Character Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing Required Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('data.csv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>8.1</th>\n",
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       "      <th>6.1</th>\n",
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       "      <th>19997</th>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "       T  2  8  3  5  1  8.1  13  0  6  6.1  10  8.2  0.1  8.3  0.2  8.4\n",
       "19997  S  2  3  4  2  1    8   7  2  6   10   6    8    1    9    5    8\n",
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
       "      <th>8.1</th>\n",
       "      <th>13</th>\n",
       "      <th>0</th>\n",
       "      <th>6</th>\n",
       "      <th>6.1</th>\n",
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       "      <th>8.2</th>\n",
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       "   T  2   8  3  5  1  8.1  13  0  6  6.1  10  8.2  0.1  8.3  0.2  8.4\n",
       "0  I  5  12  3  7  2   10   5  5  4   13   3    9    2    8    4   10\n",
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   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2be1dbef98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2be1dbea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: 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": [
    "# SPLITTING DATASET INTO TRAIN AND TEST DATA\n",
    "\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=data.drop(['T'],axis=1)\n",
    "y=data['T']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()\n",
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          A       0.88      0.88      0.88       225\n",
      "          B       0.59      0.80      0.68       226\n",
      "          C       0.65      0.73      0.69       213\n",
      "          D       0.73      0.78      0.76       241\n",
      "          E       0.65      0.54      0.59       243\n",
      "          F       0.77      0.67      0.72       245\n",
      "          G       0.50      0.40      0.44       237\n",
      "          H       0.38      0.41      0.39       222\n",
      "          I       0.84      0.82      0.83       208\n",
      "          J       0.85      0.75      0.80       217\n",
      "          K       0.54      0.54      0.54       202\n",
      "          L       0.86      0.80      0.83       249\n",
      "          M       0.86      0.88      0.87       232\n",
      "          N       0.85      0.69      0.76       244\n",
      "          O       0.58      0.49      0.53       233\n",
      "          P       0.82      0.81      0.81       233\n",
      "          Q       0.63      0.76      0.69       240\n",
      "          R       0.64      0.78      0.70       241\n",
      "          S       0.48      0.47      0.47       217\n",
      "          T       0.84      0.77      0.81       230\n",
      "          U       0.78      0.90      0.84       225\n",
      "          V       0.89      0.75      0.82       245\n",
      "          W       0.83      0.88      0.85       235\n",
      "          X       0.67      0.76      0.72       239\n",
      "          Y       0.77      0.74      0.75       229\n",
      "          Z       0.75      0.76      0.76       229\n",
      "\n",
      "avg / total       0.72      0.71      0.71      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.8, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc=SVC(C=0.8)\n",
    "svc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction=svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          A       1.00      1.00      1.00       225\n",
      "          B       0.91      0.98      0.94       226\n",
      "          C       0.99      0.95      0.97       213\n",
      "          D       0.91      0.98      0.94       241\n",
      "          E       0.97      0.95      0.96       243\n",
      "          F       0.93      0.94      0.94       245\n",
      "          G       0.95      0.97      0.96       237\n",
      "          H       0.95      0.86      0.90       222\n",
      "          I       0.99      0.96      0.98       208\n",
      "          J       0.97      0.96      0.97       217\n",
      "          K       0.95      0.93      0.94       202\n",
      "          L       0.99      0.99      0.99       249\n",
      "          M       0.99      0.98      0.98       232\n",
      "          N       0.97      0.95      0.96       244\n",
      "          O       0.96      0.98      0.97       233\n",
      "          P       0.95      0.93      0.94       233\n",
      "          Q       0.99      0.98      0.99       240\n",
      "          R       0.90      0.95      0.92       241\n",
      "          S       1.00      1.00      1.00       217\n",
      "          T       0.99      0.95      0.97       230\n",
      "          U       0.97      1.00      0.99       225\n",
      "          V       0.98      0.96      0.97       245\n",
      "          W       0.99      0.99      0.99       235\n",
      "          X       0.97      0.98      0.98       239\n",
      "          Y       0.97      0.99      0.98       229\n",
      "          Z       1.00      1.00      1.00       229\n",
      "\n",
      "avg / total       0.97      0.97      0.97      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(n_estimators=300)\n",
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction=rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          A       0.99      0.99      0.99       225\n",
      "          B       0.90      0.97      0.94       226\n",
      "          C       0.99      0.96      0.97       213\n",
      "          D       0.92      0.97      0.94       241\n",
      "          E       0.95      0.94      0.94       243\n",
      "          F       0.93      0.95      0.94       245\n",
      "          G       0.95      0.97      0.96       237\n",
      "          H       0.96      0.89      0.92       222\n",
      "          I       0.98      0.96      0.97       208\n",
      "          J       0.97      0.95      0.96       217\n",
      "          K       0.96      0.94      0.95       202\n",
      "          L       0.99      0.99      0.99       249\n",
      "          M       0.95      0.99      0.97       232\n",
      "          N       0.97      0.95      0.96       244\n",
      "          O       0.95      0.97      0.96       233\n",
      "          P       0.95      0.94      0.95       233\n",
      "          Q       0.98      0.96      0.97       240\n",
      "          R       0.93      0.95      0.94       241\n",
      "          S       1.00      0.97      0.98       217\n",
      "          T       0.98      0.96      0.97       230\n",
      "          U       0.99      0.99      0.99       225\n",
      "          V       0.98      0.97      0.98       245\n",
      "          W       0.98      0.99      0.99       235\n",
      "          X       0.97      0.97      0.97       239\n",
      "          Y       1.00      0.98      0.99       229\n",
      "          Z       0.97      0.98      0.98       229\n",
      "\n",
      "avg / total       0.96      0.96      0.96      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  }
 ],
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