Iris flowers Classification ML project - Task 1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bca9e87",
   "metadata": {},
   "source": [
    "# Iris flowers Classification ML project "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27614a90",
   "metadata": {},
   "source": [
    "### Author:- Janvi Gulhane"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3756f079",
   "metadata": {},
   "source": [
    "#### The iris flowers dataset contains numeric attributes, and it is perfect for beginners to learn about supervised ML algorithms, mainly how to load and handle data. Also, since this is a small dataset, it can easily fit in memory without requiring special transformations or scaling capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e0fed8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc06b563",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d8cc87b",
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"E:\\Jyputer\\Iris.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dae3205c",
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns =[\"Id\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63e30642",
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count     150.000000    150.000000     150.000000    150.000000\n",
       "mean        5.843333      3.054000       3.758667      1.198667\n",
       "std         0.828066      0.433594       1.764420      0.763161\n",
       "min         4.300000      2.000000       1.000000      0.100000\n",
       "25%         5.100000      2.800000       1.600000      0.300000\n",
       "50%         5.800000      3.000000       4.350000      1.300000\n",
       "75%         6.400000      3.300000       5.100000      1.800000\n",
       "max         7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to display stats about data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bd5cff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      " 4   Species        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6bc997c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "742f8bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab21540",
   "metadata": {},
   "source": [
    "## Preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fbf41dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the null values in the dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e0d2e9",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1f3f9cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "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": [
    "##histogram\n",
    "df['SepalLengthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1eefca2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "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": [
    "df['SepalWidthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d90b05f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "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": [
    "df['PetalLengthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95528e54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df['PetalWidthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f1275b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scatterplot\n",
    "color = ['red','Orange','Blue']\n",
    "species = ['Iris-virginica','Iris-versicolor','Iris-setosa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "277cabbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x208316cb7c0>"
      ]
     },
     "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": [
    "for i in range(3):\n",
    "    x = df[df['Species'] == species[i]]\n",
    "    plt.scatter(x['SepalLengthCm'], x['SepalWidthCm'], c = color[i], label = species[i])\n",
    "    \n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2088b0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20831761dc0>"
      ]
     },
     "execution_count": 17,
     "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": [
    "for i in range(3):\n",
    "    x = df[df['Species'] == species[i]]\n",
    "    plt.scatter(x['PetalLengthCm'], x['PetalWidthCm'], c = color[i], label = species[i])\n",
    "    \n",
    "plt.xlabel(\"petal Length\")\n",
    "plt.ylabel(\"petal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3445206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x208317ca220>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEGCAYAAACAd+UpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAArwElEQVR4nO3deZhU9Z3v8feXbgw0qIyCmShC4xYD3dAoi1sUxLhEQjSJ12jH4EykFa4JMTczmsc8ahzJcidP9CZzMZcxY0xol0jGjHGyiisaYxrEIIJJFHBanYjgArJIN9/7x6mCpruqzunuc05tn9fz1NNdv1N1zrcO+u1T3/NbzN0REZHKNKDYAYiISHKU5EVEKpiSvIhIBVOSFxGpYEryIiIVrLbYAXQ1fPhwr6+vL3YYIiJlY/ny5W+4+4h820sqydfX19PW1lbsMEREyoaZbSi0XeUaEZEKpiQvIlLBEkvyZvZBM1vZ5fGOmX0xqeOJiEhPidXk3f0FoAnAzGqAV4D7erufXbt20d7ezo4dO+INUPpl0KBBjBw5koEDBxY7FBEpIK0brzOAF9294A2CXNrb29l///2pr6/HzBIITXrL3dm0aRPt7e2MGTOm2OGISAFp1eQ/DdyVa4OZtZhZm5m1bdy4scf2HTt2cPDBByvBlxAz4+CDD9a3K6lura1QXw8DBgQ/W1uLHVFOiSd5M9sPmAXcm2u7uy9y90nuPmnEiNxdPZXgS4/+TaSqtbZCSwts2ADuwc+WlpJM9GlcyZ8DrHD3v6ZwLBGR5F17LWzbtm/btm1Be4lJI8lfRJ5STbkYOnRo3m0nnXRSv/d/3XXX8eCDD/bqPffffz/f/OY3C77m1Vdf5VOf+lR/QhORXF5+uXftRWRJLhpiZnXAfwFHuPvbYa+fNGmSdx/xumbNGj70oQ8lFGE0Q4cOZevWrfu0dXZ2UlNTk+hx0zhGf5TCv41IUdTXByWa7kaPhvXrUw3FzJa7+6R82xO9knf3be5+cJQEH5sEb4Y88sgjTJ8+nYsvvpjGxkZg71X+a6+9xqmnnkpTUxMNDQ08/vjj+7z37bffpr6+nt27dwOwbds2Dj/8cHbt2sWll17KkiVLgGBqhxtvvJFTTjmFe++9l1/84hcce+yxnHLKKXzhC19g5syZAPzwhz/kyiuvBODSSy/lC1/4AieddBJHHHHEnn2tX7+ehoYGIPiD8eUvf5nGxkbGjx/P9773PQBuvPFGJk+eTENDAy0tLWilMJEIFiyAurp92+rqgvYSU1Jz1/Rb9mZItlaWvRkC0NwcyyGefvppnnvuuR5dB++8807OOussrr32Wjo7O9nWrV534IEHMmHCBB599FGmT5/Oz3/+c84666yc/cwHDRrEsmXL2LFjB0cffTSPPfYYY8aM4aKLLsob12uvvcayZctYu3Yts2bN6lGmWbRoEevWreOZZ56htraWzZs3A3DllVdy3XXXAXDJJZfwwAMP8LGPfaxP50akamTzybXXBiWaUaOCBB9TnolTZU1rkMLNkClTpuTsGz558mRuv/12brjhBlatWsX+++/f4zUXXngh99xzDwB33303F154Yc5jZNvXrl3LEUccsed4hZL8eeedx4ABAxg7dix//WvPe9wPPvggV1xxBbW1wd/1gw46CICHH36YqVOn0tjYyEMPPcTq1asLfXwRyXriCWhvD3rXtLcHz0tQZSX5FG6GDBkyJGf7qaeeymOPPcZhhx3GJZdcwo9+9CPuu+8+mpqaaGpqoq2tjVmzZvHLX/6SzZs3s3z5ck4//fSCx+hN6eR973vfnt9zvc/de3R73LFjB/PmzWPJkiWsWrWKOXPmqO+7SBTz5sGtt0JnZ/C8szN4Pm9ecePKobKS/KhRvWuP0YYNGzjkkEOYM2cOn/vc51ixYgXnn38+K1euZOXKlUyaNImhQ4cyZcoU5s+fz8yZM0Nvqh577LG89NJLrM/cyMl+C+iLM888k+9///t0dHQAsHnz5j0Jffjw4WzdunVPLV9EQixa1Lv2IqqsJF/EmyGPPPIITU1NTJw4kZ/+9KfMnz8/5+suvPBCFi9enLdU09XgwYNZuHAhZ599Nqeccgrvf//7OfDAA/sU32WXXcaoUaMYP348EyZM4M4772TYsGHMmTOHxsZGzjvvPCZPntynfYtUpEKdOLJX8N3lay8mdy+Zx/HHH+/dPf/88z3aClq82H30aHez4Ofixb17f4nZsmWLu7vv3r3b586d69/5zneKHNFevf63ESkXixe719W5BxX34FFXtzef1NTsuy37qKlJPVSgzQvk1cq6kofg7vb69bB7d/CzBO9298a//uu/0tTUxLhx43j77be5/PLLix2SSOUL68SR7bXXXb72IqqsLpQV6KqrruKqq64qdhgi1SWsE8fChcHPRYuCEk1NTZDgs+0lpPKu5EVE+itKJ46FC6GjIyjUdHSUZIIHJXkRkZ7KaERrGCV5EZHumpuDUszo0WAW/Fy0qCzv8SnJi4jkkkYnjhQWHlGSjyDpqYb7oz/TCU+bNo3us36KSEpSWnhESb6POjODHp588slUjpcdqdrdoYcemtpI1c5SHOghUq5SWnik8pL8ulb4WT3cOSD4ua70pxp+8cUXOfvsszn++OP58Ic/zNq1a4FgCuEvfelLTJ8+nauvvppHH310z1w4EydOZMuWLZGmE166dCkTJ06ksbGRv//7v2fnzp09Pttdd91FY2MjDQ0NXH311Xvahw4dynXXXcfUqVP53e9+F9u5FOmzMllbNVRaC48UGimV9qPfI15fWux+d517K3sfd9cF7f0wZMgQd3d/+OGHva6uzl966aUe27797W/7TTfd5O7uHR0d/s477/TYz6xZs/yhhx5yd/e7777bP/e5z7m7++mnn+5/+tOf3N39qaee8unTp7u7++zZs/3cc8/1jo4Od3efOXOmL1u2zN2DkbC7du3ydevW+bhx49zdfeHChf6JT3zCd+3a5e7umzZt8u3bt/vIkSP9hRdecHf3Sy65xG+++WZ3dz/ttNP8D3/4g7/yyit++OGH++uvv+67du3y6dOn+3333efu7oDfc889Oc+LRrxK6sJGopaT0aNzj5odPbpXu6GqRrw+ey10dvv607ktaI9J3FMNb926lSeffJILLriApqYmLr/8cl577bU977ngggv2TGR28skn86UvfYnvfve7vPXWW3umDc7KNZ3wCy+8wJgxYzjmmGMAmD17No899tg+7/vDH/7AtGnTGDFiBLW1tTQ3N+95TU1NDZ/85Cf7erpE4lVGa6uGSqmbZmUl+W15vubka++DuKca3r17N8OGDdszW+XKlStZs2ZNzuNdc8013HbbbWzfvp0TTjhhT1kny3NMJ+wRpisu9JpBgwaV9BKEUmXKaG3VUCl106ysJF+XZ5RavvYY9XWq4QMOOIAxY8Zw7733AkHCffbZZ3Me48UXX6SxsZGrr76aSZMm9UjyuaYTPvbYY1m/fj1/+ctfAPjxj3/Maaedts/7pk6dyqOPPsobb7xBZ2cnd911V4/XiJSEIk4nnogUumlWVpKfsABqun39qakL2hPWn6mGW1tb+cEPfsCECRMYN24c//Ef/5HzvbfccgsNDQ1MmDCBwYMHc8455+yzPdd0woMGDeL222/nggsuoLGxkQEDBnDFFVfs874PfOADfOMb32D69OlMmDCB4447jo9//OP9PCMiCaigkahpsShf59MyadIk795ve82aNXzoQx+KvpN1rUENftvLwRX8hAUwpvxGqZWDXv/biMShtbUs1lZNi5ktd/dJ+bZX1pU8BAn9vPVw8e7gpxK8SHkJ6yIZpcSRRjfLMunKqamGRaR0ZEeBZnvQZEeBQvSr9Tj2UQrHiEnlXcmLSPmKo4tkGt0sy6grZ6JJ3syGmdkSM1trZmvM7MQkjydSksrka31JiKOLZBrdLMuoK2fSV/L/B/iVux8LTADWhLxepLKkNAlVxTjooN6155JGN8sy6sqZWJI3swOAU4EfALj7e+7+VlLHEylJZfS1vmKk0c2yjLpyJnklfwSwEbjdzJ4xs9vMrMdwUTNrMbM2M2vbuHFjguH0XbGmGv7617+e2L4lJWX0tb4kbN7cu/Zc0hhJWkaLiiSZ5GuB44Bb3X0i8C5wTfcXufsid5/k7pNGjBiRYDjxSmOqYSX5ClBGX+tTu3dQ6Dhxna80FvxI4xgxSDLJtwPt7v77zPMlBEk/UUn+d9qfqYYBVq9ezZQpU2hqamL8+PH8+c9/BmDx4sV72i+//HI6Ozu55ppr2L59O01NTTRn/uP5zne+Q0NDAw0NDdxyyy0AvPvuu5x77rlMmDCBhoaGPROg3XjjjUyePJmGhgZaWloizWEjCSiXr/Vp3TsIO065nK9yUmiKyv4+gMeBD2Z+vwH450Kv7+9Uw0nNQhrXVMNXXnmlL84Es3PnTt+2bZs///zzPnPmTH/vvffc3X3u3Ll+xx137LNvd/e2tjZvaGjwrVu3+pYtW3zs2LG+YsUKX7JkiV922WV7XvfWW2+5ezDNcNZnPvMZv//++/t3EnLQVMMRLV4cTB9rFvwsxWlxY5r2NpbjlMP5KiEUearhzwOtZvZHoAlItP6Qxj2u/kw1fOKJJ/L1r3+db33rW2zYsIHBgwezdOlSli9fzuTJk2lqamLp0qW89NJLPd67bNkyzj//fIYMGcLQoUP5xCc+weOPP05jYyMPPvggV199NY8//jgHHnggAA8//DBTp06lsbGRhx56iNWrV8d3EqR34vhaH8dX1EL7SOvege5RpC7RJO/uKz2ot4939/Pc/c0kj5fGfz/9mWr44osv5v7772fw4MGcddZZPPTQQ7g7s2fP3jNb5QsvvMANN9zQY/+ep9xyzDHHsHz5chobG/nKV77CjTfeyI4dO5g3bx5Llixh1apVzJkzhx07dsR3EiRdcZRSwvaR1r2DsOOoy2n8Cl3mp/3ob7kmqW+cXcs15557bs5t69ev37Mi08033+zz58/vsZ8XX3zRd+/e7e7u8+fP95tvvtlXr17tRx11lP/1r39196DMsn79end3HzZs2J4yzvLly72xsdHfffdd37p1q48bN85XrFjhr7zyim/fvt3d3e+77z7/+Mc/7m+++aYfcsghvm3bNt+yZYuPGzfOr7/++v6dhBxUrklJHP9hh+0jrRWXwo6TVtmoglBNK0MV855NlKmG77nnHhoaGmhqamLt2rV89rOfZezYsdx0002ceeaZjB8/no985CN7VoZqaWlh/PjxNDc3c9xxx3HppZcyZcoUpk6dymWXXcbEiRNZtWrVnpu2CxYs4Ktf/SrDhg1jzpw5NDY2ct555zF58uTkT4AkJ41RoFG7BPa3bBR2HJVz4lfoL0Daj36v8eq6Z5MmXcmnJI6r2yFDcu+jy839UGlc7etKvteopit5KJuuqyLRxfEVdfv23rXnkkbPBnWhjF3FJXmRihPH6Mrdu3vXnksapZQyGklaLsoiyQffSKSUVM2/SRxdF+fNg9raIGnV1gbPe7Md+v8VNd9i7L1ZpD2tHjj6Oh6rkk/ygwYNYtOmTdWTVMqAu7Np0yYGDRpU7FCSFUd3vnnz4NZbITMNBp2dwfNsIg/bHpdp03rXnotKKWWp5Nd43bVrF+3t7ernXWIGDRrEyJEjGThwYLFDSU59fZDYuxs9OrjCjKK2dm8C76qmBjo6wrfHJY7PAlpftQSFrfFa8klepGgGDAiu4Lszi17LNsu/zT18e1Z/k2uUz6IEXpaqbyFvkbjEUYMOq4XnS/Jd2+MoG2mkadVSkhfJJ44adHZx53zteabJ2Kc9jq6LYZ9Fi5tULCV5kXyidOcL632zcCHMnbv3yr2mJni+cGHw/N13cx+7a3scXRfjGmmq9WrLT6GRUmk/co14FSlZcYwAjTLCM41RoFGnAE5jfhvpFaptxKtIatIoo0R9TX9FOYZKOmVJSV6kr9Ioo0R9TX9FOYYmDytLSvIifRXXCNAnnoD29qAA0t4ePO+uFNYsLaf1amUPJXmRvoqjjJLWiNc4aMRrWVKSF+mrOMooixb1rr2YNHlYWVKSF+mP/pZRck1pkKs96TVeo9LkYWWnttgBiFS1mpr8c9dkZUejZnu2ZEejQvQkG8c+pCzpSl6kmMJGxEI8XRfV/bFq6UpepJiyI18XLQqu6GtqggSfbYd01niViqUreSlfZ5wR3ADMPs44Y9/tUWrQ61rhZ/Vw54Dg57oEhulHmfqgoyPoQtnRsW+Ch3i6LsbV/VHTGpSfQsNh035oWgOJbMaM3MPwZ8wItkcZgv/SYve769xb2fu4uy5oj0scUwHMnZv7s86dm24cmtagJBEyrYHmk5fyFDYPe5RFMn5WD9tyvKZuNJy3vv8xQjyLdZTKgh9xxSGxKup88ma23sxWmdlKM1P2luj6WxaIUoPeluc1+dr7opTq6ScBtwCLMz9P6t3bVdcvT2nU5Ke7e1OhvzQi+0hjkQwAPyj3a/K198VBefaVrz2XOOrp61rh6ZbMNxcPfj7d0rt7EJrWoCzpxquUnijd/WbMyP3ebHuUIfg/AXZ2e//OTHspiWM6gWevhc5u57RzW9CeZhySuqSTvAO/MbPlZpazQ7CZtZhZm5m1bdy4MeFwpGQUKsdEKQs8+GDPRD9jRtAO0Ybg/3wz3AZsBHZnft6WaY8SZxSbN+cuk2zeXOBN3cQxnUAcpanmZlg0G/6lJvgs/1ITPNdgqpKW6I1XMzvU3V81s0OA3wKfd/fH8r1eN16rRPfRlxBcEWYTV1o3+MKOExZnFGcNhU+/C+/r0rYTuHsI/Hpr32PvrThuMmdLPl2/EdTUwZRFMEaJvliKeuPV3V/N/HwduA+YkuTxpEyElWPSKgukse7px7bvm+AheP6x7b2Ntn8mLAgSclc1dUF7VHGUfCR1iSV5MxtiZvtnfwfOBJ5L6nhSRsLKMWnNdhjXuqeF/M3u3O3D8rQnZUxzcMVdNxqw4Gdvr8DT6I0ksUvySv79wDIzexZ4GvhPd/9VgseTchGll0Ycsx32dzRrlDjDavZv1ZDT23nakzSmOSjNXLw7+Nk9wYedr7o85yNfu5SExJK8u7/k7hMyj3HurlvwEkijHBOly2BYV82wOKN09RzWkrsHz7A8E5MVS5TzFUfJR1IX6carmZ0E1NNlQjN3/1HcwejGaxXp7+jLMFFuNEa5wVsozqg3iFvnwVuL4MDO4Ap+WAs0L+z5vmKKemN2XWtQg9/2cnAFP2GBbroWWdiN19BZKM3sx8CRwEogO/G1A7Eneakizc3Jdr2LUj+OUnM/+gn4Rmb9VWuHI58AelmzP+lkePYXwbEPGwkTTo70EWJXKEFHrbePaS6c1PVHoOREmWp4EjDWS2mSG5EwdaPyXJl2qR+PGpX7Sjxbc396Hvzl1r3t3rn3+ZSF4e+Hnt0Os2UQSDf5hcUR5Xz19xhSFFFq8s8Bf5t0ICKxilI/Dqu5v5hnndVse5R7C6XS7TAsDnWxrFh5k7yZ/dzM7geGA8+b2a/N7P7sI70QRfogSpfBsC6Unmf91Wx7lK6ecXU7DOv5ErY9LA51saxYeW+8mtlphd7o7o/GHYxuvEpJuXMAwe2n7izohhhFGiNNo4xETWNa5TSOIT30ecSruz+aSeQfzf7etS2JYEVKSu2Q3rXnkkYZJEqZJI3uj+piWZKi1OQ/kqPtnLgDESk5He/2rj2XNMogUcokccQRJo1jSK8VqsnPNbNVwAfN7I9dHuuAP6YXYhUpl/Uz41gXtb815rg8PQ/uqoU7Lfj59Ly926KM8IwSZ9IjTaOORA2LIw5pHEN6pdCV/J3Ax4D7Mz+zj+Pd/TMpxFZd4lgoIw1xLD4Rto84jhFFtotk9kZqtotkNtGHlR/SOBdR4lCZRAoIHfFqZrmWsNni7rviDqaqb7yWy/qZcdxcC9tHWjfw7qrN3YPGauCijuD3QoN70jgXWWGDjDQIqWr1e8QrsAI4HHgTMGAY8JqZvQ7McfflcQRa9cpl/cwo9d+whBNHjTkOYV0kofAIz6hxpjHSNAr9IahKUW68/oqgh81wdz+Y4KbrT4B5QIlNwFHGymX9zLD6b5TyQ1w15v6yPDNB5mvvLmrNvtD5iOOzRjnnaZXApORESfKT3P3X2Sfu/hvgVHd/ip7LIUhflcv6mWH13zi686VVYz4yz0yQ+dq7ixJnqYw01WjUqhUlyW82s6vNbHTm8Y/Am2ZWQ7AypsQhrYUy+iusm1wc3fnS6oo3ZSEcNXfvlbvVBM+nRPyCOqYZxsze9/1jZveuNBVlH2GinHONRq1aUW68DgeuB04hqMkvA74GvA2Mcve/xBVMVd94rRTVNOoxjpGmcaybGuWcV9O/S5Xp9xqv7v6Gu3/e3Se6e5O7X+nuG939vTgTvFSIaurOF0dpKo4ySpRzXk3/LrKPKPPJHwN8mZ6LhpyeXFhStrJXn9XQiyNqaQr637umkCjnvJr+XWQfUco1zwLfB5azd9EQkug6qXJNFQnrzvf0vGBKX+8M6tRHtkSvlUc9Rn+l2U9eJI9+l2uADne/1d2fdvfl2UeMMUq1CevOFzYSNY5jxCGOEojKKJKwKEn+52Y2z8w+YGYHZR+JRyaVK6wOHbZYRxzHiEMcvYA0qZckLMqI19mZn//Qpc2BI+IPR6pCWB06ykhUiGckaX/FMRI1jn2I5BGa5N19TBqBSBXZ7yB4b1PudiDoqZtnsY6sNNYsFakAoeUaM6szs6+a2aLM86PNbGbyoUnFynevP9seZbGONEaSilSAKDX524H3gJMyz9uBm6IewMxqzOwZM3ugD/FJOSo0RzvArs2535dtj7JYR1xrlpbKvPYiCYlSkz/S3S80s4sA3H27mVnYm7qYD6wBDuhLgFJmsj1jsrI9Y2BvF8iwUkqUUsvAg2BXjpLPwC59AsJq3WEln7DtImUgypX8e2Y2mMyXaTM7EtgZZedmNhI4F7itzxFKeYnSMyaOCcryXWb05vIjjrVTRUpclCR/PcF0w4ebWSuwFPjHiPu/JfPavBOZmVmLmbWZWdvGjRsj7lZKVtQ52vs7Qdl7eUo++dpzKZV57UUSFKV3zW/NbAVwAsF10nzgyLD3ZW7Ovu7uy81sWoH9LwIWQTDiNVrY0mdJjwK1mvyrLfVGWKkljt4zcZSNREpclCt53H2Tu/+nuz/g7m8A90Z428nALDNbD9wNnG5mi/seqvRbGqNAo8zRHkcch360d+25lMq89iIJipTkcwitfLr7V9x9pLvXA58GHtIC4EWWRo05yhztccTx6i96155LqcxrL5KgKL1rclFZpRylVWMecXKQbLe9DINHBs/jjiOuzxJWFtJoVClzeZO8mf2c/MMOD+7NQdz9EeCR3rxHEpBGjTlKt8M06ukiAhS+kv92H7dJqZqwIPcqRHHWmAuVYrJJPo440vgsIhUgb5J390fTDERSkMbCEXEspBGFFsEQiSR00ZA0adGQCnDv8DwjUQ+GC95IPx6RChfHoiEi0cUxElVEYqMkL/GKYySqiMSmL71rAHD3WYlEJOUtaq+XpEfeigjQ9941IrlF6fWi2R1FUqPeNRKvKL1eonSzFJFYhI54NbOjgW8AY4FB2XZ31xqvaSuXEkfYKNEo3SzL5bOKlLioK0PdCnQA04EfAT9OMijJIY3JxdKSb1Rqtr2SPqtIkUVJ8oPdfSlBn/oN7n4DcHqyYUkPlbSARdjsjpX0WUWKLMoEZTvMbADwZzO7EngFOCTZsKSHSlrAIqxuX0mfVaTIoiT5LwJ1wBeAfyIo2Xw2wZgkl0qbkKtQ3b7SPqtIEUUp19S7+1Z3b3f3v3P3TwL6vy1t1bSARTV9VpGERUnyX4nYJkmqpgUsqumziiSs0IjXc4CPAoeZ2Xe7bDqAoKeNxE3dBvfSYh0isShUk38VaANmAcu7tG8BrkoyqKoUNgpUo0RFpA9Cpxo2s4EEfwxGufsLSQZT1VMN/6w+z83G0XDe+vDtIlKV4phq+GxgJfCrzA6bzOz+eMKTPcK6DapboYj0QZQkfwMwBXgLwN1XAvVJBVS1wkaBhm0XEckhSpLvcPe3E4+k2oV1G1S3QhHpgyhJ/jkzuxioMbOjzex7wJMJx1V9wroNqluhiPRBlBuvdcC1wJmZpl8DN7n7jriDqeobryIifRB247VQP/lBwBXAUcAq4ER3V/94EZEyUqhccwcwiSDBn4NWihIRKTuFBkONdfdGADP7AfB0b3ac+SbwGPC+zHGWuPv1fQ1UItCIWRHpplCS35X9xd07zKy3+94JnO7uWzMDqpaZ2S/d/ak+xClhNCJWRHIoVK6ZYGbvZB5bgPHZ383snbAde2Br5unAzKPwXV7pOy20ISI5FFrIu6a/OzezGoJ5b44C/q+7/z7Ha1qAFoBRozSwp880IlZEcojST77P3L3T3ZuAkcAUM2vI8ZpF7j7J3SeNGDEiyXAqm0bEikgOiSb5LHd/C3iEYB4cSYJGxIpIDokleTMbYWbDMr8PBs4A1iZ1vKqnEbEikkOUNV776gPAHZm6/ADgJ+7+QILHEy20ISLdJJbk3f2PwMSk9i8iIuFSqcmLiEhxKMmLiFQwJXkRkQqmJC8iUsGU5EVEKpiSvIhIBVOSFxGpYEryIiIVTEleRKSCKcmLiFQwJXkRkQqmJC8iUsGU5EVEKpiSvIhIBVOSFxGpYEryIiIVTEleRKSCKcmLiFQwJXkRkQqmJC8iUsGU5EVEKpiSvIhIBVOSFxGpYEryIiIVLLEkb2aHm9nDZrbGzFab2fykjiUiIrnVJrjvDuB/ufsKM9sfWG5mv3X35xM8poiIdJHYlby7v+buKzK/bwHWAIcldTwREekplZq8mdUDE4Hf59jWYmZtZta2cePGNMIREakaiSd5MxsK/BT4oru/0327uy9y90nuPmnEiBFJh1M0ra1QXw8DBgQ/W1vL8xgiUl6SrMljZgMJEnyru/97kscqZa2t0NIC27YFzzdsCJ4DNDeXzzFEpPyYuyezYzMD7gA2u/sXo7xn0qRJ3tbWlkg8xVRfHyTd7kaPhvXry+cYIlJ6zGy5u0/Ktz3Jcs3JwCXA6Wa2MvP4aILHK1kvv9y79lI9hoiUn8TKNe6+DLCk9l9ORo3KfZU9alR5HUNEyo9GvKZgwQKoq9u3ra4uaC+nY4hI+VGST0FzMyxaFNTHzYKfixbFe0O0uRlmz4aamuB5TU3wPImbrvPmQW1t8Flqa4PncVNPIZGYuHvJPI4//niXvlm82L2uzh32PurqgvY4zZ277zGyj7lz4ztGWp9FpBIAbV4grybWu6YvKrV3TRrS6l1TWwudnT3ba2qgoyOeY6inkEh0xexdIylKq3dNrgRfqL0v1FNIJD5K8jEJqyHHUcc+44zg/dnHGWfs3ZavF03cvWuyNf+o7X2R1mcRqQZK8jHIjjbdsCGoIGdHm2YT/bx5cOute692OzuD571J9GecAUuX7tu2dOneRP/RPCMQ8rX3VXYUbdT2vlBPIZEYFSrYp/0o1xuvo0fnvhk5enSwvaYm9/aamujHyPX+7CNKDHGaO3fvZ6qpifema9bixUHsZsFP3XQVyY2QG6+6ko8grBQTVkOOWscuVI4JE7WOHVY2UtdFkQpT6C9A2o9SvJKP0p0vjiv5GTNyv2bGjGB72JX8oEG5tw0atPcYYd0fo3xWdaEUKS2EXMkXPbF3fZRiko9SBglLSlESY1gS32+/3Nv22y/a+93D/9hE+axxlJ7iOOciEghL8irXhIhSBgkb0bpwIcydu+9o1Llzg/aodu3qXXsuYWWjKJ9VXShFykvFJ/n+1pijdudrbg4G6uzeHfyMezqBOLoVhnV/jHKMKF0o0zrnIhKuopN8WNfGKI46qnftuUTpQjl2bO73ZtvD4jj00Nzbu7ZPm5b7Ndn2KF0Xw7pQxnHO1YVSJEaFajlpP+KuycdR242jBh1lH/29eRvHMdyjdV0s1IUyrnq6ulCKREM11+Sj1nYLlRei1qD7u4/+dsOM4xgQrex08skwcmRw/2HkyOB5b44RRdLlL5FqUdFJvvtX/lztYeWFqDXo/u4jrA49IM+/VLY9jmNEEfZZDzoo9/vytYtIsio6yW/fHt5+7bV7F7/O2rYtaIdow/jj2EdYHXrw4Nz7yLbHcYwowj6riJSYQrWctB9x1+Sj9B03y73dbO9rwobxx7EP98J16DSOEUVYHFHijIvq9iLhNfmKnk8+ytznccxdnsb858OHw6ZNPdsPPhjeeCOeY0QR9lnTmgs+Wzbq+q2iri7+FbdESl1VzyefVgmjmrr8hX3WtM6FykYiERW6zE/7kcS0BmmUMOLaRyFplkHChH3WNMoopXQ+RIqJSi/XtLYGV28vvxz0ElmwoDK/rmtJvH3pfIgEKrpcE8foynJRTSWhKHQ+RKIp6yRfTXXZsEnQqo3Oh0g0iZVrzOzfgJnA6+7eEOU9vS3XDBgQXMH3PHYwUjIu1VISEpHyU8xyzQ+BsxPcfyqzFVZTSUhEKk9iSd7dHwM2J7V/SKcuW00lIRGpPEWvyZtZi5m1mVnbxo0be/XeNOqyWsBCRMpZol0ozaweeCCpmnwa1FVPREpZRXehTIO66olIOVOSD6GueiJSzmqT2rGZ3QVMA4abWTtwvbv/IKnjJam5WUldRMpTYkne3S9Kat8iIhKNyjUiIhVMSV5EpIIpyYuIVDAleRGRClZS88mb2UYgx9Cj1AwHUlxMr88UZ/zKJVbFGa9yiRPyxzra3Ufke1NJJfliM7O2QiPHSoXijF+5xKo441UucULfY1W5RkSkginJi4hUMCX5fS0qdgARKc74lUusijNe5RIn9DFW1eRFRCqYruRFRCqYkryISAWryiRvZjVm9oyZPZBj2zQze9vMVmYe1xUjxkws681sVSaOHqupWOC7ZvYXM/ujmR1XonGWxDk1s2FmtsTM1prZGjM7sdv2kjifEWMt+jk1sw92Of5KM3vHzL7Y7TVFP6cR4yz6+czEcZWZrTaz58zsLjMb1G1778+nu1fdA/gScCfBqlXdt03L1V6kONcDwwts/yjwS8CAE4Dfl2icJXFOgTuAyzK/7wcMK8XzGTHWkjinXeKpAf6bYGBOSZ7TkDiLfj6Bw4B1wODM858Al/b3fFbdlbyZjQTOBW4rdiwx+DjwIw88BQwzsw8UO6hSZGYHAKcCPwBw9/fc/a1uLyuJ8xkx1lIzA3jR3buPWC+Jc9pFvjhLRS0w2MxqgTrg1W7be30+qy7JA7cA/wjsLvCaE83sWTP7pZmNSyesnBz4jZktN7OWHNsPA/6ry/P2TFvawuKE4p/TI4CNwO2ZUt1tZjak22tK5XxGiRWKf067+jRwV472UjmnWfnihCKfT3d/Bfg28DLwGvC2u/+m28t6fT6rKsmb2UzgdXdfXuBlKwi+yk0Avgf8LI3Y8jjZ3Y8DzgH+p5md2m275XhPMfrEhsVZCue0FjgOuNXdJwLvAtd0e02pnM8osZbCOQXAzPYDZgH35tqco60o/bZD4iz6+TSzvyG4Uh8DHAoMMbPPdH9ZjrcWPJ9VleSBk4FZZrYeuBs43cwWd32Bu7/j7lszv/8CGGhmw1OPNDj+q5mfrwP3AVO6vaQdOLzL85H0/HqXuLA4S+SctgPt7v77zPMlBIm0+2uKfj6JEGuJnNOsc4AV7v7XHNtK5ZxCgThL5HyeAaxz943uvgv4d+Ckbq/p9fmsqiTv7l9x95HuXk/wte0hd9/nL6WZ/a2ZWeb3KQTnaFPasZrZEDPbP/s7cCbwXLeX3Q98NnPH/QSCr3evlVqcpXBO3f2/gf8ysw9mmmYAz3d7WdHPJ0SLtRTOaRcXkb8EUhLnNCNvnCVyPl8GTjCzukwsM4A13V7T6/OZ2Bqv5cTMrgBw9+8DnwLmmlkHsB34tGdua6fs/cB9mf/uaoE73f1X3WL9BcHd9r8A24C/K9E4S+Wcfh5ozXxtfwn4uxI8n1lhsZbEOTWzOuAjwOVd2krunEaIs+jn091/b2ZLCEpHHcAzwKL+nk9NayAiUsGqqlwjIlJtlORFRCqYkryISAVTkhcRqWBK8iIiFUxJXsqCmV2bmZ3vjxbMEjg15v1Ps/yzkvZoj/G4w8xsXlrHk+qjfvJS8iyYZncmcJy778yMRNyvyGHFZRgwD1hY5DikQulKXsrBB4A33H0ngLu/kZ1KwcyON7NHM5Oj/To7I5+ZPWJmt5jZkxbMzT0l0z4l0/ZM5ucH8x61ADM708x+Z2YrzOxeMxuaaV9vZl/LtK8ys2Mz7SPM7LeZ9v9nZhsyf6y+CRyZ+Xbyz5ndD7W9c8m3ZkdiivSFkryUg98Ah5vZn8xsoZmdBmBmAwkmk/qUux8P/BuwoMv7hrj7SQRXyv+WaVsLnJqZ+Os64Ou9DSaTnL8KnJGZmK2NYI2CrDcy7bcCX860XU8wjcZxBPP7jMq0X0Mw9W2Tu/9Dpm0i8EVgLMGMlCf3NkaRLJVrpOS5+1YzOx74MDAduMfMriFIrg3AbzMXuzUEU7Rm3ZV5/2NmdoCZDQP2B+4ws6MJZu8b2IeQTiBIwE9kjrsf8Lsu2/8983M58InM76cA52fi+ZWZvVlg/0+7ezuAma0E6oFlfYhTREleyoO7dwKPAI+Y2SpgNkESXe3uJ+Z7W47n/wQ87O7nm1l9Zp+9ZcBv3f2iPNt3Zn52svf/sd6UXHZ2+b3rPkR6TeUaKXkWrNF5dJemJmAD8AIwInNjFjMbaPsu9nBhpv0Ugtn63gYOBF7JbL+0jyE9BZxsZkdl9l9nZseEvGcZ8D8yrz8T+JtM+xaCbxciiVCSl3IwlKDE8ryZ/ZGgVHKDu79HMHvgt8zsWWAl+86//aaZPQl8H/hcpu1/A98wsycIyjtRzDCz9uwDOIrgD8RdmXieAo4N2cfXgDPNbAXBvOavAVvcfRNB2ee5LjdeRWKjWSilIpnZI8CX3b2t2LEAmNn7gE5378h887jV3ZuKHJZUAdX6RNIxCviJmQ0A3gPmFDkeqRK6khcRqWCqyYuIVDAleRGRCqYkLyJSwZTkRUQqmJK8iEgF+/+iB4W01DHrZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x = df[df['Species'] == species[i]]\n",
    "    plt.scatter(x['SepalLengthCm'], x['PetalLengthCm'], c = color[i], label = species[i])\n",
    "    \n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Petal Length\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f1a2ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2083185ef70>"
      ]
     },
     "execution_count": 19,
     "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": [
    "for i in range(3):\n",
    "    x = df[df['Species'] == species[i]]\n",
    "    plt.scatter(x['SepalWidthCm'], x['PetalWidthCm'], c = color[i], label = species[i])\n",
    "    \n",
    "plt.xlabel(\"Sepal Width\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5790c2a",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2233676c",
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
       "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
       "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
       "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a703d4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "fig, ax = plt.subplots(figsize = (8,6))\n",
    "sns.heatmap(corr, annot = True, ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71890e7e",
   "metadata": {},
   "source": [
    "## Label Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7253b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "64aeb073",
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "0            5.1           3.5            1.4           0.2        0\n",
       "1            4.9           3.0            1.4           0.2        0\n",
       "2            4.7           3.2            1.3           0.2        0\n",
       "3            4.6           3.1            1.5           0.2        0\n",
       "4            5.0           3.6            1.4           0.2        0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'] = le.fit_transform(df['Species'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89dc4f9",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37657de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(columns = [\"Species\"])\n",
    "Y = df['Species']\n",
    "x_train, x_test,y_train, y_test = train_test_split(X ,Y ,test_size = 0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "247b7835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "56ec2ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8ee31a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  97.77777777777777\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \",model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c993ab9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# knn - K-nearest neighbor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31d314b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cb605da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  100.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \",model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c04f1f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# decision tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "22e01ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c3e1750c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  95.55555555555556\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \",model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35879114",
   "metadata": {},
   "source": [
    "### Received the accuracy of 97.77 in the test dataset in model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}