classification_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification\n",
    "\n",
    "The aim of this is to use the normalised college data to train a model that predicts the likelihood of college acceptance, given several factors. Some factors have to do with the student (e.g. GPA, minority), and some have to do with the college (e.g. acceptance rate, private/public). Together, these predictors should fit a model that returns probability of being accepted by a college. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load required packages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import TIdatabase as ti\n",
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")\n",
    "from matplotlib import rcParams\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning & Checking\n",
    "\n",
    "We read in the normalized data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>studentID</th>\n",
       "      <th>classrank</th>\n",
       "      <th>admissionstest</th>\n",
       "      <th>AP</th>\n",
       "      <th>averageAP</th>\n",
       "      <th>SATsubject</th>\n",
       "      <th>GPA</th>\n",
       "      <th>GPA_w</th>\n",
       "      <th>program</th>\n",
       "      <th>schooltype</th>\n",
       "      <th>...</th>\n",
       "      <th>alumni</th>\n",
       "      <th>outofstate</th>\n",
       "      <th>acceptStatus</th>\n",
       "      <th>acceptProb</th>\n",
       "      <th>name</th>\n",
       "      <th>acceptrate</th>\n",
       "      <th>size</th>\n",
       "      <th>public</th>\n",
       "      <th>finAidPct</th>\n",
       "      <th>instatePct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S50C3UECT8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.838909</td>\n",
       "      <td>7</td>\n",
       "      <td>1.067927</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.188104</td>\n",
       "      <td>0.059072</td>\n",
       "      <td>Biomedical engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice</td>\n",
       "      <td>0.151</td>\n",
       "      <td>6621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBWZQQRBEV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.666993</td>\n",
       "      <td>7</td>\n",
       "      <td>0.661638</td>\n",
       "      <td>2</td>\n",
       "      <td>0.493061</td>\n",
       "      <td>0.398012</td>\n",
       "      <td>Classics</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice</td>\n",
       "      <td>0.151</td>\n",
       "      <td>6621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MXXLWO1HQ2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.208552</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.395752</td>\n",
       "      <td>-1.035963</td>\n",
       "      <td>Biological Science</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice</td>\n",
       "      <td>0.151</td>\n",
       "      <td>6621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5KSL7C8SLZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.297350</td>\n",
       "      <td>7</td>\n",
       "      <td>0.864783</td>\n",
       "      <td>4</td>\n",
       "      <td>0.103824</td>\n",
       "      <td>-0.384156</td>\n",
       "      <td>Physics</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice</td>\n",
       "      <td>0.151</td>\n",
       "      <td>6621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RQWLNGGZ49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.323162</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.354087</td>\n",
       "      <td>2</td>\n",
       "      <td>0.541716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice</td>\n",
       "      <td>0.151</td>\n",
       "      <td>6621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    studentID  classrank  admissionstest  AP  averageAP  SATsubject       GPA  \\\n",
       "0  S50C3UECT8        NaN        0.838909   7   1.067927           3 -0.188104   \n",
       "1  GBWZQQRBEV        NaN        0.666993   7   0.661638           2  0.493061   \n",
       "2  MXXLWO1HQ2        NaN        0.208552   0        NaN           3  0.395752   \n",
       "3  5KSL7C8SLZ        NaN        1.297350   7   0.864783           4  0.103824   \n",
       "4  RQWLNGGZ49        NaN        0.323162   1  -0.354087           2  0.541716   \n",
       "\n",
       "      GPA_w                 program  schooltype     ...      alumni  \\\n",
       "0  0.059072  Biomedical engineering           0     ...           0   \n",
       "1  0.398012                Classics           0     ...           0   \n",
       "2 -1.035963      Biological Science           1     ...           0   \n",
       "3 -0.384156                 Physics           1     ...           0   \n",
       "4       NaN                     NaN           1     ...           0   \n",
       "\n",
       "  outofstate  acceptStatus  acceptProb  name  acceptrate  size  public  \\\n",
       "0          0             1         NaN  Rice       0.151  6621       0   \n",
       "1          1             1         NaN  Rice       0.151  6621       0   \n",
       "2          1             0         NaN  Rice       0.151  6621       0   \n",
       "3          1             0         NaN  Rice       0.151  6621       0   \n",
       "4          1             1         NaN  Rice       0.151  6621       0   \n",
       "\n",
       "   finAidPct  instatePct  \n",
       "0          0           0  \n",
       "1          0           0  \n",
       "2          0           0  \n",
       "3          0           0  \n",
       "4          0           0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"collegedata_normalized.csv\", index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we check how much of the data is missing. The cell below shows that 23% of all entries are null. This is way too high. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction nulls =  0.232593074511\n"
     ]
    }
   ],
   "source": [
    "x = df.isnull().sum(axis=1).tolist()\n",
    "y = float(sum(x)) / (df.shape[0]*df.shape[1])\n",
    "print 'Fraction nulls = ', y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To decrease the proportion of NaNs, we get rid of columns which are almost all null. From the `df.head()` above, it appears like some columns have almost exclusively nulls. Therefore the 23% above is probably driven mainly by columns that are basically all null. To see this, we print and plot the % null values for each column. We see the histogram is very bimodal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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xMVevXn2s+UFJsKls3aQ9e/ao6LKIiEglqSaP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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105e41b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentnulls = {}\n",
    "for i in range(len(df.columns)):\n",
    "    percentnulls[df.columns[i]] = 100.0 * df[df.columns[i]].isnull().sum() / len(df[df.columns[i]])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=1, figsize = (16,4))\n",
    "plt.hist(percentnulls.values(),bins=20,alpha=0.7,edgecolor=\"white\")\n",
    "plt.xlabel(\"% null values in feature\")\n",
    "plt.ylabel(\"Number of features\")\n",
    "plt.title(\"% Null Values Per Feature\")\n",
    "plt.gca().xaxis.grid(True,color='0.95')\n",
    "plt.gca().yaxis.grid(True,color='0.95')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['addInfo']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in percentnulls.keys() if percentnulls[i] > 20 and percentnulls[i] < 80]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows us that (apart from the `addInfo` feature), if a feature has >50% missing values, it has >80% null values, and if there are $<50%$ nulls, there are < 20% null values. This means if we remove columns with >50% missing data, we will be removing most of the nulls in the data frame, and it won't actually remove much information. As for the `addInfo` feature, this is additional information about the candidate, and at this stage we are not using it in the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We thus remove any column that has 50% or more null values. This takes out any predictors that would have been useless anyway. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns to drop:  ['classrank', 'canAfford', 'firstinfamily', 'artist', 'workexp', 'visited', 'acceptProb']\n"
     ]
    }
   ],
   "source": [
    "cols_to_drop = []\n",
    "for i in df.columns:\n",
    "    if 1.0* df[i].isnull().sum() / len(df[i]) >= 0.5:\n",
    "        cols_to_drop.append(i)\n",
    "print \"Columns to drop: \", cols_to_drop\n",
    "dfr = df.drop(cols_to_drop,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also drop all columns where the accept status is NaN. These are of course useless as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfr = dfr[pd.notnull(df[\"acceptStatus\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction nulls =  0.0315301778112\n"
     ]
    }
   ],
   "source": [
    "x = dfr.isnull().sum(axis=1).tolist()\n",
    "y = float(sum(x)) / (dfr.shape[0]*dfr.shape[1])\n",
    "print 'Fraction nulls = ', y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have only 3% null values. Good! The next step is to choose which columns we want to use to predict. Obviously columns like `studentID`, while crucial, are not actually predictors. Also, we remove weighted GPA in favour of GPA, as we have already normalised everything. We also do not include finAidpct and instatepct, as these columns are all 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'studentID', u'admissionstest', u'AP', u'averageAP', u'SATsubject',\n",
      "       u'GPA', u'GPA_w', u'program', u'schooltype', u'intendedgradyear',\n",
      "       u'addInfo', u'female', u'MinorityGender', u'MinorityRace',\n",
      "       u'international', u'sports', u'collegeID', u'earlyAppl', u'alumni',\n",
      "       u'outofstate', u'acceptStatus', u'name', u'acceptrate', u'size',\n",
      "       u'public', u'finAidPct', u'instatePct'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print dfr.columns\n",
    "predictor_cols = [\"admissionstest\",\"AP\",\"averageAP\",\"SATsubject\",\"GPA\",\"schooltype\",\"intendedgradyear\",\"female\",\"MinorityRace\",\"international\",\"sports\",\"earlyAppl\",\"alumni\",\"outofstate\",\"acceptrate\",\"size\",\"public\"]\n",
    "dfpredict = dfr[predictor_cols]\n",
    "dfresponse = dfr[\"acceptStatus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'admissionstest', u'AP', u'averageAP', u'SATsubject', u'GPA',\n",
       "       u'schooltype', u'intendedgradyear', u'female', u'MinorityRace',\n",
       "       u'international', u'sports', u'earlyAppl', u'alumni', u'outofstate',\n",
       "       u'acceptrate', u'size', u'public'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfpredict.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below demonstrates that if we remove all the rows with ANY nulls in it, we reduce our dataset from 13k to 11k. This reduces our dataset too much. So we will impute the missing values. We initially tried to do this using the `mice` package in R, but there does not seem to be an equivalent in Python. Since the % of nulls is just 4%, it shouldn't matter too much what method we use. Since some of the variables are factor, not numerical, we can't use mean or media. We are looking into KNN imputation, but for the time being, just use median. As stated, it shouldn't matter too much what method we use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10917, 17)\n",
      "(13310, 17)\n"
     ]
    }
   ],
   "source": [
    "print dfpredict.dropna(axis=0,how=\"any\").shape\n",
    "print dfpredict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13310, 17), (13310,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = Imputer(missing_values=\"NaN\", strategy=\"median\", axis=1)\n",
    "imp.fit(dfpredict)\n",
    "X = imp.transform(dfpredict)\n",
    "y = dfresponse\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a training and test data set. The training data will be used to fit the model and the test data to test the model accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10648, 17) (2662, 17) (10648,) (2662,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "print X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baselines\n",
    "Let's start with evaluating our starting point. What accuracy do we get with the uttermost simple classifier: predicting that all students get accepted (or all students get rejected). Whichever of these is the highest, will be our accuracy to beat. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy when predicting all accepted =  0.385424492863\n",
      "Accuracy when predicting all rejected =  0.614575507137\n"
     ]
    }
   ],
   "source": [
    "# predict all accepted\n",
    "print 'Accuracy when predicting all accepted = ', metrics.accuracy_score(y_test, np.ones(y_test.shape))\n",
    "# predict all rejected\n",
    "print 'Accuracy when predicting all rejected = ', metrics.accuracy_score(y_test,np.zeros(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our baseline is thus an accuracy of 60%, when we predict that no one gets accepted to their universities.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "\n",
    "We start with a logistic regression. We use cross validation to choose the hyperparameter c. The score for each c is the mean of the scores from the cross validation folds and is stored in a dictionary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cdict = {}\n",
    "for c in [0.001,0.01,0.1,1,10,100,1000]:\n",
    "    clf = linear_model.LogisticRegression(C=c)\n",
    "    scores = cross_val_score(clf, X_train, y_train, scoring='accuracy', cv=5)\n",
    "    cdict[c] = scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pick the c value with highest accuracy and fit the model using this value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0.66623014275845782, 100: 0.66623023092440548, 0.1: 0.66623018686212787, 1000: 0.66623023092440548, 10: 0.66623023092440548, 0.001: 0.66191098106330271, 0.01: 0.66707512518061507}\n",
      "Best parameter:  0.01 0.667075125181\n",
      "Accuracy on test set:  0.648760330579\n"
     ]
    }
   ],
   "source": [
    "print cdict\n",
    "best_c = max(cdict, key=cdict.get)\n",
    "print 'Best parameter: ', best_c, cdict[best_c]\n",
    "clflog = linear_model.LogisticRegression(C=best_c)\n",
    "clflog.fit(X_train,y_train)\n",
    "predicted = clflog.predict(X_test)\n",
    "print 'Accuracy on test set: ',metrics.accuracy_score(y_test, predicted) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got ~65% accuracy. Not bad, but different models may be able to do better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "\n",
    "Now we try a random forest, and follow the same procedure as before to choose the number of trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ntdict = {}\n",
    "for nt in [100,250,500,1000,2500]:\n",
    "    clf = RandomForestClassifier(n_estimators=nt,criterion=\"gini\")\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
    "    ntdict[nt] = scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1000: 0.74662029156443732, 500: 0.74558729532087664, 250: 0.74746505330267421, 100: 0.73995199260666022, 2500: 0.74737168587449276}\n",
      "Best parameter:  250 0.747465053303\n",
      "Accuracy on test set:  0.744552967693\n"
     ]
    }
   ],
   "source": [
    "print ntdict\n",
    "best_nt = max(ntdict, key=ntdict.get)\n",
    "print 'Best parameter: ',best_nt, ntdict[best_nt]\n",
    "clfRF_gini = RandomForestClassifier(n_estimators=nt)\n",
    "clfRF_gini.fit(X_train,y_train)\n",
    "predicted = clfRF_gini.predict(X_test)\n",
    "print 'Accuracy on test set: ',metrics.accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We repeat the random forest, but now using `entropy` instead of `gini`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1000: 0.74540003088933304, 2500: 0.74765319960065757, 250: 0.74708955461416193, 500: 0.74464797531397342, 5000: 0.7474650975305217}\n",
      "Best parameter:  2500 0.747653199601\n",
      "Accuracy on test set:  0.74154770849\n"
     ]
    }
   ],
   "source": [
    "ntdict = {}\n",
    "# We added 5000 because a few times, the optimal number of trees was on the edge\n",
    "for nt in [250,500,1000,2500,5000]:\n",
    "    clf = RandomForestClassifier(n_estimators=nt,criterion=\"entropy\")\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
    "    ntdict[nt] = scores.mean()\n",
    "print ntdict\n",
    "best_nt = max(ntdict, key=ntdict.get)\n",
    "print 'Best parameter: ',best_nt, ntdict[best_nt]\n",
    "clfRF_ent = RandomForestClassifier(n_estimators=nt)\n",
    "clfRF_ent.fit(X_train,y_train)\n",
    "predicted = clfRF_ent.predict(X_test)\n",
    "print 'Accuracy on test set: ',metrics.accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears `entropy` is pretty much the same as `gini`. This model gets about 74% accuracy, so is clearly better than logistic regression. \n",
    "Let's take a look at feature importance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create interpretable labels for the columns to make the plots more clear for general public\n",
    "better_labels = {\n",
    "    \"admissionstest\": \"ACT/SAT score\",\n",
    "    \"acceptrate\": \"College acceptance rate\",\n",
    "    \"GPA\": \"GPA\",\n",
    "    \"averageAP\": \"Average AP score\",\n",
    "    \"intendedgradyear\": \"College graduation year\",\n",
    "    \"size\": \"College size\",\n",
    "    \"AP\": \"# AP exams taken\",\n",
    "    \"SATsubject\": \"# SAT subject tests taken\",\n",
    "    \"female\": \"Gender / Female\",\n",
    "    \"schooltype\": \"Private High School\",\n",
    "    \"MinorityRace\": \"Underrepresented Minority\",\n",
    "    \"earlyAppl\": \"Early Application\",\n",
    "    \"outofstate\": \"Out of State\",\n",
    "    \"public\": \"Public College\",\n",
    "    \"alumni\": \"Legacy\",\n",
    "    \"international\": \"International Student\",\n",
    "    \"sports\": \"Varsity Sports\"\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jOKVNMbKM4mSZZxWnPLmy0aqO07No0ktFyaRIJnTrbhw378ZndDMytcSE//+f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\nmEwSERERERGR0v4f4L7LeiSzjB4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c119150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dictionary of feature importances and plot\n",
    "clf=clfRF_gini\n",
    "feature_importance = {}\n",
    "for i in range(len(dfpredict.columns)):\n",
    "    feature_importance[better_labels[dfpredict.columns[i]]] = clf.feature_importances_[i]\n",
    "# plot sorted importances\n",
    "sns.set_style(\"white\")\n",
    "plt.barh(range(len(dfpredict.columns)),sorted(feature_importance.values()), align=\"center\",alpha=0.7,edgecolor='white')\n",
    "plt.ylim([-1,17])\n",
    "plt.yticks(range(len(dfpredict.columns)), sorted(feature_importance,key=feature_importance.get))\n",
    "plt.ylabel(\"Factor\")\n",
    "plt.xlabel(\"importance\")\n",
    "plt.title(\"Relative Importance to College Admission\")\n",
    "plt.gca().yaxis.grid(False)\n",
    "plt.gca().xaxis.grid(True,color='0.95')\n",
    "plt.tight_layout()\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.savefig(\"features.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows that the admission test scores, the school's acceptance rate and the GPA score are the most important factors in the decision. This is as we expected and what most people think. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree\n",
    "\n",
    "Below is a decision tree classifier. Its performance will probably be worse than that of the random forest, as it is in some sense just a subset of a random forest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test test: 0.666040570999\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train,y_train)\n",
    "predicted = dt.predict(X_test)\n",
    "print 'Accuracy on test test:', metrics.accuracy_score(y_test, predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one got us only 67% accuracy, so again significantly worse than the random forest. So at this stage, random forest is still the best!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbours\n",
    "Now, let's see how a K-nearest neighbour classifier performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{35: 0.69637112518977118, 10: 0.6940273212012249, 15: 0.6947756296203258, 20: 0.69815652925855731, 25: 0.69928368683775344, 30: 0.69843680911677131}\n",
      "Best parameter:  25 0.699283686838\n",
      "Accuracy on test set:  0.697595792637\n"
     ]
    }
   ],
   "source": [
    "kdict = {}\n",
    "for k in [10,15,20,25,30,35]:\n",
    "    clf = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    kdict[k] = scores.mean()\n",
    "print kdict\n",
    "best_k = max(kdict, key=kdict.get)\n",
    "print 'Best parameter: ',best_k, kdict[best_k]\n",
    "clfknn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "clfknn.fit(X_train,y_train)\n",
    "predicted = clfknn.predict(X_test)\n",
    "print 'Accuracy on test set: ', metrics.accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method performs second best to our random forest with an accuracy of ~70%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM\n",
    "\n",
    "Finally, we try a Linear SVM. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0.44835344832704066, 0.1: 0.50762833653435213, 10: 0.48028291479897273, 0.001: 0.5221585324799527, 0.0001: 0.48685824301324426, 0.01: 0.60311298000430347}\n",
      "Best parameter:  0.01 0.603112980004\n",
      "Accuracy on test set:  0.614575507137\n"
     ]
    }
   ],
   "source": [
    "cdict = {}\n",
    "for c in [0.0001,0.001,0.01,0.1,1,10]:\n",
    "    clf = svm.LinearSVC(loss=\"hinge\",C=c);\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    cdict[c] = scores.mean()\n",
    "print cdict\n",
    "best_c = max(cdict, key=cdict.get)\n",
    "print 'Best parameter: ', best_c, cdict[best_c]\n",
    "clf = svm.LinearSVC(loss=\"hinge\",C=best_c);\n",
    "clf.fit(X_train,y_train)\n",
    "predicted = clf.predict(X_test)\n",
    "print 'Accuracy on test set: ',metrics.accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is our worst predictor, giving only 61% accuracy. This is barely better than the baseline, but this is maybe not surprising. We should try a kernelized SVM. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0.71092943862204772, 100: 0.65308173532734204, 0.1: 0.68548113240595732, 1000: 0.63523778606004411, 10: 0.69712635478064078, 0.001: 0.62086779447118934, 0.01: 0.62086779447118934}\n",
      "Best parameter:  1 0.710929438622\n",
      "Accuracy on test set:  0.710743801653\n"
     ]
    }
   ],
   "source": [
    "cdict = {}\n",
    "for c in [0.001,0.01,0.1,1,10,100,1000]:\n",
    "    clf = svm.SVC(C=c);\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    cdict[c] = scores.mean()\n",
    "print cdict\n",
    "best_c = max(cdict, key=cdict.get)\n",
    "print 'Best parameter: ',best_c, cdict[best_c]\n",
    "clfsvm = svm.SVC(C=best_c);\n",
    "clfsvm.fit(X_train,y_train)\n",
    "predicted = clfsvm.predict(X_test)\n",
    "print 'Accuracy on test set: ',metrics.accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RBF SVM is doing very well! It obtains an accuracy of 71% which is slightly better than the KNN one. However, random forest is still better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{200: 0.73281434206067875, 50: 0.72727271558311923, 100: 0.73056064414670563, 250: 0.73140580281797341, 150: 0.73131212653992395}\n",
      "Best parameter:  200 0.732814342061\n",
      "Accuracy on test set:  0.730653643877\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ndict={}\n",
    "for n in [50,100,150,200,250]:\n",
    "    clf = AdaBoostClassifier(n_estimators=n);\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "    ndict[n] = scores.mean()\n",
    "print ndict\n",
    "best_n = max(ndict, key=ndict.get)\n",
    "print 'Best parameter: ',best_n, ndict[best_n]\n",
    "clfada = AdaBoostClassifier(n_estimators=best_n);\n",
    "clfada.fit(X_train,y_train)\n",
    "predicted = clfada.predict(X_test)\n",
    "print 'Accuracy on test set: ', metrics.accuracy_score(y_test,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "73%! We are getting closer to our random forest accuracy, but in the end, it looks like random forest is the one. To conclude, let's summarize our best classifiers with ROC curves.\n",
    "\n",
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "def make_roc(name, clf, ytest, xtest, ax=None, labe=5, proba=True, skip=0):\n",
    "    initial=False\n",
    "    if not ax:\n",
    "        ax=plt.gca()\n",
    "        initial=True\n",
    "    if proba:#for stuff like logistic regression\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n",
    "    else:#for stuff like SVM\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    if skip:\n",
    "        l=fpr.shape[0]\n",
    "        ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    else:\n",
    "        ax.plot(fpr, tpr, '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    label_kwargs = {}\n",
    "    label_kwargs['bbox'] = dict(\n",
    "        boxstyle='round,pad=0.3', alpha=0.1,\n",
    "    )\n",
    "    if labe!=None:\n",
    "        for k in xrange(0, fpr.shape[0],labe):\n",
    "            #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n",
    "            threshold = str(np.round(thresholds[k], 2))\n",
    "            ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)\n",
    "    if initial:\n",
    "        ax.plot([0, 1], [0, 1], 'k--')\n",
    "        ax.set_xlim([0.0, 1.0])\n",
    "        ax.set_ylim([0.0, 1.05])\n",
    "        ax.set_xlabel('False Positive Rate')\n",
    "        ax.set_ylabel('True Positive Rate')\n",
    "        ax.set_title('ROC')\n",
    "    ax.legend(loc=\"lower right\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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UVQVNRdUUvCAg3dODWy6BoqK4IYZfwY+qRDQTVdVQDZUuq510JIOqqqxavpDerlYCyyHZ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BPJJGPxBHr2BqUSb//ApVRYLbx3dTs6jQZOOL70uksuvohLLr7ogO87F+WChaWvubmZ5uZmRkdH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\n0igWGUREREREpFH/BwLuol+cGvYYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10afbb650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.hls_palette(8, l=.3, s=.8):\n",
    "    ax=make_roc(\"random forest\",clfRF_gini,y_test,X_test,labe=200,skip=50,proba=True)\n",
    "    make_roc(\"adaboost\",clfada,y_test,X_test,ax=ax,labe=200,skip=50,proba=True)\n",
    "    make_roc(\"svm\",clfsvm,y_test,X_test,ax=ax,labe=200,skip=50,proba=False)\n",
    "    make_roc(\"knn\",clfknn,y_test,X_test,ax=ax,labe=100,proba=True)\n",
    "    make_roc(\"logistic\",clflog, y_test, X_test, ax=ax, labe=200, skip=50,proba=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest classifier is the best one. Its ROC curve is always reaches a higher true postive rate and lower false positive rate. It is closely followed by the Adaboost classifier. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Dropping `Intended Graduation Year` As A Predictor\n",
    "\n",
    "Given that the `intendedgradyear` in our data only stretches back as far as 2009, it is worth seeing whether dropping it as a predictor will help. This assumes that the admissions regime at all of the top 25 colleges has not drastically changed over the last few years. \n",
    "\n",
    "The reason we are dropping this is because people who use the site will input an intended graduation year of >2019, whereas all of our data has intended graduation year of 2009-2019. Therefore using it as a feature would be extrapolation by definition, so is not entirely reliable. \n",
    "\n",
    "We drop the column from the training and test sets, not from the original data frame. The reason behind this is that we don't want to compare the accuracy for different training/test splits, so we need to ensure the same split is used below as was used above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "index_igy = [index for index,value in enumerate(dfpredict.columns) if value == \"intendedgradyear\"]\n",
    "select = [i for i in range(X_train.shape[1]) if i not in index_igy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest: \n",
      "{100: 0.73318975245906592, 5: 0.7021974396159677, 1000: 0.73628935847555865, 10: 0.71205910903423453, 50: 0.73121875873921027, 500: 0.73422393900500926, 25: 0.72755440605510402, 250: 0.73337736959579303}\n",
      "Best parameter:  1000 0.736289358476\n",
      "Accuracy on test set:  0.736288504884\n"
     ]
    }
   ],
   "source": [
    "X_train1 = X_train[:,select]\n",
    "X_test1 = X_test[:,select]\n",
    "y_train1=y_train\n",
    "y_test1=y_test\n",
    "print 'Random Forest: '\n",
    "ntdict = {}\n",
    "for nt in [5,10,25,50,100,250,500,1000]:\n",
    "    clf = RandomForestClassifier(n_estimators=nt,criterion=\"gini\")\n",
    "    scores = cross_val_score(clf, X_train1, y_train1, cv=5)\n",
    "    ntdict[nt] = scores.mean()\n",
    "print ntdict\n",
    "best_nt = max(ntdict, key=ntdict.get)\n",
    "print 'Best parameter: ',best_nt, ntdict[best_nt]\n",
    "clfRF = RandomForestClassifier(n_estimators=nt)\n",
    "clfRF.fit(X_train1,y_train1)\n",
    "predicted = clfRF.predict(X_test1)\n",
    "print 'Accuracy on test set: ', metrics.accuracy_score(y_test1,predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our accuracy has dropped slightly, but with an acceptable amount. We are still gaining at least 10% compared to the baseline. Moreover, the little extra accuracy we obtained when including `intendedgradyear` may just be due to overfitting and thus not valid anyway when we want to predict for new users. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Updating the Factor Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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X4+/vn2Hdu3fvJj4+nvbt26fbPbZ3794AbNq0yaLcx8fHYjQ7V65clC1b1uJzI/K00TRX\nERGhU6dO1KhRA7i3RmvRokWcPXuW9957L10ycPr0aQCWLl3K0qVLM6zv3LlzmbaVI0cOTp8+zdq1\nazlx4gSxsbHExsYaa4zMVm6k4uTkREBAANu3bycxMZE8efKwZs0a7OzsjEdFpD1XcevWrWzdujXD\nemJjY61q936FCxe2+D0tMS5UqJBFedrU1gfv0cXFJV2dpUuXBu5NdUxbU/Xiiy9m2H7ZsmX59ddf\nuXbtGk5OTgAWyemj2NnZsX37dnbu3MmZM2eIiYnh8uXLwL1pffev/wIspjGmsbe3t1jjGBMTQ8WK\nFdOd5+DgQPny5YG/3pcyc/bsWcqVK2eRWKVJWwP5+++/Zxgf/DF99dKlS1a36+TklOFrn/bexcTE\nPFZd1vTZtNcxrc/c78E1n2fOnAHuJXMZsbGxeehrvmPHDq5fv463tzc3btww1usWKlQIZ2dnfv/9\nd3bt2oWfnx9w7zUpXLhwurXXdnZ2vPDCCxm28eDnJu3z9GC/s7W1Tdc379ezZ0+OHDnC8uXLWb58\nOYUKFaJGjRo0aNCAgIAA4F6/HT16NO+99x7Dhg1j+PDhmEwmatWqRfPmzTON8ezZswCUK1cu3bGi\nRYuSN29efv/9d4vyzD43D7sHkexOyaSIiPDiiy/i6+tr/N6oUSPatWvHoEGDuHnzpsWDxdMSofbt\n21OnTp0M63N0dMy0rbFjxzJ//nxKlSpFlSpV8Pf3p3z58pw9e5Zhw4b9qfhbtmzJ5s2b2bJlCw0b\nNmTz5s34+fkZX97Svqw1btyYVq1aZVhHzpw5/1TbQIZJCzz+MwPvH6VNk5aY2draPjLBTru/++tJ\n+wL+KElJSbzxxhscPHgQDw8PKlasSLNmzXBzc2Pp0qVERkamu+b+9Z6ZSU5OfuT9/9W+9Kh6H3Ys\no9c8jZeXFzY2No/ckGb37t2EhYUREhJC/fr1H9puRu/Rw1jTZ5OSkjKt+/4R8/vrnTVrVqaxZJSU\npomIiABg37591K1bN8NzVqxYYSSTALdv387wvMw2WMrs82StggULsmrVKr7//nu+/PJL9uzZw4YN\nG4iKiqJu3bpMnz4dgHr16lG9enV27tzJrl27+Pbbb5k1axZz585lwoQJFiOwaR7nM/ng6/s4nxuR\np42SSRERSSd37txMnTqVZs2aMWbMGCpUqGBsWPL8888D9xKl+xNQuLd5xvbt29ON1KWJjY1l/vz5\n+Pr6MnfuXIuE54cffvjT8dasWZMiRYqwadMmHBwciI+Pp2XLlsbxZ599Frj3pfvBmOHe9MTnnnvu\nT7f/V50+fdrYaCdN2q6vpUuXNkYbf/nll3TXms1mTp06Rf78+S2mnj6uTZs2cfDgQbp3707Pnj0t\njlk7Mne/Z5991hgxu198fDyDBw+mfv36lCxZEvhzfelhnn/+eX799Vfu3r2b7o8Eaa9h8eLFM72+\nSJEiVKlShQMHDnDs2LEMR47hXtK0b98+Xn/9daPd06dPp9uE5nHbvZ81fTZtWvWpU6eMz2ea+3cP\nBoxrihYtmu65sceOHeP8+fOZ7uB88eJFvv76a/Lnz89HH32U7vitW7d49913+fLLL7l48SJFihSh\nVKlSnDhxgri4OIsR25SUFGOX07/LyZMnSUxMxN3dHXd3d3r16sXVq1fp0aMH27Zt48SJEzz77LMc\nO3aMEiVK0LBhQ2Pzof379xMaGsrs2bMzTCbTXufjx4+n++yeO3eOxMRESpQo8bfdm0h2oT+RiIhI\nhooXL84HH3xASkoKgwYNMtbNubq6UqJECVatWpVuauicOXPo06cPX375ZYZ1pk2JK1OmjEUiee3a\nNePB7/evU0v7S/6jpoHlyJGDoKAg9uzZQ0REBM7OzhYjXYULF6Zy5cps376dI0eOWFwbGRlJ7969\nWb169UPb+DstXbrU4lEPsbGxREVF4eLiQtmyZSlYsCBeXl7s2rWLgwcPWly7Zs0afvvtN15++eXH\nauvBaatXr14F0k/XO3z4MN9++y02NjaP9biXB9WrV4/Lly+nWze2Zs0aY33nX+lLaTLqIw0aNODm\nzZvpHldy+vRp1q1bR8mSJTNNENO8++67APTv3z/DdXnLly9ny5YtVKhQwUg20v538uTJFufGxcWx\ndOlS8ubNS/Xq1R/abhpr+mz9+vWxs7Nj8eLFFu/VxYsXWbduncW1aesOp0+fbjG6lpiYSL9+/eje\nvTt37tzJMKaoqChSU1Np0aIFdevWTffTpEkT6tatS3JyMqtWrQL+WEs5Y8YMi7pWrVqV7hExT9o7\n77xDt27diI+PN8oKFChgTF21tbXll19+ITg4mJkzZ1pcW7FiRXLmzJnpjAU/Pz8cHR1ZtGgR165d\nszg2ZcoUgMf+TIo8zTQyKSIimWrUqBGbN29m8+bNjBkzhhEjRpAjRw5GjhxJ165dad68OcHBwZQo\nUYIDBw6wbt06KlSokG6zjTQvvvgiJUuWZMWKFeTMmZOyZcvy+++/ExERQYECBQC4efOmcX7aqNSc\nOXOoWbOmsc4pI82bN2f27Nns3LmTDh06pJvmOXz4cF5//XXatWtHcHAwZcqU4eeff2blypU8++yz\nvP3223/15frTLl26xKuvvkqrVq1ISEjg008/BbB4RuawYcNo164doaGhBAcH88ILL3DkyBEiIyN5\n7rnnHvvRAoUKFeLHH39k+fLleHt74+fnx/jx4xk1ahS//vorzs7O/Pjjj0RFRVGmTBmOHz/+p770\nv/XWW2zdupX+/fvz7bffUr58eY4fP054eDi+vr40atToL/WlNGl9ZM2aNdy9e5fmzZvTqVMntm/f\nzsSJEzl69CheXl5cuHCBTz/9FDs7uwxH1R5UqVIlRo8ezfvvv0/Dhg1p2rQpLi4uxMfHs2vXLqKj\noylVqhTTpk0zpvM2b96cTZs2ER4ezu+//46/vz/Xr18nPDycmzdvMnbs2HTTTh/mcftsiRIl6Nq1\nK9OmTaNt27Y0atSI+Ph4li9fbvyRIi1GX19fmjdvTmRkJG3atDGSy9WrV3Py5En69OmT7pEnaSIi\nIsiRI4fxuJKMdOjQgS1btrBq1Sq6detGo0aNWLt2LUuXLuXChQtUq1aNX375hYiICPLnz//Ya6Qz\nO+9h17/99tv069ePNm3a0KJFCxwdHTl8+DARERH4+/sb03lr1qzJ8uXLuXnzJl5eXty5c4e1a9dy\n+/ZtQkNDM6w7b968DB06lMGDBxMUFMSrr77KM888w44dO/jmm2/w9/c31mz/2XsTeRoomRQR+Q/L\n6Bl0D/rggw/Yv38/K1eu5OWXX8bPz48aNWrw2WefMXPmTFasWGFM6ercuTOdO3fOdLplzpw5mTNn\nDuPGjSMqKorExETKli1Lz549adasGd7e3uzevdv4AtemTRuio6NZuXIle/bsISAgINOYS5cujaen\nJ9999x3NmzdPd9zFxYWIiAimTZvG+vXruX79OkWLFqV169Z07dr1T02nzExmMWZW3rdvX06fPs30\n6dPJkSMHXl5evPPOOxajZy+99BKrV69m2rRpbNiwgevXr1O8eHHeeOMNunXrlunOmA8aOHAgn3zy\nCaNHj6Zbt25069aNWbNmMWXKFGbPng2Ah4cHYWFh2Nra0q5dO3bv3p3pZjX339v98uXLx2effca0\nadPYtm0bkZGRlChRgrfeeovOnTsbI4p/ti+lqVatGoGBgWzdupXDhw9TtWpVSpYsybJly5g1axaf\nf/45X3zxBU5OTvj7+9OtW7d0m9JkplmzZlSsWJFly5YRHR3NunXrSE5OpnTp0vTq1Ys33njDYkpo\njhw5CAsLY/78+axdu5axY8eSN29eKleuTJcuXSx2132cz541fbZHjx4ULlyYpUuX8sknn+Ds7Myr\nr77K7du3WbBggcX6vdGjR+Pu7s7KlSuZNGkS9vb2vPjii0yYMCHTZ0x+9913nD59murVqxu7Emek\nSpUqVKxYkZ9++omvvvqK2rVrM336dObMmUNERAQ7d+6kdOnSTJkyhZkzZ1q8BtZ+bh4sf/D3xo0b\nY29vz6JFi5g9ezYJCQk8//zz9OjRg86dOxvnTZ48mTlz5rB582a2bduGra0trq6uhIWFUatWrUzv\nNSgoiOLFizN79mwWLFhAcnIyZcqUYciQIcbU50d5nH4gkp3ZmPXnEBERkSwRHR1Nhw4d+PDDD3nt\ntdeyOhx5St26dYuUlBTjUS73Gz58OCtWrGD79u1awyciT5zWTIqIiIg8xX766Se8vLwICwuzKI+P\nj2f79u0UKVJEiaSI/C00zVVERETkKebh4YGLiwszZszg8uXLuLi4cO3aNSIiIoiLi2P8+PFZHaKI\n/EspmRQREclCWi8lf5WtrS2LFy9m7ty5bNmyhZUrV+Lg4IC7uzsjRozA29s7q0MUkX8prZkUERER\nERERq2nNpEg2lJCQkNUhiFhISEhQv5RsR/1SsiP1S8mO/q4+qWRSRERERERErKZkUkRERERERKym\nZFJERERERESspmRSRERERERErKZkUkRERERERKymZFJERERERESspmRSRERERERErKZkUkRERERE\nRKymZFJERERERESspmRSRERERERErKZkUkRERERERKymZFJERERERESspmRSRERERERErKZkUkRE\nRERERKymZFJERERERESspmRSRERERERErKZkUkRERERERKymZFJERERERESspmRSRERERERErKZk\nUkRERERERKymZFJERERERESspmRSRERERERErKZkUkRERERERKymZFJERERERESspmRSRERERERE\nrKZkUkRERERERKy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jI6tWrXpygYuIiIiIZDNKJuU/5csvv8TX15dnnnkmw+Nly5Zl0qRJ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u3Kzp07zWJLSkpi1KhReHl54efnx4cffljYr9eMg4MD3t7efPrpp2blWVlZ7Nu3j+eee86s/O5p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\nUkRERERERGymYFJERERERERspmBSREREREREbKZgU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7BgQQA++OADDh06RKdOnSyvBtmw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\nm/0/SZWaJ1tGTC0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3f44090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create interpretable labels for the columns\n",
    "better_labels = {\n",
    "    \"admissionstest\": \"ACT/SAT score\",\n",
    "    \"acceptrate\": \"College acceptance rate\",\n",
    "    \"GPA\": \"GPA\",\n",
    "    \"averageAP\": \"Average AP score\",\n",
    "    \"size\": \"College size\",\n",
    "    \"AP\": \"# AP exams taken\",\n",
    "    \"SATsubject\": \"# SAT subject tests taken\",\n",
    "    \"female\": \"Gender / Female\",\n",
    "    \"schooltype\": \"Private High School\",\n",
    "    \"MinorityRace\": \"Underrepresented Minority\",\n",
    "    \"earlyAppl\": \"Early Application\",\n",
    "    \"outofstate\": \"Out of State\",\n",
    "    \"public\": \"Public College\",\n",
    "    \"alumni\": \"Legacy\",\n",
    "    \"international\": \"International Student\",\n",
    "    \"sports\": \"Varsity Sports\"\n",
    "    }\n",
    "# create dictionary of feature importances\n",
    "dfpredict1 = dfpredict.drop(\"intendedgradyear\",axis=1)\n",
    "X1 = imp.transform(dfpredict1)\n",
    "y1 = dfresponse\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size=0.2)\n",
    "clf = RandomForestClassifier(n_estimators=100,criterion=\"gini\")\n",
    "clf.fit(X_train1,y_train1)\n",
    "feature_importance = {}\n",
    "for i in range(len(dfpredict1.columns)):\n",
    "    feature_importance[better_labels[dfpredict1.columns[i]]] = clf.feature_importances_[i]\n",
    "# plot sorted importances\n",
    "sns.set_style(\"white\")\n",
    "plt.barh(range(len(dfpredict1.columns)),sorted(feature_importance.values()), align=\"center\",alpha=0.7,edgecolor='white')\n",
    "plt.ylim([-1,17])\n",
    "plt.yticks(range(len(dfpredict1.columns)), sorted(feature_importance,key=feature_importance.get))\n",
    "plt.ylabel(\"Factor\")\n",
    "plt.xlabel(\"importance\")\n",
    "plt.title(\"Relative Importance to College Admission\")\n",
    "plt.gca().yaxis.grid(False)\n",
    "plt.gca().xaxis.grid(True,color='0.95')\n",
    "plt.tight_layout()\n",
    "sns.despine(left=True,bottom=True)\n",
    "plt.savefig(\"features.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the probability of getting into college\n",
    "\n",
    "Let's now use our best classifier to predict the probability that a new candidate gets into each of the universities. We use the classifiers' `predict_proba` function to obtain this information. It is the answer we want to give users of our web app."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'college': 'Princeton', 'prob': 0.36249999999999999},\n",
       " {'college': 'Harvard', 'prob': 0.20200000000000001},\n",
       " {'college': 'Yale', 'prob': 0.309},\n",
       " {'college': 'Columbia', 'prob': 0.371},\n",
       " {'college': 'Stanford', 'prob': 0.23100000000000001},\n",
       " {'college': 'UChicago', 'prob': 0.65600000000000003},\n",
       " {'college': 'MIT', 'prob': 0.32900000000000001},\n",
       " {'college': 'Duke', 'prob': 0.59599999999999997},\n",
       " {'college': 'UPenn', 'prob': 0.36099999999999999},\n",
       " {'college': 'CalTech', 'prob': 0.50700000000000001},\n",
       " {'college': 'JohnsHopkins', 'prob': 0.56000000000000005},\n",
       " {'college': 'Dartmouth', 'prob': 0.53100000000000003},\n",
       " {'college': 'Northwestern', 'prob': 0.66900000000000004},\n",
       " {'college': 'Brown', 'prob': 0.53700000000000003},\n",
       " {'college': 'Cornell', 'prob': 0.626},\n",
       " {'college': 'Vanderbilt', 'prob': 0.69099999999999995},\n",
       " {'college': 'WashU', 'prob': 0.68500000000000005},\n",
       " {'college': 'Rice', 'prob': 0.75600000000000001},\n",
       " {'college': 'NotreDame', 'prob': 0.72199999999999998},\n",
       " {'college': 'UCB', 'prob': 0.78200000000000003},\n",
       " {'college': 'Emory', 'prob': 0.72999999999999998},\n",
       " {'college': 'Georgetown', 'prob': 0.66900000000000004},\n",
       " {'college': 'CarnegieMellon', 'prob': 0.78600000000000003},\n",
       " {'college': 'UCLA', 'prob': 0.70899999999999996},\n",
       " {'college': 'USC', 'prob': 0.72799999999999998}]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### test a candidate's chances for each college\n",
    "\n",
    "candidate = [0.926899206,7,1.06733864,\n",
    "             3,-0.187109979,0,1,\n",
    "             0,0,0,0,0,\n",
    "             0]\n",
    "\n",
    "college_cols = [\"acceptrate\",\"size\",\"public\"]\n",
    "colleges = ti.College()\n",
    "preds = []\n",
    "for i, row in colleges.df.iterrows():\n",
    "    Xp1 = candidate + list(row[college_cols])\n",
    "    y_rf = clfRF.predict_proba(Xp1)[0][1]\n",
    "    p =  {'college':row.collegeID, 'prob':y_rf}\n",
    "    preds.append(p)\n",
    "    \n",
    "preds   "
   ]
  }
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