EDA_on_Students_Performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c651762a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75e18854",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"StudentsPerformance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "af8acd81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>total_score</th>\n",
       "      <th>average score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>218</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>247</td>\n",
       "      <td>82.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>278</td>\n",
       "      <td>92.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>148</td>\n",
       "      <td>49.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>229</td>\n",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>71</td>\n",
       "      <td>83</td>\n",
       "      <td>78</td>\n",
       "      <td>232</td>\n",
       "      <td>77.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>88</td>\n",
       "      <td>95</td>\n",
       "      <td>92</td>\n",
       "      <td>275</td>\n",
       "      <td>91.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male</td>\n",
       "      <td>group B</td>\n",
       "      <td>some college</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>40</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>122</td>\n",
       "      <td>40.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>male</td>\n",
       "      <td>group D</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>completed</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>195</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>38</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>148</td>\n",
       "      <td>49.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender ethnicity parental_level_of_education         lunch  \\\n",
       "0  female   group B           bachelor's degree      standard   \n",
       "1  female   group C                some college      standard   \n",
       "2  female   group B             master's degree      standard   \n",
       "3    male   group A          associate's degree  free/reduced   \n",
       "4    male   group C                some college      standard   \n",
       "5  female   group B          associate's degree      standard   \n",
       "6  female   group B                some college      standard   \n",
       "7    male   group B                some college  free/reduced   \n",
       "8    male   group D                 high school  free/reduced   \n",
       "9  female   group B                 high school  free/reduced   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \\\n",
       "0                    none          72             72             74   \n",
       "1               completed          69             90             88   \n",
       "2                    none          90             95             93   \n",
       "3                    none          47             57             44   \n",
       "4                    none          76             78             75   \n",
       "5                    none          71             83             78   \n",
       "6               completed          88             95             92   \n",
       "7                    none          40             43             39   \n",
       "8               completed          64             64             67   \n",
       "9                    none          38             60             50   \n",
       "\n",
       "   total_score  average score  \n",
       "0          218      72.666667  \n",
       "1          247      82.333333  \n",
       "2          278      92.666667  \n",
       "3          148      49.333333  \n",
       "4          229      76.333333  \n",
       "5          232      77.333333  \n",
       "6          275      91.666667  \n",
       "7          122      40.666667  \n",
       "8          195      65.000000  \n",
       "9          148      49.333333  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b571b07",
   "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>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score  reading score  writing score\n",
       "count  1000.00000    1000.000000    1000.000000\n",
       "mean     66.08900      69.169000      68.054000\n",
       "std      15.16308      14.600192      15.195657\n",
       "min       0.00000      17.000000      10.000000\n",
       "25%      57.00000      59.000000      57.750000\n",
       "50%      66.00000      70.000000      69.000000\n",
       "75%      77.00000      79.000000      79.000000\n",
       "max     100.00000     100.000000     100.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bba767f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "ethnicity                      0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2606f5da",
   "metadata": {},
   "source": [
    "#### No null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8fae807c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7293c76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "995    False\n",
       "996    False\n",
       "997    False\n",
       "998    False\n",
       "999    False\n",
       "Length: 1000, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()   #checking for dupicate records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cf3a2767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "93a16c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['math score'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "568b6c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='math score', ylabel='Count'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['math score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4efcfabb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0738bb34",
   "metadata": {},
   "source": [
    "### Our dataset do not contain any duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4655fe05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ethnicity', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x=\"ethnicity\",orient='h')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d8c84035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='parental_level_of_education'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,y=\"parental_level_of_education\",orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a2d9a537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "some college          226\n",
       "associate's degree    222\n",
       "high school           196\n",
       "some high school      179\n",
       "bachelor's degree     118\n",
       "master's degree        59\n",
       "Name: parental_level_of_education, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parental_education=data['parental_level_of_education'].value_counts()\n",
    "parental_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2b902459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of parental level of Education\n",
      "------------------------------------------\n",
      "Percenatge of some college :  22.6\n",
      "Percenatge of associate's degree :  22.2\n",
      "Percenatge of high school :  19.6\n",
      "Percenatge of some high school :  17.9\n",
      "Percenatge of bachelor's degree:  11.799999999999999\n",
      "Percenatge of master's degree :  5.8999999999999995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1ad85610d90>,\n",
       "  <matplotlib.patches.Wedge at 0x1ad856775d0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ad85688c50>,\n",
       "  <matplotlib.patches.Wedge at 0x1ad856884d0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ad8568b8d0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ad85699310>],\n",
       " [Text(0.8341981156790161, 0.717017087521336, 'some college'),\n",
       "  Text(-0.5717990621018805, 0.939705183863221, \"associate's degree\"),\n",
       "  Text(-1.0543739750814827, -0.3135211646298753, 'high school'),\n",
       "  Text(-0.11383566476996158, -1.0940938905900084, 'some high school'),\n",
       "  Text(0.8112644257554884, -0.7428660925790178, \"bachelor's degree\"),\n",
       "  Text(1.0811581857178525, -0.20272389463327067, \"master's degree\")],\n",
       " [Text(0.45501715400673604, 0.3911002295570923, '22.60%'),\n",
       "  Text(-0.31189039751011655, 0.5125664639253932, '22.20%'),\n",
       "  Text(-0.5751130773171723, -0.1710115443435683, '19.60%'),\n",
       "  Text(-0.062092180783615405, -0.5967784857763682, '17.90%'),\n",
       "  Text(0.44250786859390273, -0.4051996868612824, '11.80%'),\n",
       "  Text(0.5897226467551923, -0.1105766697999658, '5.90%')])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(\"Percentage of parental level of Education\")\n",
    "print(\"------------------------------------------\")\n",
    "\n",
    "print(\"Percenatge of some college : \",(parental_education[\"some college\"]/1000)*100)\n",
    "print(\"Percenatge of associate's degree : \",(parental_education[\"associate's degree\"]/1000)*100)\n",
    "print(\"Percenatge of high school : \",(parental_education[\"high school\"]/1000)*100)\n",
    "print(\"Percenatge of some high school : \",(parental_education[\"some high school\"]/1000)*100)\n",
    "print(\"Percenatge of bachelor's degree: \",(parental_education[\"bachelor's degree\"]/1000)*100)\n",
    "print(\"Percenatge of master's degree : \",(parental_education[\"master's degree\"]/1000)*100)\n",
    "\n",
    "plt.title(\"Distribution of Parental education\")\n",
    "plt.pie(parental_education,autopct='%.2f%%',labels=[\"some college\",\"associate's degree\",\"high school\",\"some high school\",\"bachelor's degree\",\"master's degree\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "08514e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample data (replace with your actual dataset)\n",
    "# Assume you have a DataFrame named 'df' with the specified columns\n",
    "\n",
    "# Create subplots for each subject\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))  # 1 row, 3 columns\n",
    "\n",
    "# Math Score vs. Gender\n",
    "sns.barplot(data=data, x='lunch', y='math score', ax=axs[0])\n",
    "axs[0].set_title('Math Score')\n",
    "\n",
    "# Reading Score vs. Gender\n",
    "sns.barplot(data=data, x='lunch', y='reading score', ax=axs[1])\n",
    "axs[1].set_title('Reading Score')\n",
    "\n",
    "# Writing Score vs. Gender\n",
    "sns.barplot(data=data, x='lunch', y='writing score', ax=axs[2])\n",
    "axs[2].set_title('Writing Score')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "b3eca12c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample data (replace with your actual dataset)\n",
    "# Assume you have a DataFrame named 'df' with the specified columns\n",
    "\n",
    "# Create subplots for each subject\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))  # 1 row, 3 columns\n",
    "\n",
    "# Math Score vs. Gender\n",
    "sns.barplot(data=data, x='gender', y='math score', ax=axs[0])\n",
    "axs[0].set_title('Math Score')\n",
    "\n",
    "# Reading Score vs. Gender\n",
    "sns.barplot(data=data, x='gender', y='reading score', ax=axs[1])\n",
    "axs[1].set_title('Reading Score')\n",
    "\n",
    "# Writing Score vs. Gender\n",
    "sns.barplot(data=data, x='gender', y='writing score', ax=axs[2])\n",
    "axs[2].set_title('Writing Score')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "1b09cbdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample data (replace with your actual dataset)\n",
    "# Assume you have a DataFrame named 'df' with the specified columns\n",
    "\n",
    "# Create subplots for each subject\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))  # 1 row, 3 columns\n",
    "\n",
    "# Math Score vs. Gender\n",
    "sns.barplot(data=data, x='test_preparation_course', y='math score', ax=axs[0])\n",
    "axs[0].set_title('Math Score')\n",
    "\n",
    "# Reading Score vs. Gender\n",
    "sns.barplot(data=data, x='test_preparation_course', y='reading score', ax=axs[1])\n",
    "axs[1].set_title('Reading Score')\n",
    "\n",
    "# Writing Score vs. Gender\n",
    "sns.barplot(data=data, x='test_preparation_course', y='writing score', ax=axs[2])\n",
    "axs[2].set_title('Writing Score')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "94b9970c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample data (replace with your actual dataset)\n",
    "# Assume you have a DataFrame named 'df' with the specified columns\n",
    "\n",
    "# Create subplots for each subject\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))  # 1 row, 3 columns\n",
    "\n",
    "# Math Score vs. Gender\n",
    "sns.barplot(data=data, y='parental_level_of_education', x='math score', ax=axs[0])\n",
    "axs[0].set_title('Math Score')\n",
    "\n",
    "# Reading Score vs. Gender\n",
    "sns.barplot(data=data, y='parental_level_of_education', x='reading score', ax=axs[1])\n",
    "axs[1].set_title('Reading Score')\n",
    "\n",
    "# Writing Score vs. Gender\n",
    "sns.barplot(data=data, y='parental_level_of_education', x='writing score', ax=axs[2])\n",
    "axs[2].set_title('Writing Score')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6868023",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64decbd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c460afc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1b762482",
   "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>ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>total_score</th>\n",
       "      <th>average score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>218</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>247</td>\n",
       "      <td>82.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>278</td>\n",
       "      <td>92.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>148</td>\n",
       "      <td>49.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>229</td>\n",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender ethnicity parental_level_of_education         lunch  \\\n",
       "0  female   group B           bachelor's degree      standard   \n",
       "1  female   group C                some college      standard   \n",
       "2  female   group B             master's degree      standard   \n",
       "3    male   group A          associate's degree  free/reduced   \n",
       "4    male   group C                some college      standard   \n",
       "\n",
       "  test_preparation_course  math score  reading score  writing score  \\\n",
       "0                    none          72             72             74   \n",
       "1               completed          69             90             88   \n",
       "2                    none          90             95             93   \n",
       "3                    none          47             57             44   \n",
       "4                    none          76             78             75   \n",
       "\n",
       "   total_score  average score  \n",
       "0          218      72.666667  \n",
       "1          247      82.333333  \n",
       "2          278      92.666667  \n",
       "3          148      49.333333  \n",
       "4          229      76.333333  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column \"total_score\" containing the sum of scores for each student\n",
    "data['total_score'] = data[['reading score', 'writing score', 'math score']].sum(axis=1)\n",
    "data['average score']=data[['reading score',  'writing score', 'math score']].mean(axis=1)\n",
    "# Display the DataFrame with the new \"total_score\" column\n",
    "\n",
    "data.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78a1984b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe83001a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76cd90f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "869d338a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='test_preparation_course', ylabel='math score'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=data,x='test_preparation_course',y='math score',hue='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "b0538c38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='test_preparation_course', ylabel='writing score'>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=data,x='test_preparation_course',y='writing score',hue='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "3565d7b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='test_preparation_course', ylabel='reading score'>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=data,x='test_preparation_course',y='reading score',hue='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d47a0fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='test_preparation_course', ylabel='ethnicity'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.crosstab(data['ethnicity'],data['test_preparation_course']))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2476a518",
   "metadata": {},
   "source": [
    "### no features are helpful in the above heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9a9eaa86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ethnicity', ylabel='math score'>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=data,x=\"ethnicity\",y='math score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "43398b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='parental_level_of_education', ylabel='ethnicity'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.crosstab(data['ethnicity'],data['parental_level_of_education']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc74b057",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "f3352e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_13104\\3522526226.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(data['math score'],color='orange')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='math score', ylabel='Density'>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uCityeCqIREScYlvxI0SR7DOczdIRhhvDrMEuexfM2On2kcAIvIYHd2UxxtbfYqUed/i2bj9G3ikYrjRsDa+JgzRkJiLiEHfNe7gi5diuFCKZRU7H6bhoCDvagB1twHIFiGSeBIC39J/x5Z9/ENX1iiQxqCASEXFI/OhQ1hfA5XM4TedrPOrlqVmPES5zNoxIC1QQiYg4pPHu9uGePFx2BJb/KMzUURjY+Pa95nQckSNSQSQi4gQrgrfiDQAiPXlCdQsaiz3v/rcgWu9sGJEjUEEkIuIAT1UxrmgNljcbM/3IZ2H1ZNHUEUR9R2FYIbyVbzkdR6RZKohERBzgK38FgEjWZDB68VexYcSPgPkqXgPbcjiQyOH14t9CEZHE1SuuP9RKkcwiLHcqrsg+PNXvOh1H5LBUEImIdLdoLd79/wV69/yhOJePSJ9Tgc8mkoskGhVEIiLdzFvxJoYdIRoYSDRlqNNxukUkaxI2bjz1H+Kq/8TpOCKHUEEkItLNPrs69emQJPfysr2ZmBmFAPh0lEgSkAoiEZFu5j1wTZ5kmD90sMZT8D1VazDMKmfDiHyOCiIRkW5kRPbhqV4LQCQruQoiKziIaPAYDCydgi8JRwWRiEg38u57HQMbM3UEVqCf03G6XbhxcnXFGzoFXxKKCiIRkW7UOH8mKc4uOwwzYyy2OwWXWYG7ZoPTcUTiVBCJiHSjz+5fdqazQZzi8hHJnACAr/I/DocR+YwKIhGRbuJq+BRP3RZsXESyTnU6jmPCB/bdXbMRI1zmcBqRGBVEIiLdpPHq1GbmOGxvH2fDOMj29cVMGY6BHb/BrYjTVBCJiHQTX3y47AxHcySCSJ9TAPBUvgF21OE0IiqIRES6h23jbbyhqwoizPQx2K4UXJEK3OWaSyTOU0EkItIN3LXv4w7vwXYF45OKk5rLSySzCADPzicdDiOigkhEpEsZhoFhGJ+dbp91CoYnGF9++IezmbtLpM/JALhLX8YIlzqcRpKdx+kAIiK9VdBdG79Fhb/8H7GFWeMIWjtbaOkC0+zacAnACgwgGhyMu/5j/Lsep/7oa52OJElMBZGISBcwDAPDrMIufRPMOtz73gTAtH3Yu185cmN/BvQ9vutDJoBIn1Nx139MYOdfVBCJozRkJiLSlaIhjJoPMKwGbHcKUW8edrThiA+iIadTdxszswjb8OKpXoe7er3TcSSJqSASEelinroPADBTjgNDX7tNeFKJ5sau2h3Y9ReHw0gy02+miEgXc9fGCqJo6nCHkyQms/90APy7ngCr98+dksSkgkhEpCtZIdz12wAwU0c4HCYxRXNOw/Lm4A7vwbvvX07HkSSlgkhEpAu5a7dg2FEsTxa2N9fpOInJ5SPUbxagYTNxjgoiEZEu5K7dBICZOpykucBQO4T6fQ0A/95nIVrrcBpJRo4WRKFQiLlz51JUVMSkSZNYunRps+tu2LCBWbNmUVBQwMyZM1m//rOzEWzb5sEHH2TKlCmMGzeOyy67jC1btjR5/a677uLkk09mwoQJLF68GMuyunTfREQgdkd30PyhlpgZ44gGh2BYdbGiSKSbOVoQLV68mPXr1/Pwww8zf/58lixZwnPPPXfIenV1dVx99dUUFRWxfPlyCgsLueaaa6irqwPgscceY+nSpdx000089dRTDBw4kKuuuor6+noAHnroIZ555hmWLFnCvffey8qVK3nooYe6dV9FJAmF9+Fu2A6oIDoiw8BwGfFhM/+eJ1u4knfsIdKZHCuI6urqWLZsGfPmzWP06NGcffbZXHnllTz66KOHrPvss8/i9/uZPXs2Q4cOZd68eaSmpsaLpxUrVvDtb3+bM888kyFDhrBgwQIqKytZvXo1AH/84x+57rrrKCoq4uSTT+b6668/7HZERDqTe1/spqVRf39sT4bDaRKUy4PHbROwdkHf0wHwlb1IMLSBoLWz2UfA2gmhCofDS2/i2JWqN23ahGmaFBYWxpeNHz+e3/zmN1iWhcv1Wa1WUlLC+PHj4/8iMAyDcePGsXbtWmbMmMHs2bMZOHBgfH3DMLBtm+rqavbs2cOuXbs46aSTmmxnx44d7N27l759+3bD3opIMnKX/RsAM22Uw0kSmOHGMGuwy97FNkNEAwNxN3yKe/MSzOxJzbfz+CHlDAwjB9u2uy+v9FqOFUSlpaVkZWXh8/niy3JzcwmFQlRWVpKdnd1k3WHDhjVpn5OTw+bNmwEoKipq8tqyZcswTZPx48ezZ88egCaFT25u7EyP3bt3t6kgSuYjtI37nsx90BL1UcuSqY8MLDz7XgcgmjYK2rHPhgF2W9sZzfzcVe06q200hG01YGaMxd3wKZ7Kt4j0KWq+WfTAf43k+Dy1VzL9zh1OW/bbsYKovr6+STEExJ+Hw+FWrfv59SB2NOmOO+7giiuuIC8vj48//rjJex9pOy3JyUlv0/q9kfqgZeqjliVFH+0rhnA5uAOk5I0CVxu+bj1+AFJT/NDW8z88fvB68KW2sW1723V2W+8psPcZPHVbSPeHwdvMUKMr1kfZ2Wlt3GBySorfuQ5yrCDy+/2HFCSNzwOBQKvW/fx6a9as4aqrrmLy5Mn88Ic/BJoWP36/v8l2gsFgmzKXl1eTrEdmDSP2C5XMfdAS9VHLkqmPUj78GymAmTqS+joTaP0VmA2fn7RMqK0LYZkNbdqu4fPjSzEJ14Zi90Xr4nad3zaNlMDRuBs+oWH320SyTj18OzekAfv21WBZvfzD1AHJ9Dt3OI373xqOFUT5+flUVFRgmiYeTyxGaWkpgUCAjIyMQ9YtKytrsqysrKzJcNeqVav4zne+w6mnnsrdd98dn4OUn58ff+/GeUalpaUA5OXltSmzbZOUH6iDqQ9apj5qWTL0kbfsBQDMtNHQzn21bdre1m7m565q1wVtzfQTcTd8gqdqHZE+hy+I4k2S4LPUGdRPLXPsLLNRo0bh8XhYu3ZtfFlxcTFjxoxpMqEaoKCggDVr1sQnztm2zerVqykoKADggw8+4Lvf/S6nnXYa99xzD16vN942Pz+f/v37U1xc3GQ7/fv314RqEekSRqQSz/7/AhBNG+1wmp4nkh77bnfXfgDROofTSLJwrCAKBoNMnz6dBQsWsG7dOl588UWWLl3KpZdeCsSO4jQ0xA69Tp06laqqKhYuXMiWLVtYuHAh9fX1TJs2DYCbb76Zfv36MWfOHCoqKigtLW3S/uKLL+auu+5i1apVrFq1irvvvju+HRGRzubd90rsdh2pQ7F9OU7H6XFsf1+i/n6xienV61tuINIJHBsyA5gzZw4LFizgsssuIy0tjR/84Aecc845AEyaNIlFixYxY8YM0tLSeOCBB5g/fz5PPPEEI0aM4MEHHyQlJYXS0lLWrFkDwBlnnNHk/RvbX3HFFZSXl/P9738ft9vNRRddxOWXX97NeysiycJX9iIQu2mptI+ZXoA7tAtP9TrMPhOcjiNJwLB1AYdWKytLzklpEJuYlpubntR90BL1Uct6ah+16arItk3WayNxh3bSUPh7zIjZ9onG/kzS+hVS8+lb2G2eVJ2JL28M4T3vtKlte9t1VVtXww5Sty3GNrzUDF8YP6ss3s4TIO3YL1HekKNJ1UfQU3/nOkvj/reGo0eIREQSXdBdi2FWtXp9o+YD3KGd2K4ARvZY2PNO14XrxSx/fyxvLq5IGZ6aDZgZhS03EukAFUQiIs0wDAPDrMIufROioVa18ZT+E4Bo2sjYReGS9IJ4HWYYRDIK8Je/hKe6RAWRdDkVRCIiLYm2/vo67up3Y03SRqsW6iAz/cRYQVSzAawIuLwtNxJpJ0fvdi8i0qtYIdx1HwJgZpzocJiezwocjeXpg2GFcNe+73Qc6eVUEImIdBJ37QcYRLG8udj+fKfj9HyGCzM9Vlh6q0scDiO9nQoiEZFO4qnZCOju9p3JPHCRRk/1erCjDqeR3kwFkYhIZ7BtPDWxiwiqIOo80ZRjsdxpGFYd7rotTseRXkwFkYhIJ3A1fIrL3I9t+IimDHc6Tu9huDDTxwDgqV7ncBjpzVQQiYh0Ak9N7OwyM22UzobqZGbaCQB4qt/THUqly6ggEhHpBJ4Dp9s3Hs2QzhNNHY5teHGZFbhCu5yOI72UCiIRkQ4ywuWxq1Pjwkw93uk4vY/LRzT1OAA8Ne85HEZ6KxVEIiId1DiZOppyLHhSHU7TO5lpowFwqyCSLqKCSESkg+LDZQfmukjnixdE9R9hmDUOp5HeSAWRiEhHROtw120FNH+oK9neLKL+/hjYuGs3Oh1HeiEVRCIiHeCp2YCBRdR3FLYv1+k4vVrjUSLNI5KuoIJIRKQDPNUHLsaYruGyrvZZQbRJV62WTqeCSESkvWwTT+0GQMNl3cEKDsZyp2JY9bhqtzodR3oZFUQiIu3krt2CYYWw3BlYgaOdjtP7GS6iabHLGuiq1dLZVBCJiLRT/N5l6aPB0Ndpd4gPmx04s0+ks+g3WESkPWxbp9s7wEwdiY0LV2g31HzkdBzpRVQQiYi0Q+xmrpWxm7mm6mau3cYdJJoyNPbznpedzSK9igoiEZF28FSvBRpv5upzNkySaRw2Y8+/nA0ivYoKIhGRtrJtvNUlAJjpY53NkoTiBVHZW2BWOxtGeg0VRCIibeQK7cIVLsU2PJhpuplrd7P9fbF8fcGO4N33mtNxpJdQQSQi0kaexqNDqSPBHXA4TXJqLER9ZS86nER6CxVEIiJtFJ8/lF7gbJAkFk2PDZt5y14A23Y4jfQGKohERNrAFdqDO7QbG7du1+GgaOpwcPlw13+Mu26L03GkF1BBJCLSBo3DZdHU4eBOcThNEnMHIPskAHzlGjaTjlNBJCLSBp6qxrPLNFzmuPzTAfCWv+RwEOkNVBCJiLSSES7DHfoUG5du5poI+k4GwLfv3xBtcDiM9HQqiEREWik+XJYyFNuT5nAaIX0Elr8fhlWPt/INp9NID6eCSESklbyNw2UZY50NIjGGQTj3LECn30vHqSASEWkFI1KBu+FjbAzM9BOdjiMHRHLPBsCneUTSQSqIRERaIT5cFjwW25PhcBppFMk5AxsXntqNuBo+dTqO9GAqiEREWsFTtQ4AM0NHhxKJ7c3CzCwCwFemo0TSfiqIRERaYET2467/ENDp9okonHNgHpGuRyQdoIJIRKQF7qq1GNhEA4OxvVlOx5HPaZxY7d33Clims2Gkx1JBJCLSAk/VagAiGTo6lIjMjEIsbzYucz+eqnecjiM9lAoiEZEjCe/DXfsBoOGyhGW4CWefCYCv7AWHw0hPpYJIROQIPKUvYWAR9Q/E9uU6HUeaEb8ekeYRSTupIBIROQL33ucBMDVcltAiOV8EwFu1BiNc6nAa6YlUEImINMOIVOLe9yYAEQ2XJTTLfxSRAxfM9JW/7HAa6YlUEImINMNX9k8MO0LU3w/bn+90HDmEgWGAYRgYhkEkPmz2cnxZcw+Rz/M4HUBEJFH59vwNgGhGocNJ5BAuD1gmgegubNsGwMgaC9vAX/480einYDT/b37bk0F9NLWbwkpPoIJIRORwonXxM5ZMFUSJx3CDWYNdugbbDAEQtUxslx8jXI7x0SNYwaMP39btx8g7BcOVFi+mRDRkJiJyGL7ylzGseqzAAKzAIKfjSDPsaAg72hB72CZmynAgdjHN+PLPPYiGHE4tiUgFkYjIYfj3Hhgu63sOaM5JjxFNGwmAu2aTw0mkp1FBJCLyeVYYX+lzAJh9z3Y4jLSFmToKAHf9NojWO5xGehIVRCIin+Ot+DcusxLLl4+VqflDPYntyyHq64uBhefAFcZFWkMFkYjI5/j3rAQg1Pe8I56pJIkp2niUqHajw0mkJ9FvuojIwewo/tJnAAj3vdDhMNIeZlqsIPLUbASdRSatpIJIROQgnsr/4grvxfL0IZJ9mtNxpB2iKUOxDS8usxJXeI/TcaSHUEEkInKQxrPLwnlTweVzOI20i8tHNGUoAO4aDZtJ66ggEhFpZNv49zbOH9JwWU8WHzbTPCJppXYVRNu3b+/sHCIijvNUl+Bu+ATblUI4Z4rTcaQD4hOr67aApQsxSsvaVRBNnTqVWbNm8Yc//IE9e9o/PhsKhZg7dy5FRUVMmjSJpUuXNrvuhg0bmDVrFgUFBcycOZP169cfdr3777+fG2+88ZC2I0aMaPKYMWNGu3OLSO/k23tgMnXuWeBOcTiNdITl64vlzcawo7GiSKQF7SqI/v3vfzNjxgxefvllvvjFL/KNb3yDP//5z+zbt69N77N48WLWr1/Pww8/zPz581myZAnPPffcIevV1dVx9dVXU1RUxPLlyyksLOSaa66hrq6uyXrPPPMMv/rVrw5pv2XLFkaNGsXrr78ef/z+979v206LSK/nL/0HAKG88xxOIh1mGJipsatWe3TVammFdt3cNTs7m4svvpiLL76Y8vJynn/+eV599VXuuusuCgsLOf/885k6dSrBYLDZ96irq2PZsmX89re/ZfTo0YwePZrNmzfz6KOPMnXq1CbrPvvss/j9fmbPno1hGMybN4/XXnuN5557jhkzZmCaJrfeeisrVqxg0KBD7zm0detWhg4dSl5eXnt2V0SSgKv+Ezw172LjIpx7jtNxpBNE00ZB5Rt4ajeiQTNpSYcnVZeWllJaWsru3buxLIvU1FSeeOIJzjjjDJ5//vlm223atAnTNCks/OwqsOPHj6ekpATLspqsW1JSwvjx4zEO3E/IMAzGjRvH2rVrgVhx9f777/PEE080eb9GW7du5ZhjjunormIYyf1QH6iPenMf+ctiR4fMPieDP+ezvB15xL882teu3dvv4Ha7dZvtbduKPjJTh2PjwhUuxYiUHfK605+5ZP+d6879b412HSHauHEjzz33HM899xw7duzgC1/4At/61rc466yzSE1NBeDXv/41N910E+ecc/h/aZWWlpKVlYXP99lprbm5uYRCISorK8nOzm6y7rBhw5q0z8nJYfPmzQBkZGTw2GOPNZt369atWJbFBRdcQHV1NZMnT2b27NmkpaW1ab9zctLbtH5vpD5omfqoZQnZR+teAMA75Cvk5h6Ur9YPqX6wmmnXHI8fvB587W0LpKZ043Y7mre727aqjwKQPgyqPyAtsgWyB8YWu/yQ4ic1tW1/A3qyhPydSzDtKohmzJhBUVERl19+OVOnTiUrK+uQdcaPH3/Es9Hq6+ubFENA/Hk4HG7Vup9f73AikQjbt29n4MCB3HbbbVRVVbFo0SJuuOEG7r///hbbH6y8vDppL3pqGLFfqGTug5aoj1qWqH1kmFVk7/kXBlCRMoVoWXVsuWEQsELYtSHsaEPb3tPnx5diEm5n27RMqK0LYZnds92O5u3utq3tI19wOP7qDzDL36U+9eRYWzcYdSEaGmqwE+mD2AUS9XeuuzTuf2u0qyC6/fbbOffcc/F6vU2Wh8NhXnvtNc466ywmTpzIxIkTm30Pv99/SEHT+DwQCLRq3c+vdzher5e33noLv98fz3v77bczc+ZM9uzZQ35+fovv0ci2dRV49UHL1EctS7Q+8pa9hGFHMFOGYaYcBwdnsw96tMXn36Md7O7cbkfyOtWWlvvITB2Fn2dw134AURNcnvj/z0T7HHalZNrX9mrXHKIbb7yR6urqQ5Zv3ryZH//4x616j/z8fCoqKjBNM76stLSUQCBARkbGIeuWlZU1WVZWVkbfvn1bta20tLQmxdvQobErmHbkkgEi0ns0nl0WzjvX4STS2Sz/ACx3BoYdxl2/1ek4ksBaXRD9+c9/ZuTIkYwaNQrbtjn11FMZNWpUk8dFF13ESSed1Kr3GzVqFB6PJz4xGqC4uJgxY8bgcjWNVVBQwJo1a+KHNm3bZvXq1RQUFLS4nS1btlBYWNhk+G7jxo14PB4GDx7cqqwi0otZJr6yfwIQzpvmcBjpdIZBNK3x9PsNDoeRRNbqIbOvf/3rHHfccViWxWWXXca9995LZmZm/HXDMAgGgwwfPrxV7xcMBpk+fToLFizgtttuY+/evSxdupRFixYBsaNF6enpBAIBpk6dyt13383ChQv52te+xmOPPUZ9fT3TprX85XXssccyePBgbrrpJubOnUtVVRXz589n1qxZTfKLSHLy7l+FK1KB5c0iktn8ML/0XGba8Xj3/xd3zQbI/4rTcSRBtWkOUePRn5deeon+/ftjtOV8tsOYM2cOCxYs4LLLLiMtLY0f/OAH8bPSJk2axKJFi5gxYwZpaWk88MADzJ8/nyeeeIIRI0bw4IMPkpLS8pVkXS4X999/PwsXLuSSSy7B5XJxwQUXMHv27A5lF5HewVf6LADh3C/F5pdIr2OmjsTGhTu8FyNcBsGBTkeSBNTq3/45c+Ywb9480tLSWLJkyRHXbTzK05JgMMgdd9zBHXfccchr77//fpPnJ554IitWrGjxPW+//fZDlvXr16/FzCKSnHzxq1NruKzXcgeJphyLp24Lnpr3MFUQyWHobvcikrTctZvx1G3BNrxEcr7odBzpQmbaaAA8Ne85nEQSVauPEB181Ke1R4BERBJZ43BZJPs0bE9GC2tLTxZNGw17/xq70Wsbr5UkyaFdR4hqa2u56667+PDDD7Esi9mzZzN27Fi+/vWvs2PHjs7OKCLSJeLDZbkaLuvtLF9fLG8Ohh3FXaubvcqh2lUQLViwgFdffRXDMFi5ciXPP/88t912G7m5udxyyy2dnVFEpNMZkX14K98CdLp9UjCMz4bNqt91OIwkonYVRK+++ip33nknQ4YM4Z///Cdnnnkm5557Lj/+8Y95++23OzujiEin85W/jIGFmToKK3i003GkGzQWRO7qd3XZZjlEuwoi27bxer00NDTw5ptvcvrppwOwf//+Vp0KLyLiNF9Z7Gau4dyzHU4i3SWaMgzb8OEy9+Oq1kUapal2XXTj5JNP5qabbiIlJQWXy8VZZ53Fm2++ya233sqUKVM6O6OISOeyLXzlLwIqiJKKy4OZOgJvzbu4y16BPvp/L59p1xGi2267jeOPPx6fz8d9991HWloa77//Pqeffjrz5s3r7IwiIp3KU12CK1yK5U4j0ucUp+NIN4qmHQ+Au+xVh5NIomnXEaL09HR++tOfNll2+eWXd0YeEZEu1zhcFsk+HVw+h9NIdzIPFESuqnUYoVJsX67DiSRRtKsgikQiPP3007z77ruYphm/6WojXadIRBKZ5g8lL9vbh2hgEO6G7fjKnqeh/9edjiQJol1DZvPmzWPhwoVUVFQcUgyJiCQyI7IPz/7Y2bDhHBVEySiaPgYAX9k/HU4iiaRdR4heeOEF7rvvPk499dTOziMi0mUMw8C379UDp9uPxE45miPdorqD96+WBGWmj8FX+ize8pfACmvYVIAOzCHKz8/v7CwiIl0m6K7FMKvwlf4VADvnCwStnUduZLjwuCJEuiGfdB8reAyWLxdXuAxvxb91HzsB2lkQffe732XhwoX89Kc/ZfDgwXg87XobEZFuYRgGhlmFvfc/uEtjp9ubrgzs3a8cuaEvAyN7OEc8jCQ9j+EimjcF144n8O/9uwoiAdpZEP32t79l7969nH/++Yd9fePGjR0KJSLSFVy1W3GZVdiGD9M/qMWbfBpRfzclk+4Wzfsi3h1PxG7wO/JujY9K+wqi22+/vbNziIh0OXf1egCiqcPBpSPbySyadQq2OxV3aCeeqjWYmeOcjiQOa9c3woQJEwCoqanhk08+YdiwYYTDYdLS0jo1nIhIZ/LUvAeAmTbK4STiOLefcO7Z+Pc8ja/0GRVE0r7T7sPhMD/96U+ZMGECF110EXv27OHGG2/kiiuuYP/+/Z2dUUSk4yJVuOo+BMBMVUEkEM47DwD/3r87nEQSQbsKosWLF7NlyxZWrFiB3x8bY//BD35ARUUFP/vZzzo1oIhIZ3Dv+w8GFlFfPrYvx+k4kgDCeV/CNtx4ajfiqtvqdBxxWLsKoueff5558+YxYsSI+LIRI0Zw66238tprr3VaOBGRzuIui303RTVcJgfY3iwiWacB4N/7rMNpxGntKohqa2sJBoOHLLcsi2g02uFQIiKdyrZxl/8b0HCZNBXKOxcAX6mGzZJduwqiKVOm8POf/5yampr4su3bt/Ozn/2M008/vdPCiYh0Bnf1u7jCpdiGj2jKMKfjSAJpnEfkrXwLI1zmcBpxUrsKoptvvhmPx8PEiROpr69n5syZnH322WRkZHDTTTd1dkYRkQ7xlT0PQDRthE63lyas4CAi6WMxsPCVPud0HHFQu74ZKisr+cpXvsLo0aMZMWIEH3/8MaeddhrHHntsZ+cTEemwxrvbm2knOJxEElE471y81Wvxlz5DaMA3nI4jDmlTQfTmm2+yaNEiNm/e3OQu94ZhsHLlSm688UaKioo6PaSISHsZkf149q8CIJqugkgOFep7Pqkf3oav/GWI1oI71elI4oBWD5m9/vrrXHnllYwcOZI//elPvPXWW7z33nusWrWKP/zhDxx77LF861vfYs2aNV2ZV0SkTbz7XsGwo1gpQ7B9uU7HkQQUTRtNNDAYw2qIFUWSlFp9hOi+++7j8ssv54YbbmiyPDMzk4kTJzJx4kQyMzO5//77efDBBzs9qIhIezQOl0VzJjucRBKWYRDqex4pn/wa/96VhPte4HQicUCrjxBt2rSJr3zlK0dcZ9asWWzYsKHDoUREOoVt4yuP3d0+mnuaw2EkkYX6fhkAX+k/wAo7nEac0OqCqKGhgczMzCOuk5WVxb59+zocSkSkM7hr3sMd2ontChLtM8HpOJJIDAPDiM2BNQyDaNbJRP1H4TL349v3anz54R7SO7V6yMy2bVyuI9dPhmE0mWwtIuKkxuGySPZp4PY7nEYShsuDx20TiO6Cg/5mWXlTcH/6Z1L2/JlwTvMT8G1PBvVRTbzubdp0ltk//vGPI97Rvrq6usOBREQ6i688VhCFc89B/66XOMONYdZgl70LZii+2PQchRfw7HmOUNYXwXAf2tbtx8g7BcOVpgMAvUyrC6L+/fuzdOnSFtfr169fhwKJiHQGw6zCW/kWAOHcs9HxITlENIQdbYg/NQMDsdypuKK1uKrXE00dcUgTFda9V6sLopdf1qmIItJzeMtfwbBNzJShWCnHgrXT6UiS6Aw3ZvqJ+CrfxFNVctiCSHqvdt26Q0Qk0cWHy3LOdjiJ9CRmegEAnup1YFsOp5HupIJIRHof245PqA7nqiCS1oumHoftCuKKVuOu/9DpONKNVBCJSK/jrtlw4HT7AJGsSU7HkZ7E8GCmjwHAU1XicBjpTiqIRKTXiQ+XZZ0G7qDDaaSniWjYLCmpIBKRXsdXFrs6tYbLpD2iqSOwXX5cZiWuhk+cjiPdRAWRiPQqhlmNt/JNQAWRtJPLi5k2GgCvhs2ShgoiEelVvPtexbAjmMFjsVKGOh1HeigzfSwAnuq1Ta5mLb2XCiIR6VXit+vIPcvhJNKTmWmjsA0frsg+XA2fOh1HuoEKIhHpPWxb1x+SzuHyYaaNAsBTrWGzZKCCSER6DXftJtwNn2K7/ISzT3M6jvRwjcNm3uoSDZslARVEItJrxIfLsiaBO8XhNNLTmWnHYxseXOG9uEK7nI4jXUwFkYj0Gr6yfwIQyv2Sw0mkV3AHMFNHAho2SwYqiESkVzAi+w863f4ch9NIb/HZvc1UEPV2KohEpFfw7vvXgbvbHxe7u71IJzDTT8DGjTu0CyO0x+k40oVUEIlIr+Arex7Q0SHpZO4UoqnDgQOTq6XXUkEkIj2fbeFXQSRdJJIxFgBP1VpHc0jXUkEkIj2ep7oEV3gvljuNSNYXnI4jvYyZPgYbF+7QDg2b9WIqiESkx2scLotknwEuv7NhpPdxp8aHzTxVqx0OI11FBZGI9HiNp9uHdbq9dJH4vc32FzsbRLqMCiIR6dGMcFn8j5Tubi9dJT5s1rAdo+4Tp+NIF1BBJCI9mq/sBQxsIuknYgX6Ox1Heinbk0Y09TgAPHufcziNdAVHC6JQKMTcuXMpKipi0qRJLF26tNl1N2zYwKxZsygoKGDmzJmsX7/+sOvdf//93HjjjU2W2bbNXXfdxcknn8yECRNYvHgxlmV16r6IiDN0ur10l8ZhM/ceFUS9kaMF0eLFi1m/fj0PP/ww8+fPZ8mSJTz33KEftLq6Oq6++mqKiopYvnw5hYWFXHPNNdTV1TVZ75lnnuFXv/rVIe0feughnnnmGZYsWcK9997LypUreeihh7psv0Skm1gmvvKXAM0fkq5npp8YGzarfg9X3Tan40gnc6wgqqurY9myZcybN4/Ro0dz9tlnc+WVV/Loo48esu6zzz6L3+9n9uzZDB06lHnz5pGamhovnkzTZP78+cydO5dBgwYd0v6Pf/wj1113HUVFRZx88slcf/31h92OiPQsnv1v4zIrsbxZmJlFTseRXi42bBY728y/52lnw0inc6wg2rRpE6ZpUlhYGF82fvx4SkpKDhnOKikpYfz48RiGAYBhGIwbN461a9cCseLq/fff54knnmjyfgB79uxh165dnHTSSU22s2PHDvbu3dtFeyci3SF+McacL4LhdjiNJAMzcxwAvj1/dTiJdDaPUxsuLS0lKysLn88XX5abm0soFKKyspLs7Owm6w4bNqxJ+5ycHDZv3gxARkYGjz32WLPbAejbt2+T7QDs3r27yfKWHKjHklLjvidzH7REfdSyzugj46DGjafbR/Km4nI1/6aGAdiAceDR6o0183NXt21sZoDdXdt1al872E/d2kdANLMQe+djeKuKcTd8jBUc3MaNd69k/15qy347VhDV19c3KYaA+PNwONyqdT+/3uE0NDQ0ee8jbaclOTnpbVq/N1IftEx91LJ291GoAsyq2M+126FmPeAifeB48JUfoaELTAtS/dCW8yk8fvB68LW1XWe0BVJTunG7Tu5re/PSzX0E4MqE3IlQ9ibZNc/BoOvb+AbO0PdSyxwriPx+/yEFSePzQCDQqnU/v97hHFz8+P3+JtsJBoNtylxeXo1tt6lJr2EYsV+oZO6DlqiPWtaRPjIMg4BVil36JkRDeMtewg9EU4dR/+l/j9zYl4Evazjh2hB2tKH12/T58aWYbW7XGW3TMqG2LoRlds92ndzX9ubt7j6C2MisN/cc/GVvEtn6GPvzrmlT++6W7N9LjfvfGo4VRPn5+VRUVGCaJh5PLEZpaSmBQICMjIxD1i0rK2uyrKysrFXDXfn5+fH3HjhwYPxngLy8vDZltm2S8gN1MPVBy9RHLWt3H9mAGfsj5t6/BoBI6vHYLfxBNA6+nUdbtms383NXt21sZnfjdp3a1w72U7f20YH1o3lnY2/6P7xVxRh1n2AFj27jm3Q/fS+1zLFJ1aNGjcLj8cQnRgMUFxczZswYXK6msQoKClizZg32gf+btm2zevVqCgoKWtxOfn4+/fv3p7j4s8utFxcX079//zbNHxKRBBKtxV23FYhdQVikO9n+PMysUwHw7/2bw2mkszhWEAWDQaZPn86CBQtYt24dL774IkuXLuXSSy8FYkdxGuf/TJ06laqqKhYuXMiWLVtYuHAh9fX1TJs2rVXbuvjii7nrrrtYtWoVq1at4u67745vR0R6Hk/NBgwsov5+2L5cp+NIEgrlfwUA/54VDieRzuLohRnnzJnD6NGjueyyy7jlllv4wQ9+wDnnxK42O2nSJJ599lkA0tLSeOCBByguLmbGjBmUlJTw4IMPkpKS0qrtXHHFFZx77rl8//vf54c//CFf/vKXufzyy7tqt0Ski3mq3wXATNPRIXFGKP9CbAy8+9/G1fCp03GkEzg2hwhiR4nuuOMO7rjjjkNee//995s8P/HEE1mxouVK/Pbbbz9kmdvtZs6cOcyZM6f9YUUkMVgRPDUbAQ2XiUMMAwJHYfY5BW/lG/j3/o2Gwde22MzWJJ6E5mhBJCLSVu7aTRh2GMuTiRU49Mr0Il3K5cHjtglEd2H1nQKVbxDc/TjGoK+02NT2ZFAfTe2GkNIeKohEpEfxVJUAB44OJevV5sQ5hhvDrMEuexfTSMeHETvjcfsKbG9W8+3cfoy8UzBcaTpSlKAcnUMkItImtoW7+kBBpPlD4qRoCMsVIBocAoC7chV2tKHZB9GQw4GlJSqIRKTHcFWtw2VWYbsCRFOHtdxApIuZGbHLvzQeuZSeSwWRiPQY7tKXADDTRoGhEX9xnpk+FgB3/TaMyH5nw0iHqCASkR7DEy+ITnQ4iUiM7e1DNDgEAxtPtY4S9WQqiESkR3DVbsZVuxXbcMeOEIkkiEjGWAA81WsdzSEdo4JIRHoE/96VAERThoO7bTdmFulKZnpsHpG77kMNm/VgKohEpEfw745dmNXMHOdwEpGmbG8W0eAxB4bN1jkdR9pJBZGIJDxX3VY81Wtjw2UZKogk8UQOTK7WsFnPpYJIRBJe4MANNKNZp4AnzeE0IodqPP3eXbcVw6xyOI20hwoiEUl4jcNl0fypDicROTzbm000MFjDZj2YCiIRSWju2s14at7FNjyYfc92Oo5Is+Jnm1WtdTSHtI8KIhFJaP49ywGI5EwBbx9nw4gcwWdnm23BMKsdTiNtpYJIRBKa/8D8oVB+y3cTF3GS7cshGjhaw2Y9lAoiEUlY7ppNeGo2YBtewn3PczqOSItMDZv1WCqIRCRhNQ6XhXO+iO3NcjiNSMsaT793123GMGucDSNtooJIRBKTbWu4THqc2LDZIA2b9UAqiEQkIblrNuCpfR/b8BHOO9fpOCKtZuoijT2SCiIRSUjx4bLcs7G9mQ6nEWm9xtPv3bUaNutJVBCJSOKx7XhBpOEy6WlsXy5R/0AMLDzV7zodR1pJBZGIJBzP/rfx1G3FdgUJ501zOo5Im8XPNtOwWY+hgkhEEk5g118ACOVfiO1JdziNSNtFGu9tVvsBRGsdTiOtoYJIRBJLtAH/7qcAaOj3dYfDiLSP7etL1D9Aw2Y9iAoiEUkovrJ/4DIrifoHEMme7HQckXZrHDbz6iKNPYIKIhFJKIGdfwYg1O9rYLgdTiPSfvGLNNa+r2GzHkAFkYgkDCO0F1/5iwA09NdwmfRstr8vUX+/2LBZVYnTcaQFKohEJGEEdj+BYUeJZBYRTT3O6TgiHWamFwLg2V/scBJpiQoiEUkYjcNlmkwtvYUZv0jjRojsdzaMHJEKIhFJCO7qdXhq1mMbPkJHzXA6jkinsPz5RP39Mewonj3POR1HjkAFkYgkhMajQ+G8c7G92Q6nEek8kcyTAPDs/qvDSeRIVBCJiPOsCIFdTwDQ0P9ih8OIdC4zYzw2Bu7KYlx1HzodR5qhgkhEHOcrfxFXpAzLl0c45yyn44h0KtubSTRtFAD+XY87nEaao4JIRBwX+PQhABqO+iq4vA6nEel8Zp+JAAR2Pga27XAaORwVRCLiKFf9J/jK/glAw8BvOZxGpGuYGYXY7hTc9R/i2f9fp+PIYaggEpFuZxhG/BHc8TAGNuHs07HShjd5renD6dQiHeDyY/Y9B4DAzr84HEYOx+N0ABFJMqEKAlYp2MQmU++IDZdZA2YQtHY2385w4XFFiHRPSpFOZ/abjnfX0/j3LKdm5B3g8jsdSQ6igkhEuo1hGGBWYZe+CWYI9/5iXOEyLE8GpuWF3a8039iXgZE9HHSkSHooK2sCUX9/3KGd+EqfI5z/ZacjyUE0ZCYi3S8awo424C3/FwCRzJOxrQh2tKHZB9GQw6FFOshwE+r3VQACux5zOIx8ngoiEXGEEdqDp24zNgaRrFOcjiPSLUIHrrPlK/snRrjc4TRyMBVEIuIIX+UbAETTRuvK1JI0ommjiKSPxbBN/LufdDqOHEQFkYh0PyuMtzJ26nE46wsOhxHpXqH+XwMgsPMRh5PIwVQQiUi38+wvxrDqsLzZRFNHOR1HpFs1HPVVbMOHt7oET9Uap+PIASqIRKTbectfBSDS5wtg6GtIkovtyyGUfyEAgU8fdjiNNNI3kYh0r4q1uOu3YeMmcuB2BiLJpmHA5QD4dz8BZo2zYQRQQSQi3W3r7wEwM8djezIcDiPijEjWaZjBY3FFawjsWe50HEEFkYh0I1f9dtj5LADh7DOcDSPiJMOI37sv8OlSh8MIqCASkW4U+OQ3YEcxU0diBQY4HUfEUQ39vx6bXF21Gs/+1U7HSXoqiESkWxhmNf5P/wBAJPcsZ8OIJADbl0foqK8AENz+gMNpRAWRiHSLwM4/4TKrIO1YoumjnY4jkhDqB10NgH/3UxjhMofTJDcVRCLS9ewowU9+E/t56BU61V7kADPzJCIZ4zDsMMEdf3A6TlLTt5KIdDnf3r/jrv8Iy5sNA2c4HUckoTQeJQps/z1YpsNpkpcKIhHpcimfLAGgYdCV4Ak6nEYksYSOmonlzcUd2oF/71+bXc8wjHY9pHU8TgcQkd7Ns/9tvJVvYRteGo6+ihSnA4kkGpef+kFXkvrh7QQ//hWh/BnwuUIm6K7FMKva/t4GEMpDf+5bph4SkS6V8tEvAQgddRG2/yig3NlAIgmoftBVpHx0D96q1Xgr3yCSdWr8NcMwMMwq7NI3IRpq2xt7/JByBoaRg23bnZy6d3F0yCwUCjF37lyKioqYNGkSS5c2f3GqDRs2MGvWLAoKCpg5cybr169v8vozzzzDWWedRUFBAddeey379u1r0nbEiBFNHjNmaB6DSFdz12zEv/dvANQd8/8cTiOSuGxfHg39vg5A8KN7D79SNIQdbWjTo80FVBJztCBavHgx69ev5+GHH2b+/PksWbKE55577pD16urquPrqqykqKmL58uUUFhZyzTXXUFdXB8C6deuYN28e3//+93n88cepqqpizpw58fZbtmxh1KhRvP766/HH73//+27bT5FklbLtbgBCfS8kmjbS4TQiia1+8LXYGPjL/oG75n2n4yQdxwqiuro6li1bxrx58xg9ejRnn302V155JY8++ugh6z777LP4/X5mz57N0KFDmTdvHqmpqfHi6ZFHHmHatGlMnz6dkSNHsnjxYl599VW2b98OwNatWxk6dCh5eXnxR1ZWVrfur0iycdVtxb/7SQDqhlzvcBqRxBdNPY5w3nkApHx8j7NhkpBjBdGmTZswTZPCwsL4svHjx1NSUoJlWU3WLSkpYfz48fHZ8oZhMG7cONauXRt/vaioKL5+v3796N+/PyUlJUCsIDrmmGM6nNkwkvuhPlAfteWR+tE9GFiEc88hmjm2SR/FOqodDyfaOrhNoz3b7Gn72t62TvVRB9u29HtTP+T/AeDf9Rjuho8/+73p4H46/X3g5KO1HJtUXVpaSlZWFj6fL74sNzeXUChEZWUl2dnZTdYdNmxYk/Y5OTls3rwZgL1799K3b99DXt+9ezcQK4gsy+KCCy6gurqayZMnM3v2bNLS0tqUOScnvU3r90bqg5apj4DaT2DXnwHwFc4nN/egPqktJy3VD1YzbZvj8YPXg6872zqxzca2QGpKkuxre/PSzX3UkbYuP6T4SU1t4e9O7hT45GyM3S+Qves+mHB/bHmtH9qT1xXrp+zstv29S0aOFUT19fVNiiEg/jwcDrdq3cb1Ghoamn09Eomwfft2Bg4cyG233UZVVRWLFi3ihhtu4P77729T5vLyapJ1kr5hxP7QJ3MftER99JnUjQsJWhHCWZOpMsZAWTUALpdBth9qamOTQ9vC8PnxpZiEu7GtE9tsbJuWCbV1ISyz9+9re/N2dx91KK8bjLoQDQ01LZ7t5Rn4Y/rsfgF761Iq+v8IOziAgBXCbk9eN6QB+/bVYFnJ98XU+L3cGo4VRH6//5DCp/F5IBBo1bqN6zX3ejAYxOv18tZbb+H3+/F6vQDcfvvtzJw5kz179pCfn9/qzLZN0v+hUx+0LNn7yAjtIbDjjwDUHXtDk75o0i9t7SMn2jqVt7GZ3Y3b7Wn/bxqbdGcfdaTtgZyt+X6IZJ1KuM+p+Cr/Q+CjX1I3cnG8fUc+S8n8vdQajs0hys/Pp6KiAtP87DLlpaWlBAIBMjIyDlm3rKzpTe/Kysriw2TNvZ6XlwdAWlpavBgCGDp0KAB79uzpvB0SEQBSPl6CYTUQyTyJSNZkp+OIJA7DODCvpeWrS9cP/QkAwU+X4m741OHgycGxgmjUqFF4PJ74xGiA4uJixowZg8vVNFZBQQFr1qyJH2a0bZvVq1dTUFAQf724uDi+/q5du9i1axcFBQVs2bKFwsLC+BlnABs3bsTj8TB48OAu3EOR5GOEywh++jsA6obc0LYZjSK9mcuDx20TsHYRtHa2+PD0GU60zwQMK0TalpvxuCJO70Gv59iQWTAYZPr06SxYsIDbbruNvXv3snTpUhYtWgTEjhalp6cTCASYOnUqd999NwsXLuRrX/sajz32GPX19UybNg2Aiy++mG9+85uMHTuWMWPGsHDhQs444wwGDRqEZVkMHjyYm266iblz51JVVcX8+fOZNWsWmZmZTu2+SK+Usu0ujGgtkfRCwrlfcjqOSOIw3BhmDXbZu2C27mKJoewzSKn8L56dTxIZ/LWmZ7hJp3P0woxz5sxh9OjRXHbZZdxyyy384Ac/4JxzzgFg0qRJPPvss0BsyOuBBx6guLiYGTNmUFJSwoMPPkhKSuyuSIWFhfzf//0f9913HxdffDGZmZnxwsrlcnH//feTlpbGJZdcwrXXXsspp5zC3LlzndlpkV7KVf8Jwe2xo0O1x83X0SGRw2nD1aaj/gGYaSdgYOPe2raTgKTtHL2XWTAY5I477uCOO+445LX33296lc4TTzyRFStWNPteM2bMaPZ2HP369WPJkiUdCysiR5T64SIMO0w4azKR7DOdjiPSK4TyzsNd8x7uvS/iyphI1Nu35UbSLo4eIRKR3sFdswn/zr8AOjok0pmsQH/MPicD4Nvd/EEB6TgVRCLSYalbb8XAItT3AszMk5yOI9KrhI/6CrbhxlOzAXftFqfj9FoqiESkQzz738a/dyU2LmqH3uR0HJFex/b3xRrwFQD8pSt1QaEuooJIRNrPtkndvACAhv5f1x3tRbpIdMiV2IYXd/1HeKrXOR2nV1JBJCLt5t33Mr6Kf2MbPuqOneN0HJHey59HJPes2I97/waW2UIDaSsVRCLSPpZJ2gc/BaB+0JVYwUEOBxLp3cJ5U7HcGbgiZXgrXnM6Tq+jgkhE2iWw4w94at7D8vSJXZVaRLqWO0Co7/kA+Mv+iWFWOxyod1FBJCJtZkQqSN36MwBqh87D9uU4nEgkOZiZJxENDMSwGvCVPut0nF5FBZGItFnKh7fjiuzDTB1Fw8ArnI4jkjwMF6H82Bln3so3cTXsdDhQ76GCSETaxF39LsHtDwJQM2IRuBy94L1I0ommDCOSPhYDG/+eFToNv5OoIBKR1rMt0jf+CMOO0pD/FSI5U5xOJJKUQn0vjF2sse4D3DXvOR2nV1BBJCKtFtjxMN79b2O506kdvsjpOCJJy/blED5wz8DA3qfB1mn4HaWCSERaxQjtIXXzfADqhs3DCvR3OJFIcgvnnI3lTscVLsW7799Ox+nxVBCJSMtsm/SNP8JlVhJJH0v9wKudTiQi7gDhvucBjafh1zgcqGdTQSQiLfLvfhJ/6d+xDS/Vo3+tidQiCSKSOZGofwCGVY+v7B9Ox+nRVBCJyBEZob2kvX89AHXHziaafsJnrxlGGx9O7YVIL3XwafgV/9Fp+B2gf+aJSPNsi/T3vosrUkEk/UTqjvlx/KWguxbDrGrjG7rA1ORPkc4UTT2OSPqJeKvX4d+znPqjr0X/+mg7FUQi0qzg9t/gL38B2xWg+oQHweUFDhwZMquwS9+EaKj1b+jPgL7Hd1FakeQV6jsdT80GPHWb8VSXYGaMdTpSj6MhMxE5LHf1OlI/uBmAmuELiaYdppCJhrCjDa1+tKl4EpFWs305hHO+CIB/71/BCjucqOdRQSQihzAilWSUXIphhwnlnUfDwCudjiQiLQjnfBHL0wdXZB++8n85HafHUUEkIk3ZFunrr8RT/yHRwNFUH79E8xFEegKXn1DfLwPgK38BI7LP4UA9iwoikST3+TPBUj+8HX/Z89iuAFVjHwV/rs4WE+khzIxCzOBQDDuCf+/fnI7To2hStUgS+/yZYu5dfyPw4e0AhEcuwJfWF6zDnMZruPC4IkS6K6iItI5hEDpqBu5td+GtWkOk9gPgS06n6hFUEIkkqc+fKeaueR//x78EYrcEiLiyYPcrh2/sy8DIHg46UiSScKzAQCJ9TsFX+Qb+nY/DCd9zOlKPoCEzkWQXDWHUbiHwyf0YdpRI+lhCeefqbDGRHiycdx62K4i74VP46C9Ox+kRVBCJJDlXw06Cn/waw6rHDA6hof8lYOirQaQnsz1phPLOjT3ZdDdGpMLZQD2AvvVEkphRu5XAR7/AFa0lGhhE/aBrwOVzOpaIdIJI1qlE/f0hXEFwy21Ox0l4KohEkpS7ag3Bdy7BZVYR9fen7ujvgjvodCwR6SyGm3D//wEgsP23uGs2OBwosakgEklC3n3/JvPt8zAiFUQDR8fufeROdTqWiHSyaNoo6PclDDtK2vs/Adt2OlLCUkEkkmT8Ox4hc/V0XNFqon0mUD/kx9ieNKdjiUhXGf1TbJcf375X8e1d6XSahKWCSCRZWCapH8wjY8P3MOwIofwZNBT+VsNkIr1d6iDqj/khAGkfzINovcOBEpMKIpEk4GrYSWbx+aR8/CsAao+9keoT/wDugLPBRKRb1A/5MVH/ANwNH5Oy7U6n4yQkFUQivZxv70qy3pqEr/INLHc6+0/8I3VD5+r+ZCLJxJNKzchYIZTy0S9x12x0OFDiUUEk0ksZ4XLS3/02mSWX4IqUYaaNoXLiq4TzpzsdTUQcEO57PqG8czHsCOkbfwS25XSkhKKCSKQX8u19huw3JxLY/SQ2LuqO+V8qJr5MNHWY09FExEE1I+7EdqfirXyTwM5HnI6TUFQQifQiroYdpK+7nMySr+MK78VMHUHlhBepPW4+uPxOxxMRh1nBQdQOnQdA6gc/xQiXOpwocaggEukNrBAp2+4i+z/jCexZfuCo0P+jYuK/MTOLnE4nIgmkftB3iKSfiMusjJ11JoAKIpGezbbxlf6D7DcmkLrl/zCsOiJ9TqZy4qvUHneLziITkUO5PNSMugcbg8Cux/CWv+J0ooSggkikh/JUrSVz9ZfJXPtV3PXbiPqPonrM79h/0vNEM8diGEYLD6f3QEScYmYW0TDoSgDSN16HYVY7nMh5HqcDiEjbuOo/JnXLrQR2PwGAbfiIHP1NIkO+h8uTRtDeBa25Or/hwuOKEOnauCKSoGqHzcdX+k/c9R+R+sFPqTn+l05HcpQKIpEewgjtIeWjnxPc/nsMOwxAQ7+vYh17NVbNdih7p21v6MvAyB4OOlIkkpRsTwbVo39Nn+LzCe54iFDf84nknu10LMeoIBJJcEZoLykf3UPw099jWLFL7oezz6D2uP8jmllI0NoJ+7dgRxva9r5RnXUmkuwi2ZOpG/QdUrb/hvT3rqXilP9g+/KcjuUIFUQiCcoIl8UKoe2/jRdCkcyTqB06j0j2mWAYOrgjIh1We9wCfPv+haf2fTLWX8X+wuVgJN8U4+TbY5EEZ4RLSd08n5zXx5Dy8b0YVj2RjPFUFj5F5UkvEsmZottuiEjncadQdeLD2K4gvvKXCX50j9OJHKEjRCIJwl27meAn9xHY+WcMKzb8FckYR93QuURyzznsESHVRSLSGaJpx1Mz8k7SN3yf1K23YmYUEsk50+lY3UoFkYiTbBtv5ZsEP/4VvtJnMQ6cHhbNGENkyPeI5p6JxzDwNHfmmM4UE5FO0tD/m3gr/kNg11/IWHcZlRNeIpp6nNOxuo0KIhEHGJFK/LseJ7jjITw1G+LLzYyxhHPOwkoZBlED9rx65DfSmWIi0lkMg+pRv8RdtwXv/rfJWPs/VE54Cdub7XSybqGCSKS72BaeylUEdj5CYPeT8YnStitIqN9XsY7+H6zq7bGzxaxQq95SZ4qJSKdyB9hf8Bey/nsGnrqtZK65iP3j/ortSXc6WZdTQSTSxdzV7xHYvQz/7idxN3wSX26mHU/9gG8R6vdV8GXFTp+v3u5gUhERsP192V/4JH3emYZ3/ztkrP0q+wufBHeK09G6lAoikc5m23iq1uHbuxJ/6TNNhsQsdzrh/AupH3A5ZuaE+KxojXiJSCKJph3P/sIVZK6+EF/F62SumUVVwaPY3j5OR+syKohEOkO0Ae/+N+Cjl8n6ZAXuhs+O9NiGj3Del2g4ahbh3C+BO+hgUBGR1jEzx7G/8EkyV8/EV/Fv+rwzlf2FT2EFBjgdrUuoIBI5DKOl89ltG3fdB3jLXsJX9iLeitfjc4LcgO1KIZx7FuG+5xPOm4rtzYq9b7Pb67zsIiKdxexzMpUn/YPM1RfhqdlAn1VnUj3md0SyJzsdrdOpIBL5nKC7FsOsOmS50bAHV+XbuCtW4S5/HVfDziavW/6+uPKn0JA1mWj2qeAO4AIC1MOBYqlZOn1eRBJUNP1EKie8SOaaWXhqN5JZfAF1Q66n7tjZ4Oo9J3aoIBI5iGEYGGYV9t43MOo/xV23GXdt7OGKlDVZ1zY8RFOOI5o+mmja8VgZI0nLH0101xrs0rfatmGdPi8iCcwKHk3FxJdJ2/QTgjv/SOq2O/HvWUHNiDt6zQ1hHS2IQqEQt9xyC88//zyBQIBvf/vbfPvb3z7suhs2bGD+/Pl88MEHDBs2jFtuuYUTTjgh/vozzzzDPffcQ2lpKZMmTeLWW28lOzt27QTbtrn77rt58sknsSyLiy66iOuvvx6XS3cuaasWh5IOw7YPd0XBrt1mm7Zr27hCO/BUleCtLsFXtQpXZTGuzx0lsjGwAgOJphyLmTqSaMowcPk+y2nF7kBvR0O60aqI9D7uVGpGLyGScyZp7/8ET90W+qyZSbjPqdQf8yPCB66o31M5WhAtXryY9evX8/DDD7Nz505+8pOf0L9/f6ZOndpkvbq6Oq6++mouuOACbr/9dv7yl79wzTXX8MILL5CSksK6deuYN28et9xyCyNHjmThwoXMmTOHBx54AICHHnqIZ555hiVLlmCaJjfccAM5OTlcccUVTux2zxWqIGCVHv6KyUdgezKoj6a2a5PNDV+1abtWGFe4DFe4FCNciiu8B3fdh5896rceUvwA2IabaGAw0ZShsUdwCLgD7coiItJbhI6aSTj3bFK23k5w+wP4Kv+Db+1/iAaH0HDULEJHzSCaOqrHFUeOFUR1dXUsW7aM3/72t4wePZrRo0ezefNmHn300UMKomeffRa/38/s2bMxDIN58+bx2muv8dxzzzFjxgweeeQRpk2bxvTp04FYoXXmmWeyfft2Bg0axB//+Eeuu+46ioqKALj++uv55S9/mTwFkW2D1YARrcEwq3CZ1RiNj+ihP7uaLK/CiNZi2CYQIWjWgR2NvScW2DbGgf82rZQafzbAcJHS5D7Cn61nfL66sj//HjbYFodWYUeqyhpfc5EKGHbLM3Nsw000dRRmxliM9CFEww1Effng8rbYVkQk2dieDGpH3Eb94GsJfvJrAp8+hLt+G6nbFpO6bTFR31FEsk/DzCjETD+RaHAIlr8fuBJ3po5jyTZt2oRpmhQWFsaXjR8/nt/85jdYltVkOKukpITx48fHh04Mw2DcuHGsXbuWGTNmUFJSwlVXXRVfv1+/fvTv35+SkhJ8Ph+7du3ipJNOarKdHTt2sHfvXvr27dsNe9uMA/excjXsAKJgRzHs6IGC48BzorGCwP786yaGVY8RrTvwqAerrsnz2H+rYoWNbXZK5PYOMjrz74Ro/CfbcGN587B9eVi+XKLBYz478pNybPzoj2EYBK2d2LtfgTYOe4mIJBsrMIDa4QupHToX/96/49/9BL59r+EO78a9exnsXhZf18aF5T8KKzAQy5+P7U7D9qRhu9OxvNk09L8Y25fn2L44VhCVlpaSlZWFz/fZHIzc3FxCoRCVlZXx+T+N6w4bNqxJ+5ycHDZv3gxw2MImJyeH3bt3U1paCtDk9dzcXAB2797dpoLI5frcAYwOclcW02ftrM57w2Y39Nl1byx3OrYnNf4htOPP0w8sO/Bo8jwVw+Uhw19H/Z53sW2TWGkUO/pjYxw4NHrIvdjB7cPIKiDszvtc3xnN/EyTixX6rFLsihKIhg+/7mHaAeDyY2SPJeTui234wNsHjObLOXeTZy7wpWFE23Z0yPCmAi5cvjSsth5Z8qWC4cLwprXtqFR72znU1pE+6khbh/pXn6WW6bPUOobbDxgYRuxvWJdxpRIZ8D9EBvwPREN49r+DZ//beKrX46nZiKthB4YdwR2txl27EWo3HvoW0f3UH3dTp8Zqy6idYwVRfX19k2IIiD8Ph8OtWrdxvYaGhmZfb2hoaPLeR9pOS7KzO/leLjlnwtD2zY9pr47+PgQHXtiudu2/4PsoGNT+6120b7vDIHdYy6s1t82sE1peqRm+vLHd2s6ptk70UUfaOtW/+iy1TJ+l1slu3zTOdkqHvlOBqS2uebAUOvK3ouMcO83K7/cfUpA0Pg8EAq1at3G95l4PBoOHLX4afw4GdcVgERERcbAgys/Pp6KiAtP8bG5LaWkpgUCAjIyMQ9YtK2t6DZiysrL4cFdzr+fl5ZGfnx9/74O3A5CX59xYpYiIiCQOxwqiUaNG4fF4WLt2bXxZcXExY8aMOeT6QAUFBaxZsyZ+XRnbtlm9ejUFBQXx14uLi+Pr79q1i127dlFQUEB+fj79+/dv8npxcTH9+/d3dkK1iIiIJAzHCqJgMMj06dNZsGAB69at48UXX2Tp0qVceumlQOwoTuP8n6lTp1JVVcXChQvZsmULCxcupL6+nmnTpgFw8cUX89e//pVly5axadMmZs+ezRlnnMGgQYPir991112sWrWKVatWcffdd8e3IyIiImLYHb2McAfU19ezYMECnn/+edLS0rjiiiu4/PLLARgxYgSLFi1ixowZAKxbt4758+ezdetWRowYwS233MLxxx8ff6/ly5dz7733sn//fk499VRuvfVWsrJiN9SMRqMsXryY5cuX43a7ueiii/jf//3fdl8BWURERHoXRwsiERERkUSgm3mJiIhI0lNBJCIiIklPBZGIiIgkPRVEckShUIi5c+dSVFTEpEmTWLp0qdOREsKePXu47rrrmDBhAqeddhqLFi0iFAoBsH37di6//HLGjh3Lueeey+uvv+5wWuddffXV3HjjjfHnGzZsYNasWRQUFDBz5kzWr1/vYDpnhcNhbrnlFk466SS+8IUv8POf/zx+iRH1U8yuXbu45pprGDduHFOmTOEPf/hD/DX1UewzdP7557Nq1ar4spa+h9544w3OP/98CgoKuPTSS9m+fXt3x044KojkiBYvXsz69et5+OGHmT9/PkuWLOG5555zOpajbNvmuuuuo76+nkcffZRf/OIX/Otf/+Kee+7Btm2uvfZacnNzeeqpp/jyl7/M97//fXbu3Ol0bMf8/e9/59VXX40/r6ur4+qrr6aoqIjly5dTWFjINddcQ11dnYMpnfOzn/2MN954g9///vfcfffdPPHEEzz++OPqp4P86Ec/IiUlheXLlzN37lzuueceXnjhBfURsX+0/vjHP47f2xNo8Xto586dXHvttcyYMYMnn3yS7Oxsvve975H051jZIs2ora21x4wZY7/11lvxZffdd5/9jW98w8FUztuyZYs9fPhwu7S0NL5s5cqV9qRJk+w33njDHjt2rF1bWxt/7bLLLrPvvfdeJ6I6rqKiwp48ebI9c+ZM+yc/+Ylt27a9bNkye8qUKbZlWbZt27ZlWfbZZ59tP/XUU05GdURFRYV9/PHH26tWrYove+CBB+wbb7xR/XRAZWWlPXz4cPv999+PL/v+979v33LLLUnfR5s3b7YvvPBC+4ILLrCHDx8e/65u6XvonnvuafI9XldXZxcWFjb5rk9GOkIkzdq0aROmaVJYWBhfNn78eEpKSrAsy8FkzsrLy+N3v/sdubm5TZbX1NRQUlLC8ccfT0rKZ7coHD9+fJMrsieTO+64gy9/+csMG/bZzXJLSkoYP358/DpghmEwbty4pOyj4uJi0tLSmDBhQnzZ1VdfzaJFi9RPBwQCAYLBIMuXLycSifDhhx+yevVqRo0alfR99N///peJEyfy+OOPN1ne0vdQSUkJRUVF8deCwSCjR49Omn5rjgoiaVZpaSlZWVnxG+QC5ObmEgqFqKysdC6YwzIyMjjttNPizy3L4pFHHuHkk0+mtLT0kFvC5OTksHv37u6O6bg333yTd955h+9973tNlquPPrN9+3YGDBjA008/zdSpU/niF7/Ifffdh2VZ6qcD/H4/N998M48//jgFBQVMmzaNyZMnM2vWrKTvo69//evMnTv3kBuVt9Qvyd5vzfE4HUASV319fZNiCIg/D4fDTkRKSHfeeScbNmzgySef5A9/+MNh+yzZ+isUCjF//nxuvvlmAoFAk9ea+1wlWx9BbD7Vxx9/zGOPPcaiRYsoLS3l5ptvJhgMqp8OsnXrVs4880y+9a1vsXnzZm699VZOOeUU9VEzWuoX9dvhqSCSZvn9/kN+QRqff/6PXLK68847efjhh/nFL37B8OHD8fv9hxw9C4fDSddfS5Ys4YQTTmhyJK1Rc5+rZOsjAI/HQ01NDXfffTcDBgwAYhNe//KXvzB48GD1E7EjjU8++SSvvvoqgUCAMWPGsGfPHu6//34GDRqkPjqMlr6HmvsdzMjI6K6ICUlDZtKs/Px8KioqME0zvqy0tJRAIJD0vzgAt956Kw899BB33nknX/rSl4BYn5WVlTVZr6ys7JDD073d3//+d1588UUKCwspLCxk5cqVrFy5ksLCQvXRQfLy8vD7/fFiCGDIkCHs2rVL/XTA+vXrGTx4cJMi5/jjj2fnzp3qo2a01C/NvZ6Xl9dtGRORCiJp1qhRo/B4PE0m2hUXFzNmzBhcruT+6CxZsoTHHnuMn//855x33nnx5QUFBbz33ns0NDTElxUXF1NQUOBETMf86U9/YuXKlTz99NM8/fTTTJkyhSlTpvD0009TUFDAmjVr4qf42rbN6tWrk66PIPZ5CYVCbNu2Lb7sww8/ZMCAAeqnA/r27cvHH3/c5IjGhx9+yMCBA9VHzWjpe6igoIDi4uL4a/X19WzYsCHp+y25/6rJEQWDQaZPn86CBQtYt24dL774IkuXLuXSSy91Opqjtm7dyq9//Wuuuuoqxo8fT2lpafwxYcIE+vXrx5w5c9i8eTMPPvgg69at46KLLnI6drcaMGAAgwcPjj9SU1NJTU1l8ODBTJ06laqqKhYuXMiWLVtYuHAh9fX1TJs2zenY3e7YY4/ljDPOYM6cOWzatIl///vfPPjgg1x88cXqpwOmTJmC1+vlpz/9Kdu2bePll1/mN7/5Dd/85jfVR81o6Xto5syZrF69mgcffJDNmzczZ84cBg4cyMSJEx1O7jAnz/mXxFdXV2fPnj3bHjt2rD1p0iT7oYcecjqS4x544AF7+PDhh33Ytm1/9NFH9iWXXGKfcMIJ9nnnnWf/5z//cTix837yk5/Er0Nk27ZdUlJiT58+3R4zZox90UUX2e+9956D6ZxVVVVl33DDDfbYsWPtU045xf7Vr34Vv66O+ilm8+bN9uWXX26PGzfOPuuss+yHHnpIffQ5B1+HyLZb/h565ZVX7HPOOcc+8cQT7csuu8z+5JNPujtywjFsO9kvTSkiIiLJTkNmIiIikvRUEImIiEjSU0EkIiIiSU8FkYiIiCQ9FUQiIiKS9FQQiYiISNJTQSQiIiJJTwWRiIiIJD0VRCLSa5SXl/OPf/wj/nzEiBGsWrXKwUQi0lOoIBKRXuOuu+7i1VdfdTqGiPRAKohEpNfQnYhEpL1UEImIYz799FNGjBjBK6+8wpQpUygsLORnP/sZH3zwATNmzGDs2LFcc8011NTUABAOh1m0aBGnnXYao0ePZsqUKTz++OMA/OpXv2LFihWsWLGCKVOmxLfxzjvvcMEFFzBmzBi+8Y1vsGPHjsNmiUQi/PSnP2XixIkUFhbyne98hz179sRf/+tf/8rUqVMpKCjga1/7Ghs2bIi/tnz5cqZNm8aJJ57IjBkzePvtt+OvTZkyhTvvvJNJkyYxffp0bNvmgw8+4Jvf/CYnnngiX/rSl3j00Uc7tV9FpB2cvbesiCSz7du328OHD7cvvvhie+PGjfbKlSvt4cOH22effbb9+uuv2++88449YcIE+6GHHrJt27Z/9atf2eecc469Zs0a+5NPPrF/+ctf2qNHj7ZLS0vtmpoa+4c//KH9wx/+0C4vL7dtO3YH8NNPP91+66237E2bNtnnn3++/aMf/eiwWR566CH7nHPOsdevX29v2bLF/sY3vmFfd911tm3b9muvvWaPHj3a/vOf/2x/9NFH9q233mpPmjTJDoVC9lNPPWWPHTvWXrFihb1161b7zjvvtMeOHWvv3r3btm3bPvPMM+3TTjvN3rRpk71x40a7vr7enjx5sv2LX/zC3rZtm/3SSy/ZX/jCF+wVK1Z0eX+LSPM8ThdkIiLf+973GDlyJCNHjuS2227jvPPO49RTTwXglFNO4cMPPwRg5MiRnHzyyYwdOxaA73znO9x333189NFHFBUVEQgEAMjOzo6/93e/+10mTpwIwEUXXcRjjz122Ayffvopfr+fAQMG0KdPH26//XYqKysBePzxxzn//PO5+OKLAZg9ezZer5f9+/fzpz/9iW9+85tMnz4dgOuvv563336bRx55hP/93/8F4MILL2TEiBEALFu2jJycHH70ox8BcMwxx7Bjxw7++Mc/xt9DRLqfCiIRcdygQYPiPwcCAQYMGNDkeTgcBuCss87iP//5D7fffjsffvhhfNgqGo02+95HH310/Of09HRCodBh1/vqV7/K3//+dyZNmsSECRM466yzmDFjBgDbtm3ja1/7Wnxdn8/HT37yEwC2bt3Ktdde2+S9xo4dy9atW+PPD96fDz/8kE2bNlFYWBhfFo1Gcbvdze6DiHQ9FUQi4rjPFwMu1+GnN/7iF79g2bJlzJgxg+nTpzN//vwm84UOp7n3+rzjjjuOl19+mVdeeYVXXnmFn//85zzzzDM8+uijeDzNf1X6/f5DlkWjUSzLOuw6pmlyyimncPPNN7cql4h0D02qFpEe47HHHuOmm27i+uuv59xzz6W+vh747OwywzDa/d5PP/00//rXv5g2bRp33HEHv/vd7yguLqa8vJzBgwezadOm+LrRaJQpU6ZQXFzMkCFDKCkpafJeJSUlDBky5LDbGTJkCNu2bWPgwIEMHjyYwYMHs3btWv70pz+1O7uIdJwKIhHpMfr06cO//vUvtm/fzjvvvMPs2bMB4kNqwWCQHTt2NDk7rLWqq6tZuHAhb775Jtu3b2flypUcddRRZGVl8c1vfpO//e1vrFixgo8//phFixZh2zajR4/m8ssv55FHHuHpp59m27Zt3HXXXWzatImLLrrosNu58MILaWho4Oabb2br1q28+uqrLFy4kJycnPZ3jIh0mIbMRKTHuO2221iwYAHnnXce+fn5zJo1C7fbzcaNG5k8eTJf/vKXufbaa7nwwgt566232vTel1xyCbt37+aGG25g//79nHDCCdx///243W5OOukk5s+fz3333UdpaSknnHACv/nNbwgEApx77rmUlZVx7733UlpayqhRo1i6dClDhw497HbS0tL47W9/y2233cb06dPp06cPl1xyCddcc01ndJGItJNh27qSmYiIiCQ3DZmJiIhI0lNBJCIiIklPBZGIiIgkPRVEIiIikvRUEImIiEjSU0EkIiIiSU8FkYiIiCQ9FUQiIiKS9FQQiYiISNJTQSQiIiJJTwWRiIiIJL3/DwD6r6miG2kOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['math score'],color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d9cbe80",
   "metadata": {},
   "source": [
    "#### Below code written is used for the students of different ethnicity,how their scores depeds on diff features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "8e04bf01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    test_preparation_course\n",
      "32                     none\n",
      "34                     none\n",
      "35                completed\n",
      "44                     none\n",
      "50                     none\n",
      "..                      ...\n",
      "962                    none\n",
      "968                    none\n",
      "987               completed\n",
      "990               completed\n",
      "995               completed\n",
      "\n",
      "[140 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='parental_level_of_education'>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Assuming you have a DataFrame called 'df' containing the data\n",
    "# You can filter the rows where ethnicity is 'GROUP E'\n",
    "group_e_students = data[data['ethnicity'] == 'group E']\n",
    "\n",
    "# You can then extract the parental level of education for these students\n",
    "parental_education_group_e = pd.DataFrame(group_e_students['test_preparation_course'])\n",
    "\n",
    "# Print or analyze the parental level of education for 'GROUP E' students\n",
    "print(parental_education_group_e)\n",
    "sns.countplot(data=group_e_students,y=\"parental_level_of_education\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106cc362",
   "metadata": {},
   "source": [
    "### Distribution of parental education "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     gender ethnicity parental_level_of_education         lunch  \\\n",
      "916    male   group E           bachelor's degree      standard   \n",
      "458  female   group E           bachelor's degree      standard   \n",
      "962  female   group E          associate's degree      standard   \n",
      "114  female   group E           bachelor's degree      standard   \n",
      "179  female   group D            some high school      standard   \n",
      "712  female   group D                some college      standard   \n",
      "165  female   group C           bachelor's degree      standard   \n",
      "625    male   group D                some college      standard   \n",
      "903  female   group D           bachelor's degree  free/reduced   \n",
      "149    male   group E          associate's degree  free/reduced   \n",
      "685  female   group E             master's degree      standard   \n",
      "957  female   group D             master's degree      standard   \n",
      "566  female   group E           bachelor's degree  free/reduced   \n",
      "594  female   group C           bachelor's degree      standard   \n",
      "717  female   group C          associate's degree      standard   \n",
      "855  female   group B           bachelor's degree      standard   \n",
      "970  female   group D           bachelor's degree      standard   \n",
      "546  female   group A            some high school      standard   \n",
      "451  female   group E                some college      standard   \n",
      "886  female   group E          associate's degree      standard   \n",
      "403  female   group D                 high school      standard   \n",
      "106  female   group D             master's degree      standard   \n",
      "381    male   group C          associate's degree      standard   \n",
      "995  female   group E             master's degree      standard   \n",
      "263  female   group E                 high school      standard   \n",
      "623    male   group A                some college      standard   \n",
      "864    male   group C          associate's degree      standard   \n",
      "377  female   group D             master's degree  free/reduced   \n",
      "979  female   group C          associate's degree      standard   \n",
      "571    male   group A           bachelor's degree      standard   \n",
      "2    female   group B             master's degree      standard   \n",
      "919    male   group B                some college      standard   \n",
      "562    male   group C           bachelor's degree      standard   \n",
      "229  female   group C                some college      standard   \n",
      "802  female   group E          associate's degree      standard   \n",
      "503  female   group E          associate's degree      standard   \n",
      "373  female   group D                some college      standard   \n",
      "612    male   group C           bachelor's degree      standard   \n",
      "539    male   group A          associate's degree      standard   \n",
      "934    male   group C          associate's degree      standard   \n",
      "268  female   group D          associate's degree      standard   \n",
      "6    female   group B                some college      standard   \n",
      "104    male   group C                some college      standard   \n",
      "580  female   group D            some high school      standard   \n",
      "122  female   group C                some college      standard   \n",
      "501  female   group B          associate's degree      standard   \n",
      "121    male   group B          associate's degree      standard   \n",
      "755  female   group E          associate's degree      standard   \n",
      "334  female   group C           bachelor's degree      standard   \n",
      "474  female   group B          associate's degree      standard   \n",
      "\n",
      "    test_preparation_course  math score  reading score  writing score  \\\n",
      "916               completed         100            100            100   \n",
      "458                    none         100            100            100   \n",
      "962                    none         100            100            100   \n",
      "114               completed          99            100            100   \n",
      "179               completed          97            100            100   \n",
      "712                    none          98            100             99   \n",
      "165               completed          96            100            100   \n",
      "625               completed         100             97             99   \n",
      "903               completed          93            100            100   \n",
      "149               completed         100            100             93   \n",
      "685               completed          94             99            100   \n",
      "957                    none          92            100            100   \n",
      "566               completed          92            100            100   \n",
      "594               completed          92            100             99   \n",
      "717               completed          96             96             99   \n",
      "855                    none          97             97             96   \n",
      "970                    none          89            100            100   \n",
      "546               completed          92            100             97   \n",
      "451                    none         100             92             97   \n",
      "886               completed          93            100             95   \n",
      "403               completed          88             99            100   \n",
      "106                    none          87            100            100   \n",
      "381               completed          87            100             95   \n",
      "995               completed          88             99             95   \n",
      "263                    none          99             93             90   \n",
      "623               completed         100             96             86   \n",
      "864                    none          97             93             91   \n",
      "377               completed          85             95            100   \n",
      "979                    none          91             95             94   \n",
      "571                    none          91             96             92   \n",
      "2                      none          90             95             93   \n",
      "919               completed          91             96             91   \n",
      "562               completed          96             90             92   \n",
      "229               completed          88             95             94   \n",
      "802                    none          87             94             95   \n",
      "503               completed          95             89             92   \n",
      "373               completed          82             97             96   \n",
      "612               completed          94             90             91   \n",
      "539               completed          97             92             86   \n",
      "934               completed          98             87             90   \n",
      "268               completed          88             92             95   \n",
      "6                 completed          88             95             92   \n",
      "104               completed          98             86             90   \n",
      "580                    none          81             97             96   \n",
      "122               completed          88             93             93   \n",
      "501               completed          94             87             92   \n",
      "121               completed          91             89             92   \n",
      "755                    none          84             95             92   \n",
      "334                    none          83             93             95   \n",
      "474               completed          90             90             91   \n",
      "\n",
      "     total_score  average score  \n",
      "916          300     100.000000  \n",
      "458          300     100.000000  \n",
      "962          300     100.000000  \n",
      "114          299      99.666667  \n",
      "179          297      99.000000  \n",
      "712          297      99.000000  \n",
      "165          296      98.666667  \n",
      "625          296      98.666667  \n",
      "903          293      97.666667  \n",
      "149          293      97.666667  \n",
      "685          293      97.666667  \n",
      "957          292      97.333333  \n",
      "566          292      97.333333  \n",
      "594          291      97.000000  \n",
      "717          291      97.000000  \n",
      "855          290      96.666667  \n",
      "970          289      96.333333  \n",
      "546          289      96.333333  \n",
      "451          289      96.333333  \n",
      "886          288      96.000000  \n",
      "403          287      95.666667  \n",
      "106          287      95.666667  \n",
      "381          282      94.000000  \n",
      "995          282      94.000000  \n",
      "263          282      94.000000  \n",
      "623          282      94.000000  \n",
      "864          281      93.666667  \n",
      "377          280      93.333333  \n",
      "979          280      93.333333  \n",
      "571          279      93.000000  \n",
      "2            278      92.666667  \n",
      "919          278      92.666667  \n",
      "562          278      92.666667  \n",
      "229          277      92.333333  \n",
      "802          276      92.000000  \n",
      "503          276      92.000000  \n",
      "373          275      91.666667  \n",
      "612          275      91.666667  \n",
      "539          275      91.666667  \n",
      "934          275      91.666667  \n",
      "268          275      91.666667  \n",
      "6            275      91.666667  \n",
      "104          274      91.333333  \n",
      "580          274      91.333333  \n",
      "122          274      91.333333  \n",
      "501          273      91.000000  \n",
      "121          272      90.666667  \n",
      "755          271      90.333333  \n",
      "334          271      90.333333  \n",
      "474          271      90.333333  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming you have a DataFrame called 'df' containing the data\n",
    "# You can choose which score you want to use for ranking, e.g., 'math_score'\n",
    "score_column = 'total_score'\n",
    "\n",
    "# Sort the DataFrame by the chosen score in descending order\n",
    "sorted_df = data.sort_values(by=score_column, ascending=False)\n",
    "\n",
    "# Specify the number of top-ranking students you want to retrieve\n",
    "top_rank_count = 50  # Change this number as needed\n",
    "\n",
    "# Get the top-ranking students\n",
    "top_ranking_students = sorted_df.head(top_rank_count)\n",
    "\n",
    "# Print or analyze the top-ranking students\n",
    "print(top_ranking_students)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466da348",
   "metadata": {},
   "source": [
    "#### Extracting toppers and failiues in the above code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1037506f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "50ab50ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}\n"
   ]
  }
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