Handwriting Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('mnist.csv')"
   ]
  },
  {
   "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>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=data.iloc[3,1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=a.reshape(28,28).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x200aaa20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KfQVvgnYB/ujpInwlVidcLVBsJMoetQPCAWWPyAGl3R7EOIkwixHJJEkUmalyxGSHyICK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\nmDgTeqIEbkD40RIuZGoR2qWDWC8dvnoJ6MX3OslnPyzhAeGSbqzg1Es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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb989c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_x=data.iloc[:,1:]\n",
    "df_y=data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.2, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>27750</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16211</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "32576       0       0       0       0       0       0       0       0       0   \n",
       "34735       0       0       0       0       0       0       0       0       0   \n",
       "3476        0       0       0       0       0       0       0       0       0   \n",
       "27750       0       0       0       0       0       0       0       0       0   \n",
       "16211       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel9    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "32576       0    ...            0         0         0         0         0   \n",
       "34735       0    ...            0         0         0         0         0   \n",
       "3476        0    ...            0         0         0         0         0   \n",
       "27750       0    ...            0         0         0         0         0   \n",
       "16211       0    ...            0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "32576         0         0         0         0         0  \n",
       "34735         0         0         0         0         0  \n",
       "3476          0         0         0         0         0  \n",
       "27750         0         0         0         0         0  \n",
       "16211         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32576    5\n",
       "34735    0\n",
       "3476     3\n",
       "27750    0\n",
       "16211    3\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier(n_estimators=100)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pred=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 0, ..., 0, 3, 1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s=y_test.values\n",
    "count=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(pred)):\n",
    "    if pred[i]==s[i]:\n",
    "        count=count+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8076"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8400"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9614285714285714"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8076/8400.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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