Kaggle - Digit Recognizer (ML).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read the dataset and check if anything missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import necessary modules to manipulate the datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read training and test sets\n",
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "[8 rows x 785 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train.describe()"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pixel0   pixel1   pixel2   pixel3   pixel4   pixel5   pixel6   pixel7  \\\n",
       "count  28000.0  28000.0  28000.0  28000.0  28000.0  28000.0  28000.0  28000.0   \n",
       "mean       0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "std        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "min        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "50%        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "75%        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "max        0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        pixel8   pixel9    ...         pixel774      pixel775      pixel776  \\\n",
       "count  28000.0  28000.0    ...     28000.000000  28000.000000  28000.000000   \n",
       "mean       0.0      0.0    ...         0.164607      0.073214      0.028036   \n",
       "std        0.0      0.0    ...         5.473293      3.616811      1.813602   \n",
       "min        0.0      0.0    ...         0.000000      0.000000      0.000000   \n",
       "25%        0.0      0.0    ...         0.000000      0.000000      0.000000   \n",
       "50%        0.0      0.0    ...         0.000000      0.000000      0.000000   \n",
       "75%        0.0      0.0    ...         0.000000      0.000000      0.000000   \n",
       "max        0.0      0.0    ...       253.000000    254.000000    193.000000   \n",
       "\n",
       "           pixel777      pixel778  pixel779  pixel780  pixel781  pixel782  \\\n",
       "count  28000.000000  28000.000000   28000.0   28000.0   28000.0   28000.0   \n",
       "mean       0.011250      0.006536       0.0       0.0       0.0       0.0   \n",
       "std        1.205211      0.807475       0.0       0.0       0.0       0.0   \n",
       "min        0.000000      0.000000       0.0       0.0       0.0       0.0   \n",
       "25%        0.000000      0.000000       0.0       0.0       0.0       0.0   \n",
       "50%        0.000000      0.000000       0.0       0.0       0.0       0.0   \n",
       "75%        0.000000      0.000000       0.0       0.0       0.0       0.0   \n",
       "max      187.000000    119.000000       0.0       0.0       0.0       0.0   \n",
       "\n",
       "       pixel783  \n",
       "count   28000.0  \n",
       "mean        0.0  \n",
       "std         0.0  \n",
       "min         0.0  \n",
       "25%         0.0  \n",
       "50%         0.0  \n",
       "75%         0.0  \n",
       "max         0.0  \n",
       "\n",
       "[8 rows x 784 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#find nan values\n",
    "print(np.sum(np.sum(train.isnull(), axis = 1)))\n",
    "print(np.sum(np.sum(test.isnull(), axis = 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every sample is a 28*28 img\n",
    "\n",
    "The first column \"label\" ranges from 0 to 9, which also should be the output value\n",
    "\n",
    "No missing/NaN values in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#set features and labels\n",
    "features = train.iloc[:, 1:]\n",
    "label = train.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Try sklearn / machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import ML modules\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#logistic regression\n",
    "clf = GaussianNB()\n",
    "clf.fit(features, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55719047619047624"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(features, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All other ML algorithms take a lot of time training.\n",
    "\n",
    "And Gaussian got a horrible score.\n",
    "\n",
    "There's no way implementing them without normalization and grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import preprocessing module\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "normalized_features = MinMaxScaler().fit_transform(features.loc[:, features.columns])\n",
    "normalized_test = MinMaxScaler().fit_transform(test.loc[:, test.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 784)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55571428571428572"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(normalized_features, label)\n",
    "clf.score(normalized_features, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Nov 28 22:22:07 2017\n",
      "0.93080952381\n",
      "Tue Nov 28 22:23:03 2017\n"
     ]
    }
   ],
   "source": [
    "# Try LogisticRegression\n",
    "print(time.asctime())\n",
    "clf = LogisticRegression()\n",
    "clf.fit(normalized_features, label)\n",
    "print(clf.score(normalized_features, label))\n",
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pretty good score\n",
    "#first try on test and upload to Kaggle\n",
    "#normalized_test = MinMaxScaler().fit_transform(test.loc[:, test.columns])\n",
    "prediction = pd.DataFrame(clf.predict(normalized_test), columns = ['Label']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .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>ImageId</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>9</th>\n",
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       "      <td>3</td>\n",
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       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
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       "      <th>12</th>\n",
       "      <td>13</td>\n",
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       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
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       "      <th>15</th>\n",
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       "      <th>16</th>\n",
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       "      <td>0</td>\n",
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       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
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       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
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       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
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       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
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       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>27970</th>\n",
       "      <td>27971</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>27971</th>\n",
       "      <td>27972</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>27972</th>\n",
       "      <td>27973</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27973</th>\n",
       "      <td>27974</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27974</th>\n",
       "      <td>27975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27975</th>\n",
       "      <td>27976</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27976</th>\n",
       "      <td>27977</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27977</th>\n",
       "      <td>27978</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27978</th>\n",
       "      <td>27979</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27979</th>\n",
       "      <td>27980</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>27980</th>\n",
       "      <td>27981</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>27981</th>\n",
       "      <td>27982</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27982</th>\n",
       "      <td>27983</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27983</th>\n",
       "      <td>27984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27984</th>\n",
       "      <td>27985</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27985</th>\n",
       "      <td>27986</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27986</th>\n",
       "      <td>27987</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27987</th>\n",
       "      <td>27988</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27988</th>\n",
       "      <td>27989</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27989</th>\n",
       "      <td>27990</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27990</th>\n",
       "      <td>27991</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27991</th>\n",
       "      <td>27992</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27992</th>\n",
       "      <td>27993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27993</th>\n",
       "      <td>27994</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27994</th>\n",
       "      <td>27995</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27995</th>\n",
       "      <td>27996</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27996</th>\n",
       "      <td>27997</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27997</th>\n",
       "      <td>27998</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27998</th>\n",
       "      <td>27999</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27999</th>\n",
       "      <td>28000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ImageId  Label\n",
       "0            1      2\n",
       "1            2      0\n",
       "2            3      9\n",
       "3            4      7\n",
       "4            5      3\n",
       "5            6      7\n",
       "6            7      0\n",
       "7            8      3\n",
       "8            9      0\n",
       "9           10      3\n",
       "10          11      5\n",
       "11          12      7\n",
       "12          13      4\n",
       "13          14      0\n",
       "14          15      4\n",
       "15          16      3\n",
       "16          17      3\n",
       "17          18      1\n",
       "18          19      9\n",
       "19          20      0\n",
       "20          21      9\n",
       "21          22      1\n",
       "22          23      1\n",
       "23          24      5\n",
       "24          25      7\n",
       "25          26      4\n",
       "26          27      2\n",
       "27          28      7\n",
       "28          29      7\n",
       "29          30      7\n",
       "...        ...    ...\n",
       "27970    27971      5\n",
       "27971    27972      0\n",
       "27972    27973      4\n",
       "27973    27974      8\n",
       "27974    27975      0\n",
       "27975    27976      3\n",
       "27976    27977      6\n",
       "27977    27978      0\n",
       "27978    27979      1\n",
       "27979    27980      9\n",
       "27980    27981      3\n",
       "27981    27982      1\n",
       "27982    27983      8\n",
       "27983    27984      0\n",
       "27984    27985      4\n",
       "27985    27986      5\n",
       "27986    27987      2\n",
       "27987    27988      2\n",
       "27988    27989      1\n",
       "27989    27990      6\n",
       "27990    27991      7\n",
       "27991    27992      6\n",
       "27992    27993      1\n",
       "27993    27994      4\n",
       "27994    27995      7\n",
       "27995    27996      9\n",
       "27996    27997      7\n",
       "27997    27998      3\n",
       "27998    27999      9\n",
       "27999    28000      2\n",
       "\n",
       "[28000 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.rename(columns = {'index': 'ImageId'}, inplace = True)\n",
    "prediction.iloc[:, 0] += 1\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction.to_csv('logisticML.csv', index = False)\n",
    "#accuracy = 0.91642"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Nov 28 23:30:10 2017\n",
      "Tue Nov 28 23:30:23 2017\n"
     ]
    }
   ],
   "source": [
    "#how about knn\n",
    "#import time\n",
    "print(time.asctime())\n",
    "clf = KNeighborsClassifier()\n",
    "clf.fit(normalized_features, label)\n",
    "#print(clf.score(normalized_features, label)) #0.979142857143\n",
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#even better!\n",
    "#normalized_test = MinMaxScaler().fit_transform(test.loc[:, test.columns])\n",
    "prediction = pd.DataFrame(clf.predict(normalized_test), columns = ['Label']).reset_index()\n",
    "prediction.rename(columns = {'index': 'ImageId'}, inplace = True)\n",
    "prediction.iloc[:, 0] += 1\n",
    "prediction.to_csv('knnML.csv', index = False)\n",
    "#0.96800\n",
    "#but took a lot of time to predict!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 02:16:47 2017\n",
      "1.0\n",
      "Wed Nov 29 02:16:58 2017\n"
     ]
    }
   ],
   "source": [
    "#decision tree\n",
    "print(time.asctime())\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(normalized_features, label)\n",
    "print(clf.score(normalized_features, label))\n",
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 02:16:58 2017\n",
      "Wed Nov 29 02:16:58 2017\n"
     ]
    }
   ],
   "source": [
    "print(time.asctime())\n",
    "prediction = pd.DataFrame(clf.predict(normalized_test), columns = ['Label']).reset_index()\n",
    "prediction.rename(columns = {'index': 'ImageId'}, inplace = True)\n",
    "prediction.iloc[:, 0] += 1\n",
    "prediction.to_csv('dtML.csv', index = False)\n",
    "print(time.asctime())\n",
    "#0.85485 sucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 02:18:08 2017\n",
      "0.999404761905\n",
      "Wed Nov 29 02:18:11 2017\n"
     ]
    }
   ],
   "source": [
    "#random forest\n",
    "print(time.asctime())\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(normalized_features, label)\n",
    "print(clf.score(normalized_features, label))\n",
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 02:18:23 2017\n",
      "Wed Nov 29 02:18:23 2017\n"
     ]
    }
   ],
   "source": [
    "print(time.asctime())\n",
    "prediction = pd.DataFrame(clf.predict(normalized_test), columns = ['Label']).reset_index()\n",
    "prediction.rename(columns = {'index': 'ImageId'}, inplace = True)\n",
    "prediction.iloc[:, 0] += 1\n",
    "prediction.to_csv('rfML.csv', index = False)\n",
    "print(time.asctime())\n",
    "#0.94000 no better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 02:19:34 2017\n",
      "0.940523809524\n",
      "Wed Nov 29 05:39:03 2017\n"
     ]
    }
   ],
   "source": [
    "#SVC\n",
    "print(time.asctime())\n",
    "clf = SVC()\n",
    "clf.fit(normalized_features, label)\n",
    "print(clf.score(normalized_features, label))\n",
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 05:39:03 2017\n",
      "Wed Nov 29 09:16:42 2017\n"
     ]
    }
   ],
   "source": [
    "print(time.asctime())\n",
    "prediction = pd.DataFrame(clf.predict(normalized_test), columns = ['Label']).reset_index()\n",
    "prediction.rename(columns = {'index': 'ImageId'}, inplace = True)\n",
    "prediction.iloc[:, 0] += 1\n",
    "prediction.to_csv('svcML.csv', index = False)\n",
    "print(time.asctime())\n",
    "#0.93585"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 09:16:42 2017\n",
      "0.737428571429\n",
      "Wed Nov 29 09:17:07 2017\n"
     ]
    }
   ],
   "source": [
    "#AdaBoost\n",
    "print(time.asctime())\n",
    "clf = AdaBoostClassifier()\n",
    "clf.fit(normalized_features, label)\n",
    "print(clf.score(normalized_features, label))\n",
    "print(time.asctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov 29 09:17:07 2017\n",
      "Wed Nov 29 09:17:08 2017\n"
     ]
    }
   ],
   "source": [
    "print(time.asctime())\n",
    "prediction = pd.DataFrame(clf.predict(normalized_test), columns = ['Label']).reset_index()\n",
    "prediction.rename(columns = {'index': 'ImageId'}, inplace = True)\n",
    "prediction.iloc[:, 0] += 1\n",
    "prediction.to_csv('adaML.csv', index = False)\n",
    "print(time.asctime())\n",
    "#guess it would not get over 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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