ML-digit logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "digits= load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "for i in range(2):\n",
    "    plt.matshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
       "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
       "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
       "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
       "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measure the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(digits.data[0:6])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted=model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 29,  0,  0,  0,  0,  0,  0,  3,  0],\n",
       "       [ 0,  0, 35,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 49,  0,  1,  0,  0,  1,  1],\n",
       "       [ 0,  0,  0,  0, 30,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0, 29,  0,  0,  0,  1],\n",
       "       [ 0,  0,  0,  0,  1,  1, 36,  0,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0, 35,  1,  1],\n",
       "       [ 0,  1,  0,  1,  0,  0,  0,  0, 31,  2],\n",
       "       [ 0,  0,  0,  0,  0,  1,  0,  1,  1, 36]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(y_test,y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(123.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize=(16,7))\n",
    "sn.heatmap(cm,annot=True)\n",
    "plt.xlabel(\"predicted\")\n",
    "plt.ylabel(\"Truth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}