diff --git a/.ipynb_checkpoints/grad_school-checkpoint.ipynb b/.ipynb_checkpoints/grad_school-checkpoint.ipynb
new file mode 100644
index 0000000..a00bb9e
--- /dev/null
+++ b/.ipynb_checkpoints/grad_school-checkpoint.ipynb
@@ -0,0 +1,933 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import combinations\n",
+    "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.model_selection import cross_val_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('grad.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 5,
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "school = pd.crosstab(index=df['admit'], columns=df['rank']) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc9656a0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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oJalwBr0kFc6gl6TCGfSSVDiDXpIK18zTKyWp7XoO76q8z66neyrvs52c0UtS4Qx6SSqc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\nzqCXpMIZ9JJUOINekgpn0EtS4f4fGl8qtI7Ba0kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc94d160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "school.plot(kind=\"bar\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xd0c1ac8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZXo5Cvofi8iTkXEcwvG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\nhpucYzQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd0e5668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['gre'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xcc4b860>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TJSucRZI0AK8UlaRCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljo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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd12b828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['gpa'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xd203be0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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dQ/b8O8A5wD1HuP8C4Cv03oN0LvCNUc6/XM7o+7mkwibghm77C8B5SRZ649ZysWjPVXV7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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd1372e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['admit'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = df.groupby(['admit'])['admit'].agg(['count']).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>127</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   admit  count\n",
+       "0      0    273\n",
+       "1      1    127"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "admit_count = pd.crosstab(index=df2['admit'], columns=df2['count']) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xd525278>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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11W1TktQTlYT7EcD6DtstbWM7Mx34j652RERTRDRHRPPGjRsr71KS1COVhHt0MZZdFkb8\nFdAIzOpqf2bOyczGzGwcNmxY5V1KknqkroKaFuDIDtv1wIbORRFxNnANcGZmflqd9iRJvVHJzH0Z\nMDIiRkTEQOBCYEHHgog4GfgX4NzMfKf6bUqSeqLbcM/MbcAM4HFgFTA/M1dGxA0RcW5b2SzgQODB\niFgeEQt2cjhJ0m5QybIMmbkQWNhp7NoOt8+ucl+SpF3gO1QlqUCGuyQVyHCXpAIZ7pJUIMNdkgpk\nuEtSgQx3SSqQ4S5JBTLcJalAhrskFchwl6QCGe6SVCDDXZIKZLhLUoEMd0kqkOEuSQUy3CWpQIa7\nJBXIcJekAhnuklQgw12SCmS4S1KBDHdJKpDhLkkFMtwlqUCGuyQVyHCXpAIZ7pJUIMNdkgpkuEtS\ngQx3SSqQ4S5JBaoo3CNiYkSsjog1EXF1F/v3i4gH2vY/HxEN1W5UklS5bsM9IvoDtwGTgNHAlIgY\n3alsOvB+Zn4N+Blwc7UblSRVrpKZ+1hgTWauzcwtwDxgcqeaycA9bbcfAsZHRFSvTUlST9RVUHME\nsL7Ddgtw2s5qMnNbRHwIHAK827EoIpqAprbNjyNidW+aVpeG0un7vUe63v/z9zVx817ys7n3+Eol\nRZWEe1f/GrMXNWTmHGBOBY+pHoqI5sxsrHUfUmf+bNZGJcsyLcCRHbbrgQ07q4mIOuAg4L1qNChJ\n6rlKwn0ZMDIiRkTEQOBCYEGnmgXAX7fdPh9YlJk7zNwlSbtHt8sybWvoM4DHgf7AnZm5MiJuAJoz\ncwHwb8B9EbGG1hn7hX3ZtLrkcpf2VP5s1kA4wZak8vgOVUkqkOEuSQUy3CWpQJWc5649TEQcS+u7\ngo+g9f0EG4AFmbmqpo1J2mM4c9/LRMQ/0HoJiABeoPVU1QDmdnVRN0n7Js+W2ctExO+A4zJza6fx\ngcDKzBxZm86kLxYR38vMu2rdx77CmfveZztweBfjw9v2SXuq62vdwL7ENfe9z98DT0bE//D/F3Q7\nCvgaMKNmXUlARKzY2S7gsN3Zy77OZZm9UET0o/VSzEfQ+o+mBViWmf9b08a0z4uIPwB/BrzfeRfw\nX5nZ1W+d6gPO3PdCmbkdeK7WfUhdeBQ4MDOXd94REUt2fzv7LmfuklQgX1CVpAIZ7pJUIMNd+7yI\nuCQi/rmH91kYEUPavn7QV71JvWW4S72Qmedk5gfAEMBw1x7HcFfxIuJXEfFiRKxs+5B2IuJ7EfG7\niHgK+EaH2rsj4ucRsTgi1kbEmRFxZ0Ssioi7O9Sti4ihwE3A0RGxPCJm7e7nJu2Mp0JqX3BpZr4X\nEV8ClkXEr2l9t+SpwIfAYuDlDvV/BPwpcC7wCK3h/zdt9z2p02l+VwNjMvOk3fA8pIo5c9e+4PKI\neIXW9wYcCUwDlmTmxszcAjzQqf6Rts8A/i3wh8z8bdt7C1YCDbuxb6nXDHcVLSLGAWcDf5KZJ9I6\nQ/9vWi+VvDOftv25vcPtz7b9bVd7BcNdpTsIeD8zP2m7Dv7pwJeAcRFxSEQMAP5iF47/ETC4Cn1K\nVWW4q3SPAXVtF7T6Ea1LM28D1wHPAk8AL/X24Jm5CVgaEa/6gqr2JF5+QJIK5MxdkgpkuEtSgQx3\nSSqQ4S5JBTLcJalAhrskFchwl6QC/R/MkKy4QyWc0AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd5b2cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "admit_count.plot(kind=\"bar\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u0ZDHpUMdGGPpjEbIlyuAIVeuEvVqX16qQcCRyQLjmTLpQol82eLbAGqHAEDy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\nHud8Jbhhcw8HRnP0JcIL+t1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c8fMfA398oWUX8kff3Mczx2tds//o68/x1//u5YuMVGTu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\n/j/gEh1JqJc2hwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdae3978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, alpha=0.2, figsize =(12,12))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['admit', 'gre', 'gpa', 'rank'], dtype='object')"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>Combos</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gre</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rank</td>\n",
+       "      <td>0.664887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gre, gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gre, rank</td>\n",
+       "      <td>0.694461</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Combos     Score\n",
+       "0        gre  0.682807\n",
+       "1        gpa  0.682807\n",
+       "2       rank  0.664887\n",
+       "3   gre, gpa  0.682807\n",
+       "4  gre, rank  0.694461"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,4):\n",
+    "    combos = list(combinations(['gre', 'gpa', '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",
+    "        score = cross_val_score(model, X, y, cv=20, scoring='accuracy').mean()\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Combos':s,})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>Combos</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gre</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rank</td>\n",
+       "      <td>0.664887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gre, gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gre, rank</td>\n",
+       "      <td>0.694461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>gpa, rank</td>\n",
+       "      <td>0.709355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>gre, gpa, rank</td>\n",
+       "      <td>0.707105</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Combos     Score\n",
+       "0             gre  0.682807\n",
+       "1             gpa  0.682807\n",
+       "2            rank  0.664887\n",
+       "3        gre, gpa  0.682807\n",
+       "4       gre, rank  0.694461\n",
+       "5       gpa, rank  0.709355\n",
+       "6  gre, gpa, rank  0.707105"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Combos    gpa, rank\n",
+       "Score      0.709355\n",
+       "Name: 5, dtype: object"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[df1.Score.idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "m1 = LogisticRegression().fit(df[['gpa', 'rank']], df.admit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.58204208, -0.61585648]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.26281992])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df.admit\n",
+    "X = pd.DataFrame(df, columns=['gpa','rank'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_hat = m1.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.84848484848484851"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[94,  5],\n",
+       "       [26,  7]], dtype=int64)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test, y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recall = 7/(7+26)\n",
+    "precision = 7/(7+5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.21212121212121213"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5833333333333334"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.76515151515151514"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.score(X_test, y_test)"
+   ]
+  }
+ ],
+ "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/grad_school.ipynb b/grad_school.ipynb
new file mode 100644
index 0000000..a00bb9e
--- /dev/null
+++ b/grad_school.ipynb
@@ -0,0 +1,933 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import combinations\n",
+    "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.model_selection import cross_val_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('grad.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 5,
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "school = pd.crosstab(index=df['admit'], columns=df['rank']) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc9656a0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc94d160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "school.plot(kind=\"bar\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xd0c1ac8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JlTDQJakSBrokVcJAl6RKGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZXo5Cvofi8iTkXEcwvG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\nhpucYzQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd0e5668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['gre'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xcc4b860>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TJSucRZI0AK8UlaRCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljo\nklQIC12SCjEa7z3Zos19voXtrm3zQ3nbW0layCN0SSqEhS5JhRiZUy79nvqQpKcaj9AlqRAWuiQV\nwkKXpEIMVOgRsSMiPhcRX4iIa1cqlCSpf8su9IhYA/wu8BrgfOANEXH+SgWTJPVnkCP0i4AvZOb9\nmfm/wAxw+crEkiT1a5BCPxv4z675B6tlkqQWRGYub8WI1wOvzsw3V/NvAi7KzGsWjJsGpqvZ84DP\n9XlXZwH/s6yQ7TBvc0YpK5i3aaOUd9CsL8jM5/YaNMiFRQ8C53bNnwM8vHBQZu4Gdi/3TiJiX2ZO\nLHf9YTNvc0YpK5i3aaOUd1hZBznl8m/A1ojYEhFnAFcCt69MLElSv5Z9hJ6Z8xHxk8DfAWuAP87M\ne1csmSSpLwO9l0tmfgj40AplWcqyT9e0xLzNGaWsYN6mjVLeoWRd9pOikqTVxUv/JakQrRV6RJwb\nEXdFxMGIuDci3r7ImDdGxGeqr49HxAVdtx2OiAMRcU9E7FsleScj4kSV6Z6IeGfXbUN7m4SaWX+2\nK+dnI+LxiNhY3TbsbfuMiPjXiPh0lfddi4x5ekTcVG2/uyNic9dtv1At/1xEvHqV5P3piLiv2nf3\nRsQLum57vGvbN/5Cgpp5r4qI/+7K9eau23ZGxKHqa+cqyPqerpyfj4jjXbcNddt23e+aiPhURNyx\nyG3D23czs5UvYBPwsmr6WcDngfMXjPleYEM1/Rrg7q7bDgNnrbK8k8Adi6y7Bvgi8B3AGcCnF647\n7KwLxv8g8NEWt20AY9X06cDdwMsXjHkr8PvV9JXATdX0+dX2fDqwpdrOa1ZB3ingmdX0W07mrebn\nhrVt+8h7FfA7i6y7Ebi/+r6hmt7QZtYF46+h84KMVrZt1/3+NPD+JX7/h7bvtnaEnpmPZOYnq+mv\nAgdZcKVpZn48M49Vs5+g81r3VtTJewpDfZuEZWR9A/CBpvL0kh1z1ezp1dfCJ3cuB/ZU07cAl0RE\nVMtnMvMbmfkl4At0tnereTPzrsz8ejXb9r5bZ/su5dXAnZn5aPW7eCewo4GYwLKytrrvAkTEOcCl\nwB8tMWRo++6qOIdePQR5KZ2/xku5GvjbrvkEPhIR+6NzNerQ9Mj7PdXDxb+NiBdVy1p7m4Re2zYi\nnknnF/QvuxYPfdtWD1nvAY7SKZCFef9/G2bmPHACeA4tbdsaebst3HefERH7IuITEXFFo0ErNfP+\ncHWK6JaIOHnR4NC3b91tW53G2gJ8tGvx0Lct8F7g54BvLnH70Pbd1gs9IsbolMk7MvMrS4yZovNL\n8fNdiy/OzJfRORXztoj4vsbD0jPvJ+lconsB8NvAX51cbZEf1fjLi+psWzqnW/45Mx/tWjb0bZuZ\nj2fmhXSOZC+KiBcvGLLUNmxl29bIC0BE/BgwAfxG1+LnZ+eqwR8F3hsR37kK8v41sDkzXwL8Pd86\nohz69q27bemcvrglMx/vWjbUbRsRrwWOZub+Uw1bZFkj+26rhR4Rp9MpnPdl5geXGPMSOg9lLs/M\nL59cnpkPV9+PArfS8MPsOnkz8ysnHy5m5zX6p0fEWdR8m4RhZu1yJQsesraxbbvu+zgwy5Mf1v//\nNoyI04B1wKO0sG27nSIvEfFK4JeAyzLzG13rnNy+91frvnQYWav7XDRvZn65K+MfAtur6da276m2\nbeVU++6wtu3FwGURcZjOqdRXRMSfLxgzvH130CcDlvtF56/TnwLvPcWY59M5r/S9C5avBZ7VNf1x\nYMcqyPttfOu1/RcBD1TrnUbnyaQtfOtJ0Re1mbUad3LHWtvytn0usL6aPhP4R+C1C8a8jSc+sXRz\nNf0invjE0v00/6RonbwvpfMk19YFyzcAT6+mzwIO0eAT5H3k3dQ1/UPAJ6rpjcCXqtwbqumNbWat\nbjuPzpP30ea2XZBpksWfFB3avjvQlaIDuhh4E3CgOl8G8It0SpzM/H3gnXTONf1e5zkE5rPzcGoc\nuLVadhrw/sz88CrI+zrgLRExDzwGXJmd/7lhv01CnazQ+cX9SGZ+rWvdNrbtJmBPdD405Wl0dvg7\nIuLdwL7MvB24AfiziPgCnT9CV1b/lnsj4mbgPmAeeFs+8SF4W3l/AxgD/qLalg9k5mXAC4E/iIhv\nVutel5n3rYK8PxURl9HZho/SedULmfloRPwKnfduAnh3PvH0XBtZofNk6Ez1+3VSG9t2UW3tu14p\nKkmFaP1JUUnSyrDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqxP8BfANmIPPKUjYAAAAA\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd12b828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['gpa'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xd203be0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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dQ/b8O8A5wD1HuP8C4Cv03oN0LvCNUc6/XM7o+7mkwibghm77C8B5SRZ649ZysWjPVXV7\nVT3R3byT3vsVlrN+L53xQeBPgP9cyuLGpJ+e3wZ8rKoeB6iqA0tc46j103MBv9xtn8TT3oez3FTV\nHcBjRxmyCfh09dwJnJxkzajmXy5B388lFf53TFUdAg4Cv7Ik1Y3HsV5G4jJ6ZwTL2aI9J3kxsK6q\nvryUhY1RP9/nFwAvSPLPSe5Mcv6SVTce/fR8JfDGJPvovXrvXUtT2sSM9bIxY3t55YgtekmFPscs\nJ333k+SNwDTwu2OtaPyO2nOSZwEfAS5dqoKWQD/f5+PpLd/M0Pur7Z+SnFlV/zHm2saln54vAq6v\nqmuS/DbwV13PPx9/eRMx1vxaLmf0i15SYf6YJMfT+3PvaH8qPdP10zNJfg/4I+DVVfWzJaptXBbr\n+UTgTGA2yV56a5k7lvkTsv3+bG+vqv+uqu8DD9AL/uWqn54vA24CqKqvA79I7zo4rerr3/uglkvQ\n93NJhR3AJd3264B/rO5ZjmVq0Z67ZYy/pBfyy33dFhbpuaoOVtXqqlpfVevpPS/x6qraOZlyR6Kf\nn+2/pffEO0lW01vK+d6SVjla/fT8IHAeQJIX0gv6f1/SKpfWDuDN3atvzgUOVtX+UT34sli6qSNc\nUiHJHwM7q2oHcB29P+/20DuTv3ByFQ+vz57/FFgF/HX3vPODVfXqiRU9pD57bkqfPX8VeHmS+4An\ngT+sqh9Orurh9NnzFcAnk/wBvSWMS5fziVuSz9FbelvdPe/wAeDZAFX1CXrPQ1wA7AGeAN4y0vmX\n8ddOktSH5bJ0I0kakEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/gc/JtZHM6Sv+wAA\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd1372e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['admit'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = df.groupby(['admit'])['admit'].agg(['count']).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>127</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   admit  count\n",
+       "0      0    273\n",
+       "1      1    127"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "admit_count = pd.crosstab(index=df2['admit'], columns=df2['count']) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xd525278>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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11W1TktQTlYT7EcD6DtstbWM7Mx34j652RERTRDRHRPPGjRsr71KS1COVhHt0MZZdFkb8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\nSSqQ4S5JBTLcJalAhrskFchwl6QC/R/MkKy4QyWc0AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd5b2cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "admit_count.plot(kind=\"bar\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u0ZDHpUMdGGPpjEbIlyuAIVeuEvVqX16qQcCRyQLjmTLpQol82eLbAGqHAEDy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\nHud8Jbhhcw8HRnP0JcIL+t1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c8fMfA398oWUX8kff3Mczx2tds//o68/x1//u5YuMVGTu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\n/j/gEh1JqJc2hwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdae3978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, alpha=0.2, figsize =(12,12))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['admit', 'gre', 'gpa', 'rank'], dtype='object')"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>Combos</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gre</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rank</td>\n",
+       "      <td>0.664887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gre, gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gre, rank</td>\n",
+       "      <td>0.694461</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Combos     Score\n",
+       "0        gre  0.682807\n",
+       "1        gpa  0.682807\n",
+       "2       rank  0.664887\n",
+       "3   gre, gpa  0.682807\n",
+       "4  gre, rank  0.694461"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,4):\n",
+    "    combos = list(combinations(['gre', 'gpa', '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",
+    "        score = cross_val_score(model, X, y, cv=20, scoring='accuracy').mean()\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Combos':s,})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>Combos</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gre</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rank</td>\n",
+       "      <td>0.664887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gre, gpa</td>\n",
+       "      <td>0.682807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gre, rank</td>\n",
+       "      <td>0.694461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>gpa, rank</td>\n",
+       "      <td>0.709355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>gre, gpa, rank</td>\n",
+       "      <td>0.707105</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Combos     Score\n",
+       "0             gre  0.682807\n",
+       "1             gpa  0.682807\n",
+       "2            rank  0.664887\n",
+       "3        gre, gpa  0.682807\n",
+       "4       gre, rank  0.694461\n",
+       "5       gpa, rank  0.709355\n",
+       "6  gre, gpa, rank  0.707105"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Combos    gpa, rank\n",
+       "Score      0.709355\n",
+       "Name: 5, dtype: object"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[df1.Score.idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "m1 = LogisticRegression().fit(df[['gpa', 'rank']], df.admit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.58204208, -0.61585648]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.26281992])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df.admit\n",
+    "X = pd.DataFrame(df, columns=['gpa','rank'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=22)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_hat = m1.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.84848484848484851"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[94,  5],\n",
+       "       [26,  7]], dtype=int64)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test, y_hat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recall = 7/(7+26)\n",
+    "precision = 7/(7+5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.21212121212121213"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5833333333333334"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.76515151515151514"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.score(X_test, y_test)"
+   ]
+  }
+ ],
+ "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
+}