Digit_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#for plotting the digit\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "#import digit dataset\n",
    "\n",
    "from sklearn import svm\n",
    "\n",
    "#get access to SVM algorithm\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]]\n",
      "[0 1 2 ... 8 9 8]\n"
     ]
    }
   ],
   "source": [
    "digits = datasets.load_digits()\n",
    "\n",
    "#load dataset\n",
    "\n",
    "print(digits.data)\n",
    "\n",
    "#display features\n",
    "\n",
    "print(digits.target)\n",
    "\n",
    "#display output column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "\n",
    "#create object of class svm\n",
    "\n",
    "clf = svm.SVC(gamma=0.001, C=100)       \n",
    "\n",
    "X,y = digits.data[:-10], digits.target[:-10]\n",
    "\n",
    "#X - input features , y - output\n",
    "\n",
    "clf.fit(X,y)\n",
    "\n",
    "#fit the model\n",
    "\n",
    "print(clf.predict(digits.data[-5].reshape(1,-1)))\n",
    "\n",
    "plt.imshow(digits.images[-5], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "\n",
    "#display image of digit with specification\n",
    "\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "\n",
    "#create object of class svm\n",
    "\n",
    "clf = svm.SVC(gamma=0.001, C=100)       \n",
    "\n",
    "X,y = digits.data[:-10], digits.target[:-10]\n",
    "\n",
    "#X - input features , y - output\n",
    "\n",
    "clf.fit(X,y)\n",
    "\n",
    "#fit the model\n",
    "\n",
    "print(clf.predict(digits.data[-5].reshape(1,-1)))\n",
    "\n",
    "plt.imshow(digits.images[-5], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "\n",
    "#display image of digit with specification\n",
    "\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}