diff --git a/.ipynb_checkpoints/GradSchoolAnswers-checkpoint.ipynb b/.ipynb_checkpoints/GradSchoolAnswers-checkpoint.ipynb
new file mode 100644
index 0000000..17daa59
--- /dev/null
+++ b/.ipynb_checkpoints/GradSchoolAnswers-checkpoint.ipynb
@@ -0,0 +1,919 @@
+{
+ "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 import linear_model\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "from itertools import combinations\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Load in the dataset into pandas: grad.csv."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "graddf = pd.read_csv('grad.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graddf.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2  Use the pandas describe method to get some preliminary summary statistics on the data. In particular look at the mean values of the features.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>admit</th>\n",
+       "      <th>gre</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.317500</td>\n",
+       "      <td>587.700000</td>\n",
+       "      <td>3.389900</td>\n",
+       "      <td>2.48500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.466087</td>\n",
+       "      <td>115.516536</td>\n",
+       "      <td>0.380567</td>\n",
+       "      <td>0.94446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>220.000000</td>\n",
+       "      <td>2.260000</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>520.000000</td>\n",
+       "      <td>3.130000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>580.000000</td>\n",
+       "      <td>3.395000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>660.000000</td>\n",
+       "      <td>3.670000</td>\n",
+       "      <td>3.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            admit         gre         gpa       rank\n",
+       "count  400.000000  400.000000  400.000000  400.00000\n",
+       "mean     0.317500  587.700000    3.389900    2.48500\n",
+       "std      0.466087  115.516536    0.380567    0.94446\n",
+       "min      0.000000  220.000000    2.260000    1.00000\n",
+       "25%      0.000000  520.000000    3.130000    2.00000\n",
+       "50%      0.000000  580.000000    3.395000    2.00000\n",
+       "75%      1.000000  660.000000    3.670000    3.00000\n",
+       "max      1.000000  800.000000    4.000000    4.00000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graddf.describe()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3  Use the pandas crosstab method to see how many applicants from each rank of school were accepted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xtab = pd.crosstab(graddf['admit'], graddf['rank'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xtab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a bar plot of the percent of applicants from each rank who were accepted. You can do .plot(kind=\"bar\") on a pandas dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "percentxtab = pd.crosstab(graddf['admit'], graddf['rank'], normalize='columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>0.459016</td>\n",
+       "      <td>0.642384</td>\n",
+       "      <td>0.768595</td>\n",
+       "      <td>0.820896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.540984</td>\n",
+       "      <td>0.357616</td>\n",
+       "      <td>0.231405</td>\n",
+       "      <td>0.179104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "rank          1         2         3         4\n",
+       "admit                                        \n",
+       "0      0.459016  0.642384  0.768595  0.820896\n",
+       "1      0.540984  0.357616  0.231405  0.179104"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percentxtab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc763ac8>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npyPGho26wwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc51cef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "percentxtab.iloc[1].plot(kind = 'bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4  What does the distribution of the GPA and GRE scores look like? Do the distributions differ much?\n",
+    "\n",
+    "Hint: Use the pandas hist method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAALECAYAAAAsDndWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd021dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(graddf, alpha=0.7, figsize=(12,12))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000D021588>]], dtype=object)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6JJUCAtdkgphoUtSISx0SSqEhS5Jhfj//anFSVvzILEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xde42b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graddf.hist(column='gpa')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000CEE4518>]], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcf39240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graddf.hist(column='gre')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Distributions are similar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5   One of the issues with classification can be unbalanced classes. What percentage of the data was admitted? Do you think this will be a problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer:  32% of all applicants were admitted.   This is a conservative acceptance rate.  I don't think its a problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6   Perform a Logistic Regression. (Do you need to change the rank column?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y = graddf.admit\n",
+    "X = graddf[['gre']]"
+   ]
+  },
+  {
+   "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": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.68181818181818177"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression().fit(X_train, y_train) \n",
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.00139291]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.5491761])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = graddf.admit\n",
+    "rows = []\n",
+    "for i in range(1,4):\n",
+    "    combos = list(combinations(['gre', 'gpa', 'rank'],i))\n",
+    "    for combo in combos:\n",
+    "        model = LogisticRegression(fit_intercept=True)\n",
+    "        accuracy = cross_val_score(model, graddf[list(combo)], graddf.admit, cv=10, scoring='accuracy').mean()\n",
+    "        rows.append({'Accuracy': accuracy, 'Columns': combo})\n",
+    "df1 = pd.DataFrame(rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Columns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.659994</td>\n",
+       "      <td>(rank,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.699762</td>\n",
+       "      <td>(gre, rank)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.717079</td>\n",
+       "      <td>(gpa, rank)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.702073</td>\n",
+       "      <td>(gre, gpa, rank)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Accuracy           Columns\n",
+       "0  0.682570            (gre,)\n",
+       "1  0.682570            (gpa,)\n",
+       "2  0.659994           (rank,)\n",
+       "3  0.682570        (gre, gpa)\n",
+       "4  0.699762       (gre, rank)\n",
+       "5  0.717079       (gpa, rank)\n",
+       "6  0.702073  (gre, gpa, rank)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "best fit is GPA & rank"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now I would try to dummify the rank column to see if I like that better.  I don't have time though"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = pd.get_dummies(graddf['rank'])\n",
+    "#i have to mess with this more to get this to work correctly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   1  2  3  4\n",
+       "0  0  0  1  0\n",
+       "1  0  0  1  0\n",
+       "2  1  0  0  0\n",
+       "3  0  0  0  1\n",
+       "4  0  0  0  1"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/GradSchoolAnswers.ipynb b/GradSchoolAnswers.ipynb
new file mode 100644
index 0000000..17daa59
--- /dev/null
+++ b/GradSchoolAnswers.ipynb
@@ -0,0 +1,919 @@
+{
+ "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 import linear_model\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "from itertools import combinations\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Load in the dataset into pandas: grad.csv."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "graddf = pd.read_csv('grad.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graddf.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2  Use the pandas describe method to get some preliminary summary statistics on the data. In particular look at the mean values of the features.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>admit</th>\n",
+       "      <th>gre</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.317500</td>\n",
+       "      <td>587.700000</td>\n",
+       "      <td>3.389900</td>\n",
+       "      <td>2.48500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.466087</td>\n",
+       "      <td>115.516536</td>\n",
+       "      <td>0.380567</td>\n",
+       "      <td>0.94446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>220.000000</td>\n",
+       "      <td>2.260000</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>520.000000</td>\n",
+       "      <td>3.130000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>580.000000</td>\n",
+       "      <td>3.395000</td>\n",
+       "      <td>2.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>660.000000</td>\n",
+       "      <td>3.670000</td>\n",
+       "      <td>3.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            admit         gre         gpa       rank\n",
+       "count  400.000000  400.000000  400.000000  400.00000\n",
+       "mean     0.317500  587.700000    3.389900    2.48500\n",
+       "std      0.466087  115.516536    0.380567    0.94446\n",
+       "min      0.000000  220.000000    2.260000    1.00000\n",
+       "25%      0.000000  520.000000    3.130000    2.00000\n",
+       "50%      0.000000  580.000000    3.395000    2.00000\n",
+       "75%      1.000000  660.000000    3.670000    3.00000\n",
+       "max      1.000000  800.000000    4.000000    4.00000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graddf.describe()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3  Use the pandas crosstab method to see how many applicants from each rank of school were accepted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xtab = pd.crosstab(graddf['admit'], graddf['rank'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xtab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a bar plot of the percent of applicants from each rank who were accepted. You can do .plot(kind=\"bar\") on a pandas dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "percentxtab = pd.crosstab(graddf['admit'], graddf['rank'], normalize='columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>0.459016</td>\n",
+       "      <td>0.642384</td>\n",
+       "      <td>0.768595</td>\n",
+       "      <td>0.820896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.540984</td>\n",
+       "      <td>0.357616</td>\n",
+       "      <td>0.231405</td>\n",
+       "      <td>0.179104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "rank          1         2         3         4\n",
+       "admit                                        \n",
+       "0      0.459016  0.642384  0.768595  0.820896\n",
+       "1      0.540984  0.357616  0.231405  0.179104"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percentxtab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc763ac8>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npyPGho26wwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc51cef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "percentxtab.iloc[1].plot(kind = 'bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4  What does the distribution of the GPA and GRE scores look like? Do the distributions differ much?\n",
+    "\n",
+    "Hint: Use the pandas hist method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAALECAYAAAAsDndWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd021dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(graddf, alpha=0.7, figsize=(12,12))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000D021588>]], dtype=object)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6JJUCAtdkgphoUtSISx0SSqEhS5Jhfj//anFSVvzILEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xde42b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graddf.hist(column='gpa')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000CEE4518>]], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcf39240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graddf.hist(column='gre')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Distributions are similar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5   One of the issues with classification can be unbalanced classes. What percentage of the data was admitted? Do you think this will be a problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer:  32% of all applicants were admitted.   This is a conservative acceptance rate.  I don't think its a problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6   Perform a Logistic Regression. (Do you need to change the rank column?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y = graddf.admit\n",
+    "X = graddf[['gre']]"
+   ]
+  },
+  {
+   "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": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.68181818181818177"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression().fit(X_train, y_train) \n",
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.00139291]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.5491761])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = graddf.admit\n",
+    "rows = []\n",
+    "for i in range(1,4):\n",
+    "    combos = list(combinations(['gre', 'gpa', 'rank'],i))\n",
+    "    for combo in combos:\n",
+    "        model = LogisticRegression(fit_intercept=True)\n",
+    "        accuracy = cross_val_score(model, graddf[list(combo)], graddf.admit, cv=10, scoring='accuracy').mean()\n",
+    "        rows.append({'Accuracy': accuracy, 'Columns': combo})\n",
+    "df1 = pd.DataFrame(rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Columns</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gpa,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.659994</td>\n",
+       "      <td>(rank,)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.682570</td>\n",
+       "      <td>(gre, gpa)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.699762</td>\n",
+       "      <td>(gre, rank)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.717079</td>\n",
+       "      <td>(gpa, rank)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.702073</td>\n",
+       "      <td>(gre, gpa, rank)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Accuracy           Columns\n",
+       "0  0.682570            (gre,)\n",
+       "1  0.682570            (gpa,)\n",
+       "2  0.659994           (rank,)\n",
+       "3  0.682570        (gre, gpa)\n",
+       "4  0.699762       (gre, rank)\n",
+       "5  0.717079       (gpa, rank)\n",
+       "6  0.702073  (gre, gpa, rank)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "best fit is GPA & rank"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now I would try to dummify the rank column to see if I like that better.  I don't have time though"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = pd.get_dummies(graddf['rank'])\n",
+    "#i have to mess with this more to get this to work correctly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   1  2  3  4\n",
+       "0  0  0  1  0\n",
+       "1  0  0  1  0\n",
+       "2  1  0  0  0\n",
+       "3  0  0  0  1\n",
+       "4  0  0  0  1"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}