diff --git a/.ipynb_checkpoints/Answers_2018-01-18-checkpoint.ipynb b/.ipynb_checkpoints/Answers_2018-01-18-checkpoint.ipynb
new file mode 100644
index 0000000..9d2e72b
--- /dev/null
+++ b/.ipynb_checkpoints/Answers_2018-01-18-checkpoint.ipynb
@@ -0,0 +1,1025 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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 pandas.plotting import scatter_matrix\n",
+    "from itertools import combinations\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('grad.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>admit</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33</td>\n",
+       "      <td>54</td>\n",
+       "      <td>28</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "rank    1   2   3   4\n",
+       "admit                \n",
+       "0      28  97  93  55\n",
+       "1      33  54  28  12"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xtab = pd.crosstab(df['admit'], df['rank'])\n",
+    "xtab\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oJalwBr0kFc6gl6TCGfSSVDiDXpIK18zTKyWp7XoO76q8z66neyrvs52c0UtS4Qx6SSqc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\nzqCXpMIZ9JJUOINekgpn0EtS4f4fGl8qtI7Ba0kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbe46390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "my_plot = xtab.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000C2E6898>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C3661D0>], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BQ7OWYZ/LgL+pqkeq6u+BjwHXrk9Z0uC4P2hQ1hL+FwF/O2/5aNcmtcj9QYOyljH/LNBW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\nvyQ1yPCXpAYZ/pLUIMNfkhr0/wBOsfHTr+JLQAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbe46668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gre', by=df.admit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000C3483C8>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C850BE0>], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bxCt8JalBlr8kNcjyl6QGWf6S1CDLX5Ia5Hj+UsP8Vrt2uecvSQ2y/CWpQZa/JDXI8pek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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc36d0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gpa', by=df.admit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 400 entries, 0 to 399\n",
+      "Data columns (total 4 columns):\n",
+      "admit    400 non-null int64\n",
+      "gre      400 non-null int64\n",
+      "gpa      400 non-null float64\n",
+      "rank     400 non-null int64\n",
+      "dtypes: float64(1), int64(3)\n",
+      "memory usage: 12.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3KZfTwdGWEMfb0q9hPJli+4kOfC4n4ViSt1dXAbDxSBu/qKmjLRTjSHOID924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\nxBI88MQ+IvEkm4+18y9vnxk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yBcXk+12snV/E3CI/cwp95HidrJybP6p6llbkUhRwU5zj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\nIo20uAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc869160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, figsize=(12,12))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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",
+       "      <th>rank1</th>\n",
+       "      <th>rank2</th>\n",
+       "      <th>rank3</th>\n",
+       "      <th>rank4</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   admit  gre   gpa  rank  rank1  rank2  rank3  rank4\n",
+       "0      0  380  3.61     3      0      0      1      0\n",
+       "1      1  660  3.67     3      0      0      1      0\n",
+       "2      1  800  4.00     1      1      0      0      0\n",
+       "3      1  640  3.19     4      0      0      0      1\n",
+       "4      0  520  2.93     4      0      0      0      1"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['rank1'] = df['rank'].map({1:1, 2:0, 3:0, 4:0})\n",
+    "df['rank2'] = df['rank'].map({1:0, 2:1, 3:0, 4:0})\n",
+    "df['rank3'] = df['rank'].map({1:0, 2:0, 3:1, 4:0})\n",
+    "df['rank4'] = df['rank'].map({1:0, 2:0, 3:0, 4:1})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>Accuracy</th>\n",
+       "      <th>Columns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank2,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank3,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank4,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.674878</td>\n",
+       "      <td>(gre, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.714576</td>\n",
+       "      <td>(gpa, rank1)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.670000</td>\n",
+       "      <td>(rank1, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.687320</td>\n",
+       "      <td>(gre, gpa, rank1)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.680006</td>\n",
+       "      <td>(gre, gpa, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.680006</td>\n",
+       "      <td>(gre, gpa, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, rank1, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.677567</td>\n",
+       "      <td>(gre, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.714576</td>\n",
+       "      <td>(gpa, rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.712137</td>\n",
+       "      <td>(gpa, rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.670000</td>\n",
+       "      <td>(rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0.670000</td>\n",
+       "      <td>(rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.659994</td>\n",
+       "      <td>(rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0.677567</td>\n",
+       "      <td>(gre, gpa, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.677631</td>\n",
+       "      <td>(gre, gpa, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.707076</td>\n",
+       "      <td>(gpa, rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>0.712076</td>\n",
+       "      <td>(gpa, rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>0.712137</td>\n",
+       "      <td>(gpa, rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>0.714640</td>\n",
+       "      <td>(gpa, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, gpa, rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0.702262</td>\n",
+       "      <td>(gre, gpa, rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>0.699701</td>\n",
+       "      <td>(gre, gpa, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, rank1, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>0.712137</td>\n",
+       "      <td>(gpa, rank1, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Accuracy                            Columns\n",
+       "0   0.682570                             (gre,)\n",
+       "1   0.682570                             (gpa,)\n",
+       "2   0.684817                           (rank1,)\n",
+       "3   0.682570                           (rank2,)\n",
+       "4   0.682570                           (rank3,)\n",
+       "5   0.682570                           (rank4,)\n",
+       "6   0.682570                         (gre, gpa)\n",
+       "7   0.689759                       (gre, rank1)\n",
+       "8   0.682570                       (gre, rank2)\n",
+       "9   0.674878                       (gre, rank3)\n",
+       "10  0.682570                       (gre, rank4)\n",
+       "11  0.714576                       (gpa, rank1)\n",
+       "12  0.682570                       (gpa, rank2)\n",
+       "13  0.682570                       (gpa, rank3)\n",
+       "14  0.682570                       (gpa, rank4)\n",
+       "15  0.670000                     (rank1, rank2)\n",
+       "16  0.684817                     (rank1, rank3)\n",
+       "17  0.684817                     (rank1, rank4)\n",
+       "18  0.682570                     (rank2, rank3)\n",
+       "19  0.682570                     (rank2, rank4)\n",
+       "20  0.682570                     (rank3, rank4)\n",
+       "21  0.687320                  (gre, gpa, rank1)\n",
+       "22  0.682570                  (gre, gpa, rank2)\n",
+       "23  0.680006                  (gre, gpa, rank3)\n",
+       "24  0.680006                  (gre, gpa, rank4)\n",
+       "25  0.687259                (gre, rank1, rank2)\n",
+       "26  0.689759                (gre, rank1, rank3)\n",
+       "27  0.689759                (gre, rank1, rank4)\n",
+       "28  0.677567                (gre, rank2, rank3)\n",
+       "29  0.682570                (gre, rank2, rank4)\n",
+       "..       ...                                ...\n",
+       "32  0.714576                (gpa, rank1, rank3)\n",
+       "33  0.712137                (gpa, rank1, rank4)\n",
+       "34  0.682570                (gpa, rank2, rank3)\n",
+       "35  0.682570                (gpa, rank2, rank4)\n",
+       "36  0.682570                (gpa, rank3, rank4)\n",
+       "37  0.670000              (rank1, rank2, rank3)\n",
+       "38  0.670000              (rank1, rank2, rank4)\n",
+       "39  0.684817              (rank1, rank3, rank4)\n",
+       "40  0.659994              (rank2, rank3, rank4)\n",
+       "41  0.692259           (gre, gpa, rank1, rank2)\n",
+       "42  0.692259           (gre, gpa, rank1, rank3)\n",
+       "43  0.692259           (gre, gpa, rank1, rank4)\n",
+       "44  0.677567           (gre, gpa, rank2, rank3)\n",
+       "45  0.682570           (gre, gpa, rank2, rank4)\n",
+       "46  0.677631           (gre, gpa, rank3, rank4)\n",
+       "47  0.689759         (gre, rank1, rank2, rank3)\n",
+       "48  0.689759         (gre, rank1, rank2, rank4)\n",
+       "49  0.692259         (gre, rank1, rank3, rank4)\n",
+       "50  0.687259         (gre, rank2, rank3, rank4)\n",
+       "51  0.707076         (gpa, rank1, rank2, rank3)\n",
+       "52  0.712076         (gpa, rank1, rank2, rank4)\n",
+       "53  0.712137         (gpa, rank1, rank3, rank4)\n",
+       "54  0.714640         (gpa, rank2, rank3, rank4)\n",
+       "55  0.684817       (rank1, rank2, rank3, rank4)\n",
+       "56  0.687259    (gre, gpa, rank1, rank2, rank3)\n",
+       "57  0.692259    (gre, gpa, rank1, rank2, rank4)\n",
+       "58  0.702262    (gre, gpa, rank1, rank3, rank4)\n",
+       "59  0.699701    (gre, gpa, rank2, rank3, rank4)\n",
+       "60  0.687259  (gre, rank1, rank2, rank3, rank4)\n",
+       "61  0.712137  (gpa, rank1, rank2, rank3, rank4)\n",
+       "\n",
+       "[62 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for i in range(1,6):    \n",
+    "    combos = list(combinations(['gre','gpa','rank1','rank2','rank3','rank4'],i))    \n",
+    "    for combo in combos:        \n",
+    "        model = LogisticRegression(fit_intercept=True)        \n",
+    "        accuracy = cross_val_score(model, df[list(combo)], df.admit, cv=10, scoring='accuracy').mean()        \n",
+    "        rows.append({'Accuracy': accuracy, 'Columns': combo})\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#0.714576 (gpa, rank1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#higher precision"
+   ]
+  },
+  {
+   "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Answers_2018-01-18.ipynb b/Answers_2018-01-18.ipynb
new file mode 100644
index 0000000..9d2e72b
--- /dev/null
+++ b/Answers_2018-01-18.ipynb
@@ -0,0 +1,1025 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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 pandas.plotting import scatter_matrix\n",
+    "from itertools import combinations\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('grad.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>admit</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33</td>\n",
+       "      <td>54</td>\n",
+       "      <td>28</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "rank    1   2   3   4\n",
+       "admit                \n",
+       "0      28  97  93  55\n",
+       "1      33  54  28  12"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xtab = pd.crosstab(df['admit'], df['rank'])\n",
+    "xtab\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oJalwBr0kFc6gl6TCGfSSVDiDXpIK18zTKyWp7XoO76q8z66neyrvs52c0UtS4Qx6SSqc\nQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0\nklQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVriVBHxFfiIhnIuK5iLiqFWNIkoam8qCPiFHA\nvwBzgSOA8yPiiKrHkSQNTStm9J8FnsvM5zPzd8CtwOktGEeSNASjW9DnFOClAcc14NhtG0XEAmBB\n/fC9iHimBbUMe9GSXtdOAN6osseW/EoWrfnqVQ2/N3cLnx5Ko1YE/WD/hfJjJzIXA4tbMP6IFxEr\nM7O73XVI2/J7sz1asXRTAw4ZcNwBvNKCcSRJQ9CKoP8VcFhETI2IvYDzgKUtGEeSNASVL91k5uaI\n+Crw78Ao4IeZua7qcfSJXBLTcOX3ZhtE5seWzyVJBfHOWEkqnEEvSYUz6CWpcK3YR69dKCIOp//O\n4yn036/wCrA0M3vaWpikYcMZ/W4sIr5B/yMmAniM/q2tAdziw+QkfchdN7uxiPg1cGRmbtrm/F7A\nusw8rD2VSZ8sIi7OzB+1u46Rwhn97m0r8EeDnJ9cf00arq5pdwEjiWv0u7crgBUR8Sy/f5DcocCf\nAl9tW1USEBGrt/cScPCurGWkc+lmNxcRe9D/aOgp9P8PVAN+lZlb2lqYRryIeA34S+DNbV8C/isz\nB/ttVC3gjH43l5lbgV+2uw5pEMuAsZn55LYvRMRDu76ckcsZvSQVzjdjJalwBr0kFc6glwaIiIsi\n4sadvObnETGu/u+yVtUmNcqgl5qUmSdn5lvAOMCg17Bj0GtEiYi7IuLxiFhX/wP1RMTFEfHriHgY\nOG5A2x9HxHcj4sGIeD4i/iIifhgRPRHx4wHteiNiAnAt8CcR8WRE/OOu/tqk7XF7pUaaSzLzNxGx\nD/CriLiX/rs0ZwJvAw8Cqwa0PxA4ATgNuIf+HwSX1q89Zputg1cBR2XmMbvg65CGzBm9RpqvRcT/\n0H/vwSHAl4CHMrMvM38H3LZN+3uyfw/yGuC1zFxTv3dhHdC5C+uWGmbQa8SIiDnA54HPZebR9M/c\nn6b/8c7b80H949YBn3947G/E2i0Y9BpJDgDezMzf1p/jPwvYB5gTEeMjYk/gnCb6fxfYr4I6pUoZ\n9BpJlgOj6w/b+gf6l2/WA1cD/w3cDzzRaOeZuQF4JCLW+mashhMfgSBJhXNGL0mFM+glqXAGvSQV\nzqCXpMIZ9JJUOINekgpn0EtS4f4fGl8qtI7Ba0kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbe46390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "my_plot = xtab.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000C2E6898>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C3661D0>], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BQ7OWYZ/LgL+pqkeq6u+BjwHXrk9Z0uC4P2hQ1hL+FwF/O2/5aNcmtcj9QYOyljH/LNBW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\nvyQ1yPCXpAYZ/pLUIMNfkhr0/wBOsfHTr+JLQAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbe46668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gre', by=df.admit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000C3483C8>,\n",
+       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C850BE0>], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bxCt8JalBlr8kNcjyl6QGWf6S1CDLX5Ia5Hj+UsP8Vrt2uecvSQ2y/CWpQZa/JDXI8pek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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc36d0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gpa', by=df.admit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 400 entries, 0 to 399\n",
+      "Data columns (total 4 columns):\n",
+      "admit    400 non-null int64\n",
+      "gre      400 non-null int64\n",
+      "gpa      400 non-null float64\n",
+      "rank     400 non-null int64\n",
+      "dtypes: float64(1), int64(3)\n",
+      "memory usage: 12.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3KZfTwdGWEMfb0q9hPJli+4kOfC4n4ViSt1dXAbDxSBu/qKmjLRTjSHOID924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\nxBI88MQ+IvEkm4+18y9vnxk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yBcXk+12snV/E3CI/cwp95HidrJybP6p6llbkUhRwU5zj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\nIo20uAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc869160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, figsize=(12,12))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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",
+       "      <th>rank1</th>\n",
+       "      <th>rank2</th>\n",
+       "      <th>rank3</th>\n",
+       "      <th>rank4</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   admit  gre   gpa  rank  rank1  rank2  rank3  rank4\n",
+       "0      0  380  3.61     3      0      0      1      0\n",
+       "1      1  660  3.67     3      0      0      1      0\n",
+       "2      1  800  4.00     1      1      0      0      0\n",
+       "3      1  640  3.19     4      0      0      0      1\n",
+       "4      0  520  2.93     4      0      0      0      1"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['rank1'] = df['rank'].map({1:1, 2:0, 3:0, 4:0})\n",
+    "df['rank2'] = df['rank'].map({1:0, 2:1, 3:0, 4:0})\n",
+    "df['rank3'] = df['rank'].map({1:0, 2:0, 3:1, 4:0})\n",
+    "df['rank4'] = df['rank'].map({1:0, 2:0, 3:0, 4:1})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>Accuracy</th>\n",
+       "      <th>Columns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank2,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank3,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank4,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.674878</td>\n",
+       "      <td>(gre, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.714576</td>\n",
+       "      <td>(gpa, rank1)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.670000</td>\n",
+       "      <td>(rank1, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.687320</td>\n",
+       "      <td>(gre, gpa, rank1)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.680006</td>\n",
+       "      <td>(gre, gpa, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.680006</td>\n",
+       "      <td>(gre, gpa, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, rank1, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.677567</td>\n",
+       "      <td>(gre, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.714576</td>\n",
+       "      <td>(gpa, rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.712137</td>\n",
+       "      <td>(gpa, rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.670000</td>\n",
+       "      <td>(rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0.670000</td>\n",
+       "      <td>(rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.659994</td>\n",
+       "      <td>(rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0.677567</td>\n",
+       "      <td>(gre, gpa, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.677631</td>\n",
+       "      <td>(gre, gpa, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.689759</td>\n",
+       "      <td>(gre, rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>0.707076</td>\n",
+       "      <td>(gpa, rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>0.712076</td>\n",
+       "      <td>(gpa, rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>0.712137</td>\n",
+       "      <td>(gpa, rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>0.714640</td>\n",
+       "      <td>(gpa, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0.684817</td>\n",
+       "      <td>(rank1, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, gpa, rank1, rank2, rank3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>0.692259</td>\n",
+       "      <td>(gre, gpa, rank1, rank2, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0.702262</td>\n",
+       "      <td>(gre, gpa, rank1, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>0.699701</td>\n",
+       "      <td>(gre, gpa, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>0.687259</td>\n",
+       "      <td>(gre, rank1, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>0.712137</td>\n",
+       "      <td>(gpa, rank1, rank2, rank3, rank4)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Accuracy                            Columns\n",
+       "0   0.682570                             (gre,)\n",
+       "1   0.682570                             (gpa,)\n",
+       "2   0.684817                           (rank1,)\n",
+       "3   0.682570                           (rank2,)\n",
+       "4   0.682570                           (rank3,)\n",
+       "5   0.682570                           (rank4,)\n",
+       "6   0.682570                         (gre, gpa)\n",
+       "7   0.689759                       (gre, rank1)\n",
+       "8   0.682570                       (gre, rank2)\n",
+       "9   0.674878                       (gre, rank3)\n",
+       "10  0.682570                       (gre, rank4)\n",
+       "11  0.714576                       (gpa, rank1)\n",
+       "12  0.682570                       (gpa, rank2)\n",
+       "13  0.682570                       (gpa, rank3)\n",
+       "14  0.682570                       (gpa, rank4)\n",
+       "15  0.670000                     (rank1, rank2)\n",
+       "16  0.684817                     (rank1, rank3)\n",
+       "17  0.684817                     (rank1, rank4)\n",
+       "18  0.682570                     (rank2, rank3)\n",
+       "19  0.682570                     (rank2, rank4)\n",
+       "20  0.682570                     (rank3, rank4)\n",
+       "21  0.687320                  (gre, gpa, rank1)\n",
+       "22  0.682570                  (gre, gpa, rank2)\n",
+       "23  0.680006                  (gre, gpa, rank3)\n",
+       "24  0.680006                  (gre, gpa, rank4)\n",
+       "25  0.687259                (gre, rank1, rank2)\n",
+       "26  0.689759                (gre, rank1, rank3)\n",
+       "27  0.689759                (gre, rank1, rank4)\n",
+       "28  0.677567                (gre, rank2, rank3)\n",
+       "29  0.682570                (gre, rank2, rank4)\n",
+       "..       ...                                ...\n",
+       "32  0.714576                (gpa, rank1, rank3)\n",
+       "33  0.712137                (gpa, rank1, rank4)\n",
+       "34  0.682570                (gpa, rank2, rank3)\n",
+       "35  0.682570                (gpa, rank2, rank4)\n",
+       "36  0.682570                (gpa, rank3, rank4)\n",
+       "37  0.670000              (rank1, rank2, rank3)\n",
+       "38  0.670000              (rank1, rank2, rank4)\n",
+       "39  0.684817              (rank1, rank3, rank4)\n",
+       "40  0.659994              (rank2, rank3, rank4)\n",
+       "41  0.692259           (gre, gpa, rank1, rank2)\n",
+       "42  0.692259           (gre, gpa, rank1, rank3)\n",
+       "43  0.692259           (gre, gpa, rank1, rank4)\n",
+       "44  0.677567           (gre, gpa, rank2, rank3)\n",
+       "45  0.682570           (gre, gpa, rank2, rank4)\n",
+       "46  0.677631           (gre, gpa, rank3, rank4)\n",
+       "47  0.689759         (gre, rank1, rank2, rank3)\n",
+       "48  0.689759         (gre, rank1, rank2, rank4)\n",
+       "49  0.692259         (gre, rank1, rank3, rank4)\n",
+       "50  0.687259         (gre, rank2, rank3, rank4)\n",
+       "51  0.707076         (gpa, rank1, rank2, rank3)\n",
+       "52  0.712076         (gpa, rank1, rank2, rank4)\n",
+       "53  0.712137         (gpa, rank1, rank3, rank4)\n",
+       "54  0.714640         (gpa, rank2, rank3, rank4)\n",
+       "55  0.684817       (rank1, rank2, rank3, rank4)\n",
+       "56  0.687259    (gre, gpa, rank1, rank2, rank3)\n",
+       "57  0.692259    (gre, gpa, rank1, rank2, rank4)\n",
+       "58  0.702262    (gre, gpa, rank1, rank3, rank4)\n",
+       "59  0.699701    (gre, gpa, rank2, rank3, rank4)\n",
+       "60  0.687259  (gre, rank1, rank2, rank3, rank4)\n",
+       "61  0.712137  (gpa, rank1, rank2, rank3, rank4)\n",
+       "\n",
+       "[62 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for i in range(1,6):    \n",
+    "    combos = list(combinations(['gre','gpa','rank1','rank2','rank3','rank4'],i))    \n",
+    "    for combo in combos:        \n",
+    "        model = LogisticRegression(fit_intercept=True)        \n",
+    "        accuracy = cross_val_score(model, df[list(combo)], df.admit, cv=10, scoring='accuracy').mean()        \n",
+    "        rows.append({'Accuracy': accuracy, 'Columns': combo})\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#0.714576 (gpa, rank1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#higher precision"
+   ]
+  },
+  {
+   "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}