diff --git a/.ipynb_checkpoints/grad_mh-checkpoint.ipynb b/.ipynb_checkpoints/grad_mh-checkpoint.ipynb
new file mode 100644
index 0000000..44c666e
--- /dev/null
+++ b/.ipynb_checkpoints/grad_mh-checkpoint.ipynb
@@ -0,0 +1,820 @@
+{
+ "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",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('grad.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>admit</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>0.3175</td>\n",
+       "      <td>0.466087</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gre</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>587.7000</td>\n",
+       "      <td>115.516536</td>\n",
+       "      <td>220.00</td>\n",
+       "      <td>520.00</td>\n",
+       "      <td>580.000</td>\n",
+       "      <td>660.00</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gpa</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>3.3899</td>\n",
+       "      <td>0.380567</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>3.13</td>\n",
+       "      <td>3.395</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rank</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>2.4850</td>\n",
+       "      <td>0.944460</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2.000</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count      mean         std     min     25%      50%     75%    max\n",
+       "admit  400.0    0.3175    0.466087    0.00    0.00    0.000    1.00    1.0\n",
+       "gre    400.0  587.7000  115.516536  220.00  520.00  580.000  660.00  800.0\n",
+       "gpa    400.0    3.3899    0.380567    2.26    3.13    3.395    3.67    4.0\n",
+       "rank   400.0    2.4850    0.944460    1.00    2.00    2.000    3.00    4.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = pd.crosstab(index=df['admit'], columns=df['rank'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc1d21d0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1Lh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\nkgpn0EtS4f4fGl8qtI7Ba0kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xeab7eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table.plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000EE24D68>]], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xeeb9cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gre', bins=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "gre is slightly skewed to the left, but doesn't differ greatly from the gpa histogram, which is also skewed slightly to the left. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000EFA0390>]], dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LUiEsdEkqhIUuSYWw0CWpEHU+JPqjEXEsIr7Utmx9RNwREYeq7+v6G1OS1EmdM/Qbge1z\nll0D7M/MLcD+al6S1KCOhZ6Zfwk8OmfxDmBvNb0X2LnEuSRJXVrsNfThzHwEoPr+kqWLJElajMjM\nzoMiNgG3Z+YF1fxjmbm2bf3xzJz3OnpEjAPjAMPDw9smJia6CjgzM8PQ0FBX2zRpKfNOHzmxJD/n\nVIbPhKNP9f1mlsRKygrm7beVlHfzmlU99cLY2NiBzBzpNG6xn1h0NCI2ZOYjEbEBOLbQwMzcA+wB\nGBkZydHR0a5uaHJykm63adJS5h3EJwnt3jrLddMr44OrVlJWMG+/raS8N25fPZAeW+wll33Armp6\nF3Db0sSRJC1WnactfgL4a+C8iHgoIq4GrgXeEBGHgDdU85KkBnW8v5KZb1lg1aVLnEWS1ANfKSpJ\nhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBViZbz3ZIM2dfkW\ntru3zg7kbW8laS7P0CWpEBa6JBVixVxy6fbShyQ913iGLkmFsNAlqRAWuiQVwkKXpEL0VOgRsT0i\nvhwR90fENUsVSpLUvUUXekSsAn4XeCNwPvCWiDh/qYJJkrrTyxn6xcD9mflAZn4bmAB2LE0sSVK3\nIjMXt2HEFcD2zPzZav5twA9m5jvnjBsHxqvZ84Avd3lTZwNfX1TIZpi3f1ZSVjBvv62kvL1m/b7M\nfHGnQb28sCjmWfasvw6ZuQfYs+gbiZjKzJHFbj9o5u2flZQVzNtvKynvoLL2csnlIeDctvlzgId7\niyNJWqxeCv1vgS0RsTkizgCuBPYtTSxJUrcWfcklM2cj4p3A/wJWAR/NzHuXLNl3LfpyTUPM2z8r\nKSuYt99WUt6BZF30g6KSpOXFV4pKUiEsdEkqRGOFHhHnRsSdEXEwIu6NiHfPM+atEfF31dfnIuLC\ntnWHI2I6Iu6JiKllknc0Ik5Ume6JiPe3rRvY2yTUzPqf2nJ+KSKejoj11bpB79sXRMTfRMQXq7wf\nmGfM8yPipmr/3R0Rm9rWvbda/uWI+LFlkvc9EXFfdezuj4jva1v3dNu+7/sTCWrmvSoi/rkt18+2\nrdsVEYeqr13LIOuH23L+Q0Q81rZuoPu27XZXRcQXIuL2edYN7tjNzEa+gA3Aa6rps4B/AM6fM+aH\ngXXV9BuBu9vWHQbOXmZ5R4Hb59l2FfAV4PuBM4Avzt120FnnjP93wF80uG8DGKqmTwfuBl47Z8wv\nAB+ppq8Ebqqmz6/25/OBzdV+XrUM8o4BL6ym334ybzU/M6h920Xeq4DfmWfb9cAD1fd11fS6JrPO\nGf8uWk/IaGTftt3ue4CPL/D7P7Bjt7Ez9Mx8JDM/X00/ARwENs4Z87nMPF7N3kXrue6NqJP3FAb6\nNgmLyPoW4BP9ytNJtsxUs6dXX3Mfrd8B7K2mbwEujYiolk9k5rcy86vA/bT2d6N5M/POzPxmNdv0\nsVtn/y7kx4A7MvPR6nfxDmB7H2ICi8ra6LELEBHnAJcBv7/AkIEdu8viGnp1F+TVtP4aL+Rq4NNt\n8wl8NiIOROvtBQamQ94fqu4ufjoiXlkt2wh8rW3MQ9T/Y9CTTvs2Il5I6xf0j9sWD3zfVndZ7wGO\n0SqQuXm/sw8zcxY4AbyIhvZtjbzt5h67L4iIqYi4KyJ29jVopWben6guEd0SESdfNDjw/Vt331aX\nsTYDf9G2eOD7Frge+M/AvyywfmDHbuOFHhFDtMrklzLz8QXGjNH6pfiVtsWXZOZraF2KeUdEvK7v\nYemY9/O03nPhQuC3gT85udk8P6rvzxets29pXW75v5n5aNuyge/bzHw6My+idSZ7cURcMGfIQvuw\nkX1bIy8AEfFTwAjwobbFL8vWy8B/Erg+Iv7VMsj7p8CmzHwV8Od894xy4Pu37r6ldfnilsx8um3Z\nQPdtRLwJOJaZB041bJ5lfTl2Gy30iDidVuF8LDM/tcCYV9G6K7MjM79xcnlmPlx9PwbcSp/vZtfJ\nm5mPn7y7mJn/Ezg9Is6mgbdJqLNvK1cy5y5rE/u27bYfAyZ59t367+zDiDgNWAM8SsNvQXGKvETE\njwD/Bbg8M7/Vts3J/ftAte2rB5G1us1582bmN9oy/h6wrZpubP+eat9WTnXsDmrfXgJcHhGHaV1K\nfX1E/NGcMYM7dnt9MGCxX7T+Ov0BcP0pxryM1nWlH56zfDVwVtv052i982PTeV/Kd1+sdTHwYLXd\nabQeTNrMdx8UfWWTWatxJw+s1Q3v2xcDa6vpM4H/A7xpzph38MwHlm6upl/JMx9YeoD+PyhaJ++r\naT3ItWXO8nXA86vps4FD9PEB8i7ybmib/nHgrmp6PfDVKve6anp9k1mrdefRevA+mty3czKNMv+D\nogM7dnt5t8VeXQK8DZiurpcBvI9WiZOZHwHeT+ta039vPYbAbLbuTg0Dt1bLTgM+npmfWQZ5rwDe\nHhGzwFPAldn6nxvU2yR0kxVav7ifzcwn27ZtYt9uAPZG60NTnkfrgL89Ij4ITGXmPuAG4A8j4n5a\nf4SurP4t90bEzcB9wCzwjnzmXfCm8n4IGAI+We3LBzPzcuAVwP+IiH+ptr02M+9bBnl/MSIup7UP\nH6X1rBcy89GI+DVa790E8MF85uW5JrJC68HQier366Qm9u28mjp2fem/JBWi8QdFJUlLw0KXpEJY\n6JJUCAtdkgphoUtSISx0SSqEhS5Jhfj//anFSVvzILEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xee10400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gpa', bins=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31.75"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percent_admit = sum(df.admit)/len(df.admit)*100\n",
+    "percent_admit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001003A780>]], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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tuH0A+IGFA5JsBjZ3N+eT3DPgXGuAzw947FDye5OYFZhgzxNkz6vDqut54+8N1fN39TNo\nXEGfRfbV192o2gZsG3qiZE9VzQz7OCuJPa8O9rw6LEfP41q6OQCsW3D7TOCBMc0lSTqGcQX9x4EN\nSc5K8o3AxcCuMc0lSTqGsSzdVNVjSV4NfBA4CXhrVd05jrkYwfLPCmTPq4M9rw5j7zlVtfQoSdKK\n5StjJalxBr0kNW7FBP1Sb6mQ5KlJ3tnd/y9J1i9/laPVR8+/nOSuJJ9KsjtJX9fUnsj6feuMJC9J\nUklW/KV4/fSc5GXd9/rOJH+93DWOWh8/29+Z5CNJ/q37+b5oEnWOSpK3JjmU5I6j3J8kb+6+Hp9K\n8tyRFlBVJ/wHvSd07wWeCXwj8Eng7CeM+Tngz7vti4F3TrruZeh5I/At3farVkPP3binAbcAtwIz\nk657Gb7PG4B/A07vbj9j0nUvQ8/bgFd122cD+ydd95A9/zDwXOCOo9x/EfABeq9BOh/4l1HOv1LO\n6Pt5S4VNwI3d9t8AFyRZ7IVbK8WSPVfVR6rqK93NW+m9XmEl6/etM94A/D7wX8tZ3Jj00/PPAn9a\nVQ8DVNWhZa5x1PrpuYBv7bZPZYW/DqeqbgEeOsaQTcDbq+dW4LQkZ4xq/pUS9Iu9pcLao42pqseA\nw8C3LUt149FPzwtdSe+MYCVbsuck3w+sq6r3LWdhY9TP9/lZwLOS/FOSW5NcuGzVjUc/Pb8e+Okk\nB4CbgZ9fntIm5nj/vR+Xcb0Fwqgt+ZYKfY5ZSfruJ8lPAzPAj4y1ovE7Zs9JvgF4E3DFchW0DPr5\nPp9Mb/lmlt5fbf+Y5JyqemTMtY1LPz1fAtxQVVuT/CDwl13PXxt/eRMx1vxaKWf0/bylwv+NSXIy\nvT/3jvWn0omur7eRSPKjwG8AL6yqry5TbeOyVM9PA84B5pLsp7eWuWuFPyHb78/2zqr6n6r6LHAP\nveBfqfrp+UrgJoCq+hjwTfTe8KxVY33bmJUS9P28pcIu4PJu+yXAP1T3LMcKtWTP3TLGX9AL+ZW+\nbgtL9FxVh6tqTVWtr6r19J6XeGFV7ZlMuSPRz8/2e+k98U6SNfSWcj6zrFWOVj893wdcAJDke+kF\n/X8ua5XLaxdwWXf1zfnA4ap6cFQPviKWbuoob6mQ5LeBPVW1C7ie3p93++idyV88uYqH12fPfwBM\nAe/qnne+r6peOLGih9Rnz03ps+cPAj+W5C7gceBXquoLk6t6OH32fDVwXZJforeEccVKPnFL8g56\nS29ruucdXgc8BaCq/pze8xAXAfuArwCvGOn8K/hrJ0nqw0pZupEkDcigl6TGGfSS1DiDXpIaZ9BL\nUuMMeklqnEEvSY37X5O7ULSpRpRuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10001390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='admit', bins=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y=df.admit\n",
+    "X=df[['gpa', 'gre', 'rank']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression().fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75757575757575757"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>Combos</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>gpa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>gre</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.659994</td>\n",
+       "      <td>rank</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>gpa, gre</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.717079</td>\n",
+       "      <td>gpa, rank</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.699762</td>\n",
+       "      <td>gre, rank</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.702073</td>\n",
+       "      <td>gpa, gre, rank</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Accuracy          Combos\n",
+       "0  0.682570             gpa\n",
+       "1  0.682570             gre\n",
+       "2  0.659994            rank\n",
+       "3  0.682570        gpa, gre\n",
+       "4  0.717079       gpa, rank\n",
+       "5  0.699762       gre, rank\n",
+       "6  0.702073  gpa, gre, rank"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,5):\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, shuffle=True, random_state=22)\n",
+    "        model = LogisticRegression(fit_intercept = True)\n",
+    "        accuracy = cross_val_score(model, X, y, cv=10, scoring='accuracy').mean()\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Accuracy':accuracy, 'Combos':s})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1.head(10) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The accuracy for this model is .717079, using gpa and rank in the model to predict admit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "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>Combos</th>\n",
+       "      <th>Precision</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gpa</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gre</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rank</td>\n",
+       "      <td>0.153333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gpa, gre</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gpa, rank</td>\n",
+       "      <td>0.623333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>gre, rank</td>\n",
+       "      <td>0.543333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>gpa, gre, rank</td>\n",
+       "      <td>0.558333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Combos  Precision\n",
+       "0             gpa   0.000000\n",
+       "1             gre   0.000000\n",
+       "2            rank   0.153333\n",
+       "3        gpa, gre   0.000000\n",
+       "4       gpa, rank   0.623333\n",
+       "5       gre, rank   0.543333\n",
+       "6  gpa, gre, rank   0.558333"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,5):\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, shuffle=True, random_state=22)\n",
+    "        model = LogisticRegression(fit_intercept = True)\n",
+    "        precision = cross_val_score(model, X, y, cv=10, scoring='precision').mean()\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Precision':precision, 'Combos':s})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1.head(10) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The precision score for gpa and rank to predict admit is .62333"
+   ]
+  }
+ ],
+ "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/golf.csv b/golf.csv
new file mode 100644
index 0000000..7ac26d7
--- /dev/null
+++ b/golf.csv
@@ -0,0 +1,15 @@
+Outlook,Temperature,Humidity,Windy,Result
+sunny,85,85,FALSE,Don't Play
+sunny,80,90,TRUE,Don't Play
+overcast,83,78,FALSE,Play
+rain,70,96,FALSE,Play
+rain,68,80,FALSE,Play
+rain,65,70,TRUE,Don't Play
+overcast,64,65,TRUE,Play
+sunny,72,95,FALSE,Don't Play
+sunny,69,70,FALSE,Play
+rain,75,80,FALSE,Play
+sunny,75,70,TRUE,Play
+overcast,72,90,TRUE,Play
+overcast,81,75,FALSE,Play
+rain,71,80,TRUE,Don't Play
diff --git a/grad_mh.ipynb b/grad_mh.ipynb
new file mode 100644
index 0000000..44c666e
--- /dev/null
+++ b/grad_mh.ipynb
@@ -0,0 +1,820 @@
+{
+ "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",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('grad.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>admit</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>0.3175</td>\n",
+       "      <td>0.466087</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gre</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>587.7000</td>\n",
+       "      <td>115.516536</td>\n",
+       "      <td>220.00</td>\n",
+       "      <td>520.00</td>\n",
+       "      <td>580.000</td>\n",
+       "      <td>660.00</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gpa</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>3.3899</td>\n",
+       "      <td>0.380567</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>3.13</td>\n",
+       "      <td>3.395</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rank</th>\n",
+       "      <td>400.0</td>\n",
+       "      <td>2.4850</td>\n",
+       "      <td>0.944460</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2.000</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count      mean         std     min     25%      50%     75%    max\n",
+       "admit  400.0    0.3175    0.466087    0.00    0.00    0.000    1.00    1.0\n",
+       "gre    400.0  587.7000  115.516536  220.00  520.00  580.000  660.00  800.0\n",
+       "gpa    400.0    3.3899    0.380567    2.26    3.13    3.395    3.67    4.0\n",
+       "rank   400.0    2.4850    0.944460    1.00    2.00    2.000    3.00    4.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = pd.crosstab(index=df['admit'], columns=df['rank'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc1d21d0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xeab7eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table.plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000EE24D68>]], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xeeb9cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gre', bins=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "gre is slightly skewed to the left, but doesn't differ greatly from the gpa histogram, which is also skewed slightly to the left. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000EFA0390>]], dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LUiEsdEkqhIUuSYWw0CWpEHU+JPqjEXEsIr7Utmx9RNwREYeq7+v6G1OS1EmdM/Qbge1z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\n6JJUCAtdkgphoUtSISx0SSqEhS5Jhfj//anFSVvzILEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xee10400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='gpa', bins=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31.75"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percent_admit = sum(df.admit)/len(df.admit)*100\n",
+    "percent_admit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001003A780>]], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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tuH0A+IGFA5JsBjZ3N+eT3DPgXGuAzw947FDye5OYFZhgzxNkz6vDqut54+8N1fN39TNo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\nUuMMeklqnEEvSY37X5O7ULSpRpRuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10001390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='admit', bins=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y=df.admit\n",
+    "X=df[['gpa', 'gre', 'rank']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression().fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75757575757575757"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>Combos</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>gpa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>gre</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.659994</td>\n",
+       "      <td>rank</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>gpa, gre</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.717079</td>\n",
+       "      <td>gpa, rank</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.699762</td>\n",
+       "      <td>gre, rank</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.702073</td>\n",
+       "      <td>gpa, gre, rank</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Accuracy          Combos\n",
+       "0  0.682570             gpa\n",
+       "1  0.682570             gre\n",
+       "2  0.659994            rank\n",
+       "3  0.682570        gpa, gre\n",
+       "4  0.717079       gpa, rank\n",
+       "5  0.699762       gre, rank\n",
+       "6  0.702073  gpa, gre, rank"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,5):\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, shuffle=True, random_state=22)\n",
+    "        model = LogisticRegression(fit_intercept = True)\n",
+    "        accuracy = cross_val_score(model, X, y, cv=10, scoring='accuracy').mean()\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Accuracy':accuracy, 'Combos':s})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1.head(10) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The accuracy for this model is .717079, using gpa and rank in the model to predict admit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n",
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "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>Combos</th>\n",
+       "      <th>Precision</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gpa</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gre</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rank</td>\n",
+       "      <td>0.153333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gpa, gre</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gpa, rank</td>\n",
+       "      <td>0.623333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>gre, rank</td>\n",
+       "      <td>0.543333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>gpa, gre, rank</td>\n",
+       "      <td>0.558333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Combos  Precision\n",
+       "0             gpa   0.000000\n",
+       "1             gre   0.000000\n",
+       "2            rank   0.153333\n",
+       "3        gpa, gre   0.000000\n",
+       "4       gpa, rank   0.623333\n",
+       "5       gre, rank   0.543333\n",
+       "6  gpa, gre, rank   0.558333"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,5):\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, shuffle=True, random_state=22)\n",
+    "        model = LogisticRegression(fit_intercept = True)\n",
+    "        precision = cross_val_score(model, X, y, cv=10, scoring='precision').mean()\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Precision':precision, 'Combos':s})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1.head(10) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The precision score for gpa and rank to predict admit is .62333"
+   ]
+  }
+ ],
+ "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
+}