LInear_regression-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Student Scores Prediction - Linear Regression Analysis\n",
    "## Introduction\n",
    "In this project, we will analyze the relationship between the number of hours a student studies and the percentage of marks they score. We will use linear regression, a simple yet effective technique, to build a predictive model for student scores based on studying hours.\n",
    "\n",
    "## Data Exploration and Preparation\n",
    "Let's start by loading the data and exploring its structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import urllib\n",
    "\n",
    "# Load the data from the given URL\n",
    "url = 'https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv'\n",
    "data = pd.read_csv(urllib.request.urlopen(url))\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset consists of two columns: \"Hours\" and \"Scores\". Each row represents a student's number of study hours and the corresponding score they achieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "To gain insights into the relationship between study hours and scores, let's visualize the data using a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "# Scatter plot of study hours vs. scores\n",
    "plt.scatter(data['Hours'], data['Scores'])\n",
    "plt.title('Study Hours vs. Scores')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter plot allows us to visually inspect any potential linear relationship between study hours and scores. From the plot, we can observe a positive linear trend, indicating that as study hours increase, scores tend to increase as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Splitting\n",
    "Next, we will split the data into input (study hours) and output (scores) variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the input and output variables\n",
    "X = data['Hours'].values.reshape(-1, 1)\n",
    "y = data['Scores'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we reshape the input variable X into a 2-dimensional array, as required by scikit-learn's 'LinearRegression' model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training and Evaluation\n",
    "Now, let's train a linear regression model on the data and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 18.943211722315272\n",
      "R2 Score: 0.9678055545167994\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\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 an instance of the Linear Regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Fit the model to the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test data\n",
    "y_pred = model.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 the evaluation metrics\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R2 Score:\", r2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After splitting the data into training and testing sets, with 80% for training and 20% for testing, we proceed to create an instance of the LinearRegression model. The model is then fitted to the training data, allowing us to predict scores based on the test data. Subsequently, we evaluate the model's performance using two metrics: mean squared error (MSE) and R-squared (R2) score.\n",
    "\n",
    "The mean squared error (MSE) provides an average of the squared differences between the predicted and actual scores. It gives us a measure of how close the predicted scores are to the actual scores. In our case, the MSE is calculated to be 18.94.\n",
    "\n",
    "The R-squared (R2) score represents the proportion of the variance in the scores that can be explained by the model. It ranges from 0 to 1, with a higher value indicating a better fit. In our analysis, we obtained an R2 score of 0.97, indicating that approximately 97% of the variability in the scores can be explained by our model.\n",
    "\n",
    "These results suggest that the linear regression model performs well in predicting the scores based on the number of hours studied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAHHCAYAAACle7JuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABraUlEQVR4nO3deXxM1/sH8M8kkklktWSVVRJC7HsQscRepUnsKpaWKiWKli7WqlZra/1Qu7Z2QkuLopaIUHu1VNEgSEJVEmswOb8/7ndGbhayTHJnJp/36zWvuOfeufPcZJJ5nOecc1VCCAEiIiIiI2SmdABEREREhcVEhoiIiIwWExkiIiIyWkxkiIiIyGgxkSEiIiKjxUSGiIiIjBYTGSIiIjJaTGSIiIjIaDGRISIiIqPFRMbEXblyBSqVCitXrlQ6FCqCAQMGwMfHR+kwTBq/x3nL7e/I5MmToVKplAsqG/6tK72YyBixlStXQqVS4fjx40qHUmy0fyy1DwsLC/j4+GDkyJFITU1VOjzKQ2pqKqysrKBSqXD+/PlCn2fBggX8YIKUZGX9PbC3t0ft2rUxa9YsZGRkKB1egRjCz/TKlSsYOHAg/Pz8YGVlBVdXV7Ro0QKTJk1SNC4qnDJKB0DFy9vbG48ePYKFhYXSoRTJwoULYWtriwcPHmDv3r34+uuvcfLkSRw6dEjp0ErEkiVLkJmZqXQY+bZx40aoVCq4urpi9erV+OSTTwp1ngULFqBixYoYMGCAfgM0Qmq1GkuXLgUgJYqbN2/G2LFjcezYMaxbt67E4/noo48wfvz4Aj9P6Z/ppUuX0LBhQ1hbW2PQoEHw8fFBUlISTp48ic8//xxTpkxRJC4qPCYyJk6lUsHKykrpMF7o4cOHKFu27AuPiYyMRMWKFQEAQ4cORa9evbB+/Xr89ttvaNSoUUmECQDIzMzEkydPSvx7amyJ6Pfff49OnTrB29sba9asKXQiQ8+VKVMG/fr1022//fbbaNy4MdavX4/Zs2fD3d09x3OEEHj8+DGsra2LJZ4yZYzvI2TOnDm4f/8+Tp8+DW9vb9m+W7dulWgsDx48gI2NTYm+piliacnE5VY3HjBgAGxtbXHjxg1069YNtra2cHJywtixY6HRaGTPz8zMxNy5cxEUFAQrKyu4uLhg6NChuHv3ruy4H374AZ07d4a7uzvUajX8/Pwwbdq0HOdr2bIlatSogRMnTqBFixYoW7YsPvjggwJfV0hICADg8uXLsvajR4+iQ4cOcHBwQNmyZREaGoq4uLgcz9+/fz8aNGgAKysr+Pn54Ztvvsm15q9SqTBixAisXr0aQUFBUKvV2LlzJwDgxo0bGDRoEFxcXKBWqxEUFITly5fneK2vv/4aQUFBKFu2LMqVK4cGDRpgzZo1uv337t1DdHQ0fHx8oFar4ezsjLZt2+LkyZO6Y3Ibv/HgwQOMGTMGnp6eUKvVqFq1Kr788ktkv6G99hq2bt2KGjVq6GLVXoe+Xbt2DbGxsejVqxd69eqFhIQEHD58ONdjv//+ezRq1Ej3vWnRogV++eUXAICPjw/+/PNPHDhwQFdSadmyJYC8x2doy61XrlzRteX3vZkfr7zyCipXrpzrvuDgYDRo0EC3vXv3bjRv3hyOjo6wtbVF1apVC/Vez4uZmZnu+6G9Xh8fH7zyyivYtWsXGjRoAGtra3zzzTcApF6c6Oho3fvF398fn3/+eY6evtTUVAwYMAAODg5wdHREVFRUrmXcvH4Ghf2ZFkeMubl8+TI8PDxyJDEA4OzsnKNtx44dCA0NhZ2dHezt7dGwYUPZ7y8g9UDWr18f1tbWqFixIvr164cbN27IjtH+3b18+TI6deoEOzs79O3bF0D+/84eP34c7du3R8WKFWFtbQ1fX18MGjQoX9dtyowvnSa90Gg0aN++PRo3bowvv/wSe/bswaxZs+Dn54dhw4bpjhs6dChWrlyJgQMHYuTIkUhISMD8+fNx6tQpxMXF6XoKVq5cCVtbW7z77ruwtbXFr7/+iokTJyI9PR1ffPGF7LXv3LmDjh07olevXujXrx9cXFwKHL/2D3e5cuV0bb/++is6duyI+vXrY9KkSTAzM8OKFSvQunVrxMbG6npuTp06hQ4dOsDNzQ1TpkyBRqPB1KlT4eTklOtr/frrr9iwYQNGjBiBihUrwsfHBykpKWjSpIkuSXBycsKOHTswePBgpKenIzo6GoBUEho5ciQiIyMxatQoPH78GL///juOHj2KPn36AADeeustbNq0CSNGjED16tVx584dHDp0COfPn0e9evVyjUkIgVdffRX79u3D4MGDUadOHezatQvjxo3DjRs3MGfOHNnxhw4dQkxMDN5++23Y2dnhq6++QkREBK5du4YKFSoU+Pv/ImvXroWNjQ1eeeUVWFtbw8/PD6tXr0bTpk1lx02ZMgWTJ09G06ZNMXXqVFhaWuLo0aP49ddf0a5dO8ydOxfvvPMObG1t8eGHHwJAod4rBXlvvkzPnj3Rv39/HDt2DA0bNtS1X716FUeOHNGd788//8Qrr7yCWrVqYerUqVCr1bh06VKuSXVRaBP5rD/DCxcuoHfv3hg6dCjefPNNVK1aFQ8fPkRoaChu3LiBoUOHwsvLC4cPH8aECROQlJSEuXPnApDeV127dsWhQ4fw1ltvoVq1atiyZQuioqLyFU9RfqYlFaO3tzf27NmDX3/9Fa1bt37hsStXrsSgQYMQFBSECRMmwNHREadOncLOnTt1v7/av48NGzbEjBkzkJKSgnnz5iEuLg6nTp2Co6Oj7nzPnj1D+/bt0bx5c3z55Ze6nuj8/J29desW2rVrBycnJ4wfPx6Ojo64cuUKYmJi8nXdJk2Q0VqxYoUAII4dO5bnMQkJCQKAWLFiha4tKipKABBTp06VHVu3bl1Rv3593XZsbKwAIFavXi07bufOnTnaHz58mOO1hw4dKsqWLSseP36sawsNDRUAxKJFi/J1jZMmTRIAxIULF8Tt27fFlStXxPLly4W1tbVwcnISDx48EEIIkZmZKQICAkT79u1FZmamLC5fX1/Rtm1bXVuXLl1E2bJlxY0bN3RtFy9eFGXKlBHZfyUACDMzM/Hnn3/K2gcPHizc3NzEv//+K2vv1auXcHBw0H0/unbtKoKCgl54jQ4ODmL48OEvPCYqKkp4e3vrtrdu3SoAiE8++UR2XGRkpFCpVOLSpUuya7C0tJS1nTlzRgAQX3/99QtftzBq1qwp+vbtq9v+4IMPRMWKFcXTp091bRcvXhRmZmbitddeExqNRvb8rD+/oKAgERoamuM1tO+L7LS/EwkJCbq2/L43s3+Pc5OWlibUarUYM2aMrH3mzJlCpVKJq1evCiGEmDNnjgAgbt++/cLz5VdUVJSwsbERt2/fFrdv3xaXLl0Sn376qVCpVKJWrVq647y9vQUAsXPnTtnzp02bJmxsbMTff/8tax8/frwwNzcX165dE0I8f1/NnDlTd8yzZ89ESEhIjr8j2X8GRf2ZFkeMufnjjz+EtbW1ACDq1KkjRo0aJbZu3ar7W6KVmpoq7OzsROPGjcWjR49yvZ4nT54IZ2dnUaNGDdkx27dvFwDExIkTdW3av7vjx4+XnSu/f2e3bNny0r/3pRVLS6XYW2+9JdsOCQnBP//8o9veuHEjHBwc0LZtW/z777+6R/369WFra4t9+/bpjs1ag7937x7+/fdfhISE4OHDh/jrr79kr6NWqzFw4MACxVq1alU4OTnBx8cHgwYNgr+/P3bs2KH7H83p06dx8eJF9OnTB3fu3NHF+uDBA7Rp0wYHDx5EZmYmNBoN9uzZg27dusnGFPj7+6Njx465vnZoaCiqV6+u2xZCYPPmzejSpQuEELLvTfv27ZGWlqYrCzk6OuL69es4duxYntfm6OiIo0eP4ubNm/n+fvz8888wNzfHyJEjZe1jxoyBEAI7duyQtYeFhcHPz0+3XatWLdjb28t+3vrw+++/4+zZs+jdu7eurXfv3vj333+xa9cuXdvWrVuRmZmJiRMnwsxM/mdI31N6C/LefBl7e3t07NgRGzZskJXw1q9fjyZNmsDLywsAdP8L/+GHH/Q2SPvBgwdwcnKCk5MT/P398cEHHyA4OBhbtmyRHefr64v27dvL2jZu3IiQkBCUK1dO9n4NCwuDRqPBwYMHAUjvqzJlysh6Zc3NzfHOO++8NL6i/kxLIkYACAoKwunTp9GvXz9cuXIF8+bNQ7du3eDi4oIlS5bojtu9ezfu3buH8ePH5xgTp72e48eP49atW3j77bdlx3Tu3BmBgYH46aefcrx+1ri1152fv7Pa99T27dvx9OnTfF1racHSUillZWWVo5RSrlw5WU324sWLSEtLy7VuDMgHxv3555/46KOP8OuvvyI9PV12XFpammy7UqVKsLS0LFC8mzdvhr29PW7fvo2vvvoKCQkJsg+oixcvAsALu5fT0tLw+PFjPHr0CP7+/jn259YGSB8MWd2+fRupqalYvHgxFi9enOtztN+b999/H3v27EGjRo3g7++Pdu3aoU+fPmjWrJnu2JkzZyIqKgqenp6oX78+OnXqhP79++c5FgOQShnu7u6ws7OTtVerVk23PyvtB2xW2X/euUlOTpZtOzg4vHDg6Pfffw8bGxtUrlwZly5dAiC913x8fLB69Wp07twZgFQSMTMzkyWIxaUg78386NmzJ7Zu3Yr4+Hg0bdoUly9fxokTJ3SlD+0xS5cuxRtvvIHx48ejTZs2CA8PR2RkZI4P+fyysrLCtm3bAEj/GfD19YWHh0eO47K/XwHp9+P333/Ps3yqfb9evXoVbm5usLW1le2vWrXqS+Mr6s+0JGLUqlKlCr777jtoNBqcO3cO27dvx8yZMzFkyBD4+voiLCxMV7arUaNGnufR/p7l9tqBgYE5ZlWWKVMmx88sv39nQ0NDERERgSlTpmDOnDlo2bIlunXrhj59+kCtVuf72k0RE5lSytzc/KXHZGZmwtnZGatXr851v/YPTmpqKkJDQ2Fvb4+pU6fq1mY4efIk3n///Rz/Iy3MDIoWLVroZi116dIFNWvWRN++fXHixAmYmZnpXuOLL75AnTp1cj2Hra0tHj9+XODXzh6v9rX69euXZ+JUq1YtAFJiceHCBWzfvh07d+7E5s2bsWDBAkycOFE3zbNHjx4ICQnBli1b8Msvv+CLL77A559/jpiYmDx7iQoqr5+3yDYwODs3NzfZ9ooVK/KcNiuEwNq1a/HgwYNcP8xu3bqF+/fv5/gAKoy8/oeffQBvQd+b+dGlSxeULVsWGzZsQNOmTbFhwwaYmZmhe/fuumOsra1x8OBB7Nu3Dz/99BN27tyJ9evXo3Xr1vjll1/y9fuXnbm5OcLCwl56XG6/X5mZmWjbti3ee++9XJ9TpUqVAsejb0rEaG5ujpo1a6JmzZoIDg5Gq1atsHr16nx9nwtDrVbnSGTz+3dWpVJh06ZNOHLkCLZt24Zdu3Zh0KBBmDVrFo4cOaKX3ytjxUSG8uTn54c9e/agWbNmL0w+9u/fjzt37iAmJgYtWrTQtSckJBRLXLa2tpg0aRIGDhyIDRs2oFevXrqyib29/Qv/CDk7O8PKykrXW5BVbm25cXJygp2dHTQaTb7+4NnY2KBnz57o2bMnnjx5gvDwcEyfPh0TJkzQdUe7ubnh7bffxttvv41bt26hXr16mD59ep6JjHbA4r1792S9MtpSSW4zMgpj9+7dsu2goKA8jz1w4ACuX7+OqVOn6nqGtO7evYshQ4Zg69at6NevH/z8/JCZmYlz587lmXgCeScs2kHeqampssGU2XuiiuO9qR3IvHHjRsyePRvr169HSEhIjunPZmZmaNOmDdq0aYPZs2fj008/xYcffoh9+/YV2wdlXvz8/HD//v2Xvq63tzf27t2bI+G8cOFCvl6jKD/TkojxRbQzzpKSknTxAMAff/yRZ2+t9vfswoULOQYOX7hwIV+/h/n9O6vVpEkTNGnSBNOnT8eaNWvQt29frFu3Dm+88cZLn2uqOEaG8tSjRw9oNBpMmzYtx75nz57ppjtq/3eZ9X/3T548wYIFC4ottr59+8LDwwOff/45AKB+/frw8/PDl19+ifv37+c4/vbt27pYw8LCsHXrVtmYlEuXLuUYV5IXc3NzREREYPPmzfjjjz/yfC1AmqGVlaWlJapXrw4hBJ4+fQqNRpOjvOHs7Ax3d/cXrtjaqVMnaDQazJ8/X9Y+Z84cqFQqvfXkhIWFyR7Ze2iy0paVxo0bh8jISNnjzTffREBAgO5/nd26dYOZmRmmTp2ao1ck6/vIxsYm12m12g8Z7bgJQBpDsmrVKtlxxfXe7NmzJ27evImlS5fizJkz6Nmzp2z/f//9l+M52g/3rD/Xv/76C9euXStSLPnRo0cPxMfHy8YpaaWmpuLZs2cApPfVs2fPsHDhQt1+jUaDr7/++qWvUdSfaUnECACxsbG5jjH5+eefATwvE7Vr1w52dnaYMWNGjp5c7fU0aNAAzs7OWLRokeznumPHDpw/f15XSn2R/P6dvXv3bo4e1NzeU6URe2RMwPLly3NdE2TUqFFFOm9oaCiGDh2KGTNm4PTp02jXrh0sLCxw8eJFbNy4EfPmzUNkZCSaNm2KcuXKISoqCiNHjoRKpcJ333330rJFUVhYWGDUqFEYN24cdu7ciQ4dOmDp0qXo2LEjgoKCMHDgQFSqVAk3btzAvn37YG9vrxtfMHnyZPzyyy9o1qwZhg0bpksIatSogdOnT+fr9T/77DPs27cPjRs3xptvvonq1avjv//+w8mTJ7Fnzx7dB1m7du3g6uqKZs2awcXFBefPn8f8+fPRuXNn2NnZITU1FR4eHoiMjETt2rVha2uLPXv24NixY5g1a1aer9+lSxe0atUKH374Ia5cuYLatWvjl19+wQ8//IDo6GjZwN6SkJGRgc2bN6Nt27Z5Lhb46quvYt68ebh16xb8/f3x4YcfYtq0aQgJCUF4eDjUajWOHTsGd3d3zJgxA4CUoC5cuBCffPIJ/P394ezsjNatW6Ndu3bw8vLC4MGDMW7cOJibm2P58uVwcnKSJQbF9d7UrgMyduxYXWKb1dSpU3Hw4EF07twZ3t7euHXrFhYsWAAPDw80b95cd1y1atUQGhqK/fv3Fymelxk3bhx+/PFHvPLKKxgwYADq16+PBw8e4OzZs9i0aROuXLmCihUrokuXLmjWrBnGjx+PK1euoHr16oiJicnXWKKi/kxLIkYA+Pzzz3HixAmEh4frSsAnT57Et99+i/Lly+uWTrC3t8ecOXPwxhtvoGHDhujTpw/KlSuHM2fO4OHDh1i1ahUsLCzw+eefY+DAgQgNDUXv3r110699fHwwevTol8aT37+zq1atwoIFC/Daa6/Bz88P9+7dw5IlS2Bvb49OnTrl69pNlgIzpUhPtFNN83okJibmOf3axsYmx/nymtK6ePFiUb9+fWFtbS3s7OxEzZo1xXvvvSdu3rypOyYuLk40adJEWFtbC3d3d/Hee++JXbt2CQBi3759uuNCQ0NfOh05t5hym8aalpYmHBwcZFM5T506JcLDw0WFChWEWq0W3t7eokePHmLv3r2y5+7du1fUrVtXWFpaCj8/P7F06VIxZswYYWVlJTsOQJ5To1NSUsTw4cOFp6ensLCwEK6urqJNmzZi8eLFumO++eYb0aJFC108fn5+Yty4cSItLU0IIURGRoYYN26cqF27trCzsxM2Njaidu3aYsGCBbLXym1q8L1798To0aOFu7u7sLCwEAEBAeKLL76QTXV90TV4e3uLqKioXK+toDZv3iwAiGXLluV5zP79+wUAMW/ePF3b8uXLRd26dYVarRblypUToaGhYvfu3br9ycnJonPnzsLOzk4AkP2sT5w4IRo3biwsLS2Fl5eXmD17dq7Tr/P73szP9Ous+vbtKwCIsLCwHPv27t0runbtKtzd3YWlpaVwd3cXvXv3zjG1OPs15SWv39nsvL29RefOnXPdd+/ePTFhwgTh7+8vLC0tRcWKFUXTpk3Fl19+KZ48eaI77s6dO+L1118X9vb2wsHBQbz++uvi1KlTL51+rVWUn6m+Y8xNXFycGD58uKhRo4ZwcHAQFhYWwsvLSwwYMEBcvnw5x/E//vijaNq0qbC2thb29vaiUaNGYu3atbJj1q9fr7vm8uXLi759+4rr16/LjnnZz/Blf2dPnjwpevfuLby8vIRarRbOzs7ilVdeEcePH3/h9ZYGKiGK8b/NREakW7du+PPPP3UzoIiIyPBxjAyVSo8ePZJtX7x4ET///LNsuXQiIjJ87JGhUsnNzQ0DBgxA5cqVcfXqVSxcuBAZGRk4deoUAgIClA6PiIjyiYN9qVTq0KED1q5di+TkZKjVagQHB+PTTz9lEkNEZGTYI0NERERGi2NkiIiIyGgxkSEiIiKjZfJjZDIzM3Hz5k3Y2dnp/a66REREVDyEELh37x7c3d1feLNVk09kbt68CU9PT6XDICIiokJITEzM9U7vWiafyGhvqJeYmAh7e3uFoyEiIqL8SE9Ph6enp+zGuLkx+URGW06yt7dnIkNERGRkXjYshIN9iYiIyGgxkSEiIiKjxUSGiIiIjJbJj5HJL41Gg6dPnyodBpVyFhYWMDc3VzoMIiKjUeoTGSEEkpOTkZqaqnQoRAAAR0dHuLq6ct0jIqJ8KPWJjDaJcXZ2RtmyZfnhQYoRQuDhw4e4desWAOkO3URE9GKlOpHRaDS6JKZChQpKh0MEa2trAMCtW7fg7OzMMhMR0UuU6sG+2jExZcuWVTgSoue070eO2SIierlSnchosZxEhoTvRyKi/CvVpSUiIqLSSqMBYmOBpCTAzQ0ICQGMsZrNHhnSO5VKha1btyodBhER5SEmBvDxAVq1Avr0kb76+EjtxoaJjBGLj4+Hubk5OnfuXODn+vj4YO7cufoPKh9u376NYcOGwcvLC2q1Gq6urmjfvj3i4uIUiYeIqDSJiQEiI4Hr1+XtN25I7caWzLC0pAdKdc8tW7YM77zzDpYtW4abN2/C3d29+F9UDyIiIvDkyROsWrUKlStXRkpKCvbu3Ys7d+4U22s+efIElpaWxXZ+IiJjoNEAo0YBQuTcJwSgUgHR0UDXrsZTZmKPTBEp1T13//59rF+/HsOGDUPnzp2xcuXKHMds27YNDRs2hJWVFSpWrIjXXnsNANCyZUtcvXoVo0ePhkql0g0unTx5MurUqSM7x9y5c+Hj46PbPnbsGNq2bYuKFSvCwcEBoaGhOHnyZL7jTk1NRWxsLD7//HO0atUK3t7eaNSoESZMmIBXX31VdtzQoUPh4uICKysr1KhRA9u3b9ft37x5M4KCgqBWq+Hj44NZs2bJXsfHxwfTpk1D//79YW9vjyFDhgAADh06hJCQEFhbW8PT0xMjR47EgwcPdM9bsGABAgICYGVlBRcXF0RGRub72oiIDF1sbM6emKyEABITpeOMBROZIlCye27Dhg0IDAxE1apV0a9fPyxfvhwiS4r9008/4bXXXkOnTp1w6tQp7N27F40aNfpf3DHw8PDA1KlTkZSUhKSkpHy/7r179xAVFYVDhw7hyJEjCAgIQKdOnXDv3r18Pd/W1ha2trbYunUrMjIycj0mMzMTHTt2RFxcHL7//nucO3cOn332mW5NlRMnTqBHjx7o1asXzp49i8mTJ+Pjjz/Okcx9+eWXqF27Nk6dOoWPP/4Yly9fRocOHRAREYHff/8d69evx6FDhzBixAgAwPHjxzFy5EhMnToVFy5cwM6dO9GiRYt8f2+IiAxdfv/cF+BjQXnCxKWlpQkAIi0tLce+R48eiXPnzolHjx4V+LzPngnh4SGElL/mfKhUQnh6SscVh6ZNm4q5c+cKIYR4+vSpqFixoti3b59uf3BwsOjbt2+ez/f29hZz5syRtU2aNEnUrl1b1jZnzhzh7e2d53k0Go2ws7MT27Zt07UBEFu2bMnzOZs2bRLlypUTVlZWomnTpmLChAnizJkzuv27du0SZmZm4sKFC7k+v0+fPqJt27aytnHjxonq1avLrq9bt26yYwYPHiyGDBkia4uNjRVmZmbi0aNHYvPmzcLe3l6kp6fnGXtJKMr7kojoRfbty/tzK+sjy8eJYl70+Z0Ve2QKScnuuQsXLuC3335D7969AQBlypRBz549sWzZMt0xp0+fRps2bfT+2ikpKXjzzTcREBAABwcH2Nvb4/79+7h27Vq+zxEREYGbN2/ixx9/RIcOHbB//37Uq1dP16Ny+vRpeHh4oEqVKrk+//z582jWrJmsrVmzZrh48SI0Go2urUGDBrJjzpw5g5UrV+p6hWxtbdG+fXtkZmYiISEBbdu2hbe3NypXrozXX38dq1evxsOHD/N9XUREhi4kBPDwkMbC5EalAjw9peOMBROZQlKye27ZsmV49uwZ3N3dUaZMGZQpUwYLFy7E5s2bkZaWBuD5UvcFYWZmJitPATlXl42KisLp06cxb948HD58GKdPn0aFChXw5MmTAr2WlZUV2rZti48//hiHDx/GgAEDMGnSpELHnhsbGxvZ9v379zF06FCcPn1a9zhz5gwuXrwIPz8/2NnZ4eTJk1i7di3c3NwwceJE1K5dmzcUJSKTYW4OzJsn/Tt7MqPdnjvXeAb6AkxkCi2/9/PT933/nj17hm+//RazZs3K8YHs7u6OtWvXAgBq1aqFvXv35nkeS0tLWe8FADg5OSE5OVmWzJw+fVp2TFxcHEaOHIlOnTrpBtv++++/Rb6u6tWr6wbd1qpVC9evX8fff/+d67HVqlXLMVU7Li4OVapUeeG9ierVq4dz587B398/x0M7o6lMmTIICwvDzJkz8fvvv+PKlSv49ddfi3x9RESGIjwc2LQJqFRJ3u7hIbWHhysTV2Fx+nUhabvnbtzIfRqbSiXt13f33Pbt23H37l0MHjwYDg4Osn0RERFYtmwZ3nrrLUyaNAlt2rSBn58fevXqhWfPnuHnn3/G+++/D0Ca1XPw4EH06tULarUaFStWRMuWLXH79m3MnDkTkZGR2LlzJ3bs2AF7e3vdawQEBOC7775DgwYNkJ6ejnHjxhWoB+XOnTvo3r07Bg0ahFq1asHOzg7Hjx/HzJkz0bVrVwBAaGgoWrRogYiICMyePRv+/v7466+/oFKp0KFDB4wZMwYNGzbEtGnT0LNnT8THx2P+/PlYsGDBC1/7/fffR5MmTTBixAi88cYbsLGxwblz57B7927Mnz8f27dvxz///IMWLVqgXLly+Pnnn5GZmYmqVavm+/qIiIxBeLg0xdoUVvblYN8iDKrcvFka1KtS5Rzoq1JJ+/XtlVdeEZ06dcp139GjRwUA3cDZzZs3izp16ghLS0tRsWJFER4erjs2Pj5e1KpVS6jVapH1bbBw4ULh6ekpbGxsRP/+/cX06dNlg31PnjwpGjRoIKysrERAQIDYuHFjjoHDeMFg38ePH4vx48eLevXqCQcHB1G2bFlRtWpV8dFHH4mHDx/qjrtz544YOHCgqFChgrCyshI1atQQ27dv1+3ftGmTqF69urCwsBBeXl7iiy++kL1OboOZhRDit99+E23bthW2trbCxsZG1KpVS0yfPl0IIQ38DQ0NFeXKlRPW1taiVq1aYv369bleR3HiYF8iovwP9lUJkVt/gulIT0+Hg4MD0tLSZD0LAPD48WMkJCTA19cXVlZWhTp/TIy0uFDWgb+enlKN0di658gw6ON9SURk7F70+Z0VS0tFZFLdc0REREaGiYwemJsDLVsqHQUREVHpw1lLREREZLSYyBAREZHRYiJDRERERouJDBERERktJjJERERktJjIEBERkdFiIkNERERGS9FE5t69e4iOjoa3tzesra3RtGlTHDt2TLdfCIGJEyfCzc0N1tbWCAsLw8WLFxWMuPQZMGAAunXrpttu2bIloqOjSzyO/fv3Q6VS8U7UREQko2gi88Ybb2D37t347rvvcPbsWbRr1w5hYWG4ceMGAGDmzJn46quvsGjRIhw9ehQ2NjZo3749Hj9+rGTYihswYABUKhVUKhUsLS3h7++PqVOn4tmzZ8X+2jExMZg2bVq+ji3p5OPMmTN49dVX4ezsDCsrK/j4+KBnz564detWibw+ERGVPMUSmUePHmHz5s2YOXMmWrRoAX9/f0yePBn+/v5YuHAhhBCYO3cuPvroI3Tt2hW1atXCt99+i5s3b2Lr1q1KhW0wOnTogKSkJFy8eBFjxozB5MmT8cUXX+R67JMnT/T2uuXLl4ednZ3ezqcvt2/fRps2bVC+fHns2rUL58+fx4oVK+Du7o4HDx4U2+s+ffq02M5NREQvp1gi8+zZM2g0mhw3xbO2tsahQ4eQkJCA5ORkhIWF6fY5ODigcePGiI+Pz/O8GRkZSE9Plz1MkVqthqurK7y9vTFs2DCEhYXhxx9/BPC8HDR9+nS4u7ujatWqAIDExET06NEDjo6OKF++PLp27YorV67ozqnRaPDuu+/C0dERFSpUwHvvvYfs9xTNXlrKyMjA+++/D09PT6jVavj7+2PZsmW4cuUKWrVqBQAoV64cVCoVBgwYAADIzMzEjBkz4OvrC2tra9SuXRubNm2Svc7PP/+MKlWqwNraGq1atZLFmZu4uDikpaVh6dKlqFu3Lnx9fdGqVSvMmTMHvr6+uuP+/PNPvPLKK7C3t4ednR1CQkJw+fJlXVxTp06Fh4cH1Go16tSpg507d+qee+XKFahUKqxfvx6hoaGwsrLC6tWrAQBLly5FtWrVYGVlhcDAQCxYsED3vCdPnmDEiBFwc3ODlZUVvL29MWPGjBdeDxER5Y9i91qys7NDcHAwpk2bhmrVqsHFxQVr165FfHw8/P39kZycDABwcXGRPc/FxUW3LzczZszAlClTCheUEMDDh4V7blGVLQuoVIV+urW1Ne7cuaPb3rt3L+zt7bF7924AUs9B+/btERwcjNjYWJQpUwaffPIJOnTogN9//x2WlpaYNWsWVq5cieXLl6NatWqYNWsWtmzZgtatW+f5uv3790d8fDy++uor1K5dGwkJCfj333/h6emJzZs3IyIiAhcuXIC9vT2sra0BSD+j77//HosWLUJAQAAOHjyIfv36wcnJCaGhoUhMTER4eDiGDx+OIUOG4Pjx4xgzZswLr9/V1RXPnj3Dli1bEBkZCVUu38sbN26gRYsWaNmyJX799VfY29sjLi5OV5KbN28eZs2ahW+++QZ169bF8uXL8eqrr+LPP/9EQECA7jzjx4/HrFmzULduXV0yM3HiRMyfPx9169bFqVOn8Oabb8LGxgZRUVH46quv8OOPP2LDhg3w8vJCYmIiEhMT8//DJSKivAkFXbp0SbRo0UIAEObm5qJhw4aib9++IjAwUMTFxQkA4ubNm7LndO/eXfTo0SPPcz5+/FikpaXpHomJiQKASEtLy3Hso0ePxLlz58SjR4+khvv3hZDSmZJ/3L+f7+9bVFSU6Nq1qxBCiMzMTLF7926hVqvF2LFjdftdXFxERkaG7jnfffedqFq1qsjMzNS1ZWRkCGtra7Fr1y4hhBBubm5i5syZuv1Pnz4VHh4eutcSQojQ0FAxatQoIYQQFy5cEADE7t27c41z3759AoC4e/eu7OdTtmxZcfjwYdmxgwcPFr179xZCCDFhwgRRvXp12f73338/x7my++CDD0SZMmVE+fLlRYcOHcTMmTNFcnKybv+ECROEr6+vePLkSa7Pd3d3F9OnT5e1NWzYULz99ttCCCESEhIEADF37lzZMX5+fmLNmjWytmnTpong4GAhhBDvvPOOaN26tex7/yI53pdERKVQWlpanp/fWSk62NfPzw8HDhzA/fv3kZiYiN9++w1Pnz5F5cqV4erqCgBISUmRPSclJUW3LzdqtRr29vayhynavn07bG1tYWVlhY4dO6Jnz56YPHmybn/NmjVhaWmp2z5z5gwuXboEOzs72NrawtbWFuXLl8fjx49x+fJlpKWlISkpCY0bN9Y9p0yZMmjQoEGeMZw+fRrm5uYIDQ3Nd9yXLl3Cw4cP0bZtW10ctra2+Pbbb3UlnvPnz8viAIDg4OCXnnv69OlITk7GokWLEBQUhEWLFiEwMBBnz57VxRsSEgILC4scz01PT8fNmzfRrFkzWXuzZs1w/vx5WVvW78mDBw9w+fJlDB48WHY9n3zyie56BgwYgNOnT6Nq1aoYOXIkfvnll3x8p4iIKD8UKy1lZWNjAxsbG9y9exe7du3CzJkz4evrC1dXV+zduxd16tQBIH3YHD16FMOGDSueQMqWBe7fL55z5+e1C6BVq1ZYuHAhLC0t4e7ujjJl5D9KGxsb2fb9+/dRv3593ZiOrJycnAoeL6ArFRXE/f99f3/66SdUqlRJtk+tVhcqjqwqVKiA7t27o3v37vj0009Rt25dfPnll1i1alWh4s1N1u+t9nqWLFmSI/kyNzcHANSrVw8JCQnYsWMH9uzZgx49eiAsLCzHuCAiIio4RROZXbt2QQiBqlWr4tKlSxg3bhwCAwMxcOBAqFQqREdH45NPPkFAQAB8fX3x8ccfw93dXbauiV6pVEC2BMBQ2djYwN/fP9/H16tXD+vXr4ezs3OevVRubm44evQoWrRoAUAakH3ixAnUq1cv1+Nr1qyJzMxMHDhwQDYoW0vbI6TRaHRt1atXh1qtxrVr1/LsyalWrZpu4LLWkSNHXn6Ruby+n5+fbtZSrVq1sGrVKjx9+jRHr4y9vT3c3d0RFxcniysuLg6NGjXK8zVcXFzg7u6Of/75B3379s3zOHt7e/Ts2RM9e/ZEZGQkOnTogP/++w/ly5cv8HURERkCjQaIjQWSkgA3NyAkBPjf/99KlKKJTFpaGiZMmIDr16+jfPnyiIiIwPTp03UfMu+99x4ePHiAIUOGIDU1Fc2bN8fOnTtzzHSil+vbty+++OILdO3aVTcz5+rVq4iJicF7770HDw8PjBo1Cp999hkCAgIQGBiI2bNnv3ANGB8fH0RFRWHQoEG6wb5Xr17FrVu30KNHD3h7e0OlUmH79u3o1KkTrK2tYWdnh7Fjx2L06NHIzMxE8+bNkZaWhri4ONjb2yMqKgpvvfUWZs2ahXHjxuGNN97AiRMnsHLlyhde3/bt27Fu3Tr06tULVapUgRAC27Ztw88//4wVK1YAAEaMGIGvv/4avXr1woQJE+Dg4IAjR46gUaNGqFq1KsaNG4dJkybBz88PderUwYoVK3D69Olce7GymjJlCkaOHAkHBwd06NABGRkZOH78OO7evYt3330Xs2fPhpubG+rWrQszMzNs3LgRrq6ucHR0LOBPkYjIMMTEAKNGAdevP2/z8ADmzQPCw0s4mBIZsaOgFw0WMtZBlVkH+xZkf1JSkujfv7+oWLGiUKvVonLlyuLNN9/UfW+ePn0qRo0aJezt7YWjo6N49913Rf/+/fMc7CuE9D0cPXq0cHNzE5aWlsLf318sX75ct3/q1KnC1dVVqFQqERUVJYSQBijPnTtXVK1aVVhYWAgnJyfRvn17ceDAAd3ztm3bJvz9/YVarRYhISFi+fLlLxzse/nyZfHmm2+KKlWqCGtra+Ho6CgaNmwoVqxYITvuzJkzol27dqJs2bLCzs5OhISEiMuXLwshhNBoNGLy5MmiUqVKwsLCQtSuXVvs2LFD91ztYN9Tp07leP3Vq1eLOnXqCEtLS1GuXDnRokULERMTI4QQYvHixaJOnTrCxsZG2NvbizZt2oiTJ0/meh3a76kxvi+JqHTYvFkIlSrnnBWVSnps3qyf18nvYF+VENkWCjEx6enpcHBwQFpaWo6SyuPHj5GQkABfX1/28pDB4PuSiAyVRgP4+Mh7YrJSqaSemYSEopeZXvT5nRVvGklERET5EhubdxIDSH0ziYnScSWFiQwRERHlS1KSfo/TByYyRERElC9ubvo9Th+YyBAREVG+hIRIY2DyuqOOSgV4ekrHlRQmMkCOGyMSKYnvRyIyVObm0hRrIGcyo92eO7dk15Mp1YmMdr2ah0rdKJIoF9r3Y263UiAi/dNogP37gbVrpa9Z1vCkXISHA5s2AdkWZ4eHh9Re0uvIGMQtCpRibm4OR0dH3Lp1CwBQtmzZXO+aTFQShBB4+PAhbt26BUdHR90tDoio+BjUwm5GJDwc6NrVMFb2LdXryADSh0dycvILV7AlKkmOjo5wdXVlUk1UzGJigMhIacpwVtpfPSV6F+i5/K4jU+oTGS2NRoOnT5+WYGREOVlYWLAnhqgElOTCblQ4+f38LtWlpazMzc35AUJEZOK0Nzrcuzf/C7u1bFli4VEhMJEhIqJSIbfxMC9Tkgu7UeEwkSEiIpOX13iYlynJhd2ocJjIEBGRSdNopJ6YgiQx2jEyJbmwGxVOqV5HhoiITN/LbnSYnVILu1HhMJEhIiKTVtBxLkot7EaFw9ISERGZtPyOc/noI6BNG+UWdqPCYSJDREQmTXujwxs3ch8nox0PM3kyExhjxNISERGZNEO80SHpDxMZIiIyeYZ2o0PSH5aWiIioVDCkGx2S/jCRISKiUsPc3EBuOZCaKq3SN2iQ0pEYPSYyREREJem334CePYErVwA7O6B7d6UjMmocI0NERFQShJBGHTdvLiUxlStLDyoS9sgQEREVt7t3pTLS1q3SdmQksHQp4OCgaFimgD0yRERExem334B69aQkxtISmD8f2LCBSYyeMJEhIiIqDkJIC9RkLSXFxwPDh+dc0IYKjaUlIiIifbt7Fxg4EPjhB2m7e3dgyRL2whQD9sgQERHp09GjQN26UhJjaQn83/8B69cziSkmTGSIiIj0QQhgzhyplHT1KuDnJ5WS3n6bpaRixNISERFRUf33n1RK+vFHaZulpBLDRIaIiKgojh6VFri7elUqJc2dC7z1FnthSoiipSWNRoOPP/4Yvr6+sLa2hp+fH6ZNmwaR5T7rQghMnDgRbm5usLa2RlhYGC5evKhg1ERERJBKSbNny0tJR44Aw4YxiSlBiiYyn3/+ORYuXIj58+fj/Pnz+PzzzzFz5kx8/fXXumNmzpyJr776CosWLcLRo0dhY2OD9u3b4/HjxwpGTkREpdp//wHdugFjxgDPngE9egAnT0qDfKlEqUTW7o8S9sorr8DFxQXLli3TtUVERMDa2hrff/89hBBwd3fHmDFjMHbsWABAWloaXFxcsHLlSvTq1eulr5Geng4HBwekpaXB3t6+2K6FiIhKiSNHpFLStWuAWi2VkoYOZS+MnuX381vRHpmmTZti7969+PvvvwEAZ86cwaFDh9CxY0cAQEJCApKTkxEWFqZ7joODAxo3boz4+HhFYiYiolJKW0oKCZGSGH9/aVYSx8MoStHBvuPHj0d6ejoCAwNhbm4OjUaD6dOno2/fvgCA5ORkAICLi4vseS4uLrp92WVkZCAjI0O3nZ6eXkzRExFRqfHff8CAAcC2bdJ2z57A4sUAe/oVp2iPzIYNG7B69WqsWbMGJ0+exKpVq/Dll19i1apVhT7njBkz4ODgoHt4enrqMWIiIip1jhyRxr5s2yaVkhYuBNauZRJjIBRNZMaNG4fx48ejV69eqFmzJl5//XWMHj0aM2bMAAC4uroCAFJSUmTPS0lJ0e3LbsKECUhLS9M9EhMTi/ciiIjINAkBzJr1vJQUECAlNSwlGRRFE5mHDx/CzEwegrm5OTIzMwEAvr6+cHV1xd69e3X709PTcfToUQQHB+d6TrVaDXt7e9mDiIioQO7cAV59FRg7VpqV1LMncPw4UKeO0pFRNoqOkenSpQumT58OLy8vBAUF4dSpU5g9ezYGDRoEAFCpVIiOjsYnn3yCgIAA+Pr64uOPP4a7uzu6deumZOhERGSq4uOlxCUxUSolzZsHDBnCXhgDpWgi8/XXX+Pjjz/G22+/jVu3bsHd3R1Dhw7FxIkTdce89957ePDgAYYMGYLU1FQ0b94cO3fuhJWVlYKRExGRycnMlGYlTZgg9cIEBAAbNrAXxsApuo5MSeA6MkRE9FJ37kizkrZvl7Z79ZJmJdnZKRpWaZbfz2/ea4mIiEq3w4elxEVbSvrqK+DNN1lKMhKKDvYlIiJSTGYm8MUXQIsWUhJTpYp0A0iOhzEq7JEhIqLS584dICoK+Oknabt3b+Cbb1hKMkJMZIiIqHQ5fFialXT9ulRK+vpr4I032AtjpFhaIiKi0iEzE5g5UyolXb/+vJTE8TBGjT0yRERk+v79Vyol/fyztM1SkslgIkNERKYtLk6alXT9OmBlJc1KYinJZLC0REREpklbSgoNZSnJhLFHhoiITM+//wL9+wM7dkjbffoAixaxlGSCmMgQEZFpOXRIKiXduCGVkr7+Ghg8mL0wJoqJDBERmQbtAncffghoNEDVqtK9kmrVUjqyQtNogNhYICkJcHMDQkIAc3OlozIsTGSIiMj4ZS8l9esHLFwI2NoqG1cRxMQAo0ZJw3u0PDykm3GHhysXl6HhYF8iIjJuhw5Jd6jesUMqJS1dCnz7rdEnMZGR8iQGkKplkZHSfpIwkSEiIuOUmQl89hnQsqX0CR8YCPz2m9GPh9FopJ4YIXLu07ZFR0vHERMZIiIyRrdvA507AxMmSJ/o/foBx44BNWsqHVmRxcbm7InJSgjpHpexsSUXkyHjGBkiIjIusbHSyrzaWUn/93/AwIFG3QuTVVKSfo8zdeyRISIi45CZCcyYAbRqJS8lDRpkMkkMIM1O0udxpo6JDBERGb7bt4FOnYAPPpBKSa+/bjKlpOxCQqTZSXnlZioV4OkpHUdMZIiIyNDFxkqzknbtAqytgeXLgVWrjHpW0ouYm0tTrIGcyYx2e+5criejxUSGiIgMU2Ym8Omn0qykmzefl5JMaDxMXsLDgU2bgEqV5O0eHlI715F5joN9iYjI8Ny+Lc1E+uUXabt/f2lQr4n2wuQmPBzo2pUr+74MExkiIjIsBw9Ks5Ju3pRKSQsWAAMGKB2VIszNpQ4pyhtLS0REZBgyM4Hp06VZSTdvAtWqSQN6S2kSQ/nDHhkiIlLerVvSTCRtKSkqSiol2dgoGxcZPCYyRESkrAMHpFJSUlKpLyVRwbG0REREytCWklq3lpKY6tVZSqICY48MERGVvFu3pFlJu3dL2wMGAPPns5REBcZEhoiISlbWUlLZslIpKSpK6ajISLG0REREJUOjAT75JGcpiUkMFQF7ZIiIqPilpEilpD17pO2BA4Gvv2YpiYqMiQwRERWv/fulUlJyMktJpHcsLRERUfHQaIBp04A2baQkhqUkKgaKJjI+Pj5QqVQ5HsOHDwcAPH78GMOHD0eFChVga2uLiIgIpKSkKBkyERHlR0oK0KEDMHGiNM164EDpho/VqysdGZkYRROZY8eOISkpSffY/b9peN27dwcAjB49Gtu2bcPGjRtx4MAB3Lx5E+G85ScRkWHbtw+oU0caD1O2LLBqFbB8OcfDULFQCSGE0kFoRUdHY/v27bh48SLS09Ph5OSENWvWIDIyEgDw119/oVq1aoiPj0eTJk3ydc709HQ4ODggLS0N9vb2xRk+EVHpptFIC9xNmSL1wgQFARs3SvdMIiqg/H5+G8wYmSdPnuD777/HoEGDoFKpcOLECTx9+hRhYWG6YwIDA+Hl5YX4+HgFIyUiohxSUoD27YFJk6QkZtAgqZTEJIaKmcHMWtq6dStSU1Mx4H9LUycnJ8PS0hKOjo6y41xcXJCcnJzneTIyMpCRkaHbTk9PL45wiYhIa98+oE+f57OSFi2SbgCpZxoNEBsrLUHj5gaEhADm5np/GTIyBpPILFu2DB07doS7u3uRzjNjxgxMmTJFT1EREVGeHj+WbvKoVaMGsGFDsfTCxMQAo0YB168/b/PwAObNAzh0snQziNLS1atXsWfPHrzxxhu6NldXVzx58gSpqamyY1NSUuDq6prnuSZMmIC0tDTdIzExsbjCJiIqvXbulCcxNWsCR48WWxITGSlPYgDgxg2pPSZG7y9JRsQgEpkVK1bA2dkZnTt31rXVr18fFhYW2Lt3r67twoULuHbtGoKDg/M8l1qthr29vexBRER6FBwMdOwob/v9d6mspGcajdQTk9u0FG1bdLR0HJVOiicymZmZWLFiBaKiolCmzPNKl4ODAwYPHox3330X+/btw4kTJzBw4EAEBwfne8YSERHp0ePHgEoFHDnyvO2zz3LPMvQkNjZnT0xWQgCJidJxVDopPkZmz549uHbtGgYNGpRj35w5c2BmZoaIiAhkZGSgffv2WLBggQJREhGVcjt35uyFSUyUBqoUo6Qk/R5Hpseg1pEpDlxHhoioiBo1km4tkFUJfXTs3w+0avXy4/btA1q2LO5oqCQZ3ToyRERkYB49kkpJWZOYzz8vsSQGkKZYe3hIYeRGpQI8PaXjqHRiIkNERDnt2JFz8O7168B775VoGObm0hRrIGcyo92eO5fryZRmTGSIiAyERiOVUtaulb4qNhOnYUOgUyd5mxBApUqKhBMeDmzalPPlPTykdq4jU7opPtiXiIgMZMG3R49y9sLMnAmMG1dCAeQtPBzo2pUr+1JOHOxLRKQw7YJv2f8aa0snJdLr8NNPwCuvyNtu3ACKuNo6UWFxsC8RkREwiAXf6tfPmcQIwSSGjAITGSIiBSm64NvDh1K3z8mTz9u+/LJEZyURFRXHyBARKUixBd+2bwe6dJG3sZRERoiJDBGRgtzc9HtcvtStC5w+LW9jLwwZKZaWiIgUVKILvmlLSVmTmFmzmMSQUWMiQ0SkoBJb8G3bNsDGRt528ybw7rtFPDGRspjIEBEprNgXfKtdG3j1VXmbEHquVxEpg2NkiIgMQLEs+PbwYc5emDlzpPncRCaCiQwRkYEwN9fjHZx//FHKjLJKSgJcXfX0AkSGgYkMEZGpqVkT+OMPeRsH9JKJ4hgZIiJT8eCBNEI4axIzdy6TGDJp7JEhIjIFP/wAdOsmb2MpiUoBJjJERMauRg3gzz/lbeyFoVKCpSUiImOlLSVlTWLmzWMSQ6UKe2SIiIzRli05F5hJTgZcXJSJh0ghTGSIiIxNtWrAX3/J29gLQ6UUS0tERMbi/n2plJQ1ifn6ayYxVKqxR4aIyBjExAAREfI2lpKImMgQERm8qlWBv/+Wt7EXhggAS0tERIZLW0rKmsTMn88khigL9sgQERmizZuByEh5W0oK4OysTDxEBoqJDBGRoQkIAC5der5tZgZoNMrFQ2TAWFoiIjIU9+5JpaSsScyCBUxiiF6APTJERIZgwwagZ095261bgJOTMvEQGQkmMkRESqtcGUhIeL5tYQE8eaJcPERGhKUlIiKlaEtJWZOYRYuYxBAVAHtkiIiUkFsp6fZtoGJFZeIhMlKK98jcuHED/fr1Q4UKFWBtbY2aNWvi+PHjuv1CCEycOBFubm6wtrZGWFgYLl68qGDERGRqNBpg/35g7Vrpa7GPrfX1lScxlpbS2jBMYogKTC+JTHp6OrZu3Yrz588X6Hl3795Fs2bNYGFhgR07duDcuXOYNWsWypUrpztm5syZ+Oqrr7Bo0SIcPXoUNjY2aN++PR4/fqyP0ImolIuJAXx8gFatgD59pK8+PlK73qWnS6WkK1eet33zDZCRUQwvRlQ6qIQo+BKRPXr0QIsWLTBixAg8evQItWvXxpUrVyCEwLp16xCR/X4geRg/fjzi4uIQGxub634hBNzd3TFmzBiMHTsWAJCWlgYXFxesXLkSvXr1eulrpKenw8HBAWlpabC3t8//RRKRyYuJkdacy/5XUKWSvm7aBISH6+nF1q0DeveWt7GURJSn/H5+F6pH5uDBgwgJCQEAbNmyBUIIpKam4quvvsInn3yS7/P8+OOPaNCgAbp37w5nZ2fUrVsXS5Ys0e1PSEhAcnIywsLCdG0ODg5o3Lgx4uPjCxM6EREAqXw0alTuq/1r26Kj9VRm8vaWJzHW1iwlEelJoRKZtLQ0lC9fHgCwc+dOREREoGzZsujcuXOBxq/8888/WLhwIQICArBr1y4MGzYMI0eOxKpVqwAAycnJAACXbHd3dXFx0e3LLiMjA+np6bIHEVF2sbHA9et57xcCSEyUjiu0hw+l7p1r1563LV4stRORXhRq1pKnpyfi4+NRvnx57Ny5E+vWrQMgjXmxsrLK93kyMzPRoEEDfPrppwCAunXr4o8//sCiRYsQFRVVmNAwY8YMTJkypVDPJaLSIylJv8flEBcHNG8ub/v3X6BChUKekIhyU6gemejoaPTt2xceHh5wc3NDy5YtAUglp5o1a+b7PG5ubqhevbqsrVq1arj2v/+9uLq6AgBSUlJkx6SkpOj2ZTdhwgSkpaXpHomJifmOh4hKDzc3/R4nM2yYPIlp2lTq4mESQ6R3heqRefvtt9GoUSMkJiaibdu2MDOT8qHKlSsXaIxMs2bNcOHCBVnb33//DW9vbwCAr68vXF1dsXfvXtSpUweANPjn6NGjGDZsWK7nVKvVUKvVhbgqIipNQkIADw/gxo3cx8moVNL+/w0HzJ+HDwEbG3nb3r1A69ZFipWI8lboBfEaNGiAWrVqISEhAX5+fihTpgw6d+5coHOMHj0aTZs2xaeffooePXrgt99+w+LFi7F48WIAgEqlQnR0ND755BMEBATA19cXH3/8Mdzd3dGtW7fChk5EBHNzYN48adaSSiVPZrSzlubOlY7Ll0OHcmY96emAnZ0+wiWiPBSqtPTw4UMMHjwYZcuWRVBQkK4U9M477+Czzz7L93kaNmyILVu2YO3atahRowamTZuGuXPnom/fvrpj3nvvPbzzzjsYMmQIGjZsiPv372Pnzp0FGotDRJSb8HBpinWlSvJ2D48CTr0eOlSexPTvL2VGTGKIil2h1pEZNWoU4uLiMHfuXHTo0AG///47KleujB9++AGTJ0/GqVOniiPWQuE6MkT0MhqNNDspKUkaExMSks+emNxKSb/+Kq2qR0RFkt/P70KVlrZu3Yr169ejSZMmUGn7YAEEBQXh8uXLhTklEZFizM2B/81ZyL/YWKBFC3nbvXuAra2+wiKifChUaen27dtwdnbO0f7gwQNZYkNEZJKGDJEnMVFRUimJSQxRiStUItOgQQP89NNPum1t8rJ06VIEBwfrJzIiIkPz4IE0EjjLCuTYtw9YuVKxkIhKu0KVlj799FN07NgR586dw7NnzzBv3jycO3cOhw8fxoEDB/QdIxGR8g4eBEJD5W0sJREprlA9Ms2bN8eZM2fw7Nkz1KxZE7/88gucnZ0RHx+P+vXr6ztGIiJlvfGGPIkZMIClJCIDUeAemadPn2Lo0KH4+OOPZTd4JCIyOQ8e5ExW9u0rxMhgIiouBe6RsbCwwObNm4sjFiIiw3HgQM4k5v59JjFEBqZQpaVu3bph69ateg6FiMhADB4sT1gGDZJKSdnXjCEixRVqsG9AQACmTp2KuLg41K9fHzbZfrlHjhypl+CIiEpUbqWk/ftzDvIlIoNRqJV9fX198z6hSoV//vmnSEHpE1f2JaJ82b8/54q89++zF4ZIIcW6sm9CQkKhAyMiMjgDB8rXghk8GFi6VLFwiCj/Cn33ay1thw5X9CUio3P/fs4bOx44kPPWA0RksAo12BcAvv32W9SsWRPW1tawtrZGrVq18N133+kzNiKi4rN/f84k5v59JjFERqZQiczs2bMxbNgwdOrUCRs2bMCGDRvQoUMHvPXWW5gzZ46+YyQi0q+oKPl4mCFDOCuJyEgVerDvlClT0L9/f1n7qlWrMHnyZIMaQ8PBvkSkk1sp6eBBICREmXiIKE/FOtg3KSkJTZs2zdHetGlTJCUlFeaURGTiNBogNhZISgLc3KTcwdy8BAPYtw9o3Vre9uABULZsCQZBRPpWqNKSv78/NmzYkKN9/fr1CAgIKHJQRGRaYmIAHx+pmtOnj/TVx0dqLxH9+8uTGG0piUkMkdErVI/MlClT0LNnTxw8eBDNmjUDAMTFxWHv3r25JjhEVHrFxACRkVLekNWNG1L7pk1AeHgxvfi9e0D2LunYWKB582J6QSIqaYUaIwMAJ06cwJw5c3D+/HkAQLVq1TBmzBjUrVtXrwEWFcfIEClHo5F6Xq5fz32/SgV4eAAJCcVQZvr1V6BNG3kbS0lERiO/n9+FTmSMBRMZIuXktlhubvR+Q+nXXwe+//759ltvAQsX6vEFiKi4Fetg359//hnm5uZo3769rH3Xrl3IzMxEx44dC3NaIjIx+R37r7c5ArmVkg4dAv5XAici01Oowb7jx4+HRqPJ0S6EwPjx44scFBGZBjc3/R73Qnv35kxiHj5kEkNk4gqVyFy8eBHVq1fP0R4YGIhLly4VOSgiMg0hIdIYmLzuYKJSAZ6eeljGpV8/ICzs+fawYdLoYmvrIp6YiAxdoRIZBweHXO9wfenSJdhwZUwi+h9zc2DePOnf2ZMZ7fbcuUUY6HvvnnSi1auft8XFAQsWFPKERGRsCpXIdO3aFdHR0bh8+bKu7dKlSxgzZgxeffVVvQVHRMYvPFyaYl2pkrzdw6OIU6/37Mm9lJTLYp1EZLoKNWspLS0NHTp0wPHjx+Hh4QEASExMRIsWLRATEwNHR0d9x1lonLVEZBj0urJvnz7A2rXPt4cPB+bP10ucRGQYinXWkoODAw4fPozdu3fjzJkzsLa2Ru3atRHC+5UQUR7MzfUwxTo9HXBwkLcdPgwEBxfxxERkrApUWoqPj8f27dsBACqVCu3atYOzszO+/PJLREREYMiQIcjIyCiWQImolNu9O2cS8/AhkxiiUq5AiczUqVPx559/6rbPnj2LN998E23btsX48eOxbds2zJgxQ+9BElEp17s30K7d8+0RIzgriYgAFLC0dPr0aUybNk23vW7dOjRq1AhLliwBAHh6emLSpEmYPHmyXoMkolIqt1JSfDzQpIky8RCRwSlQj8zdu3fh4uKi2z5w4IBsFd+GDRsiMTFRf9ERUen1yy+5l5KYxBBRFgVKZFxcXJCQkAAAePLkCU6ePIkmWf6o3Lt3DxYWFvqNkIhKn549gay3QBk5kqUkIspVgRKZTp06Yfz48YiNjcWECRNQtmxZ2Uyl33//HX5+fvk+3+TJk6FSqWSPwMBA3f7Hjx9j+PDhqFChAmxtbREREYGUlJSChExExiQtTVrgbsOG521HjjxfVY+IKJsCJTLTpk1DmTJlEBoaiiVLlmDJkiWwtLTU7V++fDnaZR2Qlw9BQUFISkrSPQ4dOqTbN3r0aGzbtg0bN27EgQMHcPPmTYQXevUsIjJou3YB2degevQIaNxYkXCIyDgUaLBvxYoVcfDgQaSlpcHW1hbm2Vaz2rhxI2xtbQsWQJkycHV1zdGelpaGZcuWYc2aNWjdujUAYMWKFahWrRqOHDkiK2kRkZHr3l1a5ldr1Cjp3gVERC9R6HstZU9iAKB8+fKyHpr8uHjxItzd3VG5cmX07dsX165dAwCcOHECT58+RViWG8EFBgbCy8sL8fHxhQmbiAyNtpSUNYk5coRJDBHlW6FW9tWXxo0bY+XKlahatSqSkpIwZcoUhISE4I8//kBycjIsLS1z3O7AxcUFycnJeZ4zIyNDtihfenp6cYVPREWxcyeQZdYjAKmUZGWlTDxEZJQUTWSyTt2uVasWGjduDG9vb2zYsAHWhZydMGPGDEyZMkVfIRJRcYiMBDZvfr4dHQ3MmaNYOERkvApVWioujo6OqFKlCi5dugRXV1c8efIEqampsmNSUlJyHVOjNWHCBKSlpekeXNeGyICkpkqlpKxJzNGjTGKIqNAMKpG5f/8+Ll++DDc3N9SvXx8WFhbYu3evbv+FCxdw7do1BL/g3ipqtRr29vayBxEZgB07gHLl5G2PHgGNGikTDxGZBEUTmbFjx+LAgQO4cuUKDh8+jNdeew3m5ubo3bs3HBwcMHjwYLz77rvYt28fTpw4gYEDByI4OJgzloiMTUQE0KnT8+1335UWuON4GCIqIkXHyFy/fh29e/fGnTt34OTkhObNm+PIkSNwcnICAMyZMwdmZmaIiIhARkYG2rdvjwULFigZMhEVRGpqzl6Y334DGjZUJBwiMj0qIYRQOojilJ6eDgcHB6SlpbHMRFSSfv4Z6NxZ3vb4MaBWKxMPERmV/H5+G9QYGSIyEa+9Jk9ixoyRSklMYohIzxQtLRGRicmtlHTsGNCggSLhEJHpYyJDRPrx00/AK6/I2/RcStJogNhYICkJcHMDQkKAXBYZJ6JShKUlIiq6rl3lSczYsXovJcXEAD4+QKtWQJ8+0lcfH6mdiEov9sgQUeHdvQuULy9vO34cqF9fry8TEyMtBpx9asKNG1L7pk1AeLheX5KIjAR7ZIiocLZvz5nEPH6s9yRGo5Fuhp3b/EptW3S0dBwRlT5MZIio4F59FejS5fn2uHHFNispNha4fj3v/UIAiYnScURU+rC0RET5V0KlpKySkvR7HBGZFvbIEFH+bNuWM4nJyCjWJAaQZifp8zgiMi1MZIjo5bp0kcpJWu+/L9V0LC2L/aVDQgAPD+mm2blRqQBPT+k4Iip9WFoiorz99x9QoYK87cQJoF69EgvB3ByYN0+anaRSyQf9apObuXO5ngxRacUeGSLK3Y8/5kxiMjJKNInRCg+XplhXqiRv9/Dg1Gui0o49MkSUU+fO0k0ftcaPB2bMUC4eSMlK165c2ZeI5JjIENFzuZWSTp4E6tZVJp5szM2Bli2VjoKIDAlLS0Qk+eGH3EtJBpLEEBHlhokMEQGdOgHduj3f/uCDEpuVRERUFCwtEZVmd+4AFSvK206dAurUUSQcIqKCYo8MUWm1das8iVGppFISkxgiMiJMZIhKow4dgNdee7794YdAZiZLSURkdFhaIipNWEoiIhPDHhmi0mLLFnkSY24OPHnCJIaIjBoTGaLSoH17+fK3H30EPHsGWFgoFxMRkR6wtERkyv79F3BykredPg3Urq1IOERE+sZEhugFNBojXhI/JgaIiHi+bWEBPHjAXhgiMiksLRHlISYG8PEBWrUC+vSRvvr4SO0Gr21beRIzcaI0HoZJDBGZGPbIEOUiJgaIjJQWt83qxg2p3WDvuJxbKenMGaBWLWXiISIqZuyRIcpGowFGjcqZxADP26KjpeMMyubN8iTG0lLqhWESQ0QmjIkMUTaxscD163nvFwJITJSOMxht2khdRVqTJkmr9LKUREQmjqUlomySkvR7XLG6fRtwdpa3/f47ULOm3l/KqAc+E5HJYo8MUTZubvo9rths2iRPYtRqqZRUDEmMUQ98JiKTxkSGKJuQEMDDQ7qHYm5UKsDTUzpOMa1bA927P9+eMgV4/LhYSknagc/Zy23agc9MZohISUxkiLIxNwfmzZP+nT2Z0W7PnatQWeX2bSmIffuet509K02vLgZGO/CZiEoNJjJEuQgPlyo3lSrJ2z08FJx6vXGjvJRkbQ08fQrUqFFsL2mUA5+JqFQxmETms88+g0qlQnR0tK7t8ePHGD58OCpUqABbW1tEREQgJSVFuSCpVAkPB65ckTo/1qyRviYkKJTEtGwJ9OjxfHvqVODhQ6BM8Y7XN6qBz0RUKhnErKVjx47hm2++Qa1s612MHj0aP/30EzZu3AgHBweMGDEC4eHhiIuLUyhSKm3MzaUcQjG3bgEuLvK2P/4AgoJK5OWNZuAzEZVaivfI3L9/H3379sWSJUtQrlw5XXtaWhqWLVuG2bNno3Xr1qhfvz5WrFiBw4cP48iRIwpGTFRCNmyQJzE2NlIpqYSSGMBIBj4TUammeCIzfPhwdO7cGWFhYbL2EydO4OnTp7L2wMBAeHl5IT4+Ps/zZWRkID09XfYgMipCAC1aAD17Pm+bNg24f7/YS0nZGfTAZyIiKFxaWrduHU6ePIljx47l2JecnAxLS0s4OjrK2l1cXJCcnJznOWfMmIEpU6boO1SikqFwKSk32oHPo0bJB/56eEhJjEHec4qISg3FemQSExMxatQorF69GlZWVno774QJE5CWlqZ7JCYm6u3cRMVq/Xp5EmNrW+KlpLwY1MBnIqIsFOuROXHiBG7duoV69erp2jQaDQ4ePIj58+dj165dePLkCVJTU2W9MikpKXB1dc3zvGq1Gmq1ujhDJ9IvbSnp0KHnbZ98Anz4oXIx5ULxgc9ERLlQLJFp06YNzp49K2sbOHAgAgMD8f7778PT0xMWFhbYu3cvIiIiAAAXLlzAtWvXEBwcrETIRPqXkgJkT8z//BOoXl2ZeIiIjIxiiYydnR1qZFvIy8bGBhUqVNC1Dx48GO+++y7Kly8Pe3t7vPPOOwgODkaTJk2UCJlIv9atA3r3fr5tZwf891+JD+glIjJmBv0Xc86cOTAzM0NERAQyMjLQvn17LFiwQOmwiIpGCKB5c+Dw4edt06cDH3ygXExEREZKJURud1ExHenp6XBwcEBaWhrs7e2VDodKu9xKSefOAdWqKRMPEZGByu/nt+LryBCVGmvWyJMYBwfg2TMmMURERcBEhqi4CQE0bQr07fu8bcYMIDWVK8kRERWRQY+RITJ6yck5b0R0/jwQGKhMPEREJoY9MkTFZc0aeRJTrpxUSmISQ0SkN0xkiPRNCKBJk5ylpP/+YymJiEjPWFoi0qekJMDdXd72119A1arKxENEZOLYI0OkL99/L09iKlSQSklMYoiIig0TGaKiEgJo1Ah4/fXnbZ9/Dvz7L0tJRETFjKUloqJgKYmISFHskSEqrOylJCenIpeSNBpg/35g7Vrpq0ZT5CiJiEwaExmighICaNhQXkqaORO4datIpaSYGMDHB2jVCujTR/rq4yO1ExFR7lhaIiqImzeBSpXkbRcuAFWqFOm0MTFAZKSUI2V144bUvmkTEB5epJcgIjJJ7JEhyq9vv5UnMdpSUhGTGI0GGDUqZxIDPG+LjmaZiYgoN0xkiF5GCKBBAyAq6nnbF18UuZSkFRsLXL/+4pdPTJSOIyIiOZaWiF6kmEpJWSUl6fc4IqLShD0yRHlZtUqexLi66qWUlF32e0oW9TgiotKEiQxRdkIA9eoBAwY8b5s1S+oSKYYF7kJCAA8PQKXKfb9KBXh6SscREZEcS0tEWd24IWUVWf39NxAQUGwvaW4OzJsnzU5SqeSDfrXJzdy5XCSYiCg37JEh0lq5Up7EuLlJU4WKMYnRCg+XplhnH47j4cGp10REL8IeGSIhgLp1gTNnnrfNng2MHl2iYYSHA127SrOTkpKkPCokhD0xREQvwkSGSrfcSkkXLwL+/oqEY24OtGypyEsTERkllpao9FqxQp7EVKoklZIUSmKIiKjgmMhQ6SMEULs2MGjQ87Y5c6RV6cz4K0FEZExYWqLS5fp1aS5zVgqWkoiIqGj4308qPZYvlycxnp4sJRERGTkmMmT6hABq1gQGD37eNm8ecO0aS0lEREaOpSUybYmJgJeXvO3SJcDPT5l4iIhIr/jfUTJdy5bJkxgvL6mUxCSGiMhkMJEh0yMEUKMG8MYbz9u++gq4epWlJCIiE8PSEpmWa9cAb2952+XLQOXKysRDRETFiv89JdOxZIk8ifH2lkpJTGKIiEwWe2TIYGk0+bzvkBBAUBBw/vzztq+/BkaMKLFYgQLES0REeqNoj8zChQtRq1Yt2Nvbw97eHsHBwdixY4du/+PHjzF8+HBUqFABtra2iIiIQEpKioIRU0mJiQF8fIBWrYA+faSvPj5Su4x2CnXWJOaff0o8icl3vEREpFeKJjIeHh747LPPcOLECRw/fhytW7dG165d8eeffwIARo8ejW3btmHjxo04cOAAbt68ifDwcCVDphIQEwNERkqL8GZ144bUrksOFi+Wl5J8faVuEV/fEosVKEC8RESkdyohhFA6iKzKly+PL774ApGRkXBycsKaNWsQGRkJAPjrr79QrVo1xMfHo0mTJvk6X3p6OhwcHJCWlgZ7e/viDJ30QKORejKyJwVaKhXgUUngqk01qC5ceL7j//4PePvtEokxq3zF6wEkJLDMRERUEPn9/DaYMTIajQYbN27EgwcPEBwcjBMnTuDp06cICwvTHRMYGAgvL68XJjIZGRnIyMjQbaenpxd77KQ/sbF5JwUA4CGu4dr1bLOSEhKkbEIBL4tXCGlNvthYoGXLEguLiKjUUHzW0tmzZ2Frawu1Wo233noLW7ZsQfXq1ZGcnAxLS0s4OjrKjndxcUFycnKe55sxYwYcHBx0D8/sNwgkg5aUlPe+IfgG15AlifHze94lopAXxVuY44iIqGAUT2SqVq2K06dP4+jRoxg2bBiioqJw7ty5Qp9vwoQJSEtL0z0SExP1GC0VNze33FoFLqAKvsFbupa/R/2fdKsBhRe4yz3ewh9HREQFo3hpydLSEv7/u/tw/fr1cezYMcybNw89e/bEkydPkJqaKuuVSUlJgaura57nU6vVUKvVxR02FZOQEGlMyY0bUlnGC1dxFT6yY5q6JSB2lk+uzy9p2ePNTjtGJiSk5GMjIioNFO+RyS4zMxMZGRmoX78+LCwssHfvXt2+Cxcu4Nq1awgODlYwQipO5ubSjakB4C0skiUxF+EPc2gwdr6PwQyczRqvSiXfp92eO5cDfYmIiouiPTITJkxAx44d4eXlhXv37mHNmjXYv38/du3aBQcHBwwePBjvvvsuypcvD3t7e7zzzjsIDg7O94wlMk7hrwmkuVaFXdJFXdswLMBPnsOwcS5gaDPww8OBTZuAUaPkA389PKQkxtDiJSIyJYomMrdu3UL//v2RlJQEBwcH1KpVC7t27ULbtm0BAHPmzIGZmRkiIiKQkZGB9u3bY8GCBUqGTMXtyhXA1xd2WZp+mHcFPWt5Y74Br5QbHg507cqVfYmISprBrSOjb1xHxogsXChfCyYgALhwIWfNhoiITJ7RrSNDpZgQUtJy+fLztkWLgKFDlYuJiIiMAhMZUtb/Skk52ry9czu62PCGj0RExsngZi1RKfJ//ydPYqpWBTIzSzyJ4Q0fiYiMFxMZKnmZmUDlyvI7VH/zDfDXXyU+HoY3fCQiMm4c7Esl69q1nD0uV68CXl4lHgpv+EhEZLjy+/nNHhkqWVmTmGrVpN4ZBZIYoGA3fCQiIsPERIZK1pdfSl+/+QY4d07RqdW84SMRkfFjIkMla8wYqatjyBClI+ENH4mITAATGSq1tDd8zKtTSKUCPD15w0ciIkPGRIYMlkYD7N8PrF0rfdVo9Ht+3vCRiMj4MZEhg1RSa7tob/hYqZK83cNDaucNH4mIDBunX5PB0a7tkv2dqe0lKY4Egyv7EhEZlvx+fjORIYPCtV2IiAjgOjJkpLi2CxERFQRvGllKGEvphGu7EBFRQTCRKQViYoBRo+Q9HR4e0owdQxvMyrVdiIioIFhaMnHGdlNEru1CREQFwUTGhGk0Uk9MbsO5tW3R0fpfn6UouLYLEREVBBMZE2asA2e5tgsREeUXx8iYMGMeOBseDnTtahwDlImISDlMZEyYsQ+cNTcHWrZUOgoiIjJkLC2ZMA6cJSIiU8dExoRx4CwREZk6JjImjgNniYjIlHGMTCnAgbNERGSqmMiUEhw4S0REpoilJSIiIjJaTGSIiIjIaDGRISIiIqPFRIaIiIiMFhMZIiIiMlqctWRgNBpOkyYiIsovRXtkZsyYgYYNG8LOzg7Ozs7o1q0bLly4IDvm8ePHGD58OCpUqABbW1tEREQgJSVFoYiLV0wM4OMDtGoF9OkjffXxkdqJiIgoJ0UTmQMHDmD48OE4cuQIdu/ejadPn6Jdu3Z48OCB7pjRo0dj27Zt2LhxIw4cOICbN28i3ASXo42JASIjgevX5e03bkjtTGaIiIhyUgkhhNJBaN2+fRvOzs44cOAAWrRogbS0NDg5OWHNmjWIjIwEAPz111+oVq0a4uPj0aRJk5eeMz09HQ4ODkhLS4O9vX1xX0KhaDRSz0v2JEZLpZJuKZCQwDITERGVDvn9/Daowb5paWkAgPLlywMATpw4gadPnyIsLEx3TGBgILy8vBAfH5/rOTIyMpCeni57GLrY2LyTGAAQAkhMlI4jIiKi5wwmkcnMzER0dDSaNWuGGjVqAACSk5NhaWkJR0dH2bEuLi5ITk7O9TwzZsyAg4OD7uHp6VncoRdZUpJ+jyMiIiotDCaRGT58OP744w+sW7euSOeZMGEC0tLSdI/ExEQ9RVh83Nz0exwREVFpYRDTr0eMGIHt27fj4MGD8PDw0LW7urriyZMnSE1NlfXKpKSkwNXVNddzqdVqqNXq4g5Zr0JCpDEwN25IZaTstGNkQkJKPjYiIiJDpmiPjBACI0aMwJYtW/Drr7/C19dXtr9+/fqwsLDA3r17dW0XLlzAtWvXEBwcXNLhFhtzc2DePOnfKpV8n3Z77lwO9CUiIspO0R6Z4cOHY82aNfjhhx9gZ2enG/fi4OAAa2trODg4YPDgwXj33XdRvnx52Nvb45133kFwcHC+ZiwZk/BwYNMmYNQo+cBfDw8piTHBGedERERFpuj0a1X27of/WbFiBQYMGABAWhBvzJgxWLt2LTIyMtC+fXssWLAgz9JSdsYw/TorruxLRESU/89vg1pHpjgYWyJDRERERrqODBEREVFBGMSsJWPD8g8REZFhYCJTQDExuQ/InTePA3KJiIhKGktLBcAbOxIRERkWJjL5pNFIPTG5DY3WtkVHS8cRERFRyWAik0+8sSMREZHhYSKTT7yxIxERkeFhIpNPvLEjERGR4WEik0/aGzvmsRgxVCrA05M3diQiIipJTGTyiTd2JCIiMjxMZApAe2PHSpXk7R4eUjvXkSEiIipZXBCvgMLDga5dubIvERGRIWAiUwjm5kDLlkpHQURERCwtERERkdFiIkNERERGi4kMERERGS0mMkRERGS0mMgQERGR0WIiQ0REREaLiQwREREZLSYyREREZLSYyBAREZHRMvmVfYUQAID09HSFIyEiIqL80n5uaz/H82Lyicy9e/cAAJ6engpHQkRERAV17949ODg45LlfJV6W6hi5zMxM3Lx5E3Z2dlCpVIU6R3p6Ojw9PZGYmAh7e3s9R6g8U74+U742gNdnzEz52gBenzEzlGsTQuDevXtwd3eHmVneI2FMvkfGzMwMHh4eejmXvb29yb1hszLl6zPlawN4fcbMlK8N4PUZM0O4thf1xGhxsC8REREZLSYyREREZLSYyOSDWq3GpEmToFarlQ6lWJjy9ZnytQG8PmNmytcG8PqMmbFdm8kP9iUiIiLTxR4ZIiIiMlpMZIiIiMhoMZEhIiIio8VEhoiIiIwWE5kXOHjwILp06QJ3d3eoVCps3bpV6ZD0ZsaMGWjYsCHs7Ozg7OyMbt264cKFC0qHpTcLFy5ErVq1dAs6BQcHY8eOHUqHVSw+++wzqFQqREdHKx2KXkyePBkqlUr2CAwMVDosvbpx4wb69euHChUqwNraGjVr1sTx48eVDksvfHx8cvz8VCoVhg8frnRoRabRaPDxxx/D19cX1tbW8PPzw7Rp0156LyBjcu/ePURHR8Pb2xvW1tZo2rQpjh07pnRYL2TyK/sWxYMHD1C7dm0MGjQI4eHhSoejVwcOHMDw4cPRsGFDPHv2DB988AHatWuHc+fOwcbGRunwiszDwwOfffYZAgICIITAqlWr0LVrV5w6dQpBQUFKh6c3x44dwzfffINatWopHYpeBQUFYc+ePbrtMmVM50/V3bt30axZM7Rq1Qo7duyAk5MTLl68iHLlyikdml4cO3YMGo1Gt/3HH3+gbdu26N69u4JR6cfnn3+OhQsXYtWqVQgKCsLx48cxcOBAODg4YOTIkUqHpxdvvPEG/vjjD3z33Xdwd3fH999/j7CwMJw7dw6VKlVSOrzcCcoXAGLLli1Kh1Fsbt26JQCIAwcOKB1KsSlXrpxYunSp0mHozb1790RAQIDYvXu3CA0NFaNGjVI6JL2YNGmSqF27ttJhFJv3339fNG/eXOkwSsyoUaOEn5+fyMzMVDqUIuvcubMYNGiQrC08PFz07dtXoYj06+HDh8Lc3Fxs375d1l6vXj3x4YcfKhTVy7G0RACAtLQ0AED58uUVjkT/NBoN1q1bhwcPHiA4OFjpcPRm+PDh6Ny5M8LCwpQORe8uXrwId3d3VK5cGX379sW1a9eUDklvfvzxRzRo0ADdu3eHs7Mz6tatiyVLligdVrF48uQJvv/+ewwaNKjQN+01JE2bNsXevXvx999/AwDOnDmDQ4cOoWPHjgpHph/Pnj2DRqOBlZWVrN3a2hqHDh1SKKqXM53+Wiq0zMxMREdHo1mzZqhRo4bS4ejN2bNnERwcjMePH8PW1hZbtmxB9erVlQ5LL9atW4eTJ08afO26MBo3boyVK1eiatWqSEpKwpQpUxASEoI//vgDdnZ2SodXZP/88w8WLlyId999Fx988AGOHTuGkSNHwtLSElFRUUqHp1dbt25FamoqBgwYoHQoejF+/Hikp6cjMDAQ5ubm0Gg0mD59Ovr27at0aHphZ2eH4OBgTJs2DdWqVYOLiwvWrl2L+Ph4+Pv7Kx1e3pTuEjIWMOHS0ltvvSW8vb1FYmKi0qHoVUZGhrh48aI4fvy4GD9+vKhYsaL4888/lQ6ryK5duyacnZ3FmTNndG2mVFrK7u7du8Le3t5kyoIWFhYiODhY1vbOO++IJk2aKBRR8WnXrp145ZVXlA5Db9auXSs8PDzE2rVrxe+//y6+/fZbUb58ebFy5UqlQ9ObS5cuiRYtWggAwtzcXDRs2FD07dtXBAYGKh1anpjI5JOpJjLDhw8XHh4e4p9//lE6lGLXpk0bMWTIEKXDKLItW7bo/shoHwCESqUS5ubm4tmzZ0qHqHcNGjQQ48ePVzoMvfDy8hKDBw+WtS1YsEC4u7srFFHxuHLlijAzMxNbt25VOhS98fDwEPPnz5e1TZs2TVStWlWhiIrP/fv3xc2bN4UQQvTo0UN06tRJ4YjyxjEypZQQAiNGjMCWLVvw66+/wtfXV+mQil1mZiYyMjKUDqPI2rRpg7Nnz+L06dO6R4MGDdC3b1+cPn0a5ubmSoeoV/fv38fly5fh5uamdCh60axZsxxLHfz999/w9vZWKKLisWLFCjg7O6Nz585Kh6I3Dx8+hJmZ/GPT3NwcmZmZCkVUfGxsbODm5oa7d+9i165d6Nq1q9Ih5YljZF7g/v37uHTpkm47ISEBp0+fRvny5eHl5aVgZEU3fPhwrFmzBj/88APs7OyQnJwMAHBwcIC1tbXC0RXdhAkT0LFjR3h5eeHevXtYs2YN9u/fj127dikdWpHZ2dnlGMtkY2ODChUqmMQYp7Fjx6JLly7w9vbGzZs3MWnSJJibm6N3795Kh6YXo0ePRtOmTfHpp5+iR48e+O2337B48WIsXrxY6dD0JjMzEytWrEBUVJRJTZ3v0qULpk+fDi8vLwQFBeHUqVOYPXs2Bg0apHRoerNr1y4IIVC1alVcunQJ48aNQ2BgIAYOHKh0aHlTukvIkO3bt08AyPGIiopSOrQiy+26AIgVK1YoHZpeDBo0SHh7ewtLS0vh5OQk2rRpI3755Relwyo2pjRGpmfPnsLNzU1YWlqKSpUqiZ49e4pLly4pHZZebdu2TdSoUUOo1WoRGBgoFi9erHRIerVr1y4BQFy4cEHpUPQqPT1djBo1Snh5eQkrKytRuXJl8eGHH4qMjAylQ9Ob9evXi8qVKwtLS0vh6uoqhg8fLlJTU5UO64VUQpjQkoRERERUqnCMDBERERktJjJERERktJjIEBERkdFiIkNERERGi4kMERERGS0mMkRERGS0mMgQERGR0WIiQ0REREaLiQwRlbgBAwagW7duOdr3798PlUqF1NTUEo+JiIwTExkiKlWePn2qdAhEpEdMZIjIYG3evBlBQUFQq9Xw8fHBrFmzZPtVKhW2bt0qa3N0dMTKlSsBAFeuXIFKpcL69esRGhoKKysrrF69GlevXkWXLl1Qrlw52NjYICgoCD///HMJXRUR6ZPp3JaUiEzKiRMn0KNHD0yePBk9e/bE4cOH8fbbb6NChQoYMGBAgc41fvx4zJo1C3Xr1oWVlRXefPNNPHnyBAcPHoSNjQ3OnTsHW1vb4rkQIipWTGSISBHbt2/PkTxoNBrdv2fPno02bdrg448/BgBUqVIF586dwxdffFHgRCY6Ohrh4eG67WvXriEiIgI1a9YEAFSuXLmQV0FESmNpiYgU0apVK5w+fVr2WLp0qW7/+fPn0axZM9lzmjVrhosXL8oSnvxo0KCBbHvkyJH45JNP0KxZM0yaNAm///574S+EiBTFRIaIFGFjYwN/f3/Zo1KlSgU6h0qlghBC1pbbYF4bGxvZ9htvvIF//vkHr7/+Os6ePYsGDRrg66+/LvhFEJHimMgQkUGqVq0a4uLiZG1xcXGoUqUKzM3NAQBOTk5ISkrS7b948SIePnyYr/N7enrirbfeQkxMDMaMGYMlS5boL3giKjEcI0NEBmnMmDFo2LAhpk2bhp49eyI+Ph7z58/HggULdMe0bt0a8+fPR3BwMDQaDd5//31YWFi89NzR0dHo2LEjqlSpgrt372Lfvn2oVq1acV4OERUT9sgQkUGqV68eNmzYgHXr1qFGjRqYOHEipk6dKhvoO2vWLHh6eiIkJAR9+vTB2LFjUbZs2ZeeW6PRYPjw4ahWrRo6dOiAKlWqyBIkIjIeKpG9wExERERkJNgjQ0REREaLiQwREREZLSYyREREZLSYyBAREZHRYiJDRERERouJDBERERktJjJERERktJjIEBERkdFiIkNERERGi4kMERERGS0mMkRERGS0mMgQERGR0fp/X/a5htkAXNcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Plotting the data\n",
    "plt.scatter(X_train, y_train, color='blue', label='Actual Scores')\n",
    "plt.plot(X_test, y_pred, color='red', label='Predicted Scores')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Linear Regression - Actual vs. Predicted Scores')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict the score for a student who studies for 9.25 hours per day\n",
    "Now we will use ourr model to compute the probable test acore if a given studnt studies forr 9.25 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted score if a student studies for 9.25 hours/day: 92.38611528261494\n"
     ]
    }
   ],
   "source": [
    "# Predict the score for a student who studies for 9.25 hours per day\n",
    "hours = [[9.25]]\n",
    "predicted_score = model.predict(hours)\n",
    "\n",
    "print(\"Predicted score if a student studies for 9.25 hours/day:\", predicted_score[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In conclusion, based on our analysis and linear regression model, if a student studies for 9.25 hours per day, **we predict that their score would be approximately 92.39**. This prediction is derived from the relationship we observed between the number of hours studied and the scores achieved by the students in our dataset.\n",
    "\n",
    "Our analysis showed a strong positive correlation between the number of hours studied and the scores obtained. As the number of hours increased, the scores tended to rise as well. This relationship allowed us to develop a reliable model for predicting scores based on study hours.\n",
    "\n",
    "The model's performance evaluation indicated that it performed well, with a mean squared error (MSE) of 18.94 and an R-squared (R2) score of 0.97. These metrics suggest that **our model accurately captures the patterns and trends in the data**, explaining approximately 97% of the variability in the scores.\n",
    "\n",
    "It's important to note that while our model provides a reasonable estimation, individual student outcomes may vary due to various factors beyond study hours alone. Other factors such as individual learning abilities, motivation, and external influences can also impact a student's performance.\n",
    "\n",
    "Overall, our analysis demonstrates the predictive power of the linear regression model in estimating scores based on study hours. It provides valuable insights into the relationship between study time and academic performance, highlighting the importance of dedicating sufficient time to studying for achieving higher scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Plotting the data\n",
    "plt.scatter(X_train, y_train, color='blue', label='Actual Scores')\n",
    "plt.scatter(9.25, model.predict([[9.25]]), color='green', label='Predicted Score for 9.25 hours')\n",
    "plt.plot(X_test, y_pred, color='red', label='Predicted Scores')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Linear Regression - Actual vs. Predicted Scores')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.2 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}