Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"folder/subfolder/db4-2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 1:13].values\n",
    "y = df.iloc[:, 13].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joel\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression( multi_class='multinomial', solver='lbfgs', penalty='l2', C=1.0)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.89578125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix using daubechies 4 level 2')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Fs9MnsbUD1oxb3oBACedvZL7Xp/JFX9dQEHLHX80MoEZjHpkDne98BknnL6i2rK+h6ynpCiHZYvy0xRdcoy4aRkP/rkTVtlgf4V3kMxBmij2BHYHPgubzl2BqZJ2I6gZdotYtyuwLCzvGqU8rnQO0nQBlkS8TqjKm2xbN2cx/p6unHDlkpjrfD2tgKb5FXTqXT2B/urxufzxk08p35rFlx+2SnWo9VZZKUb+sC9nHdqf3v020uN7pbzyZEfOO7Ifvz6xL8Wrc7jwusW1b6gBumr4flxycj+uv2Afhp65gr6HrNu2bPDQIt57JbNrjwOHrGdtURPmz2yW7lDqrMKU0KOuzGymmXUws55m1pMgdxxoZiuAccBwSbmSdidooX5iZsuBDZIGhd17Z1O97zKqdCbIhKu8ki6SNFnS5JLi5N5lrmhRHsVL8/jbCf246fsHsm5FLrcN7cf6Vdv7tKa9vGPtsUpOntF3SDEzJzT85s+mDU2YMaklBx+5jrVFOVRWCjPxxtMd6N1vU7rDq5fiVU0BWFfclA8nFNJ7/40AZGUbhx1bzPuvZXaC7HPIJgYdu54xH8/h2vsW0e8HG7n67kXpDqtWVVfSJPKojaSngY+A3pKWSrog5n7NZgNjgTnAG8BIM6vquP0V8CAwH/iKWgZoIL2j2LGqwjsI+yRGA+y7f9OkdqF13ruUP0/5dNvrm75/IFe9PIOCwqDJWVkJ019ryyVjt5+CtWVTFt9uyqZVhzIqymHOu23Yc8D6ZIaVNK0KyygvE5s2NKFpbiUH/GAdY+/vRGH7rRSvDpLLYceV8PUXmdcMzc2vICsLNm/KJje/ggN/sI6n7glaUQcctpalC/IpWpGb5ih3ziM3d+KRm4Oep/0P3chPR6zib5f0SHNUialM3ij2z2pZ3rPG61HAqCjrTQZ2GDCOJ50JchxwsaRngIHAurAanFJjLunFV5NasbGkCTcOOogTrljCoNNXxVz/q49b0nq3rbTrvn10e2tpNg/+Yh/KtwqrEL0OW8dhZ66IuY10KuxQxlW3LSA725Dg/VcL+eSdNvz2jq/YY5+gy2Dl0lzu+l3P9AZaD23alXH9Pz8HILuJMfHl9kz5bxsAjhxaxMQMb15nsuBmFZl/mrUsRTOPhdXiwUA7YCVwI5ADYGb3h/0A9xCMdJcC54UZPq59929qY19pn5KY0+mK3kelO4SUUZPMP80mmspNmdktkYi37N9TzOzg+r5/9/0K7A/P75/Quud+76Od2lcqpeybm0C12ICRqdq/cy59zEjaieLp1Dh/2p1zaaa6nCjeYHmCdM4lneE1SOeci6kxDNJ4gnTOJZ0hv2Guc85FE0z7mvnpJfOPwDnXACV8r8cGzROkcy7pjORdSZNOniCdcynhNUjnnIvCTF6DdM65aIJBmgY4B0kdeYJ0zqVA8uakSSdPkM65pAsGabwP0jnnovIraZxzLgq/ksY55+Ko54RcDYonSOdc0plBWWXmJ8jMPwLnXIMTNLGzEnrURtLDklZJmhVRdqukzyXNkPSCpNYRy66VNF/SPEnHRZQfJGlmuOyucFaDuDxBOudSoiK8Hru2RwIeJZiaJdIEoK+Z7Q98AVwLIKkPMBzYN3zPvZKqTsi8D7iIYCrYXlG2uQNPkM65pKs6zSeRR63bMnsfKK5RNt7MysOXkwhmRQUYBjxjZlvMbCHBFK8DJHUCWprZR+F0L48BJ9e2b++DdM6lQJ0uNWwnKXLCvtHhVM+JOh94NnzehSBhVlkalpWFz2uWx+UJ0jmXEnWYk6aovrMaSroOKAeerCqKsprFKY8r4xLk0lktuHLvo9MdRtKd8dn8dIeQMk8PPzbdIaTG9DnpjqDBCkaxU3sttqRzgKHAMbZ9/uqlQLeI1boCy8LyrlHK4/I+SOdc0lWdKJ6MPshoJB0P/B9wkpmVRiwaBwyXlCtpd4LBmE/MbDmwQdKgcPT6bOCl2vaTcTVI51xmSNa0r5KeBgYT9FUuBW4kGLXOBSaEZ+tMMrMRZjZb0lhgDkHTe6SZVYSb+hXBiHg+8Hr4iMsTpHMu6ZJ5swoz+1mU4ofirD8KGBWlfDLQty779gTpnEsJv2Guc85FYSbKPUE651x0fjcf55yLwm+Y65xzcXiCdM65KPyGuc45F0eyzoNMJ0+QzrmkM4PyRnDDXE+QzrmU8Ca2c85F4X2QzjkXh3mCdM656HyQxjnnojDzPkjnnItBVPgotnPORed9kM45F4Vfi+2cc7FY0A+Z6TxBOudSojGMYmd+L6pzrsGxcJAmkUdtJD0saZWkWRFlhZImSPoy/LdNxLJrJc2XNE/ScRHlB0maGS67K5y8Ky5PkM65lDBL7JGAR4Hja5RdA7xtZr2At8PXSOoDDAf2Dd9zr6Sq+WfvAy4imOmwV5Rt7sCb2BGysoy7XppF0cqm/OEXvTnzsqUcf/oq1hXnADDmtm58OrF1eoOM4aPfteGbiXnkta1k6MsrAfjszpYsfTsPZUFuYSWH3lxMs46V296zaVk2rwztyH4j19Pngo0AfP1KPrP+1RLJyO9QyWG3FpPXpjLqPne1du028ZurJtGmzbeYwetv7MVLL/XmgvOnMXDgN5SXZ7F8eQvu+PtANm1qylGDv+YnP5m77f27776WSy49ngUL2sTZS8PSdc9v+d39i7a93q37Vh6/dTdeeLB9GqNKTLJGsc3sfUk9axQPI5jpEGAMMJFgGthhwDNmtgVYKGk+MEDS10BLM/sIQNJjwMnUMrNhyhKkpDzgfYKpGZsA/zazG2usI+BO4ESgFDjXzKamKqbaDDtvBYu/yqdZQcW2shcf7sR/HuyUrpAStsePN9H7zI18eE3htrI+F2yg32XrAfj8sQJm3tuSgTet3bZ8ys2t6Hz4t9teV5bD5L+0ZuirK8lrU8nUW1vxxRMF7H/J+l12HPFUVGTxwIMH8NVXheTnl3HXXW8ybepuTJu2G4882o/KyizOP286p582h4cf6c+7E3vy7sSeAPTsuZYbrn8/o5IjwNKv8vj1/+sNBD/gT06dw/9eb5XmqGoX1A5T2gfZMZzrGjNbLqlDWN4FmBSx3tKwrCx8XrM8rlQ2sbcAR5tZP6A/cLykQTXWOYHt1d2LCKrAadFuty0MOGotbz7b8H+Zo+l4yFaatqpe08sp2N5+Kd8sIntclryVR0G3ClrtVb690IJHeakwg7KNIr/D9h+LdCspyeerr4IfgM2bc1iyuCVt25UydVonKsO+rM8/b0u7dqU7vPfIIxfx3ns9dmm8ydb/8I0sX9SUVd80TXcoCak0JfQgmO96csTjop3YbbSsbHHK40pZDdLMDNgYvswJHzUDGgY8Fq47SVJrSZ2qfhl2pV9ev4iHbulOfvPqCeFHZ6/gmFNW8+XMAh4Y1Z2N6zOrV2L631uy8KVm5LQwhoxZDQQJcM4DLTj64SLmPtxi27pZOTDgxrW8elJHmjQzWvQo55Ab1qYp8vg6dNjInnuWMO/zdtXKjz12Ae+9332H9Y88YjE3/fHwXRVeSgweVsLEFzOnBlyH03yKzOzgOm5+ZVWukNQJWBWWLwW6RazXFVgWlneNUh5XSgdpJGVLmk4Q/AQz+7jGKl2AJRGvo1Z7JV1U9euylS1Jj3PA0SWsXZPD/FnNq5W/+mRHzh/cn5E/3I/iVTlceN3ipO871fpfsZ4fT1xBz6GlfPFEAQAz7m7J3uduJKd59W9wZRl88UxzTnxhJae8v5w23ytj9ugW0TabVnl5Zfz+ug/41+gDKd2cs618+OmzqajI4t13e1Zbv3fvIr7dks2iRa13baBJ1CSnkkHHruf9lxt+8xrC251VZiX0qKdxwDnh83OAlyLKh0vKlbQ7Qev0k7DStUHSoLBr7+yI98SU0uqQmVUA/SW1Bl6Q1NfMZkWsklC118xGA6MBWmW1Tfrpp30O2sCgY0o4ZPBacnKNZgUV/PaO+dx65V7b1nn9mQ7c9OC8ZO96l+k5tJSJI9qx/6XrKZrRlMVv5jPt1lZs3ZCFsiA712jXbysALboHtejuJ5Qy54GGlSCzsyv5/XUf8O7Ennz44faKwpBjFjBgwDdc+7ujqfm1OvKIxbw3MbOb14ccvYH5M/NZW5RT+8oNRLL+o0p6mmBApp2kpcCNwC3AWEkXAIuBUwHMbLakscAcoBwYGeYhgF8RjIjnEwzOxB2ggV00im1mayVNJBhWj0yQsarDu9Sjt3bn0VuDZtl+A9fzkwuXc+uVe9Gm/VZKVgf9PYcdV8yiL/J3dWg7Zf3XTWjZM+hj/OadPFruXgbAsU+u3rbOjLtb0qRZJb3P2kTpyizWfZXDt8VZ5BVWsuLDPFruUR512+lhXH75xyxZ0pIXXth7W+lBBy3j1FPncvXVx7BlS/WvtGQcfvhifnv1kF0dbFINPnltRjWvSeIgjZn9LMaiY2KsPwoYFaV8MtC3LvtO5Sh2e6AsTI75wBDgrzVWGwdcLOkZYCCwLh39j7FccM1i9uhTCgYrl+Zy13W7pzukmD64spCVn+aypSSL54/cjf0vWc+y9/JY/3UOktG8cwUDbiqJu41mHSvZb+R6JpzVnqwmwXsOvTn+e3alffsUMeSYr1m4sBX33B38+I8Z048RI6aQk1PJqFHvAvD5vHbcc88hAPTtu4qiomasWFGQtrh3Vm5+JQcevoE7r+5a+8oNSSO41FCWogsmJe1PcH5SNkFf51gz+6OkEQBmdn/YF3APQc2yFDgvzPIxtcpqa4PyTkxJzOn0s+nz0x1Cyjw9/Nh0h5ASldPnpDuElHnL/j2lHgMn2+Tt2cW63fKrhNadf9r1O7WvVIpZg5R0N3F+A8zs0ngbNrMZwAFRyu+PeG7AyIQidc5lDAMqKzP/Wux4Tey4NTnnnIvJgMZ8uzMzGxP5WlJzM9uU+pCcc41BY7jdWa0nIUk6VNIcYG74up+ke1MemXMus1mCjwYskbM0/wEcB6wBMLPPgCNSGJNzLuMJs8QeDVlCp/mY2ZIat05rOBfoOucapgZeO0xEIglyiaTDAJPUFLiUsLntnHNRGVgjGMVOpIk9guBUnC7ANwR35vFTc5xztVCCj4ar1hqkmRUBZ+6CWJxzjUkjaGInMoq9h6SXJa0O54V4SdIeuyI451wG+46MYj8FjAU6AZ2B54CnUxmUcy7DVZ0onsijAUskQcrMHjez8vDxBA0+7zvn0i2Jk3alTbxrsasmN3lX0jXAMwSJ8XTg1V0Qm3MukzWCUex4gzRTqD6Xwy8jlhnwp1QF5ZzLfGrgtcNExLsWu+He/NA517BlwABMIhK6kkZSX6APkFdVZmaPpSoo51yma/gDMImoNUFKupFgPog+wGsEU7V+AHiCdM7F1ghqkImMYv+UYO6HFWZ2HtAPyE1pVM65zFeZ4CMBkq6QNFvSLElPS8qTVChpgqQvw3/bRKx/raT5kuZJOq6+h5BIgtxsZpVAuaSWBFO4+onizrnYkngepKQuBPeAONjM+hJM4zIcuAZ428x6AW+Hr5HUJ1y+L8F0LvdKyq7PYSSSICeH07Y+QDCyPRX4pD47c859d8gSeySoCZAvqQnQjGD202EE814R/nty+HwY8IyZbTGzhcB8YEB9jiGRa7F/HT69X9IbQMtwvhnnnIst8eTXTlLkFC+jzWz0ts2YfSPpNoL5rzcD481svKSOVbOgmtlySR3Ct3QBJkVsb2lYVmfxThQ/MN4yM5tanx0651wNRfFmNQz7FocBuwNrgecknRVne9Ha7fUaMopXg7w9zjIDjq7PDndadhZq3iwtu06lZ0/4frpDSJnTX38r3SGkxNN9uqU7hNRJwi2xk3ii+BBgoZmtBpD0PHAYsFJSp7D22IlgfASCGmPkH6crQZO8zuKdKH5UfTbonHPBvK9JOw9yMTBIUjOCJvYxBLOubgLOAW4J/30pXH8c8JSkOwhusNOLeo6bJHSiuHPO1VmSapBm9rGkfxMMEJcD04DRQAEwVtIFBEn01HD92ZLGAnPC9UeaWb3qxJ4gnXMpkcxrsc3sRuDGGsVbCGqT0dYfBYza2f16gnTOpcZ34UoaBc6SdEP4urukep1T5Jz7DvmO3FH8XuBQ4Gfh6w3AP1MWkXMu4yV6knhDvyVaIk3sgWZ2oKRpAGZWEk7/6pxzsTXyG+ZWKQuvYzQASe1J+BJz59x3VUOvHSYikSb2XcALQAdJowhudfaXlEblnMt8jaAPMpFrsZ+UNIVgOF3AyWY2N+WROecyVwb0LyYikRvmdgdKgZcjy8xscSoDc85luO9CgiSYwbBq8q48ggvG5xHca80556JSIxipSKSJvV/k6/AuP7+MsbpzzjUadb6SxsymSjokFcE45xqR70ITW9KVES+zgAOB1SmLyDmX+b4rgzRAi4jn5QR9kv9JTTjOuUajsSfI8ATxAjP77S6KxznXWDTmBCmpiZmVx5t6wTnnohGNfxT7E4L+xumSxgHPEdzBFwAzez7FsTnnMtV3qA+yEFhDMAdN1fmQBniCdM7F1sgTZIdwBHsW2xNjlUZw6M65lGoEWSJegswmmPMhaVMoOue+Oxp7E3u5mf1xl0XSAJz88yUcd8oyzMTXXzbn79fvTdnWbH50xlJ+NHwpFRXi0/fb8vDf90p3qHFddu00Bnx/JWtLchn582ByyjPO/5zjTlrM+rXBrTzH/GsfJn/Ukf6HrOK8EXNpklNJeVkWD/2zDzOmtk9n+NVM+l1rlk3MJa9tJSe+HJx+O+POFix9Ow9lQV5hBQNvXkuzjttHBDYty+a1oe3pO3ID+1wQdJt/9vcWfP1SM7auF6dOXZGWY0nUlbctYuCQdawtasIvh/QBYI99SrnkliXkN69g5ZKm/PWS3SndmJ3mSGuRxAQpqTXwINA33PL5BJc8Pwv0BL4GTjOzknD9a4ELCCawvdTM3qzPfuPd7myn73Yp6WtJMyVNlzQ5ynJJukvSfEkz0jli3rbDFk46YymXDT+YX58ygOxs48gTVrH/ISUMOqqIX/9kAL/68UD+M6Z7ukJM2FuvdeeGKwftUP7Ss3twybmDueTcwUz+qCMA69fmctP/DWTk2Udxx58P4Kobpu3qcOPa48elDH6guFrZPhds5MRxqznhxdV0HryF2fe2qLZ86s0t6XT4lmplXY76lmPHZsb1DeOfK+S6s6r/CF9+62IevrkzI4b04X9vtOanI1amKboEWTCKncgjQXcCb5jZ3kA/YC5wDfC2mfUC3g5fI6kPMJzgfhHHA/eGpyzWWbwEGXW2sHo4ysz6m9nBUZadQDBnbS/gIuC+JO2zXrKbGE1zK8nKriQ3r5I1q3L54enf8NxD3SkvCz6qdcUN/2bqsz9ry4b1icW54MtWFBflAbBoYQuaNq2gSU4SZo1Pkg6HbKVpq+r/i3IKtldNyjer2k/50rfyKOhWQau9yqu9p13/MvI7ZMZ5J7M+bsGGtdX/P3fd81tmTioAYNr7LfnBiWvTEFkdJel+kJJaAkcADwGY2VYzWwsMA8aEq40BTg6fDwOeMbMtZrYQmA/Uax6tmAnSzIpjLUuiYcBjFpgEtJbUaRfsdwdrVuXy/KPdGDPhI55850M2bWzCtI8K6dxjM/seuI6/PzmZvz4ylV77rk9HeEkx9CcLuWfMu1x27TQKWmzdYfn3By9nwRetKC9r4E03gibzS4M7suiVfPa7dAMA5aVizgMF9B25Ic3RJd+iefkceuw6AA4fWkL7zjv+/RqaOsxJ007S5IjHRTU2tQfB5c2PSJom6UFJzYGOZrYcIPy3Q7h+F2BJxPuXhmV1lsgdxXeGAeMlTYly0JDggUi6qOrD21r5bUoCLWhZxqCjijjv+EGcdcxh5OVXcNTQFWRnGwUty7jizIN46Pa9uPa22WTiGNVrL/TkF6cN4ZJzB1OyJo8LLp5dbXn33ddz3q/ncPet/dIUYd30u2IDwyaupMfQzXz5RHMAZt7dgr3P3UhO88z7+9Tmjqt68KNzVnPPa3PJL6ikvCwD5ntJvAZZZGYHRzxG19hSE4Jzsu8zswMIzse+Js6ekzawnOp5sb9vZsskdQAmSPrczN6PWJ7QgYQf2GiAVjntU/Lt7z+ohBXf5LO+JGia/u+t9uzTbx1FK3P58K32gPhiVkvMoGWbsm3rZYq1JXnbnr8xrgc33vrxttdt22/m93/5lNv/dAArvmmejvDqrefQzbw3opD9Lt3Amhk5LHkzj+m3tmTrhiyUBdm5xvfOKk13mDttyVd5/O7MXgB02f1bBh6zLs0R1SK50yksBZaaWdWX9t8ECXKlpE5mtjxsea6KWL9bxPu7Asvqs+OU1iDNbFn47yqCeW1q9gMk7UB21urluey9/zpy8yoAo//AEpYsbM6kd9rRb2AJAF16lNIkx1hfkpOOEHdKm7bba96HHbmcRQuCgY3mBWX84daPefRf+zB3Ztt0hVcnG77e3gXwzTt5tNw96G8c8uQaTnpnFSe9s4reZ2+iz0UbGkVyBGjVtgwAyTjjshW88ni7NEcUn0jetK9mtgJYIql3WHQMMAcYB5wTlp0DvBQ+HwcMl5QraXeCMY5P6nMcKatBhn0EWWa2IXx+LFDztKFxwMWSngEGAuuq+hR2tXkzW/HBhA7cNXYyFeViwecFvP5cZzC4/E+fc+/zn1BeJu64bh+SMMCfUlf/YQr7HVBEy9ZbGfPCeJ58qDf7HbCGPXqtwwxWrWjG3X8LmtJDf7KQzl038bNzv+Bn534BwO8vP5R1a3PTeQjb/O/K1qz6NJctJVm8eGRH9rtkA8vey2XD101A0LxzBYfctLbW7Uy7tSWLXsmnfLN48ciO7PnTUva7pGH2VV5zz0L2P3QDrQrLeeLTmTx+eyfym1fyo3OCUfj/vd6a8c82/B+zJJ8HeQnwZDjl9ALgPIIK3lhJFwCLgVMBzGy2pLEESbQcGGlm9Rp5lFlq+msk7UFQa4QgET9lZqMkjQAws/slCbiHYCi+FDjPzHY4HShSq5z2dmjrU1ISczqpZYvaV8pQp77+UbpDSImn+3SrfaUM9VbFs1NinHmSkGYdu1mv4VfWviIw464rd2pfqZSyGqSZLSA4X6lm+f0Rzw0YmaoYnHNp1AjGylI9SOOc+y76Dt3Nxznn6s4TpHPORdfYb5jrnHP15k1s55yLJrkniqeNJ0jnXGp4gnTOuR1VXUmT6TxBOudSQpWZnyE9QTrnks/7IJ1zLjZvYjvnXCyeIJ1zLjqvQTrnXCyeIJ1zLgrzSw2dcy4qPw/SOefiSdHNuHclT5DOuZTwGqRzzkXTSE4UT/W82M657yhVJvZIeHtStqRpkl4JXxdKmiDpy/DfNhHrXitpvqR5ko6r7zF4gnTOpUSyEyRwGTA34vU1wNtm1gt4O3yNpD7AcGBfggkB75WUTT14gnTOJZ8RDNIk8kiApK7AD4EHI4qHAWPC52OAkyPKnzGzLWa2EJgPDKjPYWRcH6SVV1CxpjjdYSRfybp0R5AyT+/dOd0hpMSby6akO4SUye6089uowyBNO0mR0z2PNrPRNdb5B3A1EDk/ckczWw5gZssldQjLuwCTItZbGpbVWcYlSOdchkg8QRbFmxdb0lBglZlNkTQ4ge1pp6KJ4AnSOZd0ST5R/PvASZJOBPKAlpKeAFZK6hTWHjsBq8L1lwLdIt7fFVhWnx17H6RzLvnMUGVij9o3ZdeaWVcz60kw+PKOmZ0FjAPOCVc7B3gpfD4OGC4pV9LuQC/gk/ochtcgnXOpkfrzIG8Bxkq6AFgMnApgZrMljQXmAOXASDOrqM8OPEE651IiFVfSmNlEYGL4fA1wTIz1RgGjdnZ/niCdc8lngM9J45xzMWR+fvQE6ZxLDb9ZhXPOxeDTvjrnXDSN5G4+niCdc0kXnCie+RnSE6RzLjV8ThrnnIvOa5DOOReN90E651wsiV1n3dB5gnTOpYY3sZ1zLgqr83QKDZInSOdcangN0jnnYsj8/OgJ0jmXGqrM/Da2J0jnXPIZfqK4c85FI8xPFG/ssrKMu9/4gjXLc7jhnD3SHU69tO+0ld/e+TVt2pdhleK1p9rx4kMdOPyHJfz8yuV06/Utlw7tzZczmqc71Dq78o7FDByygbVFTfjl0b0BaNG6nN/dv4iOXbeycmlTRv2yBxvXNcyv+e1XdOPjt1rSul05o9+dB8Djt+3G608V0qowmCHgvGuXMeCYDbzzfBueu7fDtvcunJvHP9/8gj37buaRW3bjrecK2bgum5fmz0zLsUTVCBJkSiftknSZpFmSZku6PMpySbpL0nxJMyQdmMp46urkXxSx5Mu8dIexUyoqxOg/duXCo/blspN686NzVtO912a+npfHHy/cg5kfF6Q7xHob/2wh1525e7Wy0y5exbQPCjj/B/sw7YMCTr94VYx3p9+xpxcz6skFO5T/+MLV3PfWPO57ax4DjtkAwNGnlGwru/ruRXTstpU9+24GYND/W89dr32xS2NPiFlij1pI6ibpXUlzw1xyWVheKGmCpC/Df9tEvOfaMK/Mk3RcfQ8hZQlSUl/gQmAA0A8YKqlXjdVOIJhxrBdwEXBfquKpq3adtjLgmPW8/lRhukPZKcWrcpg/qxkAmzdls+TLPNrtVsaS+fksXZDZyX/WxwVsKKleOzz0uPW8NTb4m701tpBDj1+fjtASst+gTbRoU/e5pN59sQ2DTy7Z9nqfg0pp27E8maHtvKo+yEQetSsHrjKzfYBBwEhJfYBrgLfNrBfwdviacNlwYF/geOBeSdn1OYxU1iD3ASaZWamZlQPvAT+usc4w4DELTAJah/Pbpt2Im5bx4J87YZXR5iDPTB27bmHPvqV8Pi3zmtOJatOujOJVOUDw49C6bQNLHAl4+ZH2jDimN7df0Y0Na3f8f/3+uNYcdfLaXR9YHamyMqFHbcxsuZlNDZ9vAOYCXQjyx5hwtTHAyeHzYcAzZrbFzBYC8wkqanWWygQ5CzhCUltJzYATqT6ZNwQHuSTi9dKwLK0GDlnP2qImzJ/ZLN2hJE1eswquH72A+//QldKN9foxdbvA0HOKeOSjOdw7YR6FHcsYfVPnass/n9qM3PxKeu79bZoiTFSCzes69lNK6gkcAHwMdDSz5RAkUaCqkzZpeSVlCdLM5gJ/BSYAbwCfEVSVI0Wrnu3wiUm6SNJkSZPL2JL0WGvqc8gmBh27njEfz+Ha+xbR7wcbufruRSnfb6pkNzGuH72Ad14o5H+vt6n9DRmspCiHwg5lABR2KGPtmoY5QBNLm/blZGdDVhaccGYx86ZX/5Ge+FLras3rBsuoS4JsV/X/O3xcFG2TkgqA/wCXm1m8vpOE8koiUjpIY2YPmdmBZnYEUAx8WWOVpVSvVXYFlkXZzmgzO9jMDs4hN3UBhx65uRNnHdyHcwb24eZf9eCzDwr42yU9Ur7f1DCuvG0RS+bn8fwDHdMdTMpNGt+SIacVAzDktGI+erNlmiOqmzUrtyf0D19vRc/e22uKlZXw31daM3jY2jREVg+J90EWVf3/Dh+ja25KUg5BcnzSzJ4Pi1dWdcmF/1aNyCWUVxKR0p9XSR3MbJWk7sApwKE1VhkHXCzpGWAgsK6qyuySY99DNjHkp8UsmJvHvW/OBeCRv3Ymp6nx6z8toVVhOX8a8xVfzc7nurNqjqE1bNfcu4j9D91Iq8Jynpg8h8dv78iz93TguvsXcfzwYlZ9E5zm01Dd/KsezPiogHXFTTjzoD78/KoVzPiogK9m5yNBx65bufRv21uKMycV0K5TGZ16bK22nQf/1Il3X2zDls1ZnHlQH47/WTE//82KXX04O0jWeZCSBDwEzDWzOyIWjQPOAW4J/30povwpSXcAnQkGgT+p174thecqSfov0BYoA640s7cljQAws/vDA7+HYKSpFDjPzCbH22ZLFdpAHZOymNMmqxH3C1bWfaQ2E7y5bHq6Q0iZ7E7zp5jZwfV9f6v8TnZYz3MTWveNz2+Juy9JPwD+C8xk+7j37wj6IccC3YHFwKlmVhy+5zrgfIJuvcvN7PX6HEdKa5BmdniUsvsjnhswMpUxOOfSwAwqknOtoZl9QPR+RYCotSUzGwWM2tl9Z1YPtnMuczSCK2k8QTrnUsMTpHPORWGAz0njnHPRGFjm3+/ME6RzLvmMpA3SpJMnSOdcangfpHPOxeAJ0jnnoqn7jSgaIk+QzrnkM4KLxzOcJ0jnXGp4DdI556JJ3qWG6eQJ0jmXfAbm50E651wMfiWNc87F4H2QzjkXhZmPYjvnXExeg3TOuWgMq8j8O8l7gnTOJZ/f7sw55+Lw03ycc25HBpjXIJ1zLgrzG+Y651xMjWGQJqXzYqeCpNXAol20u3ZA0S7a167WWI/Njys5ephZ+/q+WdIbBDEnosjMjq/vvlIp4xLkriRp8s5Mnt6QNdZj8+NyyZSV7gCcc66h8gTpnHMxeIKMb3S6A0ihxnpsflwuabwP0jnnYvAapHPOxeAJ0jnnYvAECUh6WNIqSbNiLJekuyTNlzRD0oG7Osa6kpQn6RNJn0maLemmKOtk3HFVkfS1pJmSpkuaHGV5Rh6bpMskzQr/ZpdHWZ6Rx5WpPEEGHgXinah6AtArfFwE3LcLYtpZW4Cjzawf0B84XtKgGutk4nFFOsrM+sc4PzDjjk1SX+BCYADQDxgqqVeN1TLuuDKZJ0jAzN4HiuOsMgx4zAKTgNaSOu2a6OonjHVj+DInfNQckcu446qDTDy2fYBJZlZqZuXAe8CPa6yTiceVsTxBJqYLsCTi9dKwrEGTlC1pOrAKmGBmH9dYJSOPK2TAeElTJF0UZXkmHtss4AhJbSU1A04EutVYJxOPK2P5zSoSoyhlDf78KDOrAPpLag28IKmvmUX2s2bkcYW+b2bLJHUAJkj6PGwJVMm4YzOzuZL+CkwANgKfAeU1Vsu448pkXoNMzFKq/5J3BZalKZY6M7O1wER27GfN2OMys2Xhv6uAFwj67SJl5LGZ2UNmdqCZHUHQ7fNljVUy8rgylSfIxIwDzg5HEAcB68xsebqDikdS+7DmiKR8YAjweY3VMu64ACQ1l9Si6jlwLEHzNFKmHluH8N/uwCnA0zVWycjjylTexAYkPQ0MBtpJWgrcSDCogZndD7xG0B80HygFzktPpHXSCRgjKZvgh3Csmb0iaQRk9HEBdCToMoDgO/yUmb3RSI7tP5LaAmXASDMraSTHlZH8UkPnnIvBm9jOOReDJ0jnnIvBE6RzzsXgCdI552LwBOmcczF4gmyEJFWEd7mZJem58LK1+m7rUUk/DZ8/KKlPnHUHSzqsHvv4WtIOM+DFKq+xzsZ4y6Os/wdJv6lrjO67yRNk47Q5vMtNX2ArMCJyYXhuZJ2Z2S/MbE6cVQYDdU6QzjVUniAbv/8Ce4W1u3clPQXMDG9kcaukT8P7Cv4Stt1v8B5JcyS9CnSo2pCkiZIODp8fL2mqgvtNvi2pJ0EiviKsvR4eXs3zn3Afn0r6fvjetpLGS5om6V9Ev764GkkvhjemmF3z5hSSbg9jeVtS+7BsT0lvhO/5r6S9k/Jpuu8Uv5KmEZPUhOD+gW+ERQOAvma2MEwy68zsEEm5wP8kjQcOAHoD+xFcsTIHeLjGdtsDDwBHhNsqNLNiSfcDG83stnC9p4C/m9kH4aVzbxLc0utG4AMz+6OkHxLc17A254f7yAc+lfQfM1sDNAemmtlVkm4It30xwSRXI8zsS0kDgXuBo+vxMbrvME+QjVN+eJszCGqQDxE0fT8xs4Vh+bHA/lX9i0ArgpuwHgE8Hd4JaJmkd6JsfxDwftW2zCzWvTSHAH3CSwIBWobXUB9BcJ0xZvaqpJIEjulSSVX3RuwWxroGqASeDcufAJ6XVBAe73MR+85NYB/OVeMJsnHabGb9IwvCRLEpsgi4xMzerLHeidR++ywlsA4EXTiHmtnmKLEkfI2rpMEEyfZQMyuVNBHIi7G6hftdW/MzcK6uvA/yu+tN4FeScgAkfS+8M877wPCwj7ITcFSU934EHClp9/C9hWH5BqBFxHrjCZq7hOv1D5++D5wZlp0AtKkl1lZASZgc9yaowVbJAqpqwWcQNN3XAwslnRruQ5L61bIP53bgCfK760GC/sWpCiYr+xdBi+IFgnsQziSY7+S9mm80s9UE/YbPS/qM7U3cl4EfVw3SAJcCB4eDQHPYPpp+E8Gds6cSNPUX1xLrG0ATSTOAPwGTIpZtAvaVNIWgj/GPYfmZwAVhfLMJpipwrk78bj7OOReD1yCdcy4GT5DOOReDJ0jnnIvBE6RzzsXgCdI552LwBOmcczF4gnTOuRj+P9ycBoYPTjRwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "plot_confusion_matrix(clf, X_test, y_test) \n",
    "plt.title(\"Confusion Matrix using daubechies 4 level 2\")"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}