report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Blood Donation\n",
    "\n",
    "_Spencer Pease_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\spenc\\.conda\\envs\\info370\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Load packages\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from __future__ import division\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train_raw = pd.read_csv('data/training_data.csv')\n",
    "df_test_raw = pd.read_csv('data/test_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>Months since Last Donation</th>\n",
       "      <th>Number of Donations</th>\n",
       "      <th>Total Volume Donated (c.c.)</th>\n",
       "      <th>Months since First Donation</th>\n",
       "      <th>Made Donation in March 2007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>441</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Months since Last Donation  Number of Donations  \\\n",
       "0         619                           2                   50   \n",
       "1         664                           0                   13   \n",
       "2         441                           1                   16   \n",
       "3         160                           2                   20   \n",
       "4         358                           1                   24   \n",
       "\n",
       "   Total Volume Donated (c.c.)  Months since First Donation  \\\n",
       "0                        12500                           98   \n",
       "1                         3250                           28   \n",
       "2                         4000                           35   \n",
       "3                         5000                           45   \n",
       "4                         6000                           77   \n",
       "\n",
       "   Made Donation in March 2007  \n",
       "0                            1  \n",
       "1                            1  \n",
       "2                            1  \n",
       "3                            1  \n",
       "4                            0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(576, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensions\n",
    "df_train_raw.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>last_donate</th>\n",
       "      <th>num_donate</th>\n",
       "      <th>total_vol</th>\n",
       "      <th>first_donate</th>\n",
       "      <th>march_donate</th>\n",
       "      <th>donate_per_visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>12500</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3250</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>5000</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>6000</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   last_donate  num_donate  total_vol  first_donate  march_donate  \\\n",
       "0            2          50      12500            98             1   \n",
       "1            0          13       3250            28             1   \n",
       "2            1          16       4000            35             1   \n",
       "3            2          20       5000            45             1   \n",
       "4            1          24       6000            77             0   \n",
       "\n",
       "   donate_per_visit  \n",
       "0             250.0  \n",
       "1             250.0  \n",
       "2             250.0  \n",
       "3             250.0  \n",
       "4             250.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train_raw.copy()\n",
    "\n",
    "\n",
    "# Rename columns\n",
    "df_train.columns = ['id', 'last_donate', 'num_donate', 'total_vol', 'first_donate', 'march_donate']\n",
    "\n",
    "# Drop ids\n",
    "df_train = df_train.drop('id', axis = 1)\n",
    "\n",
    "# Add metrics\n",
    "df_train = df_train.assign(donate_per_visit = df_train.total_vol / df_train.num_donate)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Seperate data into features and outcomes\n",
    "\n",
    "features = df_train.drop('march_donate', axis = 1) \n",
    "outcomes = df_train.march_donate\n",
    "\n",
    "# Split into train and validation\n",
    "train_features, val_features, train_outcome, val_outcome = train_test_split(features, outcomes, test_size=0.20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create KNN classifier\n",
    "knn_clf = KNeighborsClassifier(n_neighbors = 9)\n",
    "\n",
    "# Fit classifier to training data\n",
    "knn_fit = knn_clf.fit(train_features, train_outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7931034482758621"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test accuracy\n",
    "knn_preds = knn_fit.predict(val_features)\n",
    "test_acc = (knn_preds == val_outcome).sum()/len(val_outcome)\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Format test data\n",
    "df_test = df_test_raw\n",
    "df_test.columns = ['id', 'last_donate', 'num_donate', 'total_vol', 'first_donate']\n",
    "df_test = df_test.assign(donate_per_visit = df_train.total_vol / df_train.num_donate)\n",
    "\n",
    "df_test.head()\n",
    "\n",
    "\n",
    "# Generate test pobabilities\n",
    "test_preds = knn_fit.predict_proba(df_test.drop('id', axis = 1))[0:, 1]\n",
    "\n",
    "result = pd.DataFrame({'': df_test.id, 'Made Donation in March 2007': test_preds})\n",
    "\n",
    "result.to_csv('data/result.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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