From 0bfccbdf53019345dc74e05109e83b7079abfadd Mon Sep 17 00:00:00 2001
From: Musunuri <Siva.Musunuri@McKesson.com>
Date: Wed, 17 Jan 2018 13:23:41 -0600
Subject: [PATCH] answers

---
 ...-01-17_morning assignment-checkpoint.ipynb | 741 ++++++++++++++++++
 2018-01-17_morning assignment.ipynb           | 741 ++++++++++++++++++
 2 files changed, 1482 insertions(+)
 create mode 100644 .ipynb_checkpoints/2018-01-17_morning assignment-checkpoint.ipynb
 create mode 100644 2018-01-17_morning assignment.ipynb

diff --git a/.ipynb_checkpoints/2018-01-17_morning assignment-checkpoint.ipynb b/.ipynb_checkpoints/2018-01-17_morning assignment-checkpoint.ipynb
new file mode 100644
index 0000000..4e04a85
--- /dev/null
+++ b/.ipynb_checkpoints/2018-01-17_morning assignment-checkpoint.ipynb	
@@ -0,0 +1,741 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn import linear_model\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "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>admit</th>\n",
+       "      <th>gre</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>380</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>660</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>800</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>640</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>520</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   admit  gre   gpa  rank\n",
+       "0      0  380  3.61     3\n",
+       "1      1  660  3.67     3\n",
+       "2      1  800  4.00     1\n",
+       "3      1  640  3.19     4\n",
+       "4      0  520  2.93     4"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('grad.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "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>admit</th>\n",
+       "      <th>gre</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.317500</td>\n",
+       "      <td>587.700000</td>\n",
+       "      <td>3.389900</td>\n",
+       "      <td>2.48500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.466087</td>\n",
+       "      <td>115.516536</td>\n",
+       "      <td>0.380567</td>\n",
+       "      <td>0.94446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>220.000000</td>\n",
+       "      <td>2.260000</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>520.000000</td>\n",
+       "      <td>3.130000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>580.000000</td>\n",
+       "      <td>3.395000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>660.000000</td>\n",
+       "      <td>3.670000</td>\n",
+       "      <td>3.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            admit         gre         gpa       rank\n",
+       "count  400.000000  400.000000  400.000000  400.00000\n",
+       "mean     0.317500  587.700000    3.389900    2.48500\n",
+       "std      0.466087  115.516536    0.380567    0.94446\n",
+       "min      0.000000  220.000000    2.260000    1.00000\n",
+       "25%      0.000000  520.000000    3.130000    2.00000\n",
+       "50%      0.000000  580.000000    3.395000    2.00000\n",
+       "75%      1.000000  660.000000    3.670000    3.00000\n",
+       "max      1.000000  800.000000    4.000000    4.00000"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.crosstab(index=[df['admit']], columns=[df['rank']], margins=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "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>rank</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>admit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>97</td>\n",
+       "      <td>93</td>\n",
+       "      <td>55</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33</td>\n",
+       "      <td>54</td>\n",
+       "      <td>28</td>\n",
+       "      <td>12</td>\n",
+       "      <td>127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>61</td>\n",
+       "      <td>151</td>\n",
+       "      <td>121</td>\n",
+       "      <td>67</td>\n",
+       "      <td>400</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "rank    1    2    3   4  All\n",
+       "admit                       \n",
+       "0      28   97   93  55  273\n",
+       "1      33   54   28  12  127\n",
+       "All    61  151  121  67  400"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10c5ae80>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f8f5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df2.plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10cbffd0>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SxxkEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEg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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c8c7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot( x='gpa', y='gre', kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001127C3C8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011311C50>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000112CC160>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000117DD7F0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011831AC8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011831B00>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000010C331D0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001191A1D0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000119715C0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000119E4128>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011A48CC0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011A85F98>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011B17630>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011B28978>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011BE3470>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011C36B38>]], dtype=object)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CQA/gmeC4RERERETGZUITa2vtdiBijNkApIATxphPZw5/E3gJeBl4eCLjEhEREREZL9dE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v/64YN3NmJFWabDHTVcGI1yqF2V+bodQ9doq/UxHE7RUTcTFO9sqaQu4MalC2oKQaQQgheOdjAR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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f71320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, figsize=(12,12))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df.admit\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = df[['gre','gpa','rank']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression().fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.71999999999999997"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.0012195 ,  0.37446307, -0.64983504]])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.23945284])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import combinations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "for i in range(1,11):\n",
+    "    total += len(list(combinations(['gpa', 'gre', 'rank'],i)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "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>Coef</th>\n",
+       "      <th>Columns</th>\n",
+       "      <th>Int</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[[0.319303457297]]</td>\n",
+       "      <td>gpa</td>\n",
+       "      <td>[-1.81404627438]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[[0.00139291052464]]</td>\n",
+       "      <td>gre</td>\n",
+       "      <td>[-1.54917610063]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[[-0.504582785938]]</td>\n",
+       "      <td>rank</td>\n",
+       "      <td>[0.429516683081]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[[0.12683616908, 0.00124646717199]]</td>\n",
+       "      <td>gpa, gre</td>\n",
+       "      <td>[-1.891342199]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[[0.504696239715, -0.568382775255]]</td>\n",
+       "      <td>gpa, rank</td>\n",
+       "      <td>[-1.10398498603]</td>\n",
+       "      <td>0.757576</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Coef    Columns               Int     Score\n",
+       "0                   [[0.319303457297]]        gpa  [-1.81404627438]  0.681818\n",
+       "1                 [[0.00139291052464]]        gre  [-1.54917610063]  0.681818\n",
+       "2                  [[-0.504582785938]]       rank  [0.429516683081]  0.681818\n",
+       "3  [[0.12683616908, 0.00124646717199]]   gpa, gre    [-1.891342199]  0.681818\n",
+       "4  [[0.504696239715, -0.568382775255]]  gpa, rank  [-1.10398498603]  0.757576"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for i in range(1,11):\n",
+    "    combos = list(combinations(['gpa', 'gre', 'rank'],i))\n",
+    "    for j,com in enumerate(combos):\n",
+    "        y = df.admit\n",
+    "        X = pd.DataFrame(df, columns=com)\n",
+    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+    "        model = linear_model.LogisticRegression(fit_intercept=True).fit(X_train, y_train)\n",
+    "        score = model.score(X_test, y_test)\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Columns':s, 'Coef':model.coef_,'Int':model.intercept_})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df3 = pd.DataFrame(rows)\n",
+    "df3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_hat = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[90,  0],\n",
+       "       [32, 10]], dtype=int64)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test, y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75757575757575757"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2018-01-17_morning assignment.ipynb b/2018-01-17_morning assignment.ipynb
new file mode 100644
index 0000000..4e04a85
--- /dev/null
+++ b/2018-01-17_morning assignment.ipynb	
@@ -0,0 +1,741 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn import linear_model\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "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>admit</th>\n",
+       "      <th>gre</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>380</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>660</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>800</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>640</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>520</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   admit  gre   gpa  rank\n",
+       "0      0  380  3.61     3\n",
+       "1      1  660  3.67     3\n",
+       "2      1  800  4.00     1\n",
+       "3      1  640  3.19     4\n",
+       "4      0  520  2.93     4"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('grad.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "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>admit</th>\n",
+       "      <th>gre</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.317500</td>\n",
+       "      <td>587.700000</td>\n",
+       "      <td>3.389900</td>\n",
+       "      <td>2.48500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.466087</td>\n",
+       "      <td>115.516536</td>\n",
+       "      <td>0.380567</td>\n",
+       "      <td>0.94446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>220.000000</td>\n",
+       "      <td>2.260000</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>520.000000</td>\n",
+       "      <td>3.130000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>580.000000</td>\n",
+       "      <td>3.395000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>660.000000</td>\n",
+       "      <td>3.670000</td>\n",
+       "      <td>3.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            admit         gre         gpa       rank\n",
+       "count  400.000000  400.000000  400.000000  400.00000\n",
+       "mean     0.317500  587.700000    3.389900    2.48500\n",
+       "std      0.466087  115.516536    0.380567    0.94446\n",
+       "min      0.000000  220.000000    2.260000    1.00000\n",
+       "25%      0.000000  520.000000    3.130000    2.00000\n",
+       "50%      0.000000  580.000000    3.395000    2.00000\n",
+       "75%      1.000000  660.000000    3.670000    3.00000\n",
+       "max      1.000000  800.000000    4.000000    4.00000"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.crosstab(index=[df['admit']], columns=[df['rank']], margins=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "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>rank</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>admit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>97</td>\n",
+       "      <td>93</td>\n",
+       "      <td>55</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33</td>\n",
+       "      <td>54</td>\n",
+       "      <td>28</td>\n",
+       "      <td>12</td>\n",
+       "      <td>127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>61</td>\n",
+       "      <td>151</td>\n",
+       "      <td>121</td>\n",
+       "      <td>67</td>\n",
+       "      <td>400</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "rank    1    2    3   4  All\n",
+       "admit                       \n",
+       "0      28   97   93  55  273\n",
+       "1      33   54   28  12  127\n",
+       "All    61  151  121  67  400"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10c5ae80>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f8f5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df2.plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10cbffd0>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SxxkEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEg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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c8c7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot( x='gpa', y='gre', kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001127C3C8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011311C50>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000112CC160>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000117DD7F0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011831AC8>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011831B00>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000010C331D0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001191A1D0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000119715C0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000119E4128>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011A48CC0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011A85F98>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011B17630>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011B28978>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011BE3470>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000011C36B38>]], dtype=object)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CQA/gmeC4RERERETGZUITa2vtdiBijNkApIATxphPZw5/E3gJeBl4eCLjEhEREREZL9dE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v/64YN3NmJFWabDHTVcGI1yqF2V+bodQ9doq/UxHE7RUTcTFO9sqaQu4MalC2oKQaQQgheOdjAR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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f71320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, figsize=(12,12))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df.admit\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = df[['gre','gpa','rank']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression().fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.71999999999999997"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.0012195 ,  0.37446307, -0.64983504]])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.23945284])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import combinations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "for i in range(1,11):\n",
+    "    total += len(list(combinations(['gpa', 'gre', 'rank'],i)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "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>Coef</th>\n",
+       "      <th>Columns</th>\n",
+       "      <th>Int</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[[0.319303457297]]</td>\n",
+       "      <td>gpa</td>\n",
+       "      <td>[-1.81404627438]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[[0.00139291052464]]</td>\n",
+       "      <td>gre</td>\n",
+       "      <td>[-1.54917610063]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[[-0.504582785938]]</td>\n",
+       "      <td>rank</td>\n",
+       "      <td>[0.429516683081]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[[0.12683616908, 0.00124646717199]]</td>\n",
+       "      <td>gpa, gre</td>\n",
+       "      <td>[-1.891342199]</td>\n",
+       "      <td>0.681818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[[0.504696239715, -0.568382775255]]</td>\n",
+       "      <td>gpa, rank</td>\n",
+       "      <td>[-1.10398498603]</td>\n",
+       "      <td>0.757576</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Coef    Columns               Int     Score\n",
+       "0                   [[0.319303457297]]        gpa  [-1.81404627438]  0.681818\n",
+       "1                 [[0.00139291052464]]        gre  [-1.54917610063]  0.681818\n",
+       "2                  [[-0.504582785938]]       rank  [0.429516683081]  0.681818\n",
+       "3  [[0.12683616908, 0.00124646717199]]   gpa, gre    [-1.891342199]  0.681818\n",
+       "4  [[0.504696239715, -0.568382775255]]  gpa, rank  [-1.10398498603]  0.757576"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for i in range(1,11):\n",
+    "    combos = list(combinations(['gpa', 'gre', 'rank'],i))\n",
+    "    for j,com in enumerate(combos):\n",
+    "        y = df.admit\n",
+    "        X = pd.DataFrame(df, columns=com)\n",
+    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+    "        model = linear_model.LogisticRegression(fit_intercept=True).fit(X_train, y_train)\n",
+    "        score = model.score(X_test, y_test)\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Columns':s, 'Coef':model.coef_,'Int':model.intercept_})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df3 = pd.DataFrame(rows)\n",
+    "df3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_hat = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[90,  0],\n",
+       "       [32, 10]], dtype=int64)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test, y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75757575757575757"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}