Lab03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[ 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.]]), 'target': array([0, 1, 2, ..., 8, 9, 8]), 'frame': None, 'feature_names': ['pixel_0_0', 'pixel_0_1', 'pixel_0_2', 'pixel_0_3', 'pixel_0_4', 'pixel_0_5', 'pixel_0_6', 'pixel_0_7', 'pixel_1_0', 'pixel_1_1', 'pixel_1_2', 'pixel_1_3', 'pixel_1_4', 'pixel_1_5', 'pixel_1_6', 'pixel_1_7', 'pixel_2_0', 'pixel_2_1', 'pixel_2_2', 'pixel_2_3', 'pixel_2_4', 'pixel_2_5', 'pixel_2_6', 'pixel_2_7', 'pixel_3_0', 'pixel_3_1', 'pixel_3_2', 'pixel_3_3', 'pixel_3_4', 'pixel_3_5', 'pixel_3_6', 'pixel_3_7', 'pixel_4_0', 'pixel_4_1', 'pixel_4_2', 'pixel_4_3', 'pixel_4_4', 'pixel_4_5', 'pixel_4_6', 'pixel_4_7', 'pixel_5_0', 'pixel_5_1', 'pixel_5_2', 'pixel_5_3', 'pixel_5_4', 'pixel_5_5', 'pixel_5_6', 'pixel_5_7', 'pixel_6_0', 'pixel_6_1', 'pixel_6_2', 'pixel_6_3', 'pixel_6_4', 'pixel_6_5', 'pixel_6_6', 'pixel_6_7', 'pixel_7_0', 'pixel_7_1', 'pixel_7_2', 'pixel_7_3', 'pixel_7_4', 'pixel_7_5', 'pixel_7_6', 'pixel_7_7'], 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  0., 13., ..., 15.,  5.,  0.],\n",
      "        [ 0.,  3., 15., ..., 11.,  8.,  0.],\n",
      "        ...,\n",
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      "        [ 0.,  2., 14., ..., 12.,  0.,  0.],\n",
      "        [ 0.,  0.,  6., ...,  0.,  0.,  0.]],\n",
      "\n",
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      "        [ 0.,  0.,  8., ..., 16.,  0.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  9., 16., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  3., 13., ..., 11.,  5.,  0.],\n",
      "        [ 0.,  0.,  0., ..., 16.,  9.,  0.]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[ 0.,  0.,  1., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
      "        [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  0., 16., ..., 15.,  0.,  0.],\n",
      "        [ 0.,  0., 15., ..., 16.,  0.,  0.],\n",
      "        [ 0.,  0.,  2., ...,  6.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  2., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0., 14., ..., 15.,  1.,  0.],\n",
      "        [ 0.,  4., 16., ..., 16.,  7.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  0.,  0., ..., 16.,  2.,  0.],\n",
      "        [ 0.,  0.,  4., ..., 16.,  2.,  0.],\n",
      "        [ 0.,  0.,  5., ..., 12.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0., 10., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  2., 16., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  0., 15., ..., 15.,  0.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  4., 16., ..., 16.,  6.,  0.],\n",
      "        [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
      "        [ 0.,  1.,  8., ..., 12.,  1.,  0.]]]), 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 5620\\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\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\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"
     ]
    }
   ],
   "source": [
    "digits=load_digits()\n",
    "print(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0    1    2     3     4     5    6    7    8    9   ...   54   55   56  \\\n",
      "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  5.0  0.0  0.0   \n",
      "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  9.0  0.0  0.0   \n",
      "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "\n",
      "    57   58    59    60    61   62   63  \n",
      "0  0.0  6.0  13.0  10.0   0.0  0.0  0.0  \n",
      "1  0.0  0.0  11.0  16.0  10.0  0.0  0.0  \n",
      "2  0.0  0.0   3.0  11.0  16.0  9.0  0.0  \n",
      "3  0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
      "4  0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 64 columns]\n",
      "(1797, 64)\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame(digits.data)\n",
    "print(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=64, step=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "Length: 64, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0    1    2     3     4     5    6    7    8    9  ...   55   56   57  \\\n",
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       "\n",
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       "3  7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
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       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target']=digits.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n",
      "(1797,)\n"
     ]
    }
   ],
   "source": [
    "print(digits.data.shape)\n",
    "print(digits.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  9.,  0.,  2.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., ...,  0.,  0.,  8.],\n",
       "       [ 0.,  0.,  2., ...,  0.,  0.,  9.],\n",
       "       [ 0.,  0., 10., ...,  1.,  0.,  8.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target\n",
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x=df.drop(['target'],axis='columns')\n",
    "y=df.target\n",
    "\n",
    "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2,random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1437\n",
      "360\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train))\n",
    "print(len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score of Kernal(rbf) : 0.9916666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "model1=SVC(kernel='rbf',random_state=0, probability=True)\n",
    "model1.fit(x_train,y_train)\n",
    "\n",
    "y_pred_1=model1.predict(x_test)\n",
    "\n",
    "print(\"Model Score of Kernal(rbf) :\", model1.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score of Kernal(linear) : 0.975\n"
     ]
    }
   ],
   "source": [
    "model2=SVC(kernel='linear',random_state=0, probability=True)\n",
    "model2.fit(x_train,y_train)\n",
    "\n",
    "y_pred_2=model2.predict(x_test)\n",
    "\n",
    "print(\"Model Score of Kernal(linear) :\", model2.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score of Kernal(poly) : 0.9944444444444445\n"
     ]
    }
   ],
   "source": [
    "model3=SVC(kernel='poly',random_state=0, probability=True, C=5)\n",
    "model3.fit(x_train,y_train)\n",
    "\n",
    "y_pred_3=model3.predict(x_test)\n",
    "\n",
    "print(\"Model Score of Kernal(poly) :\", model3.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 0.9944444444444445\n"
     ]
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,y_pred_3)\n",
    "\n",
    "print('The accuracy is',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[37,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 32,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 38,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 43,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0, 39,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0, 32,  0,  0,  0,  2],\n",
       "       [ 0,  0,  0,  0,  0,  0, 29,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 42,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 32,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 34]], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=np.array(confusion_matrix(y_test,y_pred_3))\n",
    "\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08888888888888889"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse=mean_squared_error(y_test,y_pred_3)\n",
    "mse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944444444444445"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1_C=SVC(C=3)\n",
    "model1_C.fit(x_train,y_train)\n",
    "model1_C.score(x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944444444444445"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2_C=SVC(C=3)\n",
    "model2_C.fit(x_train,y_train)\n",
    "model2_C.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944444444444445"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3_C=SVC(C=3)\n",
    "model3_C.fit(x_train,y_train)\n",
    "model3_C.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(cm, annot=True, fmt=\".2f\", linewidths=.5, square = True, cmap = 'Blues_r')\n",
    "\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "\n",
    "A=f'The accuracy score :{accuracy:.2f}'\n",
    "plt.title(A)\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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}