A3_AIE22158_V1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input features (X):\n",
      "[[20  6  2]\n",
      " [16  3  6]\n",
      " [27  6  2]\n",
      " [19  1  2]\n",
      " [24  4  2]\n",
      " [22  1  5]\n",
      " [15  4  2]\n",
      " [18  4  2]\n",
      " [21  1  4]\n",
      " [16  2  4]]\n",
      "Output labels (Y):\n",
      "[[386]\n",
      " [289]\n",
      " [393]\n",
      " [110]\n",
      " [280]\n",
      " [167]\n",
      " [271]\n",
      " [274]\n",
      " [148]\n",
      " [198]]\n",
      "The dimensionality of the vector space is: 3\n",
      "The number of vectors in the vector space is: 10\n",
      "The rank of the matrix X is: 3\n",
      "The pseudo-inverse of matrix X is:\n",
      "[[-0.01008596 -0.03124505  0.01013951  0.0290728   0.0182907   0.01161794\n",
      "  -0.00771348  0.00095458  0.01743623 -0.00542016]\n",
      " [ 0.09059668  0.07263726  0.03172933 -0.09071908 -0.01893196 -0.06926996\n",
      "   0.05675464  0.03152577 -0.07641966  0.00357352]\n",
      " [ 0.00299878  0.15874243 -0.05795468 -0.06609024 -0.06295043  0.03348017\n",
      "   0.01541831 -0.01070461  0.00029003  0.05938755]]\n",
      "The cost of each product that is available for sale is:\n",
      "[ 1. 55. 18.]\n"
     ]
    }
   ],
   "source": [
    "# QUESTION - 01\n",
    "\n",
    "# Please refer to the “Purchase Data” worksheet of Lab Session1 Data.xlsx. Please load the data and segregate them into 2 matrices A & C (following the nomenclature of AX = C). Do the following activities.\n",
    "#• What is the dimensionality of the vector space for this data?\n",
    "#• How many vectors exist in this vector space?\n",
    "#• What is the rank of Matrix A?\n",
    "#• Using Pseudo-Inverse find the cost of each product available for sale.\n",
    "#(Suggestion: If you use Python, you can use numpy.linalg.pinv() function to get a pseudo-inverse.)\n",
    "\n",
    "\n",
    "# Importing the required libraries\n",
    "import pandas as pd\n",
    "\n",
    "# Provide the full path to the Excel file\n",
    "\n",
    "dataset = pd.read_excel(r\"/Users/krishnavarma/Developer/UG AI MATERIAL /4th SEM COURSE'S/ML/ML CLG PR.CODES!/A3 ML/Lab Session1 Data.xlsx\", sheet_name=\"Purchase data\")\n",
    "\n",
    "# Extracting input features (X) and output labels (Y)\n",
    "X = dataset[['Candies (#)', 'Mangoes (Kg)', 'Milk Packets (#)']].to_numpy()\n",
    "Y = dataset['Payment (Rs)'].to_numpy().reshape(-1, 1)\n",
    "\n",
    "# Printing the input features (X) and output labels (Y)\n",
    "print(f\"Input features (X):\\n{X}\")\n",
    "print(f\"Output labels (Y):\\n{Y}\")\n",
    "\n",
    "# Finding the dimensionality of the vector space\n",
    "dimensionality = X.shape[1]\n",
    "print(\"The dimensionality of the vector space is: \" + str(dimensionality))\n",
    "\n",
    "# Finding the total number of vectors in the vector space\n",
    "num_vectors = X.shape[0]\n",
    "print(\"The number of vectors in the vector space is: \" + str(num_vectors))\n",
    "\n",
    "# Calculating the rank of the matrix X\n",
    "rank_X = np.linalg.matrix_rank(X)\n",
    "print(\"The rank of the matrix X is: \" + str(rank_X))\n",
    "\n",
    "# Calculating the pseudo-inverse of matrix X\n",
    "pseudo_inverse_X = np.linalg.pinv(X)\n",
    "print(\"The pseudo-inverse of matrix X is:\\n\" + str(pseudo_inverse_X))\n",
    "\n",
    "# Calculating the cost of each product available for sale\n",
    "cost_per_product = np.dot(pseudo_inverse_X, Y)\n",
    "print(\"The cost of each product that is available for sale is:\\n\" + str(cost_per_product.flatten()))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model vector coefficients for predicting the cost of the products available with the vendor = [ 1. 55. 18.]\n"
     ]
    }
   ],
   "source": [
    "# QUESTION - 02\n",
    "# Use the Pseudo-inverse to calculate the model vector X for predicting the cost of the products available with the vendor.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Read the Excel file containing purchase data into a DataFrame\n",
    "purchase_data = pd.read_excel(\"/Users/krishnavarma/Developer/UG AI MATERIAL /4th SEM COURSE'S/ML/ML CLG PR.CODES!/A3 ML/Lab Session1 Data.xlsx\", sheet_name=\"Purchase data\")\n",
    "\n",
    "# Extracting input features (X) and output target (Y) from the DataFrame\n",
    "X_columns = ['Candies (#)', 'Mangoes (Kg)', 'Milk Packets (#)']\n",
    "X = np.array([purchase_data[col].values for col in X_columns]).T\n",
    "Y = purchase_data['Payment (Rs)'].values.reshape(-1, 1)\n",
    "\n",
    "# Calculating the pseudo-inverse of the input features matrix (X)\n",
    "pseudo_inverse_X = np.linalg.pinv(X)\n",
    "\n",
    "# Finding the model vector (coefficients for predicting the cost) using pseudo-inverse\n",
    "model_vector_coefficients = np.dot(pseudo_inverse_X, Y)\n",
    "\n",
    "# Displaying the model vector for predicting the cost of products available with the vendor\n",
    "print(f\"The model vector coefficients for predicting the cost of the products available with the vendor = {model_vector_coefficients.flatten()}\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        POOR       0.00      0.00      0.00         2\n",
      "        RICH       0.60      1.00      0.75         3\n",
      "\n",
      "    accuracy                           0.60         5\n",
      "   macro avg       0.30      0.50      0.37         5\n",
      "weighted avg       0.36      0.60      0.45         5\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    " \n",
    "#QUESTION - 03\n",
    "\n",
    " #Mark all customers (in “Purchase Data” table) with payments above Rs. 200 as RICH and others as POOR. Develop a classifier model to categorize \n",
    " # customers into RICH or POOR class based onpurchase behavior.\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Load the data from the Excel file into a DataFrame\n",
    "data = pd.read_excel(r\"Lab Session1 Data.xlsx\", sheet_name=\"Purchase data\")\n",
    "\n",
    "# Create a new column 'Category' based on the 'Payment (Rs)' column to classify customers as 'RICH' or 'POOR'\n",
    "data['Category'] = data['Payment (Rs)'].apply(lambda x: 'RICH' if x > 200 else 'POOR')\n",
    "\n",
    "# Extract features (X) and target variable (Y)\n",
    "features = data[['Candies (#)', 'Mangoes (Kg)', 'Milk Packets (#)']]\n",
    "target = data['Category'] \n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(features, target, test_size=0.5, random_state=42)\n",
    "\n",
    "# Initialize the K-Nearest Neighbors classifier with k=5\n",
    "classifier = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "# Train the classifier on the training data\n",
    "classifier.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "predictions = classifier.predict(X_test)\n",
    "\n",
    "# Print the classification report to evaluate the performance of the classifier\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(Y_test, predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing Prices: 0      2081.85\n",
      "1      2077.75\n",
      "2      2068.85\n",
      "3      2072.95\n",
      "4      2078.25\n",
      "        ...   \n",
      "244    1397.40\n",
      "245    1400.75\n",
      "246    1405.10\n",
      "247    1412.35\n",
      "248    1363.05\n",
      "Name: Price, Length: 249, dtype: float64\n",
      "Mean Closing Price: 1560.663453815261\n",
      "Variance of Closing Prices: 58732.365352539186\n",
      "Mean Price for Wednesdays: 1550.7060000000001\n",
      "Mean Price for April: 1698.9526315789474\n",
      "Probability of Making Loss: 0.4979919678714859\n",
      "Probability of Making Profit on Wednesdays: 0.08433734939759036\n",
      "Conditional Probability of Making Profit, Given it's Wednesday: 0.42\n"
     ]
    },
    {
     "data": {
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fTteuXTEYDBW69ueff06vXr0ICwvze04MGDAAj8fDr7/+6hdTUVER11xzDd999x0//PBDnR8jJcmSKJHdbgdKTxhK07Bhw2JlYWFhvqQiJSWF/Px8mjZtWuy4kspOlHfs2BGLxUJRURH33Xcfjz/+OJGRkQwbNgyr1cq3336LxWLhhhtuqFC8JcnJyQFKTxTNZjNTpkzhxx9/JCYmht69e/P8889XOGlp3LhxuY9t2rRpsbEU55xzDkCJ45sqy19//QUcH09yqhYtWvjqT7BYLERFRfmVnfz4l2Xr1q1cccUVOBwO7HY7UVFRvgHGTqfTL55TnysGg4GEhIRi5/zggw9o27YtFouFiIgIoqKi+P77733nO51Tn88nPjRP3J/rrruOHj16cOuttxITE8OwYcP47LPPqjRxOt1r7NixY2RmZvLWW28RFRXl9zN69GjgnwHQZ6qk18h3333H+eefj8ViITw8nKioKKZPn17sbz1ixAgOHDjAsmXLgONJUHJyMjfddFOZ1yztuWgymUhMTCz2XKwsp3sO7N69G6UUjz76aLG/94mxU6f7e588bikzM5OtW7fSo0cPALp3747b7Wb16tXs27ePo0eP+o6vyLV37drF/Pnzix03YMCAEmOcPHkyc+fO5YsvvvAbC1hXyWw4USK73U58fDxbtmyp0O1Kayk58S3ybL3yyisYDAbuuusuDh48yG+//ca+fftISEjg+eefJzExkUOHDvkGTJ+JLVu2oNfry0xm7r33Xi699FLmzp3LggULePTRR5k8eTK//PILHTp0KNd1Tv4mXhlKGpgKlGtwdWU505l8mZmZ9OnTB7vdzlNPPUWTJk2wWCysX7+e//u//zuj5OOjjz5i1KhRDB06lP/+979ER0ej1+uZPHkye/bsKdc5Tvd8tlqt/PrrryxevJjvv/+e+fPnM2fOHPr168dPP/1UJTMbTxfTib/VjTfeWOIaPXC8NepsnHhfOJG0Llu2jMsuu4zevXvz5ptvEhcXh9Fo5P333+eTTz7xu+2gQYOIiYnho48+onfv3nz00UfExsb6PrQDTXn/3vfff3+pA9RL+yJ4wonk57fffiMoKAiAbt26ARAZGUmzZs347bffOHjwoN/xFbm21+tl4MCBPPDAAyUed+LL1wmDBg1i/vz5PP/881xwwQV1fsaoJEuiVJdccglvvfUWK1eu9L1wz1Z0dDQWi4Xdu3cXqyup7GRHjx7lmWee4fPPP8dgMPi63E5055349/Dhw2ecLB04cIClS5fSrVu303ZBNmnShPvuu4/77ruPXbt20b59e1566SU++ugjoPTk5Uyc+AZ58jlPLEh3okXlxDfezMxMvynTJX3jLm9sjRo1AmDHjh3069fPr27Hjh2++rO1ZMkS0tLS+Oqrr+jdu7evfN++fSXGs3v3bvr27esrd7vd7N+/3y8J+OKLL0hMTOSrr77yu78nvnFXFp1OR//+/enfvz8vv/wykyZN4uGHH2bx4sUMGDCgws+Ds33eREVFERISgsfjqZIEJCcnh6+//poGDRr4prp/+eWXWCwWFixY4LeMx/vvv1/s9nq9nhtuuIFZs2YxZcoU5s6dW64u6ZOfi4mJib7yoqIi9u3bd8b39Wz/3idiMRqNZxxDdHS0LyGy2Wy0bNnS7zXcvXt3li9fzqFDh9Dr9b7344pcu0mTJuTk5JQ7xvPPP5877riDSy65hGuuuYavv/7ar+u7rpFuOFGqBx54AJvNxq233kpycnKx+j179vDaa69V6Jx6vZ4BAwYwd+5cv/FFu3fv5scffyzztg8++CC9e/dm8ODBAMTExAD4xrP8+eefAL71SCoqPT2d66+/Ho/Hw8MPP1zqcXl5eRQUFPiVNWnShJCQEAoLC31lNput0lZhPnLkCF9//bXv96ysLD788EPat2/vu78nxrGcPPYgNzeXDz74oNj5yhtbp06diI6OZsaMGX737ccff+TPP/9kyJAhZ3qX/Jz4oDy5BbKoqIg333yzWDwRERG8/fbbuN1uX/nHH39crKuvpHOuWrWKlStXVkrMcPw5c6r27dsD+P5eJ8bhlPe5UNHjT6XX67nqqqv48ssvS2wZPnbs2BmdF44v53HTTTeRnp7Oww8/7Es09Ho9mqb5tWLu37+fuXPnlniem266iYyMDMaMGUNOTk6x9ZxKMmDAAEwmE6+//rrfY/ruu+/idDrP+Ll4tn/v6OhoLrjgAmbOnMnRo0eL1Zf3792zZ082bNjATz/95BuvdEL37t1ZuXIly5Yto23btr4vchW59rXXXsvKlStZsGBBseMyMzP9Xk8nDBgwgE8//ZT58+dz0003Vdu4vEBUd9NEcVpNmjThk08+4brrruPcc89lxIgRtG7dmqKiIlasWMHnn3/OqFGjKnzeJ554gp9++okePXpw55134vF4mDp1Kq1bty51S4jVq1czZ84cNm3a5CtLSEigU6dOjBo1iltuuYV33nmHrl27lqu1Y+fOnXz00UcopcjKymLjxo18/vnn5OTk8PLLL/sSstJu279/f6699lpatmyJwWDg66+/Jjk5mWHDhvmO69ixI9OnT+eZZ56hadOmREdHF2udKa9zzjmHW265hTVr1hATE8N7771HcnKy3zf3Cy+8kIYNG3LLLbfw3//+F71ez3vvvUdUVBQHDhzwO195YzMajUyZMoXRo0fTp08frr/+epKTk3nttddISEhg/PjxZ3R/TtW9e3fCwsIYOXIkd999N5qmMXv27GLdtyaTiSeeeIJx48bRr18/rr32Wvbv38+sWbNo0qSJXyvBJZdcwldffcUVV1zBkCFD2LdvHzNmzKBly5a+MTdn66mnnuLXX39lyJAhNGrUiJSUFN58803q16/v6ypp0qQJoaGhzJgxg5CQEGw2G127di21m7djx44APPzwwwwbNgyj0cill15a5uDnUz333HMsXryYrl27ctttt9GyZUvS09NZv349P//8c4lJ3qkOHz7sayXNyclh27ZtfP755yQlJXHfffcxZswY37FDhgzxvW5uuOEGUlJSmDZtGk2bNvV7zZ7QoUMHWrduzeeff865557Leeedd9p4oqKimDhxIk8++SSDBw/msssuY8eOHbz55pt07ty5XAlXSU78ve+++24GDRqEXq/3ex2Xx7Rp0+jZsydt2rThtttuIzExkeTkZFauXMmhQ4fYuHHjac/Rs2dP3n//fdasWcPYsWP96rp3747T6cTpdDJu3LgzuvZ///tfvvnmGy655BJGjRpFx44dyc3NZfPmzXzxxRfs37+fyMjIYnENHTqU999/nxEjRmC3232LAtc5NTADT9QyO3fuVLfddptKSEhQJpNJhYSEqB49eqg33njDb7oqpUzJb9Sokd8aIkoptWjRItWhQwdlMplUkyZN1DvvvKPuu+8+ZbFYit3e6/Wqrl27qgkTJhSr2717t+rdu7cKDg5WvXv3LnX9mpPx91IEgNLpdCo0NFR16NBB3XPPPWrr1q3Fjj916YDU1FQ1duxY1aJFC2Wz2ZTD4VBdu3ZVn332md/tkpKS1JAhQ1RISIjftPYTU/nXrFlT7FqlLR0wZMgQtWDBAtW2bVtlNptVixYt1Oeff17s9uvWrVNdu3ZVJpNJNWzYUL388sslnrO02EpaZ0kppebMmaM6dOigzGazCg8PV8OHD1eHDh3yO2bkyJHKZrMVi6m0JQ1OtXz5cnX++ecrq9Wq4uPj1QMPPKAWLFhQYjyvv/66atSokTKbzapLly5q+fLlqmPHjmrw4MG+Y7xer5o0aZLvuA4dOqjvvvtOjRw5UjVq1MjvfJSydMCpU89P/VsuWrRIXX755So+Pl6ZTCYVHx+vrr/++mLTs+fNm6datmzpWx7jdMsIPP3006pevXpKp9P5Xa8ir7Hk5GQ1duxY1aBBA2U0GlVsbKzq37+/euutt8q89onznXiNaJqm7Ha7atWqlbrtttt86/uc6t1331XNmjXzPT/ff//9Mh/7559/XgFq0qRJp43nZFOnTlUtWrRQRqNRxcTEqDvvvNNvvS+lKrZ0gNvtVuPGjVNRUVFK0zRfvGVt+3Hq80Uppfbs2aNGjBihYmNjldFoVPXq1VOXXHKJ+uKLL8p1v3bs2OH7m5/6/PF6vSo0NFQBas6cOcVuW95rZ2dnq4kTJ6qmTZsqk8mkIiMjVffu3dWLL77oWzeqtPt9Yn2zU9c9qys0pSpp5K0QZ2no0KFs3bqVXbt21XQoopbxer1ERUVx5ZVX8vbbb9d0OKIcXnvtNcaPH8/+/ftLnOEnRCCRMUuiRuTn5/v9vmvXLn744QeZoipOq6CgoFj33Icffkh6ero8f2oJpRTvvvsuffr0kURJ1AoyZknUiMTEREaNGuVbH2X69OmYTKZSp7UKccLvv//O+PHjueaaa4iIiGD9+vW8++67tG7dmmuuuaamwxNlyM3N5ZtvvmHx4sVs3ryZefPm1XRIQpSLJEuiRgwePJj//e9/JCUlYTab6datG5MmTaJZs2Y1HZoIcAkJCTRo0IDXX3+d9PR0wsPDGTFiBM8995xvxW4RmI4dO8YNN9xAaGgoDz30EJdddllNhyREuciYJSGEEEKIMsiYJSGEEEKIMtS6ZGnatGkkJCRgsVjo2rUrq1evLvXYrVu3ctVVV5GQkICmabz66qvFjnniiSfQNM3vp0WLFlV4D4QQQghRm9SqMUtz5sxhwoQJzJgxg65du/Lqq68yaNAgduzYQXR0dLHj8/LySExM5Jprrilz8bxWrVrx888/+36v6JLuXq+XI0eOEBISUqlbXAghhBCi6iilyM7OJj4+Hp2ujPajGlzjqcK6dOnityCbx+NR8fHxavLkyae9baNGjdQrr7xSrPzxxx9X7dq1O6u4Dh486LfQofzIj/zIj/zIj/zUnp+DBw+W+Tlfa1qWioqKWLduHRMnTvSV6XQ6BgwYcNZ7Pe3atYv4+HgsFgvdunVj8uTJFVr748Q+PQcPHsRut59VLEIIIYSoHllZWTRo0OC0G6fXmmQpNTUVj8fj2zz1hJiYGN9Gqmeia9euzJo1i+bNm3P06FGefPJJevXqxZYtW0r94xUWFvptKpqdnQ2A3W6XZEkIIYSoZU43hKbWJEtV5aKLLvL9v23btr6NWD/77DNuueWWEm8zefJknnzyyeoKUQghhBA1qNbMhouMjESv15OcnOxXnpycTGxsbKVdJzQ0lHPOOYfdu3eXeszEiRN9O0A7nU4OHjxYadcXQgghRGCpNcmSyWSiY8eOLFq0yFfm9XpZtGgR3bp1q7Tr5OTksGfPHuLi4ko9xmw2+7rcpOtNCCGE+HerVd1wEyZMYOTIkXTq1IkuXbrw6quvkpuby+jRowEYMWIE9erVY/LkycDxQeHbtm3z/f/w4cNs2LCB4OBgmjZtCsD999/PpZdeSqNGjThy5AiPP/44er2e66+/vmbupBD/Ih6PB5fLVdNhCMBoNKLX62s6DCFqpVqVLF133XUcO3aMxx57jKSkJNq3b8/8+fN9g74PHDjgt07CkSNH6NChg+/3F198kRdffJE+ffqwZMkSAA4dOsT1119PWloaUVFR9OzZk99//52oqKhqvW9C/JsopUhKSiIzM7OmQxEnCQ0NJTY2VtaDE6KCZG+4SpCVlYXD4cDpdEqXnBDA0aNHyczMJDo6mqCgIPlwrmFKKfLy8khJSSE0NLTMYQZC1CXl/fyuVS1LQojA5/F4fIlSRERETYcj/ma1WgFISUkhOjpauuSEqIBaM8BbCFE7nBijFBQUVMORiFOdeExkHJkQFSPJkhCiSkjXW+CRx0SIMyPdcEIIIYQISEopPFlOUAqdLRid0VgjcUjLkhBClOKJJ54gJiYGTdOYO3duTYfjZ8mSJWiaJjMOxb+WOz0d50/zOfzMkxx64lHSPp+DKyWlRmKRZEkIUa1GjRqFpmlomobRaCQmJoaBAwfy3nvv4fV6azo8nz///JMnn3ySmTNncvToUb+tkYQQVcudkc7R114i9aMPcCUdxZ2WivPH7zj05CO4UpJPf4JKJsmSEKLaDR48mKNHj7J//35+/PFH+vbtyz333MMll1yC2+2u6fAA2LNnDwCXX345sbGxmM3mGo5IiLqjYO9eCvfuKVbuycoic8GPeKt5koIkS0KIamc2m4mNjaVevXqcd955PPTQQ8ybN48ff/yRWbNmAfDyyy/Tpk0bbDYbDRo04D//+Q85OTkA5ObmYrfb+eKLL/zOO3fuXGw2G9nZ2aeNYfPmzfTr1w+r1UpERAS333677/xPPPEEl156KQA6ne60A6O3bNmCTqfj2LFjAKSnp6PT6Rg2bJjvmGeeeYaePXv63eaiiy4iODiYmJgYbrrpJlJTU331Xq+XyZMn07hxY6xWK+3atSt2f0+Wl5fHRRddRI8ePaRrTtRqyu0m+9fFpdbnrFqJN+f0r/HKJMmSECIg9OvXj3bt2vHVV18Bx5OU119/na1bt/LBBx/wyy+/8MADDwBgs9kYNmwY77//vt853n//fa6++mpCQkLKvFZubi6DBg0iLCyMNWvW8Pnnn/Pzzz9z1113Ace3QTpx7qNHj3L06NEyz9eqVSsiIiJYunQpAMuWLfP7HWDp0qVccMEFAGRmZtKvXz86dOjA2rVrmT9/PsnJyVx77bW+4ydPnsyHH37IjBkz2Lp1K+PHj+fGG2/0O+cJmZmZDBw4EK/Xy8KFCwkNDS0zXiECmqaB0VR6tcEIVO/MTkmWhBABo0WLFuzfvx+Ae++9l759+5KQkEC/fv145pln+Oyzz3zH3nrrrSxYsMCXyKSkpPDDDz9w8803n/Y6n3zyCQUFBXz44Ye0bt2afv36MXXqVGbPnk1ycjLBwcG+hCM2NpbY2Ngyz6dpGr179/Zto7RkyRJGjx5NYWEh27dvx+VysWLFCvr06QPA1KlT6dChA5MmTaJFixZ06NCB9957j8WLF7Nz504KCwuZNGkS7733HoMGDSIxMZFRo0Zx4403MnPmTL9rJyUl0adPH+Li4vj2229lfStR62l6PY7+A0utt1/QD73DUY0RSbIkhAggSilfl9fPP/9M//79qVevHiEhIdx0002kpaWRl5cHQJcuXWjVqhUffPABAB999BGNGjWid+/ep73On3/+Sbt27bDZbL6yHj164PV62bFjxxnFfvKek0uXLqVfv36+BGrNmjW4XC569OgBwMaNG1m8eDHBwcG+nxYtWgDHx0rt3r2bvLw8Bg4c6HfMhx9+6BtLdcLAgQNp2rQpc+bMwWQq/du4ELWJqV49gs/vXry8fgNCevdB01Vv+iLrLAkhAsaff/5J48aN2b9/P5dccgl33nknzz77LOHh4fz222/ccsstFBUV+VpPbr31VqZNm8aDDz7I+++/z+jRo2ts4cULLriAe++9l127drFt2zZ69uzJ9u3bWbJkCRkZGXTq1MkXd05ODpdeeilTpkwpdp64uDi2bNkCwPfff0+9evX86k8daD5kyBC+/PJLtm3bRps2baro3glRvQx2B5E3jsR+QT+cCxegiooI6dUHS/PmGMOrfxslSZaEEAHhl19+YfPmzYwfP55169bh9Xp56aWX0P39DfLkLrgTbrzxRh544AFef/11tm3bxsiRI8t1rXPPPZdZs2aRm5vra11avnw5Op2O5s2bn1H8bdq0ISwsjGeeeYb27dsTHBzMBRdcwJQpU8jIyPCNVwI477zz+PLLL0lISMBgKP423LJlS8xmMwcOHPB13ZXmueeeIzg4mP79+7NkyRJatmx5RvELEWgMDgcGhwPLOc3B60VXgzNSpRtOCFHtCgsLSUpK4vDhw6xfv55JkyZx+eWXc8kllzBixAiaNm2Ky+XijTfeYO/evcyePZsZM2YUO09YWBhXXnkl//3vf7nwwgupX79+ua4/fPhwLBYLI0eOZMuWLSxevJhx48Zx0003ERMTc0b36cS4pY8//tiXGLVt25bCwkIWLVrkl/SMHTuW9PR0rr/+etasWcOePXtYsGABo0ePxuPxEBISwv3338/48eP54IMP2LNnD+vXr+eNN97wdTue7MUXX2T48OH069eP7du3n1H8QgQqndFYo4kSSLIkhKgB8+fPJy4ujoSEBAYPHszixYt5/fXXmTdvHnq9nnbt2vHyyy8zZcoUWrduzccff8zkyZNLPNeJrrnyDOw+ISgoiAULFpCenk7nzp25+uqr6d+/P1OnTj2r+9WnTx88Ho8vWdLpdPTu3RtN03zjlQDi4+NZvnw5Ho+HCy+8kDZt2nDvvfcSGhrqa0l7+umnefTRR5k8eTLnnnsugwcP5vvvv6dx48YlXvuVV17h2muvpV+/fuzcufOs7ocQwp+mlFI1HURtl5WVhcPhwOl0YrfbazocIWpUQUEB+/bto3Hjxlgsliq/3uzZsxk/fjxHjhyRAc6nUd2PjRCBrryf3zJmSQhRK+Xl5XH06FGee+45xowZI4mSEKLKSDecEKJWev7552nRogWxsbFMnDjRr27SpEl+U+5P/jnTPd5KO19wcDDLli2rjLskhAhQ0g1XCaQbToh/BEJXT3p6Ounp6SXWWa3WYtPxy2P37t2l1tWrVw+r1Vrhc1a3QHhshAgk0g0nhKizwsPDCQ8Pr9RzNm3atFLPJ4SoPaQbTgghhBCiDJIsCSGEEEKUQZIlIYQQQogySLIkhBBCCFEGSZaEEEIIIcogyZIQQgghRBkkWRJCiGoybdo0EhISsFgsdO3aldWrV9d0SEKIcpBkSQhR53hycyg6epiCPbspOnoET25OlV9zzpw5TJgwgccff5z169fTrl07Bg0aREpKSpVfWwhxdmRRSiFEneJKTyPlvZnkb9nkK7O2bkv0zWMwhkdU2XVffvllbrvtNkaPHg3AjBkz+P7773nvvfd48MEHq+y6QoizJy1LQog6w5ObUyxRAsjfsomU92ZWWQtTUVER69atY8CAAb4ynU7HgAEDWLlyZZVcUwhReSRZEkLUGZ4sZ7FE6YT8LZvwZDmr5Lqpqal4PB5iYmL8ymNiYkhKSqqSawohKo8kS0KIOsObl39W9UKIukmSJSFEnaELsp5V/ZmKjIxEr9eTnJzsV56cnExsbGyVXFMIUXkkWRJC1Bl6uwNr67Yl1llbt0Vvd1TJdU0mEx07dmTRokW+Mq/Xy6JFi+jWrVuVXFMIUXkkWRJC1Bl6WzDRN48pljCdmA2ntwVX2bUnTJjA22+/zQcffMCff/7JnXfeSW5urm92nBAicMnSAUKIOsUYHkHsnXfjyXLizctHF2RFb3dUaaIEcN1113Hs2DEee+wxkpKSaN++PfPnzy826FsIEXgkWRJC1Dl6W3CVJ0clueuuu7jrrruq/bpCiLMj3XBCCCGEEGWQZEkIIYQQogySLAkhhBBClEGSJSGEEEKIMkiyJIQQQghRBkmWhBBCCCHKIMmSEEIIIUQZJFkSQgghhCiDJEtCCCGEEGWQZEkIIYQQogySLAkhRBX79ddfufTSS4mPj0fTNObOnVvTIQkhKkCSJSFEnVPgySG96DBJBbvJKDpCgSenSq+Xm5tLu3btmDZtWpVeRwhRNWQjXSFEnZLtSmNRykwO5G/ylTW0tqV/9BhCjBFVcs2LLrqIiy66qErOLYSoetKyJISoMwo8OcUSJYAD+ZtYlDKzyluYhBC1kyRLQog6I8/jLJYonXAgfxN5Hmc1RySEqA0kWRJC1BlF3vyzqhdC1E2SLAkh6gyTznpW9UKIukmSJSFEnRGkd9DQ2rbEuobWtgTpHdUckRCiNpBkSQhRZ1j0wfSPHlMsYToxG86iD66S6+bk5LBhwwY2bNgAwL59+9iwYQMHDhyokusJISqXLB0QoJRSeJxOlNeL3mZDZzbXdEhC/CuEGCMYHHs3eR4nRd58TDorQXpHlSVKAGvXrqVv376+3ydMmADAyJEjmTVrVpVdVwhROSRZCkDuzAxy1qwi86cf8ebnEdSmHeGXXYExOhZNr6/p8ISo9Sz64CpNjk51wQUXoJSqtusJISqXJEsBxu3MJGnmVAr+3Oory1n5G7nrVlP/8Wcx12tQg9EJIYQQdU+tG7M0bdo0EhISsFgsdO3aldWrV5d67NatW7nqqqtISEhA0zReffXVsz5nVXMlHfVLlE5QRUWkffY/PPkytVkIIYSoTrUqWZozZw4TJkzg8ccfZ/369bRr145BgwaRkpJS4vF5eXkkJiby3HPPERsbWynnrGo5a1eVWpe36Q+8ebnVGI0QQgghalWy9PLLL3PbbbcxevRoWrZsyYwZMwgKCuK9994r8fjOnTvzwgsvMGzYMMylDJCu6Dmrms5sKbVOMxrRNK0aoxFCCCFErUmWioqKWLduHQMGDPCV6XQ6BgwYwMqVK6v1nIWFhWRlZfn9VJbgrt1KrQvpeQG6EHulXUsIIYQQp1drkqXU1FQ8Hg8xMTF+5TExMSQlJVXrOSdPnozD4fD9NGhQeYOuDeGRhF0ytHh5VAxhF1+KzmistGsJIYQQ4vRkNtwZmDhxom+dFICsrKxKS5j0Nhuhgy/B1rEzzl8W4s3JJrhrd6zntMAQHlEp1xBCCCFE+dWaZCkyMhK9Xk9ycrJfeXJycqmDt6vqnGazudQxUJVBHxyMPjgY8+jG4PWiGWrNwySEEEL869SabjiTyUTHjh1ZtGiRr8zr9bJo0SK6dSt9nE91n7MyaTqdJEpCCCFEDatVn8QTJkxg5MiRdOrUiS5duvDqq6+Sm5vL6NGjARgxYgT16tVj8uTJwPEB3Nu2bfP9//Dhw2zYsIHg4GCaNm1arnMKIYQQom6rVcnSddddx7Fjx3jsscdISkqiffv2zJ8/3zdA+8CBA+h0/zSWHTlyhA4dOvh+f/HFF3nxxRfp06cPS5YsKdc5hRDibE2ePJmvvvqK7du3Y7Va6d69O1OmTKF58+Y1HZoQohw0JRsWnbWsrCwcDgdOpxO7Xab2i7qtoKCAffv20bhxYyyW0tcNq0k5bg9Oj4d8j5cgvQ67Xk+woer2XRw8eDDDhg2jc+fOuN1uHnroIbZs2cK2bduw2WxVdt1T1YbHRojqVN7P71rVsiSEEGcrrcjFzMMpbMr9Z+ugtjYrY+pFE2GqmqU55s+f7/f7rFmziI6OZt26dfTu3btKrimEqDy1ZoC3EEKcrRy3p1iiBLApN5+Zh1PIcXuqJQ6n0wlAeHh4tVxPCHF2JFkKUMrrxZ2ZgTs9DW9BQU2HI8S/gtPjKZYonbApNx+np+qTJa/Xy7333kuPHj1o3bp1lV9PCHH2pBsuALkz0slevRLnwvl48/IIat2W8KFXY4yOkaUEhDgL+R7vWdVXhrFjx7JlyxZ+++23Kr+WEKJyyCdvgHE7M0ma8QYFO/70leWsXknuH2up//gkzPUrb2sVIeoaq77sxvTT1Z+tu+66i++++45ff/2V+vXrV+m1hBCVR7rhAozr6BG/ROkE5XKR9vknePLzaiAqIf4dHHo9bW3WEuva2qw49FUzI04pxV133cXXX3/NL7/8QuPGjavkOkKIqiHJUoDJWbuq1Lq8TRvw5pc83kIIcXrBBj1j6kUXS5hOzIarquUDxo4dy0cffcQnn3xCSEgISUlJJCUlkS+vZyFqBemGCzA6a1CpdZrRhIZWjdEI8e8TYTJyd4NY3zpLVr0ORxWvszR9+nQALrjgAr/y999/n1GjRlXZdYUQlUOSpQAT3KUbGd9+XWKdvfcF6EJCqjkiIf59gg1VmxydStb+FaJ2k264AGMIDyfs8quKlRtjYgm96FJ0xqpZNE8IIYQQJZOWpQCjtwUTOvAibB06krVkEZ7sLIK7dMPSrDnG8IiaDk8IIYSocyRZCkD64GD0wcGYRzYGpdB00gAohBBC1JQKJ0uFhYWsWrWKv/76i7y8PKKioujQoYNMha0CmqaBJgO6hRBCiJpU7mRp+fLlvPbaa3z77be4XC4cDgdWq5X09HQKCwtJTEzk9ttv54477iBEBiELIYQQ4l+iXP07l112Gddddx0JCQn89NNPZGdnk5aWxqFDh8jLy2PXrl088sgjLFq0iHPOOYeFCxdWddxCCCGEENWiXC1LQ4YM4csvv8RYykysxMREEhMTGTlyJNu2bePo0aOVGqQQQgghRE0pV7I0ZsyYcp+wZcuWtGzZ8owDEkIIIYQIJGc1G27Lli0sXboUj8dDjx496NixY2XFJYQQQggREM54Tvq0adPo378/S5cuZfHixfTr149nn322MmMTQgghhKhx5U6WDh486Pf71KlT2bp1K5999hlff/018+fP59VXX63s+IQQotabPn06bdu2xW63Y7fb6datGz/++GNNhyWEKKdyJ0sDBgzgtdde8+1xFBERwfz58yksLCQ7O5uff/6ZqKioKgu0LvK63XgLC2s6DCH+dQpzPDgPF3FsdwHOI0UU5niq9Hr169fnueeeY926daxdu5Z+/fpx+eWXs3Xr1iq9rhCicmiqnDs8ZmVl8eCDD7J27VreeustTCYTN910E3/88QeapnHuuefy/vvv07lz56qOOeBkZWXhcDhwOp3Y7fazPp8nJ5uio0dw/rwAb24Oti7nE9SqLcaIyEqIVoiqVVBQwL59+2jcuDEWi6WmwykmN83FipkpHN2U7yuLa2ul+5hobBHVt/dieHg4L7zwArfccku1XTPQHxshqlt5P7/LPcDbbrfz5ptvsmLFCkaNGkW/fv1YtmwZHo8Hj8dDaGhoZcRd53lyckj/9mucC37wleVt2YQhIpJ6Dz6GMSq6BqMTonYrzPEUS5QAjm7KZ8XMFHrfHYs5WF+lMXg8Hj7//HNyc3Pp1q1blV5LCFE5KjzAu3v37qxdu5awsDA6dOjAr7/+KolSJXKnp/olSr7ytFQyvp+Ht6ioBqIS4t+hwOkpliidcHRTPgXOquuO27x5M8HBwZjNZu644w6+/vprWWZFiFqi3MmS2+3mzTffZNy4ccyaNYuHHnqIb7/9lpdeeolrrrmG5OTkqoyzzshetbL0uuW/4snJrsZohPh3Kcr3nlX92WjevDkbNmxg1apV3Hnnnb5FfIUQga/cydItt9zC1KlTsdlsvP/++4wfP55zzjmHX375hcGDB9OtWzemT59elbHWCaqMliPl8UC5RpgJIUpispb9lne6+rO6tslE06ZN6dixI5MnT6Zdu3a89tprVXY9IUTlKfc7w7x58/jyyy957rnnWLhwId9//72v7pZbbuH3339n2bJlVRJkXRLcuWupdUHtz0MXFFSN0Qjx72Jx6Ilray2xLq6tFYujascrnczr9VIos12FqBXKnSzFxMTw008/UVRUxC+//EJERIRffXR0NJ988kmlB1jXGGNiCWrTvli5ZrEQefX16K0lv9ELIU7PHKyn+5joYgnTidlwVTW4e+LEifz666/s37+fzZs3M3HiRJYsWcLw4cOr5HpCiMpV7tlwU6dOZfjw4UyYMIG4uDg+++yzqoyrzjI4Qom+ZQy5G9fjXPAjnvw8gtq2J+yiSzFGx9R0eELUerYII73vjqXA6aEo34vJqsPi0FfpLLiUlBRGjBjB0aNHcTgctG3blgULFjBw4MAqu6YQovKUe50lAKUUqampsvjkKSp7naUT3FlO8HrRBdnQmUyVdl4hqpKs5RO45LERwl+lr7MEoGmaJErVyGB31HQIQgghRJ1XoWQJoEOHDmiaVqxc0zQsFgtNmzZl1KhR9O3bt1ICFEIIIUTd48nLxZ2RQe66tajCQmwdzsMQHV0jDQkVnic7ePBg9u7di81mo2/fvvTt25fg4GD27NlD586dOXr0KAMGDGDevHlVEa8QQggh/uU8uTk4f1rAwQfvJ/3zT8n45msOPfkoKW/PwJ2ZWe3xVLhlKTU1lfvuu49HH33Ur/yZZ57hr7/+4qeffuLxxx/n6aef5vLLL6+0QIUQQghRN7hSUkj/svhEsrwNf5C7fi2OfgOqNZ4Ktyx99tlnXH/99cXKhw0b5pshd/3117Njx46zj04IIYQQdYryenEu+qnU+swfv8ftzKy+gDiDZMlisbBixYpi5StWrPDNrvB6vTLTQgghhBAVprxePM6sUus9uTkob9VtTVSSCnfDjRs3jjvuuIN169bRuXNnANasWcM777zDQw89BMCCBQto3759pQYqhBBCiH8/ncFAcOcu5G1YX2J9UOu26Kt5N4sKJ0uPPPIIjRs3ZurUqcyePRs4vkHk22+/zQ033ADAHXfcwZ133lm5kQohhBCiTrC2ao0hIgJ3WppfuWY0Ej70SnTm6u29qnCyBDB8+PAyl+m3ypYcQgghhDhDxohI6j30OOlff0H2yuXg8WBt2ZrI4TfVyG4WZ5QsARQVFZGSkoL3lH7Dhg0bnnVQQgghhKjbjNHRRI26hfCrrwWl0FmD0NtsNRJLhQd479q1i169emG1WmnUqBGNGzemcePGJCQk0Lhx46qIUQgh/jWee+45NE3j3nvvrelQhAh4OrMZY0QkxsioGkuU4AxalkaNGoXBYOC7774jLi6uxNW8hRAikHlycvBkOfHm5aOzBaEPsaMPDq7y665Zs4aZM2fStm3bKr+WEKLyVDhZ2rBhA+vWraNFixZVEY8QQlQpV1oaKe/MJH/LJl+ZtU1bom8ZgzEiosqum5OTw/Dhw3n77bd55plnquw6QojKV+FuuJYtW5KamloVsQghRJXy5OQUS5QA8jdvIuXdmXhycqrs2mPHjmXIkCEMGFC9Kw8LIc5euVqWsrL+WRxqypQpPPDAA0yaNIk2bdpgNBr9jrXb7ZUboRBCVBJPlrNYonRC/uZNeLKcVdId9+mnn7J+/XrWrFlT6ecWQlS9ciVLoaGhfmOTlFL079/f7xilFJqm4fF4KjdCIYSoJN68/LLr88uuPxMHDx7knnvuYeHChbKzgRC1VLmSpcWLF1d1HEIIUeV0QWWvAaergjXi1q1bR0pKCuedd56vzOPx8OuvvzJ16lQKCwvR6/WVfl0hROUpV7LUp0+fqo5DCCGqnN7uwNqmLfmbi3fFWdu0RW93VPo1+/fvz+bNm/3KRo8eTYsWLfi///s/SZSEqAXKPcB7165dXH/99X7jl05wOp3ccMMN7N27t1KDE0KIyqQPDib6ljFY2/hP3be2aUv0rWOqZLxSSEgIrVu39vux2WxERETQunXrSr+eEKLylXvpgBdeeIEGDRqUOIDb4XDQoEEDXnjhBaZPn16pAQohRGUyRkQQ+5+7j6+zlJ+PzmpFb3dUyzpLQojaqdzJ0tKlS/noo49Krb/22mt9G+kKIUQg0wcH12hytGTJkhq7thCi4srdDXfgwAGio6NLrY+MjOTgwYOVEpQQQgghRKAod7LkcDjYs2dPqfW7d++WNZaEEEII8a9T7mSpd+/evPHGG6XWv/766/Tq1atSghJCCCGECBTlTpYmTpzIjz/+yNVXX83q1atxOp04nU5WrVrFVVddxYIFC5g4cWJVxiqEEEIIUe3KPcC7Q4cOfPHFF9x88818/fXXfnURERF89tlnfouuCSHqNqVUTYcgTiGPiRBnptzJEsAll1zCX3/9xfz589m9ezdKKc455xwuvPBCgoKCqipGIUQtcmK/yLy8PKxVsCK2OHN5eXkAxfb0FEKUrULJEoDVauWKK66oiliEEP8Cer2e0NBQUlJSAAgKCvLbW1JUP6UUeXl5pKSkEBoaKquGC1FB5UqWPv30U4YNG1auEx48eJADBw7Qo0ePswqsNNOmTeOFF14gKSmJdu3a8cYbb9ClS5dSj//888959NFH2b9/P82aNWPKlClcfPHFvvpRo0bxwQcf+N1m0KBBzJ8/v0riF6IuiI2NBfAlTCIwhIaG+h4bIQKdJzcHd1oaOat+x1tYSHDnLhhjYzE4Qqs9lnIlS9OnT+fJJ59k9OjRXHrppZx77rl+9U6nk+XLl/PRRx+xcOFC3n333SoJds6cOUyYMIEZM2bQtWtXXn31VQYNGsSOHTtKXANqxYoVXH/99UyePJlLLrmETz75hKFDh7J+/Xq/bQYGDx7M+++/7/vdbDZXSfxC1BWaphEXF0d0dDQul6umwxEc73qTFiVRW3hyssn88XsyvpnrK3Mu+AFrqzbE3PEfDKFh1RqPpso54u+bb77hjTfe4JdffsFmsxETE4PFYiEjI4OkpCQiIyMZNWoU48ePJyYmpkqC7dq1K507d2bq1KkAeL1eGjRowLhx43jwwQeLHX/dddeRm5vLd9995ys7//zzad++PTNmzACOtyxlZmYyd+7cM44rKysLh8OB0+mUtaaEEEKIs1SwZzeHnnikxLrIkTfj6D+wUrr3y/v5Xe4xS5dddhmXXXYZqamp/Pbbb/z111/k5+cTGRlJhw4d6NChAzpduVciqLCioiLWrVvntzyBTqdjwIABrFy5ssTbrFy5kgkTJviVDRo0qFhitGTJEqKjowkLC6Nfv34888wzREREVPp9EEIIIUTZlMeDc9HCUuud838guHOXau2Oq/AA78jISIYOHVoFoZQtNTUVj8dTrNUqJiaG7du3l3ibpKSkEo9PSkry/T548GCuvPJKGjduzJ49e3jooYe46KKLWLlyZalN1oWFhRQWFvp+z8rKOtO7Jf7lXN4CNHQYdKaaDkUIIWoFpRSe7OxS6735eSivtxojOoNk6d/m5IHrbdq0oW3btjRp0oQlS5bQv3//Em8zefJknnzyyeoKUdRCOe50Duf/ybasJeg1I20dA4kyN8ZmCK3p0IQQIqDpDAZCunYjb8P6EuutbdqhD7JVb0zVerWzEBkZiV6vJzk52a88OTm51NkdsbGxFToeIDExkcjISHbv3l3qMRMnTvStYO50OmUDYeEnx5XGvCPPsSD5DQ7mb2Z/3nq+OTqFRSlvkevOrOnwhBAi4FnObYkhqvjELc1sJvzyK9BV80SsWpMsmUwmOnbsyKJFi3xlXq+XRYsW0a1btxJv061bN7/jARYuXFjq8QCHDh0iLS2NuLi4Uo8xm83Y7Xa/HyEAlPKyM2claUUHitXtz1tPauFfNRCVEELULsaICOo99Cj2fgPQjEbQNILan0f9J5/FGF01k8jKUqu64SZMmMDIkSPp1KkTXbp04dVXXyU3N5fRo0cDMGLECOrVq8fkyZMBuOeee+jTpw8vvfQSQ4YM4dNPP2Xt2rW89dZbAOTk5PDkk09y1VVXERsby549e3jggQdo2rQpgwYNqrH7KWqvPE8WW7IWlVq/yfkT9awtMehkBWUhhCiLMTKKyOEjCLv8ClAKXZANfQ3tCnDGyVJRURH79u2jSZMmGAzVk3Ndd911HDt2jMcee4ykpCTat2/P/PnzfYO4Dxw44Dcjr3v37nzyySc88sgjPPTQQzRr1oy5c+f61ljS6/Vs2rSJDz74gMzMTOLj47nwwgt5+umnZa0lcYYUXuUptdaLG0X1DkwUQojaSmcyoQuv+dnp5V5n6YS8vDzGjRvnW/V6586dJCYmMm7cOOrVq1fiekf/drLOkjjBozysSv+ctRlzS6y/KOYemoWU3g0shBCi+pT387vCY5YmTpzIxo0bWbJkCRaLxVc+YMAA5syZc2bRCvEvodf0tLb3x6YvvrpslCmBOGvzGohKCCHE2ahw/9ncuXOZM2cO559/vt/qma1atWLPnj2VGpwA5fWiVeFin6Ly2Y1RXFP/KbY4F7ErZyU6TU9r+wCahXQl2BBe0+EJIYSooAonS8eOHStxH7bc3FzZWbySuHNy8GRl4kpKwpuXh6l+A3QhIZgiIms6NFEORd4CUgsPkOU6RmtHfxRejhTsIM7aDKvegV6rVfMqhBCizqvwu3anTp34/vvvGTduHIAvQXrnnXfKnJIvyseTk03hnl0kz5yGNy/3eKGmEdKjN+GXX4WxhHUnRGDJKDrCd0kvAoqduct95fvz1jO8wQuEmmTXdyGqg/Iq8p0elEehN2pYHPJFRZyZCj9zJk2axEUXXcS2bdtwu9289tprbNu2jRUrVrB06dKqiLFOcTudJE19BXXyTu1Kkf3bUkz1G2DvPwi9UaadB6oiTx6r0r8Ais+b8CgXf2b/Stfwq9Fp0rUqRFXKz3Szf2UOW+ZlkJ/pIbSBiY7DI4hqZsFkK3krKyFKU+F37J49e7Jhwwbcbjdt2rThp59+Ijo6mpUrV9KxY8eqiLFOydu43j9ROknmgh/wZqRXc0SiIlyqkNSi0heePFqwE7cqqsaIhKh7CnM8rP9fKms+SCU/8/hSHpkHi1j03FEOb8hDeSs0CVyIM1tnqUmTJrz99tuVHYsAipKTSq3zZKSjSmixEIFDrxmxG6LIcaeVWB9uikevScugEFWpIMvDnqU5Jdat+TCV6BYWbBHyOhTlV+GWpaysrBJ/srOzKSqSb8xny9qs9Knlpvj6x5d9FwHLog+mS/iVvt+tejsWXfDfv2m0sQ9Er0kXgBBVKfNAYal1BU4PRbmyMKyomAq3LIWGhpY5661+/fqMGjWKxx9/3G81bVE+lsSmmBMSCenVB0NYOHg8oNORvXwZIT16oTPJyuKBLtqcyCWx92HQWch1p6PT9Fh0weg0I3ajDNAXoqqdGJMU3cJCYq8QTDY9OSkudv2SRXaSC51RZm6LiqlwsjRr1iwefvhhRo0aRZcuXQBYvXo1H3zwAY888gjHjh3jxRdfxGw289BDD1V6wP92uTv+JGrELSTPfAPX311ymslM2KVD0VmseAsK0NuCT3MWUZMUipTC/azJ+MrXbarXjPSLug1FYg1HJ8S/X0iske53RpN7zM3GL9LJz/AQ2tBE2yvDyM/0YAmR1l1RMRXe7qR///6MGTOGa6+91q/8s88+Y+bMmSxatIjZs2fz7LPPsn379koNNlBV5nYnBXt3c+TFSXjz8orVRY64GVvHrhgdjrO6hqha+3LX8+3R50usu77+ZKIsjas5IiHqlvxsN+s+TGPvsuxidT3viqZx9xA0nbQuiSrc7mTFihV06NChWHmHDh1YuXIlcHzG3IEDByp6agEUHjxQYqIE4FzwA5qSvvZAlu/OZk0p+8IBbMpaiKeMjXaFEGevKMtbYqIEsHZ2GnkZ7mqOSNR2FU6WGjRowLvvvlus/N1336VBgwYApKWlERZWfG8scXqF+/eWWudKTkJ5JFkKZEXevFJnwgE4XSm4vAXVGJEQdU/modInGxU4PRTlyfuoqJgKj1l68cUXueaaa/jxxx/p3LkzAGvXrmX79u188cUXAKxZs4brrruuciOtI8yNSu+iMURGoemlrz2Q6TUjUaZGpSZMMebG6M9sxQ4hRDkZrWV3sekM0gUnKqbC79qXXXYZO3bsYObMmezYsQOAiy66iLlz55KQkADAnXfeWalB1iVBrdqgmc2owuJTX8OHXo0hNLT6gxLlZjU46BB6Cfvz/ii2JpZRM3NOcA+MepnRKERVCo4yYrBouAuKD8mNbm7BaJFkSVRMhQd4i+Iqc4C3Kz2NooMHSHn/LTyZGccL9Xoc/QfhuKA/pvh6lRCxqEpZrmMcLdjJirRPyXYfAyDC1JAeETcQaU4g2BBaswEK8S+XujufrCQ3K6Yn4z1piKA1VE+PO2MIiTUQEmOquQBFwCjv5/cZ9wfk5eVx4MCBYgtRtm3b9kxPKYCigwdInfMREVdeiy44GOVyozObyFn9O8c+fI+Y/9yD4SwTMlG17MYodOgYEH07Lm8hmqbDoJkJN8Zjk0RJiCqXk+pmz9Is+v43jmO7CshLcxPWyExQuIHf302h7/1xNR2iqGUqnCwdO3aM0aNH8+OPP5ZY7/HITJ+zkbthHa4jh0l5b+bxAk2DE41/moa3IA8kWQp4wcYIrIZQCjxZgEaQ3o4mm+cKUS1C65k4ujmfo5vziUg0Y7brSd7uJDvJhTlYhylIXouiYir8jLn33nvJzMxk1apVWK1W5s+fzwcffECzZs345ptvqiLGOkVnDfIvOKmXVDNbAOlrry30mh6bIQybIVQSJSGqkcGiI7798ffStL2FHNmQR3bS8Q3K21wRhilYXo+iYircsvTLL78wb948OnXqhE6no1GjRgwcOBC73c7kyZMZMmRIVcRZZwR37UbmDyUnnSE9+6APDqnmiIQQonbJd7pp2icEe4yR3UuzcBcogiIMtBwSSlGuhwKnF6NFZhaL8qtwep2bm0t09PH9rcLCwjh27PgA1jZt2rB+/frKja4O0ofYCb+q+LILpgYNCR04GH1QUAm3EkIIcUJuqptlryeTm+am223R9L43lrZXhrFveTabvszAXSjrLImKqXDLUvPmzdmxYwcJCQm0a9eOmTNnkpCQwIwZM4iLk0FzZ8sYHkFIj94EtWxNzppVePJyCWrVFnNCY4xRsgmrEEKcTnC0EaXg4NpcDq7N9avTmzQMFumGExVT4WTpnnvu4ejRowA8/vjjDB48mI8//hiTycSsWbMqO746yRgecfwnNg68Cs1sRmeSaa5CVLcct4dCpdABYUZZTLS2sIUbCG1kIvOv4it5Nx/kIChMuuBExZz1Okt5eXls376dhg0bEhkZWVlx1SqVuc6SEKLmFXi8/FVQyCfJaezNLyTMoOfyqDA62W04DJI01QY5x1ysmJFC0tZ8AHR6aDbAQdsrwrCGymNYm3iys1BehT44uNJ3sSjv53eFk6WnnnqK+++/n6BTxs7k5+fzwgsv8Nhjj51ZxLWYJEviVIWePPI9WaQW/YVeMxBuakCQPhSjTloIa4M/snOZ8tfRYuV9QkO4KTaSYIO0TNQGhTkeCrI8uAu8mGx6rKF6DGbpgqst3BkZ5G5Yj/Pnn1AuF8Fdu2Hv3adSh6RUWbKk1+s5evSob5D3CWlpaURHR9fJdZYkWRIny/dksyHzR9ZkfA1/b3mix0C/6NtpEtwZk85aswGKMqW73Dy+9xDHXCXvTP9i04bUt0jSK0RVcmdkkDTtNQp2bPcr14eGUf+xJystYSrv53eFU2ylFJpWfK2fjRs3Eh4eXtHTCfGvk1SwmzUZX8FJe8N5cLMw5U2crpSaC0yUS57HW2qiBLC3oKAaoxGibircv69YogTgycwgc+FPeF2uao2n3B23YWFhaJqGpmmcc845fgmTx+MhJyeHO+64o0qCFKK2KPDk/J0olWyTcwEXRN2CXpNunEB1ug3pg3TSjSNEVVJuN1lLfym1PmflcsIuuhhdWPU10JQ7WXr11VdRSnHzzTfz5JNP4nA4fHUmk4mEhAS6detWJUEKUVu4VRE57vRS652uFDzKJclSAAvW62kZZGFbXvEWJIMGDS3mGohKiDpE06CMgdyaTndyw321KHeyNHLkSAAaN25M9+7dMRqNVRaUELWWgihTAjnutBKrYy1N0JRsWRPIgg16bq0XzZN7D+M8aQymBtxdP5ZQGdxdq3g8Cq9LYTBpaDp57dUWwV26kbt6Vcl1Xc9HlTAcqCpVeP5knz598Hq97Ny5k5SUFLxe/5VQe/fuXWnBCVHb6DQ9bR0Xsj9vPeqUrz5GzUKjoA6yT1wtEG82MblpfTJdHnI8Hsx6HWF6PQ6DAZN0w9UKrnwv2SkudizIJDvZTcy5FhJ7hWCLMqKTpCmgKbcbdBpB7dqTt3GDX50xNg5rm7YoV/E1tKpShZOl33//nRtuuIG//vqLUyfSaZpWJ2fDCXGCVW/H5S3kwpi7+D3tM5zuZOB4a9P5EdegQ8Ogk1bZQJfldrPcmcNXKRnke71oQFe7jRtjIzHrJVkKdO4iLwfW5LD8zX8mVCRtzWfrt5kMfqI+4Y2lKzWQaUYjuN3Y2p+HrVMXclf/jtflwta2PcbYWPK2bSWsUeNqjanCydIdd9xBp06d+P7774mLiytxZpwQdZWmacRam7Is9UO6hl9NpKkhCo1DBVvZl7OB8yOurukQxWl4lGJZZjYfJ/3TlaqA37NySSly83+N4nDIat4BLT/Dw8q3is88dRcqlk9PZuDD8Vgc8hgGKk2nw9SoMcfemYE7PY2gdh3QDAZyVq3E7XQSf/+DGKp5mZ4KP1t27drFF198QdOmTasiHiFqvWBDON3DbyDPk8nu3NXoNSONbO1pYutEkMFx+hOIGpXhcvNVSkaJdXsLCkl1uSVZCnCZBwvxlrL6Q8aBIvKzPJIsBTiDw0H0bXeQt2kjWcuWolxF2Dp0xN6rD1oNrGdY4WdL165d2b17tyRLQpQiy3WM5WmfsDv3d1/ZyvRP6RQ2lDb2gYQYI2owOnE6BV5Frrf0XekPFRbRJMhSjRGJinIXlT1Vyuuu5qlUosK8hYUcffVFNKMJW4fz0AwGCnZs59BTj1H/yUkQEuAtS+PGjeO+++4jKSmJNm3aFJsV17Zt20oLTojaRinFofwtfonSCWsz5tIoqJ0kSwHOqIEeKG30ZZjsDRfwHPEmNA1ODKvV9KD+fkCDow2y5UktULBzO64jRwAo+mu/X13G998QddOoat1gvsKv+quuugqAm2++2VemaZpvZW8Z4C3qshx3GhudC0qt3+RcSJSpMSa9tEwEKqOm0d0RwjJndrG6EL2OCOmCC3h6E7S5MgxzsJ7gaCPuAi/GIB0pf+YTlmBGb5axtoHM63aTveK3Uuvz1q/De8XV6Kpx15AKv+r37dtXFXEI8a/gVR7yPcU/ZE8o8GThpfStNETNcynoF24n2eVi50kLU9r1eu6sH41Hld5FJwKDq8BLw87B/DY1mcxD/0wxb9jFRuNeIXiK5DEMZJpOh84aVHq92QzVvIRHhZOlRo0aVUUcQvwrmHRW4i0t2JmzvMT6Bta2GDVpVQpkbhQv7D/CFdHhXBoZyrEiN/a/F6L8KCmVG2IiaSh7IQc0TdNY+koS2cn++4cdWJ2LMUhH68vDaigyUR6aTodjwIXklNK65Bh4IfpqHuR9RqnZ7Nmz6dGjB/Hx8fz111/A8e1Q5s2bV6nBCVHbuFQhHcMuRa8VX0vJqrfT2NYet6rexdRExXiUIk8pPk5O47WDSXyflsm7R1KYeiiZw4Uustwy1CDQuQtUsUTphH3LspHGwcBnjI7B3m9AsXJzk6YEdz7/+JYn1ajCV5s+fToTJkzg4osvJjMz0zdGKTQ0lFdffbWy4xOiVtE4vmfRFfGPEGc55+8yjcZB5zE0/mEKPQXHjxEBK0inw/b3wpNudXwpgXzvP7OnGlhkUdFAl5teele314N0w9UC3oICDOHhxI4bT0ivPgSf353oW8dgv6Af7ozS99+sKhXuhnvjjTd4++23GTp0KM8995yvvFOnTtx///2VGpwQtY1JZyPPk8n+vPU0sLahQ+gQAA7nb+dQ3jYaBLXGoKu+GRyi4gwaXBIRSrbHS5tgK/keLyadjlSXiw3ZedjK2OBTBIaQ6NI/2vRGDVOQfGEJdDkrl5P+5WdoFivWc1uiGQykffk5nox0LC1aEnfvfehttmqL54wGeHfo0KFYudlsJjc3t1KCquu8riLcGRkU7tmFJzcXa7Nz0IdFVPuKpaLi3N4C0ooOcSBvM20dFwLHW5ZCjbFscP5IpLk+HhWLTvaHC1i5HkXr4CB+SM3g+b8yfTv8xZuN3F0/lsMFhcSZJeENZEFhBkLrm/wGd5/QbIAda5jMaAxkXpeLgt27ANC042OYNE3z7RhSdOgg3sLCwE6WGjduzIYNG4oN9J4/fz7nnntupQVWV3kLC8nbuhlvXi7mxk3A68Vb5CLr+3mEXXQJhlAZmBjI3KqQzKIjtHVcyMq0OeR6jq8EHWqMo2fEDRzJ30GkOREj0pUTqEw6+CEti5VZuTS1mqlnNpHt8bApJ48XDxzlvoaxNR2iOA2vR9Hppkg2fZ1OyvbjMxo1PST2DCG6uYWiXC8Gk3xhCVSawYCpQUMsTZpijI8nb8MfKJeLsCuuQtM0spYvQzNV73tohZOlCRMmMHbsWAoKClBKsXr1av73v/8xefJk3nnnnaqIsU5xZzkxxdcjf8c2jr03E09+HkEtW2Pv05/C5GT0dke1D2wTFaEjztqcb4++gEmzUM9yLl68pBTsYUHyVC6Pn4gOWeMlkLm8sCsvnwcaxrG3oJC9+QWEGQxMaBDHyqwccj0y3iXQZRwoYsWMFM69KJRWl4ThcSv0Bo1Df+Sy7LVkLn2+AUHSuhSwNE3D3rsPGfO+Jv2rz33lOatWYm6cSPTtd2IIDqnWmCr8bLn11luxWq088sgj5OXlccMNNxAfH89rr73GsGHDqiLGOkV53KTP+ZjcP9b5ypxHDpO98jfq/d9jeLKzMDhCay5AUSadpmezcxE9Iq7HYYzhUP429JqBTmGXcSh/G/tz/yDKlFjTYYoyuJSXG2IjeeNQsl9i9EtGFiNiIykqYysUERh0Bo2iXC8bvzg+EPjk1bwBNJ18YQlkSilcqalkL19WrK5w315y169FHxqGITi42mI6o9R6+PDhDB8+nLy8PHJycoiOjq7suOosT0aGX6J0gjc3l/RvviTy+hE1EJUoL6XcNA/pzvbsZSxP+8RX/kfm97R1XEiYKf7vRSnNNRekKJNJ0/gqJaNYC5ICPkpOZXKTBjUTmCg3e5yRoAg9LYeEERJjxFPkxWDRkbw1n6Q/8zCHSOt8IFMuF9m/Lim1PmvJYoLP7w6BnCzt27cPt9tNs2bNCAoKIijo+Cqbu3btwmg0kpCQUNkx1ik5a1eVWpf7xzoirr6+GqMRFWXQLOR7svgrb0Oxuk3On7g07gH0mgwODmRuYGd+QYl1HgWHC4poaJFkN5DpDND3/jiWT08h88A/g7wbdLbR484Yv1YmEYB0OryFhaVWq8JCqOZJMhW+2qhRo1ixYkWx8lWrVjFq1KjKiKlO03RlTEvWNGS4S2BzU8i2rMWl1m/PWoZXyXYngex0L7Ei+aQNeIXZXn59LdkvUQI4uCaXrd9l4sqXrtRApul02M7rVGp9UNt21bqJLpxBsvTHH3/Qo0ePYuXnn38+GzZsqIyY6rTgrt1Kr+vcFV2IoxqjERWloVHgLX0JjQJvtuS7Ac6oacSUMdMmQVqVAp67QJGdVMoK3r9l4/VIwhvIlMeDITwcY3y9YnW6oCBCevZGeap3Jf0KJ0uappGdXXyjUKfT6VvNW5w5nTWIkJ59ipXrHQ7CLr0SXCW/AYjAYNXbaWhtW2p9oq0TJr1sLBbI3AquiQ4vMantExpCtrzPBby8slbwdoPXJclSINMZjbhSkokcdgOOQRehdzjQBQURfH53Yu+6l+zVv6OzVu8emxUes9S7d28mT57M//73P/R/r2Tr8XiYPHkyPXv2rPQA65r8XTuw978QW8cuZC1agCc/D1v7jtjadcDjzEBnlk1YA5lO09Pa0Z9dOStwKf8+d5s+jHrWljUUmSgvhUIHTGpSH6fbQ5FXYdJp6NFwGPSkFMkXlkBniyz9o01n1NDLGksBz9auPQcffQhLk6ZEDBuOZjCQt2UzR199kXqPPIHeGlSt8VQ4WXruuefo06cPzZs3p1evXgAsW7aMrKwsfvnll0oPsK6xNGjIoScfRh8aRkivvugtZnK3bSH9yzk4Bg4m7JIrajpEUYZ8TzYHczdxRb1H+T39Mw7kbUaHjibBXegcdgW7slfhMMZg1ElXTqCy6TRCDXpWZ+UyPy2TfK9CAzqEBDEkIpSGFhmgH+hMwToc9U04S1jBu0mfEMoaGioCgy44hHoPPYonO5vC/ftQLhfBXbpiv6Af+rDqX5y5wslSq1at2LRpE1OnTmXjxo1YrVZGjBjBXXfdRXh4eFXEWKfkbdmEPiyc0AsvxhgTCx4PjqhodJpG9vJfCb3ospoOUZxGlCWRb448x7n2PrSy9wOl+CtvE18ffpqL4yagyailgOYGtubm8/WxDF+ZAtZn55HucnNX/Zgai02UT9YRF51ujGDTVxkc2/n3Ct46aNwjhJjmVoryZIB3oHNnpJO3eSNpc/7nt0hWcPcehA66GGM172ZRoWTJ5XIxePBgZsyYwaRJk6oqpjpNAdGjbyfts08oOnQAAJ3VStiQyzE1TAAlL/KApjS2OBdR4M3hj8wfCNI7UHjJ92QBcDBvC7HmpjUcpChLoRfmpzlpEWShb5gds+74muvrs3NZ7swm0+2hfk0HKU5r2RvJnHtxKK0vD8PrVugMGoc35LJ8ejKXPCdrZQUy5fXicTpJ+/STYnU5K5ZjadoMQ3R0ta7iXaFkyWg0smnTpqqKRQDB53Xi8HNP483N8ZV58/NJ++JTom+9E2TMUkBzqwJSivbSyt6fJrbOOF1J6DQDwYZwtjgXcaRgB0WqEKvsDRewXEpxUWQoJk3jk6Q0nB4PBg3Otwdzb4M4jhW6oPrWwhNnILS+CXeBl42fpxeva2CSRSkDnNftJnvFb6XWZ/3yM0Ft20M1JksVfsbceOONvPvuu1URiwAK/9rvlyidLOP7eai80qeli5qnoaNL2FUYNBPfHJ3C0tRZLD72Dj8cfZn6Qa1oZuuKDhkwEciCdRoGTeOT5OOJEhyfIfebM4dvjmXQOEjGmwU6r0fRZXRUsXKDWaPLqEhpoA90SuHJyCi12u10Ut2LDlZ4zJLb7ea9997j559/pmPHjthsNr/6l19+udKCq4sK9uwqtc519Aiy9Gyg07DobWx0/giAQTOhUHiUi2WpH3JN/adxewsxy/IBAUsB36eW/Ea9M78At7wEA567yIvyKvreH8u+FTnkpbkJTzBT/zwbrnwvHpdkS4FM0+sJat2GvI1/lFhvaXYO6Kv3S2eFk6UtW7Zw3nnnAbBz506/Ok2Tgatnyxhd+uBRQ3hENUYizoTCy/rM72gW3I3W9v4UefPQaQY0dKzP/JZtWYvpGT68psMUZShSkO0p/cP0UGERTYOkOzyQ6fU6Vr2XisGs0aCTjfBEM9lHXSx67ghmu57BTxRf7FAEDuX1Ym7SFF1ICN5T13XU6QgdPASqeb2zCidLixeXvpVDdZg2bRovvPACSUlJtGvXjjfeeIMuXbqUevznn3/Oo48+yv79+2nWrBlTpkzh4osv9tUrpXj88cd5++23yczMpEePHkyfPp1mzZpVx90pxtKsOZrRiCph8UlH/wvRjDLWJZBpaDQLPh+PcjPvyGS8HH9BGzUL/aJvI7MoGbfyyDa6AUyvHR+fUFq65DBIN2qgy/x7yQB3oWLfcv9hDQVOj2x3EuA0TUMzm6k38VFSP55N/tbNAJjqNyByxGh0NhuapXq/sJzxKLfdu3ezYMEC8vPzgeNJR1WbM2cOEyZM4PHHH2f9+vW0a9eOQYMGkZKSUuLxK1as4Prrr+eWW27hjz/+YOjQoQwdOpQtW7b4jnn++ed5/fXXmTFjBqtWrcJmszFo0CAKCkreSLOqKY+XmNvvQnfywDVNw96nP8boWJRXXuSBTSPMGM+KtP/5EiUAlyrgp+SpJNjaY6jmDSBFxRg06Gy3lVhn0+mIli8sAc8YpENngJYXOxj8RD0GPV6PgY/E06DT8YUMdQbpBQl0msmEt6iI8CuvJv7BR4h7YCJRo25GMxmPNxpUc8uSpiqY5aSlpXHttdeyePFiNE1j165dJCYmcvPNNxMWFsZLL71UVbHStWtXOnfuzNSpUwHwer00aNCAcePG8eCDDxY7/rrrriM3N5fvvvvOV3b++efTvn17ZsyYgVKK+Ph47rvvPu6//37g+LYtMTExzJo1i2HDhpUrrqysLBwOB06nE7vdflb3MW/Hn2Sv+I2g5i3QjEa8hYXoQ+zkbt6AvXsv9KFhGKU7LmBlFR3jp5RpHCnYDhwf8H18TejjL7M29oF0Dx+G2VDyh7GoeYcLCklxuZmTnMb+gn8WNQzS6fhP/WisOh2tgqt39WBRMel/FeL1KHYscP69FxyYbDraDA0jtKGJ4EgDjnrSvhuoPC4X7pRkMr76gpzVv/+9ibwGXi+mBg2JufMuNFswpkpY27G8n98V7oYbP348RqORAwcOcO655/rKr7vuOiZMmFBlyVJRURHr1q1j4sSJvjKdTseAAQNYuXJlibdZuXIlEyZM8CsbNGgQc+fOBWDfvn0kJSUxYMAAX73D4aBr166sXLmy1GSpsLCQwsJ/trLIyso607tVjN4aROiAQbjSUkEDncmM11WEo9+FFB45RFBMbKVdS1Q+Lx6y3MdItHWkZUhf3MqFTtPhVV42On8kw3UUb6kdPCIQFCnIdbsZVz+GYy43fxUUEm4w0NBiRuHlr4Liq0KLwGKwwMqZqSRv+6eHoCjXy7qP0+hwfQThDWUV9kCmPB5cycm40lKJ+c84NIMB5fGgM5nI+m0Z2b+vwN5vwOlPVIkqnCz99NNPLFiwgPr1/Zdla9asGX/99VelBXaq1NRUPB4PMTH+A6BjYmLYvn17ibdJSkoq8fikpCRf/Ymy0o4pyeTJk3nyyScrfB/Kw+v1oDNZ0BmNePNy8RYWYgiPQBUWYo6rLxvpBjgNHZ1CL6PQm8+Pya/hUccfL7PORq/Im/B4PX+3NolAFaLTiDCZeGjvITQg3Ggg1+PF6fYwLCacziGyyFKgc+UqkrcVoOkgtrUVS4ierKMu0vYWsmVuBo262JD5qAFMKYoO7Cf0wsGkff4p5kaN0Qx6CvfvI7hbDzSjCap5j8YKv2vn5uYSFFS8CTo9PR2zuW40a06cOBGn0+n7OXjwYKWdWxdkw5V0mKQZr5M07VVS3pnOkReeJWvJIsCLzFoObEbNisMYy8r0T32JEkChN5dFKW8Ra22CQSdjXgKZG/jwaCqFXkWBV3Gk0IXTfXx8xGfJ6fIqrAWcR1w07Gyj3wNxOOJMuIsU9Tse/90WYZAB3oFOA2uLlrhSjxFx3Q1oJhPK4yX04ksxRkVjiIyEap5oUeFkqVevXnz44Ye+3zVNw+v18vzzz9O3b99KDe5kkZGR6PV6kpOT/cqTk5OJjS25ayo2NrbM40/8W5FzApjNZux2u99PpSkoIHn66/7TJZUia+ki8jZtlHWWApym01ibOa/EOoWXbc4laJrMpgpkhV7FvoLj3exGTSPaaMCmO/5W6QX25heWcWsRCIKjDNRrH8QvU46yfYGTg2ty2fh5Or9NS+a84RHozTLAO5BpegOe/HxcR46QPGMa7tRUvLk5pH36Mc6f5qMPDgZd9bbQV/hqzz//PG+99RYXXXQRRUVFPPDAA7Ru3Zpff/2VKVOmVEWMAJhMJjp27MiiRYt8ZV6vl0WLFtGtW7cSb9OtWze/4wEWLlzoO75x48bExsb6HZOVlcWqVatKPWdVy9uyqcRlAwCcC39EFcobdSDzKhdOV3Kp9WmuQ34tTiIwWXQaI2IjGVc/hoHhDkbGRXJ3/RgamE3SrlQLmIP1rJ6VWuy7ZVHu8S1QZDZcgHO7wetBFxxM3N3jsSQmYoyLJ2r0bYT06EXBzp3V3nBQ4TFLrVu3ZufOnUydOpWQkBBycnK48sorGTt2LHFxcVURo8+ECRMYOXIknTp1okuXLrz66qvk5uYyevRoAEaMGEG9evWYPHkyAPfccw99+vThpZdeYsiQIXz66aesXbuWt956CzjeKnbvvffyzDPP0KxZMxo3bsyjjz5KfHw8Q4cOrdL7Upqi5NLHSrnT06oxEnEmDJqJcGM9ctwlP1ZR5gQMmgwuDWQWncaEhnF8lpzG7pNakUL0Ou6oF0M9U4XfNkU1y0p24XGV/GGatrcQT5GkvAHNYMCbmwuaxtGXX/AVZy1aiLVla+wDLgzsRSn379/PwoULcblcXH755Tz88MNVFVeJrrvuOo4dO8Zjjz1GUlIS7du3Z/78+b4B2gcOHEB3UtNc9+7d+eSTT3jkkUd46KGHaNasGXPnzqV169a+Yx544AFyc3O5/fbbyczMpGfPnsyfPx9LNS94dYK1aTOyly4qsc4YFw8GeaMOZC5PAW0dF3Igv/iG03oMJNo64lEudFrdGN9XGxmApRlZfokSHF/Ve/rhZJ5OrF/yDUXAcBeUPSbJW0oiJQKE14Pe7sA5/4diVfnbtmBt1QpLYpNqDanc6ywtXryYSy65xLcIpcFg4L333uPGG2+s0gBrg8pcZ6no6GEOPfsE3pzsYnUx/7kHc/NzMTlCz+oaoupkFCWx2bmQUFMMv6d9RoH3+OrBwYYIekXcSFLBHjqFDcVqkBlVgepQQSH/t/sgpX1v/W/DWDra5fELZJkHC/nmgYOU1GcaFGFg0OPxhERLC2+g8no8pLwzk5zffi2x3hgbR9wDD2GKKr5ZckWV9/O73GOWHn30UQYOHMjhw4dJS0vjtttu44EHHjjrQIW/7DWrib1rPKb6DX1luqAgIm4YQdHhQ1CQX4PRidMxaRYUHnZmr6BP1Ggujh3PkNgJdA27itUZXxFurodBk9lwgcylVKmJEkC6q3qb/0XF6S0azS8s+YOv88hIDFYZsxTQvF68OTmlVntyc9CqefRguft0tmzZwooVK3zjkl544QVmzpxJWloaERGyonRl8TgzSHlrGo6BgzHVa4DyeMDjJmvJIvK2bSGke8+aDlGUQWmKRFtnduasZEHyG3514ab6BOvDZcPpAGfR6bDpdeSWspluI4u0SAS6zAMu4loHEdnUwpZ5meSluQhvbKb9NREcXJeDI96INeT05xE1x9a+A3kb1pdYF9SqDeird0hKuVuWsrKyiIyM9P0eFBSE1WrF6XRWSWB1VXCnrugsVvQhdpTbhSoqQjMY0TtCsbXviFZH1rKqrTQ0Ugv2c3ncg7Sy9yNIH0qIIYrzQi9lUMw4kgv2+LY+EYFJD1wcEVpiXaLVjKWapyyLitP0YDDpOLQujya9Qug0Ioqophb2/pZNRGLNjEcV5acZDJgbJWCILN7NpplMOPoPQGeu3i8tFUrNFixYgMPh8P1+Yur+yRvTXnbZZZUXXR2kDwsnYtiNpLw3E09mxt+Fehz9LiTs0ivAIx+0gcyoWQgzx/PZoUdItHWic9gVePGwL3c9GzJ/4Jr6T0k3XIDL9yr0msbV0eH8mJZJrseLjuOb6/YJtbM/v4CGVvnSEsjssUZ+ef4oWUdc/PW7f3eO8ipiW8r63YFM0zR0DgdxE/5L5o/fk/P7CpTbjbVVGyKuHQaaDr2tescNVihZGjlyZLGyMWPG+P6vaRqeap7O92+jeTwkTXvFfz0ljwfnwh8xNWiIrUPHmgtOnFahN5f1md/jxcPu3FXszl3lV78j+zfCjPUw6eXbbaAy6TS+TEnnXJuF0XFRGDUNTYON2Xm8ejCJBxpV7RIp4uwVZnvJOlLyemZ7f8uhzRVnvwGrqFqaXk/ql5+ht1iJvv1ONJ2Ogj27SXnvbWLH3Vvt8ZQ7WfJ6ZXn46pD359ZSF57M+OYrrM3PhZBKXDFcVCqlPGQWHSm1PrXoIGUPHxY1LdxgoG9YCD+lZ7EpJ/+UOj3Rss5SwMs5VvrCr16XOu3SAqLmFR06SN66tQBkL1/mV5e1dDERV16Dpq++3RCk8z3AFB4sfTNid+ox2e4kwJn1wYSa4kutjzQ1xKiTVqVA5kUxKNxBlxCbX3mMycj9DeMwIAP0A50tsvSEVmfU0BvlMQxkXrebrCWLS63PXvYrrmpepFm+IgUYS+NEspf+UmKdMSYOZCZVQDPrg+gadhWH8rcUq9Ohp7WjHzrZGy6gZbk9ZLo9tLRZ6RtuJ8fjwarTke32kOfxYtHJazDQGcw67PHGErviEnuGIC/BAOf1Hv8pjVKBv92JqFqWps3RBQXhzcsrVhd68aVo1qAaiEpURKS5EQOi7+DXYx9QpI5341j1Di6MHovDEFPD0YnT8QI/Z2Sx0nl8YLBR03Cr43MYQ/Q6JiaU3nIoAkNumpsuI6P4Y04aaXuPD2vQNGh0fjBxra24C6WFPtDZOnchd92akus6da7WLjiQZCng5GxYT8yYu0j9ZDau5KMAaCYzoYOH4M5IR+XnwVmuEi6qllkfRPOQntS3tiLfk4WGDqveTrAhDE2Tnu9A51aK1c5/ZlC5TvoGm+3xku5ykyiTqQKaPdbI/McP0eqSMNpeFY7XrdAbNY5szmP9/1K56KkGNR2iKINSCn2IHcs5zSnYucOvTh8WTnDnrii3u1pjkmQpwKicLI59NIvQQRdjCI88vlmgppG94lfyNm8kpHuvmg5RlINeM2A3RmE3nv1y/KJ6eRRlDsHPcssA/UBndehp3COE9f8rPq6l7/2xWMOkHy6Q6QwGdBYz9r4DsHXsfHzpAFcRQW3bY23ZCldaKsZ61btH4xl9zc3MzOSdd95h4sSJpKenA7B+/XoOHz5cqcHVRUFt2+NOTyN/+zbfrLjCA/sp2LMbW6cuUM1Nj0LUNWZNw6Yv/a2xfjUvhicqzmTT0+aKMHqOjcYRb8Rg1ohuYWHwk/WIaRUkq+gHOE2vRxdsx5OZTvbyZQS1aUtwl/Mp/Gs/2ct+xdKkCYbg6l1nqdwb6Z6wadMmBgwYgMPhYP/+/ezYsYPExEQeeeQRDhw4wIcfflhVsQasytxIN/uPtRiCQ8j+7Veyf/8NVViIpek5hF1yObrgEPQhdkwxsZUUuRDiVGlFRaxw5vJxcvFWiSYWM2MbxBAvCVOtkZ/pxutRGMw6zMHyZbO2KDh4EDxuVEEBBbt3oVxFWJqdg84WjKbTY6pXD81w9p1jlb6R7gkTJkxg1KhR7Nq1C4vlnynQF198Mb/+WvIOwaL8TJHRHPvgHbKWLvK1LBXs3snR119CFRZWypNDCFE6hUZrm5UbYiKw/b21iQ7oarcxpn40BdINV6tYQw3YIoySKNUinqIiMr7+goK9e1AeD5Zm52Bp2QaFhiczk6QZU3GlHqvWmCr8ybtmzRpmzpxZrLxevXokJSVVSlB1mTs9laJDB4tXeL2kf/s1MXeMq/6ghKhDLDody3KyaWY1MzEhDoWGXgOn283BgkKaBcnobiGqknK7QSlS338HQ3gEQe3ao+kN5O/cTtGBv9CHhQf+0gFms5msrKxi5Tt37iQqSgaznq3cjRtKrSvY8efxAd9CiCoTbNDT3RFMisvN9rwC/swtIMpooKPdRqhBR6hBWihqi7x0N7lpbvIz3YTEGLGG6rHYpXU+0OnMZkJ69CJ37Wrc6WlkLV7kVx9yfje0oOpdRqfCz5rLLruMp556is8++ww4vh/cgQMH+L//+z+uuuqqSg+wrtGftFHxqXQWC8iO50JUOZeC1w8mke35Z2G8BelOxtaPAVmip1bIPFTEoueOkJv6zxTzmFYWeo6NxRYuCVNAUwpTgwZYW7clf8smvypDdAz2vv2hmlfSr/An70svvUROTg7R0dHk5+fTp08fmjZtSkhICM8++2xVxFinhHTuWmqdvd+FGOylJ1NCiLOX4/bw9pEUv0QJjudI0w8nkymtuwEvL93Fz5P9EyWA5K0FrP84FZfsDRfQdAYDrtRUokaOJvq2O7Ce2xJL02aEX3Md8fc9gDs7B10ZM1arQoXTa4fDwcKFC/ntt9/YtGkTOTk5nHfeeQwYMKAq4qtzdHYHkcNHkfrxLL9yc6PGOPoOqPZVS8WZKfDkkufJ5Gj+DnSagTjLOQQZQjHJvnABL8fjYUdeQYl1HgX78wuJNhmrOSpRETnH3OSllbxo4f6VObS/NgKjRVrpA5W3qAjnj9+jvB4ibxyJpfm5KK8X9DqcP/1E9opfafDks+iDQ6otpjNui+zZsyc9e/aszFgE4M3NpfDAfuLG/x/5O/7Em5+HpVlzlKuInD/WEtp/EJp0xQW0fHcWq9K/ZFPWAl+ZhkbvyJG0COmNWS9b1gQyz9/dbKEGPReE2alnNpLt9rIsM5t9BYUUKmmVCHR5GW40HTTobKNR12A0nYYr38uuX7JI3VWAu1Aew0CmNA19aCgFf24jf8tmDOHHB3SroiIK9+7CYHdU+57yFU6WXn/99RLLNU3DYrHQtGlTevfujV5aQM5I7h9ryV62hOzlv2JJbIpmMpG7djWe7Cz0DgchnbpiCAuv6TBFGQ4XbGd/3np6RAzHYYxGQyO5cC+rM74iznIO0frEmg5RlCFIr2NwuINzbVZ+SMvk+9RMwo16+oU5GBBup4lVWgcDXUisid73xHJ0Sx4rZ6bgLlRYw/S0HBJK/fOCMFrlC2cg0xuNOPoPxNahI2mffowr6fjWX3q7nfArr0EfHoE+pPpaleAMkqVXXnmFY8eOkZeXR1hYGAAZGRkEBQURHBxMSkoKiYmJLF68mAYNZP+dinKlpBz/j9dLwe6dfnUepxPlldGlgSzfnc3BvM30jLyJ1elfkFp0AIB4SwsGRv+HXTmriDA3RK/JANNAFWY00DrYyksHknxjuZOL3PwvOY1ejmDOC5GWwUBnCdbxx/+yOLLxnw3J8zM8rPsojY43RmBxSLIU6HQWC8nT3/CtNwjgycri2Kx3qffI4+it1buER4WfMZMmTaJz587s2rWLtLQ00tLS2LlzJ127duW1117jwIEDxMbGMn78+KqI918vqGWrUutMDRPQyViJgOZRRSQGd2Z+0mu+RAngSMF2FiS/QSNbO7yqejeAFBWT4XLzwdHUEie9LXPmkOeRLyyBrijf65conWzz1xkUZkk3XKDLWbPKL1E6WfpXX+DJza3WeCr89faRRx7hyy+/pEmTJr6ypk2b8uKLL3LVVVexd+9enn/+eVlG4AyZGzdBHxaOJyO9WF3ksBvRh5zddiqiamno2Oo8viZIa3t/Em2dUcrLtuwl7Mldw+H8P4kxJp7hroyiOuR6vBxzlZ7Q7ikooJ5FtjsJZM7DRRjMGk372YlrZcX79wTG/Suy+WtVLkV5Xmw1G6Iog9flomDnzlLrCw8cwFtYiN5WfY9ihZOlo0eP4nYXfyNxu92+Fbzj4+PJzs4+++jqIGNEJPUffIyU2e+Rv3UzKIUhPILI4aMwN5axLoHOrYoo9OZxfYPnyHAdYUf2b+g1A60d/ekSdgWrM77Go3mQ9sHAZTjN8i1BMsEi4FlD9fS+J5adPztZPN8JCgxmjWb97XS9JQq9STbSDWSawYCpfn3yNv5RYr0xKqrae1kqnCz17duXMWPG8M4779ChQwcA/vjjD+6880769esHwObNm2ncuHHlRlqHGGNiif3PPXizs1EeDzqrVQZ11xImXRAXRI1mUcpbHCnY7ivflr2E5sE9OT/8Wow6c80FKE4rWK+nZZCFbSUsH2DQoKFFHr9AZws3snR2Eml7/unGcRcq/vzBSatLQzGHSLtSINM0DXvvC8j88XvwFu8yDb/ymmpdNgDOoDPg3XffJTw8nI4dO2I2mzGbzXTq1Inw8HDeffddAIKDg3nppZcqPdi6RB9kwxgTiym+niRKtYhJZ2V/7h9+idIJO3J+I9t9TAZ3BzirTseIuEgcp8zo1YA768UgjRKBrzDX45conWzHT05ceTLuLNAZIqOIu/d+NMtJA7n1esKvuQ5L06bVH09FbxAbG8vChQvZvn07O//uU2zevDnNmzf3HdO3b9/Ki7AO8xYVotzHW5Y0Td6ha4NsdxpbsxeXWr816xfizOdgNsg320CV5XazO6+QW+tFcbTQxb78QsKMetoEB7E9N59GFhOh0o8a0LKTXaXWuQsVrnwZ4B3odCYTQW3a0nDyC7jTUlFuF8aoGPR2+/Gtv6rZGX/FbdGiBS1atKjMWMTf3NlZFB06SOaCH/Dm5RLcuSu2Dp0wRspGxYFO4cXlLXn1Z4AibwEeJdtlBLI8pZiTkka2x0sDs4l4s5G/CoqYn+bEC7SXLpyAFxRW+kebpgODWcad1QaawYAxMhJjZGRNh1LxZMnj8TBr1iwWLVpESkoK3lP6E3/55ZdKC64ucmdnk/7VZ2Qt/tlXVrBzOxnff0P9h57AGB1Tg9GJ0zFrQTQKas+WrJ9LrG8a3AWjbHkS0Fxe5dsX7mBhEQcLi/zqjxYWca6tetd4ERUTHGkgKFxPXnrxLyYJ3YKxOGTRZFExFU6v77nnHu655x48Hg+tW7emXbt2fj/i7LjTUv0SpRM8mRmkfzcXb1FRCbcSgSLI6KB96EWYdcVbHxyGGOpbW2PUy7TzQGbRaWW+MYYZZcxZoAuKMDLgoXrYIvwfq5iWFs4bHin7wokKq/Cr/tNPP+Wzzz7j4osvrop46ryc1StKr1v5G+FDr0YXHlGNEYmKMmLl6npPsDZjHntz16DTDLQI6U1bx0DMOln9OdA5DAa62G38nlV80bsgnY4GZkl2a4PQ+iYuero+uWlu8jPdhMQYsYbqsdgl2RUVV+H02mQy0bQGRqLXFcpT+niWsupE4Mj1pjPn0MNY9XYuih3PhTF3UeDJ5n8HJ1LkLXlVYRE4rHodN8ZG0uiUhSetOo2JCfGES8tSrWEJ1RMUoSeisRmLXRIlceYq/My57777eO2115g6darM0KoCwZ3Px7nghxLrbB27oA+SwaWBrNCTx6r0L3CrIjY4f2CD0/+x3Ja9lPPDr0WnSTdAIIs0GZnYKJ4Ul4v9+YVEGA00tJiJMBrQyfterZCf6WbPr9ls/TaDwmwv9ngjHW+IJLqFBXOwjFkSFVPhZOm3335j8eLF/Pjjj7Rq1Qqj0X8O7VdffVVpwdVFxqgYbOd1Inf9Wr9yXVAQEVdeWyNTJkX5uVUhaUUHS61PKtiNWxVh0uRxDHShRgOhRgPnBMlg7tqmMMfD2tmp7Fue4yvLOuJi8YtH6fGfaBJ7hqDpJOkV5VfhZCk0NJQrrriiKmIRgMHhIGrkrQR36Ubm/O/x5ucR1O48QgcMwiBLBwQ8g2bCbogmx51WYn2EqT56TRbpEaIqFTg9fonSydZ9lEZsq6Big7+FKEuFny3vv/9+VcQhTmJwhBJyfg+srdqC14MuyIbOKB+wtYFZb6Nr+NV8feTpYnUaGq3t/dFr0gVQW2S5PRQpLzo0Qg166YKrJTIPlrx6N0BBloeiXI8kS6JC5NkSwAwh1bv3jagcUeYEekeOYnnax3jU8ZWETTorF0bfhd0YXcPRifLI83jYm1/IR0lp7C8oJNSg57LIULo7QgiVAd4Bz2Qr+wuJzihJr6iYM3rVf/HFF3z22WccOHCAolPW/Vm/fn2lBFaXKaXwZGXhyckGrxddUBD64BB0ZtnAszaw6G20svcl0XYe2e40dOgJNoQTZAiVfeFqia25+bx0IMn3e6bbw4dJaezOL2R0XBQhBmkdDGQhcUaMVg1XfvE94GJaWrCEyOMnKqbCU3Jef/11Ro8eTUxMDH/88QddunQhIiKCvXv3ctFFF1VFjHWK8npxJR0la+kikl5/kSPPP0PanI8pOnQQd3ZWTYcnysmoM2M3RlPPei5x1nMIMUZKolRLpLvczDqaWmLdCmcOmW53NUckKioo1EDf++OKtSAFhevpdnu0zIYTFVbhd+8333yTt956i+uvv55Zs2bxwAMPkJiYyGOPPUZ6enpVxFinuFKPkTLrbQp2/Okry1m9ktwN66n34KMYQuw1GJ0Q/355Hi9prtITon35hTSwSCtvINMZNKJaWLn8pYYc3ZxH1tEioltYiWhsxhYh4z9FxVW4ZenAgQN0794dAKvVSnZ2NgA33XQT//vf/yo3ujrIlXTUL1E6QRUVkj73C9wZGTUQlRB1h+E0w1lsemmVqA30eo2QaCPn9HfQ6cYoGnYKlkRJnLEKJ0uxsbG+FqSGDRvy+++/A7Bv3z6UKt4/LComd8O6UuvyNm/EW1T6LA8hxNkL0etpU8pGuUZNo6FFtjsRoq6pcLLUr18/vvnmGwBGjx7N+PHjGThwINddd52sv1QJyhrErRmNsmq6EFXMZtBzS3wUYacM4tYB4xvGEiqDu4WoczRVweYgr9eL1+vFYDg+3OnTTz9lxYoVNGvWjDFjxmAy1b1vXVlZWTgcDpxOJ3b72Y0pKti3l0NPPlRinb1PPyJvHIXOWPf+xkJUtzSXi115BWzJySfWZKST3UaE0YBRJ1vVCPFvUd7P7wonS6K4ykyW3NnZZP74DZk/fOtXboiKJv6/D2OKjjmr8wshhBDiuPJ+fp/RXObMzExWr15NSkoKXq/Xr27EiBFnckrxN0NICGEXX4btvM44F/+MNzeH4E5dsZ7bCmNEZE2HJ4QQQtQ5FW5Z+vbbbxk+fDg5OTnY7Xa/MTSaptXJ5QMqs2XpZMrrRXm96AyyPo8Q1S3f48XpdpPj8WLWaTgMeuzyWhTiX6XKWpbuu+8+br75ZiZNmkRQUNBZBSnKpul0aDI+Qohql+ly80VKOr9kZHGi7TzBYubeBrHEmmX6uRB1TYU/iQ8fPszdd98tiZIQ4l/J5fXyY1omP5+UKAHsLyhk8l9HSC9jwUohxL9ThZOlQYMGsXbt2qqIRQghalym28P8NGeJdclFLo4Vuao5IiFETStXN9yJdZUAhgwZwn//+1+2bdtGmzZtMBr9m6Qvu+yyyo1QCCGqUaHXS2EZQzmTilw0L2XRSiHEv1O5kqWhQ4cWK3vqqaeKlWmahsfjOeughBCipph0OkyaRlEpCVOUUQZ5C1HXlKsb7sRClKf7kURJCFHbGYE+YSEl1kUZDThkRpwQdY5MtRJCiJPkehXNg6x0dwRz8uZCDcwmbo2P5mCB7M8oRF1T7mTpl19+oWXLlmRlZRWrczqdtGrVil9//bVSgxNCiOpm1GlMP5SMXa/nvw3juLdBLPc3jKNvmJ0Zh5Ox6OU7phB1Tbnbk1999VVuu+22EhdtcjgcjBkzhldeeYXevXtXaoBCCFGd7HodHe025qc7mZ/uPyvOqtOob5a9GYWoa8r9FWnjxo0MHjy41PoLL7yQdevWVUpQQghRU6x6PTfFRlL/lMUnzTqNBxvFEyYDvIWoc8qdLCUnJxdbJuBkBoOBY8eOVUpQJUlPT2f48OHY7XZCQ0O55ZZbyMnJKfM2BQUFjB07loiICIKDg7nqqqtITk72O0bTtGI/n376aZXdDyFE4IsyGXk4oR5PNK7HTbERjG8Qy4tNG9I0yIJe005/AiHEv0q5k6V69eqxZcuWUus3bdpEXFxcpQRVkuHDh7N161YWLlzId999x6+//srtt99e5m3Gjx/Pt99+y+eff87SpUs5cuQIV155ZbHj3n//fY4ePer7KWmpBCFE3RJmNNDCZmVIZBhdHcFEmYySKAlRR5V7I91x48axZMkS1qxZg8Vi8avLz8+nS5cu9O3bl9dff73Sg/zzzz9p2bIla9asoVOnTgDMnz+fiy++mEOHDhEfH1/sNk6nk6ioKD755BOuvvpqALZv3865557LypUrOf/884HjLUtff/31WSVIVbWRrhBCCCGqTnk/v8vdsvTII4+Qnp7OOeecw/PPP8+8efOYN28eU6ZMoXnz5qSnp/Pwww9XSvCnWrlyJaGhob5ECWDAgAHodDpWrVpV4m3WrVuHy+ViwIABvrIWLVrQsGFDVq5c6Xfs2LFjiYyMpEuXLrz33nuUM38UQgghRB1Q7pGKMTExrFixgjvvvJOJEyf6EgpN0xg0aBDTpk0jJiamSoJMSkoiOjrar8xgMBAeHk5SUlKptzGZTISGhha7Hyff5qmnnqJfv34EBQXx008/8Z///IecnBzuvvvuUuMpLCyksPCftVZKWk5BCCGEEP8OFZrW0ahRI3744QcyMjLYvXs3SimaNWtGWFjYGV38wQcfZMqUKWUe8+eff57Rucvr0Ucf9f2/Q4cO5Obm8sILL5SZLE2ePJknn3yySuMSQgghRGA4ozmwYWFhdO7c+awvft999zFq1Kgyj0lMTCQ2NpaUlBS/crfbTXp6OrGxsSXeLjY2lqKiIjIzM/1al5KTk0u9DUDXrl15+umnKSwsxGw2l3jMxIkTmTBhgu/3rKwsGjRoUOb9EEIIIUTtVKMLhkRFRREVFXXa47p160ZmZibr1q2jY8eOwPEVxb1eL127di3xNh07dsRoNLJo0SKuuuoqAHbs2MGBAwfo1q1bqdfasGEDYWFhpSZKAGazucx6IYQQQvx71IrV1c4991wGDx7MbbfdxowZM3C5XNx1110MGzbMNxPu8OHD9O/fnw8//JAuXbrgcDi45ZZbmDBhAuHh4djtdsaNG0e3bt18M+G+/fZbkpOTOf/887FYLCxcuJBJkyZx//331+TdFUIIIUQAqRXJEsDHH3/MXXfdRf/+/dHpdFx11VV+yxS4XC527NhBXl6er+yVV17xHVtYWMigQYN48803ffVGo5Fp06Yxfvx4lFI0bdqUl19+mdtuu61a75sQQgghAle511kSpZN1loQQQojap9LXWRJCCCGEqIskWRJCCCGEKIMkS0IIIYQQZZBkSQghhBCiDJIsCSGEEEKUQZIlIYQQQogySLIkhBBCCFEGSZaEEEIIIcogyZIQQgghRBkkWRJCCCGEKIMkS0IIIYQQZZBkSQghhBCiDJIsCSGEEEKUQZIlIYQQQogySLIkhBBCCFEGSZaEEEIIIcogyZIQQgghRBkkWRJCCCGEKIMkS0IIIYQQZZBkSQghhBCiDJIsCSGEEEKUQZIlIYQQQogySLIkhBBCCFEGSZaEEEIIIcogyZIQQgghRBkkWRJCCCGEKIMkS0IIIYQQZZBkSQghhBCiDJIsCSGEEEKUQZIlIYQQQogySLIkhBBCCFEGSZaEEEIIIcogyZIQQggh/r+9Ow+K6krbAP500wuL0EhkDYiKI6BxQQ0W6sfmguNSOlNTUcs4JCEm4+BEcKJFUt+MlUllUCtRJ9GMRkswBj/HuGUqRg2RbYIbgswgEmfEJSSyxIBsrSzd5/sjsSet9JVG4HLx+VV1lX3uuee+bx8u/XrvoZsksFgiIiIiksBiiYiIiEgCiyUiIiIiCSyWiIiIiCSwWCIiIiKSwGKJiIiISIJG7gCoYyajEaamRsBkgtrJCRr3gXKHRERE9FhisdQHtdXU4Lv/2w1jcREgBDSe3vB89jk4jQiB2slJ7vCIiIgeK7wN18e01X6Pb9f/CcYLhYAQAID276pRuWk9Wm5ckzk6IiKixw+LpT6m5fpVtH9/q8Ntt/Z9BFNjQy9HRERE9HhjsdTHGC+W2NzWcv0qzK2tvRgNERERsVjqY7Senja3Obi6QaXmlBEREfUmvvP2MS5hEwEbBZH7rLlwMLj3bkBERESPORZLfYzG4wn4rFgFaKz/UNFl/ES4TvkfXlkiIiLqZfzogD5GrdPB+akxCEzdiJbrV2FqbobjsOHQDPSAg6ur3OERERE9dlgs9UFqnQ5qTy9oPb3kDoWIiOixx3s6RERERBJYLBERERFJYLFEREREJIHFEhEREZEEFktEREREElgsEREREUlgsUREREQkgcUSERERkQQWS0REREQSWCwRERERSVBMsVRbW4slS5bAzc0N7u7uSEhIQFNTk+Q+H3zwAaKjo+Hm5gaVSoXbt293y7hERET0+FBMsbRkyRKUlpYiMzMTn376KfLy8vDSSy9J7mM0GjFr1iy8/vrr3TouERERPT5UQgghdxAPU1ZWhpEjR6KgoAATJ04EABw/fhyzZ8/GN998Az8/P8n9c3JyEBMTg7q6Ori7u3fbuPc0NDTAYDCgvr4ebm5uXUuSiIi6XUuTCaZWAa2TGlonxVwfoF7S2fdvRfzknD59Gu7u7paCBgCmT58OtVqNs2fP9vq4LS0taGhosHoQEVHfcbfRhG+KmnFy3U189r8V+PL9KtReb0Fbi1nu0EiBFFEsVVVVwcvLy6pNo9HAw8MDVVVVvT5uamoqDAaD5REQENDlGIiIqHu13THjq2O3kbWhEreutMBYa0JFgRFHX6vAd5fvyh0eKZCsxVJKSgpUKpXk46uvvpIzxA699tprqK+vtzwqKirkDomIiH50p74dJYfrHmgXAjizowbGunYZoiIl08h58N///vd47rnnJPsMGzYMPj4+qKmpsWpvb29HbW0tfHx8unz8ro6r1+uh1+u7fFwiIuo5tytaYWs1btN37WhtMsF5oKxvf6Qwsv60eHp6wtPT86H9IiIicPv2bRQWFmLChAkAgKysLJjNZkyaNKnLx++pcYmISD5qjUpyu0otvZ3ofopYsxQaGopZs2Zh2bJlOHfuHPLz87FixQosWrTI8hdr3377LUJCQnDu3DnLflVVVSguLsaVK1cAACUlJSguLkZtbW2nxyUiImVxf1IHtY1LAQMDddAPUMRbH/UhivmJycjIQEhICKZNm4bZs2dj6tSp+OCDDyzb29racPnyZRiNRkvbtm3bEBYWhmXLlgEAIiMjERYWhr///e+dHpeIiJTF0d0Bk1/2fqBd46jClOXecDTwFhzZRxGfs9TX8XOWiIj6ltY7ZjR/14bLn9ejsaoN3iOdMHTKALh4aqHmbTj6UWffv1leExFRv6NzUkM3WI/w5z1hahPQ6FRcq0RdxmKJiIj6LbWDCmoHFkn0aBSzZomIiIhIDiyWiIiIiCSwWCIiIiKSwGKJiIiISAKLJSIiIiIJLJaIiIiIJLBYIiIiIpLAYomIiIhIAoslIiIiIgksloiIiIgk8OtOusG97yJuaGiQORIiIiLqrHvv2/fex21hsdQNGhsbAQABAQEyR0JERET2amxshMFgsLldJR5WTtFDmc1m3Lx5E66urlCpuu8LGxsaGhAQEICKigq4ubl127h9SX/PkfkpX3/PkfkpX3/PsSfzE0KgsbERfn5+UKttr0zilaVuoFar4e/v32Pju7m59csT4Kf6e47MT/n6e47MT/n6e449lZ/UFaV7uMCbiIiISAKLJSIiIiIJLJb6ML1ej7Vr10Kv18sdSo/p7zkyP+Xr7zkyP+Xr7zn2hfy4wJuIiIhIAq8sEREREUlgsUREREQkgcUSERERkQQWS0REREQSWCzJbOvWrRgyZAgcHR0xadIknDt3TrL/xx9/jJCQEDg6OmL06NH47LPPeinSrrEnv/T0dKhUKquHo6NjL0Zrn7y8PMybNw9+fn5QqVQ4cuTIQ/fJycnB+PHjodfrMXz4cKSnp/d4nI/C3hxzcnIemEOVSoWqqqreCdhOqampePrpp+Hq6govLy8sWLAAly9ffuh+SjkPu5Kf0s7Dv/71rxgzZozlAwsjIiJw7NgxyX2UMn+A/fkpbf7ut27dOqhUKiQlJUn26+05ZLEko7/97W9YtWoV1q5di6KiIowdOxZxcXGoqanpsP+pU6ewePFiJCQk4MKFC1iwYAEWLFiAixcv9nLknWNvfsAPn9BaWVlpedy4caMXI7ZPc3Mzxo4di61bt3aq/7Vr1zBnzhzExMSguLgYSUlJePHFF3HixIkejrTr7M3xnsuXL1vNo5eXVw9F+Ghyc3ORmJiIM2fOIDMzE21tbZg5cyaam5tt7qOk87Ar+QHKOg/9/f2xbt06FBYW4vz584iNjcX8+fNRWlraYX8lzR9gf36AsubvpwoKCrB9+3aMGTNGsp8scyhINuHh4SIxMdHy3GQyCT8/P5Gamtph/2eeeUbMmTPHqm3SpEni5Zdf7tE4u8re/NLS0oTBYOil6LoXAHH48GHJPmvWrBGjRo2yalu4cKGIi4vrwci6T2dyzM7OFgBEXV1dr8TU3WpqagQAkZuba7OP0s7Dn+pMfko+D+8ZOHCg2LlzZ4fblDx/90jlp9T5a2xsFD/72c9EZmamiIqKEitXrrTZV4455JUlmbS2tqKwsBDTp0+3tKnVakyfPh2nT5/ucJ/Tp09b9QeAuLg4m/3l1JX8AKCpqQmBgYEICAh46P+elEZJ8/eoxo0bB19fX8yYMQP5+flyh9Np9fX1AAAPDw+bfZQ8j53JD1DueWgymbBv3z40NzcjIiKiwz5Knr/O5Acoc/4SExMxZ86cB+amI3LMIYslmdy6dQsmkwne3t5W7d7e3jbXd1RVVdnVX05dyS84OBi7du3CJ598go8++ghmsxmTJ0/GN9980xsh9zhb89fQ0IA7d+7IFFX38vX1xbZt23Dw4EEcPHgQAQEBiI6ORlFRkdyhPZTZbEZSUhKmTJmCp556ymY/JZ2HP9XZ/JR4HpaUlGDAgAHQ6/X4zW9+g8OHD2PkyJEd9lXi/NmTnxLnb9++fSgqKkJqamqn+ssxh5oeG5nIThEREVb/W5o8eTJCQ0Oxfft2vPnmmzJGRp0VHByM4OBgy/PJkyejvLwcmzZtwp49e2SM7OESExNx8eJFfPnll3KH0iM6m58Sz8Pg4GAUFxejvr4eBw4cQHx8PHJzc20WFEpjT35Km7+KigqsXLkSmZmZfXohOoslmQwaNAgODg6orq62aq+uroaPj0+H+/j4+NjVX05dye9+Wq0WYWFhuHLlSk+E2OtszZ+bmxucnJxkiqrnhYeH9/kCZMWKFfj000+Rl5cHf39/yb5KOg/vsSe/+ynhPNTpdBg+fDgAYMKECSgoKMBf/vIXbN++/YG+Spw/e/K7X1+fv8LCQtTU1GD8+PGWNpPJhLy8PGzZsgUtLS1wcHCw2keOOeRtOJnodDpMmDABJ0+etLSZzWacPHnS5r3oiIgIq/4AkJmZKXnvWi5dye9+JpMJJSUl8PX17akwe5WS5q87FRcX99k5FEJgxYoVOHz4MLKysjB06NCH7qOkeexKfvdT4nloNpvR0tLS4TYlzZ8tUvndr6/P37Rp01BSUoLi4mLLY+LEiViyZAmKi4sfKJQAmeawx5aO00Pt27dP6PV6kZ6eLi5duiReeukl4e7uLqqqqoQQQixdulSkpKRY+ufn5wuNRiPefvttUVZWJtauXSu0Wq0oKSmRKwVJ9ub3xhtviBMnTojy8nJRWFgoFi1aJBwdHUVpaalcKUhqbGwUFy5cEBcuXBAAxMaNG8WFCxfEjRs3hBBCpKSkiKVLl1r6X716VTg7O4vVq1eLsrIysXXrVuHg4CCOHz8uVwoPZW+OmzZtEkeOHBH/+c9/RElJiVi5cqVQq9Xiiy++kCsFScuXLxcGg0Hk5OSIyspKy8NoNFr6KPk87Ep+SjsPU1JSRG5urrh27Zr417/+JVJSUoRKpRKff/65EELZ8yeE/fkpbf46cv9fw/WFOWSxJLP33ntPDB48WOh0OhEeHi7OnDlj2RYVFSXi4+Ot+u/fv1+MGDFC6HQ6MWrUKHH06NFejtg+9uSXlJRk6evt7S1mz54tioqKZIi6c+79mfz9j3s5xcfHi6ioqAf2GTdunNDpdGLYsGEiLS2t1+O2h705rl+/XgQFBQlHR0fh4eEhoqOjRVZWljzBd0JHuQGwmhcln4ddyU9p5+ELL7wgAgMDhU6nE56enmLatGmWQkIIZc+fEPbnp7T568j9xVJfmEOVEEL03HUrIiIiImXjmiUiIiIiCSyWiIiIiCSwWCIiIiKSwGKJiIiISAKLJSIiIiIJLJaIiIiIJLBYIiIiIpLAYomI+r38/HyMHj0aWq0WCxYs6PR+6enpcHd377G4elt0dDSSkpLkDoNIcVgsEdEjee6556BSqaBSqaDVauHt7Y0ZM2Zg165dMJvNcocHAFi1ahXGjRuHa9euIT09vcM+Q4YMwebNm3s8lm3btsHV1RXt7e2WtqamJmi1WkRHR1v1zcnJgUqlQnl5eY/HRUS2sVgiokc2a9YsVFZW4vr16zh27BhiYmKwcuVKzJ0716ookEt5eTliY2Ph7+8v+5WimJgYNDU14fz585a2f/zjH/Dx8cHZs2dx9+5dS3t2djYGDx6MoKAgOUIloh+xWCKiR6bX6+Hj44Mnn3wS48ePx+uvv45PPvkEx44ds7qSs3HjRowePRouLi4ICAjAb3/7WzQ1NQEAmpub4ebmhgMHDliNfeTIEbi4uKCxsbHDY7e0tOCVV16Bl5cXHB0dMXXqVBQUFAAArl+/DpVKhe+//x4vvPACVCpVh1eWoqOjcePGDSQnJ1uukv3UiRMnEBoaigEDBlgKw5/auXMnQkND4ejoiJCQELz//vs2X6vg4GD4+voiJyfH0paTk4P58+dj6NChOHPmjFV7TEwMgB++aT41NRVDhw6Fk5MTxo4d+8BrdfHiRfz85z/HgAED4O3tjaVLl+LWrVs2Yzl69CgMBgMyMjJs9iEiFktE1ENiY2MxduxYHDp0yNKmVqvx7rvvorS0FLt370ZWVhbWrFkDAHBxccGiRYuQlpZmNU5aWhp+9atfwdXVtcPjrFmzBgcPHsTu3btRVFSE4cOHIy4uDrW1tQgICEBlZSXc3NywefNmVFZWYuHChQ+McejQIfj7++NPf/oTKisrrYoho9GIt99+G3v27EFeXh6+/vprvPrqq5btGRkZ+OMf/4i33noLZWVl+POf/4w//OEP2L17t83XJiYmBtnZ2Zbn2dnZiI6ORlRUlKX9zp07OHv2rKVYSk1NxYcffoht27ahtLQUycnJePbZZ5GbmwsAuH37NmJjYxEWFobz58/j+PHjqK6uxjPPPNNhDHv37sXixYuRkZGBJUuW2IyViAD06Nf0ElG/Fx8fL+bPn9/htoULF4rQ0FCb+3788cfiiSeesDw/e/ascHBwEDdv3hRCCFFdXS00Go3IycnpcP+mpiah1WpFRkaGpa21tVX4+fmJDRs2WNoMBoNIS0uTzCMwMFBs2rTJqi0tLU0AEFeuXLG0bd26VXh7e1ueBwUFib1791rt9+abb4qIiAibx9qxY4dwcXERbW1toqGhQWg0GlFTUyP27t0rIiMjhRBCnDx5UgAQN27cEHfv3hXOzs7i1KlTVuMkJCSIxYsXW445c+ZMq+0VFRUCgLh8+bIQ4r/f5r5lyxZhMBhsvq5EZE0jc61GRP2YEMLqltYXX3yB1NRUfPXVV2hoaEB7ezvu3r0Lo9EIZ2dnhIeHY9SoUdi9ezdSUlLw0UcfITAwEJGRkR2OX15ejra2NkyZMsXSptVqER4ejrKysm7JwdnZ2WrNkK+vL2pqagD8cOuwvLwcCQkJWLZsmaVPe3s7DAaDzTGjo6PR3NyMgoIC1NXVYcSIEfD09ERUVBSef/553L17Fzk5ORg2bBgGDx6M0tJSGI1GzJgxw2qc1tZWhIWFAQD++c9/Ijs7GwMGDHjgeOXl5RgxYgQA4MCBA6ipqUF+fj6efvrprr8wRI8RFktE1GPKysowdOhQAD+sH5o7dy6WL1+Ot956Cx4eHvjyyy+RkJCA1tZWODs7AwBefPFFbN26FSkpKUhLS8Pzzz//wBqi3qTVaq2eq1QqCCEAwLLeaseOHZg0aZJVPwcHB5tjDh8+HP7+/sjOzkZdXR2ioqIAAH5+fggICMCpU6eQnZ2N2NhYq+McPXoUTz75pNVYer3e0mfevHlYv379A8fz9fW1/DssLAxFRUXYtWsXJk6cKOtrS6QULJaIqEdkZWWhpKQEycnJAIDCwkKYzWa88847UKt/WC65f//+B/Z79tlnsWbNGrz77ru4dOkS4uPjbR4jKCgIOp0O+fn5CAwMBAC0tbWhoKDA7s8T0ul0MJlMdu3j7e0NPz8/XL161e51PzExMcjJyUFdXR1Wr15taY+MjMSxY8dw7tw5LF++HAAwcuRI6PV6fP3115bC6n7jx4/HwYMHMWTIEGg0tn+1BwUF4Z133kF0dDQcHBywZcsWu+ImehyxWCKiR9bS0oKqqiqYTCZUV1fj+PHjSE1Nxdy5c/HrX/8awA9XU9ra2vDee+9h3rx5yM/Px7Zt2x4Ya+DAgfjlL3+J1atXY+bMmfD397d5XBcXFyxfvhyrV6+Gh4cHBg8ejA0bNsBoNCIhIcGuHIYMGYK8vDwsWrQIer0egwYN6tR+b7zxBl555RUYDAbMmjULLS0tOH/+POrq6rBq1Sqb+8XExCAxMRFtbW1WBVBUVBRWrFiB1tZWy+JuV1dXvPrqq0hOTobZbMbUqVNRX1+P/Px8uLm5IT4+HomJidixYwcWL16MNWvWwMPDA1euXMG+ffuwc+dOqytdI0aMsCwq12g0vfL5UkSKJveiKSJStvj4eAFAABAajUZ4enqK6dOni127dgmTyWTVd+PGjcLX11c4OTmJuLg48eGHHwoAoq6uzqrfvcXN+/fvf+jx79y5I373u9+JQYMGCb1eL6ZMmSLOnTtn1aczC7xPnz4txowZI/R6vbj3qzEtLU0YDAarfocPHxb3/+rMyMgQ48aNEzqdTgwcOFBERkaKQ4cOSR7v2rVrAoAICQmxar9+/boAIIKDg63azWaz2Lx5swgODhZarVZ4enqKuLg4kZuba+nz73//W/ziF78Q7u7uwsnJSYSEhIikpCRhNpuFEP9d4H3PpUuXhJeXl1i1apVkrESPO5UQP958JyLqI/bs2YPk5GTcvHkTOp1O7nCI6DHH23BE1GcYjUZUVlZi3bp1ePnll1koEVGfwA+lJKI+Y8OGDQgJCYGPjw9ee+01ucMhIgIA8DYcERERkQReWSIiIiKSwGKJiIiISAKLJSIiIiIJLJaIiIiIJLBYIiIiIpLAYomIiIhIAoslIiIiIgksloiIiIgksFgiIiIikvD/Eld/4OMq1NQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#QUESTION - 04\n",
    "\n",
    " #Please refer to the data present in “IRCTC Stock Price” data sheet of the above excel file. Do the following after loading the data to your programming platform.\n",
    "#• Calculate the mean and variance of the Price data present in column D.(Suggestion: if you use Python, you may use statistics.mean() & statistics.variance() methods).\n",
    "#• Select the price data for all Wednesdays and calculate the sample mean. Compare the meanwith the population mean and note your observations.\n",
    "#• Select the price data for the month of Apr and calculate the sample mean. Compare the mean with the population mean and note your observations.\n",
    "\n",
    "\n",
    "import statistics\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "stock_data = pd.read_excel(r\"Lab Session1 Data.xlsx\", sheet_name=\"IRCTC Stock Price\")\n",
    "\n",
    "# Extracting the 'Close' column (column D)\n",
    "closing_prices = stock_data.iloc[:, 3]\n",
    "\n",
    "# Display the closing prices\n",
    "print(f\"Closing Prices: {closing_prices}\")\n",
    "\n",
    "# Calculate the mean of closing prices\n",
    "mean_price = statistics.mean(closing_prices)\n",
    "print(f\"Mean Closing Price: {mean_price}\")\n",
    "\n",
    "# Calculate the variance of closing prices\n",
    "variance_price = statistics.variance(closing_prices)\n",
    "print(f\"Variance of Closing Prices: {variance_price}\")\n",
    "\n",
    "# Convert 'Date' column to datetime format\n",
    "stock_data[\"Date\"] = pd.to_datetime(stock_data[\"Date\"])\n",
    "\n",
    "# Extract Wednesdays' data\n",
    "wednesdays_data = stock_data[stock_data[\"Date\"].dt.weekday == 2]\n",
    "wednesdays_prices = wednesdays_data['Price']\n",
    "wednesdays_mean_price = wednesdays_prices.mean()\n",
    "print(f\"Mean Price for Wednesdays: {wednesdays_mean_price}\")\n",
    "\n",
    "# Extract April data\n",
    "april_data = stock_data[stock_data[\"Date\"].dt.month == 4]\n",
    "april_mean_price = statistics.mean(april_data['Price'])\n",
    "print(f\"Mean Price for April: {april_mean_price}\")\n",
    "\n",
    "# Calculate the probability of making a loss\n",
    "loss_count = (stock_data['Chg%'] < 0).sum()\n",
    "total_samples = len(stock_data)\n",
    "loss_probability = loss_count / total_samples\n",
    "print(f\"Probability of Making Loss: {loss_probability}\")\n",
    "\n",
    "# Calculate the probability of making a profit on Wednesdays\n",
    "profit_count_on_wednesdays = (stock_data.loc[stock_data['Date'].dt.weekday == 2, 'Chg%'] > 0).sum()\n",
    "wednesday_profit_probability = profit_count_on_wednesdays / total_samples\n",
    "print(f\"Probability of Making Profit on Wednesdays: {wednesday_profit_probability}\")\n",
    "\n",
    "# Calculate the conditional probability of making a profit given that it's Wednesday\n",
    "profitable_wednesdays = wednesdays_data[wednesdays_data['Chg%'] > 0]\n",
    "num_profitable_wednesdays = len(profitable_wednesdays)\n",
    "total_wednesdays = len(wednesdays_data)\n",
    "conditional_profit_probability_wednesday = num_profitable_wednesdays / total_wednesdays\n",
    "print(f\"Conditional Probability of Making Profit, Given it's Wednesday: {conditional_profit_probability_wednesday}\")\n",
    "\n",
    "# Plotting Chg% Distribution against the day of the week\n",
    "stock_data[\"Day_of_week\"] = stock_data['Date'].dt.weekday\n",
    "sns.scatterplot(x=\"Day_of_week\", y=\"Chg%\", data=stock_data, hue=\"Day_of_week\", palette=\"hls\")\n",
    "plt.xlabel(\"Day of the Week\")\n",
    "plt.ylabel(\"Change Percentage (Chg%)\")\n",
    "plt.title(\"Chg% Distribution against the Day of the Week\")\n",
    "plt.show()\n"
   ]
  }
 ],
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