Dim. Reduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn import linear_model\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA,TruncatedSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "df_x = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "df_y = pd.DataFrame(boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_x,df_y,test_size=0.2, random_state=4)\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72647013796830096"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10, whiten='True')\n",
    "x = pca.fit(df_x).transform(df_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.08284717e+04,   6.23792169e+03,   8.16747922e+02,\n",
       "         2.66155981e+02,   5.00356236e+01,   2.78416349e+01,\n",
       "         1.60690855e+01,   9.51546794e+00,   3.26682587e+00,\n",
       "         1.17875328e+00])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,df_y,test_size=0.2, random_state=4)\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69018378692138982"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd = TruncatedSVD(n_components = 10)\n",
    "x = svd.fit(df_x).transform(df_x)\n",
    "reg = linear_model.LinearRegression()\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,df_y,test_size=0.2, random_state=4)\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68223250209842634"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.199458</td>\n",
       "      <td>0.404471</td>\n",
       "      <td>-0.055295</td>\n",
       "      <td>0.417521</td>\n",
       "      <td>-0.219940</td>\n",
       "      <td>0.350784</td>\n",
       "      <td>-0.377904</td>\n",
       "      <td>0.622029</td>\n",
       "      <td>0.579564</td>\n",
       "      <td>0.288250</td>\n",
       "      <td>-0.377365</td>\n",
       "      <td>0.452220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.199458</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.404471</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055295</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.053929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.417521</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219940</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.350784</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.377904</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.622029</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.579564</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.288250</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.377365</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.452220</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.199458  0.404471 -0.055295  0.417521 -0.219940  0.350784   \n",
       "ZN      -0.199458  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.404471 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055295 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.417521 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219940  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.350784 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.377904  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.622029 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.579564 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.288250 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "B       -0.377365  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "LSTAT    0.452220 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT  \n",
       "CRIM    -0.377904  0.622029  0.579564  0.288250 -0.377365  0.452220  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('mnist.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>label</th>\n",
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       "      <th>pixel3</th>\n",
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       "      <th>pixel776</th>\n",
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       "      <th>pixel778</th>\n",
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       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
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       "<p>5 rows × 785 columns</p>\n",
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      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0       0    ...            0         0         0         0         0   \n",
       "1       0    ...            0         0         0         0         0   \n",
       "2       0    ...            0         0         0         0         0   \n",
       "3       0    ...            0         0         0         0         0   \n",
       "4       0    ...            0         0         0         0         0   \n",
       "\n",
       "   pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0         0  \n",
       "1         0         0         0         0         0  \n",
       "2         0         0         0         0         0  \n",
       "3         0         0         0         0         0  \n",
       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_x = data.iloc[:,1:]\n",
    "df_y = data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=50, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_x,df_y,test_size=0.2, random_state=4)\n",
    "rf = RandomForestClassifier(n_estimators = 50)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = y_test.values\n",
    "count = 0\n",
    "for i in range(len(pred)):\n",
    "    if pred[i]==s[i]:\n",
    "        count = count + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615476190476191"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count/float(len(pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=25, whiten='True')\n",
    "x = pca.fit(df_x).transform(df_x)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,df_y,test_size=0.2, random_state=4)\n",
    "rf = RandomForestClassifier(n_estimators = 50)\n",
    "rf.fit(x_train,y_train)\n",
    "pred = rf.predict(x_test)\n",
    "s = y_test.values\n",
    "count = 0\n",
    "for i in range(len(pred)):\n",
    "    if pred[i]==s[i]:\n",
    "        count = count + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9434523809523809"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count/float(len(pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 334772.620528  ,  245879.21073201,  211046.60194948,\n",
       "        184721.97518117,  168065.9998401 ,  147769.76432963,\n",
       "        112531.94006026,   99313.07277501,   95013.75214537,\n",
       "         80658.80966613,   72089.56446099,   70704.85314299,\n",
       "         58464.65500507,   58129.27918648,   54294.9210048 ,\n",
       "         50933.50304145,   45317.25479694,   44048.02861847,\n",
       "         40794.10105348,   39584.60734333,   36817.50674808,\n",
       "         34860.95757231,   33127.26842033,   31336.47034327,\n",
       "         30447.66562396])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2, whiten='True')\n",
    "x = pca.fit(df_x).transform(df_x)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,df_y,test_size=0.2, random_state=4)\n",
    "rf = RandomForestClassifier(n_estimators = 50)\n",
    "rf.fit(x_train,y_train)\n",
    "pred = rf.predict(x_test)\n",
    "s = y_test.values\n",
    "count = 0\n",
    "for i in range(len(pred)):\n",
    "    if pred[i]==s[i]:\n",
    "        count = count + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4083333333333333"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count/float(len(pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tC0NBHeG66ugIgV1pjn0a2fEGfmtBnGVAGWUkbCk5Zly5euqppzLXKElRiQta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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x65850a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df_y.values\n",
    "for i in range(5000):\n",
    "    if y[i]==0:\n",
    "        plt.scatter(x[i,1],x[i,0],c='r')\n",
    "    else:\n",
    "        plt.scatter(x[i,1],x[i,0],c='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}