Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "284a2ff8",
   "metadata": {},
   "source": [
    "<b>Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression.<b><tr><tr><hr>\n",
    "<b>Random Forest is intrinsically suited for multiclass problems, while SVM is intrinsically two-class. For multiclass problem you will need to reduce it into multiple binary classification problems. Random Forest works well with a mixture of numerical and categorical features.<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dbb0dad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_digits\n",
    "digits=load_digits()\n",
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b24657bd",
   "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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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(digits.data,digits.target)\n",
    "df['target'] = digits.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f81f001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9805555555555555"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.drop('target',axis=1)\n",
    "y=df.target\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(n_estimators=80)\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "addbfbe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "import seaborn as sn\n",
    "#plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc13d40b",
   "metadata": {},
   "source": [
    "# <center><font color=crimson>+++++++++++EXERCISE+++++++++++</font><center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd493168",
   "metadata": {},
   "source": [
    "<font color=green>\n",
    "Use famous iris flower dataset from sklearn.datasets to predict flower species using random forest classifier.<br>\n",
    "    1) Measure prediction score using default n_estimators (10)<br>\n",
    "    2) Now fine tune your model by changing number of trees in your classifer and tell me what best score you can get using how many trees\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8a285317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
       "0           5.1          3.5           1.4          0.2  Setosa\n",
       "1           4.9          3.0           1.4          0.2  Setosa\n",
       "2           4.7          3.2           1.3          0.2  Setosa\n",
       "3           4.6          3.1           1.5          0.2  Setosa\n",
       "4           5.0          3.6           1.4          0.2  Setosa"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris=pd.read_csv('https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2dd208f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944444444444445"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=iris.drop('variety',axis=1)\n",
    "y=iris.variety\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(n_estimators=45)\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5d1789d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4BvMZ8JaZLQIws2VE+TaEslrcMs5Y6r4WdA7/QqnrQO02O1GYN4v89EnUDdw92D1oP5omvhBJvGZZef/WhFhZPKdSohriMLPXzGwHM/uamW1rZheH+2ea2S5mtqmZfd/MVpQ7prh60CsldQkT9E7NOyWtRQwJOqvFLeOMpbV60fUn5wS9ZYnGcc/S9OpL5N+bTZeTLqDjESdQmD2D5WP/Fkm8Zll5/9aEWFk8p1KsKX1XEsZVNLbjqv7vIKk3UG9mr5drI6tFY5Pkq9m5NUUURWMXHPrtinNOr788W9VFY1fZdTezj4CP4ojpnHNtkcL1+n2xJOecA2IYfG07T9DOOYf3oJ1zLrU+vwg7RTxBO+cc3oN2zrnU8gTtEpXU9LfFNx+dSByA7iffk1gst4axRGbOtYonaOecw3vQzjmXWlbwHrRzzqVSIe8J2jnnUsmHOJxzLqV8iMM551IqhnXj2iwTNQkhu9WHs1C9eUVTnh/9aSxH3fEsR9w2mpueewMAM+P3Y6ZxyB//yeEjRjNywsxI40I2f1dJxsriObXECqp4K0XSVyWNljRV0hRJZ4T7L5I0V9KkcCtbAjCW5Uaj0JrlRnO5HNOmjGX/A49mzpx5vPivxzn2uFOYNu3NyI8ri7HaGqfcPGgzY1ljni4damnMFzhh5POcvfe2zPx4MeNnf8zFBw4gJ7Fg6Qp6de1Ysq3WzIPO4u8qyVjVdE5RLDf69vb7Vpxz+r/6VIvxJNUTLKs8UVJ3YAJwGHAUsMTMrqk0TiZ60FmtPpyV6s2S6NIhGE1rKhRoyhcQMOqVWQz55ubkFHzWyyXn1sri7yrJWFk8p1Ki6kGb2TwzmxjeXgxMAzZYnWNKLEFL+lNcbWe1+nCWqjfnC8ZRdz7LXjc+ya791mW79XsyZ+FSnpg+l2MaxnDqqBeZtWBJpDGz+LtKMlYWz6kUM1W8SRoiaXzRNmRVbUrqB+xAUIsV4DRJr0m6XVLPcscUV1XvR768C9hT0toAZnZIC6/zqt4ZVZMT9//42yxa3shZD49jxoeLWJkv0LGmhpGDd+fpf8/jor+/yh3HDGrvQ3VrqNZMsysucN0SSd2AB4Ezw6LZNwOXABb+/F/g/5Vqo6IELembQL/i55tZqR5xX2AqMCI8GAEDwwNqkVf1bp9YSZ5Tj0517Lxhb55/+0P6dO/E3pvXA7DXZusx9PFJkcbK4u8qyVhZPKdSChGuxSGpjiA5321mDwGY2QdFj/8ReKxcO2WHOCT9GbgG+Bawc7gNLPOygQQD4xcAn5rZM8AyM3vWzJ4tF7O1slp9OCvVmxd8toJFyxsBWN6Y58VZH9K/Vzf23KyecbODCmjj3/2YDXtF+xdTFn9XScbK4jmV0pohjlIkCbgNmGZmvyvaX1/0tMOByeWOqZIe9EBga2vFdA8zKwDXShoV/vygwlirJavVh7NSvfmjJSv4zeOvUDCjYLDfFuuz+6Z9GNC3F+c/NpG7xs+kS4dahu6/fWQxIZu/qyRjZfGcSonwUu9BwHHA65ImhfvOB46WNIBgVOEd4GflGio7zS5Msqeb2bzVPVpJBwGDzOz8Sl/jVb2rhy836tpbFNPspm5yUMU5Z+u3/tq+Vb0lPUqQ6bsDUyW9DHxerbulL/pWxcz+Cvy1DcfpnHOxinIMOiqlhh0qnkztnHPVrtzYcntoMUE3f5kn6UozO6f4MUlXApF/2eecc+0ljRdVV3Khyr6r2HdA1AfinHPtqWCqeEtKqTHok4FTgE0kvVb0UHfghbgPzDnnklSosuVGRwJ/Ay4Hzi3av9jMFsR6VM45l7Cq+pLQzD4FPpV0zpce6iapm5nNjvfQXLXoddqoxGItHX97YrG6Dix5Fa7LmKr6krDIX/nP5dqdgP7AG8A2MR6Xc84lqqp60M3MbLvi+5J2JBibds65zEjhJI7WX34dLkL99TgOxjnn2ku+kL7l8csmaElnFd3NATsC77XwdOecq0opLOpdUQ+6e9HtJoIx6QfjORznnGsfRpWNQUuqAbqb2a8SOh7nnGsXhRQOQrc46CKp1szyBEvnpV5Wqw9nsXrzLbdczaxZExg/Pr71fvP5Akf9+kpOu/xWAM69voHvnn4ph591Ob+96W4am/KRx/TPRfXEWpUCqnhLSqlR8ZfDn5MkPSLpOElHNG9JHFylcrkcN1x/GQd/91i2235PfvCDw9hqq808VsriNPvzn0dx6KGDY2sf4O7Hn6H/Bv+paXfQbgN55PoLeOh/z2X5ykYeejrai2H9c1E9sVpiqOItKZV8bdkJ+BjYCzgY+G74MzWyWn04i9WbAZ5//mUWLFgYW/vvf/wJYyZO5Yi9v/H5vt123AZJSGK7TTfig48/jTSmfy6qJ1ZL8qjirRRJX5U0WtJUSVMknRHu7yXpKUlvhj/LFo0tlaC/Es7gmAy8Hv6cEv4sW6rlSwf8LUlnSdqvNa+rVFarD2exenMSrrrjIc469hByuf/+h9TYlOfRMeMYtMNWkcb0z0X1xGpJoRVbGU3AL81sa2BX4FRJWxMsmfG0mW0GPM0Xl9BYpVIJugboFm7di243by0KF/dvvv1T4MawjaGSWjyo4lLmhcLScsfu3H95dsJkeq3Vna032XCVj1824n522moTdtpqk4SPzKVdVAnazOaZ2cTw9mJgGrABcCjQED6tATis3DGVmsUxz8wuLtdAC+qKbg8B9jWzDyVdA7wIXLGqF3lV7/aJlYaKylGZNH0mz4x/nedemcqKlY0sXbac8274E5effjw3j/obnyxawm9/dWLkcf1zUT2xWtKasWVJQwhyW7PhYf768vP6ATsALwF9ikoHvg/0KRenVA+6LSPhOUk9Ja1DUPfwQwAzW0rQ/Y9UVqsPZ7F6c9zO+NEh/OPWS/j7TRdx1S9+zC7bbs7lpx/Pg0+/wAuTpnHlGYPJ5aK/Ysw/F9UTqyUFVb6Z2XAzG1i0rSo5dyO4ZuRMM1tU/FhYhLtsJ7RUD3rv1p5gkbWACQRJ3iTVm9m88IAj/wo0q9WHs1i9GaCh4QZ22+0b9O7dkxkzXuSSS66loeG+2OIBXDr8furX7clxF1wLwN5f/xonfT+6uhP+uaieWC2JcvqcpDqC5Hy3mT0U7v6gKBfWA/PLtlOuqneUJHUh6Oa/Xe65XtW7etTVtHpJl9W28KX/6qjExpcbrR5RVPV+aL1jKs45R7w/ssV4kkQwxrzAzM4s2n818LGZXRF+F9fLzM4uFSe5f1mAmX0GlE3OzjmXtIIi60EPAo4DXpc0Kdx3PsF3b/dLOhGYBRxVrqFEE7RzzqVVVH+ym9lztDyU26qhY0/QzjlH9a5m55xzmZfCmrGeoJ1zDih7CXd78ATtnHN4D9plVGM+8muPWpTk1LdFlyW3WE+PC55ILJZbNR+Dds65lErjhReeoJ1zDh/icM651PIhDuecS6m896Cdcy6dvAftnHMplcYEHf3CuO0kq9WHvXpzimPV1NLx2AvpNHgYnU64hLpBh37h4bq9jqHzGTdFGzPkn4voWSu2pGQiQWe1+rBXb055rHwTK+67muUNQ1necBG5ftuRq984iN2nH+rUJbpYRfxzEY/WLNiflEwk6KxWH/bqzVUQq3FF8DNXg2pqgtsSdXt8n5XPjoo2Vsg/F/GIsGhsZGJJ0JK+LqlHeLuzpGGSHpV0paS1oo6X1erDXr25CmJJdBp8EZ1PvY78O1MozJtJ7Q57k58xCZZ+Gm2skH8u4pFvxZaUuHrQtwOfhbevJyiBdWW4746WXuRVvV3VMWN5w0Usu+WX5Or7k+u7OTVb7EzTxKfb+8hcK0U5xCHpdknzJU0u2neRpLmSJoXbgeXaiWsWR87MmhdoGGhmO4a3nyuqMPBfvKp3+8TK4jklHYsVy8jPnk5uwy3J9fwKnX4aFq6v60Cnn1zO8hHnRRbKPxfxiHjo4k7gRuBPX9p/rZldU2kjcfWgJ0s6Ibz9qqSBAJI2BxqjDpbV6sNevTnlsTp3h46dg9u1ddT024bC+7NYdtMvWD78bJYPPxsaV0aanME/F3GJchaHmY0BFrT1mOLqQf8EuF7ShcBHwL8kvQu8Gz4WqaxWH/bqzemOpW5r0fGAEyGXA0TTG+MozHw1svZb4p+LeBRaMYFO0hBgSNGu4eEIQDmnSToeGA/80sw+KRknzqre4ReF/Qn+RzDHzD6o9LVe1du1N19utHpEUdX74o1+VHHO+e2su8vGk9QPeMzMtg3v9yHosBpwCVBvZiXXz431SkIzWwTE36Vwzrk2inv6XHEHVdIfgcfKvcYv9XbOOeK/AEVSvZnNC+8eDkwu9XzwBO2cc0DrxqDLkXQPsAfQW9IcYCiwh6QBBEMc7wA/K9eOJ2jnnCPaNTbM7OhV7L6tte14gnbOOdK5mp0naOecA/IprEroCdq5FiQ59W3xPy5LJE73fS5IJE418h60c86lVJRfEkbFE7RzzpHsQvyV8gTtnHP4EIdzzqWWf0nonHMplcYx6EyUvILsFrf04qAeq1m+UOAHF9/Oz2+4H4C5Hy7k2P+5k++efzNn3/p/NDZFX+sjS+9fOV40NiZZLW7pxUE9VrGR/xhP//p1Pr9/3YOjOXafXXj0f06mR5dOPPxctOuSZe39K6eAVbwlJRMJOqvFLb04qMdq9sGCRYx9fQZHfGt7AMyMcW/MYp+dtgTgu9/cltGvRLt+cpbev0qsSUVjT5f01TjaXpWsFrf04qAeq9nV9/2DM4/cE+WCJdcWLllG984dqa0J/gn36dmD+QsXRxYPsvX+VcJa8V9S4upBXwK8JGmspFMkrVvJi7xorHP/bcyrb9KzRxe23qi+vQ8l0/JYxVtS4prFMRPYCdgH+AEwTNIE4B7gITNb5f/qvWhs+8TK4jllKdakt+by7KQZPPf6TaxsbGLp8hVcde9TLF62gqZ8gdqaHB98soivrN09knjNsvL+VSqN86Dj6kGbmRXM7EkzOxFYH7gJ2J8geUcqq8UtvTioxwI4/Yg9ePLq0/jbFadwxZBD2XmLjbj8p4cycIuN+MeE6QA8+sJk9hgQ7ZdqWXn/KlUwq3hLSlw96C/UJjCzRuAR4BFJXaIOltXill4c1GOVcub39uCc4X/hD//3LFtsuB6Hh18gRiXr79+XRZl2Jd0OHAzML6pJ2Au4D+hHsGD/Ue1SNFbS5mbWpnfXi8a6NYmvZtc2URSNPWajwyvOOSNnPVwynqTdgSXAn4oS9FXAAjO7QtK5QE8zO6dUO7EMcbQ1OTvnXNKinMVhZmOABV/afSjQEN5uAA4r145f6u2cc0BTKwY5JA0BhhTtGh5OciilT1HR2PeBPuXieIJ2zjlo1fzm4hlnqxXLzCSVDZiJKwmdc66tEriS8ANJ9QDhz/nlXuAJ2jnnCC6fr3RbTY8Ag8Pbg4G/lHuBD3E45xzRLjcq6R5gD6C3pDnAUOAK4H5JJwKzgKPKteMJGqirSe5taMw3JRbLVY+kpr8tuiy5BYiSLLobhSgv4Tazo1t4aO/WtOMJ2jnnSOeC/Z6gnXMO2jK2HBtP0M45RzoXS/IE7ZxztG4edFI8QTvnHD4G7ZxzqZW39A1yZOZClaQqAt9yy9XMmjWB8eOTWavWq3p7rERj1dTS8dgL6TR4GJ1OuIS6QYd+4eG6vY6h8xk3RRevSPtX9V5zSl4lKsmKwH/+8ygOPXRw+SdGwKt6e6zEY+WbWHHf1SxvGMryhovI9duOXP3GQdw+/VCnyJdzD9pOQ1XvFC7Yn4kEnWRF4Oeff5kFCxbG0vaXeVVvj9UusRpXBD9zNaimJrgtUbfH91n57Kjo4hRJQ1Vva8WWlLiqeneQdLykfcL7x0i6UdKpkuqijpeGisBx8KreHqtdYkl0GnwRnU+9jvw7UyjMm0ntDnuTnzEJln4aXZwiafg3XMAq3pIS15eEd4Rtd5E0GOgGPERwmeMu/GfBkC8oXmNVNWuRy3WN6fCccy0yY3nDRdCxMx0PO41c382p2WJnVtx7ZXsfWazWpFkc25nZ1yTVAnOB9c0sL+ku4NWWXlQNVb2T5FW9PVa7xlqxjPzs6eQ23JJcz6/Q6adXBPvrOtDpJ5ezfMR5kYVKw7/hNWkWR05SB6A70AVYK9zfEYh8iCMNFYHj4FW9PVbisTp3h46dg9u1ddT024bC+7NYdtMvWD78bJYPPxsaV0aanCEd/4bTOIsjrh70bcB0oAa4ABglaSawK3Bv1MGSrAjc0HADu+32DXr37smMGS9yySXX0tBwXyyxvKq3x0o6lrqtRccDToRcDhBNb4yjMLPFP3ojk4qq3ilciyOWqt4AktYHMLP3JK0N7APMNrOXK3l9klW9fblRt6bI6nKjUVT13rH+WxXnnInznmtzvErElpnM7L2i2wuBB+KK5ZxzbRVlZ1XSO8BiIA80mdnA1WnHL/V2zjkgH/16dnua2UdtacATtHPOQaJXCFYqE1cSOudcW0U8i8OAJyVNCK/vWC3eg3bOOVrXgy6+qC40PLyOo9m3zGyupK8AT0mabmZjWntMnqCdc47WLdhffFFdC4/PDX/Ol/QwwRXUnqBXh099c2uKJKe+Lb65pcLW6RTVGLSkrkDOzBaHt/cDLl6dtjxBO+cckV7q3Qd4WBIEOXakmf19dRryBO2cc0RXk9DMZgLbR9GWJ2jnnAMshYsleYJ2zjnWrOVGnXOuqqRxsSRP0M45Rzp70Jm5kjATFZXbMVYWz8ljpTvOiqY8P/rTWI6641mOuG00Nz33BhD0ZH8/ZhqH/PGfHD5iNCMnzIw0bkvyhULFW1JiW260rVqz3Ggul2PalLHsf+DRzJkzjxf/9TjHHncK06a9GflxZTFWFs/JY7V/nHLzoM2MZY15unSopTFf4ISRz3P23tsy8+PFjJ/9MRcfOICcxIKlK+jVtWPJtjqfeE2bl/9cb+2tKs457y+clshyo5noQWeqonI7xMriOXms9MeRRJcOwShrU6FAU76AgFGvzGLINzcnF8wjLpuco2JmFW9JiS1BS9pY0q8kXS/pd5JOktQjjliZqqjcDrGyeE4eqzri5AvGUXc+y143Psmu/dZlu/V7MmfhUp6YPpdjGsZw6qgXmbVgSaQxW5LGqt6xJGhJpwO3AJ2AnQlqEX4VeFHSHiVeN0TSeEnjC4WlcRyacy5FanLi/h9/mydO3pfJ8xYy48NFrMwX6FhTw8jBu3PE9htx0d/jL7kFa1YP+qfAAWZ2KUGpq23M7AJgf+Dall5kZsPNbKCZDczlulYcLJMVlROMlcVz8ljVEwegR6c6dt6wN8+//SF9undi783rAdhrs/V4c/6iWGJ+WRq/JIxzDLp5Cl9HoBuAmc2myqt6ZzFWFs/JY6U/zoLPVrBoeSMAyxvzvDjrQ/r36saem9UzbnZQiGT8ux+zYa/KO2ttkcYhjrjmQY8Axkl6CdgNuBJA0rrAgqiDZaWicnvFyuI5eaz0x/loyQp+8/grFMwoGOy3xfrsvmkfBvTtxfmPTeSu8TPp0qGWoftHsqxFWWmc0RZnVe9tgK2AyWY2vbWvT7Kqt3MuekkuNxrFNLtuXfpXnHOWfPZ21Vf1ngJMiat955yLUlSr2UXJL/V2zjnSWTTWE7RzzgGFFC43mokrCZ1zrq2inActaX9Jb0iaIenc1T0m70E75xzRzeKQVAP8AdgXmEMwo+0RM5va2ra8B+2cc4C1YitjF2CGmc00s5XAvcChq3NMqe1BN62cu1rTWCQNCUuixyqpOB6rumJl8ZyyHKtYa3KOpCHAkKJdw4uOeQPg3aLH5gBfX51jymIPekj5p1RVHI9VXbGyeE5ZjrVaipelCLdY/oeSxQTtnHPtaS7B4nDN+ob7Ws0TtHPORWscsJmk/pI6AD8EHlmdhlI7Bt0GSY1dJTlG5rGqJ1YWzynLsSJnZk2STgOeAGqA28Mrq1sttSWvnHNuTedDHM45l1KeoJ1zLqUyk6CjurSygji3S5ovaXJcMYpifVXSaElTJU2RdEaMsTpJelnSq2GsYXHFCuPVSHpF0mMxx3lH0uuSJkkaH3OstSU9IGm6pGmSvhFTnC3C82neFkk6M6ZYvwg/D5Ml3SOpUxxxwlhnhHGmxHU+Vac115+ndSMYiH8L2BjoALwKbB1TrN2BHQnWuY77vOqBHcPb3YF/x3heArqFt+uAl4BdYzy3s4CRwGMxv4fvAL3j/l2FsRqAn4S3OwBrJxCzBngf2CiGtjcA3gY6h/fvB34c03lsC0wGuhBMXvgHsGkSv7c0b1npQUd2aWU5ZjaGGKrCtBBrnplNDG8vBqYR/KOJI5aZWXP55Lpwi+UbZEl9gYMIKu9kgqS1CP7nfRuAma00s4UJhN4beMvMZsXUfi3QWVItQfJ8r8zzV9dWwEtm9pmZNQHPAkfEFKtqZCVBr+rSylgSWXuR1A/YgaBnG1eMGkmTgPnAU2YWV6zrgLOBJNZ3NOBJSRPCy3Pj0h/4ELgjHLoZISmJYno/BO6Jo2EzmwtcA8wG5gGfmlk8hRaD3vNuktaR1AU4kC9e7LFGykqCzjRJ3YAHgTPNLLYSx2aWN7MBBFc+7SJp26hjSDoYmG9mE6JuuwXfMrMdgQOAUyXtHlOcWoKhr5vNbAdgKRDbdyEA4UUQhwCjYmq/J8Ffov2B9YGuko6NI5aZTSOoXfok8HdgEpCPI1Y1yUqCjuzSyrSRVEeQnO82s4eSiBn+aT4a2D+G5gcBh0h6h2Aoai9Jd8UQB/i8F4iZzQceJhgOi8McYE7RXx0PECTsOB0ATDSzD2Jqfx/gbTP70MwagYeAb8YUCzO7zcx2MrPdgU8IvnNZo2UlQUd2aWWaSBLBmOY0M/tdzLHWlbR2eLszwVq2rS72W46ZnWdmfc2sH8Hv6Z9mFkuvTFJXSd2bbwP7EfwpHTkzex94V9IW4a69gVav/9tKRxPT8EZoNrCrpC7hZ3Fvgu9BYiHpK+HPDQnGn0fGFataZOJSb4vw0spyJN0D7AH0ljQHGGpmt8URi6C3eRzwejg2DHC+mT0eQ6x6oCFcbDwH3G9msU6BS0Af4OEgt1ALjDSzv8cY7+fA3WEnYSZwQlyBwv/h7Av8LK4YZvaSpAeAiUAT8ArxXob9oKR1gEbg1IS+ZE01v9TbOedSKitDHM45lzmeoJ1zLqU8QTvnXEp5gnbOuZTyBO2ccynlCdrFQlI+XGltsqRR4eW7q9vWnZKODG+PkLR1iefuIanVF1OEq971Xt1jdC4OnqBdXJaZ2QAz2xZYCZxU/GC4+E6rmdlPzKzUBSB7EOPVbs4lyRO0S8JYYNOwdztW0iPA1HBxpqsljZP0mqSfQXAFpaQbw/W9/wF8pbkhSc9IGhje3l/SxHAN66fDBaVOAn4R9t53C6+QfDCMMU7SoPC160h6Mlx7eATBcqvOpUomriR06RX2lA8gWAAHgvUptjWzt8PV5T41s50ldQSel/Qkwap9WwBbE1wNOBW4/Uvtrgv8Edg9bKuXmS2QdAuwxMyuCZ83ErjWzJ4LLyF+gmBpy6HAc2Z2saSDgBNjfSOcWw2eoF1cOhddnj6WYE2RbwIvm9nb4f79gK81jy8DawGbEayrfI+Z5YH3JP1zFe3vCoxpbsvMWlqjex9g6/Byb4Ae4eqAuxOuN2xmf5X0yeqdpnPx8QTt4rIsXLr0c2GSXFq8C/i5mT3xpecdGOFx5AgqwyxfxbE4l2o+Bu3a0xPAyeGSqkjaPFwEaAzwg3CMuh7YcxWvfRHYXVL/8LW9wv2LCcqDNXuSYBEjwucNCG+OAY4J9x0A9IzqpJyLiido155GEIwvT1RQhPdWgr/qHgbeDB/7E/CvL7/QzD4EhgAPSXoVuC986FHg8OYvCYHTgYHhl5BT+c9skmEECX4KwVDH7JjO0bnV5qvZOedcSnkP2jnnUsoTtHPOpZQnaOecSylP0M45l1KeoJ1zLqU8QTvnXEp5gnbOuZT6/wHbCqBHSwFaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "import seaborn as sn\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3914b1d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}