Human Weight Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Human Weight Prediction with Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this notebook is to predict the suicide rates using Machine Learning algorithms and analyzing them to find correlated factors causing increase in suicide rates globally.\n",
    "\n",
    "The steps demonstrated in this notebook are:\n",
    "\n",
    " 1. Loading the data\n",
    " 2. Familiarizing with data\n",
    " 3. Visualizing the data\n",
    " 4. Data Preprocessing & EDA\n",
    " 5. Splitting the data\n",
    " 6. Training the data\n",
    " 7. Model Performance Comparision\n",
    " 8. Statistical Tests\n",
    " 9. Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading Data:\n",
    "\n",
    "   The dataset is borrowed from Kaggle, https://www.kaggle.com/yersever/500-person-gender-height-weight-bodymassindex. \n",
    "\n",
    "   The overview of this dataset is, it has 500 samples with 4 features. Download the dataset from the link provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>174</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>189</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>185</td>\n",
       "      <td>110</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>195</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>149</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Height  Weight  Index\n",
       "0    Male     174      96      4\n",
       "1    Male     189      87      2\n",
       "2  Female     185     110      4\n",
       "3  Female     195     104      3\n",
       "4    Male     149      61      3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading data into dataframe\n",
    "\n",
    "data = pd.read_csv(\"Person_Gender_Height_Weight_Index.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Shape of dataframe\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Height', 'Weight', 'Index'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing the features of the dataset\n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Gender  500 non-null    object\n",
      " 1   Height  500 non-null    int64 \n",
      " 2   Weight  500 non-null    int64 \n",
      " 3   Index   500 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>169.944000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>3.748000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.375261</td>\n",
       "      <td>32.382607</td>\n",
       "      <td>1.355053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>170.500000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height      Weight       Index\n",
       "count  500.000000  500.000000  500.000000\n",
       "mean   169.944000  106.000000    3.748000\n",
       "std     16.375261   32.382607    1.355053\n",
       "min    140.000000   50.000000    0.000000\n",
       "25%    156.000000   80.000000    3.000000\n",
       "50%    170.500000  106.000000    4.000000\n",
       "75%    184.000000  136.000000    5.000000\n",
       "max    199.000000  160.000000    5.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    198\n",
       "4    130\n",
       "2     69\n",
       "3     68\n",
       "1     22\n",
       "0     13\n",
       "Name: Index, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Index.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "1. None of the columns have any missing values. \n",
    "2. So no need to remove any column from the dataset.\n",
    "3. The index feature has 6 unique index groups.\n",
    "4. The index 5 has more count in dataset.\n",
    "5. Gender should be encoded.\n",
    "6. Scale required numerical features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Height'}>,\n",
       "        <AxesSubplot:title={'center':'Weight'}>],\n",
       "       [<AxesSubplot:title={'center':'Index'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pxc6wjiGbNEbX790140qGa9IY2YcAAHYmR9oAAAAGTGgDAAAYMKENAABgwIQ2AACAARPaAAAABkxoAwAAGLCpLvlfVdcleWGSh7v7GeO2n0jyz5P82Xi2146/DBQAYEN8rc7OdKLnHXaSaY+0XZ9k7zrtP9/dF49vAhsAAMAGTRXauvu2JJ/ZpFoAAABYY6s+0/bKqvpwVV1XVU/aom0AAAAsvKk+0zbBLyX5ySQ9/nltkpetnamq9iXZlyRLS0tZWVmZaqNLj0uuvujoo9qnXe/pWG/7m1XHkSNHpl7HpPqGMEab0b9pDeX5G8LzNMmk2pYeN7v6Nvo8Hbz/kXXbLzr3CSfd5hD2z6206P0DgO1u00Nbdz907H5V/XKS350w34EkB5Jkz549vby8PNV2X3fj23PtwUd359CLp1vv6bjqRB+SnrKOlZWVTDtGk+obwhhdv3fX1P2b1lCevyE8T5NMqu3qi47m+2b0/G30eZpmXDfj92/IFr1/ALDdbfrpkVV1zqqH35Pkrs3eBgAAwE4x7SX/35RkOcnZVXU4yY8nWa6qizM6PfJQkh+crkQAAICda6rQ1t1XrtP8xmnWCQAAwHFbdfVIAAAANoHQBgAAMGBCGwAAwIBtxfe0AQAADNruE32N0DUvOO1lrt+7a+qaJnGkDQAAYMCENgCYoaq6rqoerqq7VrX9+6r6SFV9uKreVlVPnLDsoao6WFV3VtXtMysagLlyeiSwME50ygIMyPVJXp/kV1e13ZrkNd19tKp+OslrkvzIhOUv7e5PbW2JAAyJI20AMEPdfVuSz6xpe2d3Hx0/fG+S82ZeGACDJbQBwLC8LMl/mjCtk7yzqu6oqn0zrAmAOXJ6JAAMRFX9aJKjSW6cMMuzu/uBqnpKklur6iPjI3dr17Mvyb4kWVpaysrKylR1XX3R0YnTpl33iRw5cuSv1z+vGrbS2j4tPe5426Q+DWEcNquG1c/vRra1kRomrW8WY3cq/d2uJo3r0Pu8kX35RMtsZX+FNgAYgKp6aZIXJnlOd/d683T3A+OfD1fV25JckuRRoa27DyQ5kCR79uzp5eXlqWq76kSXxX7xdOs+kZWVlRyrfV41bKW1fbr6oqO59uDoT7NJfRrCOGxWDauf341sayM1TFrfLMbuVPq7XU0a1+v37hp0nzeyL59oma3sr9MjAWDOqmpvRhce+e7u/uKEeXZV1eOP3U/yvCR3rTcvAItFaAOAGaqqNyX5oyRPq6rDVfXyjK4m+fiMTnm8s6reMJ73qVV1y3jRpSTvqaoPJXl/kpu7+/fm0AUAZszpkQAwQ9195TrNb5ww7wNJLhvf/1iSb9rC0gAYKEfaAAAABsyRNgCAVXZPuljFNS+YcSVsxKTnb5bbGcK+MoT6Dt7/yOSLv0yoYbPrntX+sNUcaQMAABgwoQ0AAGDAnB4Jp2AIpxjM0qKcSgAAsAgcaQMAABgwoQ0AAGDAhDYAAIABE9oAAAAGTGgDAAAYMKENAABgwIQ2AACAARPaAAAABkxoAwAAGLAz510AAAAM1e79N8+7BE7Boj9PQhuwpU70InromhdsaDkAgJ3E6ZEAAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAzVFXXVdXDVXXXqrYnV9WtVfXR8c8nTVh2b1XdW1X3VdX+2VUNwDwJbQAwW9cn2bumbX+Sd3X3hUneNX78FarqjCS/kOT5SZ6e5MqqevrWlgrAEAhtADBD3X1bks+sab48yQ3j+zckedE6i16S5L7u/lh3fynJTePlAFhwQhsAzN9Sdz+YJOOfT1lnnnOTfHLV48PjNgAW3JnzLoDJdu+/+a/vX33R0Vw1fnzomhfMqySYaPX+upXLDN2J+uR3lynVOm297oxV+5LsS5KlpaWsrKxMteGrLzo6cdq06z6RI0eO/PX6Z1nDpG1t9XaWHne8bdK25vVcbEUNq5/fjWxrM52ojo3UsN76TqW/m2kIvzOr9+lTrWGjdW/2vrKR+rbyORbaAGD+Hqqqc7r7wao6J8nD68xzOMn5qx6fl+SB9VbW3QeSHEiSPXv29PLy8lTFXXWif0a8eLp1n8jKykqO1T7LGiZta6u3c/VFR3PtwTNPuK15PRdbUcPq53cj29pMJ6p7IzWst75T6e9mGsLvzOp9+lRr2Gjdm72vbKS+6/fu2rLn2OmRADB/70jy0vH9lyZ5+zrzfCDJhVV1QVU9NskV4+UAWHBCGwDMUFW9KckfJXlaVR2uqpcnuSbJc6vqo0meO36cqnpqVd2SJN19NMkrk/x+knuSvLm7755HHwCYLadHAsAMdfeVEyY9Z515H0hy2arHtyS5ZYtKA2CgHGkDAAAYMKENAABgwIQ2AACAAZsqtFXVdVX1cFXdtartyVV1a1V9dPzzSdOXCQAAsDNNe6Tt+iR717TtT/Ku7r4wybvGjwEAANiAqUJbd9+W5DNrmi9PcsP4/g1JXjTNNgAAAHayrbjk/1J3P5gk3f1gVT1lvZmqal+SfUmytLSUlZWV6Tb6uNG3rq817XpPx3rbn6aO1etb3b+N9mlSfRtZ38H7H5k47aJzn3DaNRw5cmRmz9Wk2q++aGPre92N630H7leOw+n0bzOfp4060b68nkm/f9vBqYzrZjx/J9rWRn+fNsssf/8AgNM3t+9p6+4DSQ4kyZ49e3p5eXmq9b3uxrfn2oOP7s6hF0+33tNx1f6bJ07bSB2r13f1RUf/un8b7dOk+qat7XTWN2m56/fuyrT7wKk6Ue2bafU4rKysnHL/NvN52qjTHaPV++d2cyrjuhnP34m2tdmvHafrdPoHa+2e9Jp1zQs2dZmdZtIYbWQZ4zosm/k7c7LlNtNG9slZrm+zDa2+rbh65ENVdU6SjH8+vAXbAAAA2BG2IrS9I8lLx/dfmmT9c8cAAAA4qWkv+f+mJH+U5GlVdbiqXp7kmiTPraqPJnnu+DEAAAAbMNWHULr7ygmTnjPNegEAABjZitMjAQAA2CRCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCATXXJ/+1g9/6bJ047dM0LNnV9bNzB+x/JVRsY20nPoefp5IzRyR0bo6svOvoV++dGXjsAADbKkTYAAIABW/gjbQDA9rD2DIC1R7lPdblTsdln22zXI/Cz7NOk53e7jt1mcwbMye3kMXKkDQAAYMCENgAYgKp6WlXduer2uap69Zp5lqvqkVXz/NicygVghpweCQAD0N33Jrk4SarqjCT3J3nbOrP+YXe/cIalATBnjrQBwPA8J8mfdPcn5l0IAPMntAHA8FyR5E0Tpv2DqvpQVf2nqvrGWRYFwHw4PRIABqSqHpvku5O8Zp3JH0zy9d19pKouS/LbSS5cZx37kuxLkqWlpaysrExV09UXHT3tZU60zVNd39LjNrbtU7EZ9Z3q+k51O1vV3432ddJyG1lmveWO9Xezn4uNmMX+cOTIkaysrGx6nzb7eZpkI3Vv5e/wEB17jreC0AYAw/L8JB/s7ofWTujuz626f0tV/WJVnd3dn1oz34EkB5Jkz549vby8PFVBp3LZ/bUOvXjyNk91fVdfdDTXHtyaP1U2o75TXd+pbmer+rvRvk5abiPLrLfcsf5u9nOxEbPYH1ZWVrK8vLzpfdrs52mSjdS9lb/DQ3T93l2Z9vV2EqdHAsCwXJkJp0ZW1ddVVY3vX5LR+/inZ1gbAHOwc6LvAtnJXyy41nYdi43UvYhf6srWsK9sX1X1NUmem+QHV7X9UJJ09xuS/JMk/6Kqjib5iyRXdHfPo1YAZkdoA4CB6O4vJvlba9resOr+65O8ftZ1ATBfTo8EAAAYMEfaAIAdZ7NPr5+0viGckjzLjxJs9un/nJzx2xkcaQMAABgwoQ0AAGDAhDYAAIABE9oAAAAGTGgDAAAYMKENAABgwIQ2AACAAfM9bbDAfHfL1hjCuA6hBgBgNhxpAwAAGDChDQAAYMCENgAAgAET2gAAAAbMhUgAAHgUFzzauPXG7uqLjuaqgYyp53b7caQNAABgwIQ2AACAARPaAAAABkxoAwAAGDChDQAAYMBcPRJXENoiq8d1SFeMYnvx+wkAONIGAAAwYEIbAAxEVR2qqoNVdWdV3b7O9Kqq/1BV91XVh6vqm+dRJwCz5fRIABiWS7v7UxOmPT/JhePb30/yS+OfACwwR9oAYPu4PMmv9sh7kzyxqs6Zd1EAbC2hDQCGo5O8s6ruqKp960w/N8knVz0+PG4DYIE5PRIAhuPZ3f1AVT0lya1V9ZHuvm3V9FpnmV7bMA58+5JkaWkpKysrUxV19UVHT3uZ19349hOs79TWsfS4jW17SE5nHBahv6djSP090e/IZtU4pP7Oyk7r85EjR6Z+vZ1EaAOAgejuB8Y/H66qtyW5JMnq0HY4yfmrHp+X5IF11nMgyYEk2bNnTy8vL09V17y+suTqi47m2oM7508V/Z2fQy9enjhts/b/IfV3VnZan6/fuyvTvt5OsmWnR57sClgAwHFVtauqHn/sfpLnJblrzWzvSPL946tIfkuSR7r7wRmXCsCMbXX0PdEVsACA45aSvK2qktH782909+9V1Q8lSXe/IcktSS5Lcl+SLyb5gTnVCsAM7ZzjlQAwYN39sSTftE77G1bd7ySvmGVdAMzfVl498mRXwAIAAOAktvJI2wmvgLXZV7bayNVpTrTNg/c/sm77qV7xaq0TXT1qktXb2k5X39nIFcO2U/82Yqv7N2lfntWYbufn71T2183q36RtbfR1ZSPW21e28mpXAMD0tiy0newKWJt9ZavX3fj20746zSyuFLRZFv3qO/o3nUn78qz2Y8/f9rHevrKysrJlV7sCAKa3JadHnuIVsAAAADiJrfrX8bpXwNqibQEAACysLQltk66ABQAAwOnZyqtHAgAAMCWhDQAAYMCENgAAgAET2gAAAAZMaAMAABgwoQ0AAGDAtup72oAZ2r3/5nmXAADAFnGkDQAAYMCENgAAgAET2gAAAAZMaAMAABgwoQ0AAGDAhDYAAIABE9oAAAAGTGgDAAAYMKENAABgwIQ2ABiAqjq/qt5dVfdU1d1V9ap15lmuqkeq6s7x7cfmUSsAs3XmvAuYp937b553CQBwzNEkV3f3B6vq8UnuqKpbu/uP18z3h939wjnUB8CcONIGAAPQ3Q929wfH9z+f5J4k5863KgCGQGgDgIGpqt1JnpnkfetM/gdV9aGq+k9V9Y2zrQyAedjRp0cCwNBU1VlJ3pLk1d39uTWTP5jk67v7SFVdluS3k1y4zjr2JdmXJEtLS1lZWZmqpqsvOjrV8hu19Lj5bXse9Hd+TvQ7slk1Dqm/s7LT+nzkyJGpX28nEdoAYCCq6jEZBbYbu/uta6evDnHdfUtV/WJVnd3dn1oz34EkB5Jkz549vby8PFVdV83pM+BXX3Q01x7cOX+q6O/8HHrx8sRpm7X/D6m/s7LT+nz93l2Z9vV2EqdHAsAAVFUleWOSe7r75ybM83Xj+VJVl2T0Pv7p2VUJwDzsnOgLAMP27CQvSXKwqu4ct702yd9Oku5+Q5J/kuRfVNXRJH+R5Iru7jnUCsAMCW0AMADd/Z4kdZJ5Xp/k9bOpCIChcHokAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgJ057wIAAGCedu+/ed4lwAk50gYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADNiWhbaq2ltV91bVfVW1f6u2AwCL4mTvnTXyH8bTP1xV3zyPOgGYrS0JbVV1RpJfSPL8JE9PcmVVPX0rtgUAi+AU3zufn+TC8W1fkl+aaZEAzMVWHWm7JMl93f2x7v5SkpuSXL5F2wKARXAq752XJ/nVHnlvkidW1TmzLhSA2dqq0HZukk+uenx43AYArO9U3ju9vwLsQGdu0Xprnbb+ihmq9mV0akeSHKmqe6fc5tlJPjXlOgbrX+nftqZ/29si9a9+et3m0+nf129aMax10vfOU5xnK95j52KRfvdOhf4utp3W32Tn9fnSn566vxPfY7cqtB1Ocv6qx+cleWD1DN19IMmBzdpgVd3e3Xs2a31Do3/bm/5tb/rHjJz0vfMU59n099h52Wn7pv4utp3W32Tn9Xkr+7tVp0d+IMmFVXVBVT02yRVJ3rFF2wKARXAq753vSPL946tIfkuSR7r7wVkXCsBsbcmRtu4+WlWvTPL7Sc5Icl13370V2wKARTDpvbOqfmg8/Q1JbklyWZL7knwxyQ/Mq14AZmerTo9Md9+S0ZvLrGz700BOQv+2N/3b3vSPmVjvvXMc1o7d7ySvmHVdc7TT9k39XWw7rb/JzuvzlvW3Rq//AAAADNFWfaYNAACATbAtQltVXVdVD1fVXetM++Gq6qo6e1Xba6rqvqq6t6q+c7bVbsx6fayqn6iq+6vqzvHtslXTtlUfJz2HVfUvx324u6p+ZlX7tu9fVf3mqufuUFXduWraIvTv4qp677h/t1fVJaumLUL/vqmq/qiqDlbV71TV31w1bdv0r6rOr6p3V9U949+zV43bn1xVt1bVR8c/n7RqmW3TPxZHVT2xqn6rqj4y3l//wYn20+2uqv71+Hfyrqp6U1X9jUXr74TX1oV97ZnQ338/3qc/XFVvq6onrpq2cP1dNW0h/j5fbe5/y3b34G9Jvi3JNye5a037+Rl9YPsTSc4etz09yYeSfHWSC5L8SZIz5t2HjfQxyU8k+eF15t12fZzQv0uT/B9Jvnr8+CmL1L81069N8mOL1L8k70zy/PH9y5KsLFj/PpDkH4/vvyzJT27H/iU5J8k3j+8/Psl/GffhZ5LsH7fvT/LT27F/botzS3JDkn82vv/YJE+ctJ9u91tGX4j+8SSPGz9+c5KrFq2/E15bF/a1Z0J/n5fkzPH9n170/o7bF+bv81N4fmf2t+y2ONLW3bcl+cw6k34+yb/JV36x6OVJburuv+zuj2d0ha1L1ll2UE7Qx/Vsuz5O6N+/SHJNd//leJ6Hx+2L0r8kSVVVku9L8qZx06L0r5McO/r0hBz/rqhF6d/Tktw2vn9rkv/b+P626l93P9jdHxzf/3ySezL6g/HyjP5Izvjni8b3t1X/WAzjI9nfluSNSdLdX+ruz2byfroIzkzyuKo6M8nXZPQaulD9nfDaurCvPev1t7vf2d1Hxw/fm9F3KyYL2t+xhfn7fLV5/y27LULbeqrqu5Pc390fWjPp3CSfXPX48Lhtu3rl+JD6datOIViUPv6dJN9aVe+rqj+oqr83bl+U/h3zrUke6u6Pjh8vSv9eneTfV9Unk/xskteM2xelf3cl+e7x/e/N8S803rb9q6rdSZ6Z5H1Jlnr8/V7jn08Zz7Zt+8e29g1J/izJ/15V/7mqfqWqdmXyfrqtdff9Gb1u/mmSBzP6vr13ZkH7u8ZOfu15WZL/NL6/kP3dQX+fHzOzv2W3ZWirqq9J8qNJfmy9yeu0bddLZP5Skv8uycUZvahfO25flD6emeRJSb4lyf+c5M3jo1KL0r9jrszxo2zJ4vTvXyT51919fpJ/nfF/yLM4/XtZkldU1R0ZnVb4pXH7tuxfVZ2V5C1JXt3dnzvRrOu0Db5/bHtnZnTa0S919zOTfCGjU+cW0vifsJdndNrUU5Psqqp/Ot+q5m6hX3uq6keTHE1y47GmdWbb1v3dQX+frzazv2W3ZWjLKMhckORDVXUoo0PNH6yqr8soyZ6/at7zcvy0rW2lux/q7i93918l+eUcP6y6KH08nOStPfL+JH+V5OwsTv8yPu3lf0jym6uaF6V/L03y1vH9/08WbP/s7o909/O6+1kZhe4/GU/adv2rqsdkFNhu7O5jz9lDVXXOePo5SY6d0rHt+sdCOJzkcHe/b/z4tzIKcZP20+3uO5J8vLv/rLv/W0avpf8wi9vf1Xbca09VvTTJC5O8uMcfeMpi9ndH/H2+xsz+lt2Woa27D3b3U7p7d3fvzmhgvrm7/2uSdyS5oqq+uqouSHJhkvfPsdwNO/aiNvY9GZ2ulSxOH387ybcnSVX9nYw+eP6pLE7/ktEb80e6+/CqtkXp3wNJ/vH4/rcnOXb650L0r6qeMv75VUn+bZJjX3C8rfo3/o/fG5Pc090/t2rSOzIK3hn/fPuq9m3TPxbD+P37k1X1tHHTc5L8cSbvp9vdnyb5lqr6mvHv6HMy+rzpovZ3tR312lNVe5P8SJLv7u4vrpq0cP3dKX+fr/HbmdXfstNcxWRWt4z+y/1gkv+W0Q7w8jXTD2V8dZrx4x/N6L/i92Z8dbuh39brY5JfS3IwyYfHT/4527WPE/r32CS/nlEY/WCSb1+k/o3br0/yQ+vMv+37l+QfJbkjo6sjvS/Jsxasf6/K6EqL/yXJNUlqO/Zv/Dz1+HXkzvHtsiR/K8m7Mgrb70ry5O3YP7fFuWX0UYDbx/vqb2d0ytHE/XS735L8uyQfGb8H/lpGV5lbqP5OeG1d2NeeCf29L6PPNh17/X3DIvd3zfRD2eZ/n5/C8zuzv2VrvFIAAAAGaFueHgkAALBTCG0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtMEWqKqfqKpfn3cdAABsf0IbnEBVHaqq75h3HQAA7FxCGwAAwIAJbXAKquqqqnpPVf1sVf15VX28qp6/avoFVfUHVfX5qro1ydlrlv+Wqvo/q+qzVfWhqloet//DqvpUVZ0/fvxN43n+LzPsHgAAAya0wan7+0nuzSiQ/UySN1ZVjaf9RpI7xtN+MslLjy1UVecmuTnJ/5bkyUl+OMlbqupru/v/TPIfk9xQVY9L8mtJ/m13f2Q2XQIAYOiqu+ddAwxWVR1K8s+SnJdRmPrvx+1fk+QLSc5J8tgkH0vyhO7+wnj6byT5q+7+p1X1I0me0d0vWbXe30/yG919Q1U9Jsl7x+u5P8nz2y8mAABjjrTBqfuvx+509xfHd89K8tQkf34ssI19YtX9r0/yvePTHj9bVZ9N8o8yCnzp7v+W5Pokz0hyrcAGAMBqZ867AFgADyZ5UlXtWhXc/naSY+Hrk0l+rbv/+XoLj0+f/PEk/3uSa6vq73X3X2510QAAbA+OtMGUuvsTSW5P8u+q6rFV9Y+SfNeqWX49yXdV1XdW1RlV9Teqarmqzht/Ju76JG9M8vKMAuBPzrgLAAAMmNAGm+P/ntGFSj6T0VGzXz02obs/meTyJK9N8mcZHXn7nzP6/ftXSZaS/C/j0yJ/IMkPVNW3zrR6AAAGy4VIAAAABsyRNgAAgAET2gAAAAZMaAMAABgwoQ0AAGDAhDYAAIABG8SXa5999tm9e/fuqdbxhS98Ibt27dqcgrYx4zBiHEaMw3HGYmTacbjjjjs+1d1fu4klAQAnMYjQtnv37tx+++1TrWNlZSXLy8ubU9A2ZhxGjMOIcTjOWIxMOw5V9YnNqwYAOBVOjwQAABgwoQ0AAGDAhDYAAIABE9oAAAAG7KShrarOr6p3V9U9VXV3Vb1q3P7kqrq1qj46/vmkVcu8pqruq6p7q+o7t7IDAAAAi+xUjrQdTXJ1d//dJN+S5BVV9fQk+5O8q7svTPKu8eOMp12R5BuT7E3yi1V1xlYUDwAAsOhOGtq6+8Hu/uD4/ueT3JPk3CSXJ7lhPNsNSV40vn95kpu6+y+7++NJ7ktyySbXDQAAsCOc1mfaqmp3kmcmeV+Spe5+MBkFuyRPGc92bpJPrlrs8LgNAACA03TKX65dVWcleUuSV3f356pq4qzrtPU669uXZF+SLC0tZWVl5VRLWdeRI0emXsciMA4jxmHEOBxnLEaMAwBsP6cU2qrqMRkFthu7+63j5oeq6pzufrCqzkny8Lj9cJLzVy1+XpIH1q6zuw8kOZAke/bs6eXl5Y31YGxlZSXTrmMRGIcR4zBiHI4zFiPGAQC2n5OGthodUntjknu6++dWTXpHkpcmuWb88+2r2n+jqn4uyVOTXJjk/ZtZNACT7d5/88Rp1+/dNcNKAIDNcCpH2p6d5CVJDlbVneO212YU1t5cVS9P8qdJvjdJuvvuqnpzkj/O6MqTr+juL2924QAAADvBSUNbd78n639OLUmeM2GZn0ryU1PUBQAAQE7z6pEAAADMltAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgJw1tVXVdVT1cVXetavvNqrpzfDtUVXeO23dX1V+smvaGLawdAABg4Z15CvNcn+T1SX71WEN3/4/H7lfVtUkeWTX/n3T3xZtUHwAAwI520tDW3bdV1e71plVVJfm+JN++yXUBAACQ6T/T9q1JHuruj65qu6Cq/nNV/UFVfeuU6wcAANjRqrtPPtPoSNvvdvcz1rT/UpL7uvva8eOvTnJWd3+6qp6V5LeTfGN3f26dde5Lsi9JlpaWnnXTTTdN1ZEjR47krLPOmmodi8A4jBiHEeNw3E4ai4P3PzJx2gVPOGOqcbj00kvv6O49G14BAHDaNhzaqurMJPcneVZ3H56w3EqSH+7u20+0/j179vTtt59wlpNaWVnJ8vLyVOtYBMZhxDiMGIfjdtJY7N5/88Rp1+/dNdU4VJXQBgAzNs3pkd+R5COrA1tVfW1VnTG+/w1JLkzyselKBAAA2LlO5ZL/b0ryR0meVlWHq+rl40lXJHnTmtm/LcmHq+pDSX4ryQ9192c2s2AAAICd5FSuHnnlhPar1ml7S5K3TF8WAAAAyfRXjwQAAGALCW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAzYSUNbVV1XVQ9X1V2r2n6iqu6vqjvHt8tWTXtNVd1XVfdW1XduVeEAAAA7wakcabs+yd512n++uy8e325Jkqp6epIrknzjeJlfrKozNqtYAACAneakoa27b0vymVNc3+VJburuv+zujye5L8klU9QHAACwo505xbKvrKrvT3J7kqu7+8+TnJvkvavmOTxue5Sq2pdkX5IsLS1lZWVlilKSI0eOTL2ORWAcRozDiHE4bieNxdUXHZ04bSeNAwAsio2Gtl9K8pNJevzz2iQvS1LrzNvrraC7DyQ5kCR79uzp5eXlDZYysrKykmnXsQiMw4hxGDEOx+2ksbhq/80Tp12/d9eOGQcAWBQbunpkdz/U3V/u7r9K8ss5fgrk4STnr5r1vCQPTFciAADAzrWh0FZV56x6+D1Jjl1Z8h1Jrqiqr66qC5JcmOT905UIAACwc5309MiqelOS5SRnV9XhJD+eZLmqLs7o1MdDSX4wSbr77qp6c5I/TnI0ySu6+8tbUjkAAMAOcNLQ1t1XrtP8xhPM/1NJfmqaogAAABjZ0OmRAAAAzIbQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCAnTnvAgBm4eD9j+Sq/Tc/qv3QNS+YQzUAAKfOkTYAAIABE9oAAAAG7KShraquq6qHq+quVW3/vqo+UlUfrqq3VdUTx+27q+ovqurO8e0NW1g7AADAwjuVI23XJ9m7pu3WJM/o7v9rkv+S5DWrpv1Jd188vv3Q5pQJAACwM500tHX3bUk+s6btnd19dPzwvUnO24LaAAAAdrzq7pPPVLU7ye929zPWmfY7SX6zu399PN/dGR19+1ySf9vdfzhhnfuS7EuSpaWlZ910000b7UOS5MiRIznrrLOmWsciMA4jxmHEOBz38GceyUN/8ej2i859wuyL2WIH739k4rQLnnDGVPvEpZdeekd379nwCgCA0zbVJf+r6keTHE1y47jpwSR/u7s/XVXPSvLbVfWN3f25tct294EkB5Jkz549vby8PE0pWVlZybTrWATGYcQ4jBiH415349tz7cFHv+QdevHy7IvZYut9tcEx1+/dZZ8AgG1mw1ePrKqXJnlhkhf3+HBdd/9ld396fP+OJH+S5O9sRqEAAAA70YZCW1XtTfIjSb67u7+4qv1rq+qM8f1vSHJhko9tRqEAAAA70UlPj6yqNyVZTnJ2VR1O8uMZXS3yq5PcWlVJ8t7xlSK/Lcn/WlVHk3w5yQ9192fWXTEAAAAnddLQ1t1XrtP8xgnzviXJW6YtCgAAgJENf6YNAACArSe0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAM2ElDW1VdV1UPV9Vdq9qeXFW3VtVHxz+ftGraa6rqvqq6t6q+c6sKBwAA2AlO5Ujb9Un2rmnbn+Rd3X1hkneNH6eqnp7kiiTfOF7mF6vqjE2rFgAAYIc5aWjr7tuSfGZN8+VJbhjfvyHJi1a139Tdf9ndH09yX5JLNqdUAACAnWejn2lb6u4Hk2T88ynj9nOTfHLVfIfHbQAAAGzAmZu8vlqnrdedsWpfkn1JsrS0lJWVlak2fOTIkanXsQiMw4hxGDEOxy09Lrn6oqOPal/E8Vmvn8fYJwBg+9loaHuoqs7p7ger6pwkD4/bDyc5f9V85yV5YL0VdPeBJAeSZM+ePb28vLzBUkZWVlYy7ToWgXEYMQ4jxuG419349lx78NEveYdevDz7YrbYVftvnjjt+r277BMAsM1s9PTIdyR56fj+S5O8fVX7FVX11VV1QZILk7x/uhIBAAB2rpMeaauqNyVZTnJ2VR1O8uNJrkny5qp6eZI/TfK9SdLdd1fVm5P8cZKjSV7R3V/eotoBAAAW3klDW3dfOWHScybM/1NJfmqaogAAABjZ6OmRAAAAzIDQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYGdudMGqelqS31zV9A1JfizJE5P88yR/Nm5/bXffstHtAAAA7GQbDm3dfW+Si5Okqs5Icn+StyX5gSQ/390/uxkFAgAA7GSbdXrkc5L8SXd/YpPWBwAAQDYvtF2R5E2rHr+yqj5cVddV1ZM2aRsAAAA7TnX3dCuoemySB5J8Y3c/VFVLST6VpJP8ZJJzuvtl6yy3L8m+JFlaWnrWTTfdNFUdR44cyVlnnTXVOhaBcRgxDiPG4biHP/NIHvqLR7dfdO4TZl/MFjt4/yMTp13whDOm2icuvfTSO7p7z4ZXAACcts0IbZcneUV3P2+dabuT/G53P+NE69izZ0/ffvvtU9WxsrKS5eXlqdaxCIzDiHEYMQ7Hve7Gt+fag4/+GO+ha14wh2q21u79N0+cdv3eXVPtE1UltAHAjG3G6ZFXZtWpkVV1zqpp35Pkrk3YBgAAwI604atHJklVfU2S5yb5wVXNP1NVF2d0euShNdMAAAA4DVOFtu7+YpK/tabtJVNVBAAAwF/brKtHAgAAsAWENgAAgAET2gAAAAZsqs+0wVAdvP+RXDXhsueLeIl3AAAWl9AGC0x4BQDY/pweCQAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCAnTnNwlV1KMnnk3w5ydHu3lNVT07ym0l2JzmU5Pu6+8+nKxMAAGBn2owjbZd298XdvWf8eH+Sd3X3hUneNX4MAADABmzF6ZGXJ7lhfP+GJC/agm0AAADsCNXdG1+46uNJ/jxJJ/mP3X2gqj7b3U9cNc+fd/eT1ll2X5J9SbK0tPSsm266acN1JMmRI0dy1llnTbWORWAcRh7+zCN56C/Wn3bRuU+YbTFzZByOmzQWizgOB+9/ZOK0C55wxlSvEZdeeukdq86sAABmYKrPtCV5dnc/UFVPSXJrVX3kVBfs7gNJDiTJnj17enl5eapCVlZWMu06FoFxGHndjW/PtQfX370PvXh5tsXMkXE4btJYLOI4XLX/5onTrt+7y2sEAGwzU50e2d0PjH8+nORtSS5J8lBVnZMk458PT1skAADATrXh0FZVu6rq8cfuJ3lekruSvCPJS8ezvTTJ26ctEgAAYKea5vTIpSRvq6pj6/mN7v69qvpAkjdX1cuT/GmS752+TAAAgJ1pw6Gtuz+W5JvWaf90kudMUxQAAAAjW3HJfwAAADaJ0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGAbDm1VdX5Vvbuq7qmqu6vqVeP2n6iq+6vqzvHtss0rFwAAYGc5c4pljya5urs/WFWPT3JHVd06nvbz3f2z05cHAACws204tHX3g0keHN//fFXdk+TczSoMAACATfpMW1XtTvLMJO8bN72yqj5cVddV1ZM2YxsAAAA7UXX3dCuoOivJHyT5qe5+a1UtJflUkk7yk0nO6e6XrbPcviT7kmRpaelZN91001R1HDlyJGedddZU61gExmHk4c88kof+Yv1pF537hNkWM0fG4bhJY7GI43Dw/kcmTrvgCWdM9Rpx6aWX3tHdeza8AgDgtE0V2qrqMUl+N8nvd/fPrTN9d5Lf7e5nnGg9e/bs6dtvv33DdSTJyspKlpeXp1rHIjAOI6+78e259uD6Z/8euuYFM65mfozDcZPGYhHHYff+mydOu37vrqleI6pKaAOAGZvm6pGV5I1J7lkd2KrqnFWzfU+SuzZeHgAAwM42zdUjn53kJUkOVtWd47bXJrmyqi7O6PTIQ0l+cIptcJoO3v9IrprwX/ZFPKIAAACLbpqrR74nSa0z6ZaNlwMAAMBqm3L1SAAAALaG0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADJjQBgAAMGBnzruAzXLw/kdy1f6bH9V+6JoXzKEaAACAzeFIGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAADJrQBAAAMmNAGAAAwYEIbAADAgAltAAAAAya0AQAADNiWhbaq2ltV91bVfVW1f6u2AwAAsMi2JLRV1RlJfiHJ85M8PcmVVfX0rdgWAADAItuqI22XJLmvuz/W3V9KclOSy7doWwAAAAtrq0LbuUk+uerx4XEbAAAAp6G6e/NXWvW9Sb6zu//Z+PFLklzS3f9y1Tz7kuwbP3xaknun3OzZST415ToWgXEYMQ4jxuE4YzEy7Th8fXd/7WYVAwCc3JlbtN7DSc5f9fi8JA+snqG7DyQ5sFkbrKrbu3vPZq1vuzIOI8ZhxDgcZyxGjAMAbD9bdXrkB5JcWFUXVNVjk1yR5B1btC0AAICFtSVH2rr7aFW9MsnvJzkjyXXdffdWbAsAAGCRbdXpkenuW5LcslXrX8emnWq5zRmHEeMwYhyOMxYjxgEAtpktuRAJAAAAm2OrPtMGAADAJtj2oa2q9lbVvVV1X1Xtn3c981JV11XVw1V117xrmaeqOr+q3l1V91TV3VX1qnnXNA9V9Teq6v1V9aHxOPy7edc0T1V1RlX956r63XnXMi9VdaiqDlbVnVV1+7zrAQBO3bY+PbKqzkjyX5I8N6OvGfhAkiu7+4/nWtgcVNW3JTmS5Fe7+xnzrmdequqcJOd09wer6vFJ7kjyop22T1RVJdnV3Ueq6jFJ3pPkVd393jmXNhdV9T8l2ZPkb3b3C+ddzzxU1aEke7rbd9UBwDaz3Y+0XZLkvu7+WHd/KclNSS6fc01z0d23JfnMvOuYt+5+sLs/OL7/+ST3JDl3vlXNXo8cGT98zPi2ff9DM4WqOi/JC5L8yrxrAQDYiO0e2s5N8slVjw9nB/6BzvqqaneSZyZ535xLmYvxKYF3Jnk4ya3dvSPHIcn/K8m/SfJXc65j3jrJO6vqjqraN+9iAIBTt91DW63TtiOPJvCVquqsJG9J8uru/ty865mH7v5yd1+c5Lwkl1TVjjtttqpemOTh7r5j3rUMwLO7+5uTPD/JK8anVAMA28B2D22Hk5y/6vF5SR6YUy0MxPgzXG9JcmN3v3Xe9cxbd382yUqSvfOtZC6eneS7x5/nuinJt1fVr8+3pPno7gfGPx9O8raMTi8HALaB7R7aPpDkwqq6oKoem+SKJO+Yc03M0fgCHG9Mck93/9y865mXqvraqnri+P7jknxHko/Mtag56O7XdPd53b07o9eH/293/9M5lzVzVbVrfGGeVNWuJM9LsqOvNAsA28m2Dm3dfTTJK5P8fkYXnHhzd98936rmo6relOSPkjytqg5X1cvnXdOcPDvJSzI6onLn+HbZvIuag3OSvLuqPpzRPzdu7e4de7l7spTkPVX1oSTvT3Jzd//enGsCAE7Rtr7kPwAAwKLb1kfaAAAAFp3QBgAAMGBCGwAAwIAJbQAAAAMmtAEAAAyY0AYAADBgQhsAAMCACW0AAAAD9v8HU0Kx2audraAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x792 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(bins = 50,figsize = (15,11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation heatmap\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "sns.heatmap(data.corr(), annot=True, cmap='Oranges')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.scatter(data[\"Height\"][data[\"Gender\"]==\"Male\"],data[\"Weight\"][data[\"Gender\"]==\"Male\"],label=\"Male\")\n",
    "plt.scatter(data[\"Height\"][data[\"Gender\"]==\"Female\"],data[\"Weight\"][data[\"Gender\"]==\"Female\"],label=\"Female\")\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.legend()\n",
    "plt.title('Height Count-Weight Scatter Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above scatter plot, we can conclude that irrespective of gender, weight is not depend on height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.scatter(data[\"Index\"],data[\"Weight\"])\n",
    "plt.xlabel(\"Index\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.title('Index Count-Weight Scatter Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender and Height count bar plot\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.barplot(x = \"Gender\", y = \"Weight\", hue = \"Gender\", data = data)\n",
    "plt.title('Weight Count-Gender Bar Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age Group - Count Bar Plot Grouped by Gender\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.barplot(x = \"Index\", y = \"Weight\", hue = \"Gender\", data = data)\n",
    "plt.title(\"Index - Count Bar Plot Grouped by Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender & Sucide Count grouped by Age Group bar plot\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.barplot(x=\"Gender\", y=\"Weight\", hue=\"Index\", data=data)\n",
    "plt.title('Gender & Weight Count grouped by Index Group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, we can conclude that as index increases weight also increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAJg6gTAA64F/frQRJsEY7FgAAAA2Lv/8aCNMgjHYscB4BLAAALDxCJMAmBoBLAAAbDybZt0AAAAAAOuHM5OYOJe1AAAAwMYlTGLiXNYCAAAAG5fL3AAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGjm3dyYW9u3b8/CwkK2bt2aHTt2zLodAAAAIMIk5tjCwkJ27tw56zYAAACAAS5zAwAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJl3cwMAAJiA7du3Z2FhIVu3bs2OHTtm3Q7A1AiTAAAAJmBhYSE7d+6cdRsAU+cyNwAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoNmBs24AAIDJKpsP32sKADBJwiQAgA3mBqfff9YtAAAbmDAJAAAAaLJ9+/YsLCxk69at2bFjx6zbYUaESQAAAECThYWF7Ny5c9ZtMGNuwA0AAABAM2cmAQDABuRSFFbq71592dD5X79q957pqHXu+YgtU+sLmB/CJAAA2IBcigIMEjAzScKkGfBDDACwsTi+A+adgJlJEibNgB9iAICNxfEdrH9CYWgnTAIAAGC/JxSGdt7NDQAAAIBmwiQAAAAAmgmTAAAAAGjmnkkAAHPCzV8BgPVAmAQAMCfc/BUAWA+ESQAAAMCac0bu+iVMAgAAANacM3LXL2ESAACwJGcPADBImAQAACzJ2QMADNo06wYAAAAAWD+cmQQArBsutQEAmD1hEgCwbszjpTYCLgBgfyNMAgBYhXkMuAAApkmYRBL/VQVYCftOAAD2R8IkkvivKsBK2HcCALA/8m5uAAAAADQTJgEAAADQzGVuAPup57/h1KHzv7Lr2n66c+Q6z3/Ye6bWFwAAMN+cmQQAAABAM2cmAXt4ZyoAAACWI0wC9vDOVAAAACzHZW4AAAAANBMmAQAAANDMZW6N3EsGAAAAQJjUzL1kAAAAAIRJwCqc/4rTRi7bdeW3+unOoes99DHvbvoezgoEAACYL8IkYK45KxAAmDb/vAIYjzAJAABYdyYZAPnnFcB4hEkAAMC6IwACmB1hEgAwEfd/04uHzr9m1+VJkot2XT5ynXc8+MnTagsAgAnbNOsGAAAAAFg/hEkAAAAANBMmAQAAANBsamFSKeXlpZRLSyn/MTDvt0spnyml/Fsp5c2llBsNLDu3lPLZUsp/llJOnVZf7J+2b9+eRz3qUdm+ffusWwEAAIB1bZo34H5lkpck+fOBee9Lcm6t9dpSym8lOTfJM0opJyc5K8ntkhyf5G9KKd9Va909xf7Yj3i3D9j/3O8tDxk6/5tXX5kk2Xn1xSPXedeZb5xaXwAAsN5N7cykWusHk3xln3nvrbVe2z/8pyQ36z8/M8nraq3X1Fo/n+SzSe48rd4AAAAAWJlpnpm0nJ9K8vr+85umC5cWXdjPu55SyjlJzkmSE044YZr9AdDoaeefNnT+l3d9q5/uHLrOCx/67qn2BQAATN5MbsBdSnl2kmuTvHpx1pDV6rCvrbWeV2vdVmvdtmXLlmm1CAAAAMAQa35mUinl7CQPSHKvWutiYHRhkpsPrHazJBetdW/AxrZ9+/YsLCxk69at2bFjx6zbAWCNPPD8949ctmvX15MkF+36+tD1/vqh95paX6P4fQUwPvvOtbWmYVIp5bQkz0jyw7XWrw0semuS15RSfjfdDbhPSvLRtewN2PjciB2A9cDvK4Dx2XeuramFSaWU1ya5R5KjSykXJnleundvu0GS95VSkuSfaq2Pr7V+spTyhiSfSnf525O8kxuwP/AfFCbNmAIAYNqmFibVWh8+ZPbLllj/BUleMK1+AOaR/6AwacbU3u7/xpcOnX/NriuTJBftunLkOu94yOOm1hfAojed/+Wh83ft+vae6bB1HvzQo6faF6vjnztsdLN8NzcAAADYcPxzh41uJu/mBgAAAMD6JEwCAAAAoJnL3NaBhT/+taHzd1/xlT3TUetsfcJzptYX7M9cBw8AAOyvhEkAK+A6eAAAYH/lMjcAAAAAmgmTAAAAAGjmMjcAAADWlPtPwvomTAIAAGBNuf8kG8WlL3nvyGW7L//anumw9Y752ftOra9pEyaxIl988UNHLrv28iv66cVD1zvhyedPrS8AAABgutwzCQAAAIBmzkwCgDXyo3/9rKHzv3n1/yVJLrr6/0au884H/vrU+gIAgHE4MwkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkbcAMAMDFnnP+2ofO/tuvqJMlFu64euc5bH3r61PoCgP3JpX/0xqHzd1+xa8902DrHPPEhTfWdmQQAAABAsw1xZtJlf/yXI5ftvuKqPdNh6215wk9OrS8AAADY313y+x8bOn/35dfsmQ5b59infN9U+2LlNkSYxPr2mT88c+j8b11xdT+9aOQ6t3nSW6bWFwAAbATbt2/PwsJCtm7dmh07dsy6HWADECYBAABsYAsLC9m5c+es2wA2EPdMAgAAAKCZM5MAgKFcFgHARvTpP75k6PxvXrF7z3TYOrd9wrFT7QvWE2ESADCUyyIAABjGZW4AAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM3cgBuAufGYN582ctklu77VT3cOXe8VD3r31PoCAACuI0wCAACADWDhhZ8duWz3V7+1Zzpsva1Pu9XU+mLjcZkbAAAAAM2cmQQbwPbt27OwsJCtW7dmx44ds24HAACADUyYBBvAwsJCdu7cOes2AAAA2A8IkwAAgDXjjGqA9U+YBAAArBlnVAOsf27ADQAAAEAzYRIAAAAAzVzmBgAA69iPvfHfhs6/fNc3kyQX7/rmyHX+6iG3n1pfsBbcgwtmQ5gEAADAuuQeXDAbLnMDAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGjmBtwAAABj+Is3XTZ0/lW7du+ZjlrnkQ/eMrW+ANaKM5MAAAAAaObMJAAAAJZ15OFb9poC+y9hEgAAAMt6xGnPnnULwJwQJgEAMNL27duzsLCQrVu3ZseOHbNuBwCYA8IkAIAGD3jjq4bO/8auK5MkF+26cug6b3/I2VPta9oWFhayc+fOWbcBAMwRN+AGAAAAoJkwCQAAAIBmLnMDAACSJD/+ps8Onf+VXd9Kkly861tD13n9g2811b4AmC/OTAIAAACgmTOTAABYV7zDHADMljAJAIB1xTvMAcBsCZMAAACA/colL/7A0Pm7L//6numodY598j2m09Q6IkwCAAAA1tyWQ47aa8r6IUwCpmLz4SVJ7acAAAB7O/eUn551C6yQMGlKLv2TF49ctvuKy/dMh613zOOfPK22YM2cfi+7FwAAgI1o06wbAAAAAGD9ECYBAAAA0EyYBAAAAEAzNzUBAADm1q+9+eKh87+ya/ee6ah1nvOg46bWF8D+zJlJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADN3IAbYIPYvn17FhYWsnXr1uzYsWPW7SRJDt5cktR+CgAAbATCJIANYmFhITt37px1G3u51el+zQDA/urDr7ps5LJvXLl7z3TYeqecvWVqfQGr5zI3AAAAAJoJkwAAAABoJkwCAAAAoJmbWQAAAMAcmsc3WIFEmAQAAABzaR7fYAUSl7kBAAAAMAZnJu3jsj956dD5u6+4cs901DpbHv+4qfUFAAAAMA+EScBceM0rTx06/6orr+2nO0eu8xOPfs/U+gKAQQ9644eGzt+16xtJkot3fWPkOm9+yN2m1hcArCVhEgAAsOH94ZsvGbnsil2790yHrfekBx07tb4A1iP3TAIAAACgmTAJAAAAgGYucwM2nFe86r5D51955e5+unPoOo85+71T7QsAAGAjcGYSAAAAAM2cmQTAfqVs3pTaTwEAgPEJkwDYrxz0oMNn3QLLuP+b/njksmt2XZEkuWjXFUPXe8eDnzC1vgAA6Pi3LAAAAADNhEkAAAAANHOZGxvGx//k9JHLrrni6/30oqHr3enxb5taX7De3GBzSVL7KQAAwN6ESQDs5Xvuf8CsWwAAAOaYy9wAAAAAaObMJAAAAPZ7Nzl0y15TYDRhEgAAAPu9n737ubNugWVc8qIPj1y2+/Jv7JkOW+/Ynz9lan3tj6YWJpVSXp7kAUkurbV+dz/vxklen+TEJF9I8rBa61f7ZecmeWyS3UmeXGt9z7R6AwCAWXrIGz86ctmVu7o/iC7e9Y2h673xIXeeWl8A0GKaZya9MslLkvz5wLxnJnl/rfU3SynP7B8/o5RycpKzktwuyfFJ/qaU8l211t1T7A8AWAPliEP3mu7vHnD+q0cu+8auq5IkF+26auh6b3/oI6bWFwBAq6mFSbXWD5ZSTtxn9plJ7tF//qokH0jyjH7+62qt1yT5fCnls0nunGT0OWwAwLpw8Bl3mXUL11M2H7bXFACAdmt9z6Rja60XJ0mt9eJSyjH9/Jsm+aeB9S7s511PKeWcJOckyQknnDDFVgGAjergM+456xYAANatTbNuoFeGzKvDVqy1nldr3VZr3bZli7vsAwAAAKyltQ6TLimlHJck/fTSfv6FSW4+sN7Nkly0xr0BAAAAsIy1DpPemuTs/vOzk7xlYP5ZpZQblFJukeSkJKPf4gIAAACAmZjaPZNKKa9Nd7Pto0spFyZ5XpLfTPKGUspjk3wxyY8lSa31k6WUNyT5VJJrkzzJO7kBAAAAzJ9pvpvbw0csuteI9V+Q5AXT6gcAAACA1ZuXG3ADAAAAsA5M7cwkAAAAVuaIzVv2mgLME2ESAADAnDnj9GfPugWAkYRJAAAAsMEdfchN9prCagiTmLijD9201xQAAIDZOvfOT5l1Cyzj0j9829D5u6+4es901DrHPOn0qfU1jDCJiXvaXTbPugUAAGAD+fhLLx06/5ord++ZjlrnTo87Zmp9wf5KmMTcuvGhm5J8u58CANNy+vlvGrns67t2JUku2rVr6Hpve+iDp9YX0O5dr//yyGVf2/XtPdNh693vx4+eWl/AxiRM2s9c9IdPHTp/9xWX7ZkOW+f4J/3uVPsa5ufudsiaf0+Ylhe/+tSh8y+/6tp+unPkOk9+xHum1hcAAMC4hEkwI9u3b8/CwkK2bt2aHTt2zLodAAAAaCJMghlZWFjIzp07Z90GAAAAjEWYBAAAAGP6wu8vjFx27eW790yHrXfiU7ZOrS/mx5bDjtxrupEIkwAAAAAm7Fl3/fFZtzA13iYLAAAAgGbOTAKAJdzvLU8aueybV1+aJNl59aVD13vXmX84tb4AAGBWnJkEAAAAQDNhEgAAAADNhEkAAAAANHPPJBjiw+c9YOj8b1zxjX560ch1Tjnn7VPrCwAAAGZNmAQA69CPvvnXhs7/5q6vJEku2vWVoeu880HP2evx/d/82yO/xzW7vtrX+urQ9d7xoKc39wsAwMbhMjcAAAAAmgmTAAAAAGjmMjfYD73t5fcbOv/qK7/ZT3eOXOf0n3rX1PoCAABg/jkzCQAAAIBmwiQAAAAAmrnMDdaJ97zsR0cu+1p/edrXrrxo6HqnPvadU+sLAFqUzUfsNQUA1i9hEgAAU3fI6Q+edQsAwIS4zA0AAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkbcAMAALCmbnT4lr2mwPoiTAIAAGBNPeY+z551C8AqCJMAAIAlHXDETfaaArB/EyYBAKxC2Xz4XlPYiI484ymzbgFYYwu/+8mh83df/s0901HrbH3q7abWF/NBmAQAsAo3OOM+s25hwzrz/HcPnX/1rq8lSS7a9bWh67zloac1f49Nm4/Mt/vpRrNp81F7TQFgUoRJAADstw49/eGzbmFqjjjjCbNuAYA5s+XQzXtNV0qYBDDCn/7FqSOXXXHVtf1059D1fuaR75laXwAAACvxrB86YyJ1Nk2kCgAAAAD7BWESAAAAAM2ESQAAAAA0c88kAACACTj8iC17TQE2KmESAMza5oNT+ikA69d9z3j2rFsAWBPCJACYsYMfeNtZtwAAAM3cMwkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACg2YGzbgAAAGBcNzzi6L2mAKwdYRIAALDu3PHMZ866BYD9ljAJ2G8cfvjeUwAAAMYnTJqBLYcdutcUWBv3us8Bs24BAABg3RMmzcCz7n6XidQ5+tAb7jUFAAAAmDZh0jp27t3vOOsWAADgep785i+NXHbZrmv3TIet9+IH3XxqfQEwGZtm3QAAAAAA64czkwBYFw48oiSp/RQAAJgVYRIA68IxZ/qVBQAA82DDH5lvOfTwvaYrrnPYYXtNAQAAAPZHGz5MevbdT51QnXtOpA4AADBfDj1iy15TAJa24cMkAACApdztzHNn3QLAuuLd3AAAAABoJkwCAAAAoJkwCQAAAIBm7pkEAMBIZfMRe00BAIRJAACMdMPTz5h1CwDAnHGZGwAAAADNmsKkUspvtcwDAAAAYGNrPTPpPkPm3W+SjQAAAAAw/5a8Z1Ip5QlJnpjklqWUfxtYtDnJP0yzMQAAAADmz3I34H5Nkncl+Y0kzxyYf1Wt9StT6woAAACAubRkmFRrvSLJFUkeXko5IMmx/dccXko5vNb6xTXoEQAAAIA5sdyZSUmSUsrPJnl+kkuSfLufXZPcfjptATDMjteeOnLZV6+6tp/uHLre9oe/Z2p9AQDMwo0P27LXFFgbTWFSkqckuXWt9f+m2AvA9Rx+eElS+ykAAFznnHs+a9YtwH6pNUz6UrrL3WBduvFhZa8p68f97n3ArFsAAABgwHLv5vbU/tPPJflAKeUdSa5ZXF5r/d0p9gYTc87dbzjrFgAAAJiSLYfeaK8p07XcmUmb++kX+4+D+w8AAACAuXDuKY+ZdQv7leXeze2X16oRAAAAAOZf67u5vS3du7cNuiLJBUn+tNb6jUk3BgAAAMD82dS43ueS7EryZ/3HlUkuSfJd/WMAAGACNm0+KpuOvEk2bT5q1q0AwFCt7+Z2p1rr3Qcev62U8sFa691LKZ+cRmMAALA/OvyMx866BQBYUuuZSVtKKScsPug/P7p/+M2JdwUAAADAXGo9M+kXk3yolPI/SUqSWyR5YinlsCSvmlZzAAAAAMyXpjCp1vrOUspJSW6TLkz6zMBNt39/Sr0BAAAAMGeWDJNKKT9Sa/3bUsqD91l0y1JKaq1vmmJvAAAAAMyZ5c5M+uEkf5vk9CHLahJhEgAAAMB+ZMkwqdb6vH76mLVpBwAAAIB51nTPpFLKsUl+Pcnxtdb7lVJOTnJKrfVlU+0OAADWgbL5RtnUTwHGdfGOnUPn7/7qtXumo9Y5bvtNp9YXjNL6bm6vTPKKJM/uH/9XktcnWVGYVEr5hSSPS3ep3L8neUySQ/uaJyb5QpKH1Vq/upL6AACwlg4741GzbgFGOuLwLXtNAVarNUw6utb6hlLKuUlSa722lLJ7Jd+wlHLTJE9OcnKt9eullDckOSvJyUneX2v9zVLKM5M8M8kzVvI9AAAA6PzY/Z+9/EoAY9jUuN7VpZSbpDuTKKWUH0xyxSq+74FJDimlHJjujKSLkpyZ5FX98lcleeAq6gMAAAAwBUuemVRKeUqSf0iyPclbktyylPIPSbYk+bGVfMNa685SyguTfDHJ15O8t9b63lLKsbXWi/t1Li6lHLOS+gDAxlWOOHSvKQAAa2+5y9xuluRFSW6T5DNJ3pfkA0leX2v98kq+YSnlqHRnId0iyeVJ/qqU8pNjfP05Sc5JkhNOOGElLQAA69TBZ9x91i0AAOz3lrzMrdb6tFrrXZJsTfK0JB9J8iNJ/q2U8qkVfs97J/l8rfWyWuu3krwpyV2SXFJKOS5J+umlI3o6r9a6rda6bcsWN5ADAAAAWEut90w6JMkRSY7sPy5KFyytxBeT/GAp5dBSSklyrySfTvLWJGf365yd7rI6AAAAAObIcvdMOi/J7ZJclS48+sckv1tr/epKv2Gt9SOllPOT/EuSa5N8PMl5SQ5P8oZSymPTBU4ruicTAAAAANOz3D2TTkhygyT/nWRnkgvT3edoVWqtz0vyvH1mX5PuLCXYUD7wZ/cfOv/rV17TTy8aus49fvodU+0LAAAAVmLJMKnWelp/Kdrt0t3X6BeTfHcp5StJPtyHQgAAAADsJ5Y7Mym11prkP0oplye5ov94QJI75/pnFwEAAACwgS13z6Qnpzsj6a5JvpXkH5J8OMnLk/z71LsDAAAAYK4sd2bSiUnOT/ILtdaLp98OAAAAAPNsuXsmPXWtGgEAAABg/m2adQMAAAAArB/CJAAAAACaLftubgAAAAD7gy2H3mivKcMJkwCAocoRh+w1BQDY6M69yyNm3cK6IEwCAIY6+Mzvn3ULAADMIfdMAgAAAKCZMAkAAACAZsIkAAAAAJq5ZxIAAABM0E0OOXqvKWw0wiQAgDlRNh++1xSA9ekXTzl31i3AVAmTAADmxA1OP23WLQAALEuYRJLk6EMP3msKAAAAMIwwiSTJM+72nbNugVU48rAkKf0UAAAApkeYBBvAw37EGWUAAACsjU2zbgAAAACA9cOZSQAArCubNh+Rb/dTAGDtCZMAAFhXDjn9YbNuAQD2ay5zAwAAAKCZMAkAAACAZsIkAAAAAJq5ZxIAbCRH3DClnwIAwDQIkwBgAzn4zDvOugUAADY4l7kBAAAA0EyYBAAAAEAzl7kBzNChh5cktZ+y3pTNB6X2UwAA2F8IkwBm6C6nHTDrFliFgx50wqxbAACANecyNwAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmh046waA+XHEYSVJ7acAAABwfcIkYI8H3+ugWbcAAADAnHOZGwAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQ7cNYNAKxHhx1ektR+CgAAsP8QJgGswA/f94BZt3A9h2zuAq5uCgAAMB3CJIANYtv95i/gAgAANh73TAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoduCsGwAAAPYfBx1x9F5TANYfYRIAALBmTjzz6bNuAYBVcpkbAAAAAM2ESQAAAAA0EyYBAAAA0Mw9kwAAAGAOHX3Ilr2mMC+ESQAAADCHnrlt+6xbgKFc5gYAAABAM2ESAAAAAM2ESQAAAAA0c88kmJEbHVr2mgIAAMB6IEyCGXnEPQ+edQsAAADMuS2HHrHXdB4IkwAAAADm1LPu9uBZt3A97pkEAAAAQDNhEgAAAADNhEkAAAAANJtJmFRKuVEp5fxSymdKKZ8upZxSSrlxKeV9pZT/7qdHzaI3AAAAAEab1ZlJL0ry7lrrbZLcIcmnkzwzyftrrScleX//GAAAAIA5suZhUinliCR3T/KyJKm1frPWenmSM5O8ql/tVUkeuNa9AQAAALC0WZyZdMsklyV5RSnl46WUl5ZSDktybK314iTpp8cM++JSyjmllAtKKRdcdtlla9c1AAAAADMJkw5M8r1J/rjWeqckV2eMS9pqrefVWrfVWrdt2bJlWj0CAAAAMMQswqQLk1xYa/1I//j8dOHSJaWU45Kkn146g94AAAAAWMKah0m11oUkXyql3Lqfda8kn0ry1iRn9/POTvKWte4NAAAAgKUdOKPv+3NJXl1KOTjJ55I8Jl2w9YZSymOTfDHJj82oNwAAAABGmEmYVGv9RJJtQxbda41bAQAAAGAMszozCQAAAFhnjj7kxntN2T8Jk2AMNzqs7DUFAADYn5z7A0+adQvMAWESjOEx97jBrFsAAACAmVrzd3MDAAAAYP0SJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQLOZhUmllANKKR8vpby9f3zjUsr7Sin/3U+PmlVvAAAAAAw3yzOTfj7JpwcePzPJ+2utJyV5f/8YAAAAgDkykzCplHKzJPdP8tKB2WcmeVX/+auSPHCN2wIAAABgGbM6M+n3k2xP8u2BecfWWi9Okn56zAz6AgAAAGAJax4mlVIekOTSWuvHVvj155RSLiilXHDZZZdNuDsAAAAAljKLM5PumuSMUsoXkrwuyY+UUv4yySWllOOSpJ9eOuyLa63n1Vq31Vq3bdmyZa16BgAAACAzCJNqrefWWm9Waz0xyVlJ/rbW+pNJ3prk7H61s5O8Za17AwAAAGBps3w3t339ZpL7lFL+O8l9+scAAAAAzJEDZ/nNa60fSPKB/vP/S3KvWfYDAAAAwNLm6cwkAAAAAOacMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAAAAgGbCJAAAAACaCZMAAAAAaCZMAgAAAKCZMAkAAACAZsIkAAAAAJoJkwAAAABoJkwCAAAAoJkwCQAAAIBmwiQAAAAAmq15mFRKuXkp5e9KKZ8upXyylPLz/fwbl1LeV0r573561Fr3BgAAAMDSZnFm0rVJfrHWetskP5jkSaWUk5M8M8n7a60nJXl//xgAAACAObLmYVKt9eJa67/0n1+V5NNJbprkzCSv6ld7VZIHrnVvAAAAACxtpvdMKqWcmOROST6S5Nha68VJFzglOWbE15xTSrmglHLBZZddtma9AgAAADDDMKmUcniSNyZ5Sq31ytavq7WeV2vdVmvdtmXLluk1CAAAAMD1zCRMKqUclC5IenWt9U397EtKKcf1y49LcuksegMAAABgtFm8m1tJ8rIkn661/u7AorcmObv//Owkb1nr3gAAAABY2oEz+J53TfLIJP9eSvlEP+9ZSX4zyRtKKY9N8sUkPzaD3gAAAABYwpqHSbXWDyUpIxbfay17AQAAAGA8M303NwAAAADWF2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADNhEkAAAAANBMmAQAAANBMmAQAAABAM2ESAAAAAM2ESQAAAAA0EyYBAAAA0EyYBAAAAEAzYRIAAAAAzYRJAAAAADSbuzCplHJaKeU/SymfLaU8c9b9AAAAAHCduQqTSikHJPnDJPdLcnKSh5dSTp5tVwAAAAAsmqswKcmdk3y21vq5Wus3k7wuyZkz7gkAAACAXqm1zrqHPUopD01yWq31cf3jRyb5gVrrzw6sc06Sc/qHt07ynw2lj07y5Qm0OKk6k6w1jz1Nspae1r6Wnta+lp7Wvpae1r6Wnta+lp7Wvpae1r6Wnta+lp7Wvpae1r6WnpLvqLVuGbXwwAk1MillyLy90q5a63lJzhuraCkX1Fq3raaxSdbZ6D1Nspae1r6Wnta+lp7Wvpae1r6Wnta+lp7Wvpae1r6Wnta+lp7Wvpae1r6WnpY3b5e5XZjk5gOPb5bkohn1AgAAAMA+5i1M+uckJ5VSblFKOTjJWUneOuOeAAAAAOjN1WVutdZrSyk/m+Q9SQ5I8vJa6ycnUHqsy+LWoM4ka81jT5Ospae1r6Wnta+lp7Wvpae1r6Wnta+lp7Wvpae1r6Wnta+lp7Wvpae1r6WnZczVDbgBAAAAmG/zdpkbAAAAAHNMmAQAAABAM2ESAAAAAM32uzCplDKRbS6llEnUGag3qb5WXWexxmq3sZRyw9X2MlDryAnWOr6UcsCkX8NJmKdx0NcxzpevY5yPaZ7GQV/HOF++jnE+pnkaB30d43z5Osb5mOZpHPR1jPPl6xjnY5qncdDXMc6Xr2Ocj2klr99+ESaVUu5bSrlHKeUmtdZvr7LWLZKk1lonMMh/pJRy11LK0bXWb6/0B7CUclop5UdLKcf1dQ5YRU+PSvLYUsohq9nGUspPJvnlUsqq3zGwlHJOkneUUo6cwHP+2CSfS/I9q9y+B5dSfqGUcof+8Yr7KqXcp5RySinlqNWMT+N8rFrGeVsd47y9lnHeVss4b6tjnLfXMs7b6hjn7bWM87ZaxnlbHeO8vZZx3lZnfsZ5rXVDfyT51SSfTfInSf4lyQ8nOXSFtX4vyT8ledgE+vq1JB9P8rIkX1pFT7+S5IIkL0lyRZKD+/llhfU+keRNSR6xWCPJphU853+X5Hb7zB+7pyRPTvLRJHee0Fh4V5I3JHnpKp6jX07ysSS/k2Rnkhuvoqdf7p/z3+qfsx9KctAKt804b69nnLeNTeO8rZZx3lbLOG/fNuO8vZ5x3jY2jfO2WsZ5Wy3jvH3bjPP2esZ529icm3G+qidl3j+SHJPk7UmO6B//fP/i/WiSA8esdVqSf03yi0n+MMk9VtHX45O8L8lh/eNXJvnTVdQ5vH/8hiTfvcKeDuinr0/y4iQvSHLGCuo8Jsm3k9ywf7x18flfYV+/tPgDnOQ7k/xAkhsN9Nv0g5jkWf0P8GFJTu7HwZEr6Oe7+x+2Q/rHr0pylxVu28l9rcXn6nlJPpPk1HHGZ5JjJzjO7zfBcf6ECY3zxTqTGOcH9tPVjvOfmtNx/uwJjfPvmeA4v92ExvnWCY7zH53gOH/ihMb5Yp15GuePndNx/pwJjfPbT3CcL/5uWO04P26C4/z+ExznT5rDcX5QP13tOH/cBh/nd5jgOF/83bDace74vL3WpI7P5/W4ZR6Pzyd13LLRx/nPTHCcb+qnG/Xv0HPncJxP5O/QDX2ZW6310iTXJnlg//hF6Z6k05KckIx1WtjfJXlQktelOz3tx0spJ49ZY3Hdf0vyM7XWq/vZr0lyZWuNAe9Kct9a665Syk8kOTPJ00spLy6lHDtmrcXT2t6dLmG+PMmdSimPLqXcb4w6b0ryxiRPK6XcJ8lfJXltKeWVpZStY/aUJLdIcp9Syg/3tZ6d5I+T/HjSnea5XIH+FMf31Frv1z/nX0lylyT37pePc2rgl5MsJPnJUspZfR/bSymvKaXcZYw6SfeaX5Tul1aSvKjv7d5JjlzuixdPI621XpJVjvOBU1L/pq+z4nE+UOsTWcU4H6jz9lrrfVYzzgeeq2v7WSsa5wM9/VVWOc73OQ14VeN84NTkd61mnA/0dElWOc4Han01qxvnhyRJrXUhqx/nh/Sfvjer3J+X667F/1hWN84X67xlAuN88bla7ThffJ5el9WP80MGHq52nC8+V29f5Thf7OmirH6cL9b6clY3zjcnSa314nS/jx/YP17JON/cf/rOJA/O6sb5Yq2PJHnCKsb5Yp03JTl1leN88bn6Vj9rpeN8sae/TPLmrG6cbx54eMusbpwv1nrLKsf5Ef2nX0xyaVY3zhdrLSS5OCsc54v64/OayRyf/02Sh2SVx+e9jyd54gSOz9+W5LRJHJ/XWnf3n672+Pz1Sf46kzs+v1VWf3y+Kck7J3R8vpDkskzm+Pz/0h0HTWKcr2p/PuD9WeX+vF93U7pQasX784Hv+c6scn++qF53edVqx/n5WeX+fB/fmfn6O/TSrHJ/PuDydD83qxrnGy5MKqX8YP/kLv7AvDfJzUopJ/WrvChdUvzwZOlB0Nd6eP/wm7XWz9VadyZ5R7pfpueUUrbUWmtZ5prMvtZP9N/v4+l+ue9ZnG6n3Lp9iz19qf/eN05y23RJ4s8kOT7JLzTWOmughyT5jiSptf52ku9P8kdZZkAN9lRrvaL/3o9I8udJnprk0UkOT/JzY/aUJH+WLlV+SpKH11rPSPL3SX5o4OBmqVo/UWu9ttb6sX7ewf0fpS9M8shSypENB3aDPX0tyXuSfG+6HcoP1lofmOTCJA8bc/u+ke4X1qNKKY9P8vJ0/8G4TZKfWK5OkqeU624I994kN13pOE/yC6WUI2qt36q1fn414zzJU/s/+v4lqxjnfU+H11q/1M87Kisc5+meq8GD/ROS8cd5rnuerko3Jlc8zvuejupn/Vm6M8yekhWM8yS/WEo5tNb6L/28FY3zvqfDMoFxPlDrmnTj/JErGOenJHlpKeWO/azVjPPFWnfo9wmr2Z+fkuTlpZTbZnX788U639P3klXszxe37/YDs1cyzgefp6uzunG+WOtO/azVjPNTkryilHK7Wusn+nkrGeeLPX13kq9ndeN8sNZqxvm2JP9SSnlCP+vdWflxy7YkHy+lPL52/mcV43yx1k/VWi9I8oXBxWkf54t1zqm1LtTunhorHeeLz9XPDMxeyTgffJ6+ke5ShpWO88WentTPOi8rP27ZluQTpZTH1Vr/vZ+3knG+LcnHSik/ne6PxHdl5eN8sNbV6YLTlRy3bCulnFVKOa2f9c6sYpz3tU7t9+efXc04L6U8vJRyz1rrR5J8fnBxxhjnfZ1711p31lp3r2ac99t334HZKzk+H3yerk43nn8yKxznfa3FP+z/JKsY5/3fDfcY2J/fYCXjvO/p3rXWL6cbU9+XFY7zvta9+j4uycrG+e1KKd9bSrl5P+tdSU5Y4ThfrHWz/vh8Nfvz25VSvi/J0bXWDyf5Qil7QoxxxvlineNqrV/q9+dHZ2XjfHH7bjowe/GeUOOM88Hn6ap0+/NHZmXjfLHWd/Sz/rTfnqdk/HF+u37/efTA8fkhKxjniz0d1weU70yyLSsb53tq5bpg6tHjjvO91BWe9jWPH0lumC51/1qS7+/n3SHdL/bHJzmxn/e96dLFgxtrbevnbRpYfpd0p+A9Nd0pcD85Tl/7LH90kpf2n/98upS3qaeBZQcOfH77dP8VueE429fPv1OSn07ym0n+sx9YT09y/Djblu7UudsOPL5jkrcmucGYr9/Nkzw33UHsvfp5R6Q7rXLrmK9fGVi+LclfJDl232VL1Fk8zbH0Pbw2yc36ecenO3tt5HWrI2rdJt0v9t9O8ov9vFOT/MoSdX4i3X8VHrzP83teukvCxhnnw2qtdJxfr9bA81XSPs6XqnPQwOOWcT6q1p2SnJP2cT6qzu0y/jgf9pwvjvP/zXjjfFitlYzzoXXS/QIfd5wPq7U4zl+Y5KmN4/yH04U0DxuYd4d0gcS443xYrcHnaZxxfr1aKxznS9UZd5yPqjXuOB9V53ZJTh5znA97zm+W7jTqccf50L5WMM5Hbd+R6f7bO844H7Z9t053APvCtO/Pz0p3FudfD3zNnbKy45bBWos/Z4PHBuOM88Vabxnoa/BnpnWcD+upDOmtZZxfr9bAeBznuOV6z3k/fyXHLcNevxP65/cLGW+cD3uuDljBOB987Z62WCcrO24ZVuvkXHfc0ro/f3iSf083/j6W5J7p/vhcyXHLYK0Lkpy+z/JxxvnQWhl/f75UnXH35/vWOmNgvzDO/nxUTys5btm31mnp/jGwkuOW5V6/1nE+WOdfktynn7+S/fm+te6W7gzDcY9bHpHu+OdF6c54fHiSo9Jd0vTEjDfOB2v9RZKfXsU437evnx7YL4wzzof21NcZd5zvW+tn+vnfl/HG+aht+56Mf9yyb0+P7uc/P+OP81HP1eLlfK3jfLDOq5M8sp9/VMYf5/vWemiSQ5OcnTHG+fXqtq64Xj76AfeeJJ9MclI/735J/iDdzcFuny5N/7NRL9yQWv+R5FaDg6D//C7pTjX87yTfsYJaiwdU90m3I/i1dKeS32wldQbW+f1+G5e8YdmIWvdO91+Zd6XbOfxAkmemvx624Tm/3vPUP35RutMCW3safP1uk+7GYK9Ll1L/VrpT5Q9b5ev390leMOaYWqxzRLoDqcemu8fFb6S7VnjkjnPU9g3p6xVJzh3x9fdNd6O8B/ePb5jr7htx53QJftM4H1HrwOyzo20Z5y21Wsb5EnUWr+cd/GNmyXG+RK2D0h2UfTEN43zMnpYc58u8flvS7cybxvkYr9+S43yJOof2j383jeN8SK1D+ud78drywT8eR47zfvmTc90vtsPSBW43THeA8JKMsT/fp9ah6UKNrfus07Q/X65WGvfnI+ocN2S9388y+/MRtY5Jt+/83zTuz8foadn9+YhaR6T77+COjLE/b3z9lt2fj6hz0/7xC1vH+RLj8+j+8WAAsNT+/IlJPpzuNPo79ePvqH7Z/dLdN6J1f75vrf9JcqMh/bTsz5eqtXgg3LI/X6rOWMcty9S6d79NLfvzpuepZZyPqLX4+t0m3R9DrfvzZZ/zlnG+TJ0j0/3ste7PR47PIesuNc6PSveH1136x89Ickr/+Z0y3nHLvrW2J/nBrOy4ZVStgwfWaRnnS/aU8Y5bRtU6MN3vvi+lbZwv19PgmFpunA+tNbBsnOOW1tdvuXE+qs7ifWTGOW4ZOj5z3b5uMCRZapwfme7s6Tv1j++Z7qzAO6e7z80443zfWvdId3Pr+65gnC9bK23jfFSdU1cwzkfVune6f8q0Hp+P09Ny43zY63dFumDxsIw3zltfv+XG+aie7reCcT5qfP5I/7j5+Px6tVtXnPePXLeDfHK6/14/K8nfJjkx3S/Ck9KdcvemdNeGHjBmrb9Jl1LffmC9f1gc7KuplS4R/Ha6m2iNGuQtdU5MlwSvZPve33/9zdN4U7fGno7pe3rtCnr623SnO9463R8gj0gXCr5sQq/fA5L8xgrq3KIfUw9Pd/rjh1bxnN8yyR36ZTdKlyq/aYlxcJd0f2D8WLqd05vTpfZ/ke504y1Jnpa2cT6s1svSJfvfOeY4X7ZWrrv541LjvKXOd6ZtnI+q9RdJbpzkjo3jvKWn49I2zpd6/Rb/u/CotI3z1tdvuXG+VJ1j0/1R+9K0jfNRtV6d64LYm2SZcd6v98h0BxAnpftP4Z+nC0Zu3z/fT0/DOB9R65XpDspPGWect9TKdTetHjnOG+uclIZxvkSt/033B+22pb52zJ5umoZxPqLWq9IdIH5vv/zsNIzzMV6/Jcf5EnW+2I+p0/telh3ny/S0+AfJ0Vl+f36v9H/o94//PMnzBh6fnPbjlmG1fmng8WKg27I/X7JWP+9Ry43zhp4OSvtxy7Bazx14fJ/GMb5cnZL245blXr+j0n7c0vr6Lbc/X277HpXuj7yW/flytQ5M23HLd6Q7s+mO6c46WEj3R88/9MuOSuP+fESt16ULvQbPQmgZ58vWSsP+vLHOrRrH+bBar++357g03ty4safj0zbOR71+/5jrjoGa9udjvH7LjfNRdf6xX3afNO7Pl3jOP5z+3cDStj8/Md1x/eK7md0k3R/vX+pfu+PT3Ty7ZZwPq/XudO/cdasxx/mytdKdmbTcOF+qzuI//2+ZtnE+rNZ7+ufq6Ay5imcV27Y1beN8VE8XpjvbtKT7vd8yzltfv+XG+ag6F/Zj6ox0Z3e2jPNR2zf4+t04Dcfn16vduuI8fvQb/fR95v344gvTPyHfTneTscXlN1plrScNLL/3Kmv9bP/4tAycsr3SntL9IffTq+zp8QPLr/fWgCvo6U5JfmqVPT1xYPnQ0xNX+Ppd720wx6izeLriQRk4w2iVz9XBGfLOBX2d7QOPT08XPlyY7sDwe9O9teM7c927OC01zper9bx0O6vFWkuN85Za70l30HmfJL+wwjrP7XvalO6/FEuN8+Vq/Uq6680Xt2/UOG99ng5I98foY1bR0/P7WotnKS01zsd9/UaN85Yx9bZ0v0A3Zelx3vr6Hdg/X6ePqPO0gccPS3dA9+wkZ/fzfi7df+AWw9mlxvlytZ6U7r/2i7WG/jE6Rq3PJblBuv/2jNqfL1fnieneQvigdKdALzXOl6v15CT/NbB9o8Z5S0//k27/dHKW3p8vV+tn++1bPKtvqXE+7us3apy3jKlP57qfl6XGefNz1T9edn8++NqkexeaFw75miNbfvZG1cre/51t2p8vUWsx1LhvGvbny/WU7gyApv1543O17P68oac7pHF/3vicN+3PG2stuz9f7nlKtw9u2p83PFcHZfT+/OkDj38v3R89l+S6Y55npttHLf68LLU/X67W9nT7lsVaTcfnS9Ra3OfdKw3H58v0dFC6sxmajs9H1HpWukt+ltufj9PTd6fx+HyJ12/w93HT8Xnj67fs8fkSPX0q1+2nmo7Pl+sp3XHQqP35YJ2/SvKB/vX+43S/856XgUuGsvT+fLlav5Tk1wbWaR3no2q9oF9+akbvz5er85wkv9ovv0uSx62ip+enO/a83qXQq+jpjll6f75crecm+dWBnlrHecvr1zLOR42p5y4+RxkIqFb5XB2cIfvz5T7GWnmePtKl/P+Z7gf/9wfm3zXdTvce6S7X+vckvzyntX6lX3a90xxXUmdCPT1/kts2h8/5yFrzuH371HnxwPwHJ/nhgce3THeGy1KXZYxT62XpTxeeRF+N47xl+5Y602Pc7Rv1C2Hcnpb6T8C4PU3y9ZvE9r1igj29vLGnFw3M/8t+/mkD887PwH8wV1nrr9L/53FCfd2yYZy31Bl6YLDC7bvNhLZtkj3ddtq1xqzzxgn31PKc//7A/MU/fG6T7r/ii/cMbN3ftdQa+Ta/49bq541z3DKyzgS3b9TlIrPsaZKv36izBWbZU8tz/gcD8w9L8uvZOxx7bZKbN/a0XK3XJDlhQrVem+5M7+XGeUudYya4fTed0LYdO8Gelro1x0RqjVnndRPuqeU5f8nA/D9Id8nRn/WPz0j/x3pjT8vVGufvj6a+Gsb5XGzfPPY0w+37tbXavuU+VvyFs/5Id8rZM9Pt7D+Y6+5dcGS6O9xfmO70803p3m1nqR3LLGvdfMQP8UTqTHL75rGnGW/fRMdBY53FG2Buyt73wXhSujOTlrq31SxrbR42FiZVZ6Xbt156mvH2TXRMNfb09H7+of3XPT9dEPuYJP+WIffxmedaerJ9I+r8wsCyxTOjnpXufhPXO/Ng3mvpyfaNqLN4Y/KD0t2+YPH3xE+nuwHzljmtdUzafl8tW2fa2zePPc14+2bR0/Z+/r73g3tGkt9Jf7PrOax14LBa87h989jT/rB9y32M/QXz9JH+xlfpbrr2iVx309e7ZeAmgVnmpsjzWktPtm9EnTMHlm1Od5PPf8rAfXLWUy092b4RdR7SP75pukuRfi/dH+7ftYKeZl5LT7ZvuZ+Xfv5j0/3MjHyXn3mupSfbN6LO4vHP96S7JPh1Sf45S5zxOM+19GT7RtR54MCyzekuj/rvJLdYQU8zr6Un29fyseIvnLePJA9Jd8nQ8f3jW2elCdsc1tKT7dunzk0HHv9GGsKt9VBLT7Zvnzon9I8Xb+i/5E2R10stPdm+feos/rzcKt1/B0dekraeaunJ9u1T5zv6x6eluxnssuHWeqilJ9u3T53Fn5d7p7th88i3ol9PtfRk+0bWXm2BefpIdzf6Tyb5TEbc6G4919KT7dunzn8lecSEepqbWnqyffvU+UwG3hhgo9TSk+0bUudnJtjTXNTSk+0bqPOpdPfwOGcCPc1VLT3ZvoE6n+zrnD2Bnuaqlp5s39C6kyo0Dx/p3kXn6xl4x5WNVEtPtm+fOk+fYE9zU0tPtm+fOnPzszfJWnqyffPa00bfvnnsaaNv3zz2tNG3bx572ujbN4/HUpOspSfbN7TuJIvN8iPdjdPenIHrATdSLT3ZvnntaaNv3zz2tNG3bx572ujbN489bfTtm8eeNvr2zWNPG3375rGnjb5989jTRt++eexpo2/fPPa0P2zfvh+l/wYbQinloFrrtzZqLT2tfS09rX0tPa19LT2tfS09rX0tPa19LT2tfS09rX0tPa19LT2tfS09rX2teezpenU3UpgEAAAAwHRtmnUDAAAAAKwfwiQAAAAAmgmTAAAAAGgmTAIAAACgmTAJAKBRKWXXPo8fXUp5yTJfc0Yp5ZnLrHOPUsrbRyx7Sinl0PG7BQCYDmESAMAU1VrfWmv9zVWUeEoSYRIAMDeESQAAE1BK2VJKeWMp5Z/7j7v28/ecvVRK+c5Syj/1y39lnzOdDi+lnF9K+Uwp5dWl8+Qkxyf5u1LK381gswAArufAWTcAALCOHFJK+cTA4xsneWv/+YuS/F6t9UOllBOSvCfJbff5+hcleVGt9bWllMfvs+xOSW6X5KIk/5DkrrXWF5dSnprknrXWL094WwAAVkSYBADQ7uu11jsuPiilPDrJtv7hvZOcXEpZXHxEKWXzPl9/SpIH9p+/JskLB5Z9tNZ6YV/3E0lOTPKhiXUOADAhwiQAgMnYlOSUWuvXB2cOhEvLuWbg891xnAYAzCn3TAIAmIz3JvnZxQellDsOWeefkjyk//ysxrpXJdn3DCcAgJkRJgEATMaTk2wrpfxbKeVTSfa9J1LSvTPbU0spH01yXJIrGuqel+RdbsANAMyLUmuddQ8AAPuFUsqh6e67VEspZyV5eK31zFn3BQAwDtfiAwCsne9L8pLS3Ujp8iQ/Ndt2AADG58wkAAAAAJq5ZxIAAAAAzYRJAAAAADQTJgEAAADQTJgEAAAAQDNhEgAAAADN/j8jxrxYlRKugAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Country & Suicide_rate Bar plot\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "plot=sns.barplot( x = \"Height\",y = \"Weight\", data = data)\n",
    "plot.set_xticklabels(plot.get_xticklabels(),rotation=40)\n",
    "plt.title('Height - Weight Bar plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Index'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plpot of Index and Weight\n",
    "\n",
    "data[['Index','Weight']].groupby(['Index']).sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter matrix for checking outlier\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "attributes = ['Gender', 'Weight', 'Height','Index']\n",
    "scatter_matrix(data[attributes], figsize=(20,10))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>169.944000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>3.748000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.375261</td>\n",
       "      <td>32.382607</td>\n",
       "      <td>1.355053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>170.500000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height      Weight       Index\n",
       "count  500.000000  500.000000  500.000000\n",
       "mean   169.944000  106.000000    3.748000\n",
       "std     16.375261   32.382607    1.355053\n",
       "min    140.000000   50.000000    0.000000\n",
       "25%    156.000000   80.000000    3.000000\n",
       "50%    170.500000  106.000000    4.000000\n",
       "75%    184.000000  136.000000    5.000000\n",
       "max    199.000000  160.000000    5.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Gender  500 non-null    object\n",
      " 1   Height  500 non-null    int64 \n",
      " 2   Weight  500 non-null    int64 \n",
      " 3   Index   500 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking info of data set\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    0\n",
       "Height    0\n",
       "Weight    0\n",
       "Index     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the data for null or missing values\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 4)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#droppinf off any null rows (is any)\n",
    "\n",
    "data = data.dropna()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for outlier and removing it\n",
    "\n",
    "data=data.sort_values(by=[\"Weight\"])\n",
    "q1=data[\"Weight\"].quantile(0.25)\n",
    "q3=data[\"Weight\"].quantile(0.75)\n",
    "iqr=q3-q1\n",
    "lwo=q1-1.5*iqr\n",
    "upo=q3+1.5*iqr\n",
    "data=data[(data.Weight<upo)|(data.Weight>lwo)]\n",
    "data=data.sort_index().reset_index(drop=True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non-numerical labeled columns,Gender is to be converted to numerical label that can be done by using SkLearn's LabelEncoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding the categorical features with LabelEncoder\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = sklearn.preprocessing.LabelEncoder()\n",
    "data['Gender'] = le.fit_transform(data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>110</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>195</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>153</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>136</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>131</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Height  Weight  Index\n",
       "0         1     174      96      4\n",
       "1         1     189      87      2\n",
       "2         0     185     110      4\n",
       "3         0     195     104      3\n",
       "4         1     149      61      3\n",
       "..      ...     ...     ...    ...\n",
       "495       0     150     153      5\n",
       "496       0     184     121      4\n",
       "497       0     141     136      5\n",
       "498       1     150      95      5\n",
       "499       1     173     131      5\n",
       "\n",
       "[500 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a copy of dataset for statistical test\n",
    "\n",
    "stat_data = data.copy()\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender    int32\n",
       "Height    int64\n",
       "Weight    int64\n",
       "Index     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the data type of each column\n",
    "\n",
    "stat_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scaling the numerical data columns with RobustScalar\n",
    "\n",
    "numerical =  ['Gender', 'Weight', 'Height','Index']\n",
    "\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "rc = RobustScaler()\n",
    "stat_data[numerical] = rc.fit_transform(stat_data[numerical])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>-0.178571</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>-0.339286</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.517857</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>-0.035714</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.767857</td>\n",
       "      <td>-0.803571</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.732143</td>\n",
       "      <td>0.839286</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.482143</td>\n",
       "      <td>0.267857</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.053571</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.732143</td>\n",
       "      <td>-0.196429</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>0.446429</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender    Height    Weight  Index\n",
       "0       1.0  0.125000 -0.178571    0.0\n",
       "1       1.0  0.660714 -0.339286   -1.0\n",
       "2       0.0  0.517857  0.071429    0.0\n",
       "3       0.0  0.875000 -0.035714   -0.5\n",
       "4       1.0 -0.767857 -0.803571   -0.5\n",
       "..      ...       ...       ...    ...\n",
       "495     0.0 -0.732143  0.839286    0.5\n",
       "496     0.0  0.482143  0.267857    0.0\n",
       "497     0.0 -1.053571  0.535714    0.5\n",
       "498     1.0 -0.732143 -0.196429    0.5\n",
       "499     1.0  0.089286  0.446429    0.5\n",
       "\n",
       "[500 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Splitting the Data:\n",
    "The data is split into train & test sets, 80-20 split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((500, 3), (500,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sepratating & assigning features and target columns to X & y\n",
    "\n",
    "y = stat_data['Weight']\n",
    "X = stat_data.drop('Weight',axis=1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((400, 3), (100, 3))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets: 80-20 split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Model Building & Training:\n",
    "   Supervised machine learning is one of the most commonly used and successful types of machine learning. Supervised learning is used whenever we want to predict a certain outcome/label from a given set of features, and we have examples of features-label pairs. We build a machine learning model from these features-label pairs, which comprise our training set. Our goal is to make accurate predictions for new, never-before-seen data.\n",
    "\n",
    "   There are two major types of supervised machine learning problems, called classification and regression. Our data set comes under regression problem, as the prediction of suicide rate is a continuous number, or a floating-point number in programming terms. The supervised machine learning models (regression) considered to train the dataset in this notebook are:\n",
    "\n",
    "1. Linear Regression\n",
    "2. k-Nearest Neighbors Regression\n",
    "3. Random Forest\n",
    "4. Gradient Boosting\n",
    "5. Decision Tree\n",
    "6. Bagging Regression\n",
    "7. Support Vector Machine\n",
    "              \n",
    "  The metrics considered to evaluate the model performance are Accuracy & Root Mean Squared Error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating holders to store the model performance results\n",
    "ML_Model = []\n",
    "acc_train = []\n",
    "acc_test = []\n",
    "rmse_train = []\n",
    "rmse_test = []\n",
    "\n",
    "#function to call for storing the results\n",
    "def storeResults(model, a,b,c,d):\n",
    "  ML_Model.append(model)\n",
    "  acc_train.append(round(a, 3))\n",
    "  acc_test.append(round(b, 3))\n",
    "  rmse_train.append(round(c, 3))\n",
    "  rmse_test.append(round(d, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1. Linear Regression\n",
    "   Linear regression, or ordinary least squares (OLS), is the simplest and most classic linear method for regression. Linear regression finds the parameters w and b that minimize the mean squared error between predictions and the true regression targets, y, on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear regression model \n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# instantiate the model\n",
    "lr = LinearRegression()\n",
    "# fit the model \n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_lr = lr.predict(X_test)\n",
    "y_train_lr = lr.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression: Accuracy on training Data: 0.798\n",
      "Linear Regression: Accuracy on test Data: 0.747\n",
      "\n",
      "Linear Regression: The RMSE of the training set is: 0.25932703260614354\n",
      "Linear Regression: The RMSE of the testing set is: 0.29169819994474455\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_lr = lr.score(X_train, y_train)\n",
    "acc_test_lr = lr.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_lr = np.sqrt(mean_squared_error(y_train, y_train_lr))\n",
    "rmse_test_lr = np.sqrt(mean_squared_error(y_test, y_test_lr))\n",
    "\n",
    "print(\"Linear Regression: Accuracy on training Data: {:.3f}\".format(acc_train_lr))\n",
    "print(\"Linear Regression: Accuracy on test Data: {:.3f}\".format(acc_test_lr))\n",
    "print('\\nLinear Regression: The RMSE of the training set is:', rmse_train_lr)\n",
    "print('Linear Regression: The RMSE of the testing set is:', rmse_test_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Linear Regression', acc_train_lr, acc_test_lr, rmse_train_lr, rmse_test_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual vs Predicted Trianing data')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,y_train_lr)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"Actual vs Predicted Trianing data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual vs Predicted Testing data')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_test_lr)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"Actual vs Predicted Testing data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS: \n",
    "   The model preformance is good, and we can see that the scores on the training and test sets are very close together. This means we are likely underfitting, not overfitting.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2. k-Nearest Neighbors Regression:\n",
    " K nearest neighbors is a simple algorithm that stores all available cases and predict the numerical target based on a similarity measure (e.g., distance functions). A simple implementation of KNN regression is to calculate the average of the numerical target of the k nearest neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsRegressor(),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                         13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
       "                                         23, 24, 25, 26, 27, 28, 29, 30],\n",
       "                         'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN Regression model\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# instantiate the model\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "param_grid = {'n_neighbors':list(range(1, 31)), 'weights': ['uniform', 'distance']}\n",
    "\n",
    "# instantiate the grid\n",
    "knn_grid = GridSearchCV(knn, param_grid , cv=10)\n",
    "\n",
    "# fit the model \n",
    "knn_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 10, 'weights': 'uniform'}\n"
     ]
    }
   ],
   "source": [
    "#Checking the best parameters for the model\n",
    "knn_para = knn_grid.best_params_\n",
    "print(knn_para)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_train_knn = knn_grid.predict(X_train)\n",
    "y_test_knn = knn_grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: Accuracy on training Data: 0.848\n",
      "KNN: Accuracy on test Data: 0.806\n",
      "\n",
      "KNN: The RMSE of the training set is: 0.22475945546044898\n",
      "KNN: The RMSE of the testing set is: 0.25520993509146106\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_knn = knn_grid.score(X_train, y_train) \n",
    "acc_test_knn = knn_grid.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_knn = np.sqrt(mean_squared_error(y_train, y_train_knn))\n",
    "rmse_test_knn = np.sqrt(mean_squared_error(y_test, y_test_knn))\n",
    "\n",
    "print(\"KNN: Accuracy on training Data: {:.3f}\".format(acc_train_knn))\n",
    "print(\"KNN: Accuracy on test Data: {:.3f}\".format(acc_test_knn))\n",
    "print('\\nKNN: The RMSE of the training set is:', rmse_train_knn)\n",
    "print('KNN: The RMSE of the testing set is:', rmse_test_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('k-Nearest Neighbors Regression', acc_train_knn, acc_test_knn, rmse_train_knn, rmse_test_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating training and testing set performance with different numbers of neighbors from 1 to 30. The plot shows the training and test set accuracy on the y-axis against the setting of n_neighbors on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xc9f7040>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# try n_neighbors from 1 to 30\n",
    "neighbors_settings = range(1, 31)\n",
    "for n in neighbors_settings:\n",
    "  # fit the model\n",
    "  knn = KNeighborsRegressor(n_neighbors=n)\n",
    "  knn.fit(X_train, y_train)\n",
    "  # record training set accuracy\n",
    "  training_accuracy.append(knn.score(X_train, y_train))\n",
    "  # record generalization accuracy\n",
    "  test_accuracy.append(knn.score(X_test, y_test))\n",
    "\n",
    "#plotting the training & testing accuracy for n_neighbours from 1 to 30\n",
    "plt.plot(neighbors_settings, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(neighbors_settings, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")  \n",
    "plt.xlabel(\"n_neighbors\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS:\n",
    "This discrepancy between performance on the training set and the testing set for n_neighbors < 15\n",
    "is a clear sign of overfitting. After that, the perfomance is not so great so, moving on to the other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3. Random Forest: Ensemble of Decision Trees\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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=9)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest regression model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# instantiate the model\n",
    "forest = RandomForestRegressor(max_depth=9)\n",
    "\n",
    "# fit the model \n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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": {},
   "source": [
    "#### Performance Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest: Accuracy on training Data: 0.907\n",
      "Random Forest: Accuracy on test Data: 0.810\n",
      "\n",
      "Random Forest: The RMSE of the training set is:  0.17623318643329391\n",
      "Random Forest: The RMSE of the testing set is:  0.25301270027742473\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_forest = forest.score(X_train, y_train)\n",
    "acc_test_forest = forest.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_forest = np.sqrt(mean_squared_error(y_train, y_train_forest))\n",
    "rmse_test_forest = np.sqrt(mean_squared_error(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))\n",
    "print('\\nRandom Forest: The RMSE of the training set is: ', rmse_train_forest)\n",
    "print('Random Forest: The RMSE of the testing set is: ', rmse_test_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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, rmse_train_forest, rmse_test_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating training and testing set performance with different numbers of max_depth from 1 to 30. The plot shows the training and test set accuracy on the y-axis against the setting of max_depth on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xcfdcb80>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# try max_depth from 1 to 30\n",
    "depth = range(1, 31)\n",
    "for n in depth:\n",
    "  # fit the model\n",
    "  forest = RandomForestRegressor(max_depth=n)\n",
    "  forest.fit(X_train, y_train)\n",
    "  # record training set accuracy\n",
    "  training_accuracy.append(forest.score(X_train, y_train))\n",
    "  # record generalization accuracy\n",
    "  test_accuracy.append(forest.score(X_test, y_test))\n",
    "\n",
    "#plotting the training & testing accuracy for max_depth from 1 to 30\n",
    "plt.plot(depth, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(depth, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")  \n",
    "plt.xlabel(\"max_depth\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest gives us an accuracy of 81.4%, better than the linear models  without tuning any parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4. Bagging Regression:\n",
    "   Bagging Regressor is an ensemble estimator which fits base estimator on each random subset of the Train dataset and then aggregates their individual predictions to form a final prediction using voting or averaging method. Here the base estimator is Decision Trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(n_estimators=8)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bagging Regression model\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "#instantiate the model\n",
    "br = BaggingRegressor(n_estimators=8)\n",
    "\n",
    "#fit the model\n",
    "br.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_br = br.predict(X_test)\n",
    "y_train_br = br.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging Regression: Accuracy on training Data: 0.903\n",
      "Bagging Regression: Accuracy on test Data: 0.809\n",
      "\n",
      "Bagging Regression: The RMSE of the training set is:  0.1793558250399755\n",
      "Bagging Regression: The RMSE of the testing set is:  0.25365344262547196\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_br = br.score(X_train, y_train)\n",
    "acc_test_br = br.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_br = np.sqrt(mean_squared_error(y_train, y_train_br))\n",
    "rmse_test_br = np.sqrt(mean_squared_error(y_test, y_test_br))\n",
    "\n",
    "print(\"Bagging Regression: Accuracy on training Data: {:.3f}\".format(acc_train_br))\n",
    "print(\"Bagging Regression: Accuracy on test Data: {:.3f}\".format(acc_test_br))\n",
    "print('\\nBagging Regression: The RMSE of the training set is: ', rmse_train_br)\n",
    "print('Bagging Regression: The RMSE of the testing set is: ', rmse_test_br)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Bagging Regression',acc_train_br, acc_test_br, rmse_train_br, rmse_test_br)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating training and testing set performance with different numbers of n_estimators from 1 to 30. The plot shows the training and test set accuracy on the y-axis against the setting of n_estimators on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd250be0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# try n_estimators from 1 to 30\n",
    "est = range(1, 31)\n",
    "for n in est:\n",
    "  # fit the model\n",
    "  br = BaggingRegressor(n_estimators=n)\n",
    "  br.fit(X_train, y_train)\n",
    "  # record training set accuracy\n",
    "  training_accuracy.append(br.score(X_train, y_train))\n",
    "  # record generalization accuracy\n",
    "  test_accuracy.append(br.score(X_test, y_test))\n",
    "\n",
    "#plotting the training & testing accuracy for n_estimators from 1 to 50\n",
    "plt.plot(est, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(est, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")  \n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS: \n",
    "From the above plot, it is clear that the model performs very well on this dataset. Even with tuning of n_estimators parameters, the training accuracy always stayed above 90% & the test data accuracy is always above 80%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5. Gradient Boosted Regression Trees (gradient boosting machines): Ensemble of Decision Trees\n",
    "   The gradient boosted regression tree is another ensemble method that combines multiple decision trees to create a more powerful model. Despite the “regression” in the name, these models can be used for regression and classification. In contrast to the random forest approach, gradient boosting works by building trees in a serial manner, where each tree tries to correct the mistakes of the previous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.3)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Boosted Regression Trees model\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# instantiate the model\n",
    "gbrt = GradientBoostingRegressor(learning_rate=0.3)\n",
    "\n",
    "# fit the model \n",
    "gbrt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_gbrt = gbrt.predict(X_test)\n",
    "y_train_gbrt = gbrt.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosted Regression Trees: Accuracy on training Data: 0.896\n",
      "Gradient Boosted Regression Trees: Accuracy on test Data: 0.832\n",
      "\n",
      "Gradient Boosted Regression Trees: The RMSE of the training set is:  0.1862783042100469\n",
      "Gradient Boosted Regression Trees: The RMSE of the testing set is:  0.23793430215612169\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_gbrt = gbrt.score(X_train, y_train)\n",
    "acc_test_gbrt = gbrt.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_gbrt = np.sqrt(mean_squared_error(y_train, y_train_gbrt))\n",
    "rmse_test_gbrt = np.sqrt(mean_squared_error(y_test, y_test_gbrt))\n",
    "\n",
    "print(\"Gradient Boosted Regression Trees: Accuracy on training Data: {:.3f}\".format(acc_train_gbrt))\n",
    "print(\"Gradient Boosted Regression Trees: Accuracy on test Data: {:.3f}\".format(acc_test_gbrt))\n",
    "print('\\nGradient Boosted Regression Trees: The RMSE of the training set is: ', rmse_train_gbrt)\n",
    "print('Gradient Boosted Regression Trees: The RMSE of the testing set is: ', rmse_test_gbrt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Gradient Boosted Regression',acc_train_gbrt, acc_test_gbrt, rmse_train_gbrt, rmse_test_gbrt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating training and testing set performance with different numbers of learning_rate from 0.1 to 0.9. The plot shows the training and test set accuracy on the y-axis against the setting of learning_rate on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd2c0760>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "r = []\n",
    "# try learning_rate from 0.1 to 0.9\n",
    "rate = range(1, 10)\n",
    "for n in rate:\n",
    "  # fit the model\n",
    "  gbrt = GradientBoostingRegressor(learning_rate=n*0.1)\n",
    "  gbrt.fit(X_train, y_train)\n",
    "  r.append(n*0.1)\n",
    "  # record training set accuracy\n",
    "  training_accuracy.append(gbrt.score(X_train, y_train))\n",
    "  # record generalization accuracy\n",
    "  test_accuracy.append(gbrt.score(X_test, y_test))\n",
    "\n",
    "#plotting the training & testing accuracy for learning_rate from 0.1 to 0.9\n",
    "plt.plot(r, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(r, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")  \n",
    "plt.xlabel(\"learning_rate\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS: \n",
    "The model overfit when trained without tuning any parameters. But, the model accuracy obtained is 83.0%.So, hyperparameter tuning is performed for the model. The otimized Gradient Boosted model gives us an accuracy of 90.1%, with parameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.6. Decision Trees: Regression\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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=6)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree regression model \n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# instantiate the model \n",
    "tree = DecisionTreeRegressor(max_depth=6)\n",
    "# fit the model \n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "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": {},
   "source": [
    "#### Performance Evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: Accuracy on training Data: 0.872\n",
      "Decision Tree: Accuracy on test Data: 0.803\n",
      "\n",
      "Decision Tree: The RMSE of the training set is: 0.20618069959851093\n",
      "Decision Tree: The RMSE of the testing set is: 0.25759137752272154\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_tree = tree.score(X_train, y_train)\n",
    "acc_test_tree = tree.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_tree = np.sqrt(mean_squared_error(y_train, y_train_tree))\n",
    "rmse_test_tree = np.sqrt(mean_squared_error(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))\n",
    "print('\\nDecision Tree: The RMSE of the training set is:', rmse_train_tree)\n",
    "print('Decision Tree: The RMSE of the testing set is:', rmse_test_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "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, rmse_train_tree, rmse_test_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating training and testing set performance with different numbers of max_depth from 1 to 30. The plot shows the training and test set accuracy on the y-axis against the setting of max_depth on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd532280>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# try max_depth from 1 to 30\n",
    "depth = range(1, 31)\n",
    "for n in depth:\n",
    "  # fit the model\n",
    "  tree = DecisionTreeRegressor(max_depth=n)\n",
    "  tree.fit(X_train, y_train)\n",
    "  # record training set accuracy\n",
    "  training_accuracy.append(tree.score(X_train, y_train))\n",
    "  # record generalization accuracy\n",
    "  test_accuracy.append(tree.score(X_test, y_test))\n",
    "\n",
    "#plotting the training & testing accuracy for max_depth from 1 to 30\n",
    "plt.plot(depth, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(depth, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")  \n",
    "plt.xlabel(\"max_depth\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "\n",
    "The model of decision tree is overfit due to which accuracy for training data set comes to be 90% but when testing with test data set it comes to be 80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.7 Support Vector Regression\n",
    "\n",
    "SVR uses the same basic idea as Support Vector Machine (SVM), a classification algorithm, but applies it to predict real values rather than a class. SVR acknowledges the presence of non-linearity in the data and provides a proficient prediction model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Supprt Vector machine regression model \n",
    "from sklearn.svm import SVR\n",
    "\n",
    "# instantiate the model \n",
    "reg=SVR(kernel='rbf')\n",
    "# fit the model \n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_reg = reg.predict(X_test)\n",
    "y_train_reg = reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Regression: Accuracy on training Data: 0.842\n",
      "SVM Regression: Accuracy on test Data: 0.801\n",
      "\n",
      "SVM Regression: The RMSE of the training set is:  0.2291206338706121\n",
      "SVM Regression: The RMSE of the testing set is:  0.2587732811798846\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_reg = reg.score(X_train, y_train)\n",
    "acc_test_reg = reg.score(X_test, y_test)\n",
    "\n",
    "#computing root mean squared error (RMSE)\n",
    "rmse_train_reg = np.sqrt(mean_squared_error(y_train, y_train_reg))\n",
    "rmse_test_reg = np.sqrt(mean_squared_error(y_test, y_test_reg))\n",
    "\n",
    "print(\"SVM Regression: Accuracy on training Data: {:.3f}\".format(acc_train_reg))\n",
    "print(\"SVM Regression: Accuracy on test Data: {:.3f}\".format(acc_test_reg))\n",
    "print('\\nSVM Regression: The RMSE of the training set is: ', rmse_train_reg)\n",
    "print('SVM Regression: The RMSE of the testing set is: ', rmse_test_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Storing Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Support Vector Regression',acc_train_reg, acc_test_reg, rmse_train_reg, rmse_test_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual vs Predicted Trianing data')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FtOgrHxNOWsFDgKH6CDtHlAuXzi8t8bAIzZr78kKUW5HZbqYtwzAyaUd/C3BHWaUtX3bsgeNMVLWusX06shzM9WHlzFUbWbTyhnGmotUbBli08gb2X37t6PpOMvdB+c7zotiMxDCMTOLmEddTcRU3Mh+TzxiSCibx3sfZnmyTu/ig2Vy1fmDczMMVabV1cCi3B3ySIua+s6/clFravmzneVFMkRiGkUtkHnGVAKniRlYk4uiC6++mPpzo0zGsY0w+eXWuIuKRUd+46cFUJ7er4VVPrSuzB7wLX3MfkBoB1qqaWi7MtGUYhjetigIqeiyflrhR9FQRXMnnUaXepGxDO0dSx7t6wxclKwKsndiMxDAMbxqNAmokobHIsVwzBGBMZNmSBf18+jubnS1oi7DLtC52rXWNifZyRZq5ZEsj71q1wnleFFMkhmEUouiNrJkIJ99jZd2ok8f71AmHsOxbm8aZwooyOBQUX4xHe7l8GN2e9VnKjAZrJWbaMgyjUlpR78nVEjfteEsW9HPBOw7L3SZit+ndzrHJ88jrAZ9HM9cqLbosa3mZmCIxDKNSWlHvyadY4taEv2Tt8qNSe1kk2fHccObY+H7zesDn0ei1clX5PXf17S2p/mumLcMwKqUV9Z58QpTTjucTEhxt53se5y851Ftx+MqTd61cM5koDDm5vEiPFR9sRmIYRqX4JAuWQTTLuGjpfO9or7yZjPB8KZjFB82uPGKt0ai4rBIqRcY3iikSwzCqJydZsEyKhMgmx/b11JjZWxsVMZ5TctX6Ad7+yv7RsTN7a+wyrYuzHBnxVcsexzVjcfn4G23y5aJtPdvbwcKFC3XdunXtFsMwphSuJMb+vp7MEujtJE9mV2n4duV0pMkTV4RJemtd3PHZ47z338k92w3DmAK0qrlSmeQ118qKrqpKkWTllyR9RFlKBGBHPT1xslHMtGUYRqW4zC7trg+VRZZsURRUGlUpR5/e61m9S6rGFIlhGA3hm5/QyrIqZZHXXMuVYFiVciySX+LTBTLyA5VFWxWJiLxJRO4WkXtFZHnK+teLyJMisjH8O893W8MwqsPnCTmi6vpQVSTc5dXlGlZtSSRadG5FZkB5WfS1buFTJxxSinwRbfORiEg38AXgaOAh4BYRWaOqdySG/lxV39LgtoZhVEBRH0FV9aGqLCmyZEG/My+lr6fGM8/tHLtQnpepjI6EaQ70JGkzoKxyMY32oc+jnc72w4F7VfU+ABG5AjgJ8FEGzWxrGBOesm5WjVJm+90s8s7TR6E1c63SSs/31LoRIbVs/Yo1m3l250gpii3t3OK4zIP9jqTGKqPk2mna6gfitZUfCpcleZWIbBKR74lINB/z3RYROU1E1onIum3btpUht2G0hcjMMW/5tZy1amPlZS+yyPIFlCWPj/ksLyKsiAkuDZdZztl0a6heWl2xLMd9lnmwHT6pdiqSNENeck52K7Cfqh4G/AuwusC2wULVS1R1oaounD17dqOyGkZbid8QYfyXvap2ty6qbr+7esMAZ1+5KfemnBcRVkbByCga6rcr38za5UexZEF/Yad6I9FcrmNEM4usHu2t7lnSTtPWQ0C8JOaLgK3xAar6VOz1dSLyRRHZ02dbw+hkippb8swc0Nq8jCrb70ZK06e8h8v0FD19V5XD4jrurrWu1F4njURz5Z1bFq3uWdJORXILcICI7A8MAO8G3hMfICJ7A4+oqorI4QQzqMeBwbxtDaNTacRB7HPja3VeRlXtd/OUZny/ec2vqioY6ToulNcKN+sYi1be0Db/WBptUySqulNEzgCuB7qBr6rqZhE5PVx/MfAO4C9EZCcwBLxbg5ouqdu25UQMoyCNZEXnVan1vVnlzYQacUw38+ScRpbSTNtv2tN3dB5pWd49tW4WHzS76Ztx1lN/WYEQ8WOs3jAwrrtjpzS+slpbhtFi9l9+bapDT4Dfrnxz6jZZtZR8Qzrz6kM1cwyXAmpEMblmON0i/PO7DmvoPOPnsfig2Vy1fqCldbKKXIe0sTB+phOn6rplebW2TJEYRotptIhhsyG/WYlt/X09PPPsTgaH3L3Mi95sGy1s2GxBxLzr2+oikkXOxzXW5XuJyHoIKYM8RWIlUgyjxTQanhmPHlp27IFccP3dhbK5s0xGA4NDmUoEikc7NRox1WzUUZ6D3aVMfUqLNEKR6+Aam6VEoPyy8EWx6r+G0WLyHMR5NJrN7dMNMI8i0U55FXSzZG0m6ijPwd4tkhoRlldapFGKRI41+vlUJLo3NiMxjAlClJB45qqNDT3pLzv2QGrdzd1xFHJnQJGcWUbzKhMo82Z8rrDiYdVCtbp8a3wVqX6cpcyyPjlXgmSrsBmJYbQY14xi3QNPcONd21JnKT51l7xmCyW4RLNmQD5yQn6UWjP+oLwZn6uESN65JeXznRUWiWrLqpOV9dG1uyS/KRLDaDEuO/g3bnpwTGvX+I3JJyEx72ZywfV3Ux9x347ymiEl5U1TBD5yRrgUXyOKNkmWaSztxu5zbnGKhHAXMWVmKbmItHDmdpfkN0ViGC3GdQN1lT1ZsqA/d7bhczPJq91U1D6ftr8iPhSX4mtE0Rahmaz8eH5Kke18fT55Sg6eD2O2hETDmMIUcXpHN6asbXzzSGb01FIjs/p6aplhsS7ndJoi8D23SPGlmbAaUbRFaSQrv9Gy7kXlgmwl14m97s3ZbhgOqmiYBOnOYJcjNboxFdnGRX04vU93tNzlpD75iDne4cp5xRwjoqZRaZV5i4SyllEzy/fcGi3rXpQozPuipfMnTGdJm5EYRgpVN0yCsTZzV7Z1dNNIPqnG7eS+sj3zXPpNMFqeZctfuN8sLxu/7xN1NBtIM2HtWuuip9bt5WuJK51GHPRF/Bd5psGyTUzNhom3EstsN4wUWp39DP43wkZlm7f8Wue6+wtkRfvKmZfRnVUq5r1HzuXym7cwrEq3CNO6hWd3jp9RzeytseG8Y5rOhvehHd+JTiEvs91mJIaRQlXlx7Pwdcg2Kltfho/ElyIztUYr8/b11rhq/cCoX2ZYleGd6Q+823fUWbTyBnY8t7NwIcyiZIXxVlEMcyJhisQwUqiq/HgZFJUtuomlKZFal7DixENStkrHFVH16e9sTr1R5lXmHSdPt6CKdwgxZGeDbw2z6JspmBiN9S0dnwxXbtQUOZEw05ZhpNAKU0mjFC0CuOxbm8b1F4fG7Pouc1SSIkUJ49S6JDPXpRGSeReuisaNfuYuk1deXs5EMolZ0UbDaIB2tCutQrZPf2dzqhKZ2VvLbNfqwndGVqQoYZz6iDrLhPT11OhvYEaYPPvkzCCKxmu0yKRvuLLvdhMRM20ZhoNWtystgq9srqqxruVx005fbw1VeHKoPmrG8UmYi2g0YXFYdVzUVk+tmxUnHpKZ+9HXU2O3XaYVSqz0SfrMk7nRYpidYCYtC5uRGIYBPG/aifI6tu+oMzhUH5PjAc/ngOThSljMI5phuWZcrtyPFScewtrlRxXOr4knffqeR5xGcnw6NR+kUWxGYhge+Dhsm43MaXT7rFlEb62LHfXxYbNpkVp5Zqfo6X3ZsQc6s90jshIWs2Y00XZZTvroPHeZ1jVmtpQXDeYinvSZV1wxLsOMnhoiQeXdGT01dq11Mbij7swLKtrRciLRVkUiIm8CPk/Qd/0rqroysf69wMfDt78H/kJVN4Xr7geeBoaBnVmOIMNoBp+Q12YTGBvdPrldsp93rUvoEoj7r12RWj5mp0iuNCXic6NMRj7Fb8ZFclK276jTU+vmwqXzvartRrJlFTzMC1dOyhCPghscGi+PbxLnZKBtUVsi0g38GjgaeAi4BThZVe+IjXk1cKeqbheR44AVqnpEuO5+YKGqPuZ7TIvaMhrBJxGtaLJacvax47mdqX6L6KbsuiFltc+NiHwHjSY6+uDbT71Rmr2+zfSQz5PBR56JTicnJB4O3Kuq9wGIyBXAScCoIlHVX8bG3wS8qKUSGgZ+CYBFHLVpsw8X0QzANVPxmUU8OVRn46eOyR1XxJGeZES10qftoo5wVzBCMwEUPtd6MkViFaGdzvZ+YEvs/UPhMhcfBr4Xe6/AD0RkvYic5tpIRE4TkXUism7btm1NCWxMTXycsEUctUV6dnSLZIak+jivfaOD4mHFWfI0c4xGadQR3goZio6ZjLRTkaR9I1PtbCKymECRfDy2eJGqvgI4DviYiLw2bVtVvURVF6rqwtmzZzcrszEF8akQ6zMmqibsaz7qqXU7HdrRk29etd2i0UF5lWeLVAKO02wl5SJVepshS868ay0EM8YyK0VPFNpp2noImBN7/yJga3KQiLwc+ApwnKo+Hi1X1a3h/0dF5BoCU9nPKpXYmLQ0UhojbiIp6qh1MbO3xuCO+mj0lWt8X29t9LjrHnhitMBhl8Au07r4Q32kKQdvGZWAI5LZ9QODQyz71qYxx2lGnkZI+7whvdRJdHxXoMD2HfVJXwIlj3Y626cRONvfAAwQONvfo6qbY2PmAjcA74/7S0RkN6BLVZ8OX/8Q+Iyqfj/rmOZsN9JoZ+XYJD21bt7+yv5xoaNJ+npqbPxUa6reNsuCz/wgNZAgqtzbalzXbNdalzPgIcuBPhWqAness11Vd4rIGcD1BOG/X1XVzSJyerj+YuA84AXAFyWwzUZhvnsB14TLpgH/ladEDMNFkf7bPhTp+pdkqD7MZTc9mDtucKjuvIElZW935dmi2fVV4/q8XYo777OrslL0RKkq3NY8ElW9Drgusezi2OuPAB9J2e4+4LDKBTSmBGXeCFz5IK42t40S2eNdRLJX2aBrolL0c81zoFdVKTrvs+ukz9ZKpBhTHtcPXqGw49T1tCuCVwtaH/KqysLz59RoIcKiZDmps/qdtMMx7fq8+3pq1LrHxgDVuiXXoV9VIEDeZ9eqz9YHUyTGlCcrGidZITYP19Pu4I56bmhtFtHtrb+vJ1eJxG9irWjQlazRlbxmK048hFpXethw0etbBq4b/1sO28ddKjiDqipF53127Wi+5sJqbRlTnrw+40X8JVlmjijyp2gGebLkyEvOuc4ZFpwc62t2acbWnudjKvP6loErAuyC6+8e1wulPqJeslVRKTrvs+uk5ms2IzEMns+fcFVt9XnKW71hgGee3TluuU/OSa1LxplVemrdXLR0/ri+IVnFEpNjlx174LjZQK1LxuW3JGcUZ67ayPxP/8BrpuDzZFzG9S2TSJ7frnzz6DXrpCd8yDeZtSq3xgebkRgTinNX3z6aM9EtwslHzOH8JX5lzX1o9CnPlScys7fGp044xCvnJG1Z2lNuv0NGp9ksefdOvHdl2g8O1b2ct0WuWSc9RafJ0Emy5eXOZK1vdTSXtdo1Jgznrr49NTT2lCPnlqZMym63WkUuQREZfeSat/zazOPlnYOPPPE+7WkVeDsh7yWtLXGtW1j6p3O48a5tbQ+x9aWK3KKOzSMxjKJcfvMW5/KyFEmjGdRlm0Vcmdc+/TjKlitvbNGs/ng5947rzZF4rh4eVlbdsmVMVn7aLK1T8jmg/LwoH0yRGBMGl28gy2eQxOcH34jjtEyzSFp+wLJvbgJh9IaW1Y+jbLl8xmZds7QbW6REOinzO83ZPgKMJHrepyV8dko+B7Qnmsuc7caEwVV51rU8SV6Yatp430KDrhDiHc/tLBzamnbjrY/oGJML+OUM+Dhks65fGc7bTnNiu2h0ltZJ+RzQnkrJmYpERGZl/VUmlWGkcPIRcwotT1LkB19U6US5BMnku+076oXzJMo2O+XlOBz54pmp2+4yrasU30UnlICPyHo4aHSW1mmKsh3RXHkzkvXAuvD/NoIii/eEr9dXJpVhpHD+kkM55ci5o0/Q3SKFHO1FfvCNPGUuWdDPbruMtxYXfTqtwuyUDHWNc//j6ddlz913KcU00ylhqnkPB3ll4iOSsneSooTqEiSzyPSRqOr+ACJyMbAmrI1F2Pb2jZVJZRgOzl9yaMOO9SL+Al+lk/S5uBINizydLj5otlfhxrJuxkWfqIs6lhsJYKjCed1s4iQEDy/Jm3JaZ8l25XNEVJEgmYWvs/1PVfX06I2qfk9EPluRTIZRCUV+8D5KJ83J6qqDVeTp9Ma78jt5puWnNIqroOSMlBpZjTqWi9zYqnJe+yZOLlnQ7ww1P/mIOanBGVBer5SJiK8ieUxEzgUuI/idnAI8nr2JYbQG36fXIj94H6XjikZKy5Mo8nTqM3vpnT6t0I0565xdvvb48ngeSJKyQ0urCl8tMiN1KXPX8lbPADoN36itk4HZwDXh3+xwmWG0laJOcV987MyuG34U2upjn05z/vrMXgYGh7zO0ef6DDr6gkTL4/twUaZjuSrndRFfTac50DsdrxmJqj4B/I2I7K6qv69YJsPwpsjTa1GTSd5TpusJ1zc/wiWPT4dEwMvc43N98p7UXSVU0saWQVWlSorMSMuWoZMSFqvAa0YiIq8WkTuAO8L3h4nIFyuVzDA8cD0lpy0vO96/2Wgklzw33rXNq+S8j+w+T9Z555H3FF62Y7nKKK+8CLYqZKhq1txJ+Jq2LgSOJfSLqOom4LVVCWUYvvgmKa7eMFBKRFWcZsMss27y0Q0vT5nkye4Tmpp3HllP4VWElsblgeCzjJRmq26+ZYbQdlrCYhV4l0hR1S0y9seZPdc1jASNTu/j2/X11lBltMaUT9mU6InQRTMmk0adrOeuvt3ZMykuT5rT3zU2Dd9ItazzcO2jytyEaL/tLD1SlgN9KvhbfGckW0Tk1YCKyHQR+TvgzmYPLiJvEpG7ReReEVmesl5E5P+F628TkVf4bmt0Fo1O75Pbbd9RZ3CoProPV3GP+JN8lo0/7aZapDRKI7hCS9PkcWXMu2RPknyy7uupsWuti7NWbfQ+t3YkuMHkeZLvtITFKvCdkZwOfB7oBx4CfgD8ZTMHFpFu4AvA0eE+bxGRNap6R2zYccAB4d8RwJeAIzy3NTqAZsNG8xy9PuG2WU9+yRtiKwrwuaoYp8kTHTerx0TeTC++faPn1orw1iqSO6uQK20mnTXGZ1Y40Z3xvorkQFV9b3yBiCwC1jZx7MOBe1X1vnB/VwAnETr0Q04C/lODpik3iUifiOwDzPPY1mgzrmZPcfJuCj43jSjc1vUjzIquSv5YXU/BZ1+5CShHmWRVK07bf1wZd4ugwDPP7uTT39nMmas2jhk7Wik4ZV/tKC/uS1XJnVXIlVS+eWOKltlvd/XgRvBVJP8CvMJjWRH6gfij2UMEs468Mf2e2wIgIqcBpwHMnTu3CXGNopQRNpr1ZBqRF25bJKPdpbiGVUv7cXeLpCoTIWhElexBEm+2FG2XlokeUR9RVqzZPE7OTrbVV5XcWYVcSeXrM6Zomf1OUfC+5FX/fZWInA3MFpG/jf2tAPKrm2WTZt5O/rpcY3y2DRaqXqKqC1V14ezZswuKaDRDGWGjeYX0GvETZNn4sxRbWfZ5V7Xiri4Z50P65DW3jysf70OaoulkW30ZyZ2tlCu+vFkF3ckK3pe8Gcl0YPdw3B6x5U8B72jy2A8B8V/Ui4CtnmOme2xrtJms2YRvZ7ykWSAZteVrS/a18edFSZXx446KTsZ7z+9a6+KZ58Y/lZZJJxYXjGg2ubMqfBITm01e7LRe8Y2QV/33p8BPReRrqvpAyce+BThARPYHBoB3A+9JjFkDnBH6QI4AnlTVh0Vkm8e2RpspK2y0lXWMouOcfeWmVPOTz4/73NW3j1ESJx8xZ1zF4mQV4/1z+qYXZWbv+CivTi4u2KlKzkeuZmXv1HMvgq+P5Csi8k5VHQQQkZnAFap6bKMHVtWdInIGcD2BmeyrqrpZRE4P118MXAccD9wL7AA+mLVto7IY1dDJN64s0nIYwO/HnQztHVblspse5LKbHsychfn4gnypdQufOuGQ1HWdWlywU78rPnI1K3unnnsRRD36XYvIBlVdkLes01m4cKGuW7eu3WIYEwSfmUWSl5xzXWZUlmtGlhbh1lPrRlB21EdS9zWzt8bgjnrD5j7D8EVE1qvqQtd63xnJiIjMVdUHw53uh8O5bRiTgdUbBrhq/cCoUhhW5ar1Ayzcb1bmTTpLiYA7Gif5VDqjp4ZIkICZhgAbzjumwBkZRnX4ZrZ/EviFiHxdRL4O/Aw4pzqxDKN9rN4wwNlXbmooq9pV+yuOy2Ef1de6cOl8nt054lQiMLEcscbkx0uRqOr3CXJGVgFXAq9U1eurFMww2kFkYnLNLPKitlyhvXHylEBe/s1Ec8Qak59M05aIHKSqd8VqXEUhtnNDU9et1YpnGG5a1dc7Tp4SSIb2JomUQDJbfVh11Bmfpax8w6YNo5VkOttF5Muq+lERuTFltapq+wK8G8Cc7ZMHl3O62YS1/Zdf63T+NbL/NGUH4yPC4sfYtdaVatZqd06FMXVpytmuqh8N/y8uWzDDaIZW9/XuFslVIq4ZUnKbRStvcM56hurD7DKti55a94TOKzCmFnmmrbdlrVfVq8sVxzDGkxaGW2Vf70ZmOkUK7+XJ+ORQnQuXzs802030arHG5CIv/PeE8P8LgVcDN4TvFwM/AUyRGJXiSvDrrXWl5lfEfRiN3GzjYbiR/yIereUqH16kTH5e8uG+YVVil6yToVqsMbnIjNpS1Q+q6gcJckYOVtW3q+rbgfS0WcMoGVfvjqGdI5k9tZvpk71kQf9oscjIYZ62ffwYLtJmH1mFKH1MWJOl4ZMxefBNSJynqg/H3j8C/HEF8hjGGFxhuKpBEyjXjMN1s12xZrNXc6gdz+1sqHx4kr7e2rjS8GmznnjUVt6sYjJUizUmF76K5Ccicj1wOcHs5N1AWiSXYZSKq3dHt0im+cd1Ux0cqo+WWI9mGeseeIKr1g+MMRW58CkfHlHrFn7/h52jEViuhkdFmQzVYo3JhW9C4hnAxcBhwHzgElX9qwrlMqYgab3SXQl+eYl/vjfVofowl9+8xbtke7J8uIv+vh52mz6N+shYJViGCSrNNGZRXUY78S2RAnArcK2qngVcLyJ75G1gGL64fBoL95vFKUfOHS090i3CKUfOzS2emNcQK05efayItPLhaTf0i5bOZ+3yo3jS0cWwWRNUkUZdhtEKvExbIvJRgna1s4CXELS6vRh4Q3WiGVOJLAfy2uVH5SqOiLivY0ZPjV1rXQzuqI/6PdIS/Vzms+SY5M06r/x3lSaoTi0Hb0xNfH0kHwMOB24GUNV7ROSFlUllTDnKcCAnw2IHh+r01Lq5cOn8UYd6Wo7I21/Zz6r/2TLODBVnRDX1xh2/oUdK7KxVG9m3r4fFB81m1S1bxrTKrXWLmaCMSYevaetZVX0ueiMi07Ay8kaJlNFPPC8s1mUSWrjfrKAuewPyRaSZ5lbdsoXhZL91Hb9d0i9kGBMN3xnJT0XkE0CPiBwN/CXwnerEMqYaZbQb9ZnVuEqW1JM3/BiN5nak7bM+oqMhxJZYaEwWfBXJx4GPALcDf07QAvcrVQllTD18241mZas36pMoWm03TYYiJrhobFX1wgyj1eQqEhHpAm5T1T8BvlzGQUVkFkFvk3nA/cC7VHV7Yswc4D+BvYERgpDjz4frVgAfBbaFwz+hqteVIZvRPvIcyHlP8I3OalwKKK3arkuGGT210fyUPCLFZomFxmQh10eiqiPAJhGZW+JxlwM/VtUDgB+H75PsBM5W1ZcBRwIfE5GDY+svVNX54Z8pkSmA6wn+zFUbWbQyKAPXSFisK4x38UGzx/kvXDKIMG4ftW6h1jXW+RJXbGX4hQyjE/A1be0DbBaR/wGeiRaq6okNHvck4PXh60sJCkB+PD4gLMnycPj6aRG5kyDs+I4Gj2lMcLKe1KOZwd+/7dDCPTvSzGqLD5o9Ltvd1UMEYHBHesXe5H7jZrIy/EKG0QlkNrYaHSTyurTlqvrThg4qMqiqfbH321V1Zsb4eQR94v9EVZ8KTVsfAJ4C1hHMXLY7tj2NIAeGuXPnvvKBBx5oRGSjA1i08obM8iVQTvOnqGe7qzRL2vJGj2vl4I2JQF5jq7wOibsCpwMvJXC0/7uq7vQ88I8I/BtJPglc6qtIRGR34KfA/4n6n4jIXsBjBMGUnwX2UdUP5clkHRI7m/hNtafWxdDOEVQZ7UGycL9ZmbMCCKJ4f7vyzU3JkHeMZNMpIfgiWhtcY7LSVIdEArNTHfg5cBxwMPA3PgdW1TdmCPWIiOyjqg+LyD7Ao45xNeAq4BvxJlqq+khszJeB7/rIZHQuqzcMsOybm0aTAuO9RqIeJPB8xV/XzKSIfyFtNpBX0TfKcI9kiJQIWPiuMXXJc7YfrKqnqOq/Ae8AXlPScdcAp4avTwW+nRwgIgL8O3Cnqn4usW6f2Nu3Ar8qSS4jgyqT51as2ZyZWQ5Bb5IlC/pZu/woLlo6v6nChWkJhGet2phrOhsOM9zXLj+K/r6ecVm51hfEmIrkKZLReEZfk5YnK4GjReQe4OjwPSKyr4hEEViLgPcBR4nIxvDv+HDdP4rI7SJyG0G3xrNKlM1IoZlGUT74hM4Oq44qsHiWOjCmk6GPTGkzD59SDf2xGY+F7xpGQJ5p6zAReSp8LQSZ7U+Fr1VV/6iRg6rq46QUfFTVrcDx4etf4Chcoarva+S4RuN0SvJcmvmokezwRm72yRmP9QUxjIC8VrvdqvpH4d8eqjot9rohJWJMTKp++pacWldx4uajRtvO+t7so/L1aTkp1hfEMAJ880iMKU7VT9+eLUFGiRRYowouLYcjSV5Ir29ZF8OY7JgiMbwoO3kuGTHV5ygx4srb2Levh9UbBujKWJ933KhfyfYd9THRV0XOzfqCGEaxDonGFKbMrnxpjvtnntuZWk7k5CPmOMuXnHP17alKRMJ9JiPLkscdHKrzh/oIFy2dz4VL51vHQcNoEK/M9smCJSR2Bq4M9Zm9NXqnTxtnJnLle+SF6kKgdCKl4DpuGdnwhjGZaTYh0ZiCVF22w+W/GNxRZ8N5x4xbnmY+OmvVRq9jxSPLLFzXMKrBTFvGGKrOFwGY0VMrtDyNIk7+gcEhXnLOdfRO705db+G6htEcpkiMMTQaTlsEV6hvkRDgtNDbrM2HVXnmuWESbhjroW4YJWCmLWMMzZh/fE1i23ekZ7G7lqfhU/o9jXFVWKaOi9AwKsMUiTGGRvNFXJ0D1z3wBDfetW2McnGF9HYXmZKQ7jtZuN8sb0c8jO2hbhhGY5hpyxhDo9naLpPYN256cJy/JU2JAM7lRYgKKhZRSuZsN4zmMEVijKHRfBHXzTitOm7WTb6sqsJHvtjZJ20c5mw3jOYw09YUx+XXKGrqcZnE0hhWHdccKiLLJJaU6dzVt3P5zVsYVh1tfnX+kkMBuP/xdFkazWA3DMONzUimMGWG+haJoopmOf2OmYDLJBaX69zVt3PZTQ+OmsOi5lfnrg4q/2bNkCyD3TDKxWYkk5hkXSmRIOkvqxtgVmn4rKgs3yiqaAaw7oEn+N2Tf3DK7moYFR3n8pu3pG53+c1bOH/JofT11lKjwGb21iyL3TBKxhTJJCUZRRUviBg94bvCZNOe5otEZSWVUGR+CnI4lDM9s9Kz5Mpz2Lv89lOoIpBhtAxTJJOUvN7jkdPbt3JuVlSWq2f56g0DXLV+YPQYIwpDsV7sLpJ+jDS58kKIn3R0XHQtNwyjccxHMknxCWmNnN5xXM7nIlFZWU2n8ujv6+G9R87NlevkI+akbh8td0ViWYSWYZRPWxSJiMwSkR+KyD3h/9RYTRG5P+zNvlFE1hXdfirjc8OMO73znM9FbsB5TadcdIuwdvlRnL/k0Fy5zl9yKKccOXd0BtItwilHzh2N2rLuhYbROtpl2loO/FhVV4rI8vD9xx1jF6vqY01sPyXJ6wAY3VR9Q33T9pdngioSEgzuWYaL85ccOqo4klj3QsNoHe1SJCcBrw9fXwr8hGKKoNntJz3JG2la1FaRm2rajXneC3pY+5snxo1dfNBswK+dLTCaA7Jwv1mjPUPiSirpeykisykOw6ietjS2EpFBVe2Lvd+uquPMUyLyW2A7wT3l31T1kiLbJ7HGVuNppveIT6OoaP+umYkAv1355nFRYWlYAyrDaA9ta2wlIj8C9k5Z9ckCu1mkqltF5IXAD0XkLlX9WUE5TgNOA5g7d26RTSc9rpBe8Hvyz6oUnFRQMx15HZEZzMcxbzWxDKMzqUyRqOobXetE5BER2UdVHxaRfYBHHfvYGv5/VESuAQ4HfgZ4bR9uewlwCQQzksbPaPLhk5CYNWNx+UBm9NTGKahal9DdJQzH6rjHe4H4KAmLuDKMzqRd4b9rgFPD16cC304OEJHdRGSP6DVwDPAr3+2NfFzmpmh5XgmVZcceSC3RKarWJYgwTkHVR3SMEgHGeOrzlIRFXBlG59IuRbISOFpE7gGODt8jIvuKyHXhmL2AX4jIJuB/gGtV9ftZ2xt+rN4wwKKVN+SO8emWmEwvHMG/QVXUCwSya3VZTSzD6GzaErWlqo8Db0hZvhU4Pnx9H3BYke2NfHyc2oBXCZVPf2fzuFnG8Ig6w4Kz9mXhuq2hmeAKw3BhJVKmGL7Z5lklVGb01Fi08gbnzKOIIypu0rJw3WppNrjCMFxYiZQpRpHIJ1dhxKef3Vko0TCLKOfEqB4fU6VhNILNSDqMqk0PRbLNXTOScU7zBEVMWzfetc1zpNEsWeHahtEMNiPpIMpsNBXtb9HKG9h/+bWjLWzTnNq1bhkXfdVT626oh3qtS1KLLrqwm1jrsEKWRlWYIukgyjQ9uJQSMK4g4gXvOIwL3nnYuCKJrg6GLvr7erjgnYeNFl30wW5ircMKWRpVYaatDqJM00OWUlq7/KhUc1nasmT0Vq1LQKA+/PxspafWPS48d8mCflas2TymoVYSu4m1FouMM6rCFEkH4fJfKEFdqyI/+maUUtxP09dbY5dpXTw59HyxR/C7GYmraTvB7MVuYq3HIuOMKjBF0kFkVcstGqrpUkpJU1JeUcXtO+r01Lq5cOn8cTOOPAYd4cECVnzRMCYR5iPpIJYs6M/0TRTxl/jYw+N+lCwa9dMUde6mBQcYhtH5mCLpMJYs6Gft8qNwWYV8/SVxpeTqMlikFW4jfpoizt2yI9YMw2gdZtrqUHxNU1nk2cOLKIdGoquKOHd9KhEbhtGZmCLpUNL8Jc1EOaUlOvomJzZzXB/n7uoNA045LM/EMDofUyQdShmhmnFHerJ17VmrNqLkZ6FXHV0VmbRcWJ6JYXQ+pkg6mGZCNZMF+pLKQhP/40Q91M9f4pdU2AxZfhrLMzGMiYEpkklKEUc6wMzeGhvOO6ZCidLJMl1ZDxLDmBhY1NYkpahvwbcZVdm4TFf9fT2mRAxjgmAzkhLppKZBRar8tpMyggo66bobxlTEZiQl0Wl5EFmta9Po66lVK5ADn3yXLDrtuhvGVMRmJCXRTB5EFU/UrqgvgGXf3EQ91lOk1iWsOPGQpo7XDM0EFVj+iWG0n7YoEhGZBawC5gH3A+9S1e2JMQeGYyJeDJynqheJyArgo0DUFekTqnpdxWJnUrRIYlZobjwcNhoTNZlyheO6lJHrZjpZTEHWrMkw2k+7ZiTLgR+r6koRWR6+/3h8gKreDcwHEJFuYAC4JjbkQlX9p9aIm0+RTPS80Nyh+jAr1mzmmed2jpZrj5pMpRVvLNqLezJVgC2jAoBhGM3RLh/JScCl4etLgSU5498A/EZVH6hSqGYoUlfKJzR3cKg+pudHnGQRxWYaYkWFEuctv5aXnHMd8yZYwURr1mQY7addM5K9VPVhAFV9WERemDP+3cDliWVniMj7gXXA2UnTWISInAacBjB37tzmpM7ANxP93NX51XZ9iJtufMw7aaYvGNu4KmvW06lYsybDaD+iDfTl9tqxyI+AvVNWfRK4VFX7YmO3q+pMx36mA1uBQ1T1kXDZXsBjBFahzwL7qOqH8mRauHChrlu3ruiplMa5q2/nspsezB3XU+vOnbH09dTY+KkggXDRyhtSlVN/Xw9rlx81zvQVHWPXWldm/ki0vWEYUxsRWa+qC13rK5uRqOobXetE5BER2SecjewDPJqxq+OAWyMlEu579LWIfBn4bhkyV83lN29xrosc7pEz/cxVGzP3NThUZ8FnfoBq8DpZM6un1s3ig2Y7lcxQfThXWZnD2jAMH9rlI1kDnBq+PhX4dsbYk0mYtULlE/FW4FelSlcRwxmzv/ceOZf7V76ZxQfN5uwrN3ntb/uO+mhP9Pie+/t6ePsr+7lqvbuqrg/msDYMw4d2+UhWAleKyIeBB4F3AojIvsBXVPX48H0vcDTw54nt/1FE5hPcP+9PWd+RRCG8aVx204NeZq88+npqrF1+FItW3uBlHnt250jqOHNYG4bhS1sUiao+ThCJlVy+FTg+9n4H8IKUce+rVMCKePHsXu559JlKjzE4VHeas+L01LpHkxB9c1UMwzDSsMz2EsnLUL9v246WyJGnRJKKwhSGYRjNYIqkJHySArN8JK2gp9ZtpdkNwygdUyRNEi91kiRZ8ynLR1KEvp7aqJPdFzNXGYZRFaZIGuToz/3Ey9+xdXBoVNmUoUTiuR1JU9qO53am5oVYPohhGFViiqQBfJUIQF9vjWXf2uQsdwLBDEMkv7lUMpIqWTPLlXho0VeGYVSJKZICZJmx0uipdfNsfdipRJLmpgWf+YFTmfiYpjqpXIg1mzKMqYMpkhxc5d59eLY+zEjG+qRDftChRAS8TVOdUNm3aDViwzAmNtYhMYPVGwb42ys3js5Aino4spRIRLxKryuTfKJlmDdTjdgwjImHKZIMPnH1bYy0IGI3qmk1WUqiW7Mpw5hamCLJYEfdZ07RPNGMo9n+5Z3CZJlZGYbhh/lImiBesdfXAZ9WpTcrEmsisuzYAy16zDCmEKZIGiTZZz3PER9llUNnRFVVSSdFjxmGUT2mSBokqTSylMhUrG01GWZWhmH4YYqkYoqE7hqGYUxEzNleMTN6au0WwTAMo1JMkWTQ74gy6uupjQvTdSFSpkSGYRidhymSDBYfNDt1+VsO22dcmK6LvPpZhmEYEx3zkWRw7W0PO5efv2RsfsdLzrkutbpvt01JDMOY5LRlRiIi7xSRzSIyIiILM8a9SUTuFpF7RWR5bPksEfmhiNwT/p9ZhZyu2UTacleJ+HY3szIMw6iadpm2fgW8DfiZa4CIdANfAI4DDgZOFpGDw9XLgR+r6gHAj8P3bcVl3soyexmGYUwG2qJIVPVOVc2r4Hc4cK+q3qeqzwFXACeF604CLg1fXwosqULOPkfEVdryyVInyzAMoyid7GzvB7bE3j8ULgPYS1UfBgj/v9C1ExE5TUTWici6bdu2FRJgxYmHUOsa6+OodQkrTjxk3NjJUifLMAyjKJU520XkR8DeKas+qarf9tlFyrLCDgdVvQS4BGDhwoWFti9a6sOyuQ3DmIpUpkhU9Y1N7uIhYE7s/YuAreHrR0RkH1V9WET2AR5t8lhOTDkYhmFk08mmrVuAA0RkfxGZDrwbWBOuWwOcGr4+FfCZ4RiGYRgV0K7w37eKyEPAq4BrReT6cPm+InIdgKruBM4ArgfuBK5U1c3hLlYCR4vIPcDR4XvDMAyjDYhOoTyHhQsX6rp169othmEYxoRCRNarqjPnr5NNW4ZhGMYEwBSJYRiG0RRTyrQlItuABxrcfE/gsRLFKROTrTidKheYbI3SqbJ1qlzgL9t+qppexZYppkiaQUTWZdkI24nJVpxOlQtMtkbpVNk6VS4oTzYzbRmGYRhNYYrEMAzDaApTJP5c0m4BMjDZitOpcoHJ1iidKlunygUlyWY+EsMwDKMpbEZiGIZhNIUpEsMwDKMpTJHE6OQWwD77FpEDRWRj7O8pETkzXLdCRAZi645vlVzhuPtF5Pbw2OuKbl+VbCIyR0RuFJE7w8/+b2LrSr9mru9ObL2IyP8L198mIq/w3bZiud4bynObiPxSRA6LrUv9bFso2+tF5MnY53Se77YtkG1ZTK5ficiwiMwK11V23UTkqyLyqIj8yrG+3O+Zqtpf+Ae8DDgQ+Amw0DGmG/gN8GJgOrAJODhc94/A8vD1cuAfSpSt0L5DOX9HkEgEsAL4uwqumZdcwP3Ans2eV9myAfsArwhf7wH8OvZ5lnrNsr47sTHHA98j6MdzJHCz77YVy/VqYGb4+rhIrqzPtoWyvR74biPbVi1bYvwJwA0tum6vBV4B/MqxvtTvmc1IYmhntwAuuu83AL9R1UYz+X1p9pzbes1U9WFVvTV8/TRBpemqGtBkfXfiMv+nBtwE9EnQc8dn28rkUtVfqur28O1NBP2BWkEz513lNWtk/ycDl5d4fCeq+jPgiYwhpX7PTJEUp5QWwA1QdN/vZvyX9oxwGvvVEk1IvnIp8AMRWS8ipzWwfZWyASAi84AFwM2xxWVes6zvTt4Yn22rlCvOhwmeZiNcn20rZXuViGwSke+JSNQLu8prVmj/ItILvAm4Kra4yuuWR6nfs8o6JHYq0iEtgFN3nCFbwf1MB04Ezokt/hLwWQJZPwv8M/ChFsq1SFW3isgLgR+KyF3hU1NTlHjNdif4kZ+pqk+Fixu+Zq7DpCxLfndcYyr73hXZt4gsJlAkfxZbXMlnW0C2WwlMuL8P/VirgQM8t61atogTgLWqGp8lVHnd8ij1ezblFIl2cAvgLNlEpMi+jwNuVdVHYvsefS0iXwa+20q5VHVr+P9REbmGYAr9MzrgmolIjUCJfENVr47tu+Fr5iDru5M3ZrrHtlXKhYi8HPgKcJyqPh4tz/hsWyJbTPGjqteJyBdFZE+fbauWLcY4C0HF1y2PUr9nZtoqTrtaABfZ9zhbbHgjjXgrkBrNUYVcIrKbiOwRvQaOiR2/rddMRAT4d+BOVf1cYl3Z1yzruxOX+f1hVM2RwJOhWc5n28rkEpG5wNXA+1T117HlWZ9tq2TbO/wcEZHDCe5rj/tsW7VsoUwzgNcR+/614LrlUe73rIqIgYn6R3CzeAh4FngEuD5cvi9wXWzc8QTRPb8hMIlFy18A/Bi4J/w/q0TZUvedIlsvwY9oRmL7rwO3A7eFX4x9WiUXQQTIpvBvcyddMwITjYbXZWP4d3xV1yztuwOcDpwevhbgC+H624lFD7q+dyVdqzy5vgJsj12jdXmfbQtlOyM89iaCQIBXt+Ka+cgWvv8AcEViu0qvG8GD5MNAneCe9uEqv2dWIsUwDMNoCjNtGYZhGE1hisQwDMNoClMkhmEYRlOYIjEMwzCawhSJYRiG0RSmSAyjZETkrSKiInJQzrgzw9IZjR7nAyLyr41ubxhlYYrEMMrnZOAXBMlcWZxJkPdjGBMaUySGUSJhza5FBAlg7w6XdYvIP0nQe+I2EfkrEflrgsTIG0XkxnDc72P7eYeIfC18fYKI3CwiG0TkRyKyV6vPyzCymHK1tgyjYpYA31fVX4vIExI0DDoC2B9YoKo7RWSWqj4hIn8LLFbVx3L2+QvgSFVVEfkI8L+As6s8CcMogikSwyiXk4GLwtdXhO9fDFysqjsBdGwFWB9eBKwKa39NB35bjqiGUQ6mSAyjJETkBcBRwJ+IiBJ0m1NgPX7ly+Njdo29/hfgc6q6RkReT9C50TA6BvORGEZ5vIOg69x+qjpPVecQzB5uBU4XkWkQ9JIPxz9N0N434hEReZmIdBEUEI2YAQyEr0/FMDoMUySGUR4nA9ckll1F4FR/ELhNRDYB7wnXXQJ8L3K2E/SV/y5wA0Hl1ogVwDdF5OdAnj/FMFqOVf81DMMwmsJmJIZhGEZTmCIxDMMwmsIUiWEYhtEUpkgMwzCMpjBFYhiGYTSFKRLDMAyjKUyRGIZhGE3x/wGmC/jRB813nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,y_train_reg)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"Actual vs Predicted Trianing data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual vs Predicted Testing Set')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_test_lr)\n",
    "plt.xlabel(\"Actual Value\")\n",
    "plt.ylabel(\"Predicted Value\")\n",
    "plt.title(\"Actual vs Predicted Testing Set\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS:\n",
    "\n",
    "Support Vector Machine Regression gives 80.1% accuracy which is better than linear model and bagging regression model. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 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": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating dataframe\n",
    "results = pd.DataFrame({ 'ML Model': ML_Model,    \n",
    "    'Train Accuracy': acc_train,\n",
    "    'Test Accuracy': acc_test,\n",
    "    'Train RMSE': rmse_train,\n",
    "    'Test RMSE': rmse_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train RMSE</th>\n",
       "      <th>Test RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k-Nearest Neighbors Regression</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bagging Regression</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gradient Boosted Regression</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Support Vector Regression</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ML Model  Train Accuracy  Test Accuracy  Train RMSE  \\\n",
       "0               Linear Regression           0.798          0.747       0.259   \n",
       "1  k-Nearest Neighbors Regression           0.848          0.806       0.225   \n",
       "2                   Random Forest           0.907          0.810       0.176   \n",
       "3              Bagging Regression           0.903          0.809       0.179   \n",
       "4     Gradient Boosted Regression           0.896          0.832       0.186   \n",
       "5                   Decision Tree           0.872          0.803       0.206   \n",
       "6       Support Vector Regression           0.842          0.801       0.229   \n",
       "\n",
       "   Test RMSE  \n",
       "0      0.292  \n",
       "1      0.255  \n",
       "2      0.253  \n",
       "3      0.254  \n",
       "4      0.238  \n",
       "5      0.258  \n",
       "6      0.259  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train RMSE</th>\n",
       "      <th>Test RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosted Regression</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bagging Regression</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k-Nearest Neighbors Regression</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Support Vector Regression</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ML Model  Train Accuracy  Test Accuracy  Train RMSE  \\\n",
       "0     Gradient Boosted Regression           0.896          0.832       0.186   \n",
       "1                   Random Forest           0.907          0.810       0.176   \n",
       "2              Bagging Regression           0.903          0.809       0.179   \n",
       "3  k-Nearest Neighbors Regression           0.848          0.806       0.225   \n",
       "4                   Decision Tree           0.872          0.803       0.206   \n",
       "5       Support Vector Regression           0.842          0.801       0.229   \n",
       "6               Linear Regression           0.798          0.747       0.259   \n",
       "\n",
       "   Test RMSE  \n",
       "0      0.238  \n",
       "1      0.253  \n",
       "2      0.254  \n",
       "3      0.255  \n",
       "4      0.258  \n",
       "5      0.259  \n",
       "6      0.292  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting the datafram on accuracy\n",
    "results.sort_values(by=['Test Accuracy', 'Train Accuracy'], ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS: \n",
    "Among all the trained models, Gradient Boosted Regression performance is better. It is understandable because this model is very good in execution Speed & model performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Statistical Tests:\n",
    "Statistical tests are used in hypothesis testing. They can be used to:\n",
    "\n",
    "1. determine whether a predictor variable has a statistically significant relationship with an outcome variable.\n",
    "2. estimate the difference between two or more groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1. Test 1: To check the difference in Height between male and female\n",
    "Using independent sample t-test to check the difference in Height between male and female. The hypothesis statements for this test are:\n",
    "\n",
    "1. H0: There is no difference in the Height among male and female (Null).\n",
    "2. H1: There is difference in the Height among male and female (Alternate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.178571\n",
       "1     -0.339286\n",
       "4     -0.803571\n",
       "5     -0.035714\n",
       "6     -0.250000\n",
       "         ...   \n",
       "487   -0.464286\n",
       "488    0.535714\n",
       "494   -0.142857\n",
       "498   -0.196429\n",
       "499    0.446429\n",
       "Name: Weight, Length: 245, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#collecting male Weight data\n",
    "male = stat_data['Weight'][stat_data['Gender'] == 1]\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      0.071429\n",
       "3     -0.035714\n",
       "9     -0.053571\n",
       "11    -0.464286\n",
       "12    -0.089286\n",
       "         ...   \n",
       "492   -1.000000\n",
       "493   -0.946429\n",
       "495    0.839286\n",
       "496    0.267857\n",
       "497    0.535714\n",
       "Name: Weight, Length: 255, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#collecting female Weight data\n",
    "female = stat_data['Weight'][stat_data['Gender'] == 0]\n",
    "female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accept null hypothesis\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "ttest,pval = stats.ttest_rel(male[:245], female[:245])\n",
    "\n",
    "if pval<0.05:\n",
    "    print(\"Reject null hypothesis\")\n",
    "else:\n",
    "    print(\"Accept null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Conclusion: \n",
    "By performing T-test, the result obtained is to accept the null hypothesis. \n",
    "\n",
    "This basically means that there is no difference in weight of male & female."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2. Test 2: To find out the dependence of  Weight on the Height.\n",
    "Finding out whether there is a dependence of suicide rate on the age using the Chi- Square test. The hypothesis statements for this test are:\n",
    "\n",
    "1. H0: Weight and Height are independent (Null).\n",
    "2. H1: Weight and Height are dependent (Alternate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating Contingency Table\n",
    "contingency_table = pd.crosstab(stat_data.Weight, stat_data.Height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Significance Level 5%\n",
    "alpha=0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "chistat, p, dof, expected = stats.chi2_contingency(contingency_table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "critical_value: 6618.674185892114\n"
     ]
    }
   ],
   "source": [
    "#critical_value\n",
    "critical_value=stats.chi2.ppf(q=1-alpha,df=dof)\n",
    "print('critical_value:',critical_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significance level:  0.05\n",
      "Degree of Freedom:  6431\n",
      "chi-square statistic: 6594.908755816494\n",
      "critical_value: 6618.674185892114\n",
      "p-value: 0.07507057003703878\n"
     ]
    }
   ],
   "source": [
    "print('Significance level: ',alpha)\n",
    "print('Degree of Freedom: ',dof)\n",
    "print('chi-square statistic:',chistat)\n",
    "print('critical_value:',critical_value)\n",
    "print('p-value:',p) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, pvalue = 0.075 and a high pvalue suggests that your sample provides enough evidence that you can retain H0  for the entire population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retain H0,There is no relationship between Height & Weight.\n",
      "Retain H0,There is no relationship between Height & Weight.\n"
     ]
    }
   ],
   "source": [
    "#compare chi_square_statistic with critical_value and p-value which is the \n",
    " #probability of getting chi-square>0.09 (chi_square_statistic)\n",
    "if chistat>=critical_value:\n",
    "    print(\"Reject H0,There is a dependency between Height & Weight.\")\n",
    "else:\n",
    "    print(\"Retain H0,There is no relationship between Height & Weight.\")\n",
    "    \n",
    "if p<=alpha:\n",
    "    print(\"Reject H0,There is a dependency between Height & Weight.\")\n",
    "else:\n",
    "    print(\"Retain H0,There is no relationship between Height & Weight.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Conclusion:\n",
    "  The final take away form this project is the working of different machine learning models on a dataset and understanding their parameters. Creating this notebook helped me to learn a lot about the parameters of the models, how to tuned them and how they affect the model performance. The final conclusion on the Weight dataset are that the irrespective of Gender and Height. Index 5 is more prone to increase in Weight than remaining index."
   ]
  }
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