From 906d8e80f345214977ba2b1472a02931922a8f32 Mon Sep 17 00:00:00 2001 From: codebasics Date: Sat, 2 Oct 2021 11:15:38 -0400 Subject: [PATCH] knn --- .../Exercise/knn_exercise_digits.ipynb | 1132 ++++++++++++++++ ML/17_knn_classification/iris_petal_sepal.png | Bin 0 -> 344642 bytes .../knn_classification_tutorial.ipynb | 1159 +++++++++++++++++ ML/17_knn_classification/knn_exercise.md | 6 + 4 files changed, 2297 insertions(+) create mode 100644 ML/17_knn_classification/Exercise/knn_exercise_digits.ipynb create mode 100644 ML/17_knn_classification/iris_petal_sepal.png create mode 100644 ML/17_knn_classification/knn_classification_tutorial.ipynb create mode 100644 ML/17_knn_classification/knn_exercise.md diff --git a/ML/17_knn_classification/Exercise/knn_exercise_digits.ipynb b/ML/17_knn_classification/Exercise/knn_exercise_digits.ipynb new file mode 100644 index 00000000..15a37d13 --- /dev/null +++ b/ML/17_knn_classification/Exercise/knn_exercise_digits.ipynb @@ -0,0 +1,1132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.datasets import load_digits\n", + "digits = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, ..., 8, 9, 8])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.target" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.target_names" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "4 0.0 10.0 15.0 4.0 0.0 0.0 4 \n", + "5 15.0 16.0 2.0 0.0 0.0 0.0 5 \n", + "6 0.0 7.0 15.0 16.0 11.0 0.0 6 \n", + "7 0.0 11.0 9.0 0.0 0.0 0.0 7 \n", + "8 11.0 14.0 5.0 0.0 0.0 0.0 8 \n", + "9 7.0 16.0 16.0 13.0 11.0 1.0 9 \n", + "\n", + "[20 rows x 65 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['target'] = digits.target\n", + "df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(df.drop('target',axis='columns'), df.target, test_size=0.3, random_state=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Create KNN classifier**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1257" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "540" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier()" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.987037037037037" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.score(X_test,y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.97407407407407409" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linear_model.score(X_test,y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[51, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 56, 0, 0, 0, 1, 0, 0, 0, 0],\n", + " [ 0, 0, 55, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 56, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 50, 0, 0, 0, 1, 0],\n", + " [ 0, 0, 0, 0, 0, 50, 0, 0, 0, 1],\n", + " [ 0, 0, 0, 0, 0, 0, 55, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 60, 0, 0],\n", + " [ 0, 1, 0, 1, 0, 0, 0, 0, 48, 0],\n", + " [ 0, 0, 0, 0, 0, 1, 0, 0, 1, 52]], dtype=int64)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "y_pred = knn.predict(X_test)\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(42.0, 0.5, 'Truth')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ear4+c2UnJTudTE/YNLPRwOg6T01w9wnB784F3nP3pWY2aH/a16J6NANP6M/q1WtZs6aUiooKCgunM3zY4ETkx73uexXOWcDl551Fm7w8AA7u2D7UvLjXO6mfubLj/f/sMzLs0bj7BHcfUOc2oc5STwKGm9la4GngdDP7E7DRzPIBgp/v7at5oRUaMxtoZicE9480s+vN7Jyw8gAKevZgXdmG2sdl68spKOgRZmTW5MeVfcW4P3LRjb9h2tx/APDuhvdZWrKai2/+Hd/76X0UrXo31Hx95spOSnZa1Rne0nD3m929l7v3Ab4D/M3dLwGeBy4LXnYZsM/x8VCGzszsZ8BQoLWZzQW+BrwE/NjM+rv77SHlfua5JhxZkdP5cWQ/Pu46unXuyKat27niVw/Qp2d3Kqur2b5zF0/ecT1Fq0r50fjH+OsDP6u3fc1Bn7myk5KdTsRznd0FFJrZ5UApcOG+3hDWPpoLgK8CnwP+BfRy921mdjfwGlBvoak7XmitOpJKtW1S6Pqycnr3Kqh93KtnPuXlGzNpf0bizI8ju1vnjkDN8NjpA4+haNW7dO/ckTO+dixmxtGHfpFUytiybQedQxpC02eu7KRkpxVyoXH3l6jpLODum4AzmvL+sIbOKt29yt13AavdfRuAu+8mTYet7nhhU4sMwOIly+jXry99+vQmLy+PkSNHMGPmnIxXIpfyo87e9dHH7Nz9Ue39hW+uoF/vfE4feAyL3loJwNoN71FRWfWJgwWamz5zZSclO60Qhs6aU1g9mj1mdmBQaI7f+6SZdSTE1auqqmLstbcxe9YUWqVSTJo8leLilWHFZVV+1Nmbt27n2rsnBtnVDD35eE7ufyQVFZX89MEpfOv6O8lr3YpxYy4JbdisJlufubKTkZ1Otl8mwMIYXzSzz7n7x/U83wXId/e39rWM1m16ZveWa4F04TOR6FTuWd9s/wLb8u1BGX1fdvrzS+H9K7COUHo09RWZ4PkPgA/CyBQRSaps79G0qBM2RUQSKbsvR6NCIyKS61yFRkREQqVCIyIiYcr2Hk2LmutMRESyj3o0IiK5Lst7NCo0IiI5LtuHzlRoRERynAqN5Iy4z8zfPuWq2LLbX/xgbNlJ1jrVKrbsyuqq2LKbmwqNiIiEyyOZSSZjKjQiIjlOPRoREQmVV6tHIyIiIVKPRkREQuXaRyMiImFSj0ZEREKlfTQiIhKqEC6U3KxUaEREcly292ha3OzNg88exPKi+awoXsCNN4xJVH6Ssofe/SwX3D+Dkb+fycUPzAJg666PueLRuQwb/xeueHQu23bXe0XxZpek7Z4t2Q89dDelpa+zdOncSHMh/u+Y+ni1ZXSLSosqNKlUivvvu51zh13C0ceexkUXnccRRxyaiPwkZj98+VkU/vBcpoz5JgCPzi/ia1/KZ8b15/G1L+Xz6MvLQ29DErd73NkATzzxDMOHfzeyvL3iXu+GuGd2i0pkhcbMHg87Y+AJ/Vm9ei1r1pRSUVFBYeF0hg8bHHZsVuQnNbuul0rKGNb/EACG9T+Ev5esCz0zqds97s98wYJFbNnyYWR5e8W93g1JZI/GzJ7/1G0GcP7ex2FkAhT07MG6sg21j8vWl1NQ0COsuKzKT1q2GVz12DxGPTCLaYtWArBpx266djgQgK4dDmTzjo9CbQMkb7tnQ3ackrre+yusgwF6AcXARMABAwYAv0v3JjMbDYwGsFYdSaXaNinU7LMV2iPsH8aZn7TsSaOH0K3DgWzesZsrH5tH364dQ81rSNK2ezZkxylb1zvbT9gMa+hsALAUuBXY6u4vAbvd/WV3f7mhN7n7BHcf4O4DmlpkANaXldO7V0Ht41498ykv39jk5WQqzvykZXcLei6d2x3AaUf2pqjsAw5udwDvb9sFwPvbdtG53edDbQMkb7tnQ3acsnW9vTqzW1RCKTTuXu3u9wDfA241sz8QwaHUi5cso1+/vvTp05u8vDxGjhzBjJlzwo7NivwkZe/eU8HOjytq7y9cVU6/7gfxjcN7MeONdwCY8cY7DDqiV2ht2CtJ2z1bsuOUretd7ZbRLSqN+vI3s/8A+tR9vbvvc+e+u5cBF5rZN4FtGbax0aqqqhh77W3MnjWFVqkUkyZPpbh4ZdixWZGfpOxNOz7i+idrOsaV1dUMPaYvJ325J0f16sKNT83nuaWryO/YlrtHnRpaG/ZK0nbPlmyAxx//PaecciJdunRi1arXGDduPJMmTQ09N+71bki2D53ZvsYXzewJ4EvAMmDvJenc3a8Js2Gt2/SMf+BTIqUrbCZPkq+wWblnfbNVhxVfPiej78vDV86OpEI1pkczADjSs2GPl4iIfEa2fzs3ptAUAT2A8pDbIiIiGcj2KWgaLDTBuS8OtAeKzWwRUDunh7sPD795IiKyL1Hu2M9Euh7NbyNrhYiIZCzbDwZosNDsPd/FzH7t7jfV/Z2Z/Rpo8HwYERGJTrbvo2nMeTRn1fPc0OZuiIiIZCas82jM7PNmtsjM3jSz5Wb2i+D5zmY218zeDn52SrecBguNmV1lZm8Bh5vZ/9S5rQHeauJ2EBGRkLhbRrdG+Bg43d2PBb4KDDGzrwM/Bua5+6HAvOBxg9Lto5kC/BW481ML2e7umxvTQhERCV9YQ2fBaS07god5wc2BEcCg4PnJwEvATTQg3T6arcBWM/v0m9uZWTt3L82o5SINiPOkSZ0sGo84T5qM82TR5hbmUWdm1oqauSv7AQ+4+2tm1t3dywHcvdzMuqVbRmPOo5nFv2dg/jzQF/gncNT+NF5ERJpHpked1Z0xPzDB3Sd8ctleBXzVzA4CnjOzrzQ1Z5+Fxt2P/lTDjgOuaGqQiIiEI9MeTVBUJuzzhTWv/dDMXgKGABvNLD/ozeQD76V7b5Nnb3b314ETmvo+ERHJLWbWNejJYGYHAGcCK4DngcuCl10GTE+3nH32aMzs+joPU8BxwPtNb7KIiIQhxNNo8oHJwX6aFFDo7jPNbCFQaGaXA6XAhekW0ph9NO3r3K+kZp/NnzNrs4iINLewDgZw9/8B+tfz/CbgjMYuJ22hCapYO3e/ocktFBGRSOTsFDRm1trdK4Od/yIikqUivCpzRtL1aBZRsz9mmZk9DzwD7Nz7S3d/NuS2iYhIIzg52qOpozOwCTidf59P44AKjYhIFqjO4Uk1uwVHnBVRM7dZEbA8+FkUQdsyMvjsQSwvms+K4gXceMOYROUrO7rsoXc/ywX3z2Dk72dy8QOzANi662OueHQuw8b/hSsencu23R/vYyn7L2nbPRuyH3robkpLX2fp0rmR5qZTjWV0i0q6QtMKaBfc2te5v/eWdVKpFPffdzvnDruEo489jYsuOo8jjjg0EfnKjj774cvPovCH5zJlzDcBeHR+EV/7Uj4zrj+Pr30pn0dfXh5qflK3e9z/nz/xxDMMH/7dyPIaw7GMblFJV2jK3f2X7v6Lem6/bEqImZ1sZteb2dn72d60Bp7Qn9Wr17JmTSkVFRUUFk5n+LDBYUZmTb6y4/nM63qppIxh/Q8BYFj/Q/h7ybpQ85K63eP+zBcsWMSWLR9GltcY1RneopKu0GRc7oLLPu+9/wPgD9T0in5mZmmnk94fBT17sK5sQ+3jsvXlFBT0CCsuq/KVHW22GVz12DxGPTCLaYtWArBpx266djgQgK4dDmTzjo9CbUMSt3vc2dkq23s06Q4GaPTJOPXIq3N/NHCWu79vZr8FXgXuqu9NdSd4s1YdSaXaNinU7LMbziO89Fyc+cqONnvS6CF063Agm3fs5srH5tG3a8fQMz8tids97uxsle2HNzfYo9nPa86kzKyTmR0MmLu/HyxzJzWzCzSUOcHdB7j7gKYWGYD1ZeX07lVQ+7hXz3zKyzc2vfUZijNf2dFmdwt6Lp3bHcBpR/amqOwDDm53AO9v2wXA+9t20bnd50NtQxK3e9zZ2SqXh872R0dqrl+wBOhsZj0AzKwd+zEkty+LlyyjX7++9OnTm7y8PEaOHMGMmXPCisuqfGVHl717TwU7P66ovb9wVTn9uh/ENw7vxYw33gFgxhvvMOiIXqG2I2nbPRuys1UuD51lzN37NPCrauBbYWQCVFVVMfba25g9awqtUikmTZ5KcfHKsOKyKl/Z0WVv2vER1z/5MgCV1dUMPaYvJ325J0f16sKNT83nuaWryO/YlrtHnRpqO5K23bMhG+Dxx3/PKaecSJcunVi16jXGjRvPpElTI8uvT3V2n6+JZevYZus2PbOzYdIi6QqbyRP3FTY/+qi02crD9B4XZ/R9OeJfUyIpUaH0aEREJDrZ/q/ysPbRiIiIAOrRiIjkvGw/vFmFRkQkx1XXc25RNlGhERHJcdm+j0aFRkQkx2noTEREQpXt59Go0IiI5Lgory2TCRUaEZEcp300IjkgzrPzdyx8ILbsdidGfxXabFFZXRV3E5qNhs5ERCRUOhhARERCpaEzEREJlYbOREQkVBo6ExGRUKnQiIhIqFxDZyIiEib1aEREJFTZXmha3IXPBp89iOVF81lRvIAbb4j+ZLQ485WdjM986DV38e2b7mHkzfcy6tb7AXhw2lzOHHM7I2++l5E338srb6wIvR1J/czj/nurj2d4i0qL6tGkUinuv+92hpwzirKycl5dOJsZM+dQUvJ2i89XdrI+84m3jqZTh7afeO7SoSdz2bnfCDV3r6R+5nH/veWqUHo0ZvY1M+sQ3D/AzH5hZjPM7Ndm1jGMTICBJ/Rn9eq1rFlTSkVFBYWF0xk+bHBYcVmVr+zkfeZxSupnnq2fd7VldotKWENnjwK7gvv3AR2BXwfPPRZSJgU9e7CubEPt47L15RQU9AgrLqvylR19dmz5BlfeNZHv3HI/0+a9Vvv003MWcsFN9/DTh55h245daRaw/5L6mcf999aQ6gxvUQlr6Czl7pXB/QHuflxwf4GZLQspE6vncqbu0Y1Expmv7Oiz48qf/POr6dapA5u27uDKOyfSt6ArI8/6OqPPPwMDHnhmDr99cha/vOLC0NqQ1M887r+3hiT1YIAiM/tecP9NMxsAYGZfBioaepOZjTazJWa2pLp6Z5ND15eV07tXQe3jXj3zKS/f2OTlZCrOfGVHnx1XfrdOHQA4uGM7Th9wFEWr13Fwx/a0SqVIpVKcf/pAilavC7UNSf3M4/57a0hYBwOYWW8z+7uZlZjZcjMbGzzf2czmmtnbwc9O6ZYTVqH5PvANM1sNHAksNLN3gIeD39XL3Se4+wB3H5BKtW3oZQ1avGQZ/fr1pU+f3uTl5TFy5AhmzJyT6TrkVL6yk/GZ7/poDzt3f1x7f+FbK+nXuwfvb9lW+5q/LV5Ov17dQ2sDJPczj/vvrSEh7qOpBP6Pux8BfB0YY2ZHAj8G5rn7ocC84HGDQhk6c/etwH+aWXvgkCCnzN1DLf1VVVWMvfY2Zs+aQqtUikmTp1JcvDLMyKzJV3YyPvPNW7dz3T1PAFBZVcU5J/XnpGMP45Y/Ps0/3y3HgIKunfjJ5eeH1gZI7mce999bQ8IaOnP3cqA8uL/dzEqAnsAIYFDwssnAS8BNDS3HsmF8sT6t2/TMzoaJNDNd+CyZKvesb7bjvu784iUZfV/e/O6fGt0GM+sDzAe+ApS6+0F1frfF3RscPmtxJ2yKiCRNNZ7Rre5+8eA2ur7lm1k74M/Ate6+rb7XpNOiTtgUEUmiTIfO3H0CMCHda8wsj5oi86S7Pxs8vdHM8t293MzygffSLUM9GhGRHBfiUWcGPAKUuPv4Or96HrgsuH8ZMD3dctSjERHJcSGeR3MScCnwVp1zIG8B7gIKzexyoBRIe9KWCo2ISI4LazoZd18ANLT0Mxq7HBUaEZEcVx3pXMxNp0IjIpLjsrvMqNCIiOS8bJ/rTIVGRCTHaehMRNKK8+z83RteiS0b4ICCU2LNl2io0IiI5Ljs7s+o0IiI5DztoxERkVBpH42IiIQqu8uMCo2ISM7T0JmIiITKs7xPo0IjIpLj1KMREZFQZfvBAC3uejSDzx7E8qL5rChewI03RH8iXJz5ytZnHrZt23dw3a3jGDbqBwy7eDTLikrYum073x97C+dcdDnfH3sLW7dtD70dSdrmjRHW9Wiai7lnZyVs3aZnkxuWSqUoWf4KQ84ZRVlZOa8unM0ll15NScnbYTQxq/KVrc88k+ymzgxwy69+y3HHfoULhg+hoqKC3R99zMOPT6Vjh/Z8/9KRTHyikG3bt3P91Zc3anmZzAyQ69t8r8o965ttcv8r+lyY0Rf5Q2ufCekCA58USo/GzK4xs95hLDudgSf0Z/XqtaxZU0pFRQWFhdMZPmxwIvKVrc887OwdO3ey9M0ivh1k5OXl0aF9O/7+ykJGDD0TgBFDz+Rv8xeG1gZI1jZvrOoMb1EJa+jsV8BrZvaKmV1tZl1DyvmEgp49WFe2ofZx2fpyCgp6RBEde76yo8+OOz/q7LL1/6LTQR257fbxXPCfY/jpnfeya/dHbNryIV27dAaga5fObP5wa2htgGRt88byDP+LSliF5h2gFzUF53ig2MxeMLPLzKx9SJnUXN76k6IcGowzX9nRZ8edH3V2ZVUVJStXcdG3vsm0SQ9wwAGf55EnCkPLa0iStnljJbVH4+5e7e5z3P1yoAD4IzCEmiJULzMbbWZLzGxJdfXOJoeuLyund6+C2se9euZTXr6x6a3PUJz5yo4+O+78qLN7dOtC965dOOaowwE4e9DJFK9cxcGdDuL9DzYD8P4Hm+l8UMfQ2gDJ2uaNldQezSfKvrtXuPvz7j4K+EJDb3L3Ce4+wN0HpFJtmxy6eMky+vXrS58+vcnLy2PkyBHMmDmn6a3PUJz5ytZnHnZ2l4M706NbV9a8WwbAq0uX8aU+X2DQyV9n+l//G4Dpf/1vTjvlxNDaAMna5o2V7T2asM6juaihX7j77pAyqaqqYuy1tzF71hRapVJMmjyV4uKVYcVlVb6y9ZlHkX3LdVdx0y9+Q0VlBb0L8vnVLdfh7vyfn9zBszNfJL97V8aPuzXUNiRtmzdGdRYM36XTog5vFpGm0YXP4tOchzdf+sXzM/q+fOLdZyM5vFkzA4iI5Lhs/1e5Co2ISI7L9iloVGhERHKcZm8WEZFQafZmEREJlYbOREQkVBo6ExGRUGnoTEREQpWt50PupUIjIpLjtI8mB7VOtYotu7K6KrbsONcb4l33pIr7zPztD46KLbv9VU/Flt3cNHQmIiKh0sEAIiISKg2diYhIqHQwgIiIhEr7aEREJFTZvo8mrCtsiohIRKrxjG77YmaPmtl7ZlZU57nOZjbXzN4Ofnba13JUaEREpCGTgCGfeu7HwDx3PxSYFzxOS4VGRCTHuXtGt0Ysdz6w+VNPjwAmB/cnA+ftazktrtAMPnsQy4vms6J4ATfeMCbS7IceupvS0tdZunRupLl7JXXd41zvuPOTll1V7Vw06WV+OO01AFZs3MqlT7zCyEkvc/Hk+bxVviX0NsT991afTIfOzGy0mS2pcxvdiLju7l4OEPzstq83tKhCk0qluP++2zl32CUcfexpXHTReRxxxKGR5T/xxDMMH/7dyPLqSuq6x73eceYnMXvK0nfoe3D72sf3vlzMFSd9mcL//AZXnXwY975UEmp+3H9vDfFM/3Of4O4D6twmhNG+FlVoBp7Qn9Wr17JmTSkVFRUUFk5n+LDBkeUvWLCILVs+jCyvrqSue9zrHWd+0rI3bt/NK6vf4/xjvlD7nGHs/LgSgB0fV9K13edDbUPcf28NqXbP6JahjWaWDxD8fG9fbwil0JhZGzP7rpmdGTy+2Mz+YGZjzCwvjEyAgp49WFe2ofZx2fpyCgp6hBWXVZK67nGvd5z5Scu+e95yrh10BGb/fu6GM47inpeKGfzgXMa/VMw1px4eahvi/ntriGd4y9DzwGXB/cuA6ft6Q1jn0TwWLPtAM7sMaAc8C5wBDKzTyGZldf8CA9l+xmxzSeq6x73eceYnKXv+qo10OrANR/Y4iMWlH9Q+/8wb7/Kj04/izMMKeHHFBn7xwps8dNGJobUj7r+3hoQ1BY2ZPQUMArqYWRnwM+AuoNDMLgdKgQv3tZywCs3R7n6MmbUG1gMF7l5lZn8C3mzoTcGOqNEA1qojqVTbJoWuLyund6+C2se9euZTXr4xk/bnnKSue9zrHWd+krKXrd/My6s2suCd/2ZPVTU7P67glpmvM3/VRm484ygAzj4sn1++0ODXS7OI+++tIWEVGndvaHrtM5qynLD20aTMrA3QHjgQ6Bg8/zmgwaGzujummlpkABYvWUa/fn3p06c3eXl5jBw5ghkz52TS/pyT1HWPe73jzE9S9jXfOII5V5/FX688k7uGHccJX+jCHeceR9d2n2fJuk0ALCr9gC90avr3RlPE/ffWkLAOb24uYfVoHgFWAK2AW4FnzOwd4OvA0yFlUlVVxdhrb2P2rCm0SqWYNHkqxcUrw4r7jMcf/z2nnHIiXbp0YtWq1xg3bjyTJk2NJDup6x73eseZn9Tsun465Bh+M285VdVOm9YpfjL4mFDzsmW9Py3bZ2+2sKqamRUAuPsGMzsIOBModfdFjXl/6zY9Y9tyuvBZPHThs+RJ8oXPKves/+wOnwydUHBqRt+XizfMb7Y2pBPapJruvqHO/Q+BaWFliYgkWTYckJCOZm8WEclx2T50pkIjIpLj1KMREZFQqUcjIiKhyvYLn6nQiIjkuP2YtywSLWpSTRERyT7q0YiI5DgNnYmISKiyfehMhaYeST1DPanrDcmdDSJuncYUxpa9fcbNsWU3N/VoREQkVOrRiIhIqNSjERGRUKlHIyIioVKPRkREQuVeHXcT0lKhERHJcZrrTEREQqXZm0VEJFTZ3qNpcXOdDT57EMuL5rOieAE33jAmUfnKjj77oYfuprT0dZYunRtp7l7a7tFs96E/n8QFd05h5K+f4uK7pwIw/i8LOG/cE1x41xSumziLbbs+jqQt9XH3jG5RsWztcrVu07PJDUulUpQsf4Uh54yirKycVxfO5pJLr6ak5O0wmphV+crev+xMZwY4+eSB7Nixi0ceuYfjjz8ro2VkOjOAtvv+bfct029s9GuH/nwSU350EZ3aHVD73P8rKWXgl3vRulWKe6f/A4BrR5zU6GUeMPi/rPGtTS//oCMz+iIv/7C42dqQTovq0Qw8oT+rV69lzZpSKioqKCyczvBhgxORr+x4PvMFCxaxZcuHkeXVpe3+YWR59fmPI75A61Y1X6HH9OnBxg93xNYWz/C/qIRWaMzsS2b2IzO7z8x+Z2ZXmlnHsPIACnr2YF3ZhtrHZevLKSjoEWZk1uQrO/rsuGm7R8cwrvrjdEb95mmm/aPoM7//y6vFnHzkF2NoWY1sHzoL5WAAM7sGGAa8DJwALAN6AwvN7Gp3fymk3M88F+k4ZIz5yo4+O27a7tGZdN236daxHZu37+LKB/5C3+6dOL5fTwAefnExrVqlOGfAYbG1L6kHA/wAGOLu44AzgSPd/VZgCHBPQ28ys9FmtsTMllRX72xy6Pqycnr3Kqh93KtnPuXlG5u8nEzFma/s6LPjpu0enW4d2wHQuf2BnHbMlyh6t2Zdn3+thFeWr+WO755db/GNSrb3aMLcR7O3t/Q5oD2Au5cCeQ29wd0nuPsAdx+QSrVtcuDiJcvo168vffr0Ji8vj5EjRzBj5pxM2p6ROPOVHc9nHidt92js/riCnR/tqb2/cEUp/fIP5h/F7zLpv5dy7w/O5YA2DX6tCeGdRzMRWGxmrwKnAr8GMLOuwOaQMqmqqmLstbcxe9YUWqVSTJo8leLilWHFZVW+suP5zB9//PeccsqJdOnSiVWrXmPcuPFMmjQ1kmxt92i2+6btu7h+4iwAKqudocd/mZOO/CLDfvk4eyqruPKPfwFqDgi47aLTQmnDvmT7pJqhHd5sZkcBRwBF7r6iqe/P5PBmkUzpwmfxiHO7N+Xw5jA05+HNndr1y+j7csuOVZGM94U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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sn\n", + "plt.figure(figsize=(7,5))\n", + "sn.heatmap(cm, annot=True)\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Truth')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 51\n", + " 1 0.98 0.98 0.98 57\n", + " 2 1.00 1.00 1.00 55\n", + " 3 0.98 1.00 0.99 56\n", + " 4 1.00 0.98 0.99 51\n", + " 5 0.96 0.98 0.97 51\n", + " 6 1.00 1.00 1.00 55\n", + " 7 1.00 1.00 1.00 60\n", + " 8 0.96 0.96 0.96 50\n", + " 9 0.98 0.96 0.97 54\n", + "\n", + " accuracy 0.99 540\n", + " macro avg 0.99 0.99 0.99 540\n", + "weighted avg 0.99 0.99 0.99 540\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "\n", + "print(classification_report(y_test, y_pred))" + ] + } + ], + "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": 2 +} diff --git a/ML/17_knn_classification/iris_petal_sepal.png b/ML/17_knn_classification/iris_petal_sepal.png new file mode 100644 index 0000000000000000000000000000000000000000..0515ec77dc1552df6b534913d1f54c3911a49a7d GIT binary patch literal 344642 zcmZU)1CVAxlQ!D6ZQHi3Y1{VOwr#toZDZQDZQHh|ap&9J`~Q36o`{O5%FHM8WLCwA zQ(0M&it>{1FgP$kKtS-)Qew(LK)?zAT0IoRKT0?%>&L%Aq?M?sqO_((QEJ-9peD!XY!NHhl|d0)26eVRZdu zh2xQJKNOKP!dOnp#)~dlt0aM7#V@$8RiE5%RsQ4B_dMUxk+t551DSo8Al%Q)Ucag$ 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1027.13.05.92.12
1036.32.95.61.82
1046.53.05.82.22
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)targetflower_name
05.13.51.40.20setosa
14.93.01.40.20setosa
24.73.21.30.20setosa
34.63.11.50.20setosa
45.03.61.40.20setosa
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + "\n", + " target flower_name \n", + "0 0 setosa \n", + "1 0 setosa \n", + "2 0 setosa \n", + "3 0 setosa \n", + "4 0 setosa " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['flower_name'] =df.target.apply(lambda x: iris.target_names[x])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)targetflower_name
454.83.01.40.30setosa
465.13.81.60.20setosa
474.63.21.40.20setosa
485.33.71.50.20setosa
495.03.31.40.20setosa
507.03.24.71.41versicolor
516.43.24.51.51versicolor
526.93.14.91.51versicolor
535.52.34.01.31versicolor
546.52.84.61.51versicolor
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "45 4.8 3.0 1.4 0.3 \n", + "46 5.1 3.8 1.6 0.2 \n", + "47 4.6 3.2 1.4 0.2 \n", + "48 5.3 3.7 1.5 0.2 \n", + "49 5.0 3.3 1.4 0.2 \n", + "50 7.0 3.2 4.7 1.4 \n", + "51 6.4 3.2 4.5 1.5 \n", + "52 6.9 3.1 4.9 1.5 \n", + "53 5.5 2.3 4.0 1.3 \n", + "54 6.5 2.8 4.6 1.5 \n", + "\n", + " target flower_name \n", + "45 0 setosa \n", + "46 0 setosa \n", + "47 0 setosa \n", + "48 0 setosa \n", + "49 0 setosa \n", + "50 1 versicolor \n", + "51 1 versicolor \n", + "52 1 versicolor \n", + "53 1 versicolor \n", + "54 1 versicolor " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[45:55]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "df0 = df[:50]\n", + "df1 = df[50:100]\n", + "df2 = df[100:]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Sepal length vs Sepal Width (Setosa vs Versicolor)**" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlabel('Sepal Length')\n", + "plt.ylabel('Sepal Width')\n", + "plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'],color=\"green\",marker='+')\n", + "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color=\"blue\",marker='.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Petal length vs Pepal Width (Setosa vs Versicolor)**" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlabel('Petal Length')\n", + "plt.ylabel('Petal Width')\n", + "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color=\"green\",marker='+')\n", + "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color=\"blue\",marker='.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Train test split**" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(['target','flower_name'], axis='columns')\n", + "y = df.target" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Create KNN (K Neighrest Neighbour Classifier)**" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(n_neighbors=10)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9666666666666667" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.predict([[4.8,3.0,1.5,0.3]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Plot Confusion Matrix**" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[11, 0, 0],\n", + " [ 0, 12, 1],\n", + " [ 0, 0, 6]], dtype=int64)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "y_pred = knn.predict(X_test)\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(42.0, 0.5, 'Truth')" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sn\n", + "plt.figure(figsize=(7,5))\n", + "sn.heatmap(cm, annot=True)\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Truth')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Print classification report for precesion, recall and f1-score for each classes**" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 11\n", + " 1 1.00 0.92 0.96 13\n", + " 2 0.86 1.00 0.92 6\n", + "\n", + " accuracy 0.97 30\n", + " macro avg 0.95 0.97 0.96 30\n", + "weighted avg 0.97 0.97 0.97 30\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Do not forget to work on exercise, exercise link is below** \n", + "\n", + "\n" + ] + } + ], + "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": 2 +} diff --git a/ML/17_knn_classification/knn_exercise.md b/ML/17_knn_classification/knn_exercise.md new file mode 100644 index 00000000..55811f6f --- /dev/null +++ b/ML/17_knn_classification/knn_exercise.md @@ -0,0 +1,6 @@ +from sklearn.datasets load digits dataset and do following + 1. Classify digits (0 to 9) using KNN classifier. You can use different values for k neighbors and need to figure out a value of K that gives you a maximum score. You can manually try different values of K or use gridsearchcv + 1. Plot confusion matrix + 1. Plot classification report + +[Solution link]()