Predicting Gender From Voice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import mglearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3168, 21)\n"
     ]
    }
   ],
   "source": [
    "#Read the voice dataset\n",
    "mydata = pd.read_csv(\"voice.csv\")\n",
    "#Preview voice dataset\n",
    "mydata.head()\n",
    "print(mydata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13515509cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the histograms\n",
    "male = mydata.loc[mydata['label']=='male']\n",
    "female = mydata.loc[mydata['label']=='female']\n",
    "fig, axes = plt.subplots(10, 2, figsize=(10,20))\n",
    "ax = axes.ravel()\n",
    "for i in range(20):\n",
    "    ax[i].hist(male.iloc[:,i], bins=20, color=mglearn.cm3(0), alpha=.5)\n",
    "    ax[i].hist(female.iloc[:, i], bins=20, color=mglearn.cm3(2), alpha=.5)\n",
    "    ax[i].set_title(list(male)[i])\n",
    "    ax[i].set_yticks(())\n",
    "    \n",
    "ax[0].set_xlabel(\"Feature magnitude\")\n",
    "ax[0].set_ylabel(\"Frequency\")\n",
    "ax[0].legend([\"male\", \"female\"], loc=\"best\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitis\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\nitis\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Prepare data for modeling\n",
    "mydata.loc[:,'label'][mydata['label']==\"male\"] = 0\n",
    "mydata.loc[:,'label'][mydata['label']==\"female\"] = 1\n",
    "mydata_train, mydata_test = train_test_split(mydata, random_state=0, test_size=.2)\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(mydata_train.iloc[:,0:20])\n",
    "X_train = scaler.transform(mydata_train.iloc[:,0:20])\n",
    "X_test = scaler.transform(mydata_test.iloc[:,0:20])\n",
    "y_train = list(mydata_train['label'].values)\n",
    "y_test = list(mydata_test['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "Accuracy on training set: 1.000\n",
      "Accuracy on test set: 0.961\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Train decision tree model\n",
    "tree = DecisionTreeClassifier(random_state=0).fit(X_train, y_train)\n",
    "print(\"Decision Tree\")\n",
    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forests\n",
      "Accuracy on training set: 0.998\n",
      "Accuracy on test set: 0.976\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Train random forest model\n",
    "forest = RandomForestClassifier(n_estimators=5, random_state=0).fit(X_train, y_train)\n",
    "print(\"Random Forests\")\n",
    "print(\"Accuracy on training set: {:.3f}\".format(forest.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(forest.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting\n",
      "Accuracy on training set: 0.996\n",
      "Accuracy on test set: 0.975\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Train gradient boosting model\n",
    "gbrt = GradientBoostingClassifier(random_state=0).fit(X_train, y_train)\n",
    "print(\"Gradient Boosting\")\n",
    "print(\"Accuracy on training set: {:.3f}\".format(gbrt.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(gbrt.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine\n",
      "Accuracy on training set: 0.985\n",
      "Accuracy on test set: 0.984\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Train support vector machine model\n",
    "svm = SVC().fit(X_train, y_train)\n",
    "print(\"Support Vector Machine\")\n",
    "print(\"Accuracy on training set: {:.3f}\".format(svm.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(svm.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilayer Perceptron\n",
      "Accuracy on training set: 0.995\n",
      "Accuracy on test set: 0.981\n"
     ]
    }
   ],
   "source": [
    "#Train neural network model\n",
    "mlp = MLPClassifier(random_state=0).fit(X_train, y_train)\n",
    "print(\"Multilayer Perceptron\")\n",
    "print(\"Accuracy on training set: {:.3f}\".format(mlp.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1loodIiLisvzyftKzmK7sUuAWSdOAe6gw/buZmdVHqwkRko6MiKslndzS5xHxs6pW1uCcEGFm1n4dToggdcGGNL+RmZlZzbQ1CPcSST2A1yKiXkGvHSZpb+BiUseOPSLirc7Yb3sSIqx7aZr/bL1L6JB+i6+pdwnWhS0461M1OU6bHSIiYglpFtqubDRwbh7r1CkNk5mZVVeR3np/knSBpL0l7Vz6WdkDSuon6TFJl0maK2mipP0kTZX0pKTdJK0t6XJJ0yXNknRQ2bYPSJqZf/bMy4dLmiTphrzviUq+DHwOOC0vGy7ptrJaLsgz6CJpgaTT836bJG27st/RzMw6pmJvPWDP/PuMsmUB7NOB425FSjcfS0o4L6U/jCSlPzwK3BsRX5S0HjBN0h+Bl4CPR8TiPFX7taTIIkgJDwOAF4CpwF4RcZmkoeTIohxT1JaFEbGzpK+Rkii+3IHvaGZmK6lIV/IRVTju/IhoApC0LP1BUin9YVNgpKRT8vq9gM1JDc8FkgaRBsp+pGyf0yLi+bzPUsLDlHbWdWP+/TDwmeYfShpLalDp2ddDpMzMqqXIlVNpqvbmwa9ntL5FRZXSH5YAh0bE483qGEeavmMn0i3Jxa3ss8WEB8oSIrJezT4v7aPVhAhgPEDv/r0dCmtmViUVnzlJuhg4DDiBFAv0WWCLKtd1J3BCjhhC0uC8vA/wYs6/+wIrTtVexDPA9pLWlNQH2LezCjYzs85TpEPEnhFxFPBKRJwO7AFsVt2yOJMULTRH0tz8HuBCYIykB0m39NqV8BARz5Fm9J0DTARmdVrFZmbWaVpNiFi2gvRQROyeG4TPAC8DcyNi61oU2KicEGFm1n6dkRBRclvuMXcOMJPUU+/SDtZnZmbWqray9XpGxLvNlq0J9IqIV2tRXBG54TwiIi5ciW3/FBF7trD8SspmzG1J7/69460xa7T2cevGNcypMzOruaJXTm09c/qbpEsl7VPqmBARbzdSw5StB3ytpQ9y/FKrWmqYzMys/tpqnLYDZpDmcXpO0s8l7d7ZBUg6StIcSY9I+rWkDSX9NqdDTJe0V15vXE6NmCTpaUkn5l2cBXxY0mxJ5+QUiPskXUOaNRdJJ+c0irmSTio79uv5t3JaxKOSbgc26uzvaWZmxbUV/PoycAlwiaRNSF3Ify5pI+A3EXFqRw8uaQBwKinNYaGk9YELgPMiYoqkzUndyrfLm2wLjCAlpT8u6SLgW8AOETEo73M4sFteNl/SLsAxwO6krvAPSbo/Isp76h0CbEOafv4DpISKyzv6/czMbOUUGoQbES9I+h/gFeBkUqxPhxsnUgTSDRGxMB/nn5L2I41FKq3zPkmlaTtuj4i3gbclvURqSFoyLSLm59dDgZsi4g0ASTcCe7NiN/JhwLU56PYFSfe2tFMnRJiZ1UabjZOkXsCngcOBvYA7gG8Dd3XS8UXq/VduNVqY2iI3VkVSIGDF8U9qZZ3mKiY+OCHCzKw2Wn3mlJ/ZPEtKh7gG2CIixkTEH/IVRme4B/icpL75mOuTGr7jy+oYVGEfi2h7QsTJwMGS1pK0NukW3gMtrPN5ST0kbUy6dWhmZnXS1pXTncBXImJRtQ4eEfMk/RC4X9IS0q22E4FfSZqT65sMfLWNfbysNN3GXOAPwO3NPp+Zu4ZPy4sua/a8CeAm0i3GJuAJ4P4OfzkzM1tpFRMirGVOiDAza7/OGOdkZmZWFxV760laM/eQa3NZVyRpATCk1FuwPea9PI+BEwZ2ek1NY5o6fZ9mZl1NkSunPxdcZmZm1ilavXKS9EHgQ0DvPJ9SqUv2+4C1alBba3X1I3VpnwJ8FHgEuAI4nZTsMBr4K2kQ7ZbAm8DYiJiTewVeC2xI6iChsv0eSeqMsQbwEPC1TuyVaGZm7dDWbb1PAEeTpkz/WdnyRcB3qlhTEVuREivGAtOBI0iDbUeSansOmBURB0vaB7gKGAR8H5gSEWfk2X3HAkjajtRlfq+IeFfShaRG7qrafi0zM4O244smABMkHRoRv61hTUXMj4hSbt484J6ICElNQD/STL2HAkTEvZL65plvh5HmpCIibpf0St7fvsAuwPQ82Lc38FLzgzohwsysNorO53QE6Y/+svUj4oxqFVVAeWeMpWXvl5Jq/HcL20Sz3+UETIiIb7d1UCdEmJnVRpEOEbcAB5H+4L9R9tPIJpNuy5WCYBdGxGvNlh8AvD+vfw8wKofaIml9SVvUumgzM0uKXDltGhH7V72SzjUOuCKnTLwJjMnLTweulTSTlALxLEBEPCrpu8BdklYD3gW+DjxT68LNzKxAQoSk8cAvS894LHFChJlZ+xVNiChy5TQUOFrSfNKzHQERETt2sEYzM7MWFWmcDqh6FTUiaW/gYtJtu/dMy9Ee1UqIaI2TI8xsVVKxQ0REPANsBuyTX79ZZLsGNRo4NyIGdaRhMjOz6iqSrfd9YAhpGvMrgJ7A1aTJBxtWnrvpetIg4h7Ar4HPAZ/Is+1eSuog8XfSAN0bSVNmfIM0zungiHiqDqWbma3yitzWOwQYDMyEZVO2tzW5X6PYH3ghIj4FkAfhbgfcFhE35C7mO+Vl/wSeJs31tJukbwAnACfVpXIzs1Vckdtz70Tq0hew7IqkK2gC9pN0tqS9I+LVFtaZHhEv5oT1p1g+/XwpaWIFksZKmiFpxpJFjt0zM6uWIo3T9ZIuAdaTdCzwR9ItsYYWEU+QIomagB9LOq2F1SolTTTf5/iIGBIRQ3qs26OzSzYzs6zibb2IOFfSx4HXSM+dTouIu6teWQdJ2gT4Z0RcLel1Uojtv+pblZmZFVHkmRO5MWr4BqmZgcA5kpaSuo4fBxxf35LMzKyIVhMiJC2i5ZBUACLifdUqqitwQoSZWft1OCEiItbNOzoD+D9SV2yRxgp1hd56ZmbWRRXJ1nsoInavtKy7kHQSMD4i3mxrvd79e8dW47Zqc19OdTAzW1HRK6civfWWSBotqYek1SSNBrpzP+qTqOM09GZmVqxxOoKUrPD3/PPZvKxmJK0t6XZJj0iaK+kwSQvyGKZp+ec9lzG5QT1H0nRJcyR9JS8fLmmSpBskPSZpopITgU2A+yTdV8vvaGZmyxXpSr6ANNlgPbWU9nA28FpOdDgK+DlwYLPtvgS8GhG7SloTmCqpNNB2MDAAeAGYCuwVEedLOhkYERELq/+1zMysJUWy9TYEjuW907R/sXplvUcTcK6ks0nxQw9IArg2f34tcF4L2/0HsKOkUfl9H2Br4B1gWkQ8DyBpNun7TWmrCEljgbEAPfv27Mj3MTOzNhQZ53QL8AApGaIuz5oi4glJuwCfJKU9lK5+yntztNSzQ8AJEXHnCgtTrl55OsQSil1FjgfGQ+oQUfgLmJlZuxRpnNaKiP9X9Ura0EraA8BhwFn5959b2PRO4DhJ90bEu5I+AvytwuEWkbrK+7aemVmdFGmcbpP0yYj4fdWraV1LaQ83AGtKeojUseNwAEkjgSERcRpwGel23Uyl+4D/AA6ucKzxwB8kvRgRI6rxZczMrG1FxjktAtYmPad5h+XTtNc1IULSAlIjVJcrHCdEmJm1X4cTIkpKSRFmZma1UqS3XimyqH9EnClpM2DjiJhW9eraEBH9WlouqR+pR98OK7PfogkR816ex8AJA1v93OkQZmYrr8gg3AuBPVg+8PZ14FdVq6iOJPXACRFmZnVXpHHaPSK+DiwGiIhXgDWqWlUnkbSlpFmSvinpgrLlt+Xu5Eh6XdIZuWPFqTghwsys7oo0Tu/mK4rSNO0bkmaKbWiStgF+CxxD6qXXmrWBuRGxe0ScQUqMGOGeemZm9VOkcTofuAn4gKQfklIUflTVqjpuQ9Lg4SMjYnaFdZeQGrGKJI2VNEPSjCWLunP2rZlZfRXprTdR0sPAvnnRwRHxl+qW1WGvAs8BewHzgH+zYkPcq+z14ogo1NI4IcLMrDYKTdNO6iBQurXXu3rldJp3SINt78yJEguAr0laDfgQsFsb2zohwsyszire1pN0GjABWB/YALhC0nerXVhHRcQbpJTy/wT6AvPJAbLAzDY2LSVEuEOEmVmdFEmI+AswOCIW5/e9gZkRsV0N6mtYTogwM2u/zpwJdwErPqNZE3hqJesyMzOrqMgzp7eBeZLuJj1z+jgwRdL5ABFxYhXra7daZe45IcLMrHqKNE435Z+SSdUpxczMLCnSlXxCfs60eUQ8XoOaCpO0NnA9sCmpN+GZZZ/1JjWqv42ISyUdCZxISrd4CPgacCjw0Yg4WdI3gG9ExJaSPgxMiIihtf1GZmYGxXrrfRqYDdyR3w+SdGu1Cytof+CFiNgpB73ekZevA/wOuCY3TNuRJiTcKyIGkQbejgYmA3vnbfYGXpb0IWAoafZfMzOrgyIdIsaRxgX9CyAnLvSvYk3t0QTsJ+lsSXtHxKt5+S3AFRFxVX6/L7ALMF3S7Px+y4j4P2AdSesCmwHXAMNIDdV7GicnRJiZ1UaRxunfZX/0SxoiHSEiniA1Ok3Aj/OYLICpwAF5ug9IEyROiIhB+WebiBiXP/szKX/vcVKDtDcphX1qC8cbHxFDImJIj3V7VO17mZmt6oo0TnMlHQH0kLS1pF8Cf6pyXYVI2gR4MyKuJg2u3Tl/dBrwMmm6D4B7gFGSNsrbrS9pi/zZZOCU/HsWMAJ4u4UG2czMaqRI43QCMIDUpfxa4DXSnEeNYCAwLd+qOxX4QdlnJwG9JP0kIh4FvgvcJWkOcDewcV7vAdItvck5Y+85UritmZnVScWECGuZEyLMzNqvaEJEq13JJf2ONp4tRcTIlazNzMysTW2Nczo3//4M8EHg6vz+cFKkUcOT9HpErJNfDwB+yfIxUVcDp0fEUklHA+cAfyNFNV0SEee1te9KCRH11jT/2c7f6Tg/hjOz2mi1cYqI+wEknRkRw8o++p2kyVWvrBPlAbm3AsdFxF2S1iJNMPgNoNQIXRcRx0vqCzwu6YaIeK5OJZuZrdKKdIjYUNKWpTeS+pNmmu1KjgCmRsRdABHxJnA88M3mK0bEy8BfWd5hwszMaqxItt5/ApMkPZ3f9wO+UrWKqmMA8HD5goh4SlJvSeuVL5e0OenW3pwa1mdmZmWKZOvdIWlrYNu86LGIeLu6ZXU60XLnDpW9PkzSCGAb4NjS/FUrrCyNBcYC9Ozbsxp1mpkZxW7rQUphGADsRPojflT1SqqKecAKXRfzrcqFEfGvvOi6iBhASoj4qaQPNt+JEyLMzGqjSPDrr0k994YCu+afin3UG8xEYKik/WBZB4nzge83XzEi/gz8mtRZwszM6qDIM6chwPbRhUfrRsRbkkYCv5R0IfAh4AcRMbGVTc4GZkr6UUQsqlmhZmYGFEiIkPS/wIkR8WJtSqo+SQcDPwNGRMQzK7MPJ0SYmbVfhxMiymwAPCppGilfD+jaCRERcTNwc73rMDOzlhVpnMZVu4jOJGlT4FfA9qQkiN8D/wWMYsVxTTsCO0fEbEmTSOOa3sqf/UdEvNTWcdqbENE0pqnwumZmq7oiXcnvr0UhnSHP33QjcFFEHCSpBzAe+ElEfIPUMQJJA4Fb8sSJJaMjwvfpzMwaQFvBr4tofWxQRMT7qlbVytsHWBwRVwBExBJJ/wk8I+nUiHg9r3c4afoPMzNrQG1l661by0I6SUtJEK9JWgBsBZSulA4DDmq27RWSlpAy937QlXsnmpl1dUUH4XYVFZMgJO1Omj13btnnoyNiIGkA7t7AF1rcuTRW0gxJM5YsWtKJZZuZWbnu1ji1lATxPuADwON50edpdksvIv6Wfy8CrgF2a2nnTogwM6uN7tY43QOsVYpXyh0ifgpckAfirgZ8FvhNaQNJq0vaIL/uCRwIzH3Pns3MrGa6VeOUnxMdAoyS9CTwMrA0In6YVxkGPB8RT5dttiZwp6Q5pGdSfwMurWHZZmbWTMWEiK5M0p6kW3ifiYiHK63fHk6IMDNrv85MiOiyIuJPwBb1rsPMzNqn2zVObSREDAPOAtYA3gG+GRH35m0mUeWEiNY4OcLM7L261TOnsoSImyNia2BroDfwE2Ah8OncZXwMaVqMcqMjYlD+abNhMjOz6upWjRMtJESQppk/CngyIl5IDJXFAAANGklEQVTI680Deklasz5lmplZW7pb49RiQgSwgJQQUXIoMKvZdPNXSJot6Xv5CszMzOqkuzVORRIiBpAmE/xK2edOiDAzayDdrXFqMyEid5a4CTgqIp4qreOECDOzxtLdGqdWEyJIg21vB74dEVNLGzghwsys8XSrxqlCQsTxpOdO38vPlmZL2ggnRJiZNRwnRKwkJ0SYmbWfEyJwQoSZWVfVrW7rlZM0SdKQ/Pr3ktbrzP2XEiJKP2Zm1nm69ZVTSUR8st41mJlZcQ115SSpn6THJF0maa6kiZL2kzRV0pOSdpO0tqTLJU2XNEvSQXnb3pJ+I2mOpOtIsUWl/S4o65F3s6SHJc2TNLZsndcl/VDSI5IelPSBmp8AMzMDGqxxyrYCfgHsCGwLHAEMBU4BvgOcCtwbEbsCI4BzJK0NHEeafn1H4IfALq3s/4sRsQtpPNSJkvrm5WsDD0bETsBk4NhqfDkzM6usEW/rzY+IJgBJ84B7IiIkNQH9gE2BkZJOyev3AjYnpY6fDxARc3LX8JacKOmQ/HozUjjsy6Sk8tvy8oeBjzffMF9pjQXo2bdnR76jmZm1oREbp/K8u6Vl75eS6l0CHBoRj5dvlOPw2uwXL2k4sB+wR0S8mafK6JU/fjeW96tfQgvnJiLGA+MBevfv3X374JuZ1Vkj3tar5E7ghFI4q6TBeflkYHRetgPptmBzfYBXcsO0LfDRGtRrZmbt1BUbpzOBnsAcSXPze4CLgHXy7bz/Bqa1sO0dwOp5nTOBB2tQr5mZtVO3ToioJidEmJm1X9GEiK545WRmZt1cI3aIqKvcaeKUiDiwrfVKCRG10jT/2Zodq03jXq13BWa2CvCVk5mZNZxV7sopD9i9njReqgepY8SrwM+BhcDM+lVnZmawCjZOwP7ACxHxKQBJfUiTC+4D/BW4ro61mZkZq+ZtvSZgP0lnS9ob6E9KpXgyD8K9urUNJY2VNEPSjCWLltSqXjOzVc4q1zhFxBOk3L0m4MfASCokS5RtOz4ihkTEkB7r9qhilWZmq7ZV7raepE2Af0bE1ZJeB74K9Jf04Yh4Cji8vhWamdkq1zgBA0lJ5kuBd0lp5hsAt0taCEwBdqhjfWZmqzwnRKwkJ0SYmbWfEyLMzKzL6na39XIPvItJt+z2iIi3qnGcogkRTWOaqnF4M7NurTteOY0Gzo2IQeUNkyR3rzMz6yKq3jhJ6ifpMUmXSZoraaKk/SRNlfSkpN0krS3pcknTJc2SdFDZtg9Impl/9szLh0uaJOmGvO+JSr4MfA44LS8bLuk+SdeQuo4j6UhJ0yTNlnRJqdGSdIykJyTdL+lSSRdU+9yYmVnLanVbbyvgs6QpzqcDRwBDSWOMvgM8CtwbEV+UtB4wTdIfgZeAj0fEYklbA9cCpQdpg4EBwAvAVGCviLhM0lDgtoi4IYe47gbsEBHzJW0HHJbXfVfShcBoSXcDp5PGP70K3AfMqvI5MTOzVtSqcZofEaUrl3nAPRERkpqAfqScu5GSTsnr9wI2JzU8F0gaRJo6/SNl+5wWEc/nfc7O+5nSwrGnRcT8/HpfUgM0PU+k25vUAO4OTIqIf+T9XdfsWOTlY0kNLD379mz/WTAzs0Jq1Ti9XfZ6adn7pbmGJcChEfF4+UaSxgF/B3Yi3YJc3Mo+l9D6d3mjfJfAhIj4drPjHEyBlIiIGA+MB+jdv7f74JuZVUmjdIi4EzhB+XJG0uC8vA/wYkQsBb5AShHviHuAUZI2ysdZX9IWwEPAcEl9JfUk3YI0M7M6aZTG6UygJzBH0tz8HuBCYIykB0m32d5oZftCIuJR4LvAXZLmAHcDG0fEi8A44M/AH/G0GWZmdeWEiBZIOhoYEhHHt7aOEyLMzNrPCRFmZtZldbuEiM4QEVcCV9a5DDOzVZavnMzMrOG4cTIzs4bjxsnMzBqOGyczM2s4bpzMzKzhuHEyM7OG48bJzMwajhMiVpKkRcDjFVe0DYCF9S6iwfkcFePzVFlXOEdbRMSGlVbyINyV93iRCI5VnaQZPk9t8zkqxuepsu50jnxbz8zMGo4bJzMzazhunFbe+HoX0EX4PFXmc1SMz1Nl3eYcuUOEmZk1HF85mZlZw3HjVIGk/SU9Lumvkr7VwudrSrouf/6QpH61r7L+CpynkyU9KmmOpHskbVGPOuup0jkqW2+UpJDULXpdtVeR8yTpc/m/p3mSrql1jfVW4N/b5pLukzQr/5v7ZD3q7JCI8E8rP0AP4ClgS2AN4BFg+2brfA24OL/+PHBdvetu0PM0Algrvz5uVTtPRc5RXm9dYDLwIGk25rrX3mjnCdgamAW8P7/fqN51N+A5Gg8cl19vDyyod93t/fGVU9t2A/4aEU9HxDvAb4CDmq1zEDAhv74B2FeSalhjI6h4niLivoh4M799ENi0xjXWW5H/lgDOBH4CLK5lcQ2kyHk6FvhVRLwCEBEv1bjGeityjgJ4X37dB3ihhvV1CjdObfsQ8FzZ++fzshbXiYh/A68CfWtSXeMocp7KfQn4Q1UrajwVz5GkwcBmEXFbLQtrMEX+W/oI8BFJUyU9KGn/mlXXGIqco3HAkZKeB34PnFCb0jqPEyLa1tIVUPPujUXW6e4KnwNJRwJDgI9VtaLG0+Y5krQacB5wdK0KalBF/ltanXRrbzjpCvwBSTtExL+qXFujKHKODgeujIifStoD+HU+R0urX17n8JVT254HNit7vynvvTxeto6k1UmX0P+sSXWNo8h5QtJ+wKnAyIh4u0a1NYpK52hdYAdgkqQFwEeBW1fBThFF/83dEhHvRsR8Usbl1jWqrxEUOUdfAq4HiIg/A71IuXtdhhuntk0HtpbUX9IapA4PtzZb51ZgTH49Crg38lPIVUjF85RvWV1CaphWtWcEUOEcRcSrEbFBRPSLiH6k53IjI2JGfcqtmyL/5m4mdbBB0gak23xP17TK+ipyjp4F9gWQtB2pcfpHTavsIDdObcjPkI4H7gT+AlwfEfMknSFpZF7tf4C+kv4KnAy02kW4uyp4ns4B1gH+V9JsSc3/MXVrBc/RKq/geboTeFnSo8B9wDcj4uX6VFx7Bc/RfwHHSnoEuBY4uqv9T7MTIszMrOH4ysnMzBqOGyczM2s4bpzMzKzhuHEyM7OG48bJzMwajhsns0zSJEmfaLbsJEkXtnM/v5e0XoV1Xm9l+ZWSRrXjWF+VdFR76usoSUdL2qSWx7RVjxsns+WuJQ1oLPf5vLwiJatFxCdrFaUTERdHxFW1OBaApB6kiCU3TlZVbpzMlrsBOFDSmgB5bq5NgCmS1snzUM2U1CTpoNI6kv6Sr65mAptJWpCTC5B0s6SH87xDY8sPJumneX/3SNqweTGSdpF0f97+Tkkbt7DOOEmn5NeTJJ0naXKuaVdJN0p6UtIPyup9TNKEPM/PDZLWyp/tm+f/aZJ0edl5WCDpNElTSJltQ4CJeTB17/zZdElzJY0vpfLnes6WNE3SE5L2zst7SDo3H2eOpBOKfl9bdbhxMstyysA0oJRyXZqfK0hTWBwSETuTonN+WvojDGwDXBURgyPimWa7/WJE7EL6g36ipFJi/drAzLy/+4Hvl28kqSfwS2BU3v5y4IcFvsY7ETEMuBi4Bfg6KbPv6LJjbwOMj4gdgdeAr0nqBVwJHBYRA0nhqseV7XdxRAyNiKuBGcDoiBgUEW8BF0TErhGxA9AbOLBsu9UjYjfgpLLvOBboDwzONUzswPe1bsqNk9mKym/tld/SE/AjSXOAP5KmKPhA/uyZiHiwlf2dmCNkHiSFdZYCSpcC1+XXVwNDm223DalRuVvSbOC7FJsDqxQL1QTMi4gXc8ju0ywPC30uIqY2O/Y2wPyIeCIvnwAMK9vvdbRuhNIs0E3APsCAss9uzL8fBvrl1/uRJuj8N0BE/LMD39e6KU+ZYbaim4GfSdoZ6B0RM/Py0cCGwC4R8a5Scniv/NkbLe1I0nDSH+I9IuJNSZPKtmmupalY5kXEHu2sv5T2vrTsdel96d9782MFLU/DUK6179gLuJA0a+9zksax4ncs1bCk7PhqoYaV/b7WTfnKyaxMRLwOTCLdVirvCNEHeCk3TCOALQrsrg/wSm6YtiVNg1GyGinFHuAIYEqzbR8HNlSaiwdJPSUNoHNsXtov6RnSFOAxoJ+krfLyL5BuN7ZkEWmKD1jeEC2UtA7Lv1Nb7gK+qjTFDJLWp7rf17ogN05m73UtsBNp+uuSicAQSTNIV1GPFdjPHcDq+VbgmaRbeyVvAAMkPUy6FXZG+YZ5+u1RwNn5tuBsYM+V+zrv8RdgTK5rfeCiiFgMHENKjW8iXWld3Mr2VwIX59tvbwOXkm4j3kyazqGSy0hTOszJ3+2IKn9f64KcSm62Csk9EG/LnRfMGpavnMzMrOH4ysnMzBqOr5zMzKzhuHEyM7OG48bJzMwajhsnMzNrOG6czMys4bhxMjOzhvP/AeY4oCqISbotAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13515b205f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the variable importance\n",
    "def plot_feature_importances_mydata(model):\n",
    "    n_features = X_train.shape[1]\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), list(mydata))\n",
    "    plt.xlabel(\"Variable importance\")\n",
    "    plt.ylabel(\"Independent Variable\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13515d4e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances_mydata(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13515a7d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_feature_importances_mydata(forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1351590e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_feature_importances_mydata(gbrt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x135158d1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#Plot the heatmap on first layer weights for neural network\n",
    "plt.figure(figsize=(100, 20))\n",
    "plt.imshow(mlp.coefs_[0], interpolation='none', cmap='viridis')\n",
    "plt.yticks(range(20), list(mydata),fontsize = 50)\n",
    "plt.xlabel(\"Columns in weight matrix\", fontsize = 50)\n",
    "plt.ylabel(\"Input feature\", fontsize = 50)\n",
    "plt.colorbar().set_label('Importance',size=50)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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