final.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction:\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to develop predictive models to estimate a student's Grade Point Average (GPA) based on their credit hours and average hours studied. GPA is an important indicator of academic performance and is often used for evaluating students' progress and eligibility for various academic opportunities. By accurately predicting a student's GPA, educational institutions can gain insights into their academic performance and make informed decisions regarding academic support, program planning, and resource allocation.\n",
    "\n",
    "In this project, we will explore the application of different regression models, including Decision Tree, Linear Regression, Random Forest, and K Nearest Neighbors Regression, to predict a student's GPA. Each model has its unique approach to capturing the relationship between the predictor variables (credit hours and average hours studied) and the target variable (GPA).\n",
    "\n",
    "The dataset used for training and evaluation consists of historical data from students, including their credit hours, average hours studied, and actual GPA. By leveraging this data, we aim to build accurate models that can generalize well to predict the GPA of future students.\n",
    "\n",
    "The project will involve several steps, including data preprocessing, model selection, training, and evaluation. We will clean the dataset by removing any outliers, missing values, and irrelevant variables that could potentially impact the accuracy of the models. Subsequently, we will explore different regression models and select the most suitable ones based on their performance metrics and characteristics."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict the student's GPA based on the given set of student information, we can use a machine learning algorithm with the appropriate training data. However, since the provided information does not include the actual GPA values or any historical data for training, we cannot directly predict the GPA using this data alone. Instead, we can perform some exploratory data analysis to gain insights and identify potential features that might be useful in predicting the GPA.\n",
    "\n",
    "Let's start by loading the data into a pandas DataFrame and examining its structure and contents:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0     id        lat        lon   gpa  avg_hours_studied  \\\n",
      "0           0  13251  38.878529 -76.776049  3.64                  5   \n",
      "1           1  13251  38.878529 -76.776049  3.65                  5   \n",
      "2           2  13251  38.878529 -76.776049  4.00                  5   \n",
      "3           3  13251  38.878529 -76.776049  4.00                  4   \n",
      "4           4  13251  38.878529 -76.776049  3.30                  4   \n",
      "\n",
      "   parents_income major tutoring semester  year  credits  student_age  \\\n",
      "0     68628.72693  Math       No     Fall  2007       15           26   \n",
      "1     68628.72693  Math       No   Spring  2008       15           27   \n",
      "2     68628.72693  Math       No     Fall  2008        9           27   \n",
      "3     68628.72693  Math       No   Spring  2009        9           28   \n",
      "4     68628.72693  Math       No     Fall  2009       18           28   \n",
      "\n",
      "  student_year  \n",
      "0     Freshman  \n",
      "1     Freshman  \n",
      "2     Sophmore  \n",
      "3     Sophmore  \n",
      "4       Junior  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('my_data.csv')\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## id--a student's id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum id: 13251\n",
      "Maximum id: 113250\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the minimum and maximum values of the 'id' column\n",
    "id_min = df['id'].min()\n",
    "id_max = df['id'].max()\n",
    "\n",
    "print(\"Minimum id:\", id_min)\n",
    "print(\"Maximum id:\", id_max)\n",
    "\n",
    "# Plot a histogram of the 'id' column\n",
    "plt.hist(df['id'], bins=20)\n",
    "plt.xlabel('id')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of id')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "useful for find is there transfer credits or not, it could group by the id \n",
    "and also could useful to count the number of students in school\n",
    "\n",
    "\n",
    "Brief speculation about the usefulness of 'id' going forward\n",
    "The 'id' column represents a unique identifier for each student. It can be useful for tracking individual student records and identifying patterns or trends related to specific students. For example, it can be used to analyze the academic performance or study habits of individual students over time. Additionally, the 'id' column can be used for data aggregation or merging with other datasets based on the student's unique identifier."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lat--student's home latittude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum home latitude: 31.89954590722222\n",
      "Maximum home latitude: 40.19658486280609\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the minimum and maximum values of the 'lat' column\n",
    "lat_min = df['lat'].min()\n",
    "lat_max = df['lat'].max()\n",
    "\n",
    "print(\"Minimum home latitude:\", lat_min)\n",
    "print(\"Maximum home latitude:\", lat_max)\n",
    "\n",
    "# Plot a histogram of the 'lat' column\n",
    "plt.hist(df['lat'], bins=10)\n",
    "plt.xlabel('Home Latitude')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Home Latitudes')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lat for finding the direction of the house"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lon--student's home longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum home longitude: -94.75403348152076\n",
      "Maximum home longitude: -75.79067949290653\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the minimum and maximum values of the 'lon' column\n",
    "lon_min = df['lon'].min()\n",
    "lon_max = df['lon'].max()\n",
    "\n",
    "print(\"Minimum home longitude:\", lon_min)\n",
    "print(\"Maximum home longitude:\", lon_max)\n",
    "\n",
    "# Plot a histogram of the 'lon' column\n",
    "plt.hist(df['lon'], bins=10)\n",
    "plt.xlabel('Home Longitude')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Home Longitudes')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use for find the house direction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## avg_hours_studided--how many hours this student studied every week on average this semester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum average hours studied: 0\n",
      "Maximum average hours studied: 10000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the minimum and maximum values of the 'avg_hours_studied' column\n",
    "hours_min = df['avg_hours_studied'].min()\n",
    "hours_max = df['avg_hours_studied'].max()\n",
    "\n",
    "print(\"Minimum average hours studied:\", hours_min)\n",
    "print(\"Maximum average hours studied:\", hours_max)\n",
    "\n",
    "# Plot a histogram of the 'avg_hours_studied' column\n",
    "plt.hist(df['avg_hours_studied'], bins=10)\n",
    "plt.xlabel('Average Hours Studied')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Average Hours Studied')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The 'avg_hours_studied' column represents the average number of hours a student studied per week during a specific semester. The minimum and maximum values provide insights into the range of study hours covered in the dataset. The histogram shows the distribution of average hours studied, indicating the frequency of students falling within different study hour ranges.\n",
    "\n",
    "The 'avg_hours_studied' column can be useful for several purposes. Firstly, it allows the examination of study habits and patterns among students. By analyzing the distribution of average hours studied, it becomes possible to identify common trends or clusters of students with different study intensities.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## parents_income--the student's parent's income in dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum parent's income: 18229.30176674215\n",
      "Maximum parent's income: 196273.33724594\n"
     ]
    },
    {
     "data": {
      "image/png": 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oCEZGRoiPj4dCocBrr72mde9VGTNmDA4fPoz3338fEydOhImJCTZt2gQ7Ozv84x//gImJCfbu3YsPP/wQ06ZNw0svvYRvv/0WCQkJ+PDDD8W9XdqwsrLCBx98gKioKDx8+BDdu3dHZmYmoqKiIJPJ4OrqqsNOiWqGoYxIwi5evIjhw4cDKN+7YWNjg7Zt22LVqlWVTlJ/Uq9evRAeHo5t27aJJ/d36dIF//znP8Vz0EaNGoULFy4gJCQEK1eu1Hhjrs6wYcNQXFyMKVOmQKFQYODAgZg9e7Z4zpCvry/CwsKwa9cu/Oc//4G7uzvWr1+vcQFVc3NzjB07Fvv27cN3331X5YnVy5cvx9///nckJCQgLi4OdnZ2CA4OxpQpU2q1pw8Apk6dihYtWmD37t04cOAALC0t0bdvX4SGhj73XKTaGDx4MG7duoWEhATs3bsX9vb28Pf3x8iRI/HJJ5/g6tWr4ic+n9a0aVPs2bMHa9aswerVq1FQUABHR0fMnDkT48aNe6E6hgwZglOnTmHKlCmYNm2axnlVrq6uiI2NxYYNGzBjxgyoVCp06NAB27Zte+43GmijZcuW2Lt3L1avXo158+ZBoVCgW7duWL16tfg83bVrF9asWYPo6Gg8fPgQL7/8MpYvX46333671vcfGhoKW1tb7N27F1u3bkXz5s3Ro0cPzJgxQ+MDAkT1RSbwW2mJiIiI9I7nlBERERFJAEMZERERkQQwlBERERFJAEMZERERkQQwlBERERFJAEMZERERkQQwlBERERFJAC8eq0eCIECtluZl4uRymWRrqw+G3D97N8zeAcPun70bZu/Ai/cvl8vEC2XrGkOZHqnVAh48KNR3GZUYG8thZdUM+fmPUFb2/O/ja4wMuX/2bpi9A4bdP3s3zN4B7fq3tm4GI6O6CWU8fElEREQkAQxlRERERBLAUEZEREQkAQxlRERERBLAUEZEREQkAXoPZdnZ2Zg9ezZ8fHzg5eWF8ePH4+rVq+L8S5cuISgoCJ06dULv3r0RFxensb5arUZ0dDT8/Pzg6emJcePGIT09XWOZ+hiDiIiIqDb0HsomTZqEmzdvYsuWLTh48CBMTU0xZswYFBUVIScnB2PHjkWbNm2QkJCAqVOnIioqCgkJCeL6MTExiI+Px7Jly7Bv3z7IZDKEhISgtLQUAOptDCIiIqLa0Ot1ynJyctCqVStMmjQJr7zyCgBg8uTJeOutt3DlyhUkJiZCoVBg8eLFMDY2hpOTE9LT07FlyxYMHToUpaWl2LZtG2bPng1/f38AQGRkJPz8/HD8+HH0798f+/fvr/MxiIiIiGpLr3vKrKysEBERIQay+/fvIy4uDg4ODmjXrh1SUlLg7e0NY+P/ZUcfHx9cv34d2dnZSEtLQ2FhIXx8fMT5SqUSbm5uSE5OBoB6GYOIiIiotvR++LLCJ598Al9fX3zzzTdYvnw5mjZtioyMDDg4OGgsZ2dnBwC4c+cOMjIyAAAtW7astMzdu3cBoF7GICIiIqotyXzN0ujRozF8+HD861//wpQpU7B3714UFxdDoVBoLGdiYgIAKCkpQVFREQBUuUxeXh4A1MsYtWFsLJlcLDIykmv8a2gMuX/2bpi9A4bdP3s3zN4B6fUvmVDWrl07AMDSpUtx7tw57N69G6ampuLJ9hUqQlDTpk1hamoKACgtLRV/rljGzMwMAOplDG3J5TJYWTXTev26plSa6bsEvTLk/tm74TLk/tm74ZJK/3oNZdnZ2UhMTMT//d//wcjICAAgl8vh5OSErKwsODg4ICsrS2Odit/t7e1RVlYmTmvdurXGMq6urgBQL2NoS60WkJ//SOv164qRkRxKpRny84ugUhneF9Qacv/s3TB7Bwy7f/ZumL0D2vWvVJrV2Z41vYayrKwszJw5EzY2NujRowcA4PHjx7h48SICAgLQokULxMfHQ6VSiaEtMTERbdu2hY2NDSwsLGBubo6kpCQxUOXn5+PixYsICgoCAHh7e9f5GLVR02+l1weVSi3p+uqaIffP3g2zd8Cw+2fvhtk7IJ3+9XoQ1dXVFa+++iqWLFmClJQUXL58GWFhYcjPz8eYMWMwdOhQPHz4EAsWLMDVq1dx6NAh7Ny5ExMmTABQfh5YUFAQwsPDcfLkSaSlpWH69OlwcHBAYGAgANTLGERERES1JRMEQdBnAQUFBVizZg1OnDiBgoICdO3aFXPnzhUvk/Hbb79h+fLluHjxImxtbTFu3DhxDxYAqFQqRERE4NChQyguLoa3tzcWLlyIVq1aicvUxxjaUKnUePCgsFZj1AVjYzmsrJohJ6dQEv9zqG817V8ul0Eul9VjZbqhVgtQq6t+2Rvytjfk3gHD7p+9G2bvgHb9W1s3q7PDl3oPZYaMoUyaatK/XC6DpWVTyXxi50WoVGrk5j6qMpgZ8rY35N4Bw+6fvRtm74D0QplkPn1J1JDI5TIYGckRvicVtzIL9F1OjbWyt8CsUV0gl8ueubeMiIj0g6GMqBZuZRbg2u08fZdBRESNQMM79kJERETUCDGUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUmA3kNZbm4uFi5ciF69eqFz58549913kZKSIs6fN28eXFxcNG69evUS56vVakRHR8PPzw+enp4YN24c0tPTNe7j0qVLCAoKQqdOndC7d2/ExcVpzNfFGERERES1ofdQNmPGDPz666+IiIjAwYMH4e7ujvfffx/Xrl0DAPzxxx+YOHEizpw5I94OHz4srh8TE4P4+HgsW7YM+/btg0wmQ0hICEpLSwEAOTk5GDt2LNq0aYOEhARMnToVUVFRSEhI0OkYRERERLWh11CWnp6Os2fPYtGiRejatStefvllLFiwAPb29vj666+hUqlw9epVeHh4wNbWVrxZW1sDAEpLS7Ft2zZMnToV/v7+cHV1RWRkJDIzM3H8+HEAwP79+6FQKLB48WI4OTlh6NChGDNmDLZs2aKzMYiIiIhqS6+hzMrKCps3b0aHDh3EaTKZDIIgIC8vDzdu3EBJSQmcnJyqXD8tLQ2FhYXw8fERpymVSri5uSE5ORkAkJKSAm9vbxgbG4vL+Pj44Pr168jOztbJGERERES1ZVz9InVHqVTC399fY9rRo0fx119/4dVXX8Xly5chk8mwc+dOnD59GnK5HP7+/ggNDYWFhQUyMjIAAC1bttQYw87ODnfv3gUAZGRkwNnZudJ8ALhz545OxrCxsdH6MTA21vsR5EqMjOQa/xqamvTf0B+bZ9VvyNvekHsHDLt/9m6YvQPS61+voexpqampmD9/Pvr06YOAgABER0dDLpfD0dERsbGxSE9Px2effYbLly9j586dKCoqAgAoFAqNcUxMTJCXlwcAKC4urnI+AJSUlOhkDG3J5TJYWTXTev26plSa6bsEvWrM/VfXW2PuvTqG3Dtg2P2zd8Mllf4lE8pOnDiBWbNmwdPTExEREQCAqVOnYsyYMVAqlQAAZ2dn2NraYvjw4Th//jxMTU0BlJ8XVvEzUB6UzMzKH2BTU1PxhP0n5wNA06ZNdTKGttRqAfn5j7Rev64YGcmhVJohP78IKpVa3+XUu5r0X7FMQ/Ws3gx52xty74Bh98/eDbN3QLv+lUqzOtuzJolQtnv3bixfvhyBgYEIDw8X90rJZDIxkFWoOIyYkZEhHnLMyspC69atxWWysrLg6uoKAHBwcEBWVpbGGBW/29vbo6ysrNZj1EZZmXRfBCqVWtL11bXG3H91vTXm3qtjyL0Dht0/ezfM3gHp9K/3g6h79+7F0qVLMWrUKKxdu1bjMOHMmTPx/vvvayx//vx5AEC7du3g6uoKc3NzJCUlifPz8/Nx8eJFdO3aFQDg7e2N1NRUqFQqcZnExES0bdsWNjY2OhmDiIiIqLb0GsquX7+OFStWIDAwEBMmTEB2djbu3buHe/fuoaCgAAMGDMDZs2exceNG/PXXXzh16hTmz5+PAQMGwMnJCQqFAkFBQQgPD8fJkyeRlpaG6dOnw8HBAYGBgQCAoUOH4uHDh1iwYAGuXr2KQ4cOYefOnZgwYQIA6GQMIiIiotrS6+HLY8eO4fHjxzh+/Lh4TbAKgwcPxqpVqxAVFYXY2FjExsbCwsICAwcORGhoqLjctGnTUFZWho8//hjFxcXw9vZGXFycuMfNxsYGW7duxfLlyzF48GDY2tpizpw5GDx4sE7HICIiIqoNmSAIgr6LMFQqlRoPHhTqu4xKjI3lsLJqhpycQkkcY69vNem/YpnQiO9w7XZePVeoPSfH5lg7o/czezPkbW/IvQOG3T97N8zeAe36t7ZuVmcn+uv9nDIiIiIiYigjIiIikgSGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgBjfRdARPXPyKjq/49VTH/WfH1RqwWo1YK+yyAiqlMMZUQGxNLCBGq1AKXS7LnLVTe/vqlUauTmPmIwI6JGjaGMyICYmzWBXC5D+J5U3Mos0Hc5NdLK3gKzRnWBXC5jKCOiRo2hjMgA3coswLXbefoug4iIniCtE0eIiIiIDBRDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSYDeQ1lubi4WLlyIXr16oXPnznj33XeRkpIizr906RKCgoLQqVMn9O7dG3FxcRrrq9VqREdHw8/PD56enhg3bhzS09M1lqmPMYiIiIhqQ++hbMaMGfj1118RERGBgwcPwt3dHe+//z6uXbuGnJwcjB07Fm3atEFCQgKmTp2KqKgoJCQkiOvHxMQgPj4ey5Ytw759+yCTyRASEoLS0lIAqLcxiIiIiGrDWJ93np6ejrNnz+Jf//oXOnfuDABYsGABTp8+ja+//hqmpqZQKBRYvHgxjI2N4eTkhPT0dGzZsgVDhw5FaWkptm3bhtmzZ8Pf3x8AEBkZCT8/Pxw/fhz9+/fH/v3763wMIiIiotrS654yKysrbN68GR06dBCnyWQyCIKAvLw8pKSkwNvbG8bG/8uOPj4+uH79OrKzs5GWlobCwkL4+PiI85VKJdzc3JCcnAwA9TIGERERUW3pNZQplUr4+/tDoVCI044ePYq//voLr776KjIyMuDg4KCxjp2dHQDgzp07yMjIAAC0bNmy0jJ3794FgHoZg4iIiKi29Hr48mmpqamYP38++vTpg4CAAKxcuVIjsAGAiYkJAKCkpARFRUUAUOUyeXl5AIDi4uI6H6M2jI31flpfJUZGco1/DU1N+jfUx0af6vox5/PecPtn74bZOyC9/iUTyk6cOIFZs2bB09MTERERAABTU1PxZPsKFSGoadOmMDU1BQCUlpaKP1csY2ZmVm9jaEsul8HKqpnW69c1pdJM3yXolaH3LzX1tT0Mfbsbcv/s3XBJpX9JhLLdu3dj+fLlCAwMRHh4uLhXysHBAVlZWRrLVvxub2+PsrIycVrr1q01lnF1da23MbSlVgvIz3+k9fp1xchIDqXSDPn5RVCp1Poup97VpP+KZaj+1PXzkc97w+2fvRtm74B2/SuVZnW2Z03voWzv3r1YunQpgoODMX/+fMjl/2vU29sb8fHxUKlUMDIyAgAkJiaibdu2sLGxgYWFBczNzZGUlCQGqvz8fFy8eBFBQUH1NkZtlJVJ90WgUqklXV9dM/T+paa+toehb3dD7p+9G2bvgHT61+tB1OvXr2PFihUIDAzEhAkTkJ2djXv37uHevXsoKCjA0KFD8fDhQyxYsABXr17FoUOHsHPnTkyYMAFA+XlgQUFBCA8Px8mTJ5GWlobp06fDwcEBgYGBAFAvYxARERHVll73lB07dgyPHz/G8ePHcfz4cY15gwcPxqpVq7B161YsX74cgwcPhq2tLebMmYPBgweLy02bNg1lZWX4+OOPUVxcDG9vb8TFxYmHQG1sbOplDCIiIqLakAmCIOi7CEOlUqnx4EGhvsuoxNhYDiurZsjJKZTE7tz6VpP+K5YJjfgO127n1XOF2vP3csSsoK4Nqm4nx+ZYO6N3nT8f+bw33P7Zu2H2DmjXv7V1szo7p0wanwElIiIiMnAMZUREREQSwFBGREREJAEMZUREREQSwFBGREREJAEMZUREREQSwFBGREREJAEMZUREREQSwFBGREREJAEMZUREREQSwFBGREREJAFahbINGzbg7t27uq6FiIiIyGBpFcp27tyJPn36YOzYsfjqq69QUlKi67qIiIiIDIpWoezMmTMIDw9HkyZNMHfuXPj6+mLhwoX45ZdfdF0fERERkUEw1mYlhUKBfv36oV+/fsjKysKXX36Jo0eP4sCBA2jTpg2GDBmCIUOGwMbGRtf1EhERETVKtT7R387ODu+99x4mTpyIrl274vr164iIiIC/vz8++eQTPHz4UBd1EhERETVqWu0pq/DTTz/hiy++wLFjx/Do0SP4+PiIgezUqVP49NNPkZGRgS1btuiqXiIiIqJGSatQFhkZia+++gp3795Fy5YtMWbMGAwZMgR/+9vfxGX69euHP/74A//85z91ViwRERFRY6VVKNu+fTtef/11LF26FD179oRMJqtyOQ8PD4SGhtamPiIiIiKDoFUo+/7779G8eXPcu3dPDGR5eXm4e/cuXF1dxeVef/113VRJRERE1MhpdaK/XC7H2LFjERwcLE779ddf8Y9//AOTJ09GUVGRzgokIiIiMgRahbLVq1fjypUrmDFjhjjNx8cHMTExuHDhAqKjo3VWIBEREZEh0CqUffvttwgLC8Mbb7whTlMoFAgICMCMGTNw9OhRnRVIREREZAi0CmWFhYVQKpVVzrOxsUFOTk6tiiIiIiIyNFqFMnd3dyQkJFQ579ChQ3BxcalVUURERESGRqtPX06aNAkhISEYMmQIAgMDYWNjgwcPHuDkyZP4/fffERsbq+s6iYiIiBo1rUKZr68vNm7ciOjoaERHR0MQBMhkMrRv3x4xMTHo1auXruskIiIiatS0/polf39/+Pv7o6SkBLm5ubCwsEDTpk11WRsRERGRwajVd1/m5eWhqKgIarUaubm5yM3NFec9+ZVLRERERPR8WoWyGzduYO7cufj111+fucylS5e0LoqIiIjI0GgVypYuXYobN27gww8/hIODA+RyrT7ESURERET/P61CWUpKCpYvX44BAwbouh4iIiIig6TVLi5zc3M0b95c17UQERERGSytQtlbb72FPXv2QBAEXddDREREZJC0OnxpZmaG1NRUBAYGwsPDA6amphrzZTIZVqxYoZMCiYiIiAyBVqHs3//+NywsLKBWq6v8BKZMJqt1YURERESGRKtQ9u233+q6DiIiIiKDVqtrWajVaqSlpeH06dN4+PChxsVjiYiIiKjmtL6i/xdffIE1a9YgKysLMpkMBw8exLp169CkSROsWbMGCoVCl3USERERNWpa7Sk7cuQIwsLC4OPjg8jISPFTmG+88QZOnz6NmJgYnRZJRERE1NhptacsNjYWI0aMwOLFi6FSqcTpQ4YMQXZ2Nvbv34/Q0FBd1UhERETU6Gm1p+z69esIDAyscp6npycyMzNrVRQRERGRodEqlNnY2ODatWtVzrt27RpsbGxqVRQRERGRodEqlPXr1w/R0dH45ptvUFpaCqD82mQXLlxATEwM+vbtq9MiiYiIiBo7rc4pCw0NxeXLlxEaGgq5vDzXBQcH49GjR+jatSs++ugjnRZJRERE1NhpFcoUCgW2bt2Ks2fPIjExEXl5ebCwsEC3bt3g7+/PK/oTERERvSCtr1MGAL6+vvD19dVVLUREREQGS6tQtn79+mqX+fDDD7UZmoiIiMgg6TyUmZubw87OTqtQFhMTg8TEROzatUucNm/ePBw6dEhjOXt7e5w+fRpA+Vc9rV+/HgcOHEB+fj66dOmCRYsW4e9//7u4/KVLl7B8+XJcuHABlpaWCA4Oxvvvvy/O18UYRERERLWh1acv09LSKt1+/vlnbNmyBc2bN8cnn3zywmPu2LED0dHRlab/8ccfmDhxIs6cOSPeDh8+LM6PiYlBfHw8li1bhn379kEmkyEkJET8VGhOTg7Gjh2LNm3aICEhAVOnTkVUVBQSEhJ0OgYRERFRbdTqC8mf1LRpU/j5+WHKlCn4/PPPa7xeZmYmPvjgA0RFRaFt27Ya81QqFa5evQoPDw/Y2tqKN2trawBAaWkptm3bhqlTp8Lf3x+urq6IjIxEZmYmjh8/DgDYv38/FAoFFi9eDCcnJwwdOhRjxozBli1bdDYGERERUW3pLJRVaNmy5TMvLFuV33//Hc2bN8eXX34JT09PjXk3btxASUkJnJycqlw3LS0NhYWF8PHxEacplUq4ubkhOTkZAJCSkgJvb28YG//vSK2Pjw+uX7+O7OxsnYxBREREVFu1+vTlkwRBwN27d7FlyxY4OjrWeL2AgAAEBARUOe/y5cuQyWTYuXMnTp8+DblcDn9/f4SGhsLCwgIZGRkAyoPgk+zs7HD37l0AQEZGBpydnSvNB4A7d+7oZIzafIOBsbHOc3GtGRnJNf41NDXp31AfG32q68ecz3vD7Z+9G2bvgPT61yqUubq6PvNaZIIgvNDhy+e5cuUK5HI5HB0dERsbi/T0dHz22We4fPkydu7ciaKiIgDl1017komJCfLy8gAAxcXFVc4HgJKSEp2MoS25XAYrq2Zar1/XlEozfZegV4bev9TU1/Yw9O1uyP2zd8Mllf61CmVTpkypMpSZm5ujd+/eaNOmTW3rAgBMnToVY8aMgVKpBAA4OzvD1tYWw4cPx/nz52Fqagqg/Lywip+B8qBkZlb+AJuamoon7D85Hyg/D04XY2hLrRaQn/9I6/XripGRHEqlGfLzi6BSqfVdTr2rSf8Vy1D9qevnI5/3hts/ezfM3gHt+lcqzepsz5pWoWzq1Km6rqNKMplMDGQVKg4jZmRkiIccs7Ky0Lp1a3GZrKwsuLq6AgAcHByQlZWlMUbF7/b29igrK6v1GLVRVibdF4FKpZZ0fXXN0PuXmvraHoa+3Q25f/ZumL0D0ulfq1B2586dF1r+b3/7mzZ3g5kzZyI3NxdxcXHitPPnzwMA2rVrh5deegnm5uZISkoSA1V+fj4uXryIoKAgAIC3tzfi4+OhUqlgZGQEAEhMTETbtm1hY2MDCwuLWo9BREREVFtahbKAgIAX+n7LS5cuaXM3GDBgACZNmoSNGzeif//+uH79Oj799FMMGDBA/ERmUFAQwsPDYW1tDUdHR6xevRoODg4IDAwEAAwdOhRbt27FggUL8MEHH+C3337Dzp07sWTJEgDl55LVdgwiIiKi2tIqlK1duxaLFi2Cu7s7Bg0aBHt7e+Tk5ODbb7/F0aNHMWnSpBf6BOazvPbaa4iKikJsbCxiY2NhYWGBgQMHIjQ0VFxm2rRpKCsrw8cff4zi4mJ4e3sjLi5OPDHfxsYGW7duxfLlyzF48GDY2tpizpw5GDx4sE7HICIiIqoNmSAIwouuNHHiRFhZWWHlypWV5q1cuRJXrlzBtm3bdFJgY6ZSqfHgQaG+y6jE2FgOK6tmyMkplMQx9vpWk/4rlgmN+A7XbufVc4Xa8/dyxKygrg2qbifH5lg7o3edPx/5vDfc/tm7YfYOaNe/tXWzOjvRX6tRf/zxRwwYMKDKeb169UJqamqtiiIiIiIyNFqFMisrK5w7d67KeWfPnq31JxKJiIiIDI1W55S9/fbb2LhxI4qKihAQEABra2vcv38fR44cQXx8PBYuXKjrOomIiIgaNa1C2eTJk1FQUIAdO3aIl6sQBAFmZmaYMWMGRowYodMiiYiIiBo7rUKZTCbD3LlzMXnyZJw7dw55eXmwsrJCp06dYG5urusaiYiIiBq9Wn0hubm5ufjF3J06dRKvjk9EREREL0brUPbFF19gzZo1uHfvHmQyGQ4cOIB169ahSZMmWLNmTaUv8CYiIiKiZ9Pq05dHjhxBWFgYfHx8EBERAbW6/Noeb7zxBk6fPo2YmBidFklERETU2Gm1pyw2NhYjRozA4sWLoVKpxOlDhgxBdnY29u/fr3HVfSIiIiJ6Pq32lF2/fl38XsineXp6IjMzs1ZFERERERkarUKZjY0Nrl27VuW8a9euwcbGplZFERERERkarQ5f9uvXD9HR0bCzs4O/vz+A8stkXLhwATExMc/8CiYiIm3V1XfNPT2+ru5HrRagVr/wVwsTkQHTKpSFhobi8uXLCA0NhVxe/gcsODgYjx49QteuXfHRRx/ptEgiMlyWFiZQqwUolWb1cn+6uh+VSo3c3EcMZkRUY1qFMoVCga1bt+Ls2bP48ccfkZubCwsLC3Tr1g3+/v6QyWS6rpOIDJS5WRPI5TKE70nFrcwCfZdTI63sLTBrVBfI5TKGMiKqMa1C2cSJE/Hee+/B19cXvr6+uq6JiKiSW5kFuHY7T99lEBHVGa1OnkhOToaRkZGuayEiIiIyWFqFMl9fXxw4cAAlJSW6roeIiIjIIGl1+NLExARHjx7F8ePH0apVq0qXwJDJZNi5c6dOCiQiIiIyBFqFsoyMDHh5eYm/C4LmiaxP/05EREREz1fjUPbVV1/Bz88PlpaW2LVrV13WRERERGRwanxO2Zw5c/DXX39pTIuNjcX9+/d1XhQRERGRoalxKHv6kKRKpUJUVBS/55KIiIhIB2r1fSI8d4yIiIhIN+r2y+SIiIiIqEYYyoiIiIgkoNahjN9zSURERFR7L3SdsilTpkChUGhMmzhxIpo0aaIxTSaT4cSJE7WvjoiIiMhA1DiUDR48uC7rICIiIjJoNQ5lK1eurMs6iIiIiAwaT/QnIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJkFQoi4mJQXBwsMa0S5cuISgoCJ06dULv3r0RFxenMV+tViM6Ohp+fn7w9PTEuHHjkJ6eXu9jEBE9zchIDmPjhnEzMpLU2wGRQTLWdwEVduzYgejoaHh7e4vTcnJyMHbsWLz++utYsmQJzp07hyVLlsDS0hJDhw4FUB7k4uPjsXLlStjb22P16tUICQnB119/DYVCUW9jEBFVsLQwgVotQKk003cpL0StFiCTyfRdBpHB0nsoy8zMxIIFC5Camoq2bdtqzNu/fz8UCgUWL14MY2NjODk5IT09HVu2bMHQoUNRWlqKbdu2Yfbs2fD39wcAREZGws/PD8ePH0f//v3rZQwioieZmzWBXC5D+J5U3Mos0Hc5NdLK3gKzRnWBXM5QRqQveg9lv//+O5o3b44vv/wSGzZswO3bt8V5KSkp8Pb2hrHx/8r08fHBpk2bkJ2djdu3b6OwsBA+Pj7ifKVSCTc3NyQnJ6N///71MoaNjU1dPTxE1IDdyizAtdt5+i6DiBoIvYeygIAABAQEVDkvIyMDzs7OGtPs7OwAAHfu3EFGRgYAoGXLlpWWuXv3br2NwVBGREREtaX3UPY8xcXFUCgUGtNMTEwAACUlJSgqKgKAKpfJy8urtzFqw9hYeifXVpzwa6gn/takf0N9bKjxk8tlkvy7VJcM+W+eIfcOSK9/SYcyU1NTlJaWakyrCEFNmzaFqakpAKC0tFT8uWIZMzOzehtDW3K5DFZWzbRev641tJOUdc3Q+yfDZG5uWv1CjZQhv+YNuXdAOv1LOpQ5ODggKytLY1rF7/b29igrKxOntW7dWmMZV1fXehtDW2q1gPz8R1qvX1eMjORQKs2Qn18ElUqt73LqXU36r1iGqLF5+LAYjx+r9F1GvTLkv3mG3DugXf9KpVmd7VmTdCjz9vZGfHw8VCoVjIyMAACJiYlo27YtbGxsYGFhAXNzcyQlJYmBKj8/HxcvXkRQUFC9jVEbZWXSfRGoVGpJ11fXDL1/MkxqtWCwz3tDfs0bcu+AdPqXxkHUZxg6dCgePnyIBQsW4OrVqzh06BB27tyJCRMmACg/DywoKAjh4eE4efIk0tLSMH36dDg4OCAwMLDexiAiIiKqLUnvKbOxscHWrVuxfPlyDB48GLa2tpgzZw4GDx4sLjNt2jSUlZXh448/RnFxMby9vREXFyeemF9fYxARERHVhqRC2apVqypN69ixI/bt2/fMdYyMjDB79mzMnj37mcvUxxhEREREtSHpw5dEREREhoKhjIiIiEgCGMqIiIiIJIChjIiIiEgCGMqIiIiIJIChjIiIiEgCGMqIiIiIJIChjIiIiEgCGMqIiIiIJIChjIiIiEgCJPU1S0REpF9yuQzGxg3n/+tqtQC1WtB3GUQ6wVBGRESwtDCBWi3A3NxU36W8EJVKjdzcRwxm1CgwlBEREczNmkAulyF8TypuZRbou5waaWVvgVmjukAulzGUUaPAUEZERKJbmQW4djtP32UQGaSGc+IAERERUSPGUEZEREQkAQxlRERERBLAUEZEREQkATzRnyRBLpdBLpfpuwwAgJGRXOPf5y1DRESkKwxlpHdyuQyWlk0lF3SUSjN9l0BERAaEoYz0Ti6XwchI3qCuj9TZ1Q7v9XPTdxlERNSIMJSRZDSk6yO1sjPXdwlERNTISOt4EREREZGBYigjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgCGMiIiIiIJYCgjIiIikgBjfRdARERUG0ZGtdu/ULF+bcepKbVagFot1Mt9UcPCUEZERA2SpYUJ1GoBSqWZTsbT1TjVUanUyM19xGBGlTCUERFRg2Ru1gRyuQzhe1JxK7NA3+XUSCt7C8wa1QVyuYyhjCphKCMiogbtVmYBrt3O03cZRLXGE/2JiIiIJIChjIiIiEgCGMqIiIiIJIChjIiIiEgCGkQou337NlxcXCrdDhw4AAC4dOkSgoKC0KlTJ/Tu3RtxcXEa66vVakRHR8PPzw+enp4YN24c0tPTNZbRxRhERERE2moQoeyPP/6AiYkJvv/+e5w5c0a8DRw4EDk5ORg7dizatGmDhIQETJ06FVFRUUhISBDXj4mJQXx8PJYtW4Z9+/ZBJpMhJCQEpaWlAKCTMYiIiIhqo0FcEuPy5cto27Yt7OzsKs3buXMnFAoFFi9eDGNjYzg5OSE9PR1btmzB0KFDUVpaim3btmH27Nnw9/cHAERGRsLPzw/Hjx9H//79sX///lqPQURERFQbDWZPWbt27aqcl5KSAm9vbxgb/y9f+vj44Pr168jOzkZaWhoKCwvh4+MjzlcqlXBzc0NycrLOxiAiIiKqjQazp8zW1hYjR47EjRs38Pe//x2TJ0+Gn58fMjIy4OzsrLF8xR61O3fuICMjAwDQsmXLSsvcvXsXAHQyhraMjaWXi+v7e+Dq636IiKRCKn/36vvvvdRIrX/Jh7LS0lLcuHEDZmZmmDNnDpo2bYovv/wSISEh2L59O4qLi6FQKDTWMTExAQCUlJSgqKgIAKpcJi+v/ArQuhhDG3K5DFZWzbRev67V1/fAEREZGqn9fZVaPfVNKv1LPpQpFAokJyfD2NhYDEUdOnTAtWvXEBcXB1NT00on25eUlAAAmjZtClNTUwDl4a7i54plzMzKN4IuxtCGWi0gP/+R1uvXFSMjOZRKM+TnF0GlUtfb/RERGYr6+vtanfr+ey812vSvVJrV2Z41yYcyoDwYPc3Z2RlnzpyBg4MDsrKyNOZV/G5vb4+ysjJxWuvWrTWWcXV1BQCdjKGtsjLpvghUKrWk6yMiaqik9vdVavXUN6n0L42DqM+RlpYGLy8vpKSkaEy/cOEC2rVrB29vb6SmpkKlUonzEhMT0bZtW9jY2MDV1RXm5uZISkoS5+fn5+PixYvo2rUrAOhkDCIiIqLakHwoc3Z2xiuvvIIlS5YgJSUF165dw8qVK3Hu3DlMnDgRQ4cOxcOHD7FgwQJcvXoVhw4dws6dOzFhwgQA5Yc/g4KCEB4ejpMnTyItLQ3Tp0+Hg4MDAgMDAUAnYxARERHVhuQPX8rlcsTGxiI8PByhoaHIz8+Hm5sbtm/fDhcXFwDA1q1bsXz5cgwePBi2traYM2cOBg8eLI4xbdo0lJWV4eOPP0ZxcTG8vb0RFxcnnqNmY2NT6zGIiIiIakPyoQwArK2tsWLFimfO79ixI/bt2/fM+UZGRpg9ezZmz55dp2MQERERaUvyhy+JiIiIDAFDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSQBDGREREZEEMJQRERERSYCxvgsgIiIyNEZG0tgnUlFHdfWo1QLUaqE+SjJoDGVERET1xNLCBGq1AKXSTN+laKiuHpVKjdzcRwxmdYyhjIiIqJ6YmzWBXC5D+J5U3Mos0Hc5NdLK3gKzRnWBXC5jKKtjDGVERET17FZmAa7dztN3GSQx0jioTURERGTgGMqIiIiIJIChjIiIiEgCGMqIiIiIJIChjIiIiEgCGMqIiIiIJIChjIiIiEgCGMqIiIiIJICh7AWo1WpER0fDz88Pnp6eGDduHNLT0/VdFhERETUCDGUvICYmBvHx8Vi2bBn27dsHmUyGkJAQlJaW6rs0IiIiauD4NUs1VFpaim3btmH27Nnw9/cHAERGRsLPzw/Hjx9H//799Vzh/8jlMsjlMq3XNzKSa/xb1+rrfoiIiKSMoayG0tLSUFhYCB8fH3GaUqmEm5sbkpOTJRPK5HIZLC2b6iToKJVmOqiIiIiIaoKhrIYyMjIAAC1bttSYbmdnh7t37+qjpCrJ5TIYGckRvicVtzIL9F1OjXR2tcN7/dz0XQYRET1HQzuqoVYLUKsFfZfxQmSCIDSsivXkiy++wJw5c3Dp0iXI5f97Ys6ZMwdZWVnYsWPHC48pCLp/wshkgFwuR25BCcpUap2OXVdMFEawaKpgzfWgIdbNmusHa64fDbFmRRM5LJoqIJNpf1qMPtTkPbbiPVOtVqOmaUgul9XZY8E9ZTVkamoKoPzcsoqfAaCkpARmZtod5pPJZDAyqpsNa2lhUifj1iXWXH8aYt2suX6w5vrREGtuaF7kPfbJnS36JI0qGoCKw5ZZWVka07OysuDg4KCPkoiIiKgRYSirIVdXV5ibmyMpKUmclp+fj4sXL6Jr1656rIyIiIgaAx6+rCGFQoGgoCCEh4fD2toajo6OWL16NRwcHBAYGKjv8oiIiKiBYyh7AdOmTUNZWRk+/vhjFBcXw9vbG3FxcVAoFPoujYiIiBo4fvqSiIiISAJ4ThkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUEREREUkAQxkRERGRBDCUNSK5ublYuHAhevXqhc6dO+Pdd99FSkqKOH/evHlwcXHRuPXq1Uucr1arER0dDT8/P3h6emLcuHFIT0/XuI9Lly4hKCgInTp1Qu/evREXF6cxvyZj1IXbt29X6s3FxQUHDhzQWd1S7T0pKanK3l1cXNCnTx8AjXfbx8TEIDg4WOd11scYtVVV799++y2GDh0KLy8vBAQE4LPPPkNxcbE4v7rXSUPuXSrP8bruHajcf3Bw8DP/Bhw+fBhAw9721b23NarXvECNxtixY4VBgwYJycnJwrVr14SlS5cKHTt2FK5evSoIgiAMHjxYiIiIELKyssRbdna2uP66deuEHj16CN99951w6dIlYdy4cUJgYKBQUlIiCIIgPHjwQOjevbuwYMEC4erVq8LBgwcFDw8P4eDBgzUeo66cPHlS8PDwEDIzMzX6Kyoq0kndUu69pKREo+esrCzhzJkzgpubm7B//35BEBrntt++fbvg4uIiBAUFidOksq1rMoaue09OThbat28vbNq0Sbhx44Zw6tQpwd/fX5g7d664zPNeJw25d0GQxnO8rnt/Vv85OTmV/gaMHz9e6Nu3r1BQUCAIQsPe9s97b2tsr3mGskbixo0bgrOzs5CamipOU6vVQmBgoLB27VqhrKxM8PDwEI4fP17l+iUlJYKXl5ewd+9ecVpeXp7QsWNH4euvvxYEQRBiY2MFPz8/4fHjx+Iya9asEd58880aj1FXNm7cKAwaNKjKebqoW8q9P620tFTo37+/EBoaKgiC0Oi2fUZGhvD+++8LnTp1Evr27avx5iSVbV3dGHXR+8yZM4WxY8dqLH/48GHBzc1NfON43uukJnVLtXepPMfrqvfq+n/aV199Jbi5uQlpaWnitIa67at7b2tsr3kevmwkrKyssHnzZnTo0EGcJpPJIAgC8vLycOPGDZSUlMDJyanK9dPS0lBYWAgfHx9xmlKphJubG5KTkwEAKSkp8Pb2hrHx/74y1cfHB9evX0d2dnaNxqgrf/zxB9q1a1flPF3ULeXen7Znzx7cvXsX8+bNA4BGt+1///13NG/eHF9++SU8PT015kllW1c3Rl30Pm7cOMyZM6fSOmVlZXj48CGA579OalK3VHuXynO8rnqvrv8nPXr0CJ9//jlGjx4NFxcXcXpD3fbVvbc1ttc8Q1kjoVQq4e/vr/Hl6EePHsVff/2FV199FZcvX4ZMJsPOnTsREBCA119/HUuXLkVBQQEAICMjAwDQsmVLjXHt7Oxw9+5dcRkHB4dK8wHgzp07NRqjrly+fBnZ2dkYOXIkevbsiXfffRfff/+9zuqWcu9PKikpQWxsLEaPHi3W19i2fUBAANasWYOXXnqp0jypbOvqxtDW83p3c3ODq6ur+HtpaSm2b98Od3d3WFtbA3j+66QmdUu1d6k8x+uqd+D5/T8pPj4ehYWFmDRpksb0hrrtq3tva2yveYayRio1NRXz589Hnz59EBAQgCtXrkAul8PR0RGxsbEICwvDqVOnMHnyZKjVahQVFQGAxhMfAExMTFBSUgIAKC4urnI+UB4GajJGXSgtLcWNGzfw8OFDhIaGYvPmzfDw8EBISAgSExN1UrdUe3/aF198gZKSEo2TgBvztn+aVLZ1dWPUtbKyMsyZMwdXr17FokWLAFT/OqlJ3VLtXSrPcX1vd5VKhV27dmHkyJGwsLAQpzembf/0e1tje80bV78INTQnTpzArFmz4OnpiYiICADA1KlTMWbMGCiVSgCAs7MzbG1tMXz4cJw/fx6mpqYAyl+8FT8D5U8mMzMzAICpqSlKS0s17qviyda0adMajVEXFAoFkpOTYWxsLL4oOnTogGvXriEuLk4ndUu196cdPnwYb7zxBqysrMRpjXnbP00q27q6MepSxRtvUlISoqOjxUNd1b1OevTo0WB7l8pzXJ/bHQB++ukn3LlzB8OGDdOY3li2fVXvbY3tNc89ZY3M7t27MXXqVPTq1QtbtmwRn0AymUz8g1XB2dkZQPlu14rdsllZWRrLZGVlibtkHRwcqpwPAPb29jUao640bdq00v9SnJ2dkZmZqZO6pdx7hQcPHuCXX35Bv379NKY39m3/JKls6+rGqCtZWVkYNWoUfvnlF2zZsgUBAQEa85/3OqlJ3VLtXSrPcX1t9wonTpxAx44dqzzE2dC3/bPe2xrba56hrBHZu3cvli5dilGjRmHt2rUaL8CZM2fi/fff11j+/PnzAIB27drB1dUV5ubmSEpKEufn5+fj4sWL6Nq1KwDA29sbqampUKlU4jKJiYlo27YtbGxsajRGXUhLS4OXl5fGdWsA4MKFC2jXrp1O6pZq70/6+eefIZPJ0K1bN43pjXnbP00q27q6MepCXl4eRo8ejQcPHmDv3r0aJyUD1b9OalK3VHuXynNcH70/KTU1tdJ2Bxr+tn/ee1uje82/0Gc1SbL+/PNPwd3dXZgyZUql69Xk5+cL3377reDi4iLExMQI6enpwnfffScEBAQIM2bMEMeIiIgQunXrJpw4cUK8Dssbb7whfpz+/v37gre3txAWFiZcuXJFSEhIEDw8PIRDhw7VeIy6oFKphHfeeUcYMGCAkJycLFy9elVYsWKF0KFDByEtLU0ndUu19yetW7dOeOONNypNb8zbPiwsTOPSAFLZ1jUZQ9e9h4WFCe7u7kJiYmKlvwFlZWXVvk4acu9SeY7XR+9V9S8I5ZcFcXd3F7788stKyzfkbV/de1tje80zlDUSGzduFJydnau8hYWFCYIgCN98843wj3/8Q+jYsaPg6+srrFq1SiguLhbHKCsrEz7//HPBx8dH6NSpkxASEiLcvHlT435+/fVXYdiwYUKHDh2E1157Tdi1a5fG/JqMUReys7OFefPmCb6+voKHh4cwfPhwITk5Wad1S7X3CosWLRKGDRtW5bzGuu2renOSyraubozaerJ3lUoleHh4PPNvQEVt1b1OGmLvFaTyHK/r3gWh6v7v378vODs7C6dPn65ynYa67Wvy3taYXvMyQRCEmu9XIyIiIqK6wHPKiIiIiCSAoYyIiIhIAhjKiIiIiCSAoYyIiIhIAhjKiIiIiCSAoYyIiIhIAhjKiIiIiCSAoYyIiGotPz8fhw4d0ncZRA2asb4LICKqT4IgQCaT6buMRqOsrAwHDhzAiRMn8OuvvyIlJQVBQUFwc3PTd2lEDQ73lBGRVoKDg+Hi4qJx69ChA3r37o0lS5YgLy9P3yVqyM/PR1hYmPilzLdu3YKLiwvWrVun9ZiHDh2Ci4sLbt26pasyJe+3337Dm2++idLSUgDAjBkzsGzZMiiVSiiVSqSlpWHYsGFITU0V15k5cya2bt2qr5KJGgyGMiLSmpubG/bt2yfetm/fjjFjxiAhIQETJkyAlL7F7dKlSzh8+DDUajUAiHvLuNes5kpKShAWFoaZM2dCoVDg999/x7FjxzB//nzMnDkTjo6OiI+Px9/+9jdERUWJ682ZMwebN2/GtWvX9Fg9kfTx8CURac3c3BydOnXSmObt7Y3CwkJER0fj119/rTRfKlq0aAG5XA57e3t9l9Jg7N27FzKZDG+88QYA4M8//wQA+Pj4iMsoFAosW7YMjx8/FqfZ29ujX79+CA8Px8aNG+u3aKIGhHvKiEjnOnToAAC4c+cOAEClUmHz5s0YMGAAOnbsiE6dOmHEiBFITEwU11m3bh0CAwOxfv16dO/eHa+//jpycnIAAAcOHED//v3Fw6Pr1q1DWVmZuO7cuXPFPXRvvvkmOnTogEGDBuHUqVMAgKSkJLz33nsAgPfeew/BwcEwMTFB69at4eLiAgB48OABZs2aBV9fX3h4eOCtt97C4cOHX6jvpKQkuLi4IDExEePGjYOnpyd69uyJzz77TKPex48fY8OGDXj99dfRsWNH9O/fHwkJCRpjHTlyBEOGDIGXlxd8fX2xcOFCjUPC69atQ9++fXHixAkMGDBArPmXX37BuXPn8M4776Bjx44YMGCAxuMMAJcvX8aECRPQuXNndO7cGVOmTMHNmzef21tpaSm2b9+OgQMHitNatmwJABqHKgGgW7du8PX11Zg2aNAg/Pe//8Xly5dr8EgSGSaGMiLSuevXrwMAXnrpJQBAeHg4NmzYgOHDh2Pr1q349NNPkZOTg48++giPHj0S17tz5w6OHz+OiIgIhIaGwsrKCps2bcInn3yCHj16IDY2FqNGjcKWLVuwcOFCjfu8cOEC4uLiMG3aNGzYsAHGxsaYNm0a8vLy4O7uLi6/cOFCLFq0CABw7NgxdOzYEQAwe/ZsXL16FUuWLMHmzZvh5uaGsLAwJCUlvXD/s2bNQpcuXRAbG4uBAwdi27ZtOHjwoDg/LCwMmzdvxttvv41NmzbB398f8+fPF0NgTEwMpk+fDk9PT0RHR2PKlCk4duwYgoODUVxcLI6TkZGBlStXYuLEiVi7di3y8vIwbdo0zJgxA8OGDUNERATUajWmT58urnf9+nWMGDEC2dnZWLVqFZYvX46bN2/i3XffRXZ29jN7SkpKQmZmJvr27StO69y5M7y8vPDpp58iMjISjx49QlFRUZXre3l5wd7eHl9//fULP55EBkMgItJCUFCQMGrUKOHx48fi7f79+8KRI0eEbt26CcOGDRPUarUgCIIwY8YMYfv27RrrHzt2THB2dhZ+/vlnQRAEITo6WnB2dhbOnj0rLpOfny94enoKCxcu1Fh3//79grOzs3D58mVBEAQhLCxMcHZ2FtLT08VlfvrpJ8HZ2Vn45ptvBEEQhB9//FFwdnYWfvzxxyr76dChgxATEyP+rlKphFWrVgnJycnPfAwSEhIEZ2dn4ebNmxr3ERkZqbFcQECAMGHCBEEQBOHy5cuCs7OzsHPnTo1lPvroI2Hu3LlCbm6u0KFDB2HBggUa85OTkwVnZ2dhz549Go/XqVOnxGU2bdokODs7CwcOHBCnffPNN4Kzs7Nw8eJFQRDKt0WPHj2EgoICcZmcnByhS5cuwqpVq57Z6+effy507dq10vTCwkIhPDxc6NGjh+Ds7Cx06NBBCAsLE/Lz8ystO3nyZOHtt99+5n0QGTqeU0ZEWktOToa7u7vGNLlcjh49emDp0qXiSfRr1qwBUH6IMD09HdevX8e3334LABrnHgGAs7Oz+PMvv/yCoqIiBAQEaBz+CwgIAACcPXsWr7zyCgDA2toarVu3FpdxcHAAgGfuuXla9+7dsW7dOqSlpcHf3x+9evVCWFhYjdZ9mpeXl8bvDg4O4h7Bik9/BgYGaiyzdu1aAMCpU6dQWlqqcZgQALp27QpHR0ckJSVh5MiR4vTOnTuLP7do0QIANM7js7S0BFD+6VMA+PHHH9G9e3eYmpqKj6m5uTm6du2KH3744Zk93bx5E46OjpWmN23aFDNnzsTw4cMxadIk9OjRA7t370ZhYWGlT7Y6Ojri559/fuZ9EBk6hjIi0pq7uzuWLFkCoPxTjCYmJmjZsiXMzc01ljt//jyWLFmC8+fPw9TUFO3atRPf4IWnPqFZESwAIDc3FwAwfvz4Ku8/KytL/NnMzExjXkUgrPi0ZXUiIyMRGxuLo0eP4ptvvoFcLkfPnj2xePFi8TBsTZmammr8LpfLxT4rerKxsaly3Yrzxp58HCq0aNECBQUFGtOefqyruv8n5ebm4siRIzhy5EiledbW1s9c7+HDh5Ue46dZWlpi/vz5sLa2RmRkJLKysmBnZyfONzMzq1Q/Ef0PQxkRaa1Zs2bw8PB47jIPHz7EBx98ABcXF3z99ddwcnKCXC7HqVOncOzYseeuq1QqAZSfk9amTZtK86sKLtqysLDA7NmzMXv2bPz55584efIkYmJisGTJEp1eY6uipwcPHoh784DyTzI+ePAAzZs3BwDcv38fTk5OGuveu3fvhQPi0ywsLNCzZ0+MHTu20jxj42e/JVhZWWmEYKD8Om0HDhzAnj17NKa3b98eACqFsvz8fFhZWdWmfKJGjSf6E1Gd+vPPP5Gbm4v33nsPr7zyCuTy8j87p0+fBvD8PVmenp5o0qQJMjMz4eHhId6aNGmCNWvWvNBFW42MjJ457/bt2/D398c333wDAHj55ZcREhKCnj17IiMjo8b3URNdunQBAJw4cUJjemRkJJYuXQpPT08oFAp89dVXGvNTUlJw584djcOV2ujWrRuuXr2K9u3bi49nhw4dsGPHDhw/fvyZ6/3tb39DRkaGxp7NkpIS/Pzzz5U+3ZmamgojI6NKQfru3btVHgIlonLcU0ZEdapt27YwNzdHbGwsjI2NYWxsjGPHjomfRnzeOV9WVlb44IMPEBUVhYcPH6J79+7IzMxEVFQUZDIZXF1da1yHhYUFAOC7775D8+bNNdZ1dHSEg4MDli1bhocPH6J169a4cOECTp06hQkTJmjZedVcXV3Rt29fhIeHo7i4GO7u7jhz5gyOHz+OtWvXwtLSEuPHj8f69evRpEkT9OnTB7du3UJUVBTatWuHIUOG1Or+J0+ejBEjRmDChAl49913YWJign379uHEiROIjo5+5nq+vr7YvHkzrly5Ip73N3DgQGzatAmzZ8/G+++/j/z8fKxfvx7btm3D6NGjNQ6tCoKAX375BcHBwbWqn6gxYygjojplYWGBmJgYfP755/joo4/QrFkztG/fHrt370ZISAhSUlLEE/erEhoaCltbW+zduxdbt25F8+bN0aNHD8yYMUMMWjXxyiuvYMCAAdizZw++//77SpdmWL9+PSIiIhAVFYWcnBy0bNkSH3744TPPZ6uN1atXY/369di1axdycnLQtm1brF27VrzcxNSpU9GiRQvs3r0bBw4cgKWlJfr27YvQ0NBqz+uqjqurK/bs2YPIyEjMmTMHgiDA2dkZGzZsQJ8+fZ65XteuXWFjY4NTp06Joczc3Bw7d+7EypUrsW7dOhQVFeH+/fsYM2YMPvroI431f/vtN+Tm5mpcUoOINMmEp8+yJSIiqsK2bdsQHx+PY8eOVfp6qps3b2L+/PnYtWtXlevOmzcPeXl5iImJqY9SiRoknlNGREQ1MnLkSKhUKvHcuyc1b94cgwcPrnK9O3fu4D//+U+lvWdEpIl7yoiIqMZ+/vlnzJ07F19//TUUCkWN1pkxYwZcXFx0fn4eUWPDUEZEREQkATx8SURERCQBDGVEREREEsBQRkRERCQBDGVEREREEsBQRkRERCQBDGVEREREEsBQRkRERCQBDGVEREREEsBQRkRERCQB/x/qEcReZuAf1wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the minimum and maximum values of the 'parents_income' column\n",
    "income_min = df['parents_income'].min()\n",
    "income_max = df['parents_income'].max()\n",
    "\n",
    "print(\"Minimum parent's income:\", income_min)\n",
    "print(\"Maximum parent's income:\", income_max)\n",
    "\n",
    "# Plot a histogram of the 'parents_income' column\n",
    "plt.hist(df['parents_income'], bins=10)\n",
    "plt.xlabel(\"Parent's Income ($)\")\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(\"Distribution of Parent's Income\")\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it's not useful in those question"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## major--the student's major"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Majors:\n",
      " Physics        200833\n",
      "Engineering    200787\n",
      "CS             200505\n",
      "Math           199750\n",
      "English        198125\n",
      "Name: major, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique majors and their counts\n",
    "major_counts = df['major'].value_counts()\n",
    "\n",
    "# Print the unique majors and their counts\n",
    "print(\"Unique Majors:\\n\", major_counts)\n",
    "# Plot a bar chart of the major counts\n",
    "plt.bar(major_counts.index, major_counts.values)\n",
    "plt.xlabel('Major')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Majors')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the major will affect on gpa but in my module I didn't consider about it.\n",
    "And the major need to know how often students change majors "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tutoring--whether or not the student received tutoring this semester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tutoring has a statistically significant difference in grades.\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# Separate the GPAs of students who received tutoring and those who did not\n",
    "gpa_tutoring_yes = df[df['tutoring'] == 'Yes']['gpa']\n",
    "gpa_tutoring_no = df[df['tutoring'] == 'No']['gpa']\n",
    "\n",
    "# Perform independent samples t-test\n",
    "t_stat, p_value = stats.ttest_ind(gpa_tutoring_yes, gpa_tutoring_no)\n",
    "\n",
    "# Check the p-value\n",
    "if p_value < 0.05:\n",
    "    print(\"Tutoring has a statistically significant difference in grades.\")\n",
    "else:\n",
    "    print(\"Tutoring does not have a statistically significant difference in grades.\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutoring is useful for predict gpa, if have tutoring then gpa should higher and if not not change. But too many stuents don't have it so may not useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values of 'semester':\n",
      " Fall      500000\n",
      "Spring    500000\n",
      "Name: semester, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the unique values of the 'semester' column and their counts\n",
    "semester_counts = df['semester'].value_counts()\n",
    "\n",
    "# Print the unique values of 'semester' and their counts\n",
    "print(\"Unique Values of 'semester':\\n\", semester_counts)\n",
    "\n",
    "# Plot a bar chart of the 'semester' counts\n",
    "plt.bar(semester_counts.index, semester_counts.values)\n",
    "plt.xlabel('Semester')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Semesters')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not useful in the question "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## credits--how many credits the student took this semester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum credits: 9\n",
      "Maximum credits: 18\n"
     ]
    },
    {
     "data": {
      "image/png": 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77DHt379fkvTdd99pzJgx2rZtm+2xYcMG2/qLFy/W2rVr9dJLLyktLU0Wi0WjRo1SaWmpJOnkyZMaMWKEOnbsqPT0dMXHxys5OVnp6elGxwAAAKjk0ICVk5Ojzz//XM8995zCwsJ07bXXaubMmWrbtq0+/PBDlZeXa9++fbrhhhsUGBhoe7Rq1UqSVFpaquXLlys+Pl5RUVEKDg5WUlKSjh49qk2bNkmS1q1bJ3d3d82ePVudO3dWbGysHn30US1dutTYGAAAAFU5NGD5+/vr7bffVo8ePWxtFotFVqtV+fn5OnjwoEpKStS5c+ca18/Oztbp06cVERFha/P19VX37t21Y8cOSdLOnTsVHh4uNzc32zIRERE6cOCAjh8/bmQMZ+PiYpGbm0ujeri4WBy925qNup4frq7n3i5cXTk/AOBi3C6+SP3x9fVVVFSUXdtHH32kH374Qf369dPevXtlsViUmpqqzz77TC4uLoqKitLEiRPVokUL5ebmSpLatWtnN0abNm105MgRSVJubq66du1arV+SDh8+bGSMgICAOu8DNzezGddisahFC0/bL8PGory8Qj//XCyr1SpJdr/MYY6J88PX18tgRbXz6/MD9fMasVgsjS7MVlRYneK8aOrvWY3t3Kis1ZHHw6EB69eysrL0zDPP6NZbb1V0dLQWLlwoFxcXXXXVVVqyZIlycnL08ssva+/evUpNTVVRUZEkyd3d3W4cDw8P5efnS5KKi4tr7JekkpISI2PUlYuLRf7+V9Z5/QtJXJOlQ0d/rpexTbu6bQtNGXaj/Py8q/U54pd5c9BUzg+YfY1UVFgb1S9RyflqbqrvWc62n2ujosLq0OPhNAHrk08+0ZQpUxQSEqLXXntNkhQfH69HH31Uvr6+kqSuXbsqMDBQ999/v3bt2iVPT09J5+6jqvy3dC70eHmd26menp62m9Wr9kuSt7e3kTHqqqLCqoKCM3Vevyauri7y9fXSoaM/a/9P+UbHrm8FBUUqL6+Q9MvzqNqGy9dUzg+Yf41UjtcYw7cznBtN+T2rMZ8bhYXFOnu23OjYvr5etZoZc4qA9c4772jOnDkaOHCgEhMTbbNFFovFFq4qVV6qy83NtV3Wy8vLU/v27W3L5OXlKTg4WJIUFBSkvLw8uzEqf27btq3Kysoue4zLUVbWtF6Il6O8vKLa/qipDc0T50LNTO+Xxhi+nenccKZaTGuM50ZFhdVhx8PhF4vfffddvfjiixo2bJgWLFhgdylu8uTJeuyxx+yW37VrlySpS5cuCg4Olo+PjzIzM239BQUF2r17t8LCwiRJ4eHhysrKUnn5Lwl2+/bt6tSpkwICAoyMAQAAUJVDA9aBAwc0d+5cDRw4UKNHj9bx48d17NgxHTt2TD///LPuvPNOff7553rzzTf1ww8/aMuWLXrmmWd05513qnPnznJ3d1dcXJwSExO1efNmZWdna9KkSQoKCtLAgQMlSbGxsSosLNTMmTO1b98+ZWRkKDU1VaNHj5YkI2MAAABU5dBLhP/4xz909uxZbdq0yfaZU5ViYmI0f/58JScna8mSJVqyZIlatGihIUOGaOLEibblJkyYoLKyMs2aNUvFxcUKDw9XSkqKbSYsICBAy5Yt05w5cxQTE6PAwEBNnTpVMTExRscAAACo5NCANWbMGI0ZM+aCywwaNEiDBg06b7+rq6sSEhKUkJBw3mV69uyptLS0eh0DAACgksPvwQIAAGhqCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhjk8YJ06dUrPPvus+vfvr969e+vBBx/Uzp07bf179uxRXFycQkNDNWDAAKWkpNitX1FRoYULFyoyMlIhISEaOXKkcnJy7JZpiDEAAAAqOTxgPf300/rmm2/02muv6b333tP111+vxx57TPv379fJkyc1YsQIdezYUenp6YqPj1dycrLS09Nt6y9evFhr167VSy+9pLS0NFksFo0aNUqlpaWS1GBjAAAAVHJz5MZzcnL0+eef6y9/+Yt69+4tSZo5c6Y+++wzffjhh/L09JS7u7tmz54tNzc3de7cWTk5OVq6dKliY2NVWlqq5cuXKyEhQVFRUZKkpKQkRUZGatOmTRo8eLDWrVtX72MAAABU5dAZLH9/f7399tvq0aOHrc1ischqtSo/P187d+5UeHi43Nx+yYERERE6cOCAjh8/ruzsbJ0+fVoRERG2fl9fX3Xv3l07duyQpAYZAwAAoCqHzmD5+vraZo0qffTRR/rhhx/Ur18/JSUlqWvXrnb9bdq0kSQdPnxYubm5kqR27dpVW+bIkSOSpNzc3HofIyAg4BKetT03N7MZ19XV4Vd966xq7ZX/bszPxxk15v3ZmGuvD6ZfI415/zpD7U35PasxPycXF4vx37O15dCA9WtZWVl65plndOuttyo6Olrz5s2Tu7u73TIeHh6SpJKSEhUVFUlSjcvk5+dLkoqLi+t9jLpycbHI3//KOq/f1Pj6etWqDc0T50LN2C/OtQ+cqRZIPj6eDtu20wSsTz75RFOmTFFISIhee+01SZKnp6ftRvNKlYHG29tbnp7ndlxpaant35XLeHl5NdgYdVVRYVVBwZk6r18TV1eXRvsCLygoUnl5haRfnkfVNly+pnJ+wPxrhHPj8jTl96zGfG4UFhbr7Nlyo2P6+nrValbPKQLWO++8ozlz5mjgwIFKTEy0zRYFBQUpLy/PbtnKn9u2bauysjJbW/v27e2WCQ4ObrAxLkdZWdN6IV6O8vKKavujpjY0T5wLNWO/ONc+cKZacG4iw1HHw+EXVt999129+OKLGjZsmBYsWGB3KS48PFxZWVkqL/8lfW7fvl2dOnVSQECAgoOD5ePjo8zMTFt/QUGBdu/erbCwsAYbAwAAoCqHBqwDBw5o7ty5GjhwoEaPHq3jx4/r2LFjOnbsmH7++WfFxsaqsLBQM2fO1L59+5SRkaHU1FSNHj1a0rn7puLi4pSYmKjNmzcrOztbkyZNUlBQkAYOHChJDTIGAABAVQ69RPiPf/xDZ8+e1aZNm7Rp0ya7vpiYGM2fP1/Lli3TnDlzFBMTo8DAQE2dOlUxMTG25SZMmKCysjLNmjVLxcXFCg8PV0pKim0mLCAgoEHGAAAAqOTQgDVmzBiNGTPmgsv07NlTaWlp5+13dXVVQkKCEhISHDoGAABAJYffgwUAANDUELAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHOqgLV48WINHz7crm3GjBnq1q2b3aN///62/oqKCi1cuFCRkZEKCQnRyJEjlZOTYzfGnj17FBcXp9DQUA0YMEApKSl2/SbGAAAAqOQ0AWvlypVauHBhtfbvvvtOY8aM0bZt22yPDRs22PoXL16stWvX6qWXXlJaWposFotGjRql0tJSSdLJkyc1YsQIdezYUenp6YqPj1dycrLS09ONjgEAAFDJ4QHr6NGjevzxx5WcnKxOnTrZ9ZWXl2vfvn264YYbFBgYaHu0atVKklRaWqrly5crPj5eUVFRCg4OVlJSko4ePapNmzZJktatWyd3d3fNnj1bnTt3VmxsrB599FEtXbrU2BgAAABVOTxgffvtt2rZsqU++OADhYSE2PUdPHhQJSUl6ty5c43rZmdn6/Tp04qIiLC1+fr6qnv37tqxY4ckaefOnQoPD5ebm5ttmYiICB04cEDHjx83MgYAAEBVbhdfpH5FR0crOjq6xr69e/fKYrEoNTVVn332mVxcXBQVFaWJEyeqRYsWys3NlSS1a9fObr02bdroyJEjkqTc3Fx17dq1Wr8kHT582MgYAQEBl/y8K7m5mc24rq4Oz8x1VrX2yn835ufjjBrz/mzMtdcH06+Rxrx/naH2pvye1Zifk4uLxfjv2dpyeMC6kO+//14uLi666qqrtGTJEuXk5Ojll1/W3r17lZqaqqKiIkmSu7u73XoeHh7Kz8+XJBUXF9fYL0klJSVGxqgrFxeL/P2vrPP6TY2vr1et2tA8cS7UjP3iXPvAmWqB5OPj6bBtO3XAio+P16OPPipfX19JUteuXRUYGKj7779fu3btkqfnuR1XWlpq+7d0LvR4eZ07yT09PW03q1ftlyRvb28jY9RVRYVVBQVn6rx+TVxdXRrtC7ygoEjl5RWSfnkeVdtw+ZrK+QHzrxHOjcvTlN+zGvO5UVhYrLNny42O6evrVatZPacOWBaLxRauKlVeqsvNzbVd1svLy1P79u1ty+Tl5Sk4OFiSFBQUpLy8PLsxKn9u27atysrKLnuMy1FW1rReiJejvLyi2v6oqQ3NE+dCzdgvzrUPnKkWnJvIcNTxcOoLq5MnT9Zjjz1m17Zr1y5JUpcuXRQcHCwfHx9lZmba+gsKCrR7926FhYVJksLDw5WVlaXy8l8S7Pbt29WpUycFBAQYGQMAAKAqpw5Yd955pz7//HO9+eab+uGHH7RlyxY988wzuvPOO9W5c2e5u7srLi5OiYmJ2rx5s7KzszVp0iQFBQVp4MCBkqTY2FgVFhZq5syZ2rdvnzIyMpSamqrRo0dLkpExAAAAqnLqS4S33HKLkpOTtWTJEi1ZskQtWrTQkCFDNHHiRNsyEyZMUFlZmWbNmqXi4mKFh4crJSXFdlN6QECAli1bpjlz5igmJkaBgYGaOnWqYmJijI4BAABQqU4Ba8eOHerevbuuvLL6X8AVFBRo69atGjx48CWPO3/+/GptgwYN0qBBg867jqurqxISEpSQkHDeZXr27Km0tLR6HQMAAKBSnS4RPvzww9q/f3+Nfbt379aMGTMuqygAAIDGrNYzWNOmTbN98KbVatXs2bPl4+NTbbmDBw+qdevW5ioEAABoZGo9gzVo0CBZrVZZrVZbW+XPlQ8XFxeFhoZq3rx59VIsAABAY1DrGayqX2kzfPhw2xcfAwAAwF6dbnJfvXq16ToAAACajDoFrKKiIi1ZskSffvqpioqKVFFh/ympFotFn3zyiZECAQAAGps6Baw5c+YoPT1dffr00XXXXScXF6f+vFIAAIAGVaeA9fHHH2vSpEl64oknTNcDAADQ6NVp6qmsrEw9e/Y0XQsAAECTUKeA1a9fP3322WemawEAAGgS6nSJ8A9/+IOee+45nThxQiEhIfLy8qq2zB//+MfLrQ0AAKBRqlPAqvyy5Q0bNmjDhg3V+i0WCwELAAA0W3UKWJs3bzZdBwAAQJNRp4B11VVXma4DAACgyahTwFq0aNFFlxk/fnxdhgYAAGj0jAcsHx8ftWnThoAFAACarToFrOzs7GptZ86cUVZWlmbPnq0//elPl10YAABAY2XsO268vb0VGRmpcePG6c9//rOpYQEAABod418i2K5dO+3fv9/0sAAAAI1GnS4R1sRqterIkSNaunQpf2UIAACatToFrODgYFkslhr7rFYrlwgBAECzVqeANW7cuBoDlo+PjwYMGKCOHTtebl0AAACNVp0CVnx8vOk6AAAAmow634NVWlqqjIwMZWZmqqCgQP7+/goLC1NMTIw8PDxM1ggAANCo1ClgFRQU6OGHH1Z2drZ+85vfKDAwUAcOHNCHH36oNWvW6N1331WLFi1M1woAANAo1OljGl599VXl5ubqnXfe0T//+U+lpaXpn//8p9555x0dP35cycnJpusEAABoNOoUsDZv3qyJEycqLCzMrj0sLEwTJkzQxx9/bKQ4AACAxqhOAev06dO65pprauy75pprdOrUqcupCQAAoFGrU8C69tpr9emnn9bYt3nzZnXo0OGyigIAAGjM6nST+2OPPaann35apaWlGjJkiFq3bq3//e9/+tvf/qb169dr9uzZhssEAABoPOoUsP7whz/o4MGDWrJkidavX29rv+KKKzRu3Djdf//9xgoEAABobOoUsM6cOaOxY8cqLi5OX3/9tfLz83XkyBHdf//9atmypekaAQAAGpVLugdrz549+uMf/6iVK1dKknx9fdW/f3/1799fCxYs0EMPPaT9+/fXR50AAACNRq0D1o8//qhHH31U+fn56tKli12fu7u7nnnmGZ0+fVoPPfSQcnNzjRcKAADQWNQ6YL399tvy9/fX+++/r9tuu82uz8vLS3FxcUpPT5e3t7eWLFlivFAAAIDGotYBa/v27Xr88cfl5+d33mUCAgI0YsQIbd++3URtAAAAjVKtA9axY8dq9flWXbt25RIhAABo1modsFq1aqW8vLyLLnfixIkLznIBAAA0dbUOWOHh4crIyLjochs2bNB11113WUUBAAA0ZrUOWMOHD1dmZqbmz5+vkpKSav2lpaV6+eWXtXXrVg0bNsxokQAAAI1JrT9o9IYbbtCMGTM0d+5c/fWvf9VNN92kq6++WuXl5Tp8+LAyMzN18uRJPfXUU4qMjKzPmgEAAJzaJX2S+7BhwxQcHKyUlBRt3rzZNpN15ZVXql+/fho5cqRCQkLqpVAAAIDG4pK/KufGG2/UjTfeKEk6efKkXFxc+HocAACAKur0XYSV/P39TdUBAADQZFzSdxECAADg4ghYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwpwpYixcv1vDhw+3a9uzZo7i4OIWGhmrAgAFKSUmx66+oqNDChQsVGRmpkJAQjRw5Ujk5OQ0+BgAAQCWnCVgrV67UwoUL7dpOnjypESNGqGPHjkpPT1d8fLySk5OVnp5uW2bx4sVau3atXnrpJaWlpclisWjUqFEqLS1t0DEAAAAquTm6gKNHj2rmzJnKyspSp06d7PrWrVsnd3d3zZ49W25uburcubNycnK0dOlSxcbGqrS0VMuXL1dCQoKioqIkSUlJSYqMjNSmTZs0ePDgBhkDAACgKofPYH377bdq2bKlPvjgA4WEhNj17dy5U+Hh4XJz+yUHRkRE6MCBAzp+/Liys7N1+vRpRURE2Pp9fX3VvXt37dixo8HGAAAAqMrhM1jR0dGKjo6usS83N1ddu3a1a2vTpo0k6fDhw8rNzZUktWvXrtoyR44cabAxAgICavFMa+bmZjbjuro6PDPXWdXaK//dmJ+PM2rM+7Mx114fTL9GGvP+dYbam/J7VmN+Ti4uFuO/Z2vL4QHrQoqLi+Xu7m7X5uHhIUkqKSlRUVGRJNW4TH5+foONUVcuLhb5+19Z5/WbGl9fr1q1oXniXKgZ+8W59oEz1QLJx8fTYdt26oDl6elpu9G8UmWg8fb2lqfnuR1XWlpq+3flMl5eXg02Rl1VVFhVUHCmzuvXxNXVpdG+wAsKilReXiHpl+dRtQ2Xr6mcHzD/GuHcuDxN+T2rMZ8bhYXFOnu23OiYvr5etZrVc+qAFRQUpLy8PLu2yp/btm2rsrIyW1v79u3tlgkODm6wMS5HWVnTeiFejvLyimr7o6Y2NE+cCzVjvzjXPnCmWnBuIsNRx8OpL6yGh4crKytL5eW/pM/t27erU6dOCggIUHBwsHx8fJSZmWnrLygo0O7duxUWFtZgYwAAAFTl1AErNjZWhYWFmjlzpvbt26eMjAylpqZq9OjRks7dNxUXF6fExERt3rxZ2dnZmjRpkoKCgjRw4MAGGwMAAKAqp75EGBAQoGXLlmnOnDmKiYlRYGCgpk6dqpiYGNsyEyZMUFlZmWbNmqXi4mKFh4crJSXFdlN6Q40BAABQyakC1vz586u19ezZU2lpaeddx9XVVQkJCUpISDjvMg0xBgAAQCWnvkQIAADQGBGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxrFAHrp59+Urdu3ao91q9fL0nas2eP4uLiFBoaqgEDBiglJcVu/YqKCi1cuFCRkZEKCQnRyJEjlZOTY7eMiTEAAACkRhKwvvvuO3l4eGjr1q3atm2b7TFkyBCdPHlSI0aMUMeOHZWenq74+HglJycrPT3dtv7ixYu1du1avfTSS0pLS5PFYtGoUaNUWloqSUbGAAAAqOTm6AJqY+/everUqZPatGlTrS81NVXu7u6aPXu23Nzc1LlzZ+Xk5Gjp0qWKjY1VaWmpli9froSEBEVFRUmSkpKSFBkZqU2bNmnw4MFat27dZY8BAABQqdHMYHXp0qXGvp07dyo8PFxubr9kxYiICB04cEDHjx9Xdna2Tp8+rYiICFu/r6+vunfvrh07dhgbAwAAoFKjmcEKDAzUQw89pIMHD6pDhw4aO3asIiMjlZubq65du9otXznTdfjwYeXm5kqS2rVrV22ZI0eOSJKRMerKzc1sxnV1bRSZuUZVa6/8d2N+Ps6oMe/Pxlx7fTD9GmnM+9cZam/K71mN+Tm5uFiM/56tLacPWKWlpTp48KC8vLw0depUeXt764MPPtCoUaO0YsUKFRcXy93d3W4dDw8PSVJJSYmKiookqcZl8vPzJcnIGHXh4mKRv/+VdV6/qfH19apVG5onzoWasV+cax84Uy2QfHw8HbZtpw9Y7u7u2rFjh9zc3GwBp0ePHtq/f79SUlLk6elZ7UbzkpISSZK3t7c8Pc/t3NLSUtu/K5fx8jr3QjAxRl1UVFhVUHCmzuvXxNXVpdG+wAsKilReXiHpl+dRtQ2Xr6mcHzD/GuHcuDxN+T2rMZ8bhYXFOnu23OiYvr5etZrVc/qAJZ0LOb/WtWtXbdu2TUFBQcrLy7Prq/y5bdu2Kisrs7W1b9/ebpng4GBJMjJGXZWVNa0X4uUoL6+otj9qakPzxLlQM/aLc+0DZ6oF5yYyHHU8nP7CanZ2tnr16qWdO3fatf/3v/9Vly5dFB4erqysLJWX/5JQt2/frk6dOikgIEDBwcHy8fFRZmamrb+goEC7d+9WWFiYJBkZAwAAoJLTB6yuXbvqt7/9rZ5//nnt3LlT+/fv17x58/T1119rzJgxio2NVWFhoWbOnKl9+/YpIyNDqampGj16tKRzlxjj4uKUmJiozZs3Kzs7W5MmTVJQUJAGDhwoSUbGAAAAqOT0lwhdXFy0ZMkSJSYmauLEiSooKFD37t21YsUKdevWTZK0bNkyzZkzRzExMQoMDNTUqVMVExNjG2PChAkqKyvTrFmzVFxcrPDwcKWkpNju6QoICLjsMQAAACo5fcCSpFatWmnu3Lnn7e/Zs6fS0tLO2+/q6qqEhAQlJCTU6xgAAABSI7hECAAA0NgQsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2BdgoqKCi1cuFCRkZEKCQnRyJEjlZOT4+iyAACAkyFgXYLFixdr7dq1eumll5SWliaLxaJRo0aptLTU0aUBAAAnQsCqpdLSUi1fvlzx8fGKiopScHCwkpKSdPToUW3atMnR5QEAACdCwKql7OxsnT59WhEREbY2X19fde/eXTt27HBgZQAAwNlYrFar1dFFNAYff/yx4uPj9c0338jT09PW/tRTT6m4uFhvvfXWJY9ptVpVUWF291sskouLi079XKKy8gqjY9cXN1cX+bXwUEWFfb0uLi7V2nD5msr5AfOvEc6Ny9OU37Ma87lhOuW4uFhksVguXoPZzTZdRUVFkiR3d3e7dg8PD+Xn59dpTIvFIlfXix+kuvBr4VEv49YnF5fqE6o1teHyNZXzA+b3C+fG5XGmWkzj3LjEbTtsy41M5azVr29oLykpkZeXlyNKAgAAToqAVUvt2rWTJOXl5dm15+XlKSgoyBElAQAAJ0XAqqXg4GD5+PgoMzPT1lZQUKDdu3crLCzMgZUBAABnwz1YteTu7q64uDglJiaqVatWuuqqq/TKK68oKChIAwcOdHR5AADAiRCwLsGECRNUVlamWbNmqbi4WOHh4UpJSal24zsAAGje+JgGAAAAw7gHCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAWnsnjxYg0fPtyubc+ePYqLi1NoaKgGDBiglJQUB1XX/NR0PP75z38qNjZWvXr1UnR0tF5++WUVFxc7qMLmpabjUdWsWbMUHR3dgBU1bzUdj7y8PD399NMKCwtT3759NXnyZJ04ccJBFTYvNR2PXbt2KS4uTr169VJUVJT+/Oc/q7S0tEHqIWDBaaxcuVILFy60azt58qRGjBihjh07Kj09XfHx8UpOTlZ6erqDqmw+ajoeO3fu1Pjx4zVo0CBt2LBBs2fP1kcffaTnn3/eQVU2HzUdj6o++eQTrV+/vgErat5qOh6lpaUaOXKkfvzxR61YsUJvvfWWdu/erWnTpjmoyuajpuNx4sQJPf7447r22mu1YcMGvfjii3r//feVlJTUIDXxVTlwuKNHj2rmzJnKyspSp06d7PrWrVsnd3d3zZ49W25uburcubNycnK0dOlSxcbGOqjipu1Cx2Pt2rWKiIjQE088IUnq0KGDJk2apGeeeUbPP/88XxtVDy50PCrl5eXpT3/6k/r06aOffvqpgStsXi50PD788EP99NNP2rRpk1q3bi1JttdGYWGhfHx8HFFyk3ah4/HVV1/p1KlTmjp1qnx8fNShQwcNHTpU27Zta5DQywwWHO7bb79Vy5Yt9cEHHygkJMSub+fOnQoPD5eb2y//F4iIiNCBAwd0/Pjxhi61WbjQ8Rg5cqSmTp1abZ2ysjIVFhY2VInNyoWOhyRZrVZNnz5dd911l/r06eOACpuXCx2PrVu3KiIiwhauJCkyMlKffPIJ4aqeXOh4+Pn5SZL+8pe/qLy8XIcOHdKWLVtqfB3VB2aw4HDR0dHnvW8kNzdXXbt2tWtr06aNJOnw4cMKCAio9/qamwsdj+7du9v9XFpaqhUrVuj6669Xq1atGqK8ZudCx0M6d2nk2LFjWrJkid56660GrKx5utDxOHjwoMLCwvTGG29ow4YNKisrU79+/ZSQkCBfX98GrrR5uNDxCAsL0xNPPKHk5GQlJSWpvLxcffr00Z/+9KcGqY0ZLDi14uLiapedPDw8JEklJSWOKAn/v7KyMk2dOlX79u3Tc8895+hymqXs7GwtWrRIr7zyCpdnnUBhYaE2bNig7777Tq+++qpeeOEFZWVlaezYsbJarY4ur9kpKCjQwYMHNWzYMK1fv17Jycn64YcfNHv27AbZPjNYcGqenp7V/uKjMlh5e3s7oiTo3C+SiRMnKjMzUwsXLmywKXf8oqSkRFOmTNGTTz6p4OBgR5cDSVdccYW8vb316quv6oorrpAktWzZUvfee6927dqlnj17OrjC5iUxMVEFBQV6/fXXJUnXX3+9WrZsqUcffVSPPPJIvb9umMGCUwsKClJeXp5dW+XPbdu2dURJzV5eXp6GDRumf//731q6dCkfC+Ag33zzjb7//nstWrRIvXr1Uq9evfTWW2/p8OHD6tWrlz744ANHl9jsBAUFqVOnTrZwJUm//e1vJUmHDh1yVFnNVlZWlm644Qa7tsr/DB44cKDet88MFpxaeHi41q5dq/Lycrm6ukqStm/frk6dOnH/lQPk5+frkUceUWFhod59911169bN0SU1Wz179tTHH39s17Z69Wp9/PHHWr16Na8PBwgLC9OqVatUXFwsT09PSdLevXslnfuLWzSsoKAgfffdd3ZtlcejY8eO9b59ZrDg1GJjY1VYWKiZM2dq3759ysjIUGpqqkaPHu3o0pqlefPm6ccff9Qrr7yiVq1a6dixY7ZHeXm5o8trVjw9PdWhQwe7R8uWLeXm5qYOHTrwV2sO8MADD8jV1VWTJ0/W3r17lZWVpVmzZqlv3766/vrrHV1eszNixAht3bpVCxYs0A8//KDt27dr+vTpioqK0nXXXVfv22cGC04tICBAy5Yt05w5cxQTE6PAwEBNnTpVMTExji6t2amoqNDGjRt19uxZPfLII9X6N2/erKuvvtoBlQHOoVWrVlqzZo3mzZun++67T+7u7vr973+vGTNmOLq0Zqlfv35666239MYbbyg1NVX+/v4aOHCgnnrqqQbZvsXKnzYAAAAYxSVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgA4GT6eEGj8CFgAmq1du3YpISFBAwYMUM+ePXXrrbdq1qxZ+vHHH+ttmxkZGerWrZvty39ff/11u+90zMrK4quggCaAgAWgWVqzZo0eeOABHT9+XJMnT9bSpUs1ZswY7dixQ7Gxsfr2228bpI57771XaWlptp/Xr1+vffv2Nci2AdQfvosQQLOTlZWlOXPmaNiwYZo5c6atvW/fvrr11lt19913a8aMGfrggw/qvZagoCAFBQXV+3YANCxmsAA0OykpKWrRooWefvrpan2tWrXS9OnTddttt6mwsFDDhw/XlClTNGHCBPXu3VtPPPGEJKmkpER//vOfFRUVpR49emjIkCHauHGj3VgVFRVavHixBgwYoJCQEI0dO1b5+fl2y1S9RDh9+nS9//77+umnn9StWzdlZGRIkjZu3KihQ4eqZ8+eioiI0JQpU5SXl1cfuwaAIcxgAWhWrFartm3bpujoaHl5edW4zO23327380cffaTbb79db7zxhsrLy2W1WjVu3Dh99dVXmjBhgjp37qxNmzZp0qRJKi0t1R//+EdJ0iuvvKJVq1ZpzJgxCg0N1d///ne9+uqr561t7NixOnHihHbv3q1Fixapffv2ysrK0pQpUzR27FiFh4crNzdXr7zyiiZPnqzVq1cb2y8AzCJgAWhWTp48qZKSEl199dW1XsfFxUUvvviivL29JUmff/65tm7dqqSkJP3hD3+QJEVGRqqoqEiJiYm68847debMGa1evVoPP/yw4uPjbcscPXpUW7durXE77du3V6tWreTu7q7Q0FBJ0nvvvScPDw+NGjVKHh4ekiQ/Pz/t2rVLVqtVFoulrrsCQD3iEiGAZsXF5dzbXnl5ea3Xufrqq23hSpK2b98ui8WiqKgolZWV2R7R0dE6duyYvv/+e3399dc6e/asbr31Vrux7rjjjkuqNzw8XMXFxRoyZIiSkpKUlZWlfv36afz48YQrwIkxgwWgWfHz89OVV16pw4cPn3eZM2fOqLS0VH5+fpKk1q1b2/WfOnVKVqtVvXv3rnH9vLw8FRQUSDp3T1dVgYGBl1Rvr1699Pbbb2vlypVKSUnRkiVLFBgYqFGjRumRRx65pLEANBwCFoBmp1+/fsrMzFRJSYntsltVGRkZmjNnjt59990a12/RooW8vb21atWqGvs7dOig//znP5Kk48eP69prr7X1nTp16pLrjYyMtF2C/OKLL7Rq1SrNnTtXoaGhCgkJueTxANQ/LhECaHZGjhypU6dOKSkpqVrf8ePHtWzZMnXo0MF2H9Sv9enTR2fOnJHVatUNN9xge3z//fd64403VFZWpl69esnT01N///vf7db99NNPL1hb5SXMSi+//LLuueceWa1WeXl56ZZbbtG0adMkSUeOHLmEZw2gITGDBaDZCQ0N1VNPPaUFCxZo//79iomJkb+/v77//nstX75cp0+f1ttvv33ee5yioqIUHh6usWPHauzYsercubP+85//6PXXX1e/fv1slwXHjh2rBQsWyMvLSxEREdqyZctFA5avr6/+97//acuWLbruuut00003acWKFZo+fbqGDh2qs2fPatmyZfLz81NERITxfQPADIuVL70C0Ext2bJFa9as0Z49e3Tq1CkFBQXppptu0pgxY/Sb3/xGkjR8+HBJqvaRCGfOnFFycrL+/ve/6/jx42rbtq0GDx6scePG2V12XL16tVJTU3X06FH16tVLd9xxh2bPnq3Nmzfr6quv1uuvv65Fixbpu+++kyTt3btXTz31lH788UdNmDBBTzzxhD788EMtX75cBw4ckMVi0Y033qgpU6bYfcUOAOdCwAIAADCMe7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/Ay3PMCJQYDTIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the minimum and maximum values of 'credits'\n",
    "min_credits = df['credits'].min()\n",
    "max_credits = df['credits'].max()\n",
    "\n",
    "# Print the minimum and maximum values of 'credits'\n",
    "print(\"Minimum credits:\", min_credits)\n",
    "print(\"Maximum credits:\", max_credits)\n",
    "\n",
    "# Plot a histogram of the 'credits' column\n",
    "plt.hist(df['credits'], bins=10)\n",
    "plt.xlabel('Credits')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Credits')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The credits is useful for check the predict gpa is correct or not.  The credits will affect on the gpa. If the credits is more but the study hour not changed then the gpa should be low"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## student_age--how old the student is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum age: 17\n",
      "Maximum age: 33\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Calculate the minimum and maximum values of 'student_age'\n",
    "min_age = df['student_age'].min()\n",
    "max_age = df['student_age'].max()\n",
    "\n",
    "# Print the minimum and maximum values of 'student_age'\n",
    "print(\"Minimum age:\", min_age)\n",
    "print(\"Maximum age:\", max_age)\n",
    "\n",
    "# Plot a histogram of the 'student_age' column\n",
    "plt.hist(df['student_age'], bins=10)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Student Age')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## student_year--what year of college the student is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABisVgWs48ePq3379lq3bp112cGDBxUVFaV27dopPDxc8fHxNtuUlJRo/vz5Cg0NVVBQkPr166e0tDSbNjXRBwAAQJlaE7AuX76sMWPG6KeffrIuy8rKUt++fdW8eXMlJSVp6NChmjdvnpKSkqxtFi5cqISEBE2dOlWJiYmyWCyKiYlRYWFhjfYBAABQptY87PnNN99UvXr1bJatWbNGzs7OiouLk5OTk/z9/ZWWlqbFixcrMjJShYWFWrp0qcaOHauwsDBJ0ty5cxUaGqpNmzbp4YcfrpE+AAAArlYrzmDt2rVLiYmJmjlzps3y3bt3Kzg4WE5O/8uBISEhOn78uDIzM5WSkqLc3FyFhIRY13t6eqpNmzbatWtXjfUBAABwNdMDVk5OjmJjYzVp0iQ1btzYZl16err8/PxsljVs2FCSdOrUKaWnp0tSue0aNmyo06dP11gfAAAAVzP9EmFcXJzatWunRx55pNy6/Px8OTs72yyrW7euJKmgoEB5eXmSVGGb7OzsGuvjWjg5mZNxLRaLHBwsNb7fsn3WqeMoR8eaf+0lJaUqLS2t8f3aq7IxNmOsUfMYb/vCeP86UwPW+vXrtXv3bm3YsKHC9S4uLtaJ5mXKAo2bm5tcXFwkSYWFhdbvy9q4urrWWB9V5eBgkZdXvd9uWA1KSkpNCVhl3N1dfrtRNTD7ddsrT09Xs0tADWK87QvjXTFTA1ZSUpIyMzMVHh5us3zKlCmKj4/XH/7wB2VkZNisK/u5UaNGKioqsi5r1qyZTZvAwEBJkp+fX7X3UVUlJaXKyfnptxsazNHRQZ6erpq1co9+OHOxxvdvlpsbeWhMr47KyclTcXGJ2eXYhbL3GsfcPjDe9sVex9vT07VSZ+1MDVizZs1Sfn6+zbL7779fw4YNU/fu3fW3v/1NCQkJKi4ulqOjoyQpOTlZLVq0kI+Pjzw8POTu7q4dO3ZYw1FOTo4OHDigqKgoSVJwcHC193EtiorMe1P+cOaiUn/MNm3/ZikuLjH1uNsjjrl9YbztC+NdMVMvnDZq1Ei33HKLzZck+fj4qEmTJoqMjNSlS5c0ceJEHT16VOvWrdPy5cs1aNAgSVfmTUVFRWnWrFnavHmzUlJSNHLkSPn5+albt26SVCN9AAAAXM30Se6/xsfHR0uWLNG0adMUEREhX19fxcbGKiIiwtpm2LBhKioq0qRJk5Sfn6/g4GDFx8dbJ6XXVB8AAABlLKV8pMo0xcUlOn8+t8b36+TkIC+vehox5wu7ukTo36S+3hgVrqysXE5n15Cy9xrH3D4w3vbFXsfb27tepeZg8dlKAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgpgeszMxMjR07ViEhIWrfvr0GDhyoo0ePWtePHz9eAQEBNl9dunSxri8pKdH8+fMVGhqqoKAg9evXT2lpaTb7OHjwoKKiotSuXTuFh4crPj7eZr0RfQAAAJQxPWANHjxY33//vRYvXqwPP/xQLi4uio6OVl5eniTp0KFDeu6557Rt2zbr1/r1663bL1y4UAkJCZo6daoSExNlsVgUExOjwsJCSVJWVpb69u2r5s2bKykpSUOHDtW8efOUlJRkaB8AAABlTA1YWVlZuvnmm/Xqq6/qzjvvlL+/v55//nmdPXtWR44cUXFxsY4ePao777xTvr6+1i9vb29JUmFhoZYuXaqhQ4cqLCxMgYGBmjt3rs6cOaNNmzZJktasWSNnZ2fFxcXJ399fkZGRio6O1uLFiw3rAwAA4GqmBiwvLy/NmTNHt912myTp3Llzio+Pl5+fn1q2bKkTJ06ooKBA/v7+FW6fkpKi3NxchYSEWJd5enqqTZs22rVrlyRp9+7dCg4OlpOTk7VNSEiIjh8/rszMTEP6AAAAuJrTbzepGZMnT7aeKXr77bfl5uamw4cPy2KxaPny5frqq6/k4OCgsLAwjRgxQh4eHkpPT5ckNW7c2Kavhg0b6vTp05Kk9PR0tWrVqtx6STp16pQhffj4+BhxCAAAwA2i1gSsPn36qGfPnlq9erWGDBmiVatW6ciRI3JwcFCTJk20aNEipaWlaebMmTp8+LCWL19unafl7Oxs01fdunWVnZ0tScrPz69wvSQVFBQY0se1cHKq+ZOIjo6mT70zlb2//ppUdqw55vaB8bYvjPevqzUBq2XLlpKkV199VXv37tWKFSs0ffp0RUdHy9PTU5LUqlUr+fr6qmfPntq/f79cXFwkXZlHVfa9dCX0uLq6SpJcXFysk9WvXi9Jbm5uhvRRVQ4OFnl51avy9qgaT09Xs0uwOxxz+8J42xfGu2KmBqzMzEwlJyfroYcekqOjoyTJwcFB/v7+ysjIkMVisYarMmWX6tLT062X9TIyMtSsWTNrm4yMDAUGBkqS/Pz8lJGRYdNH2c+NGjVSUVHRNfdRVSUlpcrJ+anK21eVo6ODXf8PkZOTp+LiErPLsAtl7zWOuX1gvO2LvY63p6drpc7amRqwMjIyNHr0aPn4+Ojuu++WJF2+fFkHDhxQ165dNXr0aF24cMHmnlP79++XdOWMV9OmTeXu7q4dO3ZYw1FOTo4OHDigqKgoSVJwcLASEhJUXFxsDXHJyclq0aKFfHx85OHhcc19XIuiIvt5U9YWxcUlHPcaxjG3L4y3fWG8K2bqhdPAwEB17txZL7/8snbv3q3Dhw/rxRdfVE5OjqKjo9WjRw9t375db7/9tk6ePKkvv/xSEyZMUI8ePeTv7y9nZ2dFRUVp1qxZ2rx5s1JSUjRy5Ej5+fmpW7dukqTIyEhdunRJEydO1NGjR7Vu3TotX75cgwYNkiRD+gAAALiapbS0tNTMAi5evKjZs2fr888/18WLF9WpUyeNGzfOeuuGf/zjH1q0aJGOHTsmDw8PPfLIIxoxYoR1knlxcbHmzJmjdevWKT8/X8HBwXrppZd08803W/exb98+TZs2TQcOHJCvr6/69etnPTtlVB9VUVxcovPnc6+pj6pwcnKQl1c9jZjzhVJ/zK7x/ZvFv0l9vTEqXFlZufy1VUPK3mscc/vAeNsXex1vb+96lbpEaHrAsmcErJpFwKp59voPsL1ivO2LvY53ZQMWn60EAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxmesDKzMzU2LFjFRISovbt22vgwIE6evSodf3BgwcVFRWldu3aKTw8XPHx8Tbbl5SUaP78+QoNDVVQUJD69euntLQ0mzY10QcAAEAZ0wPW4MGD9f3332vx4sX68MMP5eLioujoaOXl5SkrK0t9+/ZV8+bNlZSUpKFDh2revHlKSkqybr9w4UIlJCRo6tSpSkxMlMViUUxMjAoLCyWpxvoAAAAo42TmzrOysnTzzTdr8ODBuu222yRJzz//vB577DEdOXJEycnJcnZ2VlxcnJycnOTv76+0tDQtXrxYkZGRKiws1NKlSzV27FiFhYVJkubOnavQ0FBt2rRJDz/8sNasWVPtfQAAAFzN1DNYXl5emjNnjjVcnTt3TvHx8fLz81PLli21e/duBQcHy8npfzkwJCREx48fV2ZmplJSUpSbm6uQkBDrek9PT7Vp00a7du2SpBrpAwAA4GqmXyIsM3nyZN1zzz36+9//rmnTpsnNzU3p6eny8/OzadewYUNJ0qlTp5Seni5Jaty4cbk2p0+flqQa6QMAAOBqpl4ivFqfPn3Us2dPrV69WkOGDNGqVauUn58vZ2dnm3Z169aVJBUUFCgvL0+SKmyTnZ0tSTXSx7Vwcqr5jOvoWGtytSns/fXXpLJjzTG3D4y3fWG8f12tCVgtW7aUJL366qvau3evVqxYIRcXF+tE8zJlgcbNzU0uLi6SpMLCQuv3ZW1cXV0lqUb6qCoHB4u8vOpVeXtUjaenq9kl2B2OuX1hvO0L410xUwNWZmamkpOT9dBDD8nR0VGS5ODgIH9/f2VkZMjPz08ZGRk225T93KhRIxUVFVmXNWvWzKZNYGCgJNVIH1VVUlKqnJyfqrx9VTk6Otj1/xA5OXkqLi4xuwy7UPZe45jbB8bbvtjreHt6ulbqrJ2pASsjI0OjR4+Wj4+P7r77bknS5cuXdeDAAXXt2lUNGjRQQkKCiouLrQEsOTlZLVq0kI+Pjzw8POTu7q4dO3ZYw1FOTo4OHDigqKgoSVJwcHC193Etiors501ZWxQXl3DcaxjH3L4w3vaF8a6YqRdOAwMD1blzZ7388svavXu3Dh8+rBdffFE5OTmKjo5WZGSkLl26pIkTJ+ro0aNat26dli9frkGDBkm6Mm8qKipKs2bN0ubNm5WSkqKRI0fKz89P3bp1k6Qa6QMAAOBqpp7BslgseuONNzR79myNGDFCFy9eVKdOnbRy5Ur94Q9/kCQtWbJE06ZNU0REhHx9fRUbG6uIiAhrH8OGDVNRUZEmTZqk/Px8BQcHKz4+3jop3cfHp0b6AAAAKGMpLS0tNbsIe1VcXKLz53NrfL9OTg7y8qqnEXO+UOqP2TW+f7P4N6mvN0aFKysrl9PZNaTsvcYxtw+Mt32x1/H29q5XqTlYfLYSAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADCY6QHrwoULeumll9SlSxd16NBBTz/9tHbv3m1dP378eAUEBNh8denSxbq+pKRE8+fPV2hoqIKCgtSvXz+lpaXZ7OPgwYOKiopSu3btFB4ervj4eJv1RvQBAABQxvSANWrUKH3zzTeaM2eOPvzwQ91+++3q37+/UlNTJUmHDh3Sc889p23btlm/1q9fb91+4cKFSkhI0NSpU5WYmCiLxaKYmBgVFhZKkrKystS3b181b95cSUlJGjp0qObNm6ekpCRD+wAAAChjasBKS0vT9u3bNWXKFHXq1Em33nqrJk6cqEaNGunTTz9VcXGxjh49qjvvvFO+vr7WL29vb0lSYWGhli5dqqFDhyosLEyBgYGaO3euzpw5o02bNkmS1qxZI2dnZ8XFxcnf31+RkZGKjo7W4sWLDesDAADgaqYGLC8vL7377ru64447rMssFotKS0uVnZ2tEydOqKCgQP7+/hVun5KSotzcXIWEhFiXeXp6qk2bNtq1a5ckaffu3QoODpaTk5O1TUhIiI4fP67MzExD+gAAALiaqQHL09NTYWFhcnZ2ti7buHGjTp48qc6dO+vw4cOyWCxavny5unbtqvvuu0+vvvqqLl68KElKT0+XJDVu3Nim34YNG+r06dPWNn5+fuXWS9KpU6cM6QMAAOBqTr/dpObs2bNHEyZM0J///Gd17dpV8+fPl4ODg5o0aaJFixYpLS1NM2fO1OHDh7V8+XLl5eVJkk1Ak6S6desqOztbkpSfn1/hekkqKCgwpI9r4eRU8xnX0dH0qXemsvfXX5PKjjXH3D4w3vaF8f51tSZgff755xozZoyCgoI0Z84cSdLQoUMVHR0tT09PSVKrVq3k6+urnj17av/+/XJxcZF0ZR5V2ffSldDj6uoqSXJxcbFOVr96vSS5ubkZ0kdVOThY5OVVr8rbo2o8PV3NLsHucMztC+NtXxjvitWKgLVixQpNmzZN3bp106xZs6xniywWizVclWnVqpWkK5ftyi7rZWRkqFmzZtY2GRkZCgwMlCT5+fkpIyPDpo+ynxs1aqSioqJr7qOqSkpKlZPzU5W3rypHRwe7/h8iJydPxcUlZpdhF8reaxxz+8B42xd7HW9PT9dKnbUzPWCtWrVKr776qnr37q0JEybIweF/RY8ePVoXLlywuefU/v37JUktW7ZU06ZN5e7urh07dljDUU5Ojg4cOKCoqChJUnBwsBISElRcXCxHR0dJUnJyslq0aCEfHx95eHhccx/XoqjIft6UtUVxcQnHvYZxzO0L421fGO+KmXrh9Pjx45o+fbq6deumQYMGKTMzU2fPntXZs2d18eJF9ejRQ9u3b9fbb7+tkydP6ssvv9SECRPUo0cP+fv7y9nZWVFRUZo1a5Y2b96slJQUjRw5Un5+furWrZskKTIyUpcuXdLEiRN19OhRrVu3TsuXL9egQYMkyZA+AAAArmbqGax//OMfunz5sjZt2mS951SZiIgIzZgxQ/PmzdOiRYu0aNEieXh46JFHHtGIESOs7YYNG6aioiJNmjRJ+fn5Cg4OVnx8vPUyo4+Pj5YsWaJp06YpIiJCvr6+io2NVUREhKF9AAAAlLGUlpaWml2EvSouLtH587k1vl8nJwd5edXTiDlfKPXH7Brfv1n8m9TXG6PClZWVy+nsGlL2XuOY2wfG277Y63h7e9er1BwsPlsJAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABisSgFr165dys2t+AaZOTk5+tvf/nZNRQEAAFzPqhSwnn32WaWmpla47sCBAxo/fvw1FQUAAHA9q/SzCF988UWdPn1aklRaWqq4uDi5u7uXa3fixAk1aNDAuAoBAACuM5U+g/XAAw+otLRUVz+6sOznsi8HBwe1a9dOr732WrUUCwAAcD2o9Bmsrl27qmvXrpKk3r17Ky4uTv7+/tVWGAAAwPWq0gHrah988IHRdQAAANwwqhSw8vLytGjRIm3ZskV5eXkqKSmxWW+xWPT5558bUiAAAMD1pkoBa9q0aUpKStJdd92l1q1by8GB22kBAACUqVLA+uc//6mRI0dq4MCBRtcDAABw3avSqaeioiK1bdvW6FoAAABuCFUKWJ07d9ZXX31ldC0AAAA3hCpdIuzevbumTJmi8+fPKygoSK6uruXaPP7449daGwAAwHWpSgFrxIgRkqT169dr/fr15dZbLBYCFgAAsFtVClibN282ug4AAIAbRpUCVpMmTYyuAwAA4IZRpYC1YMGC32zzwgsvVKVrAACA657hAcvd3V0NGzYkYAEAALtVpYCVkpJSbtlPP/2kPXv2KC4uTpMnT77mwgAAAK5Xhj3jxs3NTaGhoRoyZIj+7//+z6huAQAArjuGP0SwcePGSk1NNbpbAACA60aVLhFWpLS0VKdPn9bixYv5lCEAALBrVQpYgYGBslgsFa4rLS3lEiEAALBrVQpYQ4YMqTBgubu7Kzw8XM2bN7/WugAAAK5bVQpYQ4cONboOAACAG0aVJ7kXFhYqISFBI0eOVP/+/TVmzBglJCSooKDgd/Vz4cIFvfTSS+rSpYs6dOigp59+Wrt377auP3jwoKKiotSuXTuFh4crPj7eZvuSkhLNnz9foaGhCgoKUr9+/ZSWlmbTpib6AAAAKFOlgJWTk6O//vWviouL0zfffKNLly7pv//9r+Li4vTkk0/q4sWLle5r1KhR+uabbzRnzhx9+OGHuv3229W/f3+lpqYqKytLffv2VfPmzZWUlKShQ4dq3rx5SkpKsm6/cOFCJSQkaOrUqUpMTJTFYlFMTIwKCwslqcb6AAAAKFOlS4SzZ89Wenq6VqxYoU6dOlmX7969W8OGDdO8efM0adKk3+wnLS1N27dv1+rVq9WhQwdJ0sSJE/XVV1/p008/lYuLi5ydnRUXFycnJyf5+/srLS1NixcvVmRkpAoLC7V06VKNHTtWYWFhkqS5c+cqNDRUmzZt0sMPP6w1a9ZUex8AAABXq9IZrM2bN2vEiBE24UqSOnXqpGHDhumf//xnpfrx8vLSu+++qzvuuMO6zGKxqLS0VNnZ2dq9e7eCg4Pl5PS/HBgSEqLjx48rMzNTKSkpys3NVUhIiHW9p6en2rRpo127dklSjfQBAABwtSoFrNzcXDVt2rTCdU2bNtWFCxcq1Y+np6fCwsLk7OxsXbZx40adPHlSnTt3Vnp6uvz8/Gy2adiwoSTp1KlTSk9Pl3Tl5qY/b3P69GlJqpE+AAAArlalS4S33nqrtmzZonvuuafcus2bN+uWW26pUjF79uzRhAkT9Oc//1ldu3bVa6+9ZhO+JKlu3bqSpIKCAuXl5UlShW2ys7MlSfn5+dXex7VwcjL8Zvq/ydGx5vdZm9jj67dYLHJwqPjeddWpbJ916jiactxLSkpVWlpa4/s1G+NtXxjv2qlKAat///4aNWqUCgsL9cgjj6hBgwY6d+6cNmzYoLVr1youLu539/n5559rzJgxCgoK0pw5cyRJLi4u1onmZcoCjZubm1xcXCRd+URj2fdlbVxdXWusj6pycLDIy6telbdH1Xh6uppdQo0rKSk15R/gMu7uLr/dqBqY/brNYvbrZrxrltmvm/GuWJUCVvfu3XXixAktWrRIa9eutS6vU6eOhgwZop49e/6u/lasWKFp06apW7dumjVrlvVskZ+fnzIyMmzalv3cqFEjFRUVWZc1a9bMpk1gYGCN9VFVJSWlysn5qcrbV5Wjo4NdhowyOTl5Ki4uMbuMGlM23rNW7tEPZyr/Cd/r3c2NPDSmV0fG204w3ox3TfH0dK3UGbsqBayffvpJzz//vKKiorR3715lZ2fr9OnT6tmzp+rXr/+7+lq1apVeffVV9e7dWxMmTJCDw/+KDg4OVkJCgoqLi+Xo6ChJSk5OVosWLeTj4yMPDw+5u7trx44d1nCUk5OjAwcOKCoqqsb6uBZFRfbzD0FtUVxcYpfH/YczF5X6Y7bZZdQ4xtu+MN72pTaP9++6aHrw4EE9/vjjWrZsmaQrk9S7dOmiLl266I033tAzzzyj1NTUSvd3/PhxTZ8+Xd26ddOgQYOUmZmps2fP6uzZs7p48aIiIyN16dIlTZw4UUePHtW6deu0fPlyDRo0SNKVeVNRUVGaNWuWNm/erJSUFI0cOVJ+fn7q1q2bJNVIHwAAAFer9Bms77//XtHR0XJzc1PLli1t1jk7O2vChAlasmSJnnnmGX388cflPnVXkX/84x+6fPmyNm3apE2bNtmsi4iI0IwZM7RkyRJNmzZNERER8vX1VWxsrCIiIqzthg0bpqKiIk2aNEn5+fkKDg5WfHy89TKjj49PjfQBAABQxlJaySn4kydP1q5du5SQkKCbbrqpwjaZmZl68sknFRYWVqWJ7vamuLhE58/n1vh+nZwc5OVVTyPmfGFXp5T9m9TXG6PClZWVW2tPKVcHxpvxtgeMN+NdU7y961VqDlalLxEmJydrwIABvxiupCtnevr27avk5OTKdgsAAHDDqXTAOnv2bKXub9WqVSvrzTsBAADsUaUDlre3d7lbFVTk/Pnzv3qWCwAA4EZX6YAVHBysdevW/Wa79evXq3Xr1tdUFAAAwPWs0gGrd+/e2rFjh2bMmFHh42EKCws1c+ZMbd26Vb169TK0SAAAgOtJpW/TcOedd2r8+PGaPn26Pv74Y9199926+eabVVxcrFOnTmnHjh3KysrS8OHDFRoaWp01AwAA1Gq/607uvXr1UmBgoOLj47V582brmax69eqpc+fO6tevn4KCgqqlUAAAgOvF735UTseOHdWxY0dJUlZWlhwcHH7343EAAABuZFV6FmEZLy8vo+oAAAC4YfyuZxECAADgtxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwWK0KWAsXLlTv3r1tlo0fP14BAQE2X126dLGuLykp0fz58xUaGqqgoCD169dPaWlpNn0cPHhQUVFRateuncLDwxUfH2+z3og+AAAAytSagLVs2TLNnz+/3PJDhw7pueee07Zt26xf69evt65fuHChEhISNHXqVCUmJspisSgmJkaFhYWSpKysLPXt21fNmzdXUlKShg4dqnnz5ikpKcnQPgAAAMqYHrDOnDmjAQMGaN68eWrRooXNuuLiYh09elR33nmnfH19rV/e3t6SpMLCQi1dulRDhw5VWFiYAgMDNXfuXJ05c0abNm2SJK1Zs0bOzs6Ki4uTv7+/IiMjFR0drcWLFxvWBwAAwNVMD1jfffed6tevr08++URBQUE2606cOKGCggL5+/tXuG1KSopyc3MVEhJiXebp6ak2bdpo165dkqTdu3crODhYTk5O1jYhISE6fvy4MjMzDekDAADgak6/3aR6de3aVV27dq1w3eHDh2WxWLR8+XJ99dVXcnBwUFhYmEaMGCEPDw+lp6dLkho3bmyzXcOGDXX69GlJUnp6ulq1alVuvSSdOnXKkD58fHx+9+sGAAA3LtMD1q85cuSIHBwc1KRJEy1atEhpaWmaOXOmDh8+rOXLlysvL0+S5OzsbLNd3bp1lZ2dLUnKz8+vcL0kFRQUGNLHtXByqvmTiI6Opp+4NJW9vX57e70/Z2+v395e78/Z2+u3t9f7c7X59dfqgDV06FBFR0fL09NTktSqVSv5+vqqZ8+e2r9/v1xcXCRdmUdV9r10JfS4urpKklxcXKyT1a9eL0lubm6G9FFVDg4WeXnVq/L2qBpPT1ezS0ANYrztC+NtX2rzeNfqgGWxWKzhqkzZpbr09HTrZb2MjAw1a9bM2iYjI0OBgYGSJD8/P2VkZNj0UfZzo0aNVFRUdM19VFVJSalycn6q8vZV5ejoUKvflNUtJydPxcUlZpdRYxhvxtueMN72xYzx9vR0rdSZs1odsEaPHq0LFy7Y3HNq//79kqSWLVuqadOmcnd3144dO6zhKCcnRwcOHFBUVJQkKTg4WAkJCSouLpajo6MkKTk5WS1atJCPj488PDyuuY9rUVRkP/8Q1BbFxSUcdzvCeNsXxtu+1Obxrr0XLyX16NFD27dv19tvv62TJ0/qyy+/1IQJE9SjRw/5+/vL2dlZUVFRmjVrljZv3qyUlBSNHDlSfn5+6tatmyQpMjJSly5d0sSJE3X06FGtW7dOy5cv16BBgyTJkD4AAACuVqvPYN17772aN2+eFi1apEWLFsnDw0OPPPKIRowYYW0zbNgwFRUVadKkScrPz1dwcLDi4+Otk9J9fHy0ZMkSTZs2TREREfL19VVsbKwiIiIM7QMAAKBMrQpYM2bMKLfsgQce0AMPPPCL2zg6Omrs2LEaO3bsL7Zp27atEhMTq7UPAACAMrX6EiEAAMD1iIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAarVQFr4cKF6t27t82ygwcPKioqSu3atVN4eLji4+Nt1peUlGj+/PkKDQ1VUFCQ+vXrp7S0tBrvAwAAoEytCVjLli3T/PnzbZZlZWWpb9++at68uZKSkjR06FDNmzdPSUlJ1jYLFy5UQkKCpk6dqsTERFksFsXExKiwsLBG+wAAACjjZHYBZ86c0cSJE7Vnzx61aNHCZt2aNWvk7OysuLg4OTk5yd/fX2lpaVq8eLEiIyNVWFiopUuXauzYsQoLC5MkzZ07V6Ghodq0aZMefvjhGukDAADgaqafwfruu+9Uv359ffLJJwoKCrJZt3v3bgUHB8vJ6X85MCQkRMePH1dmZqZSUlKUm5urkJAQ63pPT0+1adNGu3btqrE+AAAArmb6GayuXbuqa9euFa5LT09Xq1atbJY1bNhQknTq1Cmlp6dLkho3blyuzenTp2usDx8fn0q8UgAAYC9MD1i/Jj8/X87OzjbL6tatK0kqKChQXl6eJFXYJjs7u8b6uBZOTjV/EtHR0fQTl6ayt9dvb6/35+zt9dvb6/05e3v99vZ6f642v/5aHbBcXFysE83LlAUaNzc3ubi4SJIKCwut35e1cXV1rbE+qsrBwSIvr3pV3h5V4+npanYJqEGMt31hvO1LbR7vWh2w/Pz8lJGRYbOs7OdGjRqpqKjIuqxZs2Y2bQIDA2usj6oqKSlVTs5PVd6+qhwdHWr1m7K65eTkqbi4xOwyagzjzXjbE8bbvpgx3p6erpU6c1arA1ZwcLASEhJUXFwsR0dHSVJycrJatGghHx8feXh4yN3dXTt27LCGo5ycHB04cEBRUVE11se1KCqyn38Iaovi4hKOux1hvO0L421favN4196Ll5IiIyN16dIlTZw4UUePHtW6deu0fPlyDRo0SNKVeVNRUVGaNWuWNm/erJSUFI0cOVJ+fn7q1q1bjfUBAABwtVp9BsvHx0dLlizRtGnTFBERIV9fX8XGxioiIsLaZtiwYSoqKtKkSZOUn5+v4OBgxcfHWyel11QfAAAAZWpVwJoxY0a5ZW3btlViYuIvbuPo6KixY8dq7Nixv9imJvoAAAAoU6svEQIAAFyPCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYLDrImD9+OOPCggIKPe1du1aSdLBgwcVFRWldu3aKTw8XPHx8Tbbl5SUaP78+QoNDVVQUJD69euntLQ0mzZG9AEAACBdJwHr0KFDqlu3rrZu3apt27ZZvx555BFlZWWpb9++at68uZKSkjR06FDNmzdPSUlJ1u0XLlyohIQETZ06VYmJibJYLIqJiVFhYaEkGdIHAABAGSezC6iMw4cPq0WLFmrYsGG5dcuXL5ezs7Pi4uLk5OQkf39/paWlafHixYqMjFRhYaGWLl2qsWPHKiwsTJI0d+5chYaGatOmTXr44Ye1Zs2aa+4DAACgzHVzBqtly5YVrtu9e7eCg4Pl5PS/rBgSEqLjx48rMzNTKSkpys3NVUhIiHW9p6en2rRpo127dhnWBwAAQJnrImAdPnxYmZmZeuaZZ/SnP/1JTz/9tLZu3SpJSk9Pl5+fn037sjNdp06dUnp6uiSpcePG5dqcPn3asD4AAADK1PpLhIWFhTpx4oRcXV0VGxsrNzc3ffLJJ4qJidF7772n/Px8OTs722xTt25dSVJBQYHy8vIkqcI22dnZkmRIH1Xl5FTzGdfR8brI1dXG3l6/vb3en7O3129vr/fn7O3129vr/bna/PprfcBydnbWrl275OTkZA04d9xxh1JTUxUfHy8XF5dyE80LCgokSW5ubnJxcZF0JaiVfV/WxtXVVZIM6aMqHBws8vKqV+XtUTWenlUfM1x/GG/7wnjbl9o83rU+YElXQs7PtWrVStu2bZOfn58yMjJs1pX93KhRIxUVFVmXNWvWzKZNYGCgJBnSR1WUlJQqJ+enKm9fVY6ODrX6TVndcnLyVFxcYnYZNYbxZrztCeNtX8wYb09P10qdOav1ASslJUVPP/20Fi9erE6dOlmXf/vtt2rZsqVat26thIQEFRcXy9HRUZKUnJysFi1ayMfHRx4eHnJ3d9eOHTus4SgnJ0cHDhxQVFSUJCk4OPia+6iqoiL7+YegtiguLuG42xHG274w3valNo937b14+f+1atVKt912m15++WXt3r1bqampeu2117R3714999xzioyM1KVLlzRx4kQdPXpU69at0/LlyzVo0CBJVy4xRkVFadasWdq8ebNSUlI0cuRI+fn5qVu3bpJkSB8AAABlav0ZLAcHBy1atEizZs3SiBEjlJOTozZt2ui9995TQECAJGnJkiWaNm2aIiIi5Ovrq9jYWEVERFj7GDZsmIqKijRp0iTl5+crODhY8fHx1jldPj4+19wHAABAmVofsCTJ29tb06dP/8X1bdu2VWJi4i+ud3R01NixYzV27Nhq7QMAAEC6Di4RAgAAXG8IWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMALW71BSUqL58+crNDRUQUFB6tevn9LS0swuCwAA1DIErN9h4cKFSkhI0NSpU5WYmCiLxaKYmBgVFhaaXRoAAKhFCFiVVFhYqKVLl2ro0KEKCwtTYGCg5s6dqzNnzmjTpk1mlwcAAGoRAlYlpaSkKDc3VyEhIdZlnp6eatOmjXbt2mViZQAAoLYhYFVSenq6JKlx48Y2yxs2bKjTp0+bURIAAKilnMwu4HqRl5cnSXJ2drZZXrduXWVnZ1epTwcHi7y9611zbb+XxXLlv3Exd6uouKTG928WJ8crf0/Ur++q0lKTi6lBjDfjbQ8Yb8a7pjg4WCrVjoBVSS4uLpKuzMUq+16SCgoK5OrqWqU+LRaLHB0rN1DV4SaPuqbt20wODvZ54pbxti+Mt31hvGuf2ltZLVN2aTAjI8NmeUZGhvz8/MwoCQAA1FIErEoKDAyUu7u7duzYYV2Wk5OjAwcOqFOnTiZWBgAAahsuEVaSs7OzoqKiNGvWLHl7e6tJkyZ6/fXX5efnp27dupldHgAAqEUIWL/DsGHDVFRUpEmTJik/P1/BwcGKj48vN/EdAADYN0tpqT193gIAAKD6MQcLAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQvV7sSJE2aXAABAjSJgodpFRUVp3759ZpcBALgGr776qk6ePGl2GdcNAhaqnbOzs5yceOwlcCNKSUnR+PHj9dRTT+nMmTNauXKl/vOf/5hdFqrB+vXr5eBAbKgsfuuh2j366KMaMGCAHnvsMd1yyy1ycXGxWf/444+bUxiqRWZmpubOnas9e/bo8uXL+vnjTjdv3mxSZTDat99+q2eeeUZBQUH69ttvVVhYqIMHD2r69OlasGCB7r33XrNLhIHCw8O1YsUKDR06VPXq1TO7nFqPhz2j2gUGBv7iOovFooMHD9ZgNahuQ4YM0e7du/X444/Lw8Oj3PoXXnjBhKpQHaKjoxUUFKSRI0eqffv2+uSTT9S0aVPNnDlTO3fuVFJSktklwkBPP/20vv76a1ksFvn4+Khu3bo26/njyRZnsFDtUlJSzC4BNWj79u166623dM8995hdCqrZt99+qylTppRb/vTTTyshIcGEilCd7rnnHv6//h0IWKgxx44d06FDh1SnTh35+/urRYsWZpeEauDm5qbGjRubXQZqQJ06dXTp0qVyy0+dOiVXV1cTKkJ14uzz70PAQrUrLCzUmDFj9M9//tO6zGKx6N5779Ubb7whZ2dnE6uD0R5//HHFx8frlVdekaOjo9nloBrdd999mj17tubOnWtdlpqaqmnTpik8PNy8wlBtvvvuO8XHx+vQoUNycnJSy5Yt1adPH7Vt29bs0mod5mCh2s2cOVMbN27UlClTFBwcrOLiYu3atUtTp07VI488otGjR5tdIgwUGxurjRs3ysPDQ82aNSsXoN9//32TKoPRLl26pAEDBuibb75RaWmpPDw8dOnSJQUGBuq9997TTTfdZHaJMNDu3bvVt29ftWrVSp06dVJxcbH++9//6vDhw1q+fLk6duxodom1CgEL1S40NFRTp05VWFiYzfItW7bo5Zdf1hdffGFOYagW48eP/9X1r732Wg1Vgup26dIlubu7Kzk5WQcOHFBJSYlatWql0NBQPs5/A3rmmWcUGBiol156yWb5yy+/rKNHj+qDDz4wqbLaiUuEqHaXLl3SLbfcUm55ixYtdP78eRMqQnUiQNmPxx57TPPnz9fdd9+tu+++2+xyUM2+++47TZ06tdzyqKgoPfnkkyZUVLsRsFDtWrVqpb///e967rnnbJZ/9tlnTHS/QZ0/f17Hjx9XSUmJJKm0tFSFhYX65ptvNGTIEJOrg1EKCgrK3dcONy4vLy9lZmbq1ltvtVmemZnJXNoKELBQ7QYPHqznn39eKSkp6tChgywWi3bv3q1NmzZp1qxZZpcHg/3tb3/ThAkTVFBQIIvFotLSUlksFklSkyZNCFg3kF69emno0KHq1auXmjVrVi5sBQcHm1QZqsO9996rV199VXPnzpW/v78k6ejRo5o2bRo3la0Ac7BQIz7//HO9++67Onz4sEpLS9WqVSv1799fDz74oNmlwWCPPPKI2rZtq5iYGP31r3/V0qVLlZGRoZdfflmjRo3SY489ZnaJMAg3EbYv2dnZ6tu3rw4ePCgPDw9ZLBbl5OSoVatWeu+99+Tt7W12ibUKAQuAoe688059/PHHuvXWW9WnTx/1799fXbp00T//+U8tWrRI69atM7tEGOTHH3/81fVNmjSpoUpQU0pKSrR161YdOXLE+sdy586duSVLBbhEiGqxYMEC9e/fX66urlqwYMGvtuXmdTeWunXrqk6dOpKk5s2b68iRI+rSpYvuuOMOpaWlmVwdjFQWoC5duqRjx46pTp06atq0qdzd3U2uDNXFwcFBYWFh5T4VjvIIWKgW69atU69eveTq6vqrZywsFgsB6wbTtm1bJSQkaOzYsWrZsqW2bNmi/v376+jRo9bghRtDaWmp/u///k8rVqxQUVGRSktL5ezsrJ49e2rChAnWuXe4frVu3Vrbtm2Tj4+PAgMDf3VMuSRsi4CFavGvf/2rwu9x4xsyZIj69+8vb29vPfHEE1qwYIEefvhhnT59Wt27dze7PBjo3XffVVJSkl588UV16tRJJSUl2rVrl9566y01atRIAwYMMLtEXKPp06dbH9o+ffp0QvPvwBws1Jhz587p8uXL+vlb7g9/+INJFaG6nDlzRoWFhWratKlSU1O1evVqNW7cWL179+bj3DeQrl27avTo0Xr44Ydtlm/YsEFvvvmmzeOxAHtDwEK127t3r1588UWdPHnSZnnZx/c5rQxcn4KCgrRhwwY1a9bMZvnJkyf18MMPa//+/SZVhupQUlKiTz/9VHv27Cn3x7LFYtH06dNNrK724RIhqt3UqVNVv359LViwwHqqGTeu77//XrNmzdKRI0dUUFBQbv3mzZtNqArVoXnz5tq+fXu5gLVt2zbOTN+AZs6cqffff1+BgYF8kKESCFiodocOHdKaNWvUunVrs0tBDYiNjdXZs2f10EMPqW7dumaXg2rUt29fvfTSS/rhhx9sbiK8cuVKjR071uzyYLCPP/5YkyZNUq9evcwu5bpAwEK1a9y4sS5fvmx2GaghBw8e1MqVK3X77bebXQqq2eOPP64LFy5oyZIlio+PlyT5+Pho2LBhioqKMrk6GK2goEChoaFml3Hd4HHnqHbPP/+8pk+frkOHDhG07ECLFi30008/mV0Gakh0dLS2bdumf//739q+fbu2b9+u/v37m10WqkFoaKi2bt1qdhnXDSa5o9p17dpVGRkZKi4urnA9k9xvLHv37lVcXJx69+6tm2++WQ4Otn/H8Xy6G8vRo0d1+PBhFRYWllv3+OOP13xBqDaLFy/WggULFBoaKn9//3L3teOehrYIWKh2H3300a+uj4iIqKFKUBPWrl2rl19+WUVFReXW8anRG8u7776rOXPmVLiOsb7xdO3a9RfXWSwWPsDyMwQsAIYKCwtTeHi4nn32Wbm4uJRbz/Ppbhz33HOPnnrqKQ0aNIj7mwE/wyR31Igvv/xS8fHxOnbsmBITE5WUlKRmzZpxCeEGlJ2drZiYGN18881ml4JqdvnyZT366KOEK6ACTHJHtdu+fbteeOEF/eEPf1BOTo5KSkpUXFysCRMmKCkpyezyYLAuXbroP//5j9lloAY89thjWrNmjdlloIYEBgaqdevWv/gFW1wiRLV76qmn9OCDDyo6Olrt27fXJ598oqZNmyo+Pl4fffSRPv30U7NLhIFWrFih2bNnq0uXLmrRooWcnGxPlDMR9sZx5swZPfroo3Jzc9PNN99c7jl177//vkmVoTqsW7fOZoyLiop04sQJffTRRxo3bpweffRRE6urfbhEiGp36NAh/d///V+55ffff7/mz59vQkWoTkuXLpWXl5f2799f7lEpFouFgHUDmTRpkqQrj8xxdXU1uRpUtyeeeKLC5YGBgfr4448JWD9DwEK18/Dw0JkzZ8o9TuPIkSOqX7++SVWhuvzrX/8yuwTUkJ07d2rZsmVq37692aXARB06dNDkyZPNLqPWIWCh2j3yyCOaNm2apk2bJovFotzcXH355Zd69dVX1b17d7PLQzU5f/68CgoK9PNZCDyj7sbRoEED1atXz+wyYLK//e1v/LFcAeZgodpdvnxZ48aN09/+9jeb5eHh4Zo3bx7Pq7vB7Nu3TyNGjNDp06dtlpeWlnJvpBvMZ599poSEBE2ZMkXNmzeXo6Oj2SWhGnXt2tVmDlZpaalyc3OVk5OjkSNHauDAgSZWV/sQsFBjTp48qW+//VaS1LBhQ3Xq1MnkilAdIiMj5ezsrP79+8vT07Pc+rvuusuEqlAd7r//fp06dYqnNNiJBQsWlFtWp04ddejQgSc0VIBLhKg2b731lt5//32tWbNGt9xyi86dO6eXXnpJubm5kqSQkBC9/fbbFd6MEtevI0eOaN26dWrZsqXZpaCaDR482OwSUM3Wr19v/f7n97ZzdHSUt7e32rRpU8NVXR8IWKgWiYmJeueddxQdHS0fHx9J0oQJE+Tm5qbExES5u7tr6NCheueddzR8+HCTq4WR/Pz8lJ+fb3YZqAE85urGN27cuN9sU79+fS1atIgPO/wMlwhRLZ588kk98cQTeuaZZyRdmZfz17/+VWPGjNGAAQMkSVu2bNGMGTP0j3/8w8xSYbCPPvpIiYmJmj59ulq0aFHu3ki4cZSWluqjjz7St99+q/z8fJsPNFgsFk2fPt3E6lATsrOz9dprryk1NVVr1641u5xahTNYqBapqan605/+ZP35P//5jywWi8LCwqzLWrZsqVOnTplRHgwWGBhYbvLrww8/XGFb5uXcOGbOnKlly5YpICCgwvl2uPHVr19fAwcO/MV7ZNkzAhaqzdW/cPfs2SNvb2/ddttt1mW5ubncnPAGMX36dM5U2aH169dr+vTp/HK1c/v27VODBg3MLqPWIWChWgQEBGjXrl265ZZblJOTox07duiBBx6wabNx40a1atXKpAphJH7B2qeCggL98Y9/NLsMmOTw4cMaNWqUUlNT9eKLL5pdTq3Dw55RLXr16mW9uWi/fv1UWFio3r17S5IyMjIUHx+v+Ph4/eUvfzG5UhitsLBQixYtUlpamiRp4sSJat++vfr376+srCyTq4ORQkNDtWXLFrPLgEl8fHzUpUsXLV68WNHR0WaXU+swyR3V5sMPP9Tq1avl4OCgmJgY3X///ZKkqVOnKiEhQTExMXyC8AY0ffp0ffzxx1q6dKkuXLigmJgYDRs2TFu2bNGtt96q1157zewScQ2uvhfS2bNnlZSUpPvvv1/NmzeXg4Pt3+w8dxL2jICFGnfmzBk5OzvLy8vL7FJQDbp06aLXXntN99xzj1555RUdO3ZMy5Yt0/79+zVw4EAlJyebXSKuQdeuXSvVzmKxaPPmzdVcDVB7MQcLNa5Ro0Zml4BqdOHCBfn7+0uStm/frieffFKS5OXlxf2xbgA8zBuoHAIWAEM1a9ZM+/fv1/nz55WWlqbQ0FBJ0ueff17uTtC4/pWUlCg5OVmHDx+Wg4ODbr/9dh6DBYiABcBgAwYM0KhRo+Tg4KCQkBAFBgbqrbfe0ltvvcWNJ28wGRkZ6t+/v44cOSJPT0+Vlpbq4sWL6tChg9555x15eHiYXSJgGuZgATBcSkqKfvjhB3Xp0kXOzs766quv5OTkZHPzWVz/XnjhBZ0+fVqvv/66br31VklXnkX54osvKiAggA80wK4RsABUi8LCQv3www9q1qyZSktLVadOHbNLgsE6dOig999/X3fccYfN8m+++UYxMTHauXOnSZUB5uM+WAAMVVpaqlmzZik4OFg9evTQ6dOn9eKLL2r8+PG6fPmy2eXBQJ6enhWOqaOjo5ycmIEC+0bAAmCoDz74QB9//LGmTJkiZ2dnSdJ9992nf/3rX5o3b57J1cFIw4YN00svvaSUlBTrsu+//17Tpk3TkCFDTKwMMB+XCAEY6uGHH9aIESPUrVs3tW/fXp988omaNm2qzz//XNOmTePO3zeQsLAwZWZmqri4WPXq1ZOTk5Oys7NVWlpa7tmUPOQb9oZzuAAM9cMPP6h169bllgcEBOjcuXMmVITqMmLECLNLAGotAhYAQzVp0kT79u0rd8+rL7/8Uk2bNjWpKlSHiIgIs0sAai0CFgBD9e/fXy+//LLOnDmj0tJSJScnKyEhQR988IHGjx9vdnkw2JYtW7Ro0SIdOnRITk5Oatmypfr3769u3bqZXRpgKuZgATBcYmKi3n77baWnp0uSfHx8NGDAAPXt29fkymCkzz//XEOHDlW3bt3UqVMnlZSUaNeuXdqyZYvefPNN/fnPfza7RMA0BCwAhvrkk08UFham+vXr6/z58yotLZWPj4/ZZaEaRERE6L777iv3icEFCxboiy++0IcffmhSZYD5uE0DAENNnTrVOpnd29ubcHUDS01NVY8ePcot79Gjh44cOWJCRUDtQcACYKjmzZvr0KFDZpeBGtCwYUOdOHGi3PITJ07wHELYPSa5AzDUbbfdpjFjxmjJkiVq3ry56tata7Oe59PdOHr06KGXX35ZU6ZMUceOHSVJe/bs0SuvvKIHH3zQ5OoAcxGwABjq5MmT1l+2Z8+eNbkaVKfBgwfr8OHDGjRokPXGoqWlpQoLC9Po0aNNrg4wF5PcAVyz1157TcOHD5ebm5vZpcAEqampOnz4sEpLS9WwYUN16tTJ7JIA0zEHC8A1e//995WXl2ezrH///srIyDCpIlSnt956S3/84x+VlpYmScrOztbkyZM1evRo9e7dW3379lV+fr7JVQLmImABuGYVnQj/73//q4KCAhOqQXVKTEzUO++8o549e1o/ITphwgS5ubnp008/1RdffKHc3Fy98847JlcKmIuABQCotLVr12rcuHEaNWqU3N3dtW/fPp04cULPPvus/P391ahRIw0ePFifffaZ2aUCpiJgAQAqLTU1VX/605+sP//nP/+RxWJRWFiYdVnLli116tQpM8oDag0CFgBDlH2KDDe+q8d6z5498vb21m233WZdlpubK1dXVzNKA2oNbtMAwBBTp061uefV5cuX9frrr6tevXo27bgP1vUtICBAu3bt0i233KKcnBzt2LFDDzzwgE2bjRs3qlWrViZVCNQOBCwA1yw4OLjcPa/at2+vrKwsZWVlmVQVqkOvXr300ksv6dChQ/r6669VWFio3r17S5IyMjK0YcMGxcfHa9q0aSZXCpiL+2ABAH6XDz/8UKtXr5aDg4NiYmJ0//33S7pyFjMhIUExMTEaPny4yVUC5iJgAQAMcebMGTk7O8vLy8vsUgDTEbAAAAAMxqcIAQAADEbAAgAAMBgBC8AN43qZ8XC91Amg6ghYAGq1w4cPa+TIkbrnnnt0xx13qHPnzhoxYoQOHDhg027Pnj0aNGiQIfvcsWOHAgICtGPHDkP6u9rmzZv14osv/uL6MWPGKCAgQNu2batw/datWxUQEKCZM2caXhsA4xCwANRaR44cUc+ePXX+/HlNnDhRS5cuVWxsrE6dOqWePXtq79691rZr167V0aNHzSu2kpYtW6bTp0//4vpJkybJ19dXU6ZMUV5ens263NxcTZkyRbfddptGjhxZ3aUCuAYELAC11nvvvaebbrpJS5YsUffu3XXXXXfp0Ucf1bJly+Tt7a2FCxeaXaLhbrrpJsXFxemHH37QG2+8YbNu9uzZysjI0Ouvvy5nZ2dzCgRQKQQsALXWuXPnJJWfs+Tm5qbx48froYcekiSNGzdOH330kX788UcFBARo3bp1+uGHH6zfX23cuHHq2rWrzbKEhAQ98MADatu2raKioip8UPGpU6c0atQo3XXXXQoKClKfPn1sLlOW7W/jxo0aNmyY2rdvr+DgYE2cOFG5ubmSpN69e2vnzp3auXPnr16CvO+++9SjRw998MEH2r9/vyTpv//9r1atWqUXXnhBrVu3rlRNZXXFxsaqc+fOuv3223X33XcrNjbW5g77Xbt21fTp09WnTx916NBBL7300i+MCIDKImABqLXCw8N16tQpPfXUU1q5cqVSU1OtYevBBx9URESEJOn5559XWFiYfH19lZiYqPDw8ErvY8WKFZoyZYpCQ0O1cOFCBQUFafLkyTZtzp8/r6eeekrfffedJk+erNmzZ6ukpES9evVSamqqTdspU6aoSZMmWrhwoQYMGKCkpCQtWrTIuq5NmzZq06aNEhMTdfvtt/9iXZMmTZKXl5deeeUVXb58WXFxcQoKClJMTEyla8rLy9Ozzz6r1NRUTZkyRfHx8YqKitKnn36qOXPm2Oxv5cqVCggI0JtvvqnHHnus0scPQMV4FiGAWuuZZ57R2bNnFR8fr1deeUWS5OXlpc6dO6t3794KCgqSJDVr1kze3t5ydnZWu3btJEk//fTTb/ZfWlqqhQsX6oEHHtCkSZMkSZ07d9alS5eUkJBgbbd8+XJduHBBq1evVpMmTSRJXbp0Uffu3TVv3jzNnz/f2jYsLMw6if3uu+/W9u3b9cUXX2j06NFq2bKl3N3dJcla5y/x8vJSXFycXnjhBfXr108nT57U+vXr5ejoWOmaTpw4IT8/P82YMUPNmjWTJIWEhGj//v3auXOnzf4aNmyocePGycGBv7sBI/B/EoBabfjw4dq6datmz56tJ598Uu7u7tqwYYN69uyp5cuXX1Pfx44dU2Zmpv785z/bLC+79FgmOTlZrVu3VqNGjVRUVKSioiI5ODioS5cu+ve//23T9ufByc/Pr1JhryLdunVT9+7dtXPnTo0dO1bNmzf/XTW1bt1aq1at0s0336zvv/9eW7du1dKlS3Xs2DFdvnzZZl/+/v6EK8BAnMECUOvVr19fPXr0UI8ePSRJBw4cUGxsrGbNmqVHH320ys++y87OliR5e3vbLPf19bX5+cKFC0pLS/vFS3pXf9rP1dXVZp2Dg8M13fcqNDRUn332mcLCwn53Ta6urnrvvff0zjvvKCsrSw0aNNDtt98uV1dXXbx40aZ9gwYNqlwjgPIIWABqpTNnzigyMlLDhw/XX/7yF5t1bdq00YgRIzRkyBB9//33FQYsi8UiSSouLrZZfvXZpLLtMjMzbdpcuHDB5mcPDw/dddddio2NrbBWMz7RV5maNmzYoBkzZmj06NF68sknrUFy+PDh1snzAKoH54MB1EoNGjSQk5OTVq1apYKCgnLrjx07prp16+qWW26RpHKXt8rmOqWnp1uXXb58Wfv27bP+3Lx5czVu3Fh///vfbbbdsmWLzc933XWXjh8/rhYtWujOO++0fn3yySdau3atdV5UZRh1Ga4yNe3Zs0ceHh4aOHCgNVzl5uZqz549KikpMaQOABUjYAGolRwdHRUXF6fDhw8rMjJSq1ev1s6dO/Xll19q+vTpmjdvnl544QXVr19fkuTp6alz587pyy+/VEZGhurXr6/27dtrxYoV+uSTT7R161YNGTJE+fn51n1YLBaNGTNGW7Zs0aRJk7Rt2zYtWLBAq1evtqklOjpaJSUlio6O1meffabk5GRNnjxZ77//vm699dbf9bo8PT11/PhxJScnWy9RVkVlamrbtq0uXryoGTNmaMeOHdqwYYN69eqlc+fOlbuJKQBjEbAA1Frh4eFas2aNWrVqpUWLFql///4aNWqUDh48qLlz52rgwIHWtk888YSaNGmiIUOGaP369ZKkGTNm6I477tDkyZM1btw4tW7dWn369LHZR48ePTR37lzt3btXgwcP1pYtW6yfWCzTqFEjJSQkqEmTJoqLi9Nzzz2nffv2adq0aYqOjv5dr6lXr16qU6eOYmJi9NVXX1XpuFS2poiICA0ZMkQbN25UTEyM5s+fr06dOumVV17RhQsXros73wPXK0spTx0FAAAwFGewAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAg/0/oPikPQ3zusIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"my_data.csv\")\n",
    "\n",
    "# Plot histogram of student_year column value counts\n",
    "df['student_year'].value_counts().plot(kind='bar')\n",
    "\n",
    "# Set plot title and labels\n",
    "plt.title(\"Student Year Distribution\")\n",
    "plt.xlabel(\"Student Year\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "# Display the histogram\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "student year is useful to find the transfer student, find some one have freshman but  don't have senior and senior but don't have freshman."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## student_age--how old the student is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum student age: 17\n",
      "Maximum student age: 33\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the minimum and maximum values of 'student_age'\n",
    "min_age = df['student_age'].min()\n",
    "max_age = df['student_age'].max()\n",
    "\n",
    "# Print the minimum and maximum values of 'student_age'\n",
    "print(\"Minimum student age:\", min_age)\n",
    "print(\"Maximum student age:\", max_age)\n",
    "\n",
    "# Plot a histogram of the 'student_age' column\n",
    "plt.hist(df['student_age'], bins=10)\n",
    "plt.xlabel('Student Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Student Age')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not useful for in those question"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## year--what year it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum year: 2005\n",
      "Maximum year: 2025\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the minimum and maximum values of 'year'\n",
    "min_year = df['year'].min()\n",
    "max_year = df['year'].max()\n",
    "\n",
    "# Print the minimum and maximum values of 'year'\n",
    "print(\"Minimum year:\", min_year)\n",
    "print(\"Maximum year:\", max_year)\n",
    "\n",
    "# Plot a histogram of the 'year' column\n",
    "plt.hist(df['year'], bins=10)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Years')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not useful in the question "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does this school have transfer students?\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore each variable and answer the question about transfer students, we need the provided student information in a pandas DataFrame. Let's assume the DataFrame is named df. We'll go through each column, examine its statistics, create histograms, and provide observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDs of Transfer Students:\n"
     ]
    }
   ],
   "source": [
    "# Filter for students who have all four years\n",
    "filtered_data = df.groupby('id')['student_year'].nunique() < 4\n",
    "transfer_students = filtered_data[filtered_data].index\n",
    "\n",
    "# Print the IDs of transfer students\n",
    "print(\"IDs of Transfer Students:\")\n",
    "for student_id in transfer_students:\n",
    "    print(student_id)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No output print out, so No transfer student "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the median length of attendence at this university?\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore each variable and answer the questions, let's analyze the relevant columns using pandas and create visualizations where applicable.\n",
    "\n",
    "First, let's calculate the median length of attendance at the university. To do this, we'll need the \"year\" and \"student_year\" columns. Assuming the \"year\" column represents the current year, we can calculate the median length of attendance by subtracting the \"student_year\" from the \"year\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Length of Attendance:  5.0 years\n"
     ]
    }
   ],
   "source": [
    "\n",
    "median_length_of_attendance = df.groupby('id')['year'].apply(lambda x: x.max() - x.min()).median()\n",
    "\n",
    "print(\"Median Length of Attendance: \", median_length_of_attendance, \"years\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Median Length of Attendance:  5 years"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do you think this university has any one credit classes?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine if the university offers any one-credit classes, we can examine the unique values in the \"credits\" column and check if there is a one-credit course option available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The university does not offer one-credit classes.\n"
     ]
    }
   ],
   "source": [
    "# Check if there are any one-credit classes\n",
    "one_credit_classes = 1 in df['credits'].unique()\n",
    "\n",
    "if one_credit_classes:\n",
    "    print(\"The university offers one-credit classes.\")\n",
    "else:\n",
    "    print(\"The university does not offer one-credit classes.\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The university does not offer one-credit classes."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is grade inflation a problem at this university?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine if grade inflation is a problem at the university, we can analyze the distribution of GPA values in the dataset. If there is a significant proportion of high GPAs (e.g., close to the maximum value of 4.0) and a lack of variation in lower GPAs, it may indicate grade inflation.\n",
    "Let's plot a histogram of the GPA values and visually inspect the distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting histogram of GPAs\n",
    "plt.hist(df['gpa'], bins=10)\n",
    "plt.xlabel('GPA')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of GPAs')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By examining the histogram, we can gain insights into the distribution of GPA values. If there is a substantial concentration of GPAs near the high end of the scale (e.g., above 4.0), and few or no GPAs in the lower range, it may suggest grade inflation.\n",
    "\n",
    "Additionally, we can calculate the percentage of GPAs that fall into certain ranges (e.g., above 4.0) to further assess the presence of grade inflation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of GPAs above 4.0 : 8.8361 %\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percentage of GPAs above a certain threshold (e.g., 4.0)\n",
    "threshold = 4.0\n",
    "percentage_above_threshold = (df['gpa'] > threshold).mean() * 100\n",
    "\n",
    "print(\"Percentage of GPAs above\", threshold, \":\", percentage_above_threshold, \"%\")\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the information you provided, the percentage of GPAs above 4.0 is approximately 8.8361%. Since the maximum GPA value in the dataset is 4.0, this indicates that the percentage of GPAs above the maximum value is not possible. Therefore, based on this dataset, it suggests that grade inflation is not a problem at this university, as no GPAs are reported above the maximum value of 4.0.\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In what area do you think the Univesrity might be located?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the latitude and longitude values provided in the dataset, we can make an estimation of the general area where the university might be located. Let's calculate the average latitude and longitude values to get an approximate location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Latitude:  37.57247703917055\n",
      "Average Longitude:  -84.17148162331742\n"
     ]
    }
   ],
   "source": [
    "# Group the data by 'id' and calculate the average latitude and longitude\n",
    "grouped_data = df.groupby('id').agg({'lat': 'mean', 'lon': 'mean'})\n",
    "\n",
    "# Get the average latitude and longitude\n",
    "average_latitude = grouped_data['lat'].mean()\n",
    "average_longitude = grouped_data['lon'].mean()\n",
    "\n",
    "print(\"Average Latitude: \", average_latitude)\n",
    "print(\"Average Longitude: \", average_longitude)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the average latitude and longitude values calculated from the dataset, the estimated location of the university is approximately at latitude 37.572 and longitude -84.171.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does tutoring make a stastically significant difference in grade?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine if tutoring makes a statistically significant difference in grades, we can perform a hypothesis test. We can compare the GPAs of students who received tutoring to those who did not receive tutoring and assess if there is a significant difference between the two groups.\n",
    "\n",
    "Here, we can conduct a two-sample t-test to compare the means of the GPA for students who received tutoring and those who did not. The null hypothesis (H0) would be that there is no significant difference in GPAs between the two groups, and the alternative hypothesis (H1) would be that there is a significant difference.\n",
    "\n",
    "\n",
    "Let's assume:\n",
    "\n",
    "Group 1: Students who received tutoring (tutoring_yes)\n",
    "Group 2: Students who did not receive tutoring (tutoring_no)\n",
    "We can use the scipy.stats module in Python to perform the t-test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /Users/yuliangpeng/opt/anaconda3/lib/python3.9/site-packages (1.9.1)\n",
      "Requirement already satisfied: numpy<1.25.0,>=1.18.5 in /Users/yuliangpeng/opt/anaconda3/lib/python3.9/site-packages (from scipy) (1.23.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a statistically significant difference in grades between students who received tutoring and those who did not.\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# Select GPAs for students who received tutoring\n",
    "gpa_tutoring_yes = df[df['tutoring'] == 'Yes']['gpa']\n",
    "\n",
    "# Select GPAs for students who did not receive tutoring\n",
    "gpa_tutoring_no = df[df['tutoring'] == 'No']['gpa']\n",
    "\n",
    "# Perform two-sample t-test\n",
    "t_statistic, p_value = stats.ttest_ind(gpa_tutoring_yes, gpa_tutoring_no, equal_var=False)\n",
    "\n",
    "alpha = 0.05  # Significance level\n",
    "\n",
    "if p_value < alpha:\n",
    "    print(\"There is a statistically significant difference in grades between students who received tutoring and those who did not.\")\n",
    "else:\n",
    "    print(\"There is no statistically significant difference in grades between students who received tutoring and those who did not.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting output will indicate whether there is a statistically significant difference in grades between students who received tutoring and those who did not.\n",
    "\n",
    "It's important to note that the interpretation of statistical significance should consider the context and limitations of the dataset and the specific analysis conducted. Additionally, performing additional statistical tests and considering potential confounding variables would provide a more comprehensive analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How often do students switch majors?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine how often students switch majors, we can analyze the frequency of changes in the \"major\" column. We'll count the number of unique majors and calculate the percentage of students who changed their major at least once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of students who have changed their major: 6.78%\n"
     ]
    }
   ],
   "source": [
    "# Group the data by 'id' and count the number of unique majors for each student\n",
    "grouped_data = df.groupby('id')['major'].nunique()\n",
    "\n",
    "# Count the number of students who have changed their major at least once\n",
    "students_changed_major = (grouped_data > 1).sum()\n",
    "\n",
    "# Calculate the percentage of students who have changed their major at least once\n",
    "percentage_changed_major = (students_changed_major / len(grouped_data)) * 100\n",
    "\n",
    "print(\"Percentage of students who have changed their major: {:.2f}%\".format(percentage_changed_major))\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code groups the data by 'id' and counts the number of unique majors for each student. Then, it counts the number of students who have changed their major at least once by checking if the number of unique majors is greater than 1. Finally, it calculates the percentage of students who have changed their major at least once.\n",
    "\n",
    "The resulting percentage indicates the frequency of students switching majors based on the available dataset. Please note that this analysis assumes that each row in the dataset represents a unique semester for a student and the 'major' column accurately represents the student's major."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do different majors have different GPA distributions?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assess whether different majors have different GPA distributions, we can compare the GPA distributions across different majors in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the distribution of GPAs for each major\n",
    "sns.boxplot(x='major', y='gpa', data=df)\n",
    "plt.xlabel('Major')\n",
    "plt.ylabel('GPA')\n",
    "plt.title('Distribution of GPAs by Major')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "English are better and others are looks same. But totally close and same"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The data cleaning steps in your code snippet performed the following operations:\n",
    "\n",
    "Remove rows with GPAs over 4.0:\n",
    "\n",
    "Filtered the DataFrame to keep only rows where the GPA is less than or equal to 4.0.\n",
    "Identify IDs with missing information:\n",
    "\n",
    "Identified the unique IDs for rows that have duplicated IDs and contain any missing values.\n",
    "Remove rows with missing information for identified IDs:\n",
    "\n",
    "Filtered the DataFrame to exclude rows with IDs that were identified as having missing information.\n",
    "Remove rows where students have GPA but 0 studied hours:\n",
    "\n",
    "Filtered the DataFrame to keep only rows where the average hours studied are greater than 0 or where the GPA is null.\n",
    "Assign the cleaned dataset to 'df':\n",
    "\n",
    "Assigned the cleaned DataFrame to the variable 'df'.\n",
    "By applying these data cleaning steps, you have eliminated rows with GPAs over 4.0, removed rows with missing information for specific IDs, and removed rows where students have a GPA but 0 studied hours. This helps to ensure that the dataset is more reliable and suitable for analysis or further processing."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I apply log transformation on latitude, longitude and parents_income. For longitude, I do the log operation on the number and keep the sign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'id', 'lat', 'lon', 'gpa', 'avg_hours_studied',\n",
      "       'parents_income', 'year', 'credits', 'student_age', 'major_CS',\n",
      "       'major_Engineering', 'major_English', 'major_Math', 'major_Physics',\n",
      "       'student_year_Freshman', 'student_year_Junior', 'student_year_Senior',\n",
      "       'student_year_Sophmore', 'tutoring_No', 'tutoring_Yes', 'semester_Fall',\n",
      "       'semester_Spring'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "df = pd.read_csv(\"my_data.csv\")\n",
    "\n",
    "df[['lat', 'parents_income']] = df[['lat', 'parents_income']].apply(np.log) \n",
    "df['lon'] = df['lon'].apply(lambda x: -np.log(abs(x)))\n",
    "df.loc[df['avg_hours_studied'] > 10, 'avg_hours_studied'] = df[df['avg_hours_studied'] < 10]['avg_hours_studied'].median()\n",
    "columns = ['major', 'student_year', 'tutoring', 'semester'] \n",
    "for c in columns:\n",
    "    dummies = pd.get_dummies(df[c], prefix=c) \n",
    "    for i in dummies.columns:\n",
    "        df[i] = dummies[i]\n",
    "    df = df.drop(c, axis=1)\n",
    "    df = df.drop(df[df[\"year\"] > 2022].index)\n",
    "print(df.columns)\n",
    "\n",
    "# Remove rows with GPAs over 4.0\n",
    "df_cleaned = df[df['gpa'] <= 4.0]\n",
    "\n",
    "# Identify IDs with missing information\n",
    "missing_ids = df_cleaned[df_cleaned['id'].duplicated(keep=False) & df_cleaned.isnull().any(axis=1)]['id'].unique()\n",
    "\n",
    "# Remove rows with missing information for identified IDs\n",
    "df_cleaned = df_cleaned[~df_cleaned['id'].isin(missing_ids)]\n",
    "# Remove rows where students have GPA but 0 studied hours\n",
    "df_cleaned = df_cleaned[(df_cleaned['avg_hours_studied'] > 0) | df_cleaned['gpa'].isnull()]\n",
    "\n",
    "\n",
    "\n",
    "df = df_cleaned\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evalutating "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I utilized four different regression models: Decision Tree, Linear Regression, Random Forest, and K Nearest Neighbors Regression. The objective was to predict a student's GPA based on their credit hours and average hours studied. Specifically, I focused on predicting the GPA of a student who took 14 credits (the average number of credits) and studied for 4 hours (the average studied hours).\n",
    "\n",
    "To evaluate the models, I compared the predicted GPA for the student with 14 credits and 4 hours of study with the predicted GPAs for students who had 13 and 15 credits but the same study hours. The expected outcome was that the predicted GPA for the student with 14 credits would fall between the predicted GPAs for the students with 13 and 15 credits.\n",
    "\n",
    "The Decision Tree, Linear Regression, Random Forest, and K Nearest Neighbors Regression models were trained using the available data. After training, I used each model to predict the GPA for the student with 14 credits and 4 hours of study. I also obtained the predicted GPAs for the students with 13 and 15 credits but the same study hours.\n",
    "\n",
    "By comparing the predicted GPAs, I could determine if the models correctly predicted that the GPA for the student with 14 credits would fall between the predicted GPAs for the students with 13 and 15 credits.\n",
    "\n",
    "It's important to note that the success of the models in accurately predicting the GPA depends on the quality and representativeness of the data, as well as the appropriateness of the chosen models for the given task.\n",
    "\n",
    "Overall, this evaluation approach provides insights into how well the models can predict the GPA for a student with average credits and study hours compared to students with slightly higher or lower credits."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Modeling"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will predict the  gpa when the stuided hours are 4 and credits 13. And compare with the actual gpa when credits are 12 and 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average hours studied: 5.5978344526179615\n",
      "Average credits: 13.594395631787805\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average hours studied\n",
    "average_hours_studied = df['avg_hours_studied'].mean()\n",
    "\n",
    "\n",
    "# Calculate the average credits\n",
    "average_credits = df['credits'].mean()\n",
    "\n",
    "# Print the results\n",
    "print(\"Average hours studied:\", average_hours_studied)\n",
    "print(\"Average credits:\", average_credits)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume Average hours to 4 hours \n",
    "Average credits to 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_credits = 13\n",
    "average_hours_studied = 4\n",
    "\n",
    "subset_df = df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "data = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.32831965250145134\n",
      "R-squared Score: 0.07214820031469482\n",
      "Predicted GPA: [3.55072927]\n",
      "Predicted GPA: [3.5507745]\n",
      "Predicted GPA: [3.55081973]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Assuming you have already loaded the dataset and selected the relevant features and target variable\n",
    "X = data[['credits', 'avg_hours_studied']]\n",
    "y = data['gpa']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and fit a Linear Regression model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared Score:\", r2)\n",
    "\n",
    "# Predict GPA for new data\n",
    "new_datal3 = pd.DataFrame({'credits': [13], 'avg_hours_studied': [3]})\n",
    "predicted_gpal3 = regressor.predict(new_datal3)\n",
    "\n",
    "new_datal4 = pd.DataFrame({'credits': [13], 'avg_hours_studied': [4]})\n",
    "predicted_gpal4 = regressor.predict(new_datal4)\n",
    "\n",
    "new_datal5 = pd.DataFrame({'credits': [13], 'avg_hours_studied': [5]})\n",
    "predicted_gpal5 = regressor.predict(new_datal5)\n",
    "\n",
    "print(\"Predicted GPA:\", predicted_gpal3)\n",
    "\n",
    "print(\"Predicted GPA:\", predicted_gpal4)\n",
    "print(\"Predicted GPA:\", predicted_gpal5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual GPA when credits are 12 points:\n",
      "Average GPA when study hours are 3 hours: 3.240417090125636\n",
      "Average GPA when study hours are 4 hours: 3.5723291267639055\n",
      "Average GPA when study hours are 5 hours: 3.9470085347486368\n",
      "Predicted GPA: [3.55072927]\n",
      "Predicted GPA: [3.5507745]\n",
      "Predicted GPA: [3.55081973]\n",
      "Actual GPA when credits are 15 points:\n",
      "Average GPA when study hours are 3 hours: 3.0163553331744852\n",
      "Average GPA when study hours are 4 hours: 3.395554876354547\n",
      "Average GPA when study hours are 5 hours: 3.828745073108724\n"
     ]
    }
   ],
   "source": [
    "# Filter the DataFrame for credits equal to 12\n",
    "filtered_credits_df = df[df['credits'] == 12]\n",
    "# Filter the DataFrame for study hours equal to 4\n",
    "filtered_study_hours_4_df = filtered_credits_df[filtered_credits_df['avg_hours_studied'] == 4]\n",
    "# Calculate the average GPA for study hours equal to 4\n",
    "average_gpa_study_hours_4 = filtered_study_hours_4_df['gpa'].mean()\n",
    "\n",
    "# Filter the DataFrame for study hours equal to 5\n",
    "filtered_study_hours_5_df = filtered_credits_df[filtered_credits_df['avg_hours_studied'] == 5]\n",
    "# Calculate the average GPA for study hours equal to 5\n",
    "average_gpa_study_hours_5 = filtered_study_hours_5_df['gpa'].mean()\n",
    "\n",
    "# Filter the DataFrame for study hours equal to 3\n",
    "filtered_study_hours_3_df = filtered_credits_df[filtered_credits_df['avg_hours_studied'] == 3]\n",
    "# Calculate the average GPA for study hours equal to 3\n",
    "average_gpa_study_hours_3 = filtered_study_hours_3_df['gpa'].mean()\n",
    "\n",
    "# Print the results\n",
    "print(\"Actual GPA when credits are 12 points:\")\n",
    "\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5)\n",
    "\n",
    "\n",
    "# Filter the DataFrame for credits equal to 15\n",
    "filtered_credits_df_12 = subset_df[subset_df['credits'] == 15]\n",
    "\n",
    "# Filter the DataFrame for study hours equal to 3\n",
    "filtered_study_hours_3_df_12 = filtered_credits_df_12[filtered_credits_df_12['avg_hours_studied'] == 3]\n",
    "# Calculate the average GPA for study hours equal to 3\n",
    "average_gpa_study_hours_3_12 = filtered_study_hours_3_df_12['gpa'].mean()\n",
    "\n",
    "# Filter the DataFrame for study hours equal to 4\n",
    "filtered_study_hours_4_df_12 = filtered_credits_df_12[filtered_credits_df_12['avg_hours_studied'] == 4]\n",
    "# Calculate the average GPA for study hours equal to 4\n",
    "average_gpa_study_hours_4_12 = filtered_study_hours_4_df_12['gpa'].mean()\n",
    "\n",
    "# Filter the DataFrame for study hours equal to 5\n",
    "filtered_study_hours_5_df_12 = filtered_credits_df_12[filtered_credits_df_12['avg_hours_studied'] == 5]\n",
    "# Calculate the average GPA for study hours equal to 5\n",
    "average_gpa_study_hours_5_12 = filtered_study_hours_5_df_12['gpa'].mean()\n",
    "\n",
    "print(\"Predicted GPA:\", predicted_gpal3)\n",
    "\n",
    "print(\"Predicted GPA:\", predicted_gpal4)\n",
    "print(\"Predicted GPA:\", predicted_gpal5)\n",
    "\n",
    "# Print the results\n",
    "print(\"Actual GPA when credits are 15 points:\")\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3_12)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4_12)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5_12)\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this result we could find when then credits is 13 and the study hours are 4 the predicted gpa is 3.52 this is bigger than when credits are 12 (4 hours) and samller than 15 (4 hours), so that points is should be correct  but when the study hours are 3 the predicte gpa should be smaller than 12 credits and 5 hours should bigger than 15 points so those are not correct. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dicision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.14785117558902927\n",
      "R-squared Score: 0.5821633633239105\n",
      "Predicted GPA when credits 13 and studied 3 hours: [3.23962514]\n",
      "Predicted GPA when credits 13 and studied 4 hours: [3.57323552]\n",
      "Predicted GPA when credits 13 and studied 5 hours: [3.94676513]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "\n",
    "# Assuming you have already loaded the dataset and selected the relevant features and target variable\n",
    "\n",
    "# Separate the features (studied hours in each credit) and the target variable (GPA)\n",
    "X = data[['credits', 'avg_hours_studied']]\n",
    "y = data['gpa']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and fit a decision tree regressor\n",
    "regressor = DecisionTreeRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared Score:\", r2)\n",
    "\n",
    "# Predict GPA for new data\n",
    "new_data3 = pd.DataFrame({'credits': [13], 'avg_hours_studied': [3]})\n",
    "new_data4 = pd.DataFrame({'credits': [13], 'avg_hours_studied': [4]})\n",
    "new_data5 = pd.DataFrame({'credits': [13], 'avg_hours_studied': [5]})\n",
    "\n",
    "predicted_gpa3 = regressor.predict(new_data3)\n",
    "predicted_gpa4 = regressor.predict(new_data4)\n",
    "predicted_gpa5 = regressor.predict(new_data5)\n",
    "\n",
    "print(\"Predicted GPA when credits 13 and studied 3 hours:\", predicted_gpa3)\n",
    "print(\"Predicted GPA when credits 13 and studied 4 hours:\", predicted_gpa4)\n",
    "print(\"Predicted GPA when credits 13 and studied 5 hours:\", predicted_gpa5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual GPA when credits are 12 points:\n",
      "Average GPA when study hours are 3 hours: 3.240417090125636\n",
      "Average GPA when study hours are 4 hours: 3.5723291267639055\n",
      "Average GPA when study hours are 5 hours: 3.9470085347486368\n",
      "Predicted  GPA when credits are 13 points:\n",
      "Predicted GPA when credits 13 and studied 3 hours: [3.23962514]\n",
      "Predicted GPA when credits 13 and studied 4 hours: [3.57323552]\n",
      "Predicted GPA when credits 13 and studied 5 hours: [3.94676513]\n",
      "Actual GPA when credits are 15 points:\n",
      "Average GPA when study hours are 3 hours: 3.0163553331744852\n",
      "Average GPA when study hours are 4 hours: 3.395554876354547\n",
      "Average GPA when study hours are 5 hours: 3.828745073108724\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "print(\"Actual GPA when credits are 12 points:\")\n",
    "\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5)\n",
    "\n",
    "\n",
    "print(\"Predicted  GPA when credits are 13 points:\")\n",
    "print(\"Predicted GPA when credits 13 and studied 3 hours:\", predicted_gpa3)\n",
    "print(\"Predicted GPA when credits 13 and studied 4 hours:\", predicted_gpa4)\n",
    "print(\"Predicted GPA when credits 13 and studied 5 hours:\", predicted_gpa5)\n",
    "\n",
    "# Print the results\n",
    "print(\"Actual GPA when credits are 15 points:\")\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3_12)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4_12)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5_12)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All predicted gpa should be correct "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.14784899865704632\n",
      "R-squared Score: 0.5821695154693663\n",
      "Predicted GPA when credits 13 and studied 3 hours: [3.23981216]\n",
      "Predicted GPA when credits 13 and studied 4 hours: [3.57319187]\n",
      "Predicted GPA when credits 13 and studied 5 hours: [3.94677087]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Assuming you have already loaded the dataset and selected the relevant features and target variable\n",
    "\n",
    "# Separate the features (studied hours in each credit) and the target variable (GPA)\n",
    "X = data[['credits', 'avg_hours_studied']]\n",
    "y = data['gpa']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and fit a random forest regressor\n",
    "regressor = RandomForestRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared Score:\", r2)\n",
    "\n",
    "predicted_gpa3r = regressor.predict(new_data3)\n",
    "predicted_gpa4r = regressor.predict(new_data4)\n",
    "predicted_gpa5r = regressor.predict(new_data5)\n",
    "\n",
    "print(\"Predicted GPA when credits 13 and studied 3 hours:\", predicted_gpa3r)\n",
    "print(\"Predicted GPA when credits 13 and studied 4 hours:\", predicted_gpa4r)\n",
    "print(\"Predicted GPA when credits 13 and studied 5 hours:\", predicted_gpa5r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual GPA when credits are 12 points:\n",
      "Average GPA when study hours are 3 hours: 3.240417090125636\n",
      "Average GPA when study hours are 4 hours: 3.5723291267639055\n",
      "Average GPA when study hours are 5 hours: 3.9470085347486368\n",
      "Predicted  GPA when credits are 13 points:\n",
      "Predicted GPA when credits 13 and studied 3 hours: [3.23981216]\n",
      "Predicted GPA when credits 13 and studied 4 hours: [3.57319187]\n",
      "Predicted GPA when credits 13 and studied 5 hours: [3.94677087]\n",
      "Actual GPA when credits are 15 points:\n",
      "Average GPA when study hours are 3 hours: 3.0163553331744852\n",
      "Average GPA when study hours are 4 hours: 3.395554876354547\n",
      "Average GPA when study hours are 5 hours: 3.828745073108724\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual GPA when credits are 12 points:\")\n",
    "\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5)\n",
    "\n",
    "\n",
    "print(\"Predicted  GPA when credits are 13 points:\")\n",
    "print(\"Predicted GPA when credits 13 and studied 3 hours:\", predicted_gpa3r)\n",
    "print(\"Predicted GPA when credits 13 and studied 4 hours:\", predicted_gpa4r)\n",
    "print(\"Predicted GPA when credits 13 and studied 5 hours:\", predicted_gpa5r)\n",
    "\n",
    "# Print the results\n",
    "print(\"Actual GPA when credits are 15 points:\")\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3_12)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4_12)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5_12)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### result "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "all should be correct"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors (KNN) Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.16788280125907462\n",
      "R-squared Score: 0.5255527407584771\n",
      "Predicted GPA: [3.324]\n",
      "Predicted GPA: [3.752]\n",
      "Predicted GPA: [3.978]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "\n",
    "# Assuming you have already loaded the dataset and selected the relevant features and target variable\n",
    "X = data[['credits', 'avg_hours_studied']]\n",
    "y = data['gpa']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and fit a KNN regressor with k=5\n",
    "regressor = KNeighborsRegressor(n_neighbors=5)\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared Score:\", r2)\n",
    "\n",
    "# Predict GPA for new data\n",
    "new_data3r = pd.DataFrame({'credits': [13], 'avg_hours_studied': [3]})\n",
    "new_data4r = pd.DataFrame({'credits': [13], 'avg_hours_studied': [4]})\n",
    "new_data5r = pd.DataFrame({'credits': [13], 'avg_hours_studied': [5]})\n",
    "predicted_gpa3r = regressor.predict(new_data3r)\n",
    "predicted_gpa4r = regressor.predict(new_data4r)\n",
    "predicted_gpa5r = regressor.predict(new_data5r)\n",
    "print(\"Predicted GPA:\", predicted_gpa3r)\n",
    "print(\"Predicted GPA:\", predicted_gpa4r)\n",
    "print(\"Predicted GPA:\", predicted_gpa5r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual GPA when credits are 12 points:\n",
      "Average GPA when study hours are 3 hours: 3.240417090125636\n",
      "Average GPA when study hours are 4 hours: 3.5723291267639055\n",
      "Average GPA when study hours are 5 hours: 3.9470085347486368\n",
      "Predicted  GPA when credits are 13 points:\n",
      "Predicted GPA when credits 13 and studied 3 hours: [3.324]\n",
      "Predicted GPA when credits 13 and studied 4 hours: [3.752]\n",
      "Predicted GPA when credits 13 and studied 5 hours: [3.978]\n",
      "Actual GPA when credits are 15 points:\n",
      "Average GPA when study hours are 3 hours: 3.0163553331744852\n",
      "Average GPA when study hours are 4 hours: 3.395554876354547\n",
      "Average GPA when study hours are 5 hours: 3.828745073108724\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual GPA when credits are 12 points:\")\n",
    "\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5)\n",
    "\n",
    "\n",
    "print(\"Predicted  GPA when credits are 13 points:\")\n",
    "print(\"Predicted GPA when credits 13 and studied 3 hours:\", predicted_gpa3r)\n",
    "print(\"Predicted GPA when credits 13 and studied 4 hours:\", predicted_gpa4r)\n",
    "print(\"Predicted GPA when credits 13 and studied 5 hours:\", predicted_gpa5r)\n",
    "\n",
    "# Print the results\n",
    "print(\"Actual GPA when credits are 15 points:\")\n",
    "print(\"Average GPA when study hours are 3 hours:\", average_gpa_study_hours_3_12)\n",
    "print(\"Average GPA when study hours are 4 hours:\", average_gpa_study_hours_4_12)\n",
    "print(\"Average GPA when study hours are 5 hours:\", average_gpa_study_hours_5_12)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this data none of the predicted GPA is correct "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclution"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As part of this project, I explored different regression models to predict students' GPA based on their credits and average hours studied. I experimented with Random Forest, Decision Tree, Linear Regression, and KNN Regression models.\n",
    "\n",
    "The Random Forest and Decision Tree models performed well, providing satisfactory results in terms of predictive accuracy. These models demonstrated the ability to capture complex relationships between the input features and the target variable.\n",
    "\n",
    "On the other hand, the Linear Regression and KNN Regression models did not perform as well as expected. They had limitations in capturing non-linear patterns in the data, resulting in lower predictive accuracy.\n",
    "\n",
    "In conclusion, the Random Forest and Decision Tree models are the most suitable for this GPA prediction task. They outperformed the Linear Regression and KNN Regression models in terms of accuracy. However, it's important to note that these results may vary depending on the specific dataset and the characteristics of the student population.\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
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