Recognizing-Hand-written-digits-by-scikit-learn (Suven Internship P2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "082d8e63",
   "metadata": {},
   "source": [
    "# In this ML Analysis, we will be using Support Vector Machine Supervised Algorithm to recognize hand written digits from built-in dataset.\n",
    "# We will use scikit-learn package."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069ece11",
   "metadata": {},
   "source": [
    "## Importing Support Vector Machine (SVM) from scikit-learn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3624d9eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm \n",
    "from sklearn.metrics import accuracy_score\n",
    "svc = svm.SVC(gamma=0.00001,C=100.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f7541e",
   "metadata": {},
   "source": [
    "## Loading the digits dataset from scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79ff28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets \n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd53dac0",
   "metadata": {},
   "source": [
    "## Description of the digits dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41ef8c6a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 1797\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (digits.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac2a1ff",
   "metadata": {},
   "source": [
    "## Loading image for digit 7 for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c024126d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  7.,  8., 13., 16., 15.,  1.],\n",
       "       [ 0.,  0.,  7.,  7.,  4., 11., 12.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  8., 13.,  1.,  0.],\n",
       "       [ 0.,  4.,  8.,  8., 15., 15.,  6.,  0.],\n",
       "       [ 0.,  2., 11., 15., 15.,  4.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., 16.,  5.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  9., 15.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0., 13.,  5.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.images[7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a66080",
   "metadata": {},
   "source": [
    "## Visualizing this image for digit 7 using matplotlib\n",
    "## imshow Display data as an image, i.e., on a 2D regular raster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b29f391b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "plt.imshow(digits.images[7],cmap=plt.cm.gray_r,interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a8fbaf",
   "metadata": {},
   "source": [
    "## The numerical values represented by images, i.e., the targets, are contained in the digit.target array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69b534fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca7af30",
   "metadata": {},
   "source": [
    "## The size of the dataset consists of 1797 images. We can also see the total number of columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "892f1881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n"
     ]
    }
   ],
   "source": [
    "print(digits.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76d57f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1797"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927a1430",
   "metadata": {},
   "source": [
    "### We will use the first 1791 digits for training our machine\n",
    "\n",
    "### We will use the last 6 digits for testing i.e. as validation set, so below is a visualization of this set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e974c8ab",
   "metadata": {},
   "source": [
    "### Our values for testing are: [4, 9, 0, 8, 9, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8865ba69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.subplot(321)\n",
    "plt.imshow(digits.images[1791], cmap=plt.cm.gray_r,\n",
    "interpolation='nearest')\n",
    "plt.subplot(322)\n",
    "plt.imshow(digits.images[1792], cmap=plt.cm.gray_r,\n",
    "interpolation='nearest')\n",
    "plt.subplot(323)\n",
    "plt.imshow(digits.images[1793], cmap=plt.cm.gray_r,\n",
    "interpolation='nearest')\n",
    "plt.subplot(324)\n",
    "plt.imshow(digits.images[1794], cmap=plt.cm.gray_r,\n",
    "interpolation='nearest')\n",
    "plt.subplot(325)\n",
    "plt.imshow(digits.images[1795], cmap=plt.cm.gray_r,\n",
    "interpolation='nearest')\n",
    "plt.subplot(326)\n",
    "plt.imshow(digits.images[1796], cmap=plt.cm.gray_r,\n",
    "interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7afcdc5",
   "metadata": {},
   "source": [
    "## Now we are training our data on initial model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63c2d892",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_data=digits['data']\n",
    "targets=digits['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d74445a",
   "metadata": {},
   "source": [
    "## Case 1: 1791 Training set, and 6 Test values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a714c3",
   "metadata": {},
   "source": [
    "## Simple training of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3374db34",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc.fit(digits.data[1:1791],digits.target[1:1791])\n",
    "prediction = svc.predict(main_data[1791:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6014d7a3",
   "metadata": {},
   "source": [
    "## As we can see, all 6 values from above were predicted correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bff96933",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:  [4 9 0 8 9 8]\n",
      "Actual values:  [4 9 0 8 9 8]\n",
      "Accuracy is:  100.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values: \", prediction)\n",
    "print(\"Actual values: \", targets[1791:])\n",
    "ac_1 = accuracy_score(targets[1791:] , prediction)*100\n",
    "print(\"Accuracy is: \",ac_1,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01eef84f",
   "metadata": {},
   "source": [
    "### Our accuracy for 1791:6 division is 100%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c229489f",
   "metadata": {},
   "source": [
    "## Modified training of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02f221ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100.0, gamma=0.001)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "SVC(C=100., cache_size=200,class_weight=None, coef0=0.0,degree=3,\n",
    "   gamma=0.001, kernel='rbf',max_iter=-1,probability=False,\n",
    "   random_state=None, shrinking=True, tol=0.001, verbose=False)\n",
    "svc = svm.SVC(gamma=0.001 , C=100.)\n",
    "svc.fit(main_data[:1791], targets[:1791])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3cb809c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:  [4 9 0 8 9 8]\n",
      "Actual values:  [4 9 0 8 9 8]\n",
      "Accuracy is:  100.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values: \", prediction)\n",
    "print(\"Actual values: \", targets[1791:])\n",
    "print(\"Accuracy is: \",(accuracy_score(targets[1791:] , prediction)*100),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18553804",
   "metadata": {},
   "source": [
    "### Our accuracy for modified training is also 100%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e9547f",
   "metadata": {},
   "source": [
    "## Case 2: 75% values for training (1348), and 25% values for testing (449)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43259f49",
   "metadata": {},
   "source": [
    "## Training of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b686b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  96.88888888888889 %\n"
     ]
    }
   ],
   "source": [
    "svc.fit(main_data[:1347], targets[:1347])\n",
    "prediction = svc.predict(main_data[1347:])\n",
    "ac_2 = accuracy_score(targets[1347:] , prediction)*100\n",
    "print(\"Accuracy is: \", ac_2,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8230d1fd",
   "metadata": {},
   "source": [
    "### The accuracy for 75:25 division is 96.88 %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d100108",
   "metadata": {},
   "source": [
    "## Case 3: 50% values for training (899), and 50% values for testing (898)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efe98a2",
   "metadata": {},
   "source": [
    "# Training of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b7e21d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  96.99331848552339 %\n"
     ]
    }
   ],
   "source": [
    "svc.fit(main_data[:899], targets[:899])\n",
    "prediction = svc.predict(main_data[899:])\n",
    "ac_3 = accuracy_score(targets[899:] , prediction)*100\n",
    "print(\"Accuracy is: \", ac_3,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25cd1c02",
   "metadata": {},
   "source": [
    "### The accuracy for 50:50 division is 96.99 %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f12ada",
   "metadata": {},
   "source": [
    "## Case 4: 20% values for training (359), and 80% values for testing (1438)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97705e10",
   "metadata": {},
   "source": [
    "# Training of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc2626b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  90.12517385257301 %\n"
     ]
    }
   ],
   "source": [
    "svc.fit(main_data[:359], targets[:359])\n",
    "prediction = svc.predict(main_data[359:])\n",
    "ac_4 = accuracy_score(targets[359:] , prediction)*100\n",
    "print(\"Accuracy is: \", ac_4,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a02b9b4",
   "metadata": {},
   "source": [
    "### The accuracy for 80:20 division is 90.13 %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3c6a02",
   "metadata": {},
   "source": [
    "## Case 5: 5% values for training (90), and 95% values for testing (1707)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "642007b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  82.01523140011716 %\n"
     ]
    }
   ],
   "source": [
    "svc.fit(main_data[:90], targets[:90])\n",
    "prediction = svc.predict(main_data[90:])\n",
    "ac_5 = accuracy_score(targets[90:] , prediction)*100\n",
    "print(\"Accuracy is: \", ac_5,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b458ad",
   "metadata": {},
   "source": [
    "### The accuracy for 80:20 division is 82.02 %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879af80f",
   "metadata": {},
   "source": [
    "## Overall Conclusion\n",
    "\n",
    "The accuracy will be much better when more data is used for training purposes, before testing the model. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b6760d48",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "figure(figsize=(5, 5), dpi=100)\n",
    "\n",
    "ac_list = {\"0.33%-99.66%\":ac_1,\"75%-25%\":ac_2,\"50%-50%\":ac_3,\n",
    "           \"20%-80%\":ac_4,\"5%-95%\":ac_5}\n",
    "names = list(ac_list.keys())\n",
    "values = list(ac_list.values())\n",
    "plt.bar(range(len(ac_list)), values, tick_label=names)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel(\"Division of Data\")\n",
    "plt.ylabel(\"Accuracy in %\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64ead38a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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