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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1> The Challenge:</h1>\n",
"\n",
"Based off this dataset with school financial, enrollment, and achievement data, we are interested in what information is a useful indicator of student performance at the state level.\n",
"\n",
"This question is a bit too big for a checkpoint, however. Instead, we want you to look at smaller questions related to our overall goal. Here's the overview:\n",
"\n",
"1. Choose a specific test to focus on\n",
">Math/Reading for 4/8 grade\n",
"* Pick or create features to use\n",
">Will all the features be useful in predicting test score? Are some more important than others? Should you standardize, bin, or scale the data?\n",
"* Explore the data as it relates to that test\n",
">Create 2 well-labeled visualizations (graphs), each with a caption describing the graph and what it tells us about the data\n",
"* Create training and testing data\n",
">Do you want to train on all the data? Only data from the last 10 years? Only Michigan data?\n",
"* Train a ML model to predict outcome \n",
">Pick if you want to do a regression or classification task. For both cases, defined _exactly_ what you want to predict, and pick any model in sklearn to use (see sklearn <a href=\"https://scikit-learn.org/stable/modules/linear_model.html\">regressors</a> and <a href=\"https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html\">classifiers</a>).\n",
"* Summarize your findings\n",
">Write a 1 paragraph summary of what you did and make a recommendation about if and how student performance can be predicted\n",
"\n",
"** Include comments throughout your code! Every cleanup and preprocessing task should be documented.\n",
"\n",
"\n",
"Of course, if you're finding this assignment interesting (and we really hope you do!), you are welcome to do more than the requirements! For example, you may want to see if expenditure affects 4th graders more than 8th graders. Maybe you want to look into the extended version of this dataset and see how factors like sex and race are involved. You can include all your work in this notebook when you turn it in -- just always make sure you explain what you did and interpret your results. Good luck!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# feel free to import other libraries! "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('states_edu.csv') #dataset import"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chosen test: **<hit `Enter` to edit>**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Cleanup (optional)</h2>\n",
"\n",
"_Use this space to rename columns, deal with missing data, etc._"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['PRIMARY_KEY', 'STATE', 'YEAR', 'ENROLL', 'TOTAL_REVENUE',\n",
" 'FEDERAL_REVENUE', 'STATE_REVENUE', 'LOCAL_REVENUE',\n",
" 'TOTAL_EXPENDITURE', 'INSTRUCTION_EXPENDITURE',\n",
" 'SUPPORT_SERVICES_EXPENDITURE', 'OTHER_EXPENDITURE',\n",
" 'CAPITAL_OUTLAY_EXPENDITURE', 'GRADES_PK_G', 'GRADES_KG_G',\n",
" 'GRADES_4_G', 'GRADES_8_G', 'GRADES_12_G', 'GRADES_1_8_G',\n",
" 'GRADES_9_12_G', 'GRADES_ALL_G', '4MATH', '8MATH', '4READING',\n",
" '8READING', 'PERCENT_EXPENDITURE', 'MONEY_PER_STUDENT',\n",
" 'PERCENT_PER_STUDENT', 'MONEY_PER_STUDENT_AVG',\n",
" 'PERCENT_PER_STUDENT_AVG'],\n",
" dtype='object')"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#renaming columns to make them easily accessible\n",
"df.rename({'AVG_MATH_4_SCORE' : '4MATH', 'AVG_READING_4_SCORE' : '4READING', 'AVG_MATH_8_SCORE' : '8MATH', 'AVG_READING_8_SCORE' : '8READING'}, axis = 1, inplace = True)\n",
"#keep only documented values\n",
"df.dropna(subset=['4MATH', '8MATH', '4READING', '8READING', 'STATE'], inplace=True)\n",
"df.columns #for my reference later"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Feature Selection </h2>\n",
"\n",
"_Use this space to modify or create features_"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.004382533196235\n",
"1.7261459623412743\n",
"12.327728713667957\n",
"3.4526777193699165\n"
]
}
],
"source": [
"df['PERCENT_EXPENDITURE'] = 100*df['INSTRUCTION_EXPENDITURE']/df['TOTAL_EXPENDITURE'] # percent of total expenditure directed towards instruction\n",
"df['MONEY_PER_STUDENT'] = df['INSTRUCTION_EXPENDITURE']/df['GRADES_ALL_G'] #amount of instruction expenditure per student\n",
"df['PERCENT_PER_STUDENT'] = 100*df['PERCENT_EXPENDITURE']/df['GRADES_ALL_G'] #percent of instruction percentage per student\n",
"arr = df['STATE'].values[0:51] #list of states\n",
"avgdf = pd.DataFrame(columns=['STATE', 'MONEY_PER_STUDENT', 'PERCENT_PER_STUDENT', '8READING', '8MATH', '4READING', '4MATH', 'STUDENT_POP'], index = range (51)) #new df for averages\n",
"avgdf['STATE'] = arr #population of new df\n",
"for i in range (0, 51):\n",
" avgdf['MONEY_PER_STUDENT'][i] = df.groupby('STATE')['MONEY_PER_STUDENT'].mean()[i]\n",
" avgdf['PERCENT_PER_STUDENT'][i] = df.groupby('STATE')['PERCENT_PER_STUDENT'].mean()[i]\n",
" avgdf['8READING'][i] = df.groupby('STATE')['8READING'].mean()[i]\n",
" avgdf['8MATH'][i] = df.groupby('STATE')['8MATH'].mean()[i]\n",
" avgdf['4READING'][i] = df.groupby('STATE')['4READING'].mean()[i]\n",
" avgdf['4MATH'][i] = df.groupby('STATE')['4MATH'].mean()[i]\n",
" avgdf['STUDENT_POP'][i] = df.groupby('STATE')['GRADES_ALL_G'].mean()[i]\n",
"meanmoney = avgdf['MONEY_PER_STUDENT'].mean()\n",
"stdmoney = avgdf['MONEY_PER_STUDENT'].std()\n",
"maxmoney = avgdf['MONEY_PER_STUDENT'].max()\n",
"minmoney = avgdf['MONEY_PER_STUDENT'].min()\n",
"print(meanmoney)\n",
"print(stdmoney)\n",
"print(maxmoney)\n",
"print(minmoney)\n",
"avgdf = avgdf.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Final feature list: **<LIST FEATURES HERE\\>**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feature selection justification: **<BRIEFLY DESCRIBE WHY YOU PICKED THESE FEATURES\\>**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> EDA </h2>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization 1"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, '4th grade reading score')"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#4 scatter plots for money\n",
"avgdf.plot.scatter(x='MONEY_PER_STUDENT',y='4MATH')\n",
"plt.xlabel('4th grade money per student ($)')\n",
"plt.ylabel('4th grade math score')\n",
"avgdf.plot.scatter(x='MONEY_PER_STUDENT',y='8MATH')\n",
"plt.xlabel('8th grade money per student ($)')\n",
"plt.ylabel('8th grade math score')\n",
"avgdf.plot.scatter(x='MONEY_PER_STUDENT',y='8READING')\n",
"plt.xlabel('8th grade money per student ($)')\n",
"plt.ylabel('8th grade reading score')\n",
"avgdf.plot.scatter(x='MONEY_PER_STUDENT',y='4READING')\n",
"plt.xlabel('4th grade money per student ($)')\n",
"plt.ylabel('4th grade reading score')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<CAPTION FOR VIZ 1>**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization 2"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, '4th grade math score')"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#4 scatter plots for percentage\n",
"avgdf.plot.scatter(x='PERCENT_PER_STUDENT',y='8READING')\n",
"plt.xlabel('8th grade percent of revenue per student (%)')\n",
"plt.ylabel('8th grade reading score')\n",
"avgdf.plot.scatter(x='PERCENT_PER_STUDENT',y='4READING')\n",
"plt.xlabel('4th grade percent of revenue per student (%)')\n",
"plt.ylabel('4th grade reading score')\n",
"avgdf.plot.scatter(x='PERCENT_PER_STUDENT',y='8MATH')\n",
"plt.xlabel('8th grade percent of revenue per student (%)')\n",
"plt.ylabel('8th grade math score')\n",
"avgdf.plot.scatter(x='PERCENT_PER_STUDENT',y='4MATH')\n",
"plt.xlabel('4th grade percent of revenue per student (%)')\n",
"plt.ylabel('4th grade math score')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<CAPTION FOR VIZ 2>**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Data Creation </h2>\n",
"\n",
"_Use this space to create train/test data_"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {},
"outputs": [],
"source": [
"X\n",
"y = avgdf.loc[X.index]['8MATH']\n",
"#based on money per student, can the score be predicted for 8th grade math tests?"
]
},
{
"cell_type": "code",
"execution_count": 318,
"metadata": {},
"outputs": [],
"source": [
"X1_train, X1_test, y1_train, y1_test = train_test_split(\n",
" X, y, test_size=.75, random_state=0) #25% of data allocated to prediction\n",
"\n",
"X2_train, X2_test, y2_train, y2_test = train_test_split(\n",
" X, y, test_size=.65, random_state=0) #35% of data allocated to prediction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Prediction </h2>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chosen ML task: **<REGRESSION/CLASSIFICATION>**"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {},
"outputs": [],
"source": [
"# import your sklearn class here\n",
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [],
"source": [
"# create your model here\n",
"model = LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": 321,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7.884823712911691"
]
},
"execution_count": 321,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X1_train, y1_train)\n",
"model.score(X1_test, y1_test)\n",
"np.mean(model.predict(X1_test)-y1_test)\n",
"np.mean(np.abs(model.predict(X1_test)-y1_test))\n",
"np.mean((model.predict(X1_test)-y1_test)**2)**0.5"
]
},
{
"cell_type": "code",
"execution_count": 322,
"metadata": {},
"outputs": [],
"source": [
"y1_pred = model.predict(X1_test)\n",
"y2_pred = model.predict(X2_test)"
]
},
{
"cell_type": "code",
"execution_count": 323,
"metadata": {},
"outputs": [],
"source": [
"# for classification:\n",
"#from sklearn.metrics import plot_confusion_matrix\n",
"\n",
"#plot_confusion_matrix(model, X_test, y_test,\n",
" # cmap=plt.cm.Blues)"
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Prediction with 35% trained')"
]
},
"execution_count": 324,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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r1jF8+HAGDhzI8OHD+eKLLwBPIj5+/HjAs6PhXXfdxahRo+jRo8dZiXZoaGhx/1GjRpGQkEDv3r2ZOnUqZzYBevfdd+nduzfx8fHMmDGjeFwRERGRxqhxr0N97tY7mZmeY6jyYodPPfUUW7ZsYdOmTYAnMV23bh1btmwhMjKSjIyMMq+dNm0aL730Ej179uTTTz9l+vTprFixovh8REQE99xzD6GhoTz44IMAzJ07ly+//JLly5cTGBjIsWPHWLVqFU2aNGH58uU88sgjLFy48Lx7paWl8cEHH5Cbm0uvXr249957adq06Vl9Nm7cyNatW+nSpQtxcXGsWbOGwYMHc/fdd7Nq1SoiIyO55ZZbqvRzEhEREWkoGndCPXv22ftYgud49uwaXT38sssuIzIystw+eXl5rF27lilTphS3nTx5skLjT5kyhcDAQABycnK444472LFjB2bG6dOnS73m+uuvJygoiKCgIDp27Mi+ffsICws7L+4zbTExMWRkZBAaGkqPHj2K388tt9xCUlJSheIUERERaYgad0KdlVW59ipq0aJF8fdNmjShqKio+PjEiRMAFBUV0aZNm+KZ7aqO/+ijjzJ69GgWL15MRkYGo0aNKvWaoKCg4u8DAwNLrb8urc+Zsg8RERER8WjcNdTh4ZVrr4CWLVuSm5tb5vlOnTqxf/9+Dh06xMmTJ3n77bcBaNWqFZGRkSxYsAAA5xyfffZZpcfPycmha9euAMybN6/K76MsvXv35quvviouXXn99ddr/B4iIiIi9UnjTqgTEyEk5Oy2kBBPexW1a9eOuLg4+vfvz8yZM88737RpUx577DEuv/xyxo8fT+/evYvPJScnM3fuXKKjo+nXrx9Lliw57/obbriBxYsXFz+UeK6HHnqIhx9+mLi4uBpZVeRczZs358UXX2TcuHHEx8fTqVMnWrduXeP3EREREakvrD7/Cn/w4MFu/fr1Z7Vt376dPn36VHyQ5GRPzXRWlmdmOjGxRuunG6K8vDxCQ0NxznHffffRs2dPfvGLX/g1pkp/7iIiIiKVYGapzrnBpZ1r3DPU4EmeMzKgqMjzqmT6gv77v/+bmJgY+vXrR05ODnfffbe/QxIRERHxm8b9UKJUyS9+8Qu/z0iLiIiI1BU+m6E2s2AzW2dmn5nZVjN7wtve1sz+ZWY7vK8XlbjmYTPbaWZfmNk1vopNRERERKSm+LLk4yRwpXMuGogBxpnZUGAW8L5zrifwvvcYM+sL/ADoB4wDXjSzQB/GJyIiIiJSbT5LqJ1HnvewqffLATcC873t84GJ3u9vBF5zzp10zn0N7AQu81V8IiIiIiI1wacPJZpZoJltAvYD/3LOfQp0cs7tBfC+dvR27wrsKnF5trft3DGnmdl6M1t/4MABX4YvIiIiInJBPk2onXOFzrkYIAy4zMz6l9PdShuilDGTnHODnXODO3ToUFOh1lkrV65k/PjxACxdupSnnnqqzL5Hjx7lxRdfrPQ95syZwzPPPHNe+5tvvsm2bdsqPd6F4gTYs2cPCQkJlR5bREREpK6plWXznHNHgZV4aqP3mdnFAN7X/d5u2UC3EpeFAXtqIz5/qMqmKxMmTGDWrFllnq9qQl2W8hLq0rYqP+NCcQJ06dKFlJSUasUnIiIiUhf4cpWPDmbWxvt9c+AqIA1YCtzh7XYHcGY7wKXAD8wsyMwigZ7AOl/Fd0by5mQi/hhBwBMBRPwxguTNydUaLyMjg969e3PHHXcQFRVFQkIC+fn5AERERPDkk08SHx/PggULWLZsGcOGDWPQoEFMmTKFvDxPyfk///lPevfuTXx8PIsWLSoee968edx///0A7Nu3j0mTJhEdHU10dDRr165l1qxZpKenExMTU7xL49NPP82QIUOIiori8ccfLx4rMTGRXr16cdVVV/HFF1+c9z7Wrl3L0qVLmTlzJjExMaSnpzNq1CgeeeQRrrjiCp5//nneeustLr/8cgYOHMhVV13Fvn37zovzzjvvZMaMGQwfPpwePXoUJ9EZGRn079+/uP9NN93EuHHj6NmzJw899FBxHHPnzuXSSy9l1KhR/OQnPykeV0RERKSu8OU61BcD870rdQQAbzjn3jazj4E3zOxHQBYwBcA5t9XM3gC2AQXAfc65mt87u4TkzclMe2sa+ac9CW9mTibT3poGwNQBVd/g5YsvvmDu3LnExcVx11138eKLL/Lggw8CEBwczOrVqzl48CA33XQTy5cvp0WLFvz+97/n2Wef5aGHHuInP/kJK1as4JJLLuHmm28u9R4zZszgiiuuYPHixRQWFpKXl8dTTz3Fli1b2LRpEwDLli1jx44drFu3DuccEyZMYNWqVbRo0YLXXnuNjRs3UlBQwKBBg4iNjT1r/OHDhzNhwgTGjx9/VmnG0aNH+fDDDwE4cuQIn3zyCWbG//zP//CHP/yB//zP/zwv1r1797J69WrS0tKYMGFCqaUemzZtYuPGjQQFBdGrVy9++tOfEhgYyK9//Ws2bNhAy5YtufLKK4mOjq7ahyIiIiLiIz5LqJ1znwMDS2k/BIwp45pEINFXMZ1r9vuzi5PpM/JP5zP7/dnVSqi7detGXFwcAD/84Q954YUXihPqMwnyJ598wrZt24r7nTp1imHDhpGWlkZkZCQ9e/Ysvj4pKem8e6xYsYJXXnkFgMDAQFq3bs2RI0fO6rNs2TKWLVvGwIGejyEvL48dO3aQm5vLpEmTCAkJATwlGhVVMsHPzs7m5ptvZu/evZw6dYrIyMhSr5k4cSIBAQH07du3eBb7XGPGjKF169YA9O3bl8zMTA4ePMgVV1xB27ZtAZgyZQpffvllhWMVERERqQ2NeuvxrJysSrVXlJmVedyiRQsAnHNcffXVbNq0iU2bNrFt2zbmzp1b6vVV5Zzj4YcfLr7Hzp07+dGPflSte5yJH+CnP/0p999/P5s3b+avf/0rJ06cKPWaoKCgs2K6UJ/AwEAKCgrK7CsiIiJSlzTqhDq8dXil2isqKyuLjz/+GIBXX32V+Pj48/oMHTqUNWvWsHPnTgDy8/P58ssv6d27N19//TXp6enF15dmzJgx/OUvfwE8DzgeO3aMli1bkpubW9znmmuu4eWXXy6uzd69ezf79+9n5MiRLF68mOPHj5Obm8tbb71V6j3OHe9cOTk5dO3qWdlw/vz5Zfarqssuu4wPP/yQI0eOUFBQwMKFC2v8HiIiIiLV1agT6sQxiYQ0DTmrLaRpCIljqld10qdPH+bPn09UVBSHDx/m3nvvPa9Phw4dmDdvHrfccgtRUVEMHTqUtLQ0goODSUpK4vrrryc+Pp7u3buXeo/nn3+eDz74gAEDBhAbG8vWrVtp164dcXFx9O/fn5kzZzJ27FhuvfVWhg0bxoABA0hISCA3N5dBgwZx8803ExMTw+TJkxkxYkSp9/jBD37A008/zcCBA4sT/JLmzJnDlClTGDFiBO3bt6/Wz6w0Xbt25ZFHHuHyyy/nqquuom/fvsVlISIiIiJ1hdXnX6sPHjzYrV+//qy27du306dPnwqPkbw5mdnvzyYrJ4vw1uEkjkmsVv10RkYG48ePZ8uWLVUeQ76Tl5dHaGgoBQUFTJo0ibvuuotJkyad16+yn7uIiIhIZZhZqnNucGnnfLnKR70wdcDUaiXQ4ltz5sxh+fLlnDhxgrFjxzJx4sQLXyQiIiJSixp9Ql3TIiIiNDtdg0rbwVFERESkLmnUNdQiIiIiItWlhFpEREREpBqUUIuIiIiIVIMSahERERGRalBCXcetXLmS8ePHA7B06VKeeuqpMvsePXqUF198sdL3mDNnTqkP/7355pts27at0uMBbNq0iXfffbf4+EKxi4iIiNRXSqj9pLCwsNLXTJgwgVmzZpV5vqoJdVlqMqG+UOwiIiIi9VWjT6iTkyEiAgICPK/JydUbLyMjg969e3PHHXcQFRVFQkIC+fn5gGdJvSeffJL4+HgWLFjAsmXLGDZsGIMGDWLKlCnFW4T/85//pHfv3sTHx7No0aLisefNm8f9998PwL59+5g0aRLR0dFER0ezdu1aZs2aRXp6OjExMcycOROAp59+miFDhhAVFcXjjz9ePFZiYiK9evXiqquu4osvvjjvfaxdu5alS5cyc+ZMYmJiSE9PJz09nXHjxhEbG8uIESNIS0sDYMGCBfTv35/o6GhGjhzJqVOneOyxx3j99deJiYnh9ddfPyv2O++8kxkzZjB8+HB69OhBSkoKAEVFRUyfPp1+/foxfvx4rrvuuuJzIiIiInVVo16HOjkZpk0Db75LZqbnGGBqNfZ6+eKLL5g7dy5xcXHcddddvPjiizz44IMABAcHs3r1ag4ePMhNN93E8uXLadGiBb///e959tlneeihh/jJT37CihUruOSSS7j55ptLvceMGTO44oorWLx4MYWFheTl5fHUU0+xZcsWNm3aBMCyZcvYsWMH69atwznHhAkTWLVqFS1atOC1115j48aNFBQUMGjQIGJjY88af/jw4UyYMIHx48eTkJAAwJgxY3jppZfo2bMnn376KdOnT2fFihU8+eSTvPfee3Tt2pWjR4/SrFkznnzySdavX8+f/vQnwPOfgZL27t3L6tWrSUtLY8KECSQkJLBo0SIyMjLYvHkz+/fvp0+fPtx1111V/yBEREREakGjTqhnz/4umT4jP9/TXp2Eulu3bsTFxQHwwx/+kBdeeKE4oT6TIH/yySds27atuN+pU6cYNmwYaWlpREZG0rNnz+Lrk5KSzrvHihUreOWVVwAIDAykdevWHDly5Kw+y5YtY9myZQwcOBDwbOO9Y8cOcnNzmTRpEiEhIYCnHONC8vLyWLt2LVOmTCluO3nyJABxcXHceeedfP/73+emm26q0M9o4sSJBAQE0LdvX/bt2wfA6tWrmTJlCgEBAXTu3JnRo0dXaCwRERERf2rUCXVWVuXaK8rMyjxu0aIFAM45rr76al599dWz+m7atOm866vKOcfDDz/M3XfffVb7H//4x0rfo6ioiDZt2hTPfpf00ksv8emnn/LOO+8QExNTap9zBQUFnRVnyVcRERGR+qRR11CHh1euvaKysrL4+OOPAXj11VeJj48/r8/QoUNZs2YNO3fuBCA/P58vv/yS3r178/XXX5Oenl58fWnGjBnDX/7yF8DzgOOxY8do2bIlubm5xX2uueYaXn755eLa7N27d7N//35GjhzJ4sWLOX78OLm5ubz11lul3qPkeK1atSIyMpIFCxYAnuT3s88+AyA9PZ3LL7+cJ598kvbt27Nr167zYqmI+Ph4Fi5cSFFREfv27WPlypWVul5ERETEHxp1Qp2YCN6qh2IhIZ726ujTpw/z588nKiqKw4cPc++9957Xp0OHDsybN49bbrmFqKgohg4dSlpaGsHBwSQlJXH99dcTHx9P9+7dS73H888/zwcffMCAAQOIjY1l69attGvXjri4OPr378/MmTMZO3Yst956K8OGDWPAgAEkJCSQm5vLoEGDuPnmm4mJiWHy5MmMGDGi1Hv84Ac/4Omnn2bgwIGkp6eTnJzM3LlziY6Opl+/fixZsgSAmTNnMmDAAPr378/IkSOJjo5m9OjRbNu2rfihxIqYPHkyYWFh9O/fn7vvvpvLL7+c1q1bV/CnLiIiIuIfVp9/zT548GC3fv36s9q2b99Onz59KjxGcrKnZjoryzMznZhYvfrpjIwMxo8fz5YtW6o+SCOWl5dHaGgohw4d4rLLLmPNmjV07tz5gtdV9nMXERERqQwzS3XODS7tXKOuoQZP8lydBFpq1vjx4zl69CinTp3i0UcfrVAyLSIiIuJPjT6hrmkRERGana4G1U2LiIhIfdOoa6hFRERERKqrQSbU9bkuXCpPn7eIiIj4U4NLqIODgzl06JCSrEbCOcehQ4cIDg72dygiIiLSSDW4GuqwsDCys7M5cOCAv0ORWhIcHExYWJi/wxCR2lbTyzSJiFRRg0uomzZtSmRkpL/DEBERX0pOhmnTID/fc5yZ6TkGJdUiUusaXMmHiIg0ArNnf5dMn5Gf72kXEallSqhFRKT+ycqqXLuIiA8poRYRkfonPLxy7SIiPqSEWkRE6p/ERAgJObstJChkZ5UAACAASURBVMTTLiJSy5RQi4hI/TN1KiQlQffuYOZ5TUrSA4ki4hcNbpUPERFpJKZOVQItInWCz2aozaybmX1gZtvNbKuZ/czbHm1mH5vZZjN7y8xaedsjzOy4mW3yfr3kq9hERERERGqKL2eoC4D/cM5tMLOWQKqZ/Qv4H+BB59yHZnYXMBN41HtNunMuxocxiYiIiIjUKJ/NUDvn9jrnNni/zwW2A12BXsAqb7d/AZN9FYOIiIiIiK/VykOJZhYBDAQ+BbYAE7ynpgDdSnSNNLONZvahmY0oY6xpZrbezNZre3EREZHzJSdDRAQEBHhek5P9HZFIw+bzhNrMQoGFwM+dc8eAu4D7zCwVaAmc8nbdC4Q75wYCDwB/P1NfXZJzLsk5N9g5N7hDhw6+Dl9ERKReObMre2YmOPfdruxKqkV8x6cJtZk1xZNMJzvnFgE459Kcc2Odc7HAq0C6t/2kc+6Q9/tUb/ulvoxPRESkodGu7CK1z5erfBgwF9junHu2RHtH72sA8CvgJe9xBzML9H7fA+gJfOWr+ERERBoi7couUvt8OUMdB9wGXFliKbzrgFvM7EsgDdgD/M3bfyTwuZl9BqQA9zjnDvswPhERkQZHu7KL1D6fLZvnnFsNWBmnny+l/0I85SEiIiJSRYmJnprpkmUf2pVdxLe09biIiEgDol3ZRWqfth4XERFpYLQru0jt0gy1iIiIiEg1KKEWEREREakGJdQiIiIiItWghFpEREREpBqUUIuIiIiIVIMSahERERGRalBCLSIiIiJSDUqoRaT6kpMhIgICAjyvycn+jkhERKTWaGMXEame5OSz9znOzPQcg3aWEBGRRkEz1CJSPbNnf5dMn5Gf72kXERFpBJRQi0j1ZGVVrl1ERKSBUUItItUTHl65dhERkQZGCbWIVE9iIoSEnN0WEuJpFxERaQSUUItI9UydCklJ0L07mHlek5L0QKI0CoVFjlVfHiA187C/QxERP9IqHyJSfVOnKoGWRiX9QB4LU7NZtGE33xw7wbX9OxPbva2/wxIRP1FCLSIiUgE5x0/z9ud7SEnNZmPWUQIDjCsu7cBjN/RlTJ+O/g5PRPxICbWIiEgZCoscH+04QEpqNsu27eNUQRG9OrXkket6M3FgVzq2DPZ3iCJSByihFhEROceOfbmkbMjmzY272XfsJG1CmnLrZeEkxIbRr0srzMzfIYpIHaKEWkREBDiaf4q3PttDyobdfLbLU9IxulcHnpgQxujeHQlqEujvEEWkjlJCLSIijVZBYRGrdhxgYepu/rVtH6cKi+jduSW/ur4PN8Z0pUPLIH+HKCL1gBJqERFpFJI3JzP7/dlk5WQRHnI58R0fJG1Xaw7mnaRti2ZMHXqmpKO1v0MVkXpGCbWIiNRLZyXIrcNJHJPI1AGlL9+YvDmZu5f8BwEnh9Cp4OdwvCcfHSqgb1guv500jFG9OtKsibZmEJGqUUItIiL1TvLmZKa9NY380/kAZOZkMu2taQBnJdWnC4tY+cUBZqd8TbuTL2E05aTt5HDTJL4NXIkVXMTYfhn+eAsi0oAooRYRkXpn9vuzi5PpM/JP5zP7/dlMHTCV7XuPkZLqWaXj0LenKCSCb5u8TV7g+5wOyCi+Jisnt5YjF5GGSAm1iIjUO1k5Wee1BbhWHDkUzXXPf8S2vcdoGmhc1acTkweF8eP3hnHk2NfnXRPeOrw2whWRBk4JtYiI1DvhrcPJzMkE14TmRYMJLRhD86IhGE1oEmg8MaEfE6K7cFGLZgAkFv76rBIRgJCmISSOSfTXWxCRBkQJtYiI1Dv3DXyKp99fRdCpeAJpTSFHON7sXWZdPYoHRlx/Xv8zddUVfYhRRKQyzDnn7xiqbPDgwW79+vX+DkNERGrBgdyTLNm0m5TUbNK+ySUwwFHYbCP7i5bS8aJDJF71ayXIIuIzZpbqnBtc2jnNUIuISJ11sqCQD9L2k5KazQdfHKCwyBHdrQ2/vrEfN0R3oU3IeOBRf4cpIo2cEmoREalTnHNs2X2MlNRdLPlsD0fzT9OxZRA/HhFJwqAwenZq6e8Qv5OcDLNnQ1YWhIdDYiJM1Sy5SGPjs4TazLoBrwCdgSIgyTn3vJlFAy8BoUAGMNU5d8x7zcPAj4BCYIZz7j1fxSciInXL/mMneNNb0vHlvjyaNQngmn6dmTyoK/GXtKdJYB3beCU5GaZNg3zvg46ZmZ5jUFIt0sj4rIbazC4GLnbObTCzlkAqMBGYDzzonPvQzO4CIp1zj5pZX+BV4DKgC7AcuNQ5V1jWPVRDLSJSv504Xcj72/eTkrqLVTsOUljkGBTehsmxYYyP6kLr5k39HWLZIiI8SfS5uneHjIzajkZEfMwvNdTOub3AXu/3uWa2HegK9AJWebv9C3gPTwHcjcBrzrmTwNdmthNPcv2xr2IUEZHa55zjs+wcFqZms/SzPeQcP03nVsFMG9mDhNgwvtch1N8hVkzW+Wthl9suIg1WrdRQm1kEMBD4FNgCTACWAFOAbt5uXYFPSlyW7W07d6xpwDSA8HAtyC8iUl98k3OCxRt3s3BDNjv35xHUJIBx/TszeVAYcZe0JzDA/B1i5YSHlz5DrX+bRBodnyfUZhYKLAR+7pw75i3zeMHMHgOWAqfOdC3l8vPqUZxzSUASeEo+fBO1iIjUhBOnC/nXtn2kpGbz0Y4DFDkY3P0ifnfTAK6PuphWwXW4pONCEhPPrqEGCAnxtItIo+LThNrMmuJJppOdc4sAnHNpwFjv+UuBMyvwZ/PdbDVAGLDHl/GJiEjNc86xcdfR4pKO3BMFdGkdzL2jvkdCbDci27fwd4g148yDh1rlQ6TR8+VDiYbnAcTDzrmfl2jv6Jzbb2YBwDxgpXPuZTPrB/yd7x5KfB/oqYcSRUTqh705x1m0wVPS8dWBbwluGsB1/S9mcmwYw3q0I6C+lXSIiJTgr41d4oDbgM1mtsnb9gjQ08zu8x4vAv4G4JzbamZvANuAAuC+8pJpERHxv+OnClm27RtSUrNZvfMgzsFlkW25Z+T3uHZAZ1rW55IOEZEK0tbjIiJSKc45UjOPkJKazduf7yXvZAFd2zRn8qCuTI4No3u7BlLSISJSgrYeFxGRatt99DiLN2STkppNxqF8mjcN5LoBFzM5titDI1XSISKNlxJqEREpU/6pAt7b6inpWJt+COdgaI+23Df6Eq4dcDGhQfpnREREfxOKiMhZnHP8O+MIKam7eHfzN+SdLKBb2+b8bExPJg8Ko1vbEH+HKCJSpyihFhERAHYdzi9epSPrcD4tmgVyfdTFTB4UxpCItirpEBEpgxJqEZFG7NuTBfxjyzekpO7ik68OAzD8e+34+VU9Gde/MyHN9M+EiMiF6G9KEZFGpqjIsS7jMCmp2by7eS/5pwrp3i6EB66+lJsGdSXsIpV0iIhUxgUTajPrBPwW6OKcu9bM+gLDnHNzfR6diIjUmKxD+SzckM3CDdlkHzlOaFATJkR3ISE2jNjuF+HZj0tERCqrIjPU8/BsvjLbe/wl8DqghFpEpI7LO1nAu5v3kpKazbqvD2MG8Ze0Z+Y1vRjbtzPNmwX6O0QRkXqvIgl1e+fcG2b2MIBzrsDMtIOhiEgdVVTk+OSrQ6SkZvOPLd9w/HQhPdq3YOY1vZg0sCtd2jT3d4giIg1KRRLqb82sHeAAzGwokOPTqEREpNIyDn7Lwg3ZLNqwm91Hj9MyqAkTB3YlITaMQeFtVNIhIuIjFUmoHwCWAt8zszVAByDBp1GJiEiF5J44zTuf72Xhhmz+nXGEAIP4nh345bW9Gdu3E8FNVdIhIuJr5SbUZhYIXOH96gUY8IVz7nQtxCYiIqUoLHKsTT/IwtRs/rn1G06cLuJ7HVrw0Lhe3DQwjM6tg/0doohIoxJQ3knnXCFwo3OuwDm31Tm3Rcm0iMiFJSdDRAQEBHhek5Or1qekrw7k8fR7acT/fgW3zV3HirT9JMSG8eZ9cSx/4Aqmj7pEybSIiB9UpORjjZn9Cc/KHt+eaXTObfBZVCIi9VhyMkybBvn5nuPMTM8xwNSpFe8DkHPcU9KRkrqLDVlHCTC44tIO/Or6vozp01ElHSIidYA558rvYPZBKc3OOXelb0KquMGDB7v169f7OwwRkbNERHgS5HN17w4ZGRfuk/6VY/XOg6SkZvPe1m84VVBEz46hJMSGMWlgVzq20iy0iEhtM7NU59zg0s5dcIbaOTe65kMSEWm4srIu3F5anybtcsmJ2M3wp7LZd+wkrZs35ebB3ZgyOIwBXVtrlQ4RkTqqIjsltgYeB0Z6mz4EnnTOaek8EZFShIeXPvscHn5+n4Cg04T02UPogGyCuhyFIqN/lw7MuSGMK/t0JKiJSjpEROq6itRQvwxsAb7vPb4Nz86JN/kqKBGR+iwx8ez6aICQEE87QEFhEbf/8iBJy7MJityHNSni1IGW5K3uw29+3IV77lRJh4hIfVKRhPp7zrnJJY6fMLNNvgpIRKS+O/NQ4ezZntKO8HBPMj3kqlx+9242izbu5kDuSdr2bkr+9nD2fhzGxUGteDrRznogUURE6oeKJNTHzSzeObcawMzigOO+DUukjkhOPj8rUsYjFTB1qufraP4pln62h9dTs5n9XA5NAozRvTsyeVAYV/buSLMm5a5eKiIi9UBFEup7gfneWmqAI8CdPotIpK6o6LpmIuc4XVjEqi8PsHBDNsu37edUYRF9L27FY+P7MiGmC+1Dg/wdooiI1KALLptX3NGsFYBz7phPI6oELZsnPlWRtc9ESkj75hgp67N5c9MeDuadpF2LZtwY05XJsV3p16X1hQcQEZE6q1rL5pnZb4E/OOeOeo8vAv7DOfermg1TpI6pyNpn0ugd/vYUSzbtZuGGbLbsPkbTQOPK3h1JiO3GqF4daBqokg4RkYauIiUf1zrnHjlz4Jw7YmbXAUqopWGryNpn0iidLizig7T9LNyQzYq0/ZwudPTv2oo5N/RlQkxX2rZo5u8QRUSkFlUkoQ40syDn3EkAM2sOqABQGr4LrX0mjc7WPTksTN3Nkk27OfTtKdqHBnHHsAgSBofRu3Mrf4cnIiJ+UpGE+v+A983sb4AD7gLm+zQqkbqgrLXP9EBio3Iw7yRLNu0hJTWb7Xs9JR1X9+3E5EFhjLxUJR0iIlLBhxLNbBxwFWDAMufce74OrCL0UKKI+MKpgiJWpO0nJTWblV/sp6DIER3WmsmxYdwQ1YWLVNIhItLoVPehxBZ4kuh/mlkvoJeZNXXOna7pQEVE/MU5x9Y9x0hJzWbJpt0cyT9Nx5ZB/Cg+ksmxYVzaqaW/QxQRkTqqIiUfq4AR3tU9lgPrgZsB/d5bROq9A7knWbJpNymp2aR9k0uzwACu7teJhNgwRlzSniYq6aiWhr43UvLmZGa/P5usnCzCW4eTOCaRqQMa0BsUkQqpSEJtzrl8M/sR8F/OuT+Y2UZfByYi4isnCwp5f/t+FqZms/LLAxQWOWK6teE3E/tzQ1QXWoc09XeIDUJD3xspeXMy096aRv5pzxvMzMlk2lueN6ikWqRxuWANtTd5ng48B/zIObfVzDY75wbURoDlUQ21iFSUc47Ps3NYuCGbJZv2kHP8NJ1aBTFpYBgJsWFc0jHU3yE2OA19b6SIP0aQmXP+G+zeujsZP8+o/YBExKeqVUMN/Ax4GFjsTaZ7AB/UZIAiIr6y/9gJFm/0lHTs2J9HUJMArunXmYTYMOIuaU9ggPk7xAaroe+NlJVT+hspq11EGq4LJtTOuVV46qjPHH8FzLjQdWbWDXgF6AwUAUnOuefNLAZ4CQgGCoDpzrl1ZhYBbAe+8A7xiXPunkq9GxER4MTpQpZv30dKajarvjxAkYPY7hfxu5sGcH3UxbQKVklHbWjoeyOFtw4vdYY6vHUDeYMiUmEVmaGuqgI8W5RvMLOWQKqZ/Qv4A/CEc+4f3h0X/wCM8l6T7pyL8WFMItJAOefYtOsoKanZvPXZHo6dKODi1sFMH3UJNw3qSo8ONVjS0dCftKshDX1vpMQxiWfVUAOENA0hcUwDeYMiUmE+S6idc3uBvd7vc81sO9AVz+YwZ7YUaw3s8VUMItLwfZNzpqRjF+kHviW4qaekY0psN4Z9r13Nl3Q09CftalBD3xvpzIOHWuVDRCq0sUu1b+Ip51gF9MeTVL+HZ5OYAGC4cy7T22cr8CVwDPiVc+6jUsaaBkwDCA8Pj80s7feJItKgnThdyHtbv2Hhht2s3uEp6RgScREJsWFcN+BiWvqypKOhP2nXkOk3CyJSDeU9lFjmAqtm1tbMHjOzH5vHbDN728ye9q5JXdGbhwILgZ87544B9wK/cM51A34BzPV23QuEO+cGAg8AfzezVueO55xLcs4Nds4N7tChQ0XDEBE/SE725J8BAZ7X5OSqj+WcIzXzMA8v2syQ3yznZ69tIn1/HvePvoSVD45iwT3DuXlIuG+Taai1J+1q8mcnfPebhcxMcO673yzoBysiNaDMGWozexfYjKc8o4/3+zeAq4Fo59yNFxzcrCnwNvCec+5Zb1sO0MY558zMgBzn3HmJs5mtBB50zpW5Lp6WzROpu86tjABP/WxSUuUmBfccPV68SsfXB7+ledNArh3gWaVjaGQ7Amp7lY5amKGuqZ+dlKDfLIhINZU3Q11eQr3JORfjTXqznXNdzz13gZsaMB847Jz7eYn27cC9zrmVZjYG+INzLtbMOnj7FnqX5vsIGOCcO1zWPZRQi9Rd1clfjp/ylHSkpGazJv0gzsHlkW1JiA3j2gEXExrky+epL6AWsl1/534Ncve/gADPzPS5zKCoqPbjEZF6p6rrUAd4SztaAqFmFuGcyzCzdkCzCtw3DrgN2Gxmm7xtjwA/AZ43sybACbz10MBI4EkzKwAKgXvKS6ZFpG6rbGWEc471mUdIWZ/NO5v3kneygLCLmjPjyp5MHhRGeLsQ3wVbGbXwpJ0/129usLv/NfQ1/ETEr8qbob4F+KP3cDqe2mcH9MWz7F1SrURYDs1Qi9QNpc1ozr5haoVmWbOP5LNow24Wbsgm81A+Ic0Cubb/xUwZHMZlEW1rv6SjDvDnDHWD3f1PdTQiUk1VmqF2zr1qZm/gSboLzGwJEAPs9i6JJyJS5ozmHfd0Z/6v40tdgzj/VAH/2Owp6fj4q0MADOvRjp9e2ZNr+3emhT9LOuoAf67f3GB3/2voa/iJiF/VyrJ5vqIZahH/K29GM7FDRon8xfHjhw+T0zabdzfv5dtThYS3DSEhNoxJA7vSrW0dKemoI/y1wluDnaEWEammKj2UWB8ooRbxv4AnAnCc//eIYRQ9XsSuw/ks3JDNwg3Z7Dp8nNCgJlw3oDMJsd0YEnERnueXpa449zcO4Nn9L+mGpPpdQy0iUk1VfShRROSCwluHnzejaa454c0mcPNfP+bTrw9jBnHfa88DV1/KNf06E9JMf/XUVdr9T0Sk8io0Q21mgUAnSiTgzjm/F9Rphlqk+qpbWlA8o3nqOMFFA2hROIYWhXEYwUS2b0FCbBgTB3ala5vmvnsTIiIiPlatGWoz+ynwOLAPOLNYpwOiaizC+kRb10oDcu7CB2c2j4OK/7GOu3gi3w8P5oNt+VDUFuw4gy8xHr56GIPCG2ZJR4Ncp1lERKrsgjPUZrYTuNw5d6h2Qqq4Wp+h1rJL0sBUdXm23BOneXfzXlJSs/l3xhHMIP6S9iTEhnFNv84ENw30Vch+pxpjEZHGqVoPJZrZB8DVzrkCXwRXHbWeUPt7+zKRGlaZzeMKixwfpx8iJXUX/9z6DSdOF9GjQ4viVToubt04Sjq0CoaISONUpZIPM3vA++1XwEozewc4eea8c+7ZGo2yPvDn9mUiPlCRzeO+PvgtC1OzWbQhmz05J2gZ3ISbBoWREBvGwG5tGmRJR3ka7DrNIiJSZeXVULf0vmZ5v5rx3Zbj9XetverQ1rVSh1WlvL+sDUQeffI0r67zlHSkZh4hwGDkpR14+Lo+XN23U4Mu6biQ0lY1OdMuIiKNU3k7JT4BYGZTnHMLSp4zsym+DqxO8uf2ZSLlqOrDhWdtHrfLET7kIFE3ZvOHL7/h5LYiLukYyqxrezNpYFc6tQr27ZuoJxLHJJZaQ504Rn8PiIg0VhWpod7gnBt0oTZ/8MuyeVrlQ+qg6pT3px/I85Z07OabYydo3bwpE6K7MDk2jOiw1o2upKMitMqHiEjjU6WHEs3sWuA64PvA6yVOtQL6Oucuq+lAK0vrUIt4VObhQoCc/NO89fkeFm7IZmPWUQIDjJE925MQ240xfTo26pIOERGR0lR1Heo9wHpgApBaoj0X+EXNhSci1VWR8v6CwiI+2nmQhanZLNu2j1MFRfTq1JLZ1/XhxoFd6NhSJR0iIiJVUV4N9WfAZ2b2d+fc6VqMSUQqqbzy/h37cknZkM3iDbvZn3uSNiFNufWycCYPCqN/11Y1VtKhaigREWmsLrhTIhBhZr8D+gLFU1jOuR4+i0pEKuWshwuzIPySU9xw/x7eOJLN7OdyCAwwRvfqQEJsGKN7dySoSc2WdNTEjosiIiL1VUUeSlyNZ+vx54AbgP/nve5x34dXPtVQS33li9ncgsIiVu04QEpqNsu37edUYRG9O7ckITaMG2O60qFlUM0EXwrteSQiIg1dVWuoz2junHvfzMw5lwnMMbOP8CTZIlJJNT2b+8U3uaSk7mLxxj0czDtJ2xbNmDr0TElH65oLvBza80hERBqziiTUJ8wsANhhZvcDu4GOvg1LpOGaPfvsWmfwHM+eXfGE+si3p1j62R5SUrPZvDuHJgHG6N4dmRIbxqheHWnWJKDmAy+H9jwSEZHGrCIJ9c+BEGAG8GvgSuAOXwYl0pCcW95RWuIJF57NPV1YxMovDrAwNZv30/ZxutDRr0srHhvflxtjutAu1HclHReiPY9ERKQxu2BC7Zz7t/fbPDz10yJSQaWVd5iVvmZ0WbO52/ceIyU1myWbdnMw7xTtQ5tx+7AIJg8Ko2+XVr4LvhLOeyhSq3yIiEgjUmZCbWZLy7vQOTeh5sMRaVhKK+9w7vyk+tzZ3EN5J1myybPxytY9x2gaaIzp3YmE2DCu6NWBpoG1W9JREVOnKoEWEZHGqbwZ6mHALuBV4FNA+w+LVFJZZRzOeVbAODObe911MPvRIqY9uZ8OQ7IJCNtPEY4BXVvzxIR+TIjuwkUtmtV4fFo7WkREpPrKS6g7A1cDtwC3Au8ArzrnttZGYCINQVk102eWk3PO8czcYzydkk3Q+D10CDlFYV4Q+RsimXVzGA/8qKXPYtPa0SIiIjXjgutQA5hZEJ7E+mngSefcf/k6sIrQOtRS152btIKnvOPZP58k8Hu7SUnNJu2bXFxBAPk7O5K3uRsnvm4PLsDnazjXlbWjNUsuIiL1QZXXofYm0tfjSaYjgBeARTUdoEhDddbDersLCb98P72vzeYPXx6gMM0R3a0Nh5f149vtXSg6cXZJh6/XcK4La0drllxERBqCMp9sMrP5wFpgEPCEc26Ic+7XzrndtRadiJ8lb06m/Q9nYG0yMCuifZc8kpMrfr1zjqgrchh63xbC738f4jeQtj+H4W0j+dcvRrLkvjjaHo44L5kG36/hXNb4tbl2dHlrcouIiNQX5S0VcBtwKfAzYK2ZHfN+5ZrZsdoJT6R2JSd7SiECAqB9lzxu/3Eeh974HeREAAEc2hvKD3/oMPP0mz7d82oGTZpQ3P6XeSdIWpXOuD9+xA1/Ws3H+3bx7Vft2ffGEDL/60oWPd6Hdcs99dGJiZ4ykJJqYw1nf923JH/Nkpf8nCMiqNR/kkRERM5VoRrquko11FKTSqt3hiLK/39nCYGFhFyynxYDdtE88gAWAAPD27BxYRi71nSh6GTTs7qXrFX2Vx2xv+uX/VHHXVZde1KSykxERKRs5dVQK6EW8SoruSufo9nFOYT230VI3z0EBhdQkBvMt1u60vpwGBmbQwkIKH0jFzMoKqqBwOsxfyS3deVhTBERqV+q/FCiSGNSmTKDwNATtOi3mxb9s2nWPo+i0wEc/7IzeVvCOJHZHpyR4125vayl82qzVrmu8scOi3XhYUwREWlYlFCLeJWV+J4p+7AmhTTvuY/Q/tkER3hKOk5kX8Shfwzg27SLcaeanjceeBLE0mZha7NWuS6r7R0W9R8cERGpaT5LqM2sG/AKng1iioAk59zzZhYDvAQEAwXAdOfcOu81DwM/AgqBGf+/vXuPsrOu7z3+/s7kxiQkQAgQMmQSMJEaQiGJlkrxQnB5Fw+xKs6hWk9Nbb2AtdUjaetarUM93tG2tlOleuwU9SSgQG0VI61yVDhMiIkQFIEk7iSQG7mQ62Tme/7Ye2BnmJkk7NmzdzLv11qzZu/f8+xnvrN5hvnMk+/z+2Xmd6tVn9RXf8F3zLguTnrpN2kcPZPx5+2mYdwhDu0ax66fPo+nfj6NQ09O6PdY5YG5FldhNTD/wJEkDbWq9VBHxFRgamauiIiTgU7gjcDngM9m5r9HxGuAD2XmyyLiBRSXOX8RcDbwfWB2ZnYP9DXsodZQ671Jb8P2fUy7dAOnv7DA1oN7GDe6gddcMJVTdjRz099MZv36eHrJ8O98p3jFs7ERuruLvbgG5vpW65sxJUnHn5r0UGfmJmBT6fHuiFgDTAMSmFjabRKwsfT4SuDrmXkAeCwifkUxXP+kWjVK5fYd7GbCnMe57C8K3P2rrWTCuWefxofmn8er557FyeOKLR1/+Yc1LlQVG+42E0nSiW1YeqgjYgZwMXAPcB3w3Yj4FMX5yF5c2m0a8NOylxVKY32PtRhYDDDdpscRp2N1B0uWL2H9zvVMnzSdtoVttM597skoM+lc9yRLOwvcsWoTTx04RPOpJ/G+y2exaN40WiaPH8LqJUnSiajqgToiJgDLgOsyc1dEfAz4o0KQHAAAGalJREFUQGYui4g3A18GrgCin5c/qx8lM9uBdii2fFSvctWbjtUdLL59MXu7is2v63auY/HtpXWqV7Ue0z/hb9ixj1tXFFjaWWDttr00jWnk1RdMZdH8aVwyczINDf2djpIkSc9W1UAdEaMphumOzLylNPx2iqsvAvwf4EulxwXgnLKXN/NMO4hGsN5+13XrroZJl8LC6+HCmwHY27WXa//XPey7tfXpm8zWrSvedAaHh+q9Bw/xHz9/nGUrCvz4kW1kwiXnnsZ7L5/Fqy84i/FjnfRGkiQdu2rO8hEUrz6vyczPlG3aCLwU+E/gcuDh0vhtwL9GxGco3pQ4C7i3WvXp+HD4wh8NxSXAb/+n4sZSqN52y1/AvsNft3dvMYS/7W3JvY9tZ9mKAv+2ahN7DnYz/bQmrls4m6vmTeOc0/qsvS1JknSMqnlJ7lLgGmB1RKwsjV0PvAu4MSJGAfsp9UNn5gMR8U3gQYrT6b1nsBk+NDIsWdJ3KXCgazwsv6EYqFddDftOf9brGifuZUfzBl76yQLrt+9l/JhGXnvhVBbNa+ZFM0+j+PeeJElS5ao5y8fd9N8XDTB/gNe0Ac4GO8KVT2k24KyOO4s3pMYPPk6WTrMYfYim5z/OhAt+zbiW7ZDQfOpkPvCKWbxyzlk0jbGlQ5IkDT0ThupGRwdcey1s25YM/LdYyaT1tExqYd3OZsaes40Jcws0PX8TDWO66drexI4fzuZj75rGe3/flg5JklRdBmrVhcN7pQcP02PGHWLilV9k59ZLmfbu5YyaeJCeA6PYs+Zs9qxu5sCGU5k8OXjv7w9L6ZIkaYQzUKsu9NsrfZikYWw3U164lph3N2NPegl5qIf9jet48juXs29NC3moESguI33jjcNStiRJkoFatdF36ed16wbaMxk3fRvj5z3ElAueYl9XN11xgCcbv8qexrvoPmkrzLqaxsIn6NnR7DLSkiRp2BmoNewOb+8ohumIw29AHHXKHsbPLTBhzgZGTdpH9nTxxovP47Mr38KBhjWHd4VceDM9F36dno/2DOv3IUmSBAZq1UB/7R2Z0DC2i6bnb2L83ALjmp8ke2D/ryexa/9dfP795/OOi+dy8/q9rNv57GNOn+Qy9JIkqTYM1Bp269eXPYlkXMtWJlxQ4KTZj9MwuoeubePZec9Udk/5e5pf/H0+s7CN1rmtdHTAU5/4OWxqgknrn14xsWl0E20LnW1RkiTVhoFaw276dNiw+ykmXFBg/JwNjJq4n+79o2hY28ytn23monNOKS288qWnX/NMm8iE4kBpxcTJJ53OjR/+LVrn2jQtSZJqw0CtYbNzXxd3rNrI9HcWYO+OYkvHY1N48gcvgI1n0P7FRi4eoHNjoBUTJ9z9eVr/peqlS5IkDchArarq7kl+9PAWlnYW+N6DT3DwUA+zzpjAqyaez3/8wzS2/HJccWaOLw4+M8dhbSJHMS5JkjRcDNSqil9t3s3Szg3cen+BJ3Yd4JSm0Vz9wnNYNL+ZudMmFVs6rjv64w00td5070WUJEk1ZqDWkNm5t4vbVm1kWWeBlb/eQWND8LLZU/jo65tZ+BtnMHZU43M+dlvb4VPtQXEBlzbvRZQkSTVmoFZFDnX38KOHt7J0RYE7Sy0d7DiZJ1f8BqfuOpvL/2Icr5lb+dfpbQcpXwzGBVykKum78pI/bJI0KAO1npNfPrGbpZ0Fbr1/A1t2H+DUptHMnzSd7/xdM7vWTQSCXRSvKsPQ/C5ubR25v9PNNxo2/a28NJQ/yJJ0AoosX57uOLNgwYK87777al3GiPHknoPcvmojSzsLrCrsZFRD8PLzz2DRvGYuP/8MZj+vod8+55YWWLt22Ms9YfTNN1Bsd2lvN9+oCmbM6P+GBX+QJY1wEdGZmQv63Wag1mC6unv4q/YtfO3uAjl1MzGqh6njJvKuK5q58qKzmTxh7NP7NjQcvnx4rwjocVXw58x8o2HlD7Ik9WuwQN0w3MXo+PDQ47v42B0PcvFHf8DX1t1H92nb2X1/CxtvuoxVn7yMsetmHhamYeAZN4Z0Jo6OjmLCbGgofu7oGMKD1yenDNSwGpYfZEk6sdhDradt33OQb6/cwNLOAg9s3MXoxqBr7ZlsvreZfY9OgZ7i319dFPt5+7YbVH0mjhHa2+mUgRpWTqkjScfMlo8Rrqu7h7se2syyFQV+8NBmurqTOWdP5HfnN/OGi6Zx+sljjulff6t689wI7X2wh1rDzrtgJelZ7KEeYTpWd7Bk+RLW71zP9EnTaVvYRuvcw38ZPrBxJ8s6N/DtlRvYtucgp08Yy3+7+GwWzW/m/LMmPr1fXWXYEdzbab6RJKm2BgvUtnycYDpWd7D49sXs7Spezly3cx2Lby+2Rbxy5pv49sriLB1rNu1iTGMDV7ygOEvHS2dPYVTjs1vq6+pff0dw78NInjJQkqR6502JJ5gly5c8HaYByFHk/t9kydL1XHLDcv76jgcZ3Rj89ZVzuHfJQv6+dT4Lf+PMfsM0FENce3vxinRE8XPNWg3a2oppvpy9nc9Jpfd2jsB7QyVJGpBXqE8w63euh4QxeR7jDy1kfPdLaWQS3WznD18yk0Xzm5l95snHdMy6uTrqcolDotJ7O0fovaGSJA3IHuoTyObd+3nR59/DwafmMSZnkHSxt/EnPNW4nDNP3c7aDzxa6xJVByrti6+rvnpJkoaJPdQnsAOHulm+ZjNLOwv81y+3QM9VRMPDbBv1d+xt/CE9sYem0U20XdFe61JVJyqd19p5sSVJOpyB+jiUmawq7GRpZ4HbfraRnfu6OHPiWBa/5FwWzWvmnid2sGT5g+zZuZeWSS39zvKhkavSeztH8L2hkiT1y0B9HNm8az+33l9ceOXhzU8xdlQDr5xzFovmN/M7zzudxoYA4HlntBqgNaBKZ26pq5lfJEmqAwbqOre/q5vvr3mCpZ0FfvjLLfQkzJt+Cn9z1Vxee+FUJo4bXesSdZyp9N5O7w2VJOlw3pRYhzKTlb/ewdLOArf/bCO79h9i6qRxXDVvGovmNXPulAm1LlGSJGlE8abE48TjO/dzy/0FlnUWeGTLHsaNbuBVc87iTfPP4bfPm/x0S4ckSZLqh4G6xvZ3dfO9B4stHXc/XGzpeOGMU3nXZefy2guncrItHZIkSXXNQH2MOjoq7x3NTFasf5KlnRu4Y9VGdu8/xLRTTuK9L38eV81rZsbp46tTvCRJkoZc1QJ1RJwD/G/gLKAHaM/MGyPiG8DzS7udAuzIzIsiYgawBvhFadtPM/Pd1arvuah0hbiNO/Y9PUvHY1v3cNLoRl499yzeNL+ZS2ZOpsGWDkmSpONONa9QHwI+mJkrIuJkoDMi7szMt/TuEBGfBnaWveaRzLyoijVVZMmSw6cKg+LzJUsGDtT7Dnbz3QceZ2lngf/7yFYy4bdmnsYfvew8XjN3KhPGVuE/wVBcRpckSdJRqVqgzsxNwKbS490RsQaYBjwIEBEBvBm4vFo1DLWjXSEuM/l/a59kWWeBf1u9iacOHKL51JN4/+WzWDSvmemTm6pXZKWX0SVJknRMhqWHutTOcTFwT9nwZcATmflw2djMiLgf2AX8eWb+aDjqO1pHWiGu8OReblmxgWUrCqzbtpemMY28du5UFs1v5kUzThuelo7nchldkiRJz1nVA3VETACWAddl5q6yTVcDN5c93wRMz8xtETEf+FZEzOnzGiJiMbAYYPowr3Xc1gbv/INDHNz/zNs2dsJ+3vSnW3nbPxX48SPbAHjxeZN5/+WzeNUFZzG+Gi0dgznay+iSJEkaElVNexExmmKY7sjMW8rGRwFXAfN7xzLzAHCg9LgzIh4BZgOHrdySme1AOxQXdqlm/c9yYQf5+u/D9/6SsRMnMGHeL2mavYWlhdG0TG7iT14xm6vmTaP51Cq2dBzJkS6jS5IkaUhVc5aPAL4MrMnMz/TZfAXwUGYWyvafAmzPzO6IOBeYBTxarfqeiyXLl9A1Zx0Tnr+ZyV1/TA972dP4I06e9AD/+affp/gt11hb2+E91ABNTcVxSZIkDbmGKh77UuAa4PKIWFn6eE1p21s5vN0D4CXAqoj4GbAUeHdmbq9ifcds/c5i28Texh+zdfSnKIy7hu1jvsD6fXdVNUx3dMCMGdDQUPzc0THIzq2t0N4OLS0QUfzc3n5C9E8//T5EMmNUgY5oPYo3RJIkqboic3i7JobSggUL8r777jvyjkNkxudmsG7ns9spWia1sPa6tVX5mn0n7YDiBecTJCMftX7fB/bQzrtobfr2yHtDJEnSsIqIzsxc0N+2al6hPuG0LWyjafTh/dFNo5toW1i9dorBJu0YSfp9HxjPEm4YmW+IJEmqGwbqY9A6t5X217fTMqmFIGiZ1EL769tpnVu9K6NO2lE04PvA9MF3kCRJqrJhntPt+Nc6t7WqAbovJ+0oGvB9YP0zO0iSJNWAV6jrXFtbsWe63EictKPf94E9tHH9yHxDJElS3TBQ17kTeNKOY3LY+0DS0ligncW0tvx4ZL4hkiSpbjjLhyRJknQEzvIhSZIkVYmBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSaqAgVqSJEmqgIFakiRJqoCBWpIkSapA1QJ1RJwTEXdFxJqIeCAiri2NfyMiVpY+1kbEyrLXfCQifhURv4iIV1arNkmSJGmojKrisQ8BH8zMFRFxMtAZEXdm5lt6d4iITwM7S49fALwVmAOcDXw/ImZnZncVa5QkSZIqUrUr1Jm5KTNXlB7vBtYA03q3R0QAbwZuLg1dCXw9Mw9k5mPAr4AXVas+SZIkaSgMSw91RMwALgbuKRu+DHgiMx8uPZ8G/Lpse4GyAC5JkiTVo6oH6oiYACwDrsvMXWWbruaZq9MA0c/Ls5/jLY6I+yLivi1btgxtsZIkSdIxqmqgjojRFMN0R2beUjY+CrgK+EbZ7gXgnLLnzcDGvsfMzPbMXJCZC6ZMmVKdwiVJkqSjVM1ZPgL4MrAmMz/TZ/MVwEOZWSgbuw14a0SMjYiZwCzg3mrVJ0mSJA2Fas7ycSlwDbC6bGq86zPzOxRn8yhv9yAzH4iIbwIPUpwh5D3O8CFJkqR6V7VAnZl3039fNJn5jgHG24C2atUkSZIkDTVXSpQkSZIqYKCWJEmSKmCgliRJkipgoJYkSZIqYKCWJEmSKmCgliRJkipgoJYkSZIqYKCWJEmSKmCgliRJkipgoJYkSZIqYKCWJEmSKmCgliRJkipgoJYkSZIqYKCWJEmSKmCgliRJkipgoJYkSZIqYKCWJEmSKmCgliRJkipgoFb96eiAGTOgoaH4uaOj1hVJkiQNaFStC5AO09EBixfD3r3F5+vWFZ8DtLbWri5JkqQBeIVa9WXJkmfCdK+9e4vjkiRJdchArfqyfv2xjUuSJNWYgVr1Zfr0YxuXJEmqMQO16ktbGzQ1HT7W1FQclyRJqkMGatWX1lZob4eWFogofm5v94ZESZJUt5zlQ/WntdUALUmSjhteoZYkSZIqYKCWJEmSKmCgliRJkipgoJYkSZIqYKCWJEmSKmCgliRJkipQtUAdEedExF0RsSYiHoiIa8u2vS8iflEa/0RpbEZE7IuIlaWPf6hWbZIkSdJQqeY81IeAD2bmiog4GeiMiDuBM4ErgQsz80BEnFH2mkcy86Iq1iRJkiQNqaoF6szcBGwqPd4dEWuAacC7gI9n5oHSts3VqkGSJEmqtmHpoY6IGcDFwD3AbOCyiLgnIv4rIl5YtuvMiLi/NH7ZcNQmSZIkVaLqS49HxARgGXBdZu6KiFHAqcAlwAuBb0bEuRSvZk/PzG0RMR/4VkTMycxdfY63GFhcevpURPyi2t/DceR0YGuti1Bd8xzR0fA80ZF4juhITsRzpGWgDVUN1BExmmKY7sjMW0rDBeCWzEzg3ojoAU7PzC1AbxtIZ0Q8QvFq9n3lx8zMdqC9mnUfryLivsxcUOs6VL88R3Q0PE90JJ4jOpKRdo5Uc5aPAL4MrMnMz5Rt+hZweWmf2cAYYGtETImIxtL4ucAs4NFq1SdJkiQNhWpeob4UuAZYHRErS2PXAzcBN0XEz4GDwNszMyPiJcBfRcQhoBt4d2Zur2J9kiRJUsWqOcvH3UAMsPm/97P/MortIXrubIXRkXiO6Gh4nuhIPEd0JCPqHIliK7MkSZKk58KlxyVJkqQKGKhPIBHRWJrH+45a16L6ExGnRMTSiHgoItZExG/XuibVl4j4QEQ8EBE/j4ibI2JcrWtS7UXETRGxuXTvU+/YaRFxZ0Q8XPp8ai1rVG0NcI58svT7ZlVE3BoRp9SyxmozUJ9YrgXW1LoI1a0bgf/IzPOB38RzRWUiYhrwfmBBZl4ANAJvrW1VqhNfAV7VZ+x/AsszcxawvPRcI9dXePY5cidwQWZeCPwS+MhwFzWcDNQniIhoBl4LfKnWtaj+RMRE4CUUp7IkMw9m5o7aVqU6NAo4qbQAVxOwscb1qA5k5g+BvrNuXQl8tfT4q8Abh7Uo1ZX+zpHM/F5mHio9/SnQPOyFDSMD9Ynjc8CHgJ5aF6K6dC6wBfjnUlvQlyJifK2LUv3IzA3Ap4D1FFeu3ZmZ36ttVapjZ2bmJoDS5zNqXI/q2zuBf691EdVkoD4BRMTrgM2Z2VnrWlS3RgHzgC9m5sXAHvwnWpUp9cBeCcwEzgbGR8SzpjiVpGMREUuAQ0BHrWupJgP1ieFS4A0RsRb4OnB5RPxLbUtSnSkAhcy8p/R8KcWALfW6AngsM7dkZhdwC/DiGtek+vVEREwFKH3eXON6VIci4u3A64DWPMHnaTZQnwAy8yOZ2ZyZMyjeRPSDzPTKkp6WmY8Dv46I55eGFgIP1rAk1Z/1wCUR0RQRQfEc8cZVDeQ24O2lx28Hvl3DWlSHIuJVwIeBN2Tm3lrXU23VXHpcUn15H9AREWOAR4Hfr3E9qiOZeU9ELAVWUPzn2fsZYSudqX8RcTPwMuD0iCgAHwU+DnwzIv4HxT/Gfrd2FarWBjhHPgKMBe4s/o3OTzPz3TUrsspcKVGSJEmqgC0fkiRJUgUM1JIkSVIFDNSSJElSBQzUkiRJUgUM1JIkSVIFDNSSJElSBQzUktSPiMiI+FrZ81ERsSUi7igbe2NErIqIhyJidUS8sWzbVyJiQ0SMLT0/vbSaKRExIyL2RcTKso/fi4h/jYg/KjvGb5WO3++aARGxtvR1fxYR34uIs/qM9x7782U1PVYa+1lELDzCe/C6iLi/tO+DEfGHEbGk7LjdZY/fXzr+m/oc46k+3/P9EbEmIu4traLWu987Su9v+XvygtLrMiLeV7bv35b2/7vSfg/2eT8Pq0GSqs2FXSSpf3uACyLipMzcB7wC2NC7MSJ+E/gU8IrMfCwiZlJcwODRzFxV2q0beCfwxX6O/0hmXlQ+EBHfBX5SWmBlG/C3wB9n5qFB6nx5Zm6NiBuA64H3l4/3s/+fZebSiHg5xYVbZvV30IgYXdr+oswslP4wmJGZvwDaSvs8Vf49RMRXBqmz93u+uLTvucAtEdGQmf9c2v6NzHxvnzpmUFzW+tqI+MfMPNi7LTPfU7bPHX3fT0kaLl6hlqSB/Tvw2tLjq4Gby7b9KXBDZj4GUPr8N8Cfle3zOeADA11h7iszn6AY0j8BvBtYlZl3H2WtPwSed5T7AvwEmDbI9pMpXnTZVqrtQClMD4nMfBT4E575A2AwW4DlPLPUtSTVFQO1JA3s68BbI2IccCFwT9m2OUBnn/3vK433Wg/cDVzTz7HP69PecFlp/B+AF1AM5h86hlpfB6wue35X2bE/0M/+rwK+NdDBMnM7cBuwLiJujojWiBjq3xkrgPPLnr+lz3tyUtm2jwMfjIjGIa5Bkipmy4ckDSAzV5XaCa4GvtNncwB5FGM3UAym/9Zn/FktH6Wv2RMR/wgsyMxtR1HmXRHRDawC/rxsfKCWj09GxCeAM4BLBjtwZv5BRMwFrqB4Rf4VwDsGe8lRjvWKPs/7a/noreWxiLgXeNtgNUtSLXiFWpIGdxvFNoyb+4w/ACzoMzYPeLB8IDN/BawE3nwMX7On9HE0Xp6ZF2Xm72XmjqPY/88otob8OfDVI+2cmasz87MUw/SiI+y+DTi190lEnAb0F+p7XQysOVINZW4APoy/uyTVGf+nJEmDuwn4q8xc3Wf8U8BHSlewe2+Mux74dD/HaKN4hbcuZGYPcCPQEBGv7G+fiJgQES8rG7oIWHeEQ/8nxbaNMaXn7wDuGuD4Myi+h184yrLJzIco/sHyuqN9jSQNB1s+JGkQmVmgGD77jq+MiA8Dt5dmxOgCPpSZK/vZ94GIWEHxCnav8yKifN+bMvPzQ1h6bysIFG9u/L0+NWVEfIxin/Z3+3l9AB8qtZ/sozjryTsG+4KZeUdEzAc6S1/7EYo3V/Y6LyLuB8YBu4EvlM3wAcUw/jtlz/8Y2Njny7QB9w9WhyQNt8gcrL1NkiRJ0mBs+ZAkSZIqYMuHJNW5iLgHGNtn+Jp++rqf6/FvBWb2Gf5wZvbXCiJJ6sOWD0mSJKkCtnxIkiRJFTBQS5IkSRUwUEuSJEkVMFBLkiRJFTBQS5IkSRX4/zHk3XzBXmJ0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# for regression: (pick a single column to visualize results)\n",
"\n",
"# Results from this graph _should not_ be used as a part of your results -- it is just here to help with intuition. \n",
"# Instead, look at the error values and individual intercepts.\n",
"\n",
"col_name = 'MONEY_PER_STUDENT'\n",
"col_index = X1_train.columns.get_loc(col_name)\n",
"\n",
"f = plt.figure(figsize=(12,6))\n",
"plt.scatter(X1_train[col_name], y1_train, color = \"red\")\n",
"plt.scatter(X1_train[col_name], model.predict(X1_train), color = \"green\")\n",
"plt.scatter(X1_test[col_name], model.predict(X1_test), color = \"blue\")\n",
"\n",
"new_x1 = np.linspace(X1_train[col_name].min(),X1_train[col_name].max(),100)\n",
"intercept = model.predict([X1_train.sort_values(col_name).iloc[0]]) - X1_train[col_name].min()*model.coef_[col_index]\n",
"plt.plot(new_x1, intercept+new_x1*model.coef_[col_index])\n",
"\n",
"plt.legend(['controlled model','true training','predicted training','predicted testing'])\n",
"plt.xlabel(col_name)\n",
"plt.ylabel('Math 8 score')\n",
"plt.title('Prediction with 25% trained')\n",
"\n",
"col_name = 'MONEY_PER_STUDENT'\n",
"col_index = X2_train.columns.get_loc(col_name)\n",
"\n",
"f = plt.figure(figsize=(12,6))\n",
"plt.scatter(X2_train[col_name], y2_train, color = \"red\")\n",
"plt.scatter(X2_train[col_name], model.predict(X2_train), color = \"green\")\n",
"plt.scatter(X2_test[col_name], model.predict(X2_test), color = \"blue\")\n",
"\n",
"new_x2 = np.linspace(X2_train[col_name].min(),X2_train[col_name].max(),100)\n",
"intercept = model.predict([X2_train.sort_values(col_name).iloc[0]]) - X2_train[col_name].min()*model.coef_[col_index]\n",
"plt.plot(new_x2, intercept+new_x2*model.coef_[col_index])\n",
"\n",
"plt.legend(['controlled model','true training','predicted training','predicted testing'])\n",
"plt.xlabel(col_name)\n",
"plt.ylabel('Math 8 score')\n",
"plt.title('Prediction with 35% trained')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Summary </h2>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<WRITE A PARAGRAPH SUMMARIZING YOUR WORK AND FINDINGS\\>**"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nLooking at the 8 scatter plots in the first section, there looks to be a slight positive correlation between money\\ninvested in each student and their test scores. The average investment per student is a bit over $6 with a standard\\ndeviation of $1.73. Excluding the high outliers, the bulk of the investments range from $3(minimum) to $7.50, and\\nwithin that subsect, as the investments increase, the math scores do as well. The training model analyzed the data\\nin the same way. In both graphs with 25% and 35% of the data allocated towards training, respectively, the training\\npredicted the math scores to rise with the investment the same way the scatter plot showed. It's a limited analysis,\\nbut the correlation shows that, if possible, increasing the investment per student relates to an increase in their\\nmath scores.\\n\""
]
},
"execution_count": 325,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"Looking at the 8 scatter plots in the first section, there looks to be a slight positive correlation between money\n",
"invested in each student and their test scores. The average investment per student is a bit over $6 with a standard\n",
"deviation of $1.73. Excluding the high outliers, the bulk of the investments range from $3(minimum) to $7.50, and\n",
"within that subsect, as the investments increase, the math scores do as well. The training model analyzed the data\n",
"in the same way. In both graphs with 25% and 35% of the data allocated towards training, respectively, the training\n",
"predicted the math scores to rise with the investment the same way the scatter plot showed. It's a limited analysis,\n",
"but the correlation shows that, if possible, increasing the investment per student relates to an increase in their\n",
"math scores.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}