])\n",
+    "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(train)) < 0.8\n",
+    "test = train[~msk]\n",
+    "train = train[msk]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8JyJeBDzYelxANUfj/la7jwA/Afb0vlpJvTfGyn80eOpE6kd9ETSAF1OdAsnW4+Ot7Z+n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\nrkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "(146, 10)"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Mg'],bins=40)\n",
-    "plt.xlabel('Mg')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "train.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SsjOASJKk7AwgkiQpOwOIJEnKzgAiSZKyM4BIkqTsDCCSJCk7A4gkScquKwJIRBwTEZdF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\ndgYQSZKUnQFEkiRl9/8BJRg89E42LPAAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "(51, 10)"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Ca'],bins=40)\n",
-    "plt.xlabel('Ca')\n",
-    "plt.ylabel(\"freq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "test.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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637237/SuJ/sycEhqXDe/VKURdhjwTaqVibtZnfMgYBfVVbU/vZ82LwOOBI6q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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "2    54\n",
+       "1    47\n",
+       "7    17\n",
+       "3    14\n",
+       "5     8\n",
+       "6     6\n",
+       "Name: Type, dtype: int64"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Si'],bins=40)\n",
-    "plt.xlabel('Si')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "train['Type'].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AngVcFxG/t61DRJwLnAOcBRxBde6PdRHx7P6XK0mSOtXNib96KjNfP/N+RJxJNf12DLix\n1fwe4MLM/EqrzxnAZuAU4Kq+FStJkroyCHs45tqHaibMrwEi4kBgP+Ab2zpk5sPA94Cj6ihQkiR1\nZqACR0QEcAlwY2b+tNW8H1UA2Tyn++bWMkmSNOBq/0pljsuAVwKvrbsQSZLUOwMTOCLi74DXA0dn\n5v0zFm0CAljG7L0cy6hOub4dq4Elc9oarZskSbu2ZrNJs9mc1TY9PV3kuQYicLTCxhuBYzNzcuay\nzNwQEZuA44HbW/33pprVcun217wGWFmgYkmShl+j0aDRmP0hfHx8nLGxsZ4/V+2BIyIuo9rl8Abg\nkYhY1lo0nZlbWj9fApwXEXcD9wIXAhuBq/tcriRJ6kLtgQM4m+qg0G/OaX8b8BmAzLw4IvYELqea\nxfJt4OTMfKKPdUqSpC7VHjgys62ZMpl5AXBB0WIkSVIRAzUtVpIkjSYDhyRJKs7AIUmSijNwSJKk\n4gwckiSpOAOHJEkqzsAhSZKKM3BIkqTiDBySJKk4A4ckSSrOwCFJkoozcEiSpOIMHJIkqTgDhyRJ\nKs7AIUmSijNwSJKk4gwckiSpOAOHJEkqzsAhSZKKM3BIkqTiDBySJKm43esuQNrVTUxMtNVv6dKl\nLF++vHA1klSGgUOqzf3AIlatWtVW78WL92T9+glDh6ShZOCQavMQsBW4Elixg74TbNmyiqmpKQOH\npKE0EIEjIo4G/hIYA14AnJKZX5qx/ArgrXMedm1mvr5/VUqlrABW1l2EJBU1KAeN7gX8CHgXkAv0\nuQZYBuzXujX6U5okSdpZA7GHIzOvBa4FiIhYoNvjmflA/6qSJEm9Mih7ONpxXERsjog7I+KyiHhe\n3QVJkqT2DMQejjZcA3we2AC8DPgQsDYijsrMhb6CkSRJA2IoAkdmXjXj7k8i4sfAPcBxwA0LP3I1\nsGROWwMP/5AkCZrNJs1mc1bb9PR0kecaisAxV2ZuiIgp4CC2GzjW4NH/kiTNr9Fo0GjM/hA+Pj7O\n2NhYz59rmI7h+P8iYn9gX6ozJ0mSpAE3EHs4ImIvqr0V22aovDQiDgV+3bqdT3UMx6ZWv4uAu4B1\n/a9WkiR1aiACB3A41Vcj2bp9tNX+aapzcxwCnAHsA9xHFTTen5lP9r9USZLUqYEIHJn5Lbb/9c5J\n/apFkiT13kAEDqnfJicnmZqa2mG/dq/kKknaPgOHdjmTk5McfPAKtmx5tO5SJGmXYeDQLmdqaqoV\nNtq5Suta4H3li5KkEWfg0C6snau0+pWKJPXCUJ6HQ5IkDRcDhyRJKs7AIUmSijNwSJKk4gwckiSp\nOAOHJEkqzsAhSZKKM3BIkqTiDBySJKk4A4ckSSrOwCFJkoozcEiSpOIMHJIkqTgDhyRJKs7AIUmS\nijNwSJKk4gwckiSpOAOHJEkqzsAhSZKKG4jAERFHR8SXIuKXEbE1It4wT58PRMR9EfFoRHwtIg6q\no1ZJktS5gQgcwF7Aj4B3ATl3YUScC5wDnAUcATwCrIuIZ/ezSEmS1J3d6y4AIDOvBa4FiIiYp8t7\ngAsz8yutPmcAm4FTgKv6VackSerOoOzhWFBEHAjsB3xjW1tmPgx8DziqrrokSVL7Bj5wUIWNpNqj\nMdPm1jJJkjTghiFwSJKkITcQx3DswCYggGXM3suxDLh1+w9dDSyZ09Zo3SRJ2rU1m02azeastunp\n6SLPNfCBIzM3RMQm4HjgdoCI2Bs4Erh0+49eA6wsXKEkScOp0WjQaMz+ED4+Ps7Y2FjPn2sgAkdE\n7AUcRLUnA+ClEXEo8OvM/AVwCXBeRNwN3AtcCGwErq6hXEmS1KGBCBzA4cANVAeHJvDRVvungbdn\n5sURsSdwObAP8G3g5Mx8oo5iJUlSZwYicGTmt9jBAayZeQFwQT/qkSRJveUsFUmSVJyBQ5IkFWfg\nkCRJxRk4JElScQYOSZJUnIFDkiQVZ+CQJEnFGTgkSVJxBg5JklScgUOSJBVn4JAkScUZOCRJUnEG\nDkmSVJyBQ5IkFTcQl6cfBJs2bWJ8fLytvkuXLmX58uWFK5IkaXQYOFpOO20VTzyxpa2+ixfvyfr1\nE4YOSZLaZOBoqcLGlcCKHfScYMuWVUxNTRk4JElqk4FjlhXAyrqLkCRp5HjQqCRJKs7AIUmSijNw\nSJKk4jyGQ301OTnJ1NRUW32dfixJo8PAob6ZnJzk4INXsGXLo231d/qxJI0OA4f6ZmpqqhU2nH4s\nSbuaoQgcEXE+cP6c5jsz85V11KOd5fRjSdrVDEXgaLkDOB6I1v2naqxFkiR1YJgCx1OZ+UDdRUiS\npM4N07TYP4iIX0bEPRFxZUS8uO6CJElSe4YlcHwXOBN4HXA2cCDwfyNirzqLkiRJ7RmKr1Qyc92M\nu3dExPeBfwHeAlyx8CNXA0vmtDVaN0mSdm3NZpNmszmrbXp6ushzDUXgmCszpyPiLuCg7fdcg7Mh\nJEmaX6PRoNGY/SF8fHycsbGxnj/XsHylMktEPIcqbNxfdy2SJGnHhiJwRMRHIuKYiHhJRPxr4IvA\nk0BzBw+VJEkDYFi+Utkf+BywL/AAcCPwmsx8sNaqJElSW4YicGSmR3lKkjTEhiJwDDOvjtofnYzz\nxMRE4WokSXMZOAry6qj90ek4S5L6z8BRkFdH7Y/OxhlgLfC+skVJkmYxcPSFV0ftj3bH2a9UJKnf\nhmJarCRJGm4GDkmSVJyBQ5IkFecxHF1qZ2ql0y/Va+2+p5xiLWnQGDg6dj+wiFWrVtVdiHYpnb3v\nnGItadAYODr2ELCV9qZgOv1SvdLJ+84p1pIGj4Gja+1MwfQrFfWaU6wlDScPGpUkScUZOCRJUnEG\nDkmSVJzHcAyYTqbSDsrUx3av1Oo0YUnadRk4Bkbn020HYeqjV2qVJLXDwDEwOpn2CIMy9bGzK7U6\nTViSdlUGjoEzrNMenSYsSVqYB41KkqTiDBySJKk4A4ckSSrOYzg00Lwq73Brd8o0dD7Nu+S6S+mk\n5scff5w99tijrb67wtjpGZ38/8Hg/B8aODSgvCrvsOt0ynQn07xLrruUzqeQ7wY83VbPUR87PaOb\nUxEMyv/hUAWOiHg38F+B/YDbgP+UmT+ot6rB12w2aTQadZfRoRJX5W0CwzYOZfTjPdHZlOnOpnn3\nat39/N3obgp5v8buWuCknV73KBj07WVn7yMYpP/DoQkcEfFnwEeBs4DvA6uBdRHx8sxsf9/SLmjQ\nf4G2r5fTbQ0c2/T3PVFyqvfOrbue341O3tP9GrsLgP9W6HmGy/BsL4fvFArDdNDoauDyzPxMZt4J\nnA08Cry93rIkSdKODEXgiIhnAWPAN7a1ZWYCXweOqqsuSZLUnmH5SmUp1RFUm+e0bwYOXvhh7exq\n39B1UZIkqT3DEjg6tbj6p70ZDrvttjtPP/0U1YFaOwop32n9W2df2BaU1q5du8NpoRs3bqTZbLJ1\n69Y21guLFi1qu++GDdsC2zCM3UbgswNQR+l17/i9sXHjRj772c8Cnf1/d9K/s/dG++/nXq575jhs\n08l4DMbvSi/Gbtvvxs6tu9TYlVz33L7zvSfqqGMhnb2PYNv/YSenD5jRd3HbD2pDVN9MDLbWVyqP\nAv8+M780o/1TwJLMfNOc/qcx/2+PJElqz+mZ+blerWwo9nBk5pMRcQtwPPAlgIiI1v2/nech64DT\ngXuBLX0qU5KkUbAYOIDqb2nPDMUeDoCIeAvwKarZKdumxb4ZeEVmPlBjaZIkaQeGYg8HQGZeFRFL\ngQ8Ay4AfAa8zbEiSNPiGZg+HJEkaXkNxHg5JkjTcDBySJKm4oQ0cEfHuiNgQEY9FxHcj4tU76H9c\nRNwSEVsi4q6IeGu/ai2pk3GIiDdFxHUR8auImI6ImyLixH7WW1Kn74kZj3ttRDwZEeOla+yHLn43\nnh0R/yMi7m39fvw8Is7sU7lFdTEWp0fEjyLikYi4LyL+MSKe1696S4iIoyPiSxHxy4jYGhFvaOMx\nI7e97HQcRnl72c17YsZju95eDmXgmHEht/OBf0V15dh1rYNK5+t/APAVqlOjHwp8HPhfEfEn/ai3\nlE7HATgGuA44meqqPzcAX46IQ/tQblFdjMW2xy0BPk11mvyh1+U4/B/gj4C3AS+nusLd+sKlFtfF\nduK1VO+FTwKvpJoFdwTwD30puJy9qA6yfxeww4P2RnV7SYfjwAhvL+l8LIAebC8zc+huwHeBj8+4\nH1SnyvurBfpfBNw+p60JrK37tfRzHBZYxx3AeXW/lrrGovU++O9Uf5TG634d/R4HqmuS/xrYp+7a\nB2As/gvwszlt5wCTdb+WHo7JVuANO+gzktvLTsdhgceNxPay27HY2e3l0O3h6PJCbq/hdxPZuu30\nH3i9uKBd6+Rpz6X6gzO0uh2LiHgbcCDVL9DQ63Ic/h3wQ+DciNgYEesj4iMR0dNTGvdbl2NxM/Di\niDi5tY5lwJ8CXy1b7cAZue1lL4zK9rJbvdheDs15OGbo5kJu+y3Qf++I2CMzH+9tiX3R5QXtZvlL\nql1rV/Wwrjp0PBYR8QfAB4F/k5lbq23J0OvmPfFS4GiqM/Ke0lrH/wSeB/x5mTL7ouOxyMybImIV\n8E+twLU71ZmNzylZ6AAaxe1lL4zK9rJjvdpeDt0eDvVGVNebeR/wp5k5VXc9/RQRi6iutXN+Zt6z\nrbnGkuq0iGqX6mmZ+cPMvBb4C+CtEbFHvaX1V0S8kup4hQuovrN/HdUnustrLEsDwO1lb7aXw7iH\nYwp4mupsozMtAzYt8JhNC/R/eIjTejfjAEBEnEp1INybM/OGMuX1Vadj8VzgcOCwiLi01baIaq/p\nE8CJmfnNQrWW1M174n7gl5n52xltE1QblP2Be+Z91ODrZizeC3wnMz/Wun9HRLwL+HZE/E1mzv3U\nP6pGcXvZtRHcXnaqZ9vLodvDkZlPAtsu5AbMupDbTQs87OaZ/VtObLUPpS7HgYhoAP8InNr6NDv0\nuhiLh4FXAYdRHYV/KPD3wJ2tn79XuOQiunxPfAd4YUTsOaPtYKq9HhsLlVpcl2OxJ/DUnLatVEfx\n70p7wEZue9mtUdxedqF328u6j5Dt8qjat1Bdrv4M4BVUuzwfBH6/tfxDwKdn9D8A+A3V0dcHU00F\negI4oe7X0udxOK31us+m+sSy7bZ33a+l32Mxz+NHZZZKp++JvYB/Af4JWEE1FXA98Pd1v5YaxuKt\nwOOt348DgddSXSjyprpfy06Ow15UfxgOowpQ/7l1/8ULjMOobi87HYdR3l52NBbzPL6r7WXtL3wn\nBuxdVJeff4wqeR8+Y9kVwPVz+h9D9YnnMeBnwH+o+zX0exyo5pE/Pc/tf9f9Oup4T8x57EgEjm7G\ngercG+uA31KFj4uBPep+HTWNxbuBH7fGYiPVOQdeUPfr2MkxOLb1R2Xe3/tdZXvZ6TiM8vaym/fE\nnMd3tb304m2SJKm4oTuGQ5IkDR8DhyRJKs7AIUmSijNwSJKk4gwckiSpOAOHJEkqzsAhSZKKM3BI\nkqTiDBySJKk4A4ekvomIKyLiC3Pa3hwRj0XE6rrqklTeMF6eXtKIiIh3AJ8A/mNmfqbueiSV4x4O\nSbWIiL8CPg78mWFDGn3u4ZDUdxHxYeCdwL/NzG/WXI6kPjBwSOq31wNvBI43bEi7Dr9SkdRvtwH3\nAh+IiL1qrkVSnxg4JPXbL4HjgBcB1xo6pF2DgUNS32XmL4Bjgf2AdYYOafQZOCTVIjM3UoWO5wPX\nRcRzay5JUkEGDkm1ycz7qELHvlRfrzyn5pIkFRKZWXcNkiRpxLmHQ5IkFWfgkCRJxRk4JElScQYO\nSZJUnIFDkiQVZ+CQJEnFGTgkSVJxBg5JklScgUOSJBVn4JAkScUZOCRJUnEGDkmSVNz/A46ozj5K\n8LGRAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "1    20\n",
+       "2    13\n",
+       "7     9\n",
+       "5     4\n",
+       "3     3\n",
+       "6     2\n",
+       "Name: Type, dtype: int64"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['K'],bins=40)\n",
-    "plt.xlabel('K')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "test['Type'].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import svm\n",
+    "from sklearn.model_selection import cross_val_score,GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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h5TFgbVfbWuDf1D+vBwJYxI6zKYuAe3a96pXA/l1tw/VDkqS5bWRkhJGRkR3axsfH+7Lt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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "{'C': 1, 'gamma': 1}"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Ba'],bins=40)\n",
-    "plt.xlabel('Ba')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "param_grid={'C':[0.01,0.1,1,5,10], 'gamma' :[0.01, 0.1, 1,6]}\n",
+    "grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=5)\n",
+    "grid_search.fit(train[feat],train['Type'])\n",
+    "grid_search.best_params_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8E7gKeA/waNfic4EVmfnlzPw34HTgucCp/a1SkiT1wkCFFOBy4EuZeXNnY0QsAhYCN421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\nkqQiGVIkSVKRDCmSJKlI/x8tJD0sUnAs6wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma=1, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Fe'],bins=40)\n",
-    "plt.xlabel('Fe')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "model=svm.SVC(kernel='rbf',C=1,gamma=1)\n",
+    "model.fit(train[feat],train['Type'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pred=model.predict(test[feat])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhwAAAF5CAYAAADUL/MIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             precision    recall  f1-score   support\n",
+      "\n",
+      "          1       0.78      0.90      0.84        20\n",
+      "          2       0.58      0.85      0.69        13\n",
+      "          3       0.00      0.00      0.00         3\n",
+      "          5       1.00      0.25      0.40         4\n",
+      "          6       1.00      0.50      0.67         2\n",
+      "          7       1.00      0.78      0.88         9\n",
+      "\n",
+      "avg / total       0.75      0.75      0.72        51\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Al'],bins=40)\n",
-    "plt.xlabel('Al')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(test['Type'],pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 80,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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lZ+CQJEnZGTgkSVJ2Bg5JkpSdgUOSJGVXm8AREU+MiI9ERCsidkXETRFRjy+QlyRplavF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\nqNEJv/gAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xcc55d85be0>"
+       "0.74509803921568629"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plt.clf()\n",
-    "plt.hist(df_bc['Na'],bins=40)\n",
-    "plt.xlabel('Na')\n",
-    "plt.ylabel(\"frq\")\n",
-    "fig1=plt.gcf()\n",
-    "fig1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#feature engineering can be done seeing the multivariate plots or get domain knowledge\n",
-    "#to tackle correlation PCA can be used or using feature importance (RFE) select few of them.\n",
-    "#and then SVM can be applied\n"
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(test['Type'],pred)"
    ]
   },
   {
@@ -1929,21 +2039,21 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python [conda env:py27]",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "conda-env-py27-py"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,