Phishing Website Detection_Models & Training.ipynb

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   "source": [
    "# **Phishing Website Detection by Machine Learning Techniques**\n",
    "\n",
    "*Final project of AI & Cybersecurity Course*"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## **1. Objective:**\n",
    "A phishing website is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages. The objective of this project is to train machine learning models and deep neural nets on the dataset created to predict phishing websites. Both phishing and benign URLs of websites are gathered to form a dataset and from them required URL and website content-based features are extracted. The performance level of each model is measures and compared.\n",
    "\n",
    "*This project is worked on Google Collaboratory.*<br>\n",
    "*The required packages for this notebook are imported when needed.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "NOq598WCqIol"
   },
   "source": [
    "## **2. Loading Data:**\n",
    "\n",
    "The features are extracted and store in the csv file. The working of this can be seen in the 'Phishing Website Detection_Feature Extraction.ipynb' file.\n",
    "\n",
    "The reulted csv file is uploaded to this notebook and stored in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "#importing basic packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
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    "colab_type": "code",
    "id": "fVPglpaf4REa",
    "outputId": "eef4a4ca-e12d-4cd3-e011-20376fc752a2"
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .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>Domain</th>\n",
       "      <th>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>DNS_Record</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
       "      <th>Web_Forwards</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>graphicriver.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ecnavi.jp</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hubpages.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>extratorrent.cc</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>icicibank.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "             Domain  Have_IP  Have_At  URL_Length  URL_Depth  Redirection  \\\n",
       "0  graphicriver.net        0        0           1          1            0   \n",
       "1         ecnavi.jp        0        0           1          1            1   \n",
       "2      hubpages.com        0        0           1          1            0   \n",
       "3   extratorrent.cc        0        0           1          3            0   \n",
       "4     icicibank.com        0        0           1          3            0   \n",
       "\n",
       "   https_Domain  TinyURL  Prefix/Suffix  DNS_Record  Web_Traffic  Domain_Age  \\\n",
       "0             0        0              0           0            1           1   \n",
       "1             0        0              0           0            1           1   \n",
       "2             0        0              0           0            1           0   \n",
       "3             0        0              0           0            1           0   \n",
       "4             0        0              0           0            1           0   \n",
       "\n",
       "   Domain_End  iFrame  Mouse_Over  Right_Click  Web_Forwards  Label  \n",
       "0           1       0           0            1             0      0  \n",
       "1           1       0           0            1             0      0  \n",
       "2           1       0           0            1             0      0  \n",
       "3           1       0           0            1             0      0  \n",
       "4           1       0           0            1             0      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "data0 = pd.read_csv(r\"E:\\Phishing-Website-Detection-by-Machine-Learning-Techniques-master\\Phishing-Website-Detection-by-Machine-Learning-Techniques-master\\DataFiles\\5.urldata.csv\")\n",
    "data0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lMXihulvMcAj"
   },
   "source": [
    "## **3. Familiarizing with Data**\n",
    "In this step, few dataframe methods are used to look into the data and its features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
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    "colab_type": "code",
    "id": "4hARIXyGKorc",
    "outputId": "bc223e7f-4529-4ebe-e7d0-7eef13e691c0"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the shape of the dataset\n",
    "data0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "Q3uBm9ObMyaG",
    "outputId": "551b4c2e-8b33-4a6a-c3e9-f168066915de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Domain', 'Have_IP', 'Have_At', 'URL_Length', 'URL_Depth',\n",
       "       'Redirection', 'https_Domain', 'TinyURL', 'Prefix/Suffix', 'DNS_Record',\n",
       "       'Web_Traffic', 'Domain_Age', 'Domain_End', 'iFrame', 'Mouse_Over',\n",
       "       'Right_Click', 'Web_Forwards', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing the features of the dataset\n",
    "data0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "colab_type": "code",
    "id": "gBMqupCMM74V",
    "outputId": "0e530eb9-699d-4ece-800d-8e853fc9d6ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Domain         10000 non-null  object\n",
      " 1   Have_IP        10000 non-null  int64 \n",
      " 2   Have_At        10000 non-null  int64 \n",
      " 3   URL_Length     10000 non-null  int64 \n",
      " 4   URL_Depth      10000 non-null  int64 \n",
      " 5   Redirection    10000 non-null  int64 \n",
      " 6   https_Domain   10000 non-null  int64 \n",
      " 7   TinyURL        10000 non-null  int64 \n",
      " 8   Prefix/Suffix  10000 non-null  int64 \n",
      " 9   DNS_Record     10000 non-null  int64 \n",
      " 10  Web_Traffic    10000 non-null  int64 \n",
      " 11  Domain_Age     10000 non-null  int64 \n",
      " 12  Domain_End     10000 non-null  int64 \n",
      " 13  iFrame         10000 non-null  int64 \n",
      " 14  Mouse_Over     10000 non-null  int64 \n",
      " 15  Right_Click    10000 non-null  int64 \n",
      " 16  Web_Forwards   10000 non-null  int64 \n",
      " 17  Label          10000 non-null  int64 \n",
      "dtypes: int64(17), object(1)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset\n",
    "data0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CfrxhE3pNfiw"
   },
   "source": [
    "## **4. Visualizing the data**\n",
    "Few plots and graphs are displayed to find how the data is distributed and the how features are related to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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WmM6ePavx48erdu3astlsuuaaa/Tvf/871/efa665RrfffrtWrVql5s2by8/PT6+//rruuusuNWvWzKHvHXfcIQ8PD33yySdW25YtW+Th4aHPPvtMknT06FE9/fTTatSokSpVqqSAgAB17txZ33zzjcO21q5dKw8PDy1atEgjR47UVVddpYoVKyotLU2StHTpUt1www3y9fXVDTfckOcpvJK0aNEiRUVFqXLlygoICFCjRo00Y8YMp9+/8oICG0pddna2/ve//zn8nTx50lp+9uxZxcXFKTg4WJMnT1aPHj0kSUuWLNHp06c1YMAAzZw5U3FxcZo5c6Z69+6d52t07txZ4eHhmjRpkq655hoNHDhQ8+bNU6dOndS8eXNNnDhRlStXVu/evXXw4EFr3dOnT6tdu3Z699131bt3b73yyiu65ZZbNGLECA0ZMqT43yAATrvmmmsUHR2t//73v1bbZ599puPHj+c6HcEYo3/84x+aNm2aOnXqpKlTp6pu3boaOnToFX/mH3/8cb322mvq0aOHXn31VT399NPy8/PTvn37rD5r1qxR27ZtlZaWptGjR+vFF1/UsWPH1KFDB23durXAr/Xpp59KUp65UJIqVKigBx54QH///bc2btwob29v3XnnnVq6dKkyMzMd+i5dulQZGRnWe5STk6N//OMfmjx5su644w7NnDlT3bt317Rp03Tffffleq01a9Zo8ODBuu+++zRjxgwupAuUIPvBzCpVquTb59SpU+rQoYM+//xz/fOf/9Szzz6rTZs25Tu79e+//1anTp3UpEkTTZkyRfXq1dPw4cOtH4L169fXuHHjJEn9+/fX//3f/+n//u//1LZt2wKN+ZNPPpGfn5/uvvvuQkR6ad9++61atGihNWvWqF+/fpoxY4a6d+9u5cq8/Pjjj2rbtq0qV66stWvXKiQkpMjGA5QHrVu3VlZWlrZs2WK1bdy4Ua1atVKrVq10/Phx7d6922FZvXr1VK1aNb3wwgvq3bu3rrvuOk2dOlWDBg3S6tWr1bZt21zXYPz777/VpUsXRUVFadKkSapVq5YGDBigt99+u9Bjvtxv0r59+2rUqFFq1qyZpk2bpnbt2mnChAl5nta6f/9+3X///erYsaNmzJihpk2bqk2bNvrmm2+sgpcxRhs3bpSnp6e+/PJLa90vv/xSnp6eVrHxp59+0tKlS3X77bdr6tSpGjp0qHbt2qV27drp8OHDuV57/PjxWr58uZ5++mm9+OKL8vHxUWJionr06CEPDw9NmDBB3bt31yOPPKKvvvrKYd2kpCTdf//9qlKliiZOnKiXXnpJ7du3L/TB3nLNACVo7ty5RpI5ePCgMcaYdu3aGUm5/uLj440xxsTHxxtJ5plnnsm1rdOnT+dqmzBhgvHw8DC//PKL1Wbfxosvvmi1/f3338bPz894eHiYRYsWWe3fffedkWRGjx5ttY0fP974+/ub77//3uG1nnnmGePl5WUOHTp0JW8FgBJgzznbtm0zs2bNMpUrV7Zyxz333GNuvfVWY4wxERERpmvXrsYYY5YuXWokmeeff95hW3fffbfx8PAwP/zwgzHGmIMHDxpJZu7cuble9+I8EhgYaBISEvIdZ05OjrnuuutMXFycycnJsdpPnz5tIiMjTceOHQscc/fu3Y0k8/fff+fb58MPPzSSzCuvvGKMMWbVqlVGkvn0008d+nXp0sVce+211vP/+7//M56enubLL7906DdnzhwjyWzcuNFqk2Q8PT3Nnj17Cjx2AIVnz3Off/65+fPPP82vv/5q3n//fVOjRg1js9nMr7/+avVt166dadeunfV8ypQpRpJZunSp1XbmzBlTr149I8l88cUXDutKMu+8847VlpGRYUJDQ02PHj2stm3btuWbGy+nSpUqpkmTJgXuf3E8eeXltm3bmsqVKzt8NzTGOOTa0aNHG0nmzz//NPv27TNhYWHmpptuMkePHi10DACM2bNnj5Fkxo8fb4wxJisry/j7+5v58+cbY4wJCQkxs2fPNsYYk5aWZry8vEy/fv3Mzz//bLy8vMwLL7zgsL1du3aZChUqOLTbc9KUKVOstoyMDNO0aVMTHBxsMjMzCzzey/0m3blzp5Fk+vbt67De008/bSSZNWvWWG0RERFGklm5cqVDX3tuXLFihTHGmG+//dZIMvfcc49p0aKF1e8f//iHufHGG63n6enpJjs722FbBw8eNDabzYwbN85q++KLL4wkc+211+b6ndy0aVNTs2ZNc+zYMastMTHRSDIRERFW27/+9S8TEBBgzp49W5C3DXlgBhtK3TXXXKOkpCSHv2HDhjn0GTBgQK71LjyP/dSpU/rf//6nVq1ayRijHTt25Orft29f63FQUJDq1q0rf39/67okklS3bl0FBQXpp59+stqWLFmiNm3aqEqVKg5HNGJiYpSdna3169c7FT+AknHvvffqzJkzWrZsmU6cOKFly5bleXroihUr5OXlpX/+858O7U899ZSMMdZMjcIICgrSli1b8jzSKEk7d+7UgQMH9MADD+ivv/6y8sypU6d02223af369QU+Jf3EiROSpMqVK+fbx77MfhS1Q4cOql69ut577z2rz99//62kpCSHmWlLlixR/fr1Va9ePYd82KFDB0nKdVewdu3aqUGDBgUaNwDnxMTEqEaNGgoPD9fdd98tf39/ffLJJ6pVq1a+66xcuVJXXXWVw+nkvr6+6tevX579K1Wq5HCNIh8fH918880O35uckZaWdsncVVh//vmn1q9fr0cffVRXX321wzIPD49c/Xfv3q127drpmmuu0eeff37J2X8A8le/fn1Vq1bNurbaN998o1OnTll3CW3VqpU1Kyo5OVnZ2dlq3bq1PvzwQ+Xk5Ojee+91+J4RGhqq6667Ltf3jAoVKuixxx6znvv4+Oixxx7TkSNHtH379kKN+VK/SVesWCFJuc5keOqppyRJy5cvd2iPjIxUXFycQ9uNN96oSpUqWb8dv/zyS9WqVUu9e/fW119/rdOnT8sYow0bNlin+EuSzWazrqGWnZ2tv/76S5UqVVLdunVzXWpEOncd3gt/J//xxx/auXOn4uPjFRgYaLV37Ngx13e0oKAgnTp1yuGSKigcbnKAUufv76+YmJh8l1eoUCHPL4eHDh3SqFGj9Mknn+jvv/92WHb8+HGH576+vqpRo4ZDW2BgoGrVqpXrC1ZgYKDD9g4cOKBvv/021/p2R44cyXfsAMqOGjVqKCYmRgsXLtTp06eVnZ2d52lIv/zyi8LCwnL9yKtfv761vLAmTZqk+Ph4hYeHKyoqSl26dFHv3r117bXXSjqXZ6RzX4ryc/z48QL92LOP+8SJEwoKCsqzz8VFuAoVKqhHjx5auHChMjIyZLPZ9OGHHyorK8uhwHbgwAHt27evwPkwMjLysuMFUDRmz56t66+/XsePH9fbb7+t9evXX/YOyb/88otq166d67tQnTp18uyf1/emKlWq6Ntvv3Vu8P9fQECAlZ+Kgr3wd8MNNxSo/x133KGQkBCtWrVKlSpVKrJxAOWNh4eHWrVqZR0g3Lhxo4KDg63c0qpVK82aNUuSrEJb69atrevdXnfddXlu9+I7BIeFheW6+d31118v6dxp8i1btizwmC/1m/SXX36Rp6dnrtwYGhqqoKCgXN8N8/r+4+XlpejoaOt00C+//FJt2rRR69atlZ2drc2bNyskJERHjx51KLDl5ORoxowZevXVV3Xw4EFlZ2dby6pVq5brdS5+bfvY8npPLy7SPfHEE1q8eLE6d+6sq666SrGxsbr33nvVqVOnPN8X5EaBDWXehVV7u+zsbHXs2FFHjx7V8OHDVa9ePfn7++v333/Xww8/nGumx8V3Lr1cu7ngQuY5OTnq2LFjrll1dvYkDqDse+CBB9SvXz+lpKSoc+fO+RagCiKv2Q+SHL742N17771q06aNPvroIyUmJurll1/WxIkT9eGHH6pz585Wznr55ZfVtGnTPLdb0B979evX19KlS/Xtt9/me90j+4/hC49c9uzZU6+//ro+++wzde/eXYsXL1a9evXUpEkTq09OTo4aNWqkqVOn5rnd8PBwh+elcccsoLy6+eabrbuIdu/eXa1bt9YDDzyg/fv3F1mxqCDfm5xRr1497dy5U5mZmaVyx9MePXpo/vz5WrBggcOsGACF17p1a3366afatWuXdf01u1atWmno0KH6/ffftWHDBoWFhenaa69VTk6OdYH/vPJNaRe+8/vud7H8vv+0bt1aL7zwgtLT0/Xll1/q2WefVVBQkG644QZ9+eWX1vUeLyywvfjii3ruuef06KOPavz48apatao8PT01aNCgPM9ucOa7V3BwsHbu3KlVq1bps88+02effaa5c+eqd+/emj9//hVvtzyhwAaXtGvXLn3//feaP3++w4W8i2M6a+3atXXy5MlLzrID4BruvPNOPfbYY9q8ebPD6ZAXioiI0Oeff64TJ044zGL77rvvrOXS+QuHX3zB3fxmuNWsWVNPPPGEnnjiCR05ckTNmjXTCy+8oM6dO6t27dqSzs3ecDbX3H777ZowYYLeeeedPAts2dnZWrhwoapUqWJdQFeS2rZtq5o1a+q9995T69attWbNmlx32atdu7a++eYb3XbbbQX+kgmg5Hl5eWnChAm69dZbNWvWLD3zzDN59ouIiNDevXtljHH4TP/www9X/NrO5IY77rhDycnJ+uCDD3T//fdf8Xbs7LOEL7yY+qW8/PLLqlChgp544glVrlw5z8sIACiY1q1bS5I2bNigjRs3atCgQdayqKgo2Ww2rV27Vlu2bFGXLl0knfueYYxRZGRkgSYxHD58WKdOnXKYxfb9999LUpHeWCkiIkI5OTk6cOCAdUaDJKWmpurYsWPWd8PLadOmjTIzM/Xf//5Xv//+u1VIa9u2rVVgu/766x1urPL+++/r1ltv1VtvveWwrWPHjql69eoFGrt0/myJC+3fvz9Xm4+Pj+644w7dcccdysnJ0RNPPKHXX39dzz33XL6zm3Ee12CDS7If0bjwiKkxplhuIXzvvfcqOTlZq1atyrXs2LFjOnv2bJG/JoDiUalSJb322msaM2aM7rjjjjz7dOnSRdnZ2dapC3bTpk2Th4eHOnfuLOlcMax69eq5rsP46quvOjzPzs7Oddp6cHCwwsLCrFu7R0VFqXbt2po8ebLDHavs/vzzzwLH2KpVK8XExGju3LlatmxZruXPPvusvv/+ew0bNszhKKenp6fuvvtuffrpp/q///s/nT17NtedQe+99179/vvv+s9//pNru2fOnNGpU6cKPE4Axat9+/a6+eabNX36dKWnp+fZJy4uTr///rs++eQTqy09PT3Pz3hB2X/oXnzwoSAef/xx1axZU0899ZT1I/lCR44c0fPPP1/g7dWoUUNt27bV22+/rUOHDjksy2vWnYeHh9544w3dfffdio+Pd3hfABRO8+bN5evrqwULFuj33393mMFms9nUrFkzzZ49W6dOnbKKcXfddZe8vLw0duzYXJ9RY4z++usvh7azZ8/q9ddft55nZmbq9ddfV40aNRQVFVVksdgLgNOnT3dot8/o79q1a4G206JFC3l7e2vixImqWrWqGjZsKOlc4W3z5s1at26dw+w16dzv3ovfiyVLluj3338v0GvWrFlTTZs21fz58x2+jyYlJWnv3r0OfS9+fz09PdW4cWNJsr6z4tKYwQaXVK9ePdWuXVtPP/20fv/9dwUEBOiDDz7IdS22ojB06FB98sknuv322/Xwww8rKipKp06d0q5du/T+++/r559/LtDRAwBlw6Wucyadm0Fx66236tlnn9XPP/+sJk2aKDExUR9//LEGDRpkzTaTzt085aWXXlLfvn3VvHlzrV+/PtePwhMnTqhWrVq6++671aRJE1WqVEmff/65tm3bpilTpkg69wXmzTffVOfOndWwYUM98sgjuuqqq/T777/riy++UEBAgD799NMCx/jOO+/otttuU7du3fTAAw+oTZs2ysjI0Icffqi1a9fqvvvu09ChQ3Otd99992nmzJkaPXq0GjVq5HCUVpIeeughLV68WI8//ri++OIL3XLLLcrOztZ3332nxYsXa9WqVdYpagBK39ChQ3XPPfdo3rx5evzxx3Mtf+yxxzRr1izdf//9+te//qWaNWtqwYIF8vX1lXRls9Fq166toKAgzZkzR5UrV5a/v79atGhRoGsyVqlSRR999JG6dOmipk2b6sEHH7R+JH/99df673//q+jo6EKN55VXXlHr1q3VrFkz9e/fX5GRkfr555+1fPly7dy5M1d/T09Pvfvuu+revbvuvfderVixwrqRC4CC8/Hx0U033aQvv/xSNpstV8GrVatW1vcge4Gtdu3aev755zVixAj9/PPP6t69uypXrqyDBw/qo48+Uv/+/fX0009b2wgLC9PEiRP1888/6/rrr9d7772nnTt36o033sh1vTZnNGnSRPHx8XrjjTd07NgxtWvXTlu3btX8+fPVvXt33XrrrQXaTsWKFRUVFaXNmzfrjjvusHJs27ZtderUKZ06dSpXge3222/XuHHj9Mgjj6hVq1batWuXFixYYM3QLYgJEyaoa9euat26tR599FEdPXpUM2fOVMOGDR0O7Pbt21dHjx5Vhw4dVKtWLf3yyy+aOXOmmjZtmus7IfJRGrcuRfllv5X8wYMHjTHnboncsGHDfPvHx8cbf3//PJft3bvXxMTEmEqVKpnq1aubfv36mW+++SbX7dnz20Z+rx0REWG6du3q0HbixAkzYsQIU6dOHePj42OqV69uWrVqZSZPnlyoW0ADKFn2nLNt27ZL9rv4c3/ixAkzePBgExYWZry9vc11111nXn75ZZOTk+Ow3unTp02fPn1MYGCgqVy5srn33nvNkSNHjCQzevRoY8y5W8YPHTrUNGnSxFSuXNn4+/ubJk2amFdffTXXOHbs2GHuuusuU61aNWOz2UxERIS59957zerVqwsd+4kTJ8yYMWNMw4YNjZ+fn6lcubK55ZZbzLx583LFYZeTk2PCw8ONJPP888/n2SczM9NMnDjRNGzY0NhsNlOlShUTFRVlxo4da44fP271k2QSEhIKPW4AhXOpPJednW1q165tateubc6ePWvatWtn2rVr59Dnp59+Ml27djV+fn6mRo0a5qmnnjIffPCBkWQ2b95s9cvve1N8fLyJiIhwaPv4449NgwYNTIUKFXJ9LyuIw4cPm8GDB5vrr7/e+Pr6mooVK5qoqCjzwgsvOOSZi+M5ePBgnq+3e/duc+edd5qgoCDj6+tr6tata5577jlr+ejRo40k8+eff1ptp0+fNu3atTOVKlVyeB8AFNyIESOMJNOqVatcyz788EMjyVSuXNmcPXvWYdkHH3xgWrdubfz9/Y2/v7+pV6+eSUhIMPv377f62HPSV199ZaKjo42vr6+JiIgws2bNKvQ4L/eb1BhjsrKyzNixY01kZKTx9vY24eHhZsSIESY9Pd2hX16/JS80dOhQI8lMnDjRob1OnTpGkvnxxx8d2tPT081TTz1latasafz8/Mwtt9xikpOTc+W/L774wkgyS5YsyfN1P/jgA1O/fn1js9lMgwYNzIcffpgrf7///vsmNjbWBAcHGx8fH3P11Vebxx57zPzxxx+XfG9wnocxRXRVUgAAAAAub/r06Ro8eLB+++03XXXVVaU9HAAAXAIFNgAAAKCcOnPmjMP1GNPT03XjjTcqOzs7z+ugAQCAvHENNgAAXEB2dvZlb3ZQqVKlUr+FPQDXctddd+nqq69W06ZNdfz4cb377rv67rvvtGDBgiJ7jTNnzuS62cvFqlatKh8fnyJ7TQD4888/lZ2dne9yHx8fVa1atQRHBHdHgQ0AABfw66+/XvYi4aNHj9aYMWNKZkAA3EJcXJzefPNNLViwQNnZ2WrQoIEWLVqU6y7Cznjvvff0yCOPXLLPF198ofbt2xfZawLATTfdpF9++SXf5e3atdPatWtLbkBwe5wiCgCAC0hPT9eGDRsu2efaa68t1F2lAKAk/PHHH9qzZ88l+0RFRalKlSolNCIA5cHGjRt15syZfJdXqVIl191NAWdQYAMAAAAAAACc4FnaAwAAAAAAAABcWbm+BltOTo4OHz6sypUry8PDo7SHA+AyjDE6ceKEwsLC5OnJ8YELkc8A10I+yx/5DHA95LT8kdMA1+JMPivXBbbDhw8rPDy8tIcBoJB+/fVX1apVq7SHUaaQzwDXRD7LjXwGuC5yWm7kNMA1XUk+K9cFtsqVK0s698YFBATk2y8rK0uJiYmKjY2Vt7d3SQ2v2BGXayEuKS0tTeHh4dZnF+cVNJ9J/FtyNcTlOshnRYN8RlyuhrjIaZdCTnPPuNwxJom4JOfyWbkusNmn6AYEBFy2wFaxYkUFBAS43T8y4nIdxHUe0+tzK2g+k/i35GqIy3WQz4oG+Yy4XA1xnUdOy42c5p5xuWNMEnFd6EryGSfIAwAAAAAAAE6gwAYAAODi1q9frzvuuENhYWHy8PDQ0qVLHZYbYzRq1CjVrFlTfn5+iomJ0YEDBxz6HD16VL169VJAQICCgoLUp08fnTx50qHPt99+qzZt2sjX11fh4eGaNGlSrrEsWbJE9erVk6+vrxo1aqQVK1YUebwAAABlDQU2AAAAF3fq1Ck1adJEs2fPznP5pEmT9Morr2jOnDnasmWL/P39FRcXp/T0dKtPr169tGfPHiUlJWnZsmVav369+vfvby1PS0tTbGysIiIitH37dr388ssaM2aM3njjDavPpk2bdP/996tPnz7asWOHunfvru7du2v37t3FFzwAAEAZUK6vwQYAAOAOOnfurM6dO+e5zBij6dOna+TIkerWrZsk6Z133lFISIiWLl2qnj17at++fVq5cqW2bdum5s2bS5JmzpypLl26aPLkyQoLC9OCBQuUmZmpt99+Wz4+PmrYsKF27typqVOnWoW4GTNmqFOnTho6dKgkafz48UpKStKsWbM0Z86cEngnAAAASgcz2AAAANzYwYMHlZKSopiYGKstMDBQLVq0UHJysiQpOTlZQUFBVnFNkmJiYuTp6aktW7ZYfdq2bSsfHx+rT1xcnPbv36+///7b6nPh69j72F8HAADAXTGDDQAAwI2lpKRIkkJCQhzaQ0JCrGUpKSkKDg52WF6hQgVVrVrVoU9kZGSubdiXValSRSkpKZd8nbxkZGQoIyPDep6Wlibp3B2/srKyLhmbffnl+rka4nItxOV+sQPAlaDAVgg3jFmljOzzt2r9+aWupTgaAHDOhTmNfAagtEyYMEFjx47N1Z6YmKiKFSsWaBtJSUlFPawygbhcS3mO6/Tp0yUwkvKD72iAa6LABgAA4MZCQ0MlSampqapZs6bVnpqaqqZNm1p9jhw54rDe2bNndfToUWv90NBQpaamOvSxP79cH/vyvIwYMUJDhgyxnqelpSk8PFyxsbEKCAi4ZGxZWVlKSkpSx44d5e3tfcm+roS4XIs7xXXDmFXWY5un0fjmOQWKyz7zFADKMwpsAAAAbiwyMlKhoaFavXq1VVBLS0vTli1bNGDAAElSdHS0jh07pu3btysqKkqStGbNGuXk5KhFixZWn2effVZZWVnWj+2kpCTVrVtXVapUsfqsXr1agwYNsl4/KSlJ0dHR+Y7PZrPJZrPlavf29i5wsaIwfV0JcbkWd4jrwrN17AoSl6vHDQBFgZscAAAAuLiTJ09q586d2rlzp6RzNzbYuXOnDh06JA8PDw0aNEjPP/+8PvnkE+3atUu9e/dWWFiYunfvLkmqX7++OnXqpH79+mnr1q3auHGjBg4cqJ49eyosLEyS9MADD8jHx0d9+vTRnj179N5772nGjBkOs8/+9a9/aeXKlZoyZYq+++47jRkzRl999ZUGDhxY0m8JAABAiWIGGwAAgIv76quvdOutt1rP7UWv+Ph4zZs3T8OGDdO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      "text/plain": [
       "<Figure size 1500x1500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the data distribution\n",
    "data0.hist(bins = 50,figsize = (15,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 821
    },
    "colab_type": "code",
    "id": "jy9fjgj3M7zc",
    "outputId": "0292cc0a-8436-49d9-c724-ad9345f1b693"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sasye\\AppData\\Local\\Temp\\ipykernel_9600\\2629205811.py:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(data0.corr())\n"
     ]
    },
    {
     "data": {
      "image/png": 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VpEgRubm5OR0bEBCQ3PESpEGDBk73f/vtN2XMmFGFChWKdm5mazD6uOnTp2vw4MFq3bp1jF83s77BlB72Wps9e7YCAgIUEBAQ7dzM2hD8SS5cuKDs2bMbHSPBatasqT179mjKlCl69913dffuXXXr1k1BQUEaNGiQPv/8c6MjxkuLFi20ZcsWjR49WqVLl5Yk7dy5Uz179tTrr7/uaIJuZn///bc8PDycLliZ2datW1WrVi01b95cQUFBat++vY4cOaLg4GBt2rTJ1L3W8uXLp3r16mnkyJFO2z///HMtX75cx48fNyhZwty8eVPvvvuu9uzZo1u3bumVV17R5cuXVa5cOa1evTpanyuz2bJli6ZOnarTp0/rhx9+UPbs2TVnzhz5+vrqtddeMzpenPx3EZInMfsUTasaPHiwZs2apcGDB6tdu3b6/fff5efnp4ULF2rcuHHavn270RGBJEOxC3iGO3fuaOHChZoxY4a2b9+uUqVKqWHDhurZs6fR0WLNxcVFNpstWkP6Rx7tM+MbldatW8f62JkzZyZhkqT1tDebZvy6Pa5KlSpP3Gez2Uy/iuYjly9f1rBhwzR9+nTduXPH6DgJVqNGDc2aNUuvvPKK0/ZVq1apbdu2prog8Lg7d+6oR48emjFjhiIiIiRJKVKkUJs2bfT111+b/kO4VZ06dUojRozQwYMHFRYWphIlSqhXr14qUqSI0dESZPXq1WrYsKH8/f1VpkwZSdKuXbt04sQJLVmyxPQ9Krdu3aqQkBDH16x69epGR0qwJUuW6IMPPlCzZs00Z84cHTlyRH5+fpo4caJWr16t1atXGx0Rcr4Q/CxmuxD8OH9/f02dOlXVqlWTp6enDh48KD8/Px07dkzlypXT9evXjY4IJBmKXcAT7NixQ9OmTdMPP/ygnDlz6ujRo9qwYYMpGxSfO3cu1sfmypUrCZMA1nb9+nV98sknWrdunVKmTKnevXurY8eOGjhwoEaNGqWAgAB17dpV7733ntFRk9Q///zjNKXdLB48eKBt27apSJEiSpkypaO5ee7cuU1Z5CpRooTWr1+vDBkyqHjx4k8dkb1v375kTIa4+PPPP/Xtt9/q2LFjkqQCBQqoQ4cO8vb2NjgZYlK8eHF17dpVLVq0cCou7N+/X7Vq1TJ9A36rePxC8LNmq5j5gqKHh4eOHTumXLlyOX0/HjlyRKVLl1ZYWJjREYEkQ88uxMuzVhx7xIw9u0aPHq0ZM2bo5s2bev/997V582YVLVpUbm5ujjn8ZvN4AWvz5s0qX768UqRw/vGPjIxUcHCwqYtdVatW1dKlS5U+fXqn7f/++6/q169vmRFCeH717t1bwcHBatWqlX755Rd17dpVa9askYuLi3777TeVLVvW6IjJwoyFLklydXXVG2+8oaNHj8rX19fUV/MlqV69enJ3d5cU+6XozSimRufSw5Gh7u7uTgvNmFGOHDk0fPhwo2Mkut27d2vDhg26cuVKtNXjzDx9/fjx46pYsWK07enSpdONGzeSP1Ai6ty5s/z9/aO9v584caJOnjxpqsUuHu+xuX//fvXo0UM9e/Z0WvV09OjR0aYQm03BggW1ZcuWaO/vFy9erOLFixuUCkgeFLsQL7FZccxms5my2NWrVy/16tVLgwcPlqurq9FxEl2VKlV06dKlaCtX3bx5U1WqVDH11auNGzfq/v370bbfu3dPW7ZsMSBR4tq0aZNGjRqlo0ePSnr4BuZRHyGzadCggYKCguTl5RWt79p/manX2s8//6ygoCBVrVpVHTt2lJ+fn4oVK2aZD6oZM2ZUaGioMmXKpAwZMjz1avi1a9eSMVniKVy4sE6fPi1fX1+joyRYhgwZHFOgW7durRw5csS6/46ZpE+f/qnfizly5FCrVq00YMAAU57/jRs3tGvXrhiLQmZdXGD48OH68ssvlS9fPr388stOXz+zLzCTNWtWnTx5Mtoqflu3bjX9IjNLliyJsWF9+fLlNWLECFMVux4v/jRq1Ejjx493mhYcEBAgb29v9evXz9QXC/r376+WLVvqwoULioqK0tKlS3X8+HHNnj1bP/30k9HxgCRFsQvxYuUVx4YMGaKZM2dqzpw5ev/99/XBBx+ocOHCRsdKNE8arn316lVTTtORnPsuHDlyxGmKwIMHD7RmzRrTNwT//vvv1bp1azVo0MBRRN62bZuqVaumoKAgNW3a1OCEcZMuXTrH92G6dOkMTpN4Ll68qAIFCkh6uJBHqlSp1Lx5c4NTJZ6xY8fK09NTkkz1oSYuhg4dqh49emjIkCEqWbJktN+LMa3Q+7zq1q2bmjRpolSpUsnX1zfGCx1WEBQUpL59+6pVq1aORQV27dqlWbNm6csvv9Tff/+tUaNGyd3dXV988YXBaeNm5cqVatasmcLCwuTl5RWtKGTWYtc333yjGTNmqFWrVkZHSXTt2rVTly5dNGPGDNlsNl28eFHbt29Xjx49TL/q8NWrV2P8m+3l5aV//vnHgESJ49ChQzFe4PD19dWRI0cMSJR46tWrp5UrV2rw4MFKkyaN+vfvrxIlSmjlypWqUaOG0fGAJEXPLiSLIkWKaPXq1abqL7Fp0ybNmDFDixcvlr+/vw4fPqxNmzapQoUKRkeLl0ejZ5YvX66aNWs6prZIDwtCISEhypcvn9asWWNUxHh71HdBUoxN+D08PDRhwgR9+OGHyR0t0RQoUEAfffSRunbt6rR9zJgx+t///ucY7QVjubq66vLly8qcObMkydPTUyEhIZYYJSQ9LJ4MGTJEadKkeeKUaLN7fOTP44UFMy7ikTNnTvXp00e1a9eWr6+v9uzZ88Qppjlz5kzmdImnWrVqat++vRo3buy0fdGiRZo6darWr1+vOXPmaNiwYY6+V2aRN29e1a5dW8OHD1fq1KmNjpNosmXLps2bNytPnjxGR0l0drtdw4cPV2BgoGMxEnd3d0cR3cwKFy6sDh06qGPHjk7bJ0yYoMmTJ5u2MFSiRAkVLlxY06ZNc0x7vn//vtq2bavff/+dnoaASVHsQrJ4vCGi2dy6dUvz5s3TjBkztHfvXpUuXVrvvvuuunXrZnS0OHm0auGsWbPUuHFjeXh4OPalTJlSPj4+ateunSl77Zw7d052u11+fn7atWuXo9AgPTy3LFmymH5Kqru7uw4fPix/f3+n7SdPnlThwoV17949g5LhcS4uLipcuLCjABQSEqL8+fNH6xlk1jfObm5u+vPPP/Xyyy/L1dXVkiOFNm3a9NT9lSpVSqYkCffdd9+pU6dOioyMfOIxZizi/ZeHh4dCQkKiFU5OnDihokWL6s6dOzpz5owKFSpkupVQ06RJo0OHDpny/dPTjBw5UhcvXrTsCFHpYbHk5MmTCgsLU8GCBZU2bVqjIyXYjBkz1LFjR/Xs2VNVq1aVJK1fv16jR4/WuHHj1K5dO4MTxs+uXbtUp04d2e12R6/GkJAQ2Ww2rVy50jFi1Mz27Nnj1AajZMmSBicCkh7FLiQLMxe7Hnfo0CFNnz5d8+bN05UrV4yOEy+DBg1Sjx49TDtl8UXl7++vnj17qn379k7bp0yZotGjR+vEiRMGJUscixcv1qJFi3T+/PlofdfMVBgaNGhQrI4bMGBAEidJGnny5FHjxo31xhtvqEqVKvrxxx+VIUOGGI+NqUEzkt+tW7d07tw5BQQE6Ndff33iQitFixZN5mSJJ2/evGrQoIFGjBjhtL1379768ccfdfz4ce3Zs0f16tXThQsXDEoZPw0aNFCTJk2ijVozu6ioKL311lsKDQ1VwYIF5ebm5rTfTL0a/+v7779XgwYNLDUS73GTJ0/WsGHDdPHiRUkPp+wPHDjQtFNqH7l9+7bmzp3rtOpp06ZNTf9++c8//9T777+vbdu2ORZwunHjhsqXL68FCxYoR44cxgYEkhDFLiQLqxS7HomIiHC8MTPjFE0rO378uCZMmOC4elWgQAF17NhR+fPnNzhZwkyePFmfffaZPvzwQ5UvX17Sw55dQUFB+uabb6IVwcxk/Pjxjn473333nVq3bq1Tp05p9+7d+vTTTzVs2DCjI+L/W7ZsmTp06KArV644lmyPidlHCt24cUPTp093/B4pVKiQPvzwQ9P1lxs/frw++ugjpUqVKsZRvVaxYsUKNWrUSPnz59err74q6eEohmPHjmnx4sV6++23NXnyZJ04ccJ0q/xNnz5dgwcPVuvWrVWkSJFoRaG6desalCxhOnbsqGnTpqlKlSrRGtRL0syZMw1KlnCZM2fW3bt3VbduXTVv3lxvvvmm6UeXx+Tvv/+Wh4eH6UesRUREKH/+/Prpp58cPTetpGbNmrpx44ZmzZqlfPnySXr4Xrl169by8vIyZfsSILYodiFZWK3Y9TiznVvx4sVjbFBvs9mUKlUq+fv7q1WrVqpSpYoB6RJmyZIlatKkiUqVKuVYOnrHjh3avXu3FixYoIYNGxqcMGF+/PFHjR492qmQ17NnT9WrV8/gZAmTP39+DRgwQO+//77Tz1P//v117do1TZw40eiI+I9HzbKPHz/+xGmMZisMPbJnzx69+eab8vDwcExd2b17t+7evau1a9eqRIkSBieMvRQpUujixYuOqdxWnHb6yNmzZzV16lQdP35ckpQvXz61b98+2op4ZvO01SPNXFT29PTUggUL9NZbbxkdJdFFRkZqzZo1mj9/vpYvX67UqVOrUaNGatasmeNiFZ4v2bNn16+//mrJYpeHh4eCg4NVvHhxp+179+7V66+/brqp3UBcUOxCsjBbQSguzHZuffr00eTJk1WkSBGnD3IhISFq1aqVjhw5ovXr12vp0qWmK6Lkzp1bzZo10+DBg522DxgwQN9//71OnTplUDI8TerUqXX06FHlypVLWbJk0bp161S0aFGdOHFCZcuW1dWrV42OGGsZMmSIsZicLl065c2bVz169DD16kePN6h/tGCH1RrUv/766/L399f//vc/x7lFRkaqbdu2On36tDZv3mxwwth7URrUP83vv/9uqRWVrSJXrlz65ZdfTD/q+lnu3LmjH3/8UfPmzdOvv/6qHDlymO69SIkSJbR+/XplyJDhiRdMHzFT24HHDR8+XKGhoZo2bZrl/qblzZtX33//fbS+Y7t27VLTpk118uRJg5IBSc9aP80Anumff/5R9+7doy1/PXToUJ07d05r167VgAEDNGTIENMVuy5duhRjz4jmzZvr66+/NiARYiNr1qy6du2acuXKpZw5c2rHjh0qWrSozpw588Rpcs+rJzVbvnHjhvbu3au3335bixcvVp06dZI3WCKZMGGCevXqpTRp0qhq1aqWHCm0Z88ep0KX9HCE1Oeff65SpUoZmCzuvvzyS3Xq1EkdO3aUzWZzTPF7nBUa1P/XrVu3NH/+fE2bNk179+611LlZxcCBAzVgwADNnDnTsr2tpIcXc958801dv35d586dM+XKyfXq1XOs4F2/fn1jwySR3bt3a/369Vq7dq2KFCkSrU+XmXvIff311+rUqZMmTZrk+Bu2Z88edenSRaNGjTI4HZC0GNmFRHPv3j2lSpUqxn3z5s1TvXr1TN/kMSZmG9mVLl067d27N8ZV/UqWLKmbN2/q2LFjevXVV3Xr1i2DUsZP7dq11ahRI8fKk4/MnDlTCxYs0C+//GJQsvjJmDGjQkNDlSlTpieOGHrk2rVryZgscbVt21be3t4aMGCAJk2apJ49e6pChQras2ePGjRooOnTpxsdMdGMGTNGixcvVnBwsNFR4uVFaFD/8ssva86cOXrjjTectv/yyy9q0aKF/vrrL4OSxc+L0KD+kc2bN2v69OlasmSJXnnlFTVo0EANGzaMscj3vKtdu7bmz5/vmA48YsQIdejQwdFg+urVq3r99dd15MgRA1PGX/HixXXq1CnZ7Xb5+PhE60Vm1hFCjzwa0TV37lytX79e3t7eev/999WsWTPLj2Yzo/++b/wvs/WQ++97xtu3bysyMtJptHKKFCmUJk0aU79/BJ6FkV1IkKioKA0bNkxTpkzRX3/9pdDQUPn5+alfv37y8fFRmzZtJElNmzY1OCkeSZUqlYKDg6MVu4KDgx3FyqioqCcWLp9ndevWVa9evbR3716VLVtW0sOeXT/88IMGDRqkFStWOB37vBs7dqw8PT0d/35ascvMvvvuO0VFRUmSPv30U7300ksKDg5W3bp1Td14PyZvv/22hg4danSMePv666/VoUMHBQYGymaz6Z133onxODOPFHrvvffUpk0bjRo1ymkxiJ49e+r99983OF3ceXp6qnDhwpo5c6YqVKjgGKFhFZcvX1ZQUJCmT5+uf//9V40bN1Z4eLiWLVumggULGh0v3n755ReFh4c77g8fPlyNGzd2FLsiIyMd/cnMyKojhCSpSZMm+umnn5Q6dWo1btxY/fr1c/QRxfPJbMWsZ3nSKHPgRcPILiTI4MGDNWvWLA0ePFjt2rXT77//Lj8/Py1cuFDjxo3T9u3bjY6Y5Mw2smvo0KEaPny42rVr57javXv3bk2bNk1ffPGF+vbtq7Fjx2r16tVat26dwWnj5mmNfB9n5g/iMLdDhw6pRo0aunz5stFREsTKDerv37+vnj17asqUKYqMjJQkubm56eOPP9aIESMsVywyszp16mjz5s1666231KxZM9WsWVOurq5yc3PTwYMHTV3scnFx0eXLlx0/X/99r/HXX3/plVde4W/Zc6hZs2Zq1qyZZVZhfNbI8scxSgjA84RiFxLE399fU6dOVbVq1ZzeiB07dkzlypXT9evXjY6YKKw2RXPu3LmaOHGi06pVnTp1cozAu3v3rmN1RjwfnrSS2tWrV5UlSxbTf+C5d++eQkJCdOXKFccor0fMMAovtj777DMdO3bMEkt9W7VB/SN37txxNJLOnTu36fsKPXjwQGPHjtWiRYt0/vx53b9/32m/GT+kpkiRQp07d9bHH3+sPHnyOLZT7AISz6xZs2J9bMuWLZMwSdJavHjxE38/mn1a7SP37t2Ldm5eXl4GpQGSnjXfoSLZXLhwIdp0OOnhNLiIiAgDEiUeK0/RfHTV8Uk8PDySMU3SeFqB0oyedF0iPDxcKVOmTOY0iWvNmjVq0aKF/vnnn2j7zDYKr1u3bjFuv3nzpvbt26fQ0FBTreb3NL6+vrp48eIT95t1db+bN2/qwYMHypgxo4oUKeLYfu3aNaVIkcK0HwwGDRqkadOmqXv37vryyy/Vt29fnT17VsuWLVP//v2NjhcvW7du1fTp01WyZEkVKFBAH3zwgZo0aWJ0rERhs9mijaax0lR2KxZfpYfTS8eOHav58+crNDRU0sPV8Jo2baouXbpE601mBmYuYMXW+PHj1bdvX7Vq1UrLly9X69atderUKe3evVuffvqp0fES5Pbt2+rVq5cWLVoU4+rWZnqPBcQVxS4kSMGCBbVlyxblypXLafvixYtVvHhxg1IljqFDh2rWrFkaOXKk2rVr59heuHBhjRs3zlHsMqv79+/HOIrGrB9QpYd/sIcPH/7MAqWZjB8/XtLDDznTpk1T2rRpHfsePHigzZs3m77ZbadOndSoUSP1799fL7/8stFxEmT//v0xbvfy8lKNGjW0dOlS+fr6JnOqpOHj4/PUD99mfQPdpEkT1alTR5988onT9kWLFmnFihVavXq1QckSZu7cufrf//6nt956SwMHDtT777+v3LlzKyAgQDt27FDnzp2NjhhnZcuWVdmyZTVu3DgtXLhQM2bMULdu3RQVFaV169bJ29vb0ffQbOx2u1q1auWYNnvv3j116NDBMYr88X5eZmTF4uvdu3dVo0YNbd++XdWrV3cs0nH06FH16tVLK1as0Nq1a015Ie7ixYsaM2aM+vfvH63gf/PmTQ0dOlQ9evQw7d/wb7/9Vt99953ef/99BQUF6fPPP5efn5/69+9v2sLrI59//rk2bNigyZMn64MPPtCkSZN04cIFTZ06VSNGjDA6HpCkmMaIBFm+fLlatmypPn36aPDgwRo0aJCOHz+u2bNn66efflKNGjWMjhhvVp2ieeLECX344YfRVoOzwvLzVuwh96gwcu7cOeXIkcOp/0fKlCnl4+OjwYMHq0yZMkZFTDAvLy/t379fuXPnNjoK4uDgwYNO9yMiIrR//36NGTNGw4YNU4MGDQxKljAZM2bUtm3bVKBAAaftx44dU4UKFWK8Mm4GadKk0dGjR5UzZ05ly5ZNq1atUokSJXT69GkVL15cN2/eNDpiojh+/LimT5+uOXPm6MaNG6pRo4bT4iRm8azV4R4xa2Pt3Llza/z48Xrrrbfk6empAwcOOLbt2LFD8+bNMzpinA0YMEBBQUFauXKlAgICnPYdPHhQdevWVevWrTVw4EBjAiZAjx499O+//+q7776LcX+HDh2ULl06ffXVV8mcLHGkTp1aR48eVa5cuZQlSxatW7dORYsW1YkTJ1S2bFnT/t6XHl7Enj17tipXriwvLy/t27dP/v7+mjNnjubPn2/aCzhArNiBBNq8ebO9evXq9syZM9s9PDzsFSpUsP/yyy9Gx0qwVKlS2c+ePWu32+32tGnT2k+dOmW32+32w4cP29OkSWNktAQpX768vWLFivbVq1fb9+/fbz9w4IDTzcxy585t//XXX+12u/PX7OjRo/b06dMbGS3BKleubL927ZrRMZJE69at7dOmTTM6RqKaMWOG/c6dO0bHMMRPP/1kr1SpktEx4i116tT2kJCQaNtDQkLsHh4eBiRKHHnz5rXv2LHDbrfb7RUqVLAHBgba7Xa7fcGCBfbMmTMbGS1JREZG2n/88Ud7nTp1HNv++OMP+4MHDwxMlXTMdm6pU6e2nzt3zm632+1Zs2a1792712632+2nTp2ye3l5GRkt3vLmzWtfvHjxE/cvWrTInidPnmRMlHgKFSpk37JlyxP3b9u2zV6wYMFkTJS4fH197fv27bPb7XZ7yZIl7VOmTLHb7Xb7L7/8Ys+QIYOR0RIsTZo0jp+17Nmz23fu3Gm32+3206dPm/rzDBAbTGNEgr3++uumW7UvNqw6RfPAgQPau3ev6ae+xcTKPeQ2bNhgdIQkM3HiRDVq1EhbtmxRkSJFovU0MeP0qt69e6tLly5q1KiR2rRpo/LlyxsdKdnky5dPu3fvNjpGvJUuXVrfffedJkyY4LR9ypQpKlmypEGpEu6dd97R+vXrVaZMGXXq1EnNmzfX9OnTdf78eXXt2tXoeInO1dVV9evXV/369R3bChYsqAMHDphm9eS4MNu55ciRQ5cuXVLOnDmVO3durV27ViVKlNDu3btNu+LpuXPnVLp06SfuL1u2rM6fP5+MiRLPmTNnntrmIkeOHDp79mzyBUpkVatW1YoVK1S8eHG1bt1aXbt21eLFi7Vnzx7TjlJ+xM/Pz/H1y58/vxYtWqTSpUtr5cqVpl01GYgtil1IkLZt26p58+aqXLmy0VESXf/+/dWyZUtduHBBUVFRWrp0qdMUTbMqWLBgjI3ArcCqBUpJatiwoUqXLq1evXo5bR85cqR2796tH374waBkCTd//nxHH5ONGzc69YGy2WymLHZduHBBK1euVFBQkCpXriw/Pz+1bt1aLVu2VNasWY2Olyj+/fdfp/t2u12XLl3SwIEDnVbGM5uhQ4eqevXqOnjwoKpVqyZJWr9+vXbv3q21a9canC7+Hu/N8t577ylXrlwKDg5Wnjx5VKdOHQOTJR+7hTt3mO3crFh89fLy0pUrV+Tt7R3j/suXL5u2h5yHh4fOnj37xILX2bNnTb240XfffefoYfvpp5/qpZdeUnBwsOrWrav27dsbnC5hWrdurYMHD6pSpUrq3bu36tSpo4kTJyoiIkJjxowxOh6QtAweWQaTq1u3rt3d3d2eI0cOe48ePez79+83OlKisuIUzfXr19vLlStn37Bhg/2ff/6x37x50+lmZsuWLbOnS5fOPmLECHvq1KntX3/9tb1t27b2lClT2teuXWt0vATJlCnTE6dWZcmSxYBEiefll1+2Dxs2zFRTcOLi8uXL9lGjRtmLFClid3Nzs9epU8e+bNky05+vzWazu7i4ON1sNps9Z86c9uDgYKPjJcj+/fvt77//vr1gwYL2kiVL2lu3bm0PDQ01Ola83b9/3966dWv76dOnjY5iqMent1uN2c9t+/bt9tGjR9tXrFhhdJR4a9y4sb1BgwZP3N+gQQN7o0aNkjFR4qldu7a9bdu2T9zfpk0be61atZIxEeLr7Nmz9iVLltg3bdpkb9eundFxgCRFg3ok2PXr1/XDDz9o3rx52rJli/Lnz69mzZqpadOm8vHxMToe/sPFxUVS9CXM7RZoUC9JW7Zs0eDBg3Xw4EGFhYWpRIkS6t+/v9544w2joyWIh4eHDhw4oHz58jltP3bsmIoXL667d+8alCzhMmbMqN27d1u6Qf3OnTs1Y8YMzZo1S9myZdP169eVIUMGzZw507QjYzdt2uR038XFRZkzZ5a/v79SpGDg+PMmXbp0OnDggGVWA42PxxebsRoznVtERITat2+vfv36Wer78ciRIypTpowKFSqkbt26KX/+/LLb7Tp69KjGjh2rI0eOaMeOHSpUqJDRUeNsw4YNqlGjhj777DP17NnTseriX3/9pZEjR+qbb77R2rVrVbVqVYOTxk/FihVVuXJlVapUSRUqVDDliplxdfDgQZUoUcL07/uBp6HYhUT1559/av78+ZoxY4ZOnDihyMhIoyPFm1WnaP73A+p/VapUKZmSIC5Kly6tt99+O9qS7AMHDtTKlSu1d+9eg5IlXNeuXZU5c2Z98cUXRkdJVH/99ZfmzJmjmTNn6vTp06pfv77atGmj6tWr6/bt2xo8eLAWLFigc+fOGR0V/3Hq1CnH123cuHHKkiWLfv75Z+XMmdOUH1QlqWXLlipWrJhpp4glBjMVhOLKbOdm1eLrjh071KZNGx09etRxUdFutyt//vyaPn26ypUrZ3DC+Js6daq6dOmiiIgIeXl5yWaz6ebNm3Jzc9PYsWP18ccfGx0x3oYOHarNmzcrODhYkZGRKlWqlFPxK3Xq1EZHTHQUu/AioNiFRBMREaFVq1bp+++/16pVq5QxY0ZduHDB6FjxVq9ePf3yyy/KnDmzmjRpombNmqlYsWJGx8JT2O127d27V2fPnpXNZpOfn5+KFSsWbRSbGa1cuVINGjRQ06ZNHVdO169fr/nz5+uHH35wasJsNp07d9bs2bNVtGhRBQQERGtQb8aeEnXq1NEvv/yivHnzqm3btmrRooUyZszodMyVK1eUNWtWR58QswgNDdWNGzecGjGvX79eQ4cO1e3bt1W/fn1TFy43bdqkWrVqqUKFCtq8ebOOHj0qPz8/jRgxQnv27NHixYuNjhgvQ4cO1ejRo1WtWjWVLFlSadKkcdpvxt54ceXl5WWqJu5xYbZzs3rx9cCBAwoNDZUk5c2b1zLvHy9cuKBFixbp5MmTstvtyps3r959913lyJHD6GiJIjIyUrt379amTZu0ceNG/fbbb3JxcdG9e/eMjpboKHbhRcA8AyTYhg0bNG/ePC1ZskRRUVFq0KCBfvrpJ9MOZX5k+fLlTlM0x4wZY5kpmlu2bNHUqVN1+vRp/fDDD8qePbvmzJkjX19fvfbaa0bHi5cNGzaoTZs2OnfunKNRr81mk6+vr2bMmKGKFSsanDBh6tSpo2XLlmn48OFavHixPDw8FBAQoF9//dX0o/EOHTrkWEDg999/d9pn1kJllixZtGnTpqdexc+cObPOnDmTjKkSR69evVSkSBFHsevMmTOqU6eOXn/9dQUEBCgwMFCpU6fWZ599ZmzQeOrdu7eGDh2qbt26OTWTrlq1qiZOnGhgsvjx8/PT7t27NX36dKVPn1579+6NNhLUrAtBxJWVr++a7dzy5MmjwYMHa9u2bZYsvhYrVuyZBS6zFSglKXv27LEqUL711luaNm2asmXLlgypEs/p06d16NAhHTx4UCEhIfL09DT9+0fgRcbILiRI9uzZde3aNdWsWVPNmjVTnTp1TLtk9LNYZYrmkiVL9MEHH6hZs2aaM2eOjhw5Ij8/P02cOFGrV6/W6tWrjY4YZydPnlTRokVVpkwZdenSxdEn48iRIxo/frz27NmjkJAQU72hBJ5X3t7eWrRokaOQN3ToUC1evFgHDhyQJE2fPl0TJkxw3DebtGnT6tChQ/L19XWaGnb27Fnlz5/fdFf4XVxcdPnyZWXJksXoKIb7448/9Morr8jV1dXoKInObOf2tOmLNptNp0+fTsY0xjDb1NO4MNu5NW3aVJs2bVJ4eLgqVqyoSpUqqXLlygoICDDtRbcGDRo8df+NGze0adMmRnbB0hjZhQQZOHCgGjVqpPTp0xsdJUlFRERoz5492rlzp86ePetozGlGQ4cO1ZQpU9SiRQstWLDAsb1ChQoaOnSogcnib9y4cSpbtqzWr1/vtD1//vx65513VL16dY0dO1YTJkwwKGHiuHHjhhYvXqzTp0+rR48eypgxo/bt26eXX35Z2bNnNzpeovjzzz8lyRJTItavX6/169frypUr0aYqzpgxw6BUCffPP/84fX02bNigOnXqOO5XrlxZ3bt3NyJaokifPr0uXboU7cP4/v37LfNzZjW3b9/WiBEjnvjz9qhw4u3tbUS8BLHauUVFRcnFxcWUo1phXQsWLFCmTJnUtm1bVa1aVa+99prp+3SlS5fumftbtGiRTGkAY1DsQoK0a9fO6AhJyopTNI8fPx7jkOx06dLpxo0byR8oEWzcuFGBgYEx7rPZbPrss8/Up0+fZE6VuEJCQlS9enWlS5dOZ8+eVdu2bZUxY0YtXbpU58+f1+zZs42OGG9RUVGOfkJhYWGSHl4V7t69u/r27etYQdRMBg0apMGDB6tUqVLKli2baa8MxyRjxoy6dOmSvL29FRUVpT179qhbt26O/ffv3zfdlKrHNWnSRL169dIPP/wgm82mqKgobdu2TT169DDtB4NffvnlmR986tatm0xpEl/btm21adMmffDBB5b7ebPaubm5uenSpUuOkYY9e/ZUnz59ovU0BJLT1atXtWXLFm3cuFF9+vTR0aNHVaxYMVWuXFmVK1c25YreM2fONDoCYDiKXUiwPXv2aNGiRTp//rzu37/vtG/p0qUGpUq4x6dofvfdd5aZopk1a1adPHkyWs+xrVu3mma4+X+dP39eRYoUeeL+woULm37Fu27duqlVq1YaOXKkUx+h2rVrq2nTpgYmS7i+fftq+vTpGjFihCpUqCDp4ffjwIEDde/ePQ0bNszghHE3ZcoUBQUF6YMPPjA6SqKrXLmyhgwZom+//VY//PCDoqKinFatPXLkiKl7Gg4fPlyffvqpvL299eDBAxUsWFAPHjxQ06ZN1bdvX6PjxUvLli2fut9ms5l6KsvPP/+sVatWOX5/WInVzu2/hfCpU6fq448/ptgFQ2XIkEF169Z1FP1PnjypoUOH6uuvv9ZXX31l6t+PwIuMYhcSZMGCBWrRooXefPNNrV27Vm+88YZCQ0P1119/6Z133jE6XoJYdYpmu3bt1KVLF82YMUM2m00XL17U9u3b1aNHD/Xr18/oePESFhb21OHmqVOn1p07d5IxUeLbvXu3pk6dGm179uzZdfnyZQMSJZ5Zs2Zp2rRpTiNLAgIClD17dn3yySemLHbdv39f5cuXNzpGkhg2bJiqV6+uXLlyydXVVePHj3dqLj1nzhxTj35NmTKl/ve//6l///46dOiQwsLCVLx4ceXJk8foaPFm9Z5dGTJksGyxxMrnJpmvsX5iMvsoPSu5evWqYwXGjRs36siRI0qfPr3q1Klj+kWAgBcZxS4kyPDhwzV27Fh9+umn8vT01DfffCNfX1+1b9/edCuw/JdVp2j27t1bUVFRqlatmu7cuaOKFSvK3d1dPXr0UKdOnYyOF29Hjhx5YtHnn3/+SeY0ic/d3V3//vtvtO2hoaHKnDmzAYkSz7Vr15Q/f/5o2/Pnz69r164ZkCjh2rZtq3nz5pm2gPw0Pj4+OnbsmA4fPqzMmTPrlVdecdo/aNAgS/Rc8/b2duqDtHTpUg0cOFAhISEGpoq7F+ED9ZAhQ9S/f3/NmjXL9H12/svK5/aie5ELfc+bLFmyKFOmTHr99dfVrl07Va5c+akzBgCYA6sxIkHSpEmjw4cPy8fHRy+99JI2btyoIkWK6OjRo6pataouXbpkdMQEseoUTenhyJOTJ08qLCxMBQsWVKpUqXTlypVoH1zNwMXFRTabLcY3jo+2m32aTtu2bXX16lUtWrRIGTNmVEhIiFxdXVW/fn1VrFhR48aNMzpivJUpU0ZlypTR+PHjnbZ36tRJu3fv1o4dOwxKFn9dunTR7NmzFRAQoICAALm5uTntHzNmjEHJEu71119X/fr1VadOHeXNm9foOIlq6tSpWrdunVKmTKkuXbqoTJky+u2339S9e3eFhoaqRYsWmjx5stEx4+RFWI2xePHiOnXqlOx2u3x8fKL9vO3bt8+gZAlntXNzcXHRRx995CjcTZo0Sc2bN4/WU87MvyMfuX//vs6cOaPcuXMrRYro4wu2bt2qV1991VQtMjZv3qzy5ctHO5/IyEgFBwc7esIGBgbq448/NsXsiEerd/v6+lJQBiyGkV1IkAwZMujWrVuSHk6n+v3331WkSBHduHHD9NPGrDxFU3o4VadgwYKO+wcPHlSJEiVMWRB6EVZ1Gj16tN59911lyZJFd+/eVaVKlXT58mWVK1fOlNP8Hjdy5Ei99dZb+vXXX1WuXDlJ0vbt2/XHH39o9erVBqeLn5CQEBUrVkyS9PvvvzvtM/tIm3bt2mn58uUaOHCgcuTI4ehzUr58eVOf24gRI9S/f38FBATo2LFjWr58ufr27asJEyaoS5cuat++vTJkyGB0zDhr2bKlPDw8Yn38iBEj1KFDB1N8SH2kfv36RkdIMlY7t4oVK+r48eOO++XLl3esKGkVd+7cUadOnTRr1ixJD0dg+/n5qVOnTsqePbt69+4tSXrttdeMjBkvVapUcVpg4JGbN2+qSpUqjveQZloUyG63q3jx4jp8+LCpp6sDiI6RXUiQpk2bqlSpUurWrZuGDBmiCRMmqF69elq3bp1KlChh6tFPAQEBat++vWOK5sGDB52maA4aNMjoiInKzMWuuPrkk080ePBgZcqUyegocbZt2zYdPHhQYWFhKlGihKpXr250pERx4cIFffvttzp27JgkqUCBAvrkk09MOdLwRREeHq7169dr+fLlWrlypR48eKC33npLdevW1ZtvvhmnAsvzIF++fPriiy/UsmVLbdmyRZUqVVLt2rW1cOFCp55kVufl5aUDBw6YdsESwGhdunTRtm3bNG7cONWsWVMhISHy8/NzXCTYv3+/0RHjzcXFRX/99Ve09gmhoaEqVapUjO0WzKBQoUKaPn26ypYta3QUAImIYhcS5Nq1a7p3755eeeUVRUVFaeTIkQoODlaePHn05ZdfmvIq+CNWn6L5Xy9SsctsH+YiIiLk4eGhAwcOqHDhwkbHAWK0c+dOrVixQitWrNCpU6dUtWpV9enTxzSryHl4eCg0NNTRp8vd3V3BwcEqWbKkwcmS16OLO2b5/QjzeHRhNE2aNOrWrdsTj7PZbBo9enQyJktcuXLl0sKFC1W2bFmnn6eTJ0+qRIkSpiwINWjQQJK0fPly1axZ02nq5YMHDxQSEqJ8+fJpzZo1RkVMkJUrV2rkyJGaPHky77MAC2EaI+Ll0R/qFClSKG3atI77n3zyiT755BMjoyUaK0/RfNGZrcbv5uamnDlzWrYQeeLECS1fvlxnz56VzWaTn5+f6tevL19fX6OjxUmDBg0UFBQkLy8vxweDJzHzqNcnedR7bdiwYTp9+rSWL19uqosC4eHhSpUqleN+ypQpLb0KntllzJhRoaGhypQpkzJkyPDUKbRmW+jCque2f/9+RUREOP79JGaeDi1Jf//9d4w98m7fvm3ac3vUU81ut8vT09Np5G7KlClVtmxZUy/s1KJFC925c0dFixZVypQpo41MNtPPGYD/Q7EL8ZI+ffpY/cE284fzihUrat26dSpSpIgaNWqkLl266LffftO6detUrVo1o+PF2bNWEHu8hwaeP3379tUXX3yhOXPmWOoDeGBgoPr376+oqChlyZJFdrtdf//9t3r16qXhw4erR48eRkeMtXTp0jl+L/632bIVbdiwQVWqVIlx37p169S1a9dkTpRw/fr1czQovn//voYOHWrJxtlWMHbsWHl6ekqSqRfoiIlVz23Dhg0x/ttqSpUqpVWrVjlWuH70d2HatGmOvpRmM3PmTEkPV+Pt0aOH5aZ2W+nnDMD/YRoj4mXTpk2Of9vtdtWuXVvTpk1T9uzZnY6rVKlSckdLNFabovkirFgYW2acplO8eHGdPHlSERERypUrV7Q3mmZbkUt6+GGnevXq6tevn7p06eL4mbp27ZrGjRun4cOH67fffnOs7mQGgwcPVo8ePV6IFZ3c3d3VuXNnDR8+3LFC3D///KPWrVtr69atun79usEJ46Zy5crPvIhjs9n022+/JVMiY5jx9yPwPNm6datq1aql5s2bKygoSO3bt9eRI0cUHBysTZs2vXBTowHAKBS7kCis9OY4tr0UvLy8kjhJ4jp37lysjsuVK1cSJzGeGb9fn7UgwoABA5IpSeJ57733lD59ek2dOjXG/R999JFu3bql+fPnJ3Oy+HN1dY1xpSorCg4OVosWLZQ2bVrNmzdPZ86cUZs2bZQvXz7Nnj37hfhdYkVm/P34uHv37un+/ftO28z29/pJrHxuVnPq1CmNGDHCaUGZXr16qUiRIkZHS5C//vpLPXr00Pr163XlypVoF1DNfMH0wYMHWrZsmY4ePSrpYdP6unXrytXV1eBkAOKLYhcShdnfHD/u0QioZzHzH/TYMPOKhc9ipe9XM/P19dWcOXOeuPz6li1b1KJFC505cyaZk8Wfi4uLLl++/EIUuyQpLCxMHTp00OLFixUVFaUhQ4bo888/N21fmrgw20IXsVW7dm1Nnz5d2bJlMzpKrN2+fVu9evXSokWLdPXq1Wj7zfz32srnBvOpVauWzp8/r44dOypbtmzRftfXq1fPoGQJc/LkSdWuXVsXLlxQvnz5JD1s7+Ht7a1Vq1Ypd+7cBicEEB/07AL+4/E+Ek+boml133//vXr06GGJYteNGze0evVqNW3aVJLUvHlzroY/B/766y/5+Pg8cb+vr68uX76cfIESyYtQ6HkkNDRUe/bsUY4cOXTx4kUdP35cd+7csVw/l5iY8VrhgwcP9OOPPzpGLhQoUED169dXihT/93Zw9erVRsWLt88//1wbNmzQ5MmT9cEHH2jSpEm6cOGCpk6dqhEjRhgdL0GsfG5WtW/fPrm5uTlGcS1fvlwzZ85UwYIFNXDgQKVMmdLghPG3detWbdmyRcWKFTM6SqLq3LmzcufOrR07djj6ol69elXNmzdX586dtWrVKoMTAogPRnYhUXh6eiokJMR0q6fFxos6CshK533w4EGVKFHCdFfArboi1yPPGgX1119/6ZVXXjHV183FxcWpUf2TmPHr9V8jRozQgAED9NFHH+nrr7/WyZMn9cEHH+jff//V999/b9pGzLFltt+Rhw8fVt26dXX58mXHyIXQ0FBlzpxZK1euVOHChQ1OGH85c+bU7NmzVblyZXl5eWnfvn3y9/fXnDlzNH/+fFMW8B6x8rlZ1auvvqrevXurYcOGOn36tAoWLKgGDRpo9+7deuutt0zdDL1gwYKaO3euihcvbnSURJUmTRrt2LEj2jTTgwcPqkKFCgoLCzMoGYCEYGQX4qVBgwZO9+/du6cOHTpEu5q/dOnS5IwFWIpVV+R63LRp05Q2bdoY9926dSuZ0ySOQYMGvRCrMX7zzTdatmyZatWqJUkqXLiwdu3apS+++EKVK1dWeHi4wQnxuLZt26pQoULas2ePYzGI69evq1WrVvroo48UHBxscML4u3btmqPo6OXl5Sgmv/baa/r444+NjJZgVj43qwoNDXWMfPrhhx9UqVIlzZs3T9u2bVOTJk1M/fd83Lhx6t27t6ZOnfrUkdlm4+7uHuN7jrCwMFOPxANedBS7EC///SDXvHlzg5IA1tWyZcsY/20VOXPm1P/+979nHmM2TZo0eSF6dh06dCjaNGc3Nzd9/fXXevvttw1KhSc5cOCAU6FLkjJkyKBhw4bp1VdfNTBZwvn5+enMmTPKmTOn8ufPr0WLFql06dJauXKl0qdPb3S8BLHyuVmV3W5XVFSUJOnXX391/D709vbWP//8Y2S0ePnvyPLbt28rd+7cSp06tWMl3kfMOmr57bff1kcffaTp06erdOnSkqSdO3eqQ4cOqlu3rsHpAMQXxS7Ey8yZM42OkKxepB48eH7EdmVQyZwrcp09e9boCInuRfpd8bR+fpUqVUrGJMYw29c6b968+uuvv1SoUCGn7VeuXJG/v79BqRJH69atdfDgQVWqVEm9e/dWnTp1NHHiREVERGjMmDFGx0sQK5+bVZUqVUpDhw5V9erVtWnTJk2ePFmSdObMGb388ssGp4s7M49Ei63x48erZcuWKleunKOAFxkZqbp16+qbb74xOB2A+KJnF/Af/52iuXLlSlWtWvWFm6Jppn4048ePf+r+CxcuaNSoUabq/STFfmVQ6cVYkatIkSJavXq1vL29jY7yRFZfjbFBgwYKCgqSl5dXtN+V/8XvSOM9XjDfunWrPv/8cw0cOFBly5aVJO3YsUODBw/WiBEjVLt2baNiJrpz585p79698vf3V0BAgNFxEpWVz80qQkJC1KxZM50/f17dunXTgAEDJEmdOnXS1atXNW/ePIMT4klOnDihY8eOSXq4gIfZLwQALzpGdgH/8aJO0TTzioVjx4595jFmnA73+MqgZ8+eVe/evdWqVStH4+/t27dr1qxZCgwMNCpisjp79qwiIiKMjvFUj6auWNXjzfe9vLxMN7opPiIjI3Xv3r1oveV+/vnn536V3vTp0zt9jex2uxo3buzY9uh6Z506dSxVMM+VK5dy5cpldIwkYeVzs4qAgAAdOnQo2vavv/5arq6uBiRKPE8acW6z2eTu7m66/lb9+/dX7969lTp1akkPRyzXqVPH4FQAEgsjuwBIMu+KhS+KatWqqW3btnr//fedts+bN0/fffedNm7caEywZGSGkTRWt2LFCtWqVStanxYrWLlypa5evapWrVo5tg0bNkxDhgxRZGSkqlatqoULFzr1vHrebdq0KdbHmn3q6e7du7VhwwZduXIlWtHZ7NP9rHxuMJdnjTjPkSOHWrVqpQEDBsjFxSUZk8WPq6urLl265BiN7eXlpQMHDvA+A7AIRnYBeCFcuHDhuR+F8TTbt2/XlClTom0vVaqU2rZta0AivIjeeecdXb58WZkzZ472IcHsxowZo3fffddxPzg4WP3799fgwYNVoEAB9e3bV0OGDDFVccHsBazYGj58uL788kvly5dPL7/8stOHcbOPPrTyuVnVswpCZr6oGBQUpL59+6pVq1aORu67du3SrFmz9OWXX+rvv//WqFGj5O7uri+++MLgtM/23zEfjAEBrIViFwBLu3z5soYNG6bp06frzp07RseJN29vb/3vf//TyJEjnbZPmzbtue5hBWvJnDmzduzYoTp16shut1vqw/bhw4edClmLFy9WjRo11LdvX0lSqlSp1KVLF1MVu/5ry5Ytmjp1qk6fPq0ffvhB2bNn15w5c+Tr66vXXnvN6Hjx9s0332jGjBlOo/KswsrnZlU//vij0/2IiAjt379fs2bN0qBBgwxKlThmzZql0aNHq3Hjxo5tderUUZEiRTR16lStX79eOXPm1LBhw0xR7AJgbRS7AJje9evX9cknn2jdunVKmTKlevfurY4dO2rgwIEaNWqUAgICTL+C6NixY9WwYUP9/PPPKlOmjKSHV1NPnDihJUuWGJwOL4oOHTqoXr16stlsstlsypo16xOPNdvohVu3bumll15y3N+6dasaNWrkuF+oUCFdvHjRiGiJYsmSJfrggw/UrFkz7du3T+Hh4ZKkmzdvavjw4Vq9erXBCePPxcVFFSpUMDpGkrDyuVlVvXr1om179913VahQIS1cuFBt2rQxIFXiCA4OjnGUefHixbV9+3ZJ0muvvabz588nd7R4sdlsunXrllKlSuW4gBMWFhatN5lZetgCcEaxC3hBxGbFQrPq3bu3goOD1apVK/3yyy/q2rWr1qxZIxcXF/3222+OlcfMrHbt2goNDdXkyZMdKwXVqVNHHTp0YGQXks3AgQPVpEkTnTx5UnXr1tXMmTOVPn16o2MliuzZs+vo0aPKmTOnwsLCdPDgQafFL65evepoYmxGQ4cO1ZQpU9SiRQstWLDAsb1ChQoaOnSogckSrmvXrpo0aZLGjRtndJREZ+Vze9GULVtWH330kdExEsTb21vTp0/XiBEjnLZPnz7d8V7k6tWrpultaLfblTdvXqf7xYsXd7pvs9lMd/EGwEMUu4AXhFVXLJQerooWFBSkqlWrqmPHjvLz81OxYsU0fPhwo6MlKm9vb8ud05PcuHEjWhFl6tSpevnll40JBIf8+fMrf/78GjBggBo1amTqAtDjGjVqpM8++0xffPGFVq9eraxZszoVyvfs2aN8+fIZmDBhjh8/rooVK0bbni5dOt24cSP5AyWiHj166K233lLu3LlVsGDBaAsoLF261KBkCWflc3uR3L17V+PHjzd171BJGjVqlBo1aqSff/5Zr776qqSHvxuPHTumxYsXS3q4oMJ7771nZMxYe3zVawDWQ7ELeEGcOXPG6AhJ5uLFiypQoIAkycfHR6lSpVLz5s0NTpX4rNpv56uvvpKPj4/jzXHjxo21ZMkSZc2aVatXr1bRokUlSU2bNjUyJv5jwIABioyM1K+//qpTp06padOm8vT01MWLF+Xl5aW0adMaHTFO+vfvrwsXLqhz587KmjWrvv/+e7m6ujr2z58/39RL0mfNmlUnT56Uj4+P0/atW7eafuWxzp07a8OGDapSpYpeeuklS/WSs/K5WVWGDBmcvk52u123bt1S6tSp9f333xuYLOHq1q2rY8eO6bvvvtPx48clSbVq1dKyZcscv1s+/vhjAxPGTVwX8RgxYoQ6dOhgmRHNgNXZ7Cw7AeD/M+uKha6uro4V4iTJ09NTISEh8vX1NThZ4nm8386cOXN05MgR+fn5aeLEiVq9erWp++34+vpq7ty5Kl++vNatW6fGjRtr4cKFWrRokc6fP6+1a9caHRExOHfunGrWrKnz588rPDxcoaGh8vPzU5cuXRQeHh5jXxcYJzAwUN9//71mzJihGjVqaPXq1Tp37py6du2qfv36qVOnTkZHjDdPT08tWLBAb731ltFREp2Vz82qZs2a5XTfxcVFmTNnVpkyZUwzvQ8x8/Ly0oEDB0x/gQB4UTCyC4DpVyy02+2qVq2aUqR4+Cvt7t27qlOnjlKmTOl03L59+4yIlyis3G/n8uXLjl4fP/30kxo3bqw33nhDPj4+jmb8eP506dJFpUqV0sGDB50au7/zzjtq166dgcnwuDNnzsjX11e9e/dWVFSUqlWrpjt37qhixYpyd3dXjx49TF3okqSMGTMqd+7cRsdIElY+N6tq2bKl0RESVUhIiAoXLiwXFxeFhIQ89diAgIBkSmUMxogA5kKxC3hBWHnFwv79+ztNGYhpJSSzs3K/nQwZMuiPP/6Qt7e31qxZ4yje2e12msI+x7Zs2aLg4OBoRWUfHx9TLnhRpUqVZ04Rs9lsWr9+fTIlShy5c+dWrly5VKVKFVWpUkVHjx7VrVu3FBYWpoIFC5puumlMBg4cqAEDBmjmzJmW6SH3iJXPzcpu3Lih6dOn6+jRo5Ierub64YcfKl26dAYni7tixYrp8uXLypIli4oVKyabzRZj0YdG7gCeNxS7gBeElVcsHDhwoNERkpyV++00aNBATZs2VZ48eXT16lXVqlVLkrR//375+/sbnA5PEhUVFeMHmz///FOenp4GJEqYYsWKPXHfrVu3NG/ePIWHhydfoETy22+/aePGjdq4caPmz5+v+/fvy8/PT1WrVlXVqlVVuXJl0y/8MH78eJ06dUovv/yyfHx8ojVxN/OoXiufm1Xt2bNHb775pjw8PFS6dGlJ0pgxYzRs2DCtXbtWJUqUMDhh3Jw5c8bRJuJp/V9v376dXJEAIFYodgEvCCuvWPjfZrCPpEuXTnnz5lWPHj1Uo0YNA5Ilnnbt2qlLly6aMWOGbDabLl68qO3bt6t79+7q37+/0fESZOzYsfLx8dEff/yhkSNHOkaaXLp0SZ988onB6fAkb7zxhsaNG6fvvvtO0sOr+mFhYRowYIBq165tcLq4i2nF2sjISE2aNEnDhg1T9uzZNWTIEAOSJUzlypVVuXJlSdK9e/cUHBzsKH7NmjVLERERyp8/vw4fPmxs0ASoX7++0RGSjJXPzaq6du2qunXr6n//+5+jvUJkZKTatm2rzz77TJs3bzY4YdzkypUrxn8/Eh4erkmTJmnkyJG6fPlyckYDgKeiQT3wgkiRIoX++OMPZcuWTZKUOnVq7dmzRwULFjQ4WcL9txnsIzdu3NDevXu1cOFCLV682NQrqdntdg0fPlyBgYGOvmru7u7q2bOn+vTpIw8PD4MT4kXzxx9/qGbNmrLb7Tpx4oRKlSqlEydOKFOmTNq8ebOyZMlidMQEmTt3rvr376+7d+/qyy+/1EcffeT44Gp29+/f17Zt2/Tzzz9r6tSpCgsLY/oRkEg8PDy0f/9+5c+f32n7kSNHVKpUKVP2Rg0PD9fAgQMdrTA+//xz1a9fXzNnzlTfvn3l6uqqjh07qlevXkZHTVKenp46ePCg6UfUAy8Ka7xrA/BMdrvd6YOaq6urZQokz2oGW6xYMQUGBpq62GWz2dS3b1/17NlTJ0+edPTbmTp1qnx9fU1/NfX48eOaMGGCo79JgQIF1KlTJ+XLl8/gZHgSb29vHTx4UAsXLtTBgwcVFhamNm3aqFmzZqb+3bJmzRr17t1bZ86cUY8ePdStWzelSZPG6FgJcv/+fe3YsUMbNmzQxo0btXPnTnl7e6tixYqaOHGiKlWqZHTERLF3716nHknFixc3OFHisfK5WY2Xl5fOnz8frdj1xx9/mHKKt/SwN+rUqVNVvXp1BQcHq1GjRmrdurV27NihMWPGqFGjRnJ1dTU6ZpJ7/fXXTf33DXjRUOwCXhAvwoqFT/L222+bdsXCx6+mPhrJ9ehq6jvvvCNXV1d17drV6JgJsmTJEjVp0kSlSpVSuXLlJEk7duxQ4cKFtWDBAjVs2NDghPivR1PffvrpJzVr1kzNmjUzOlKC7dq1S7169dKOHTvUoUMH/frrr8qUKZPRsRKsatWq2rlzp3x9fVWpUiW1b99e8+bNc4zytYIrV66oSZMm2rhxo9KnTy/p4cjeKlWqaMGCBY5+Q2Zk5XOzqvfee09t2rTRqFGjVL58eUnStm3b1LNnT73//vsGp4ufH374QbNnz1bdunX1+++/KyAgQJGRkTp48OAzF/YwiwcPHujHH390uuhWv359pwvFq1evNioegHhgGiPwghg4cGCs3pAMGDAgGdIkr0OHDqlGjRqmHP3Uq1cvp6upf//9t+Nq6hdffGGJq6m5c+dWs2bNNHjwYKftAwYM0Pfff69Tp04ZlAxPkz17dv36668qUKCA0VEShYuLizw8PPTRRx/J19f3icd17tw5GVMlnJubm7Jly6b69eurcuXKqlSpkl566SWjYyWq9957T6dPn9bs2bMd349HjhxRy5Yt5e/vr/nz5xucMP6sfG5Wdf/+ffXs2VNTpkxRZGSk7Ha7UqZMqY8//lgjRoyQu7u70RHjLGXKlDpz5oyyZ88u6eFUzV27dqlIkSIGJ0schw8fVt26dXX58mXHiPLQ0FBlzpxZK1euVOHChQ1OCCA+KHYBsLzPPvtMx44d05o1a4yOEmd+fn4aN26c09XUVq1aafr06Za5mpo6dWqFhIREW3nxxIkTKlq0qCn7m7wIhg8frtDQUE2bNs0Svax8fHye+TNls9l0+vTpZEqUOG7fvq0tW7Zo48aN2rBhgw4cOKC8efOqUqVKjuKX2UcHpUuXTr/++qteffVVp+27du3SG2+8oRs3bhgTLBFY+dys7s6dO46LNblz51bq1KkNThR/rq6uunz5suN3haenp0JCQp56YcBMypUrp8yZM2vWrFnKkCGDJOn69etq1aqV/v77bwUHBxucEEB8mP/dKYBYsfKKhd26dYtx+82bN7Vv3z6FhoaabvWjR/7880+VLFlSklS4cGG5u7ura9eulil0SQ9Xi9uyZUu0YtfWrVv1+uuvG5QKz7J7926tX79ea9euVZEiRaL1tVq6dKlByeLn7NmzRkdIEmnSpFHNmjVVs2ZNSdKtW7e0detWbdiwQSNHjlSzZs2UJ08e/f777wYnjb+oqCi5ublF2+7m5qaoqCgDEiUeK5+b1Xz44YexOm7GjBlJnCTx2e12tWrVyjEq7d69e+rQoYPpf+8/cuDAAe3Zs8dR6JIevm8eNmxYtEIzAPOg2AW8IMaNGxfj9kcrFr799tumXbFw//79MW738vJSjRo1tHTpUtNefXzw4IFTX7UUKVIobdq0BiZKfHXr1lWvXr20d+9elS1bVtLDnl0//PCDBg0apBX/r737C626/uM4/jrHzT+ImsfSzTnPzsQhJdqiiy5y589UNKcECiq6hZoEhQx1hATRpiBS6JU3u9mOi5xQWS1wDjpO3WZpkkoNC4YaZpHgH9JT7rid04V4fh43c7+j22ffz3k+4ODx+/1evD6oDN+fz/v7bmpKeRbDwzPPPGPd+9Ti8bjC4bAOHjyoS5cuyeVyqbCwUMuXL1d5ebkVReaxY8fK4/HI4/Fo4sSJysrKSr6jxqlCoZAqKyvV2NioqVOnSpKuXLmizZs3q7S01HC6J2Pz2mwTDofl9XpVXFws2xpnHh4EtHbtWkNJBkdRUZH+/PNPvfDCCynXr1692mcjDoBz0MYIQJK0Z88effbZZxzVHmbcbrcWL16c3E39+uuvFQqFrNlNle6tcSBcLpd6e3sHOQ0eJx6P66OPPlJTU5NisZhCoZCqq6sdP6EqkUiorKxMzc3Nmjt3rmbNmqVEIqHz58/rxx9/1LJly/Tll1+ajvl/i8fjOn36dLKNsaOjQ9FoVHl5eQoGg8mP1+s1HTVtly9f1rJly9TZ2an8/PzktdmzZ6upqUnTpk0znDB9Nq/NNu+8844aGxvl9Xq1bt06rV27Vh6Px3QsPMJff/2V/N7e3q53331X1dXVKZtu27dv165du/Taa6+ZigngCVDsAiDp3os4X3nlFV2/ft10FDxg3bp1A3quvr5+kJMA9+zYsUPV1dWaP3++xowZo5aWFq1evdqRrTkPqq+vV2Vlpb766isFg8GUe0eOHNHrr7+uvXv3qqKiwlDC9IwfP17RaFQ5OTnJwlYgENCMGTNMR3uqEomEIpFIyiS1+fPnG071dNi8Ntt0d3fr4MGDqqur04kTJ7RkyRJt2LBBCxcutOJkqE3cbnfKn8n9/xLfv/bg79loA5yJYhcASc6eWAhna2ho0MqVK/tMqIrFYjpw4IDjigu2mzlzpqqqqvTWW29Jkr755hstWbJE//zzz4BP6Q1HCxcuVCgU0rZt2/q9v3PnTh07dkwtLS1DnOzJ1NbWKhgMqqioyHSUQdFf66nP59OKFSsc33pq89oywa+//qpwOKyGhgb19PSos7PTutcQONmxY8cG/Kzf7x/EJAAGC8UuAJKcPbEQzjZixAj98ccfmjx5csr1a9euafLkyeyoDjOjRo1SV1dXsqVKkkaPHq2uri5Ht1Tl5OTo8OHDevHFF/u9f+bMGS1evJgNgWEkkUho6dKlOnTokFWtp5Lda8sUly9fVn19vcLhsGKxmH7++WeKXQAwhHhBPZAhbJ5YCGdLJBL9nlD47bffNGHCBAOJ8F96eno0evTolGvZ2dm6e/euoURPx/Xr1zVlypRH3p8yZYpu3LgxhInwOOFwWMePH1ckEnlk62lDQ4MjT4favDabPdjG2N7errKyMu3du1eLFi1y9MnXTNDW1qba2lpduHBBn376qfLy8vTxxx/L5/Pp1VdfNR0PQBoodgEZwuaJhXCm4uJiuVwuuVwulZaWKivrfz+Sent7dfHiRS1atMhgQvTn4RH0Uv9j6J02NKG3tzfl7+DDRowYoZ6eniFMhMdpbGzUe++916cYJCnZkvrJJ584siBk89ps9fbbb+vAgQPKz8/X+vXr1djYqGeffdZ0LAzA559/rvLycq1Zs0Y//PCDuru7Jd3bEN65c6cOHTpkOCGAdNDGCAAwoqamJvnr1q1bU9o7Ro4cqYKCAi1fvlwjR440FRH9sHVowsOTTx/W3d2tw4cP01Y7jNjcemrz2mzldrs1ffr05EbOozhtIyATFBcXa/PmzaqoqNC4ceN07tw5FRYW8u8McDhOdgEAjPjggw8kSQUFBVq1atUjiwwYXpxWxBqoN95447HPcIpmeLG59dTmtdmqoqKCoQEO9csvv6ikpKTP9QkTJujmzZtDHwjAU0GxCwBgVE1NjcrKyvoUu27evKmXXnpJFy5cMJQMmcTWIp7NbG49tXlttgqHw6YjIE05OTnq6upSQUFByvX29nYVFhaaCQXgiVHsAgAYdenSpX5bw7q7u3XlyhUDiQA4QX/vj3vQ/ffuOJHNawOGm40bN6qyslJ1dXVyuVz6/fff9e2336qqqkrvv/++6XgA0kSxCwBgRFNTU/J7S0tLyuTF3t5eRSKRPrusAHCfza2nNq8NGC4uXrwon8+nbdu2KR6Pq7S0VH///bdKSko0atQoVVVVadOmTaZjAkgTL6gHABhxfwy7y+XSwz+KsrOzVVBQoN27d6usrMxEPAAAYDG32y2v16tgMKhgMKhAIKBbt27p9u3bev7551MG5wBwHopdAACjfD6fvv/+e0a0AwCAIXP06NHk5+TJk4rFYiosLFQoFFIoFFIgEPjPQREAhjeKXQAA4yKRiCKRiK5evap4PJ5yr66uzlAqAACQCe7cuaMTJ04ki1+nTp3S3bt3NWvWLHV2dpqOByANFLsAAEZt375dNTU1evnll5Wbm9tndPsXX3xhKBkAAMgksVhMHR0dam5uVm1trW7fvt3vEB0Awx/FLgCAUbm5ufrwww9VXl5uOgoAAMggsVhM3333nVpbW5PtjPn5+SopKVFJSYn8fr+mT59uOiaANFDsAgAYNWnSJJ06dUozZswwHQUAAGSIUCikkydPyufzye/3a968efL7/crNzTUdDcBT4DYdAACQ2d58803t37/fdAwAAJBB2traNGnSJIVCIZWWlmrBggUUugCLcLILADDktmzZkvwej8e1b98+zZkzR3PmzFF2dnbKs3v27BnqeAAAwHLRaFRtbW06evSoWltbdfbsWRUVFcnv9ysQ2+Cn2AAAARpJREFUCMjv9+u5554zHRNAmih2AQCGXDAYHNBzLpdLR44cGeQ0AAAg0926dUvt7e3J93edO3dOM2fO1E8//WQ6GoA0ZJkOAADIPK2traYjAAAAJI0dO1Yej0cej0cTJ05UVlaWzp8/bzoWgDRxsgsAAAAAkFHi8bhOnz6dbGPs6OhQNBpVXl6egsFg8uP1ek1HBZAGil0AAAAAgIwyfvx4RaNR5eTkJAtbgUCA6dCAJSh2AQAAAAAySm1trYLBoIqKikxHATAIKHYBAAAAAADAGm7TAQAAAAAAAICnhWIXAAAAAAAArEGxCwAAAAAAANag2AUAAAAAAABrUOwCAAAAAACANSh2AQAAAAAAwBoUuwAAAAAAAGCNfwHnmQAKIEjTzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation heatmap\n",
    "\n",
    "plt.figure(figsize=(15,13))\n",
    "sns.heatmap(data0.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9cbVPMe8NxFN"
   },
   "source": [
    "## **5. Data Preprocessing & EDA**\n",
    "Here, we clean the data by applying data preprocesssing techniques and transform the data to use it in the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "colab_type": "code",
    "id": "b_HKPelgnmf8",
    "outputId": "f9fb9a1b-0ddc-470a-f4c0-db248a1a94f3"
   },
   "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>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>DNS_Record</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
       "      <th>Web_Forwards</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.0000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.005500</td>\n",
       "      <td>0.022600</td>\n",
       "      <td>0.773400</td>\n",
       "      <td>3.072000</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.090300</td>\n",
       "      <td>0.093200</td>\n",
       "      <td>0.100800</td>\n",
       "      <td>0.845700</td>\n",
       "      <td>0.413700</td>\n",
       "      <td>0.8099</td>\n",
       "      <td>0.090900</td>\n",
       "      <td>0.06660</td>\n",
       "      <td>0.99930</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.073961</td>\n",
       "      <td>0.148632</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>2.128631</td>\n",
       "      <td>0.115408</td>\n",
       "      <td>0.014141</td>\n",
       "      <td>0.286625</td>\n",
       "      <td>0.290727</td>\n",
       "      <td>0.301079</td>\n",
       "      <td>0.361254</td>\n",
       "      <td>0.492521</td>\n",
       "      <td>0.3924</td>\n",
       "      <td>0.287481</td>\n",
       "      <td>0.24934</td>\n",
       "      <td>0.02645</td>\n",
       "      <td>0.306955</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Have_IP       Have_At    URL_Length     URL_Depth   Redirection  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean       0.005500      0.022600      0.773400      3.072000      0.013500   \n",
       "std        0.073961      0.148632      0.418653      2.128631      0.115408   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      1.000000      2.000000      0.000000   \n",
       "50%        0.000000      0.000000      1.000000      3.000000      0.000000   \n",
       "75%        0.000000      0.000000      1.000000      4.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000     20.000000      1.000000   \n",
       "\n",
       "       https_Domain       TinyURL  Prefix/Suffix    DNS_Record   Web_Traffic  \\\n",
       "count  10000.000000  10000.000000   10000.000000  10000.000000  10000.000000   \n",
       "mean       0.000200      0.090300       0.093200      0.100800      0.845700   \n",
       "std        0.014141      0.286625       0.290727      0.301079      0.361254   \n",
       "min        0.000000      0.000000       0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000       0.000000      0.000000      1.000000   \n",
       "50%        0.000000      0.000000       0.000000      0.000000      1.000000   \n",
       "75%        0.000000      0.000000       0.000000      0.000000      1.000000   \n",
       "max        1.000000      1.000000       1.000000      1.000000      1.000000   \n",
       "\n",
       "         Domain_Age  Domain_End        iFrame   Mouse_Over  Right_Click  \\\n",
       "count  10000.000000  10000.0000  10000.000000  10000.00000  10000.00000   \n",
       "mean       0.413700      0.8099      0.090900      0.06660      0.99930   \n",
       "std        0.492521      0.3924      0.287481      0.24934      0.02645   \n",
       "min        0.000000      0.0000      0.000000      0.00000      0.00000   \n",
       "25%        0.000000      1.0000      0.000000      0.00000      1.00000   \n",
       "50%        0.000000      1.0000      0.000000      0.00000      1.00000   \n",
       "75%        1.000000      1.0000      0.000000      0.00000      1.00000   \n",
       "max        1.000000      1.0000      1.000000      1.00000      1.00000   \n",
       "\n",
       "       Web_Forwards         Label  \n",
       "count  10000.000000  10000.000000  \n",
       "mean       0.105300      0.500000  \n",
       "std        0.306955      0.500025  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.500000  \n",
       "75%        0.000000      1.000000  \n",
       "max        1.000000      1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XBqTcpqen_fB"
   },
   "source": [
    "The above obtained result shows that the most of the data is made of 0's & 1's except 'Domain' & 'URL_Depth' columns. The Domain column doesnt have any significance to the machine learning model training. So dropping the *'Domain'* column from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tdpRw0Bcn_K1"
   },
   "outputs": [],
   "source": [
    "#Dropping the Domain column\n",
    "data = data0.drop(['Domain'], axis = 1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "peN2Q-WdowNL"
   },
   "source": [
    "This leaves us with 16 features & a target column. The *'URL_Depth'* maximum value is 20. According to my understanding, there is no necessity to change this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "iGO3nWV1nvW1",
    "outputId": "f9cc1bae-b91b-483d-a1ca-44aea204dae5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Have_IP          0\n",
       "Have_At          0\n",
       "URL_Length       0\n",
       "URL_Depth        0\n",
       "Redirection      0\n",
       "https_Domain     0\n",
       "TinyURL          0\n",
       "Prefix/Suffix    0\n",
       "DNS_Record       0\n",
       "Web_Traffic      0\n",
       "Domain_Age       0\n",
       "Domain_End       0\n",
       "iFrame           0\n",
       "Mouse_Over       0\n",
       "Right_Click      0\n",
       "Web_Forwards     0\n",
       "Label            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the data for null or missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5xo6cAqAqJOq"
   },
   "source": [
    "In the feature extraction file, the extracted features of legitmate & phishing url datasets are just concatenated without any shuffling. This resulted in top 5000 rows of legitimate url data & bottom 5000 of phishing url data. \n",
    "\n",
    "To even out the distribution while splitting the data into training & testing sets, we need to shuffle it. This even evades the case of overfitting while model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "id": "4LZnaoU_qBsz",
    "outputId": "df212692-ea66-4d67-a4aa-00a256010f69"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>DNS_Record</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Have_IP  Have_At  URL_Length  URL_Depth  Redirection  https_Domain  \\\n",
       "0        0        0           1          3            0             0   \n",
       "1        0        0           0          3            0             0   \n",
       "2        0        0           1          2            0             0   \n",
       "3        0        0           1          7            0             0   \n",
       "4        0        0           1          2            0             0   \n",
       "\n",
       "   TinyURL  Prefix/Suffix  DNS_Record  Web_Traffic  Domain_Age  Domain_End  \\\n",
       "0        0              0           0            1           0           1   \n",
       "1        0              0           0            1           0           0   \n",
       "2        0              0           0            0           1           1   \n",
       "3        1              1           0            1           1           1   \n",
       "4        0              0           0            1           0           0   \n",
       "\n",
       "   iFrame  Mouse_Over  Right_Click  Web_Forwards  Label  \n",
       "0       0           0            1             0      0  \n",
       "1       1           1            1             1      1  \n",
       "2       0           0            1             0      0  \n",
       "3       0           0            1             0      1  \n",
       "4       0           0            1             0      0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffling the rows in the dataset so that when splitting the train and test set are equally distributed\n",
    "data = data.sample(frac=1).reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4FsQosd_nycx"
   },
   "source": [
    "From the above execution, it is clear that the data doesnot have any missing values.\n",
    "\n",
    "By this, the data is throughly preprocessed & is ready for training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fs5hu2UR4SIF"
   },
   "source": [
    "## **6. Splitting the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "FzEU-wcLN8K7",
    "outputId": "534f9839-31e6-4b19-b469-c16db57fd5a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 16), (10000,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sepratating & assigning features and target columns to X & y\n",
    "y = data['Label']\n",
    "X = data.drop('Label',axis=1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "84xKobSqAV3U",
    "outputId": "20c0a9f7-d20e-4176-f815-238727c44336"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8000, 16), (2000, 16))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets: 80-20 split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size = 0.2, random_state = 12)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ah9B035xOjs1"
   },
   "source": [
    "## **7. Machine Learning Models & Training**\n",
    "\n",
    "From the dataset above, it is clear that this is a supervised machine learning task. There are two major types of supervised machine learning problems, called classification and regression. \n",
    "\n",
    "This data set comes under classification problem, as the input URL is classified as phishing (1) or legitimate (0). The supervised machine learning models (classification) considered to train the dataset in this notebook are:\n",
    "* Decision Tree\n",
    "* Random Forest\n",
    "* Multilayer Perceptrons\n",
    "* XGBoost\n",
    "* Autoencoder Neural Network\n",
    "* Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D5Tg_ei0-xPU"
   },
   "outputs": [],
   "source": [
    "#importing packages\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DPBHdBikSXHv"
   },
   "outputs": [],
   "source": [
    "# Creating holders to store the model performance results\n",
    "ML_Model = []\n",
    "acc_train = []\n",
    "acc_test = []\n",
    "\n",
    "#function to call for storing the results\n",
    "def storeResults(model, a,b):\n",
    "  ML_Model.append(model)\n",
    "  acc_train.append(round(a, 3))\n",
    "  acc_test.append(round(b, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YOolxQWs-VbB"
   },
   "source": [
    "### **7.1. Decision Tree Classifier**\n",
    " Decision trees are widely used models for classification and regression tasks. Essentially, they learn a hierarchy of if/else questions, leading to a decision. Learning a decision tree means learning the sequence of if/else questions that gets us to the true answer most quickly.\n",
    " \n",
    "In the machine learning setting, these questions are called tests (not to be confused with the test set, which is the data we use to test to see how generalizable our model is). To build a tree, the algorithm searches over all possible tests and finds the one that is most informative about the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 117
    },
    "colab_type": "code",
    "id": "1kzsjtudy-0w",
    "outputId": "80b84eba-eeb1-48d1-d95a-412b7cfb4c45"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree model \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# instantiate the model \n",
    "tree = DecisionTreeClassifier(max_depth = 5)\n",
    "# fit the model \n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cpPk7O-MrTZi"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_tree = tree.predict(X_test)\n",
    "y_train_tree = tree.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kLn-_qOuS_9Y"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "X4wDTnFZrz3q",
    "outputId": "a8bf5873-8185-4f18-e0f0-87717975e5a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: Accuracy on training Data: 0.812\n",
      "Decision Tree: Accuracy on test Data: 0.820\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
    "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
    "\n",
    "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
    "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "LITrJdVGWwTl",
    "outputId": "363e0abd-28df-4703-b784-5f5af37cab30"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S3LlztXfvXs2ZM+e2xnR2drbbt1gsMgwjx+NVrlxZSUlJtt9N5YSbm5upfs2bN9fvv/+uTZs23bKv1WqVt7e33QYAAAAUVgU+PP2Tg4OD3nzzTb311lu6dOnSdcdDQkL03Xff2bXt378/2/O4uLgoIyPDdP8OHTrIxcVF77zzTpbH//2yiqx4eXkpMDDwlq8uf/zxx/XZZ5+pV69eWrJkiekaAQAAgMKuUIUnSerYsaMcHR314YcfXnfspZde0k8//aShQ4fq2LFjWrZsmaKioiQpW4/VBQYG6scff1RcXJz+97//KT09/ab9AwICNGXKFE2bNk09e/bU9u3b9euvv2rXrl166aWXNHbsWFPzjh49WpMnT9b06dN1/PhxHThwQO+///51/Z544gktXLhQPXr00IoVK0xfFwAAAFCYFbrw5OTkpL59++qdd97R33//bXcsKChIK1as0KpVq1SzZk3NmDFDw4cPl3T1ETazXnjhBYWEhKhevXoqUaJElm+8+7eXX35Zmzdv1u+//64nnnhCVapUUa9eveTt7Z3lG/+y0q1bN02dOlUfffSRqlevrtatW+v48eNZ9u3QoYPmz5+v5557TqtWrTJ9bQAAAEBhZTFu58c6hcD48eM1c+ZMnTp1Kq9LyXPJycny8fFRUlISv38CAABAgZCd77gF/m172fXRRx+pfv36KlasmHbt2qV33333pn9oFgAAAEDhUOge27uV48ePq23btqpWrZrGjh2r1157TaNHj76tMSdMmCBPT88st5YtW+ZO4QAAAADuKB7buwvOnj2rs2fPZnnMzc3tpn+o9l7CY3sAAAAoaHhs7x7j6+srX1/fvC4DAAAAwG3gsT0AAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAn5Njx1795dFotFvXv3vu7YK6+8IovFou7du9/9wnLoyJEjeuqpp1SiRAlZrVZVrlxZI0eO1MWLF/O6NAAAAADKx+FJkgICArRkyRJdunTJ1nb58mV99tlnKleuXB5Wlj3ffvutGjRooLS0NK1bt07Hjh3T+PHjFRUVpebNmystLe2OzW0Yhq5cuXLHxgcAAAAKinwdnurWrauAgACtWrXK1rZq1SqVK1dOderUsbWlpqaqX79+KlmypFxdXfXwww9r//79tuNRUVEqUqSI3dhffPGFLBaLbf/gwYMKCwuTl5eXvL29df/99+u7776zHd+5c6ceeeQRubm5KSAgQP369dPff/99y2swDEM9e/ZU1apVtWrVKj3wwAMqX768OnbsqC+//FJ79uzRlClTJEnPPvusOnXqZHd+enq6ihcvrgULFkiSMjMzFRkZqaCgILm5ualWrVpasWKFrX90dLQsFos2bNig+++/X1arVTt37rxlnQAAAEBhl6/DkyQ9//zzmjdvnm1/7ty56tGjh12f119/XStXrtT8+fN14MABBQcHKzw8XGfPnjU9T+fOnVW2bFnt379f33//vd544w05OztLkuLj49WiRQu1b99eP/74o5YuXaqdO3eqb9++txw3JiZGR48e1aBBg+TgYH87atWqpWbNmmnx4sW2Gr788kulpKTY+mzatEkXL17UE088IUmKjIzUggULNHPmTB05ckQDBw5Uly5dtH37drux33jjDU2cOFGxsbGqWbNmlrWlpqYqOTnZbgMAAAAKq3wfnrp06aKdO3fq119/1a+//qpdu3apS5cutuN///23ZsyYoXfffVctW7ZUtWrV9Mknn8jNzU1z5swxPc/JkyfVrFkzValSRZUqVVLHjh1Vq1YtSVcDS+fOnTVgwABVqlRJjRo10vTp07VgwQJdvnz5puMeO3ZMklS1atUsj1etWtXWJzw8XB4eHvr8889txz/77DM9/vjj8vLyUmpqqiZMmKC5c+cqPDxcFSpUUPfu3dWlSxfNmjXLbtwxY8aoefPmqlixonx9fbOcOzIyUj4+PrYtICDA3IcFAAAAFED5PjyVKFFCrVq1UlRUlObNm6dWrVqpePHituPx8fFKT0/XQw89ZGtzdnbWAw88oNjYWNPzDBo0SL169VKzZs00ceJExcfH244dPHhQUVFR8vT0tG3h4eHKzMzUiRMnTI1vGMYt+zg5Oempp57SokWLJF0NhqtXr1bnzp0lST///LMuXryo5s2b29WyYMECu3olqV69erecb9iwYUpKSrJtp06dMnUtAAAAQEHklNcF5Ibnn3/e9ojchx9+mO3zHRwcrgsv6enpdvujR4/Ws88+q3Xr1mnDhg0aNWqUlixZoieeeEIpKSl66aWX1K9fv+vGvtWLKypXrixJio2Ntfud1jWxsbG2PtLVR/eaNGmi06dPa8uWLXJzc1OLFi0kyfY437p161SmTBm7caxWq92+h4fHTeu6ds6/zwMAAAAKq3y/8iRJLVq0UFpamtLT0xUeHm53rGLFinJxcdGuXbtsbenp6dq/f7+qVasm6erq1YULF+xe8BATE3PdPJUrV9bAgQO1efNmPfnkk7bfWtWtW1dHjx5VcHDwdZuLi8tNa69du7aqVKmiKVOmKDMz0+7YwYMH9dVXX+mZZ56xtTVq1EgBAQFaunSpFi1apI4dO9p+e1WtWjVZrVadPHnyujp45A4AAAC4PQVi5cnR0dH2CJ6jo6PdMQ8PD/Xp00dDhgyRr6+vypUrp3feeUcXL15Uz549JUkNGjSQu7u73nzzTfXr10979+5VVFSUbYxLly5pyJAh6tChg4KCgvTbb79p//79at++vSRp6NChevDBB9W3b1/16tVLHh4eOnr0qLZs2aIPPvjgprVbLBbNmTNHzZs3V/v27TVs2DD5+flp7969eu2119SwYUMNGDDA7pxnn31WM2fO1LFjx7Rt2zZbu5eXlwYPHqyBAwcqMzNTDz/8sJKSkrRr1y55e3urW7duOf2IAQAAgEKvQKw8SZK3t7e8vb2zPDZx4kS1b99ezz33nOrWrauff/5ZmzZtUtGiRSVJvr6++vTTT7V+/XrVqFFDixcv1ujRo23nOzo66syZM+ratasqV66sp556Si1btlRERIQkqWbNmtq+fbuOHTumRx55RHXq1NHIkSNVunRpU7U3atRI3377rRwdHdWyZUsFBwdr2LBh6tatm7Zs2XLdo3OdO3fW0aNHVaZMGbvfcknS2LFjNWLECEVGRqpq1apq0aKF1q1bp6CgILMfJQAAAIAsWAwzbyoAJCUnJ8vHx0dJSUk3DKoAAABAfpKd77gFZuUJAAAAAO4kwtMdtmPHDrvXhv97AwAAAJA/FIgXRtzL6tWrl+Wb+wAAAADkL4SnO8zNzU3BwcF5XQYAAACA28RjewAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmMDfeUK23Tdqkxys7nd0joSJre7o+AAAAEB2sfIEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA85bLQ0FANGDAgr8sAAAAAkMv4O0+5bNWqVXJ2dpZ0NUht3779uj7p6elycuKjBwAAAPITVp5yma+vr7y8vGz7L7zwghITE+22rIJTWlra3SwTAAAAQDYRnnLZvx/bc3d3l5+fn90mSYGBgRo7dqy6du0qb29vvfjii5KkoUOHqnLlynJ3d1eFChU0YsQIpaen28YbPXq0ateurblz56pcuXLy9PTUyy+/rIyMDL3zzjvy8/NTyZIlNX78eLu6zp8/r169eqlEiRLy9vZW06ZNdfDgwTv/gQAAAAAFBM+O5aFJkyZp5MiRGjVqlK3Ny8tLUVFRKl26tA4dOqQXXnhBXl5eev3112194uPjtWHDBm3cuFHx8fHq0KGDfvnlF1WuXFnbt2/X7t279fzzz6tZs2Zq0KCBJKljx45yc3PThg0b5OPjo1mzZunRRx/VsWPH5Ovre9evHQAAAMhvWHm6wz766CN5enrattdee812rGnTpnrttddUsWJFVaxYUZL01ltvqVGjRgoMDFSbNm00ePBgLVu2zG7MzMxMzZ07V9WqVVObNm0UFhamuLg4TZ06VSEhIerRo4dCQkK0bds2SdLOnTu1b98+LV++XPXq1VOlSpU0adIkFSlSRCtWrLhh7ampqUpOTrbbAAAAgMKKlac7rHPnzho+fLhtv0iRIrZ/16tX77r+S5cu1fTp0xUfH6+UlBRduXJF3t7edn0CAwPtfldVqlQpOTo6ysHBwa7t9OnTkqSDBw8qJSVFxYoVsxvn0qVLio+Pv2HtkZGRioiIMHehAAAAQAFHeLrDfHx8FBwcnOUxDw8Pu/09e/aoc+fOioiIUHh4uHx8fLRkyRJNnjzZrt+1t/ldY7FYsmzLzMyUJKWkpMjf31/R0dHX1fDPMPdvw4YN06BBg2z7ycnJCggIuGF/AAAAoCAjPN1Ddu/erfLly9utVP3666+3PW7dunX1559/ysnJSYGBgabPs1qtslqttz0/AAAAUBDwm6d7SKVKlXTy5EktWbJE8fHxmj59uj7//PPbHrdZs2Zq2LCh2rVrp82bNyshIUG7d+/W8OHD9d133+VC5QAAAEDBR3i6hzz++OMaOHCg+vbtq9q1a2v37t0aMWLEbY9rsVi0fv16NW7cWD169FDlypX19NNP69dff1WpUqVyoXIAAACg4LMYhmHkdRHIH5KTk+Xj46OAAcvkYHW/o3MlTGx1R8cHAAAApP/7jpuUlHTdi9r+jZUnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYIJTXheA/OdwRPgtX+MIAAAAFDSsPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeMplUVFRKlKkSF6XcUvR0dGyWCw6f/58XpcCAAAA5Av5Ljx1795dFotFFotFzs7OKlWqlJo3b665c+cqMzMzr8tTp06ddOzYsVwb75/X+8+tRYsWuTYHAAAAgFtzyusCcqJFixaaN2+eMjIy9Ndff2njxo3q37+/VqxYoTVr1sjJKe8uy83NTW5ubrk65rXr/Ser1ZqrcwAAAAC4uXy38iRdDQ5+fn4qU6aM6tatqzfffFOrV6/Whg0bFBUVJUk6efKk2rZtK09PT3l7e+upp57SX3/9ZRtj9OjRql27tubOnaty5crJ09NTL7/8sjIyMvTOO+/Iz89PJUuW1Pjx4+3mfu+991SjRg15eHgoICBAL7/8slJSUmzH//3Y3rV5Fi5cqMDAQPn4+Ojpp5/WhQsXsn29/9yKFi1qO26xWDR79mw98cQTcnd3V6VKlbRmzRq7MdavX6/KlSvLzc1NYWFhSkhIMD0/AAAAgHwanrLStGlT1apVS6tWrVJmZqbatm2rs2fPavv27dqyZYt++eUXderUye6c+Ph4bdiwQRs3btTixYs1Z84ctWrVSr/99pu2b9+ut99+W2+99Zb27t1rO8fBwUHTp0/XkSNHNH/+fH399dd6/fXXb1pbfHy8vvjiC61du1Zr167V9u3bNXHixFy9/oiICD311FP68ccf9dhjj6lz5846e/asJOnUqVN68skn1aZNG8XExKhXr1564403bjlmamqqkpOT7TYAAACgsCow4UmSqlSpooSEBG3dulWHDh3SZ599pvvvv18NGjTQggULtH37du3fv9/WPzMzU3PnzlW1atXUpk0bhYWFKS4uTlOnTlVISIh69OihkJAQbdu2zXbOgAEDFBYWpsDAQDVt2lTjxo3TsmXLblpXZmamoqKidN999+mRRx7Rc889p61bt5q+rrVr18rT09NumzBhgl2f7t2765lnnlFwcLAmTJiglJQU7du3T5I0Y8YMVaxYUZMnT1ZISIg6d+6s7t2733LeyMhI+fj42LaAgADTNQMAAAAFTb78zdONGIYhi8Wi2NhYBQQE2H3Zr1atmooUKaLY2FjVr19fkhQYGCgvLy9bn1KlSsnR0VEODg52badPn7btf/XVV4qMjNRPP/2k5ORkXblyRZcvX9bFixfl7u6eZV3/nsff399uzFsJCwvTjBkz7Np8fX3t9mvWrGn7t4eHh7y9vW1zxMbGqkGDBnb9GzZseMt5hw0bpkGDBtn2k5OTCVAAAAAotApUeIqNjVVQUJDp/s7Oznb7197g9++2a2/xS0hIUOvWrdWnTx+NHz9evr6+2rlzp3r27Km0tLQbhqebjWmGh4eHgoODs30tt/v2QavVyospAAAAgP+vwDy29/XXX+vQoUNq3769qlatqlOnTunUqVO240ePHtX58+dVrVq1HM/x/fffKzMzU5MnT9aDDz6oypUr648//siN8u+oqlWr2h7hu+bbb7/No2oAAACA/Clfrjylpqbqzz//tHtVeWRkpFq3bq2uXbvKwcFBNWrUUOfOnTV16lRduXJFL7/8spo0aaJ69erleN7g4GClp6fr/fffV5s2bbRr1y7NnDkzF68sa9eu95+cnJxUvHhxU+f37t1bkydP1pAhQ9SrVy99//33trcSAgAAADAnX648bdy4Uf7+/goMDFSLFi20bds2TZ8+XatXr5ajo6MsFotWr16tokWLqnHjxmrWrJkqVKigpUuX3ta8tWrV0nvvvae3335b9913nxYtWqTIyMhcuqobu3a9/9wefvhh0+eXK1dOK1eu1BdffKFatWpp5syZ171wAgAAAMDNWQzDMPK6COQPycnJ8vHxUVJSkry9vfO6HAAAAOC2Zec7br5ceQIAAACAu43wlIdOnjx53d9v+ud28uTJvC4RAAAAwP+XL18YUVCULl1aMTExNz0OAAAA4N5AeMpDTk5Ot/z7TQAAAADuDTy2BwAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEp1wWFRWlIkWK5HUZAAAAAHJZvgtP3bt3l8VikcVikbOzs0qVKqXmzZtr7ty5yszMzOvy1KlTJx07dizXx92zZ48cHR3VqlWrXB8bAAAAwK3lu/AkSS1atFBiYqISEhK0YcMGhYWFqX///mrdurWuXLmSp7W5ubmpZMmSuT7unDlz9Oqrr+qbb77RH3/8kevjAwAAALi5fBmerFar/Pz8VKZMGdWtW1dvvvmmVq9erQ0bNigqKkqSdPLkSbVt21aenp7y9vbWU089pb/++ss2xujRo1W7dm3NnTtX5cqVk6enp15++WVlZGTonXfekZ+fn0qWLKnx48fbzf3ee++pRo0a8vDwUEBAgF5++WWlpKTYjv/7sb1r8yxcuFCBgYHy8fHR008/rQsXLpi+3pSUFC1dulR9+vRRq1atbNf4T2vWrFGlSpXk6uqqsLAwzZ8/XxaLRefPn7f12blzpx555BG5ubkpICBA/fr1099//226DgAAAKAwy5fhKStNmzZVrVq1tGrVKmVmZqpt27Y6e/astm/fri1btuiXX35Rp06d7M6Jj4/Xhg0btHHjRi1evFhz5sxRq1at9Ntvv2n79u16++239dZbb2nv3r22cxwcHDR9+nQdOXJE8+fP19dff63XX3/9prXFx8friy++0Nq1a7V27Vpt375dEydONH1ty5YtU5UqVRQSEqIuXbpo7ty5MgzDdvzEiRPq0KGD2rVrp4MHD+qll17S8OHDr6uhRYsWat++vX788UctXbpUO3fuVN++fW84b2pqqpKTk+02AAAAoNAy8plu3boZbdu2zfJYp06djKpVqxqbN282HB0djZMnT9qOHTlyxJBk7Nu3zzAMwxg1apTh7u5uJCcn2/qEh4cbgYGBRkZGhq0tJCTEiIyMvGE9y5cvN4oVK2bbnzdvnuHj42Pbz2qeIUOGGA0aNDB9zY0aNTKmTp1qGIZhpKenG8WLFze2bdtmOz506FDjvvvusztn+PDhhiTj3LlzhmEYRs+ePY0XX3zRrs+OHTsMBwcH49KlS1nOO2rUKEPSdVtSUpLp2gEAAIB7WVJSkunvuAVm5UmSDMOQxWJRbGysAgICFBAQYDtWrVo1FSlSRLGxsba2wMBAeXl52fZLlSqlatWqycHBwa7t9OnTtv2vvvpKjz76qMqUKSMvLy8999xzOnPmjC5evHjDuv49j7+/v92YNxMXF6d9+/bpmWeekSQ5OTmpU6dOmjNnjl2f+vXr2533wAMP2O0fPHhQUVFR8vT0tG3h4eHKzMzUiRMnspx72LBhSkpKsm2nTp0yVTMAAABQEDnldQG5KTY2VkFBQab7Ozs72+1fe4Pfv9uuvcUvISFBrVu3Vp8+fTR+/Hj5+vpq586d6tmzp9LS0uTu7m56HrNvBpwzZ46uXLmi0qVL29oMw5DVatUHH3wgHx8fU+OkpKTopZdeUr9+/a47Vq5cuSzPsVqtslqtpsYHAAAACroCE56+/vprHTp0SAMHDlTZsmV16tQpnTp1yrb6dPToUZ0/f17VqlXL8Rzff/+9MjMzNXnyZNvq1LJly3Kl/qxcuXJFCxYs0OTJk/Wf//zH7li7du20ePFi9e7dWyEhIVq/fr3d8f3799vt161bV0ePHlVwcPAdqxcAAAAoyPLlY3upqan6888/9fvvv+vAgQOaMGGC2rZtq9atW6tr165q1qyZatSooc6dO+vAgQPat2+funbtqiZNmqhevXo5njc4OFjp6el6//339csvv2jhwoWaOXNmLl6ZvbVr1+rcuXPq2bOn7rvvPrutffv2tkf3XnrpJf30008aOnSojh07pmXLltneyGexWCRJQ4cO1e7du9W3b1/FxMTo+PHjWr169U1fGAEAAADg/+TL8LRx40b5+/srMDBQLVq00LZt2zR9+nStXr1ajo6OslgsWr16tYoWLarGjRurWbNmqlChgpYuXXpb89aqVUvvvfee3n77bd13331atGiRIiMjc+mqrjdnzhw1a9Ysy0fz2rdvr++++04//vijgoKCtGLFCq1atUo1a9bUjBkzbG/bu/bYXc2aNbV9+3YdO3ZMjzzyiOrUqaORI0faPQ4IAAAA4MYshvGPd16jwBg/frxmzpyZqy95SE5Olo+Pj5KSkuTt7Z1r4wIAAAB5JTvfcQvMb54Ku48++kj169dXsWLFtGvXLr377rs8kgcAAADkIsJTHjp58uRNX2Bx9OjRG74J79+OHz+ucePG6ezZsypXrpxee+01DRs2LLdKBQAAAAo9HtvLQ1euXFFCQsINjwcGBsrJ6d7Jtzy2BwAAgIKGx/byCScnJ14dDgAAAOQT+fJtewAAAABwtxGeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAn8nSdk232jNsnB6n7X502Y2OquzwkAAABcw8oTAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ5uITo6WhaLRefPn8/rUux8/PHHCggIkIODg6ZOnZpl2+jRo1W7du08rRMAAAAoKApUeJo5c6a8vLx05coVW1tKSoqcnZ0VGhpq1/daKIqPj79j9XTv3l0Wi+WGW2BgYI7GTU5OVt++fTV06FD9/vvvevHFF7NsGzx4sLZu3Zq7FwUAAAAUUgUqPIWFhSklJUXfffedrW3Hjh3y8/PT3r17dfnyZVv7tm3bVK5cOVWsWPGO1TNt2jQlJibaNkmaN2+ebX///v12/dPS0kyNe/LkSaWnp6tVq1by9/eXu7t7lm2enp4qVqxYrl8XAAAAUBgVqPAUEhIif39/RUdH29qio6PVtm1bBQUF6dtvv7VrDwsLU2ZmpiIjIxUUFCQ3NzfVqlVLK1asuG7sXbt2qWbNmnJ1ddWDDz6ow4cP37IeHx8f+fn52TZJKlKkiG2/fv36Gjt2rLp27Spvb2+9+OKLkqShQ4eqcuXKcnd3V4UKFTRixAilp6dLkqKiolSjRg1JUoUKFWSxWLJsS0hIyPKxvblz56p69eqyWq3y9/dX3759zX/AAAAAQCFWoMKTdHX1adu2bbb9bdu2KTQ0VE2aNLG1X7p0SXv37lVYWJgiIyO1YMECzZw5U0eOHNHAgQPVpUsXbd++3W7cIUOGaPLkydq/f79KlCihNm3a2ALN7Zg0aZJq1aqlH374QSNGjJAkeXl5KSoqSkePHtW0adP0ySefaMqUKZKkTp066auvvpIk7du3T4mJierYseN1bQEBAdfNNWPGDL3yyit68cUXdejQIa1Zs0bBwcE3rC01NVXJycl2GwAAAFBYOeV1AbktLCxMAwYM0JUrV3Tp0iX98MMPatKkidLT0zVz5kxJ0p49e5SamqrQ0FBVq1ZNX331lRo2bCjp6srNzp07NWvWLDVp0sQ27qhRo9S8eXNJ0vz581W2bFl9/vnneuqpp26r3qZNm+q1116za3vrrbds/w4MDNTgwYO1ZMkSvf7663Jzc7M9ileiRAnbilZWbf82btw4vfbaa+rfv7+trX79+jesLTIyUhERETm7MAAAAKCAKXDhKTQ0VH///bf279+vc+fOqXLlyipRooSaNGmiHj166PLly4qOjlaFChWUkpKiixcv2kLRNWlpaapTp45d27VwJUm+vr4KCQlRbGzsbddbr16969qWLl2q6dOnKz4+XikpKbpy5Yq8vb1va57Tp0/rjz/+0KOPPmr6nGHDhmnQoEG2/eTk5CxXtAAAAIDCoMCFp+DgYJUtW1bbtm3TuXPnbKtHpUuXVkBAgHbv3q1t27apadOmSklJkSStW7dOZcqUsRvHarXelXo9PDzs9vfs2aPOnTsrIiJC4eHh8vHx0ZIlSzR58uTbmsfNzS3b51it1rv2OQAAAAD3ugIXnqSrj+5FR0fr3LlzGjJkiK29cePG2rBhg/bt26c+ffqoWrVqslqtOnnypN0jeln59ttvVa5cOUnSuXPndOzYMVWtWjXXa9+9e7fKly+v4cOH29p+/fXX2x7Xy8tLgYGB2rp1q8LCwm57PAAAAKCwKbDh6ZVXXlF6erpdKGrSpIn69u2rtLQ0hYWFycvLS4MHD9bAgQOVmZmphx9+WElJSdq1a5e8vb3VrVs327ljxoxRsWLFVKpUKQ0fPlzFixdXu3btcr32SpUq6eTJk1qyZInq16+vdevW6fPPP8+VsUePHq3evXurZMmSatmypS5cuKBdu3bp1VdfzZXxAQAAgIKswIanS5cuqUqVKipVqpStvUmTJrpw4YLtleaSNHbsWJUoUUKRkZH65ZdfVKRIEdWtW1dvvvmm3ZgTJ05U//79dfz4cdWuXVtffvmlXFxccr32xx9/XAMHDlTfvn2VmpqqVq1aacSIERo9evRtj92tWzddvnxZU6ZM0eDBg1W8eHF16NDh9osGAAAACgGLYRhGXheB/CE5OVk+Pj4KGLBMDlb3uz5/wsRWd31OAAAAFGzXvuMmJSXd8iVtBe7vPAEAAADAnUB4uk0tW7aUp6dnltuECRPyujwAAAAAuaRA/ubpbpo9e7YuXbqU5TFfX9+7XA0AAACAO4XwdJv+/fehAAAAABRMPLYHAAAAACaw8oRsOxwRfss3kQAAAAAFDStPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACTkOTwsXLtRDDz2k0qVL69dff5UkTZ06VatXr8614gAAAADgXpGj8DRjxgwNGjRIjz32mM6fP6+MjAxJUpEiRTR16tTcrA8AAAAA7gk5Ck/vv/++PvnkEw0fPlyOjo629nr16unQoUO5VhzurNDQUA0YMCCvywAAAADyhRyFpxMnTqhOnTrXtVutVv3999+3XZRZ3bt3l8VikcVikbOzs0qVKqXmzZtr7ty5yszMtPULDAyUxWLRt99+a3f+gAEDFBoaatu/ePGihg0bpooVK8rV1VUlSpRQkyZNTD+KGBoaaqvH1dVVlStXVmRkpAzDyJXrBQAAAJB3chSegoKCFBMTc137xo0bVbVq1dutKVtatGihxMREJSQkaMOGDQoLC1P//v3VunVrXblyxdbP1dVVQ4cOvelYvXv31qpVq/T+++/rp59+0saNG9WhQwedOXPGdD0vvPCCEhMTFRcXp2HDhmnkyJGaOXNmjq/vdhmGYfc5AAAAAMiZHIWnQYMG6ZVXXtHSpUtlGIb27dun8ePHa9iwYXr99ddzu8abslqt8vPzU5kyZVS3bl29+eabWr16tTZs2KCoqChbvxdffFHffvut1q9ff8Ox1qxZozfffFOPPfaYAgMDdf/99+vVV1/V888/b7oed3d3+fn5qXz58urRo4dq1qypLVu22I6npqZq8ODBKlOmjDw8PNSgQQNFR0fbjbFr1y6FhobK3d1dRYsWVXh4uM6dO2c7v1+/fipZsqRcXV318MMPa//+/bZzo6OjZbFYtGHDBt1///2yWq3auXOn/v77b3Xt2lWenp7y9/fX5MmTTV8TAAAAgByGp169euntt9/WW2+9pYsXL+rZZ5/VjBkzNG3aND399NO5XWO2NW3aVLVq1dKqVatsbUFBQerdu7eGDRtm90jfP/n5+Wn9+vW6cOHCbddgGIZ27Nihn376SS4uLrb2vn37as+ePVqyZIl+/PFHdezYUS1atNDx48clSTExMXr00UdVrVo17dmzRzt37lSbNm1sL+V4/fXXtXLlSs2fP18HDhxQcHCwwsPDdfbsWbv533jjDU2cOFGxsbGqWbOmhgwZou3bt2v16tXavHmzoqOjdeDAgZteQ2pqqpKTk+02AAAAoNAysik9Pd2YP3++8eeffxqGYRh///238ddff2V3mFzRrVs3o23btlke69Spk1G1alXDMAyjfPnyxpQpU4zTp08bXl5exoIFCwzDMIz+/fsbTZo0sZ2zfft2o2zZsoazs7NRr149Y8CAAcbOnTtN19OkSRPD2dnZ8PDwMJydnQ1Jhqurq7Fr1y7DMAzj119/NRwdHY3ff//d7rxHH33UGDZsmGEYhvHMM88YDz30UJbjp6SkGM7OzsaiRYtsbWlpaUbp0qWNd955xzAMw9i2bZshyfjiiy9sfS5cuGC4uLgYy5Yts7WdOXPGcHNzM/r373/D6xk1apQh6botKSnJ9GcCAAAA3MuSkpJMf8fN9sqTk5OTevfurcuXL0u6+phayZIlcy3M5RbDMGSxWOzaSpQoocGDB2vkyJFKS0u77pzGjRvrl19+0datW9WhQwcdOXJEjzzyiMaOHWt63s6dOysmJka7du1Sy5YtNXz4cDVq1EiSdOjQIWVkZKhy5cry9PS0bdu3b1d8fLyk/1t5ykp8fLzS09P10EMP2dqcnZ31wAMPKDY21q5vvXr17M5LS0tTgwYNbG2+vr4KCQm56bUMGzZMSUlJtu3UqVOmPwcAAACgoHHKyUkPPPCAfvjhB5UvXz6368k1sbGxCgoKuq590KBB+uijj/TRRx9leZ6zs7MeeeQRPfLIIxo6dKjGjRunMWPGaOjQoXaP392Ij4+PgoODJUnLli1TcHCwHnzwQTVr1kwpKSlydHTU999/b/eKd0ny9PSUJLm5uWX3UrPk4eFx22NYrVZZrdZcqAYAAADI/3L0m6eXX35Zr732mj744APt2bNHP/74o92W177++msdOnRI7du3v+6Yp6enRowYofHjx5v6bVO1atV05coV20pbdnh6eqp///4aPHiwDMNQnTp1lJGRodOnTys4ONhu8/PzkyTVrFlTW7duzXK8ihUrysXFRbt27bK1paena//+/apWrdoN66hYsaKcnZ21d+9eW9u5c+d07NixbF8TAAAAUFjlaOXp2ksh+vXrZ2uzWCy2R+WuvdzgbkhNTdWff/6pjIwM/fXXX9q4caMiIyPVunVrde3aNctzXnzxRU2ZMkWfffaZ3aNsoaGheuaZZ1SvXj0VK1ZMR48e1ZtvvqmwsDB5e3vnqL6XXnpJY8eO1cqVK9WhQwd17txZXbt21eTJk1WnTh3997//1datW1WzZk21atVKw4YNU40aNfTyyy+rd+/ecnFx0bZt29SxY0cVL15cffr00ZAhQ+Tr66ty5crpnXfe0cWLF9WzZ88b1uDp6amePXtqyJAhKlasmEqWLKnhw4fLwSFH2RkAAAAolHIUnk6cOJHbdeTYxo0b5e/vLycnJxUtWlS1atXS9OnT1a1btxuGA2dnZ40dO1bPPvusXXt4eLjmz5+vN998UxcvXlTp0qXVunVrjRw5Msf1+fr6qmvXrho9erSefPJJzZs3T+PGjdNrr72m33//XcWLF9eDDz6o1q1bS5IqV66szZs3680339QDDzwgNzc3NWjQQM8884wkaeLEicrMzNRzzz2nCxcuqF69etq0aZOKFi160zreffddpaSkqE2bNvLy8tJrr72mpKSkHF8XAAAAUNhYDMMw8roI5A/Jycny8fFRUlJSjlfiAAAAgHtJdr7j5mjlacGCBTc9fqPH5QAAAAAgv8rRytO/HxFLT0/XxYsX5eLiInd39+v+YGtBsGPHDrVs2fKGx1NSUu5iNXmDlScAAAAUNHd85encuXPXtR0/ftz2MoOCqF69eoqJicnrMgAAAADkkVz9zdN3332nLl266KeffsqtIXEPYeUJAAAABU12vuPm6ruqnZyc9Mcff+TmkAAAAABwT8jRY3tr1qyx2zcMQ4mJifrggw/00EMP5UphAAAAAHAvyVF4ateund2+xWJRiRIl1LRpU02ePDk36gIAAACAe0qOwlNmZmZu1wEAAAAA97Qc/eZpzJgxunjx4nXtly5d0pgxY267KAAAAAC41+TobXuOjo5KTExUyZIl7drPnDmjkiVLKiMjI9cKxL2Dt+0BAACgoLnjb9szDEMWi+W69oMHD8rX1zcnQwIAAADAPS1bv3kqWrSoLBaLLBaLKleubBegMjIylJKSot69e+d6kbi33Ddqkxys7nldBnDXJExsldclAACAe0C2wtPUqVNlGIaef/55RUREyMfHx3bMxcVFgYGBatiwYa4XCQAAAAB5LVvhqVu3bpKkoKAgNWrUSM7OznekKAAAAAC41+ToVeVNmjSx/fvy5ctKS0uzO87LBAAAAAAUNDl6YcTFixfVt29flSxZUh4eHipatKjdBgAAAAAFTY7C05AhQ/T1119rxowZslqtmj17tiIiIlS6dGktWLAgt2sEAAAAgDyXo8f2vvzySy1YsEChoaHq0aOHHnnkEQUHB6t8+fJatGiROnfunNt1AgAAAECeytHK09mzZ1WhQgVJV3/fdPbsWUnSww8/rG+++Sb3qgMAAACAe0SOwlOFChV04sQJSVKVKlW0bNkySVdXpIoUKZJrxeWl0aNHq1SpUrJYLPriiy/UvXt3tWvXLq/LkiR98cUXCg4OlqOjowYMGJBlW1RUVIG5FwAAAMC9IEfhqUePHjp48KAk6Y033tCHH34oV1dXDRw4UEOGDMnVAm+le/futj/c6+LiouDgYI0ZM0ZXrlzJ8ZixsbGKiIjQrFmzlJiYqJYtW2ratGmKiorK9ljz58/Xww8/LEk6ceKEnn32WZUuXVqurq4qW7as2rZtq59++ilbY7700kvq0KGDTp06pbFjx2bZ1qlTJx07dizb9QIAAADIWo5+8zRw4EDbv5s1a6affvpJ33//vYKDg1WzZs1cK86sFi1aaN68eUpNTdX69ev1yiuvyNnZWcOGDbPrl5aWJhcXl1uOFx8fL0lq27atLBaLJMlqteaottWrV+vxxx9Xenq6mjdvrpCQEK1atUr+/v767bfftGHDBp0/f970eCkpKTp9+rTCw8NVunTpG7ZJkpubW45qBgAAAHC9HK08/dPly5dVvnx5Pfnkk3kSnKSrwcbPz0/ly5dXnz591KxZM61Zs8b2qN348eNVunRphYSESJJOnTqlp556SkWKFJGvr6/atm2rhIQESVcf12vTpo0kycHBwRae/vnY3n//+1/5+flpwoQJthp2794tFxcXbd261dZ2+fJlbd68WY8//riOHDmi+Ph4ffTRR3rwwQdVvnx5PfTQQxo3bpwefPBBSVJ0dLQsFotdmIqJiZHFYlFCQoKio6Pl5eUlSWratKksFssN2/752J5hGGrWrJnCw8NlGIakq79bK1u2rEaOHJmLdwIAAAAouHIUnjIyMjR27FiVKVNGnp6e+uWXXyRJI0aM0Jw5c3K1wJxwc3Oz/eHerVu3Ki4uTlu2bNHatWuVnp6u8PBweXl5aceOHdq1a5c8PT3VokULpaWlafDgwZo3b54kKTExUYmJideNX6JECc2dO1ejR4/Wd999pwsXLui5555T37599eijj9r6bd26VWXKlFGVKlVUokQJOTg4aMWKFcrIyMjRdTVq1EhxcXGSpJUrVyoxMfGGbf9ksVg0f/587d+/X9OnT5ck9e7dW2XKlLlpeEpNTVVycrLdBgAAABRWOQpP48ePV1RUlN555x27x+Duu+8+zZ49O9eKyy7DMPTVV19p06ZNatq0qSTJw8NDs2fPVvXq1VW9enUtXbpUmZmZmj17tmrUqKGqVatq3rx5OnnypKKjo+Xp6WlbsfHz85Ofn1+Wcz322GN64YUX1LlzZ/Xu3VseHh6KjIy063PtkT1JKlOmjKZPn66RI0eqaNGiatq0qcaOHWsLnma4uLioZMmSkiRfX1/5+fndsO3fypQpo1mzZumNN97QsGHDtH79en366adycrrxk5uRkZHy8fGxbQEBAaZrBQAAAAqaHIWnBQsW6OOPP1bnzp3l6Ohoa69Vq1a2X36QG9auXStPT0+5urqqZcuW6tSpk0aPHi1JqlGjhl2YOHjwoH7++Wd5eXnJ09NTnp6e8vX11eXLl22/dTJr0qRJunLlipYvX65FixbZ/S7KMAx9+eWXtvAkSa+88or+/PNPLVq0SA0bNtTy5ctVvXp1bdmy5fY+AJM6duyoJ554QhMnTtSkSZNUqVKlm/YfNmyYkpKSbNupU6fuSp0AAADAvShHL4z4/fffFRwcfF17Zmam0tPTb7uo7AoLC9OMGTPk4uKi0qVL262meHh42PVNSUnR/fffr0WLFl03TokSJbI1b3x8vP744w9lZmYqISFBNWrUsB3bt2+frly5ct0jdF5eXmrTpo3atGmjcePGKTw8XOPGjVPz5s3l4HA1y177XZKkXP08L168qO+//16Ojo46fvz4LftbrdYcvygDAAAAKGhyFJ6qVaumHTt2qHz58nbtK1asUJ06dXKlsOzw8PDIMsxlpW7dulq6dKlKliwpb2/vHM+ZlpamLl26qFOnTgoJCVGvXr106NAh2yN0q1evVqtWrexW5v7NYrGoSpUq2r17t6T/C2+JiYkqWrSopKsvjMgtr732mhwcHLRhwwY99thjatWqle3xRgAAAAA3l6PH9kaOHKm+ffvq7bffVmZmplatWqUXXnhB48ePv+ff3ta5c2cVL15cbdu21Y4dO3TixAlFR0erX79++u2330yPM3z4cCUlJWn69OkaOnSoKleurOeff952fM2aNXaP7MXExKht27ZasWKFjh49qp9//llz5szR3Llz1bZtW0lScHCwAgICNHr0aB0/flzr1q3T5MmTc+W6161bp7lz52rRokVq3ry5hgwZom7duuncuXO5Mj4AAABQ0GUrPP3yyy8yDENt27bVl19+qa+++koeHh4aOXKkYmNj9eWXX6p58+Z3qtZc4e7urm+++UblypXTk08+qapVq6pnz566fPmy6ZWo6OhoTZ06VQsXLpS3t7ccHBy0cOFC7dixQzNmzFB8fLx+/vlnhYeH284pW7asAgMDFRERoQYNGqhu3bqaNm2aIiIiNHz4cEmSs7OzFi9erJ9++kk1a9bU22+/rXHjxt32Nf/3v/9Vz549NXr0aNWtW1eSFBERoVKlSql37963PT4AAABQGFiMf/7A5hYcHR2VmJhoezStU6dOmj59ukqVKnXHCsyP3nvvPX311Vdav359XpeSq5KTk6++dW/AMjlY3fO6HOCuSZjYKq9LAAAAd8i177hJSUm3XEzJ1srTv3PWhg0b9Pfff2e/wgKubNmyGjZsWF6XAQAAACAX5eiFEddkY9GqUHnqqafyugQAAAAAuSxbK08Wi0UWi+W6NgAAAAAo6LK18mQYhrp372772z+XL19W7969r/tbSqtWrcq9CgEAAADgHpCt8NStWze7/S5duuRqMQAAAABwr8rW2/ZQuGXnTSQAAABAfnDH3rYHAAAAAIUV4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeclH37t3Vrl27vC4DAAAAwB1AeDLJYrHcdBs9erSmTZumqKioXJ03NDRUAwYMuK49KipKRYoUse2PHj3aVoujo6MCAgL04osv6uzZs3bnBQYGaurUqblaIwAAAFAYOOV1AflFYmKi7d9Lly7VyJEjFRcXZ2vz9PSUp6dnXpRmU716dX311VfKyMhQbGysnn/+eSUlJWnp0qV5WhcAAABQELDyZJKfn59t8/HxkcVisWvz9PS87rG90NBQ9evXT6+//rp8fX3l5+en0aNH244///zzat26td086enpKlmypObMmZPtGp2cnOTn56cyZcqoWbNm6tixo7Zs2ZLTSwYAAADwD4SnO2z+/Pny8PDQ3r179c4772jMmDG2QNOrVy9t3LjRblVr7dq1unjxojp16nRb8yYkJGjTpk1ycXHJ8RipqalKTk622wAAAIDCivB0h9WsWVOjRo1SpUqV1LVrV9WrV09bt26VJDVq1EghISFauHChrf+8efPUsWPHHD0CeOjQIXl6esrNzU1BQUE6cuSIhg4dmuPaIyMj5ePjY9sCAgJyPBYAAACQ3xGe7rCaNWva7fv7++v06dO2/V69emnevHmSpL/++ksbNmzQ888/n6O5QkJCFBMTo/3792vo0KEKDw/Xq6++muPahw0bpqSkJNt26tSpHI8FAAAA5HeEpzvM2dnZbt9isSgzM9O237VrV/3yyy/as2ePPv30UwUFBemRRx6xHff29lZSUtJ1454/f14+Pj52bS4uLgoODtZ9992niRMnytHRURERETmu3Wq1ytvb224DAAAACivCUx4rVqyY2rVrp3nz5ikqKko9evSwOx4SEqIDBw5cd96BAwdUuXLlm4791ltvadKkSfrjjz9ytWYAAACgMOJV5feAXr16qXXr1srIyFC3bt3sjvXp00cffPCB+vXrp169eslqtWrdunVavHixvvzyy5uO27BhQ9WsWVMTJkzQBx98YGv//fffFRMTY9e3fPnyKlq0aK5dEwAAAFDQsPJ0D2jWrJn8/f0VHh6u0qVL2x2rUKGCvvnmG/30009q1qyZGjRooGXLlmn58uVq0aLFLcceOHCgZs+ebfd7pUmTJqlOnTp227p163L9ugAAAICCxGIYhpHXRRR2KSkpKlOmjObNm6cnn3wyr8u5oeTkZPn4+CgpKYnfPwEAAKBAyM53XB7by0OZmZn63//+p8mTJ6tIkSJ6/PHH87okAAAAADdAeMpDJ0+eVFBQkMqWLauoqCg5OXE7AAAAgHsV39bzUGBgoHhqEgAAAMgfeGEEAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMCEeyo8hYaGasCAAXldRoHQvXt3tWvXLq/LAAAAAAqMeyo83YrFYtEXX3xh1zZ69GjVrl37rtYRGBgoi8Uii8UiNzc3BQYG6qmnntLXX399V+u4mWnTpikqKiqvywAAAAAKjHwVnu4lY8aMUWJiouLi4rRgwQIVKVJEzZo10/jx4/O6NEmSj4+PihQpktdlAAAAAAXGPReeMjMz9frrr8vX11d+fn4aPXq0pKurPZL0xBNPyGKxKDAwUFFRUYqIiNDBgwdtK0HXVlssFotmzJihli1bys3NTRUqVNCKFSts86Slpalv377y9/eXq6urypcvr8jISNN1enl5yc/PT+XKlVPjxo318ccfa8SIERo5cqTi4uJs/bZv364HHnhAVqtV/v7+euONN3TlyhXb8dDQUL366qsaMGCAihYtqlKlSumTTz7R33//rR49esjLy0vBwcHasGGD7ZyMjAz17NlTQUFBcnNzU0hIiKZNm2ZX378f2wsNDVW/fv2y/GwBAAAA3No9F57mz58vDw8P7d27V++8847GjBmjLVu2aP/+/ZKkefPmKTExUfv371enTp302muvqXr16kpMTFRiYqI6depkG2vEiBFq3769Dh48qM6dO+vpp59WbGysJGn69Olas2aNli1bpri4OC1atMgW0HKqf//+MgxDq1evliT9/vvveuyxx1S/fn0dPHhQM2bM0Jw5czRu3Ljrrrl48eLat2+fXn31VfXp00cdO3ZUo0aNdODAAf3nP//Rc889p4sXL0q6GjDLli2r5cuX6+jRoxo5cqTefPNNLVu2LEef7Y2kpqYqOTnZbgMAAAAKLeMe0qRJE+Phhx+2a6tfv74xdOhQwzAMQ5Lx+eef2x0fNWqUUatWrevGkmT07t3brq1BgwZGnz59DMMwjFdffdVo2rSpkZmZme06y5cvb0yZMiXLY6VKlbLN8eabbxohISF2c3z44YeGp6enkZGRYRjG9dd85coVw8PDw3juuedsbYmJiYYkY8+ePTes6ZVXXjHat29v2+/WrZvRtm1b2/6tPtusjBo1ypB03ZaUlHTDcwAAAID8JCkpyfR33Htu5almzZp2+/7+/jp9+nSOxmrYsOF1+9dWnrp3766YmBiFhISoX79+2rx5c84K/hfDMGSxWCRJsbGxatiwoW1fkh566CGlpKTot99+s7X985odHR1VrFgx1ahRw9ZWqlQpSbL7HD788EPdf//9KlGihDw9PfXxxx/r5MmTN60tu5/tsGHDlJSUZNtOnTp10/EBAACAguyeC0/Ozs52+xaLRZmZmbk+T926dXXixAmNHTtWly5d0lNPPaUOHTrc1phnzpzRf//7XwUFBWXrvKyu+Z9t18LXtc9hyZIlGjx4sHr27KnNmzcrJiZGPXr0UFpaWrbnudlna7Va5e3tbbcBAAAAhdU9F55uxtnZWRkZGXZtLi4u17Vd8+233163X7VqVdu+t7e3OnXqpE8++URLly7VypUrdfbs2RzXN23aNDk4ONhe1FC1alXt2bNHhmHY+uzatUteXl4qW7ZsjufZtWuXGjVqpJdffll16tRRcHCw4uPjczweAAAAgFtzyusCsiMwMFBbt27VQw89JKvVqqJFiyowMFAnTpxQTEyMypYtKy8vL1mtVknS8uXLVa9ePT388MNatGiR9u3bpzlz5kiS3nvvPfn7+6tOnTpycHDQ8uXL5efnZ/r13hcuXNCff/6p9PR0nThxQp9++qlmz56tyMhIBQcHS5JefvllTZ06Va+++qr69u2ruLg4jRo1SoMGDZKDQ85za6VKlbRgwQJt2rRJQUFBWrhwofbv35/tFS8AAAAA5uWrlafJkydry5YtCggIUJ06dSRJ7du3V4sWLRQWFqYSJUpo8eLFtv4RERFasmSJatasqQULFmjx4sWqVq2apKuvGn/nnXdUr1491a9fXwkJCVq/fr3pUDNy5Ej5+/srODhYzz33nJKSkrR161YNHTrU1qdMmTJav3699u3bp1q1aql3797q2bOn3nrrrdv6HF566SU9+eST6tSpkxo0aKAzZ87o5Zdfvq0xAQAAANycxfjnM2UFiMVi0eeff273t45we5KTk+Xj46OkpCR+/wQAAIACITvfcfPVyhMAAAAA5BXC078sWrRInp6eWW7Vq1fP6/IAAAAA5JF89cKI7Mjp04iPP/64GjRokOWxf7/qGwAAAEDhUWDDU055eXnJy8srr8sAAAAAcI/hsT0AAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDyZFB0dLYvFovPnz0uSoqKiVKRIkTypJS/nBgAAAAqrAhmeunfvLovFIovFImdnZwUFBen111/X5cuXc22OTp066dixY7k23o0EBgZq6tSpeTI3AAAAgP/jlNcF3CktWrTQvHnzlJ6eru+//17dunWTxWLR22+/nSvju7m5yc3N7YbH09LS5OLikitzZXduAAAAALmvQK48SZLVapWfn58CAgLUrl07NWvWTFu2bJEkZWZmKjIyUkFBQXJzc1OtWrW0YsUKu/PXr1+vypUry83NTWFhYUpISLA7/u9H50aPHq3atWtr9uzZCgoKkqurqyTp/Pnz6tWrl0qUKCFvb281bdpUBw8etBvryy+/VP369eXq6qrixYvriSeekCSFhobq119/1cCBA20raVnNLUkzZsxQxYoV5eLiopCQEC1cuNDuuMVi0ezZs/XEE0/I3d1dlSpV0po1a3L02QIAAACFUYENT/90+PBh7d6927YSFBkZqQULFmjmzJk6cuSIBg4cqC5dumj79u2SpFOnTunJJ59UmzZtFBMTo169eumNN9645Tw///yzVq5cqVWrVikmJkaS1LFjR50+fVobNmzQ999/r7p16+rRRx/V2bNnJUnr1q3TE088occee0w//PCDtm7dqgceeECStGrVKpUtW1ZjxoxRYmKiEhMTs5z3888/V//+/fXaa6/p8OHDeumll9SjRw9t27bNrl9ERISeeuop/fjjj3rsscfUuXNnWx1ZSU1NVXJyst0GAAAAFFpGAdStWzfD0dHR8PDwMKxWqyHJcHBwMFasWGFcvnzZcHd3N3bv3m13Ts+ePY1nnnnGMAzDGDZsmFGtWjW740OHDjUkGefOnTMMwzDmzZtn+Pj42I6PGjXKcHZ2Nk6fPm1r27Fjh+Ht7W1cvnzZbqyKFSsas2bNMgzDMBo2bGh07tz5htdSvnx5Y8qUKXZt/567UaNGxgsvvGDXp2PHjsZjjz1m25dkvPXWW7b9lJQUQ5KxYcOGG849atQoQ9J1W1JS0g3PAQAAAPKTpKQk099xC+zKU1hYmGJiYrR3715169ZNPXr0UPv27fXzzz/r4sWLat68uTw9PW3bggULFB8fL0mKjY1VgwYN7MZr2LDhLecsX768SpQoYds/ePCgUlJSVKxYMbu5Tpw4YZsrJiZGjz766G1da2xsrB566CG7toceekixsbF2bTVr1rT928PDQ97e3jp9+vQNxx02bJiSkpJs26lTp26rTgAAACA/K7AvjPDw8FBwcLAkae7cuapVq5bmzJmj++67T9LVx+XKlCljd47Var3tOf8pJSVF/v7+io6Ovq7vtd8s3c0XPzg7O9vtWywWZWZm3rC/1Wq97c8EAAAAKCgKbHj6JwcHB7355psaNGiQjh07JqvVqpMnT6pJkyZZ9q9atep1L1P49ttvsz1v3bp19eeff8rJyUmBgYFZ9qlZs6a2bt2qHj16ZHncxcVFGRkZN52natWq2rVrl7p162Zr27Vrl6pVq5btmgEAAABkrcA+tvdvHTt2lKOjo2bNmqXBgwdr4MCBmj9/vuLj43XgwAG9//77mj9/viSpd+/eOn78uIYMGaK4uDh99tlnioqKyvaczZo1U8OGDdWuXTtt3rxZCQkJ2r17t4YPH67vvvtOkjRq1CgtXrxYo0aNUmxsrA4dOmT3OvXAwEB98803+v333/W///0vy3mGDBmiqKgozZgxQ8ePH9d7772nVatWafDgwdn/oAAAAABkqdCEJycnJ/Xt21fvvPOOhg0bphEjRigyMlJVq1ZVixYttG7dOgUFBUmSypUrp5UrV+qLL75QrVq1NHPmTE2YMCHbc1osFq1fv16NGzdWjx49VLlyZT399NP69ddfVapUKUlXX0e+fPlyrVmzRrVr11bTpk21b98+2xhjxoxRQkKCKlasaPd7qn9q166dpk2bpkmTJql69eqaNWuW5s2bp9DQ0Ox/UAAAAACyZDEMw8jrIpA/JCcny8fHR0lJSfL29s7rcgAAAIDblp3vuIVm5QkAAAAAbgfhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJTnldAPKf+0ZtkoPVPa/LwP+XMLFVXpcAAABQKLDyBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYSnQqJ79+5q165dXpcBAAAA5FsFOjyFhoZqwIAB17VHRUWpSJEikqTRo0fLYrHIYrHI0dFRAQEBevHFF3X27Fm7cwIDAzV16tRs15CQkGAb32KxyMvLS9WrV9crr7yi48eP5+CqzM0XExOT62MDAAAAhVmBDk9mVa9eXYmJiTp58qTmzZunjRs3qk+fPrk6x1dffaXExEQdPHhQEyZMUGxsrGrVqqWtW7fm6jwAAAAA7gzCkyQnJyf5+fmpTJkyatasmTp27KgtW7bk6hzFihWTn5+fKlSooLZt2+qrr75SgwYN1LNnT2VkZNj6rV69WnXr1pWrq6sqVKigiIgIXblyxXbcYrFoxowZatmypdzc3FShQgWtWLHCdjwoKEiSVKdOHVksFoWGhtrVMWnSJPn7+6tYsWJ65ZVXlJ6enqvXCQAAABRUhKd/SUhI0KZNm+Ti4nJH53FwcFD//v3166+/6vvvv5ck7dixQ127dlX//v119OhRzZo1S1FRURo/frzduSNGjFD79u118OBBde7cWU8//bRiY2MlSfv27ZP0fytdq1atsp23bds2xcfHa9u2bZo/f76ioqIUFRV1wxpTU1OVnJxstwEAAACFFeFJ0qFDh+Tp6Sk3NzcFBQXpyJEjGjp06B2ft0qVKpKuBjZJioiI0BtvvKFu3bqpQoUKat68ucaOHatZs2bZndexY0f16tVLlStX1tixY1WvXj29//77kqQSJUpI+r+VLl9fX9t5RYsW1QcffKAqVaqodevWatWq1U0fG4yMjJSPj49tCwgIyM3LBwAAAPIVp7wu4F4QEhKiNWvW6PLly/r0008VExOjV1999Y7PaxiGpKuP4knSwYMHtWvXLruVpoyMDF2+fFkXL16Uu7u7JKlhw4Z24zRs2NDUCyKqV68uR0dH276/v78OHTp0w/7Dhg3ToEGDbPvJyckEKAAAABRaBTo8eXt7Kykp6br28+fPy8fHx7bv4uKi4OBgSdLEiRPVqlUrRUREaOzYsXe0vmuP2l37nVJKSooiIiL05JNPXtfX1dX1tudzdna227dYLMrMzLxhf6vVKqvVetvzAgAAAAVBgX5sLyQkRAcOHLiu/cCBA6pcufINz3vrrbc0adIk/fHHH3estszMTE2fPl1BQUGqU6eOJKlu3bqKi4tTcHDwdZuDw//dqm+//dZurG+//VZVq1aVJNtvtf75EgoAAAAAt69Arzz16dNHH3zwgfr166devXrJarVq3bp1Wrx4sb788ssbntewYUPVrFlTEyZM0AcffGBr//333697PK58+fIqWrToLWs5c+aM/vzzT128eFGHDx/W1KlTtW/fPq1bt872KN3IkSPVunVrlStXTh06dJCDg4MOHjyow4cPa9y4cbaxli9frnr16unhhx/WokWLtG/fPs2ZM0eSVLJkSbm5uWnjxo0qW7asXF1d7VbZAAAAAORMgV55qlChgr755hv99NNPatasmRo0aKBly5Zp+fLlatGixU3PHThwoGbPnq1Tp07Z2iZNmqQ6derYbevWrTNVS7NmzeTv768aNWrojTfeUNWqVfXjjz8qLCzM1ic8PFxr167V5s2bVb9+fT344IOaMmWKypcvbzdWRESElixZopo1a2rBggVavHixqlWrJunqa9enT5+uWbNmqXTp0mrbtq3ZjwsAAADATViMa28tQL5gsVj0+eefq127dnd97uTk5Ktv3RuwTA5W97s+P7KWMLFVXpcAAACQb137jpuUlCRvb++b9i3QK08AAAAAkFsIT7epd+/e8vT0zHLr3bt3XpcHAAAAIJcU6BdG3A1jxozR4MGDszx2q2W/nOApSwAAACBvEJ5uU8mSJVWyZMm8LgMAAADAHcZjewAAAABgAitPyLbDEeF35JFEAAAA4F7GyhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAm8bQ/Zdt+oTXKwuud1GQAAACgAEia2yusSTGPlCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPhUBCQoIsFotiYmLyuhQAAAAg3yoQ4Sk0NFQDBgy4rj0qKkpFihSRJI0ePVoWi0UWi0WOjo4KCAjQiy++qLNnz9qdExgYqKlTp2a7hnsloHTv3l3t2rXL0xoAAACAgsgprwu4m6pXr66vvvpKGRkZio2N1fPPP6+kpCQtXbo0r0sDAAAAcI8rECtPZjk5OcnPz09lypRRs2bN1LFjR23ZsuWuzJ2ZmanIyEgFBQXJzc1NtWrV0ooVK2zHo6OjZbFYtHXrVtWrV0/u7u5q1KiR4uLi7MYZN26cSpYsKS8vL/Xq1UtvvPGGateuLenq6tr8+fO1evVq2ypbdHS07dxffvlFYWFhcnd3V61atbRnz567cekAAABAgVCowtM/JSQkaNOmTXJxcbkr80VGRmrBggWaOXOmjhw5ooEDB6pLly7avn27Xb/hw4dr8uTJ+u677+Tk5KTnn3/edmzRokUaP3683n77bX3//fcqV66cZsyYYTs+ePBgPfXUU2rRooUSExOVmJioRo0a2Y09ePBgxcTEqHLlynrmmWd05cqVG9acmpqq5ORkuw0AAAAorArVY3uHDh2Sp6enMjIydPnyZUnSe++9d8fnTU1N1YQJE/TVV1+pYcOGkqQKFSpo586dmjVrlpo0aWLrO378eNv+G2+8oVatWuny5ctydXXV+++/r549e6pHjx6SpJEjR2rz5s1KSUmRJHl6esrNzU2pqany8/O7ro7BgwerVatWkqSIiAhVr15dP//8s6pUqZJl3ZGRkYqIiMi9DwIAAADIxwrVylNISIhiYmK0f/9+DR06VOHh4Xr11Vfv+Lw///yzLl68qObNm8vT09O2LViwQPHx8XZ9a9asafu3v7+/JOn06dOSpLi4OD3wwAN2/f+9fzM3Gzsrw4YNU1JSkm07deqU6bkAAACAgqZArDx5e3srKSnpuvbz58/Lx8fHtu/i4qLg4GBJ0sSJE9WqVStFRERo7Nixd7S+aytD69atU5kyZeyOWa1Wu31nZ2fbvy0Wi6Srv5fKDdkd22q1XlcfAAAAUFgViJWnkJAQHThw4Lr2AwcOqHLlyjc876233tKkSZP0xx9/3MnyVK1aNVmtVp08eVLBwcF2W0BAgOlxQkJCtH//fru2f++7uLgoIyMjV+oGAAAA8H8KxMpTnz599MEHH6hfv37q1auXrFar1q1bp8WLF+vLL7+84XkNGzZUzZo1NWHCBH3wwQe29t9///26v9dUvnx5FS1a9Ja1/PvteNLVV6QPHjxYAwcOVGZmph5++GElJSVp165d8vb2Vrdu3Uxd56uvvqoXXnhB9erVU6NGjbR06VL9+OOPqlChgq1PYGCgNm3apLi4OBUrVsxu5Q0AAABAzhWI8FShQgV98803Gj58uJo1a6a0tDRVqVJFy5cvV4sWLW567sCBA9W9e3cNHTrUtgo0adIkTZo0ya7fwoUL1aVLl1vW8vTTT1/XdurUKY0dO1YlSpRQZGSkfvnlFxUpUkR169bVm2++afo6O3furF9++UWDBw/W5cuX9dRTT6l79+7at2+frc8LL7yg6Oho1atXTykpKdq2bZsCAwNNzwEAAAAgaxbDMIy8LgI517x5c/n5+WnhwoV3fK7k5GT5+PgoYMAyOVjd7/h8AAAAKPgSJrbK0/mvfcdNSkqSt7f3TfsWiJWnwuLixYuaOXOmwsPD5ejoqMWLF+urr766a3/oFwAAACjMCsQLI+6G3r17271m/J9b796970oNFotF69evV+PGjXX//ffryy+/1MqVK9WsWbO7Mj8AAABQmPHYnkmnT59WcnJylse8vb1VsmTJu1zR3cdjewAAAMhtPLZXAJUsWbJQBCQAAAAAWeOxPQAAAAAwgZUnZNvhiPBbLmkCAAAABQ0rTwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJvC2PWTbfaM25Ys/kpvXf3ANAAAABQsrTwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8mde/eXe3atbuuPTo6WhaLRefPn7/rNd1MZGSkHB0d9e677153LDQ0VAMGDLj7RQEAAAD5GOGpgJo7d65ef/11zZ07N69LAQAAAAoEwlMuOnPmjJ555hmVKVNG7u7uqlGjhhYvXmw7/vHHH6t06dLKzMy0O69t27Z6/vnnbfurV69W3bp15erqqgoVKigiIkJXrlwxXcf27dt16dIljRkzRsnJydq9e7ftWPfu3bV9+3ZNmzZNFotFFotFCQkJOb9oAAAAoJAgPOWiy5cv6/7779e6det0+PBhvfjii3ruuee0b98+SVLHjh115swZbdu2zXbO2bNntXHjRnXu3FmStGPHDnXt2lX9+/fX0aNHNWvWLEVFRWn8+PGm65gzZ46eeeYZOTs765lnntGcOXNsx6ZNm6aGDRvqhRdeUGJiohITExUQEJDlOKmpqUpOTrbbAAAAgMKK8JQNa9eulaenp93WsmVL2/EyZcpo8ODBql27tipUqKBXX31VLVq00LJlyyRJRYsWVcuWLfXZZ5/ZzlmxYoWKFy+usLAwSVJERITeeOMNdevWTRUqVFDz5s01duxYzZo1y1SNycnJWrFihbp06SJJ6tKli5YtW6aUlBRJko+Pj1xcXOTu7i4/Pz/5+fnJ0dExy7EiIyPl4+Nj224UsgAAAIDCgPCUDWFhYYqJibHbZs+ebTuekZGhsWPHqkaNGvL19ZWnp6c2bdqkkydP2vp07txZK1euVGpqqiRp0aJFevrpp+XgcPVWHDx4UGPGjLELaNdWiS5evHjLGhcvXqyKFSuqVq1akqTatWurfPnyWrp0abavd9iwYUpKSrJtp06dyvYYAAAAQEHhlNcF5CceHh4KDg62a/vtt99s/3733Xc1bdo0TZ06VTVq1JCHh4cGDBigtLQ0W582bdrIMAytW7dO9evX144dOzRlyhTb8ZSUFEVEROjJJ5+8bn5XV9db1jhnzhwdOXJETk7/d2szMzM1d+5c9ezZM1vXa7VaZbVas3UOAAAAUFARnnLRrl271LZtW9sjc5mZmTp27JiqVatm6+Pq6qonn3xSixYt0s8//6yQkBDVrVvXdrxu3bqKi4u7LqSZcejQIX333XeKjo6Wr6+vrf3s2bMKDQ3VTz/9pCpVqsjFxUUZGRm3caUAAABA4UN4ykWVKlXSihUrtHv3bhUtWlTvvfee/vrrL7vwJF19dK9169Y6cuSILWhdM3LkSLVu3VrlypVThw4d5ODgoIMHD+rw4cMaN27cTeefM2eOHnjgATVu3Pi6Y/Xr19ecOXP07rvvKjAwUHv37lVCQoI8PT3l6+tre2wQAAAAQNb4xpyL3nrrLdWtW1fh4eEKDQ2Vn59fln9Yt2nTpvL19VVcXJyeffZZu2Ph4eFau3atNm/erPr16+vBBx/UlClTVL58+ZvOnZaWpk8//VTt27fP8nj79u21YMECpaena/DgwXJ0dFS1atVUokQJu99kAQAAAMiaxTAMI6+LQP6QnJx89a17A5bJweqe1+XcUsLEVnldAgAAAO5x177jJiUlydvb+6Z9WXkCAAAAABMIT/nIokWLrvs7U9e26tWr53V5AAAAQIHGCyPykccff1wNGjTI8pizs/NdrgYAAAAoXAhP+YiXl5e8vLzyugwAAACgUOKxPQAAAAAwgZUnZNvhiPBbvokEAAAAKGhYeQIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIG37SHb7hu1SQ5W9yyPJUxsdZerAQAAAO4OVp4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCesql79+5q167dde3R0dGyWCw6f/78Xa8pK4GBgZo6dardvsVikcVikYeHh+rWravly5fnXYEAAABAPkN4KkTGjBmjxMRE/fDDD6pfv746deqk3bt353VZAAAAQL5AeLoDzpw5o2eeeUZlypSRu7u7atSoocWLF9uOf/zxxypdurQyMzPtzmvbtq2ef/552/7q1atVt25dubq6qkKFCoqIiNCVK1dyXJeXl5f8/PxUuXJlffjhh3Jzc9OXX36Z4/EAAACAwoTwdAdcvnxZ999/v9atW6fDhw/rxRdf1HPPPad9+/ZJkjp27KgzZ85o27ZttnPOnj2rjRs3qnPnzpKkHTt2qGvXrurfv7+OHj2qWbNmKSoqSuPHj8+VGp2cnOTs7Ky0tLQb9klNTVVycrLdBgAAABRWhKccWLt2rTw9Pe22li1b2o6XKVNGgwcPVu3atVWhQgW9+uqratGihZYtWyZJKlq0qFq2bKnPPvvMds6KFStUvHhxhYWFSZIiIiL0xhtvqFu3bqpQoYKaN2+usWPHatasWbddf1pamiIjI5WUlKSmTZvesF9kZKR8fHxsW0BAwG3PDQAAAORXhKccCAsLU0xMjN02e/Zs2/GMjAyNHTtWNWrUkK+vrzw9PbVp0yadPHnS1qdz585auXKlUlNTJUmLFi3S008/LQeHq7fk4MGDGjNmjF1Ae+GFF5SYmKiLFy/mqO6hQ4fK09NT7u7uevvttzVx4kS1atXqhv2HDRumpKQk23bq1KkczQsAAAAUBE55XUB+5OHhoeDgYLu23377zfbvd999V9OmTdPUqVNVo0YNeXh4aMCAAXaPyLVp00aGYWjdunWqX7++duzYoSlTptiOp6SkKCIiQk8++eR187u6uuao7iFDhqh79+7y9PRUqVKlZLFYbtrfarXKarXmaC4AAACgoCE83QG7du1S27Zt1aVLF0lSZmamjh07pmrVqtn6uLq66sknn9SiRYv0888/KyQkRHXr1rUdr1u3ruLi4q4LabejePHiuToeAAAAUJgQnu6ASpUqacWKFdq9e7eKFi2q9957T3/99ZddeJKuPrrXunVrHTlyxBa0rhk5cqRat26tcuXKqUOHDnJwcNDBgwd1+PBhjRs37m5eDgAAAADxm6c74q233lLdunUVHh6u0NBQ+fn5ZfmHdZs2bSpfX1/FxcXp2WeftTsWHh6utWvXavPmzapfv74efPBBTZkyReXLl79LVwEAAADgnyyGYRh5XQTyh+Tk5Ktv3RuwTA5W9yz7JEy88QsoAAAAgHvNte+4SUlJ8vb2vmlfVp4AAAAAwATCUz60aNGi6/7O1LWtevXqeV0eAAAAUCDxwoh86PHHH1eDBg2yPObs7HyXqwEAAAAKB8JTPuTl5SUvL6+8LgMAAAAoVHhsDwAAAABMYOUJ2XY4IvyWbyIBAAAAChpWngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwwSmvC0D+YRiGJCk5OTmPKwEAAAByx7Xvtte+694M4QmmnTlzRpIUEBCQx5UAAAAAuevChQvy8fG5aR/CE0zz9fWVJJ08efKW/4eFvJOcnKyAgACdOnVK3t7eeV0OboJ7lX9wr/IH7lP+wb3KHwrLfTIMQxcuXFDp0qVv2ZfwBNMcHK7+RM7Hx6dA/wdUUHh7e3Of8gnuVf7BvcofuE/5B/cqfygM98nswgAvjAAAAAAAEwhPAAAAAGAC4QmmWa1WjRo1SlarNa9LwU1wn/IP7lX+wb3KH7hP+Qf3Kn/gPl3PYph5Jx8AAAAAFHKsPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhPsfPjhhwoMDJSrq6saNGigffv23bT/8uXLVaVKFbm6uqpGjRpav379Xaq0cMvOfTpy5Ijat2+vwMBAWSwWTZ069e4Vimzdq08++USPPPKIihYtqqJFi6pZs2a3/G8QuSc792rVqlWqV6+eihQpIg8PD9WuXVsLFy68i9UWXtn9/1PXLFmyRBaLRe3atbuzBcImO/cqKipKFovFbnN1db2L1RZe2f1v6vz583rllVfk7+8vq9WqypUrF6rvf4Qn2CxdulSDBg3SqFGjdODAAdWqVUvh4eE6ffp0lv13796tZ555Rj179tQPP/ygdu3aqV27djp8+PBdrrxwye59unjxoipUqKCJEyfKz8/vLldbuGX3XkVHR+uZZ57Rtm3btGfPHgUEBOg///mPfv/997tceeGT3Xvl6+ur4cOHa8+ePfrxxx/Vo0cP9ejRQ5s2bbrLlRcu2b1P1yQkJGjw4MF65JFH7lKlyMm98vb2VmJiom379ddf72LFhVN271NaWpqaN2+uhIQErVixQnFxcfrkk09UpkyZu1x5HjKA/++BBx4wXnnlFdt+RkaGUbp0aSMyMjLL/k899ZTRqlUru7YGDRoYL7300h2ts7DL7n36p/LlyxtTpky5g9Xhn27nXhmGYVy5csXw8vIy5s+ff6dKxP93u/fKMAyjTp06xltvvXUnysP/l5P7dOXKFaNRo0bG7NmzjW7duhlt27a9C5Uiu/dq3rx5ho+Pz12qDtdk9z7NmDHDqFChgpGWlna3SrznsPIESVf/l4Tvv/9ezZo1s7U5ODioWbNm2rNnT5bn7Nmzx66/JIWHh9+wP25fTu4T8kZu3KuLFy8qPT1dvr6+d6pM6PbvlWEY2rp1q+Li4tS4ceM7WWqhltP7NGbMGJUsWVI9e/a8G2VCOb9XKSkpKl++vAICAtS2bVsdOXLkbpRbaOXkPq1Zs0YNGzbUK6+8olKlSum+++7ThAkTlJGRcbfKznOEJ0iS/ve//ykjI0OlSpWyay9VqpT+/PPPLM/5888/s9Ufty8n9wl5Izfu1dChQ1W6dOnr/kcK5K6c3qukpCR5enrKxcVFrVq10vvvv6/mzZvf6XILrZzcp507d2rOnDn65JNP7kaJ+P9ycq9CQkI0d+5crV69Wp9++qkyMzPVqFEj/fbbb3ej5EIpJ/fpl19+0YoVK5SRkaH169drxIgRmjx5ssaNG3c3Sr4nOOV1AQCA602cOFFLlixRdHQ0P5q+R3l5eSkmJkYpKSnaunWrBg0apAoVKig0NDSvS4OkCxcu6LnnntMnn3yi4sWL53U5uIWGDRuqYcOGtv1GjRqpatWqmjVrlsaOHZuHleGfMjMzVbJkSX388cdydHTU/fffr99//13vvvuuRo0aldfl3RWEJ0iSihcvLkdHR/3111927X/99dcNXzLg5+eXrf64fTm5T8gbt3OvJk2apIkTJ+qrr75SzZo172SZUM7vlYODg4KDgyVJtWvXVmxsrCIjIwlPd0h271N8fLwSEhLUpk0bW1tmZqYkycnJSXFxcapYseKdLbqQyo3/X+Xs7Kw6dero559/vhMlQjm7T/7+/nJ2dpajo6OtrWrVqvrzzz+VlpYmFxeXO1rzvYDH9iBJcnFx0f3336+tW7fa2jIzM7V161a7/yXonxo2bGjXX5K2bNlyw/64fTm5T8gbOb1X77zzjsaOHauNGzeqXr16d6PUQi+3/rvKzMxUamrqnSgRyv59qlKlig4dOqSYmBjb9vjjjyssLEwxMTEKCAi4m+UXKrnx31RGRoYOHTokf3//O1VmoZeT+/TQQw/p559/tv0PEZJ07Ngx+fv7F4rgJIm37eH/LFmyxLBarUZUVJRx9OhR48UXXzSKFCli/Pnnn4ZhGMZzzz1nvPHGG7b+u3btMpycnIxJkyYZsbGxxqhRowxnZ2fj0KFDeXUJhUJ271Nqaqrxww8/GD/88IPh7+9vDB482Pjhhx+M48eP59UlFBrZvVcTJ040XFxcjBUrVhiJiYm27cKFC3l1CYVGdu/VhAkTjM2bNxvx8fHG0aNHjUmTJhlOTk7GJ598kleXUChk9z79G2/bu3uye68iIiKMTZs2GfHx8cb3339vPP3004arq6tx5MiRvLqEQiG79+nkyZOGl5eX0bdvXyMuLs5Yu3atUbJkSWPcuHF5dQl3HeEJdt5//32jXLlyhouLi/HAAw8Y3377re1YkyZNjG7dutn1X7ZsmVG5cmXDxcXFqF69urFu3bq7XHHhlJ37dOLECUPSdVuTJk3ufuGFUHbuVfny5bO8V6NGjbr7hRdC2blXw4cPN4KDgw1XV1ejaNGiRsOGDY0lS5bkQdWFT3b//9Q/EZ7uruzcqwEDBtj6lipVynjssceMAwcO5EHVhU92/5vavXu30aBBA8NqtRoVKlQwxo8fb1y5cuUuV513LIZhGHm16gUAAAAA+QW/eQIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAd1FoaKgGDBiQ12UAAHKA8AQAuGd0795dFovluu3nn3/OlfGjoqJUpEiRXBkrp1atWqWxY8fmaQ03Ex0dLYvFovPnz+d1KQBwz3HK6wIAAPinFi1aaN68eXZtJUqUyKNqbiw9PV3Ozs7ZPs/X1/cOVJM70tPT87oEALinsfIEALinWK1W+fn52W2Ojo6SpNWrV6tu3bpydXVVhQoVFBERoStXrtjOfe+991SjRg15eHgoICBAL7/8slJSUiRdXVHp0aOHkpKSbCtao0ePliRZLBZ98cUXdnUUKVJEUVFRkqSEhARZLBYtXbpUTZo0kaurqxYtWiRJmj17tqpWrSpXV1dVqVJFH3300U2v79+P7QUGBmrcuHHq2rWrPD09Vb58ea1Zs0b//e9/1bZtW3l6eqpmzZr67rvvbOdcW0H74osvVKlSJbm6uio8PFynTp2ym2vGjBmqWLGiXFxcFBISooULF9odt1gsmjFjhh5//HF5eHjohRdeUFhYmCSpaNGislgs6t69uyRp48aNevjhh1WkSBEVK1ZMrVu3Vnx8vG2sa5/RqlWrFBYWJnd3d9WqVUt79uyxm3PXrl0KDQ2Vu7u7ihYtqvDwcJ07d06SlJmZqcjISAUFBcnNzU21atXSihUrbvp5AsBdZQAAcI/o1q2b0bZt2yyPffPNN4a3t7cRFRVlxMfHG5s3bzYCAwON0aNH2/pMmTLF+Prrr40TJ04YW7duNUJCQow+ffoYhmEYqampxtSpUw1vb28jMTHRSExMNC5cuGAYhmFIMj7//HO7+Xx8fIx58+YZhmEYJ06cMCQZgYGBxsqVK41ffvnF+OOPP4xPP/3U8Pf3t7WtXLnS8PX1NaKiom54jU2aNDH69+9v2y9fvrzh6+trzJw50zh27JjRp08fw9vb22jRooWxbNkyIy4uzmjXrp1RtWpVIzMz0zAMw5g3b57h7Oxs1KtXz9i9e7fx3XffGQ888IDRqFEj27irVq0ynJ2djQ8//NCIi4szJk+ebDg6Ohpff/21rY8ko2TJksbcuXON+Ph4IyEhwVi5cqUhyYiLizMSExON8+fPG4ZhGCtWrDBWrlxpHD9+3Pjhhx+MNm3aGDVq1DAyMjLsPqMqVaoYa9euNeLi4owOHToY5cuXN9LT0w3DMIwffvjBsFqtRp8+fYyYmBjj8OHDxvvvv2/897//NQzDMMaNG2dUqVLF2LhxoxEfH2/MmzfPsFqtRnR09A0/TwC4mwhPAIB7Rrdu3QxHR0fDw8PDtnXo0MEwDMN49NFHjQkTJtj1X7hwoeHv73/D8ZYvX24UK1bMtj9v3jzDx8fnun5mw9PUqVPt+lSsWNH47LPP7NrGjh1rNGzY8IY1ZRWeunTpYttPTEw0JBkjRoywte3Zs8eQZCQmJtquQ5Lx7bff2vrExsYakoy9e/cahmEY/6+dewmJco3jOP4dEc0xspuYSk2BhGOjYqR5SawoElGIaApt0cIMCwsNQ1skDCXSxcIKuijoJqgWQaQbUdw4QaDQUGFiRcwm6IIWNpjpvGcRzmnShlfOqVOc32c18zzP/Of/PLvfvPO+ubm5RkVFRdB3O51Oo6ioKGjf1dXVQWv6+voMwBgdHf3hHgzDMN6+fWsAxuPHjw3D+PuM2traAmuePn1qAMbQ0JBhGIZRWlpq5OXlzVlvYmLCsFqtxoMHD4LGy8vLjdLS0pC9iIj8KrrnSUREfitbtmzh6tWrgffR0dEAeDwe3G43jY2Ngbnp6WkmJibw+XxYrVZ6enpoamri2bNnfPz4kampqaD5f2rDhg2B158+feLFixeUl5dTUVERGJ+amiImJmZeddPS0gKv4+LiAEhNTZ019ubNG1asWAFAeHg4mZmZgTXJycksXryYoaEhsrKyGBoa4uDBg0Hfk5eXR0tLyw/3FMrIyAgNDQ08fPiQd+/e4ff7AfB6vTgcjjn3Eh8fH+g7OTmZR48e4XQ656z//PlzfD4f27dvDxqfnJwkIyPDVI8iIj+bwpOIiPxWoqOjSUpKmjU+Pj6Oy+Vi165ds+YWLFjAq1evKC4u5tChQzQ2NrJ06VL6+/spLy9ncnIyZHiyWCwYhhE0NtfDE2aC3Ew/AK2trWzcuDFo3cw9WmZ9++AJi8Xyw7GZwPJv+nZPoZSUlGCz2WhtbSUhIQG/34/D4WBycjJoXai+o6Kiflh/5jy7urpITEwMmouMjDTVo4jIz6bwJCIif4T169czPDw8Z7ACGBwcxO/309zcTFjY1+ch3blzJ2hNREQE09PTsz4bGxvL69evA+9HRkbw+Xwh+4mLiyMhIYGXL1+yb9+++W7nH5uammJgYICsrCwAhoeHGRsbw263A2C323G73ezfvz/wGbfbTUpKSsi6ERERAEHn9P79e4aHh2ltbSU/Px+A/v7+efeclpZGb28vLpdr1lxKSgqRkZF4vV4KCgrmXVtE5FdQeBIRkT9CQ0MDxcXFrFq1it27dxMWFobH4+HJkyecPn2apKQkvnz5wuXLlykpKcHtdnPt2rWgGqtXr2Z8fJze3l7S09OxWq1YrVa2bt3KlStXyMnJYXp6mrq6OlOPIXe5XBw9epSYmBgKCwv5/PkzAwMDjI6OcuzYsZ91FMDXKzxHjhzh0qVLhIeHU1VVRXZ2diBMHT9+nD179pCRkcG2bdu4f/8+d+/epaenJ2Rdm82GxWKhs7OToqIioqKiWLJkCcuWLePGjRvEx8fj9Xqpr6+fd88nTpwgNTWVw4cPU1lZSUREBH19fTidTpYvX05tbS01NTX4/X42bdrEhw8fcLvdLFq0KCgEioj8V/SochER+SPs2LGDzs5Ouru7yczMJDs7m4sXL2Kz2QBIT0/nwoULnDlzBofDwc2bN2lqagqqkZubS2VlJXv37iU2NpazZ88C0NzczMqVK8nPz6esrIza2lpT90gdOHCAtrY22tvbSU1NpaCggI6ODtasWfPvH8B3rFYrdXV1lJWVkZeXx8KFC7l9+3ZgfufOnbS0tHD+/HnWrVvH9evXaW9vZ/PmzSHrJiYm4nK5qK+vJy4ujqqqKsLCwrh16xaDg4M4HA5qamo4d+7cvHteu3Yt3d3deDwesrKyyMnJ4d69e4SHf/0t99SpU5w8eZKmpibsdjuFhYV0dXX9kvMUETHDYnz/J28RERH5rXV0dFBdXc3Y2Nh/3YqIyP+KrjyJiIiIiIiYoPAkIiIiIiJigv62JyIiIiIiYoKuPImIiIiIiJig8CQiIiIiImKCwpOIiIiIiIgJCk8iIiIiIiImKDyJiIiIiIiYoPAkIiIiIiJigsKTiIiIiIiICQpPIiIiIiIiJig8iYiIiIiImPAXvw/G9ypJiR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XpC9PAn5RTfY"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5XKvXxr9RSxl"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Decision Tree', acc_train_tree, acc_test_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LuDTMcmb-n5x"
   },
   "source": [
    "### **7.2. Random Forest Classifier**\n",
    "Random forests for regression and classification are currently among the most widely used machine learning methods.A random forest is essentially a collection of decision trees, where each tree is slightly different from the others. The idea behind random forests is that each tree might do a relatively good job of predicting, but will likely overfit on part of the data.\n",
    "\n",
    "If we build many trees, all of which work well and overfit in different ways, we can reduce the amount of overfitting by averaging their results. To build a random forest model, you need to decide on the number of trees to build (the n_estimators parameter of RandomForestRegressor or RandomForestClassifier). They are very powerful, often work well without heavy tuning of the parameters, and don’t require scaling of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "2fmB9rPSsR6y",
    "outputId": "27ddebf4-bee1-4eec-eb4e-995d4cdc08b2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# instantiate the model\n",
    "forest = RandomForestClassifier(max_depth=5)\n",
    "\n",
    "# fit the model \n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J1Qck-wrsabB"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_forest = forest.predict(X_test)\n",
    "y_train_forest = forest.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i8TybBPHT1ao"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "Oguf-37tsboO",
    "outputId": "34386ec6-a7f0-4185-b3c0-a40de3239fb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: Accuracy on training Data: 0.819\n",
      "Random forest: Accuracy on test Data: 0.824\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_forest = accuracy_score(y_train,y_train_forest)\n",
    "acc_test_forest = accuracy_score(y_test,y_test_forest)\n",
    "\n",
    "print(\"Random forest: Accuracy on training Data: {:.3f}\".format(acc_train_forest))\n",
    "print(\"Random forest: Accuracy on test Data: {:.3f}\".format(acc_test_forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "m9GZGxvZ9jnB",
    "outputId": "465186a8-d622-4427-c148-9dff349b40eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), forest.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t6U_BEF8W-FS"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YNf4EXHUW-FU"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Random Forest', acc_train_forest, acc_test_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VJ_EW8QUC0bn"
   },
   "source": [
    "### **7.3. Multilayer Perceptrons (MLPs): Deep Learning**\n",
    "Multilayer perceptrons (MLPs) are also known as (vanilla) feed-forward neural networks, or sometimes just neural networks. Multilayer perceptrons can be applied for both classification and regression problems.\n",
    "\n",
    "MLPs can be viewed as generalizations of linear models that perform multiple stages of processing to come to a decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "JSFAbsgnAxqv",
    "outputId": "2828ce2e-95ec-4dfd-e7dd-5d3da152ea09"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multilayer Perceptrons model\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# instantiate the model\n",
    "mlp = MLPClassifier(alpha=0.001, hidden_layer_sizes=([100,100,100]))\n",
    "\n",
    "# fit the model \n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gyuSg6w_A4pN"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_mlp = mlp.predict(X_test)\n",
    "y_train_mlp = mlp.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UlDx0rDXatCl"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "z2ndgKQbA64_",
    "outputId": "40ddef62-9dd4-4d55-b5ba-9932ba07a0b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilayer Perceptrons: Accuracy on training Data: 0.865\n",
      "Multilayer Perceptrons: Accuracy on test Data: 0.858\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_mlp = accuracy_score(y_train,y_train_mlp)\n",
    "acc_test_mlp = accuracy_score(y_test,y_test_mlp)\n",
    "\n",
    "print(\"Multilayer Perceptrons: Accuracy on training Data: {:.3f}\".format(acc_train_mlp))\n",
    "print(\"Multilayer Perceptrons: Accuracy on test Data: {:.3f}\".format(acc_test_mlp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zjBgfI64Xubd"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N0fsq4yEXubk"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Multilayer Perceptrons', acc_train_mlp, acc_test_mlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lVuTty-KaS4c"
   },
   "source": [
    "### **7.4. XGBoost Classifier**\n",
    "XGBoost is one of the most popular machine learning algorithms these days. XGBoost stands for eXtreme Gradient Boosting. Regardless of the type of prediction task at hand; regression or classification. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "oIIQGzxgAREc",
    "outputId": "fc27da07-7071-4fbf-9d05-05e514ad9b3e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#XGBoost Classification model\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# instantiate the model\n",
    "xgb = XGBClassifier(learning_rate=0.4,max_depth=7)\n",
    "#fit the model\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_fx9xbzfAUO-"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_xgb = xgb.predict(X_test)\n",
    "y_train_xgb = xgb.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pwoDNqDIaxB9"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "x1NNeI-NaxCA",
    "outputId": "d021057e-e9bc-487d-b584-9fb2492305de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost: Accuracy on training Data: 0.867\n",
      "XGBoost : Accuracy on test Data: 0.858\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_xgb = accuracy_score(y_train,y_train_xgb)\n",
    "acc_test_xgb = accuracy_score(y_test,y_test_xgb)\n",
    "\n",
    "print(\"XGBoost: Accuracy on training Data: {:.3f}\".format(acc_train_xgb))\n",
    "print(\"XGBoost : Accuracy on test Data: {:.3f}\".format(acc_test_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_g2HQNotaxCQ"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sFNo8jskaxCS"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('XGBoost', acc_train_xgb, acc_test_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EwQ8DP4OsmcG"
   },
   "source": [
    "### **7.5. Autoencoder Neural Network**\n",
    "An auto encoder is a neural network that has the same number of input neurons as it does outputs. The hidden layers of the neural network will have fewer neurons than the input/output neurons. Because there are fewer neurons, the auto-encoder must learn to encode the input to the fewer hidden neurons. The predictors (x) and output (y) are exactly the same in an auto encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "7cy28vy3fDab",
    "outputId": "2947530e-ed77-40b2-bc9e-c3c575ff4faf"
   },
   "outputs": [],
   "source": [
    "#importing required packages\n",
    "import keras\n",
    "from keras.layers import Input, Dense\n",
    "from keras import regularizers\n",
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "8Rca6ZpShnRN",
    "outputId": "1e8f7ecb-bbaa-4bd0-92dc-73a3ccf58fe2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 16)]              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 16)                272       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 16)                272       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 14)                238       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 16)                240       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 16)                272       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,294\n",
      "Trainable params: 1,294\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#building autoencoder model\n",
    "\n",
    "input_dim = X_train.shape[1]\n",
    "encoding_dim = input_dim\n",
    "\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "encoder = Dense(encoding_dim, activation=\"relu\",\n",
    "                activity_regularizer=regularizers.l1(10e-4))(input_layer)\n",
    "encoder = Dense(int(encoding_dim), activation=\"relu\")(encoder)\n",
    "\n",
    "encoder = Dense(int(encoding_dim-2), activation=\"relu\")(encoder)\n",
    "code = Dense(int(encoding_dim-4), activation='relu')(encoder)\n",
    "decoder = Dense(int(encoding_dim-2), activation='relu')(code)\n",
    "\n",
    "decoder = Dense(int(encoding_dim), activation='relu')(encoder)\n",
    "decoder = Dense(input_dim, activation='relu')(decoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "colab_type": "code",
    "id": "FFet6_03sBtK",
    "outputId": "bfc5a82f-b08b-453b-cd31-da9ee824480f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "100/100 [==============================] - 1s 2ms/step - loss: 2.4243 - accuracy: 0.5011 - val_loss: 1.3170 - val_accuracy: 0.7375\n",
      "Epoch 2/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.2626 - accuracy: 0.8031 - val_loss: -1.4582 - val_accuracy: 0.8487\n",
      "Epoch 3/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.5345 - accuracy: 0.8414 - val_loss: -1.6188 - val_accuracy: 0.8487\n",
      "Epoch 4/50\n",
      "100/100 [==============================] - 0s 3ms/step - loss: -1.6085 - accuracy: 0.8414 - val_loss: -1.6869 - val_accuracy: 0.8487\n",
      "Epoch 5/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6581 - accuracy: 0.8311 - val_loss: -1.7078 - val_accuracy: 0.8087\n",
      "Epoch 6/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6756 - accuracy: 0.7763 - val_loss: -1.7277 - val_accuracy: 0.7094\n",
      "Epoch 7/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.7058 - accuracy: 0.6839 - val_loss: -1.7568 - val_accuracy: 0.6944\n",
      "Epoch 8/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.7200 - accuracy: 0.6448 - val_loss: -1.7657 - val_accuracy: 0.5994\n",
      "Epoch 9/50\n",
      "100/100 [==============================] - 0s 3ms/step - loss: -1.7317 - accuracy: 0.5664 - val_loss: -1.7739 - val_accuracy: 0.5606\n",
      "Epoch 10/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7415 - accuracy: 0.5378 - val_loss: -1.7900 - val_accuracy: 0.5325\n",
      "Epoch 11/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.7408 - accuracy: 0.4606 - val_loss: -1.7920 - val_accuracy: 0.4087\n",
      "Epoch 12/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7628 - accuracy: 0.4233 - val_loss: -1.7981 - val_accuracy: 0.4550\n",
      "Epoch 13/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.7734 - accuracy: 0.4116 - val_loss: -1.8179 - val_accuracy: 0.4544\n",
      "Epoch 14/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.7871 - accuracy: 0.3767 - val_loss: -1.8286 - val_accuracy: 0.3525\n",
      "Epoch 15/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7971 - accuracy: 0.3466 - val_loss: -1.8374 - val_accuracy: 0.3369\n",
      "Epoch 16/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7979 - accuracy: 0.3573 - val_loss: -1.8407 - val_accuracy: 0.3162\n",
      "Epoch 17/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8065 - accuracy: 0.3070 - val_loss: -1.8489 - val_accuracy: 0.3019\n",
      "Epoch 18/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8153 - accuracy: 0.2941 - val_loss: -1.8579 - val_accuracy: 0.2925\n",
      "Epoch 19/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8229 - accuracy: 0.2822 - val_loss: -1.8663 - val_accuracy: 0.2956\n",
      "Epoch 20/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8323 - accuracy: 0.2864 - val_loss: -1.8759 - val_accuracy: 0.2481\n",
      "Epoch 21/50\n",
      "100/100 [==============================] - 0s 3ms/step - loss: -1.8358 - accuracy: 0.2767 - val_loss: -1.8778 - val_accuracy: 0.3013\n",
      "Epoch 22/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8069 - accuracy: 0.3144 - val_loss: -1.8461 - val_accuracy: 0.4238\n",
      "Epoch 23/50\n",
      "100/100 [==============================] - 0s 3ms/step - loss: -1.8097 - accuracy: 0.3755 - val_loss: -1.8528 - val_accuracy: 0.3519\n",
      "Epoch 24/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8280 - accuracy: 0.2959 - val_loss: -1.8894 - val_accuracy: 0.2625\n",
      "Epoch 25/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8481 - accuracy: 0.2414 - val_loss: -1.8940 - val_accuracy: 0.2294\n",
      "Epoch 26/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8533 - accuracy: 0.2163 - val_loss: -1.8988 - val_accuracy: 0.1950\n",
      "Epoch 27/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8572 - accuracy: 0.1873 - val_loss: -1.9020 - val_accuracy: 0.1731\n",
      "Epoch 28/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8606 - accuracy: 0.1538 - val_loss: -1.9056 - val_accuracy: 0.1488\n",
      "Epoch 29/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8636 - accuracy: 0.1355 - val_loss: -1.9086 - val_accuracy: 0.1469\n",
      "Epoch 30/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.5593 - accuracy: 0.1755 - val_loss: -1.1855 - val_accuracy: 0.2969\n",
      "Epoch 31/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.6644 - accuracy: 0.1864 - val_loss: -1.8823 - val_accuracy: 0.1238\n",
      "Epoch 32/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8436 - accuracy: 0.1181 - val_loss: -1.8675 - val_accuracy: 0.0894\n",
      "Epoch 33/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6408 - accuracy: 0.0328 - val_loss: -1.5976 - val_accuracy: 0.0025\n",
      "Epoch 34/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.6045 - accuracy: 0.0016 - val_loss: -1.6439 - val_accuracy: 0.0025\n",
      "Epoch 35/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.5524 - accuracy: 0.0016 - val_loss: -1.6302 - val_accuracy: 0.0025\n",
      "Epoch 36/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6242 - accuracy: 0.0016 - val_loss: -1.6972 - val_accuracy: 0.0025\n",
      "Epoch 37/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.6595 - accuracy: 0.0017 - val_loss: -1.7281 - val_accuracy: 0.0025\n",
      "Epoch 38/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6816 - accuracy: 0.0020 - val_loss: -1.7413 - val_accuracy: 0.0025\n",
      "Epoch 39/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6867 - accuracy: 0.0020 - val_loss: -1.7449 - val_accuracy: 0.0025\n",
      "Epoch 40/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.6974 - accuracy: 0.0020 - val_loss: -1.7591 - val_accuracy: 0.0025\n",
      "Epoch 41/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7056 - accuracy: 0.0019 - val_loss: -1.7415 - val_accuracy: 0.0025\n",
      "Epoch 42/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7273 - accuracy: 0.0023 - val_loss: -1.8119 - val_accuracy: 0.0037\n",
      "Epoch 43/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.7074 - accuracy: 0.0028 - val_loss: -1.8219 - val_accuracy: 0.0025\n",
      "Epoch 44/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7785 - accuracy: 0.0033 - val_loss: -1.8368 - val_accuracy: 0.0037\n",
      "Epoch 45/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.7915 - accuracy: 0.0036 - val_loss: -1.8541 - val_accuracy: 0.0037\n",
      "Epoch 46/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8007 - accuracy: 0.0039 - val_loss: -1.8616 - val_accuracy: 0.0037\n",
      "Epoch 47/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8089 - accuracy: 0.0036 - val_loss: -1.8832 - val_accuracy: 0.0037\n",
      "Epoch 48/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8261 - accuracy: 0.0034 - val_loss: -1.8856 - val_accuracy: 0.0037\n",
      "Epoch 49/50\n",
      "100/100 [==============================] - 0s 2ms/step - loss: -1.8297 - accuracy: 0.0034 - val_loss: -1.8869 - val_accuracy: 0.0037\n",
      "Epoch 50/50\n",
      "100/100 [==============================] - 0s 1ms/step - loss: -1.8333 - accuracy: 0.0030 - val_loss: -1.8966 - val_accuracy: 0.0025\n"
     ]
    }
   ],
   "source": [
    "#compiling the model\n",
    "autoencoder.compile(optimizer='adam',\n",
    "                    loss='binary_crossentropy',\n",
    "                    metrics=['accuracy'])\n",
    "\n",
    "#Training the model\n",
    "history = autoencoder.fit(X_train, X_train, epochs=50, batch_size=64, shuffle=True, validation_split=0.2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q1ONIVqSsXSP"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "TQa0u7vyj2Ml",
    "outputId": "73a8713c-683d-4a13-8619-5bd87adec0c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250/250 [==============================] - 0s 715us/step - loss: -1.8498 - accuracy: 0.0018\n",
      "63/63 [==============================] - 0s 670us/step - loss: -1.7809 - accuracy: 5.0000e-04\n",
      "\n",
      "Autoencoder: Accuracy on training Data: 0.002\n",
      "Autoencoder: Accuracy on test Data: 0.001\n"
     ]
    }
   ],
   "source": [
    "acc_train_auto = autoencoder.evaluate(X_train, X_train)[1]\n",
    "acc_test_auto = autoencoder.evaluate(X_test, X_test)[1]\n",
    "\n",
    "print('\\nAutoencoder: Accuracy on training Data: {:.3f}' .format(acc_train_auto))\n",
    "print('Autoencoder: Accuracy on test Data: {:.3f}' .format(acc_test_auto))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YG5oxuvJsflM"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xqjgA0aCsflO"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('AutoEncoder', acc_train_auto, acc_test_auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "plQeFh4fwB3a"
   },
   "source": [
    "### **7.6. Support Vector Machines**\n",
    "\n",
    "In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "VBnV71QowZ07",
    "outputId": "258bc8b4-ba81-4036-c558-11923a7b0b5e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=12)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=12)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear', random_state=12)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support vector machine model\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# instantiate the model\n",
    "svm = SVC(kernel='linear', C=1.0, random_state=12)\n",
    "#fit the model\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Nlrfq-pwZ1G"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_svm = svm.predict(X_test)\n",
    "y_train_svm = svm.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qb2FHrUpwZ1P"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "1ofewcVHwZ1R",
    "outputId": "3f83bfc7-f885-49a7-ed88-816e05097eac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: Accuracy on training Data: 0.800\n",
      "SVM : Accuracy on test Data: 0.806\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_svm = accuracy_score(y_train,y_train_svm)\n",
    "acc_test_svm = accuracy_score(y_test,y_test_svm)\n",
    "\n",
    "print(\"SVM: Accuracy on training Data: {:.3f}\".format(acc_train_svm))\n",
    "print(\"SVM : Accuracy on test Data: {:.3f}\".format(acc_test_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ToF9fRNSwZ1b"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4ZAc00sUwZ1d"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('SVM', acc_train_svm, acc_test_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3vsRppPv3rs"
   },
   "source": [
    "## **8. Comparision of Models**\n",
    "To compare the models performance, a dataframe is created. The columns of this dataframe are the lists created to store the results of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "colab_type": "code",
    "id": "RkOSzcfsv8Xl",
    "outputId": "82b2e437-b210-4b83-c3a0-dc9c5f65f9e0"
   },
   "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>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AutoEncoder</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "0           Decision Tree           0.812          0.820\n",
       "1           Random Forest           0.819          0.824\n",
       "2  Multilayer Perceptrons           0.865          0.858\n",
       "3  Multilayer Perceptrons           0.865          0.858\n",
       "4                 XGBoost           0.867          0.858\n",
       "5             AutoEncoder           0.002          0.001\n",
       "6                     SVM           0.800          0.806"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe\n",
    "results = pd.DataFrame({ 'ML Model': ML_Model,    \n",
    "    'Train Accuracy': acc_train,\n",
    "    'Test Accuracy': acc_test})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "colab_type": "code",
    "id": "eKheGBiHwDfK",
    "outputId": "8ff038a3-9eea-472a-e1e7-ac6be45c9882"
   },
   "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>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AutoEncoder</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "4                 XGBoost           0.867          0.858\n",
       "2  Multilayer Perceptrons           0.865          0.858\n",
       "3  Multilayer Perceptrons           0.865          0.858\n",
       "1           Random Forest           0.819          0.824\n",
       "0           Decision Tree           0.812          0.820\n",
       "6                     SVM           0.800          0.806\n",
       "5             AutoEncoder           0.002          0.001"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting the datafram on accuracy\n",
    "results.sort_values(by=['Test Accuracy', 'Train Accuracy'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5t9806vn601b"
   },
   "source": [
    "For the above comparision, it is clear that the XGBoost Classifier works well with this dataset.\n",
    "\n",
    "So, saving the model for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aCIIkZ7V3AFN"
   },
   "outputs": [],
   "source": [
    "# save XGBoost model to file\n",
    "import pickle\n",
    "pickle.dump(xgb, open(\"XGBoostClassifier.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PbrNHP0o3QrD"
   },
   "source": [
    "**Testing the saved model:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "-ZEm_PS33QD-",
    "outputId": "a4195d7f-94ef-4bc7-a165-35ed2ed5493f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model from file\n",
    "loaded_model = pickle.load(open(\"XGBoostClassifier.pickle.dat\", \"rb\"))\n",
    "loaded_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3vy2egEdwkqZ"
   },
   "source": [
    "## **9. References**\n",
    "* https://blog.keras.io/building-autoencoders-in-keras.html\n",
    "* https://en.wikipedia.org/wiki/Autoencoder\n",
    "* https://mc.ai/a-beginners-guide-to-build-stacked-autoencoder-and-tying-weights-with-it/\n",
    "* https://github.com/shreyagopal/t81_558_deep_learning/blob/master/t81_558_class_14_03_anomaly.ipynb\n",
    "* https://machinelearningmastery.com/save-gradient-boosting-models-xgboost-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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