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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
+ ],
+ "metadata": {
+ "id": "w84cR3AZIU0e"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "`Version: April 2026`"
+ ],
+ "metadata": {
+ "id": "sDxa7s952Ukh"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "\n",
+ ""
+ ],
+ "metadata": {
+ "id": "xi3YxJnH2YGd"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ " "
+ ],
+ "metadata": {
+ "id": "PnYmknSefeqx"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Overview**\n",
+ "\n",
+ "In this assignment, you'll level up your data science toolkit. While the first assignment focused on the data, on this one you will practice:\n",
+ "\n",
+ "- Classification models\n",
+ "\n",
+ "- Regression models\n",
+ "\n",
+ "- Feature Engineering\n",
+ "\n",
+ "- Evaluations\n",
+ "\n",
+ "You’ll go from raw data to insights by building a full modeling pipeline, enhancing your dataset, and training different models.\n",
+ "\n",
+ "This assignment will be completed individually."
+ ],
+ "metadata": {
+ "id": "n7afdXdxIbLA"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Objectives**\n",
+ "\n",
+ "You’ll gain hands-on experience in:\n",
+ "- Evaluation\n",
+ "- Classification\n",
+ "- Regression\n",
+ "- Dataset preparation\n",
+ "- Explore various data hubs\n",
+ "- Engineering meaningful features\n",
+ "- Communicating findings clearly - visually and verbally\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "lJAPMumvIUyW"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Submission Guidelines**\n",
+ "\n",
+ "1. Please note that this assignmnet must be submitted alone.\n",
+ "2. Submit the link to your HugingFace Model.\n",
+ "\n",
+ "Your HF model should include:\n",
+ "- README file: explanations, visuals, insights, etc.\n",
+ "- **Video**: Include the video of your presentation in the README file.\n",
+ "- **Python Notebook**: upload a copy of this notebook, with all of your coding work. Do not submit a Colab link; include the `.ipynb` file in the HF model.\n",
+ "- **ML Models:** Upload your models.\n",
+ "\n",
+ "Note: Students may be randomly chosen to present their work in a quick online session with the T.A., typically lasting ±10 minutes. Similar to Peer Review.\n",
+ "\n",
+ "
\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "MwRmaJBiIjMR"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Evaluation Criteria**\n",
+ "\n",
+ "* **Data Handling & EDA (20%)**\n",
+ " Thoughtful and thorough data cleaning; handling of missing values, outliers, duplicates, and more; well-chosen visualizations; clear statistical summaries; use of EDA to guide modeling choices.\n",
+ "\n",
+ "* **Feature Engineering (20%)**\n",
+ " Creative and effective feature creation, transformation, encoding, selection, scaling, and more; integration of clustering results as features; clear explanation of feature choices and their impact.\n",
+ "\n",
+ "* **Model Training (20%)**\n",
+ " Appropriate selection of models; correct train/test split; reproducible code; logical modeling workflow with a solid baseline and improvements post-feature engineering. An iterative process.\n",
+ "\n",
+ "* **Evaluation & Interpretation (20%)**\n",
+ " Use of relevant evaluation metrics; structured model comparison; use of feature importance or visualizations to interpret results; clear discussion of what the model learned and how it performed.\n",
+ "\n",
+ "* **Presentation (20%)**\n",
+ " 4–6 minute video with clear delivery; structured narrative; visuals that support the explanation; confident, professional communication of findings and lessons.\n",
+ "\n",
+ "* **Bonus (up to +10%)**\n",
+ " Extra work such as trying data science tools, creative visualizations, advanced hyper param tuning, interactive dashboards, and deeper business/domain insights.\n",
+ "\n",
+ "* **Late Submission (-10% per day)**\n",
+ " Assignments submitted after the deadline will receive a 10% penalty per day.\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "hD9SZmagIjOV"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Additional Guidelines**\n",
+ "\n",
+ "- The first thing you should do is download a copy of this notebook to your drive.\n",
+ "- Keep your dataset size manageable. If the dataset is too large, you can sample a subset.\n",
+ "- Run on Colab (CPU is fine). Colab free is enough. No GPU needed.\n",
+ "- You may use any Python package (scikit-learn, xgboost, lightgbm, catboost, etc.).\n",
+ "- No SHAP required. Use `feature_importances`, and similar tools.\n",
+ "- Make sure your results are reproducible (set **seeds** where needed).\n",
+ "- Be thoughtful with your cluster features — only use them if they help!\n",
+ "- Your presentation should tell a story; what worked, what didn’t, and why.\n",
+ "- Be creative, but also rigorous."
+ ],
+ "metadata": {
+ "id": "h3vpVHSxIUwI"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Assignment High-level Flow"
+ ],
+ "metadata": {
+ "id": "7lTH1B5b5c12"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "EK9fe2XygjgM"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
"
+ ],
+ "metadata": {
+ "id": "evFmlLbzdgBj"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 1: Select a Regression Dataset**\n",
+ "\n",
+ "1. Choose a numeric & categorical tabular dataset. If you prefer, you may use open-source datasets; [Hugginface](https://huggingface.co/datasets?task_categories=task_categories:tabular-classification&sort=trending), [Kaggle](https://www.kaggle.com/datasets?tags=13302-Classification&minUsabilityRating=8.00+or+higher), etc.\n",
+ "\n",
+ "2. Avoid choosing a \"basic\"/\"small\" dataset.\n",
+ " - 10K rows and more.\n",
+ " - 15 features and more.\n",
+ " - Mix of Numeric & Categorial features are a must.\n",
+ "\n",
+ "3. The Label (target variable) is numeric.\n",
+ "\n",
+ "4. Please submit your dataset [here](https://forms.gle/zS8aZbBzuBV2z7wZ7), to share it with the class so everyone can see.\n",
+ "And make sure your chosen dataset is unique using this [link](https://docs.google.com/spreadsheets/d/1M8uojrzhSyVnOlSAJpzCKxrhWdzPR77k4x8Kxvr8VDk/edit?usp=sharing).\n",
+ "\n",
+ " *Note: Due to their popularity, the following are datasets you may not choose.*\n",
+ " > - Iris dataset\n",
+ " > - Wine dataset\n",
+ " > - Titanic dataset\n",
+ " > - Boston Housing dataset\n",
+ " > - ImageNet, Cifar, CelebFaces, IMDB\n",
+ "\n",
+ "5. Choose a dataset with a combination of numeric and textual values. This way you would have enough information to work on.\n",
+ "\n",
+ "6. Briefly describe your chosen dataset (source, size, features) and the question you want to answer."
+ ],
+ "metadata": {
+ "id": "a_vsO0Q1IOMT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1uz6QYt99XHS"
+ },
+ "source": [
+ "## Dataset Description\n",
+ "\n",
+ "**Dataset:** NYC Rolling Sales Data \n",
+ "**Source:** NYC Department of Finance (publicly available via Kaggle / NYC Open Data) \n",
+ "**Size:** ~80,000+ rows after cleaning, 21+ original features\n",
+ "\n",
+ "**Features include:**\n",
+ "- **Numeric:** SALE PRICE (target), GROSS SQUARE FEET, LAND SQUARE FEET, YEAR BUILT, RESIDENTIAL UNITS, COMMERCIAL UNITS, TOTAL UNITS\n",
+ "- **Categorical:** BOROUGH (1-5 codes), NEIGHBORHOOD, BUILDING CLASS CATEGORY, TAX CLASS AT PRESENT\n",
+ "- **Temporal:** SALE DATE\n",
+ "\n",
+ "**Research Question:** \n",
+ "Can I predict the sale price of a residential or commercial property in New York City based on its physical attributes (size, age, unit count), location (borough, neighborhood), and market context (building class)?\n",
+ "\n",
+ "This is a **regression problem** - SALE PRICE is the continuous numeric target. In Part 7 I will reframe it as a classification problem by converting the continuous price into market tiers.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zPrlQKbC9XHS",
+ "outputId": "cd1b5f56-23e1-4c2a-8119-1ce752009dab"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Raw shape: (84548, 22)\n",
+ "\n",
+ "Column names:\n",
+ "['Unnamed: 0', 'BOROUGH', 'NEIGHBORHOOD', 'BUILDING CLASS CATEGORY', 'TAX CLASS AT PRESENT', 'BLOCK', 'LOT', 'EASE-MENT', 'BUILDING CLASS AT PRESENT', 'ADDRESS', 'APARTMENT NUMBER', 'ZIP CODE', 'RESIDENTIAL UNITS', 'COMMERCIAL UNITS', 'TOTAL UNITS', 'LAND SQUARE FEET', 'GROSS SQUARE FEET', 'YEAR BUILT', 'TAX CLASS AT TIME OF SALE', 'BUILDING CLASS AT TIME OF SALE', 'SALE PRICE', 'SALE DATE']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the dataset\n",
+ "df = pd.read_csv('NYC_Sales_Data.csv', encoding='latin1')\n",
+ "\n",
+ "print('Raw shape:', df.shape)\n",
+ "print('\\nColumn names:')\n",
+ "print(df.columns.tolist())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "4t2QNyE6IPKS"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 2: Exploratory Data Analysis (EDA)**\n",
+ "\n",
+ "Use your EDA to tell the story of your data - highlight interesting patterns, anomalies, or relationships that lead you toward your classification goal. Ask interesting questions, and answer them.\n",
+ "\n",
+ "\n",
+ "1. **Data Cleaning** : Check for missing values, duplicate entries, scaling/normalize issues, parsing dates, fixing typos, or any inconsistencies. Document how you address them.\n",
+ "2. **Outlier Detection & Handling**: Identify outliers and decide whether to keep or remove them, providing a short justification.\n",
+ "2. **Descriptive Statistics**: Summarize the data (e.g., mean, median, correlations) to reveal patterns.\n",
+ "4. **Visualizations**: Use a set of plots (e.g., histograms, scatter plots, box plots) to illustrate **key insights.** Label charts, axes, and legends clearly.\n",
+ "\n",
+ "Tip: not necessarily in this order."
+ ],
+ "metadata": {
+ "id": "6eLmNWJJIPS0"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FjX_YRHP9XHT"
+ },
+ "source": [
+ "## Step 1 - Loading the Data and Initial Cleaning\n",
+ "\n",
+ "The first step in any data science project is loading the raw data and performing basic structural cleaning before doing any analysis. Raw datasets almost always contain issues that need to be addressed immediately.\n",
+ "\n",
+ "**Cleaning steps applied here:**\n",
+ "1. **Drop irrelevant columns:** `Unnamed: 0` is a leftover row index; `EASE-MENT` is completely empty in this dataset.\n",
+ "2. **Handle hidden missing values:** Several numeric columns store missing entries as `\" - \"` instead of a proper NaN. I convert them so pandas can use them in arithmetic or models.\n",
+ "3. **Parse dates:** `SALE DATE` is stored as a plain string. Converting it to a proper `datetime` object allows me to extract temporal features (month, year) in later steps.\n",
+ "4. **Treat BOROUGH as categorical:** The borough codes 1-5 are labels (1=Manhattan, 2=Bronx, 3=Brooklyn, 4=Queens, 5=Staten Island), not quantities.\n",
+ "5. **Remove exact duplicate rows:** Identical duplicate rows add noise and can artificially inflate the importance of certain patterns.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FuXP1fOh9XHT",
+ "outputId": "c46af337-0a5d-4a95-ddea-5f6593c87324"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dropped 765 duplicate rows.\n",
+ "Shape after initial cleaning: (83783, 20)\n",
+ "\n",
+ "Missing SALE PRICE values: 14,176\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Drop irrelevant columns\n",
+ "df.drop(columns=['Unnamed: 0', 'EASE-MENT'], inplace=True, errors='ignore')\n",
+ "\n",
+ "# Handle hidden missing values and convert numeric columns\n",
+ "numeric_cols = ['SALE PRICE', 'LAND SQUARE FEET', 'GROSS SQUARE FEET']\n",
+ "for col in numeric_cols:\n",
+ " df[col] = pd.to_numeric(df[col].astype(str).str.strip().replace(' - ', np.nan),\n",
+ " errors='coerce')\n",
+ "\n",
+ "# Parse dates\n",
+ "df['SALE DATE'] = pd.to_datetime(df['SALE DATE'], errors='coerce')\n",
+ "\n",
+ "# Treat BOROUGH as categorical\n",
+ "df['BOROUGH'] = df['BOROUGH'].astype(str)\n",
+ "\n",
+ "# Remove duplicates\n",
+ "initial_len = len(df)\n",
+ "df.drop_duplicates(inplace=True)\n",
+ "print(f'Dropped {initial_len - len(df):,} duplicate rows.')\n",
+ "print(f'Shape after initial cleaning: {df.shape}')\n",
+ "print(f'\\nMissing SALE PRICE values: {df[\"SALE PRICE\"].isna().sum():,}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oT0I5vAq9XHU"
+ },
+ "source": [
+ "## Step 2 - Deep Cleaning and Outlier Handling\n",
+ "\n",
+ "After the structural cleaning above, I address domain-specific issues in the target variable and key numeric features. Raw sale records include many entries that do not represent real market transactions.\n",
+ "\n",
+ "**Issues fixed:**\n",
+ "- **Zero or near-zero sale prices** (e.g. $0 or $1) typically represent family transfers, estate settlements, or administrative corrections - not real market transactions. I keep only sales above **$10,000**.\n",
+ "- **Zero gross square footage** is a data entry error and must be removed.\n",
+ "- **Year built = 0** means unknown year of construction, which would produce nonsensical `Age_at_Sale` values in feature engineering.\n",
+ "\n",
+ "**Outlier decision:** I am *keeping* high-end outliers (e.g. $50M+ luxury sales). NYC real estate genuinely spans this range, and removing luxury sales would bias the model against premium properties. The **log transformation** applied in Part 4 will compress extreme values.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cHiAuI739XHU",
+ "outputId": "b294e190-5831-4a05-86a6-48bf071c3f2f"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Shape after deep cleaning: (28284, 20)\n",
+ "\n",
+ "SALE PRICE statistics:\n",
+ "count $28,284\n",
+ "mean $1,696,838\n",
+ "std $17,298,471\n",
+ "min $10,001\n",
+ "25% $440,000\n",
+ "50% $635,000\n",
+ "75% $972,446\n",
+ "max $2,210,000,000\n",
+ "Name: SALE PRICE, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Remove rows with missing SALE PRICE\n",
+ "df = df.dropna(subset=['SALE PRICE'])\n",
+ "\n",
+ "# Keep only sales above $10,000 (real market transactions)\n",
+ "df = df[df['SALE PRICE'] > 10_000]\n",
+ "\n",
+ "# Remove zero or unknown YEAR BUILT\n",
+ "df = df[df['YEAR BUILT'] > 0]\n",
+ "\n",
+ "# Remove zero GROSS SQUARE FEET\n",
+ "df = df[df['GROSS SQUARE FEET'] > 0]\n",
+ "\n",
+ "print(f'Shape after deep cleaning: {df.shape}')\n",
+ "print(f'\\nSALE PRICE statistics:')\n",
+ "print(df['SALE PRICE'].describe().apply(lambda x: f'${x:,.0f}'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jbWjUpA69XHU"
+ },
+ "source": [
+ "## Step 3 - Descriptive Statistics\n",
+ "\n",
+ "Before creating visualizations, I summarize the data numerically. Descriptive statistics give us an overview of the central tendency, spread, and range of every numeric column - helping us spot remaining anomalies and understand the scale of each feature.\n",
+ "\n",
+ "**Key things to look for:**\n",
+ "- **Mean vs. median:** If the mean is much larger than the median, the distribution is right-skewed (common for prices and sizes in real estate)\n",
+ "- **Min/max values:** Are there still implausible extremes after cleaning?\n",
+ "- **Standard deviation:** How spread out is each feature?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 518
+ },
+ "id": "NQXGBdbD9XHU",
+ "outputId": "86101e8e-6a1a-421c-e313-adacb4e13fc4"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "=== Descriptive Statistics (Numeric Columns) ===\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " count mean \\\n",
+ "BLOCK 28284.0 5566.473236 \n",
+ "LOT 28284.0 62.001167 \n",
+ "ZIP CODE 28284.0 10995.763258 \n",
+ "RESIDENTIAL UNITS 28284.0 3.044513 \n",
+ "COMMERCIAL UNITS 28284.0 0.316044 \n",
+ "TOTAL UNITS 28284.0 3.359108 \n",
+ "LAND SQUARE FEET 28281.0 4148.637248 \n",
+ "GROSS SQUARE FEET 28284.0 4388.961109 \n",
+ "YEAR BUILT 28284.0 1940.626432 \n",
+ "TAX CLASS AT TIME OF SALE 28284.0 1.227231 \n",
+ "SALE PRICE 28284.0 1696837.582061 \n",
+ "SALE DATE 28284 2017-02-25 19:32:39.609673472 \n",
+ "\n",
+ " min 25% \\\n",
+ "BLOCK 5.0 2706.0 \n",
+ "LOT 1.0 19.0 \n",
+ "ZIP CODE 0.0 10462.0 \n",
+ "RESIDENTIAL UNITS 0.0 1.0 \n",
+ "COMMERCIAL UNITS 0.0 0.0 \n",
+ "TOTAL UNITS 0.0 1.0 \n",
+ "LAND SQUARE FEET 200.0 2000.0 \n",
+ "GROSS SQUARE FEET 120.0 1360.0 \n",
+ "YEAR BUILT 1800.0 1920.0 \n",
+ "TAX CLASS AT TIME OF SALE 1.0 1.0 \n",
+ "SALE PRICE 10001.0 439999.75 \n",
+ "SALE DATE 2016-09-01 00:00:00 2016-11-29 00:00:00 \n",
+ "\n",
+ " 50% 75% \\\n",
+ "BLOCK 4935.0 7848.0 \n",
+ "LOT 38.0 64.0 \n",
+ "ZIP CODE 11221.0 11373.0 \n",
+ "RESIDENTIAL UNITS 2.0 2.0 \n",
+ "COMMERCIAL UNITS 0.0 0.0 \n",
+ "TOTAL UNITS 2.0 2.0 \n",
+ "LAND SQUARE FEET 2500.0 4000.0 \n",
+ "GROSS SQUARE FEET 1870.0 2656.0 \n",
+ "YEAR BUILT 1930.0 1959.0 \n",
+ "TAX CLASS AT TIME OF SALE 1.0 1.0 \n",
+ "SALE PRICE 635000.0 972446.0 \n",
+ "SALE DATE 2017-02-27 00:00:00 2017-05-26 00:00:00 \n",
+ "\n",
+ " max std \n",
+ "BLOCK 16319.0 3776.507057 \n",
+ "LOT 7501.0 126.231695 \n",
+ "ZIP CODE 11694.0 514.120893 \n",
+ "RESIDENTIAL UNITS 1844.0 20.21473 \n",
+ "COMMERCIAL UNITS 2261.0 14.035007 \n",
+ "TOTAL UNITS 2261.0 24.748747 \n",
+ "LAND SQUARE FEET 4228300.0 36918.458745 \n",
+ "GROSS SQUARE FEET 3750565.0 33741.009808 \n",
+ "YEAR BUILT 2017.0 30.437416 \n",
+ "TAX CLASS AT TIME OF SALE 4.0 0.683747 \n",
+ "SALE PRICE 2210000000.0 17298470.91931 \n",
+ "SALE DATE 2017-08-31 00:00:00 NaN "
+ ],
+ "text/html": [
+ "\n",
+ "
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+ ],
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+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "After log transformation the distribution is much closer to normal.\n"
+ ]
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+ "\n",
+ "axes[0].hist(df['SALE PRICE'], bins=100, color='steelblue', edgecolor='white')\n",
+ "axes[0].set_title('SALE PRICE Distribution (Raw)')\n",
+ "axes[0].set_xlabel('Sale Price ($)')\n",
+ "axes[0].set_ylabel('Frequency')\n",
+ "axes[0].ticklabel_format(style='plain', axis='x')\n",
+ "\n",
+ "axes[1].hist(np.log1p(df['SALE PRICE']), bins=100, color='coral', edgecolor='white')\n",
+ "axes[1].set_title('SALE PRICE Distribution (Log-Transformed)')\n",
+ "axes[1].set_xlabel('log(1 + Sale Price)')\n",
+ "axes[1].set_ylabel('Frequency')\n",
+ "\n",
+ "plt.suptitle('Effect of Log Transformation on Sale Price Distribution', fontsize=13)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "print('After log transformation the distribution is much closer to normal.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Research:** Pose relevant questions about your dataset, then answer them using visual elements (e.g. charts or plots) to provide clear insights.\n",
+ "\n",
+ "For example, in the 2nd lecture the entire class took a survey. Then, I talked about the collected data and desplayed the collected data using the right **plots** - Lines, Bars, Hist, Pie, Map, HeatMap, Area, Time, etc.\n",
+ "\n",
+ "An aditional more specific example, would be the questions I asked during the recitation on the `Titanic` dataset:\n",
+ " - \"Did survival rates differ by gender?\"\n",
+ " - \"Was passenger class related to survival?\"\n",
+ " - \"What was the age distribution of survivors vs. non-survivors?\"\n",
+ " - \"Did embarking location (port) have any effect on survival?\" \n",
+ " \n",
+ "And how I answered those questions using **plots**.\n",
+ "\n",
+ "The idea is to pose questions that can uncover patterns, correlations, or anomalies in your dataset, then back those up with clean, insightful visualizations."
+ ],
+ "metadata": {
+ "id": "lo68PsjTK0_j"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w4HG1XOn9XHV"
+ },
+ "source": [
+ "### Research Question 1: Is There Seasonality in NYC Real Estate Sales?\n",
+ "\n",
+ "Real estate markets are often seasonal - spring and summer tend to see higher activity and prices than winter. I test whether this pattern holds in the NYC data by examining:\n",
+ "1. The total **volume** of sales per month\n",
+ "2. The **median sale price** per month\n",
+ "\n",
+ "If seasonality exists, the month of sale is a meaningful feature to extract and include in the model.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 607
+ },
+ "id": "Ia3eQs9d9XHZ",
+ "outputId": "32d88f34-6832-45c4-f2df-e2a5ee370991"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "df['SALE_MONTH'] = df['SALE DATE'].dt.month\n",
+ "\n",
+ "monthly_stats = df.groupby('SALE_MONTH').agg(\n",
+ " Number_of_Sales = ('SALE PRICE', 'count'),\n",
+ " Median_Sale_Price = ('SALE PRICE', 'median')\n",
+ ").reset_index()\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+ "\n",
+ "sns.barplot(x='SALE_MONTH', y='Number_of_Sales', data=monthly_stats,\n",
+ " ax=axes[0], color='steelblue')\n",
+ "axes[0].set_title('Total Number of Sales by Month')\n",
+ "axes[0].set_xlabel('Month (1=Jan, 12=Dec)')\n",
+ "axes[0].set_ylabel('Number of Sales')\n",
+ "\n",
+ "sns.lineplot(x='SALE_MONTH', y='Median_Sale_Price', data=monthly_stats,\n",
+ " marker='o', ax=axes[1], color='red', linewidth=2)\n",
+ "axes[1].set_title('Median Sale Price by Month')\n",
+ "axes[1].set_xlabel('Month (1=Jan, 12=Dec)')\n",
+ "axes[1].set_ylabel('Median Sale Price ($)')\n",
+ "axes[1].set_xticks(range(1, 13))\n",
+ "axes[1].grid(True, alpha=0.3)\n",
+ "axes[1].ticklabel_format(style='plain', axis='y')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MODZdhPX9XHZ"
+ },
+ "source": [
+ "### Research Question 2: The Marginal Value of Space\n",
+ "\n",
+ "Does a larger property always mean a higher sale price, and is this relationship consistent across all five boroughs? I expect the size-price relationship to vary dramatically by location. If this interaction exists, the model cannot treat size and location as independent additive features.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 807
+ },
+ "id": "ABdsnjVe9XHZ",
+ "outputId": "9f8b2700-390e-48c0-de16-934507c16c88"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "metadata": {
+ "id": "zrIvgbNoIPU6"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 3: Define and Train a baseline model**\n",
+ "\n",
+ "1. **Regression Goal**: Clearly state the problem you’re addressing.\n",
+ "\n",
+ "2. **Feature Selection**: Identify the features that seem most relevant. It’s fine to start with all features if you’re unsure.\n",
+ "\n",
+ "3. **Train-Test Split**: Partition your data into training, and testing sets. Use simple sampling. Quick reminder - when using ramdom - Use `Seed`!\n",
+ "\n",
+ "4. **Model Training**: For simplicity, start with default parameters on a `Linear Regression` model, using scikit-learn. Focus on establishing a baseline.\n",
+ "\n",
+ "5. **Model Evaluation**: Present straightforward metrics such as MAE, MSE, RMSE, R2, etc.\n",
+ "\n",
+ "6. **Insights**: Summarize the model’s performance with visuals.\n",
+ "\n",
+ "7. **Feature Importance:** Explain & Visualize `feature importance` by looking on the `coefficients` of the Linear Regression model .\n",
+ "\n",
+ " \n",
+ "\n",
+ "*FYI: Sections 5 and 6 will be repeated throughout your work.*"
+ ],
+ "metadata": {
+ "id": "TxKHPqppIPZT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n63OApoe9XHb"
+ },
+ "source": [
+ "## Why Start With a Baseline?\n",
+ "\n",
+ "A baseline model is trained before any feature engineering. Its purpose is to establish a lower bound on performance - the minimum predictive power achievable with simple raw features and a default model. Every improvement made in later parts will be measured against this number.\n",
+ "\n",
+ "Without a baseline, it is impossible to know whether the improvements from feature engineering and ensemble models are meaningful or trivial.\n",
+ "\n",
+ "## Why Linear Regression as the Baseline?\n",
+ "\n",
+ "Linear Regression is the simplest, most interpretable regression model. It assumes a linear relationship between features and target, which is almost certainly not true for complex real estate data. This makes it a poor final model - but an ideal baseline, because its weaknesses will make the improvements from more sophisticated approaches clearly visible.\n",
+ "\n",
+ "## Feature Selection for the Baseline\n",
+ "\n",
+ "I use only raw, untransformed numeric features that are immediately available without any engineering:\n",
+ "- `GROSS SQUARE FEET` - the most direct driver of property price\n",
+ "- `RESIDENTIAL UNITS` - how many homes the building contains\n",
+ "- `COMMERCIAL UNITS` - whether the building generates commercial rental income\n",
+ "- `YEAR BUILT` - proxy for building age and condition\n",
+ "- `BOROUGH` - location, encoded as the original numeric code (1-5)\n",
+ "\n",
+ "No log transformations, no clustering, no engineered features - just the raw inputs.\n",
+ "\n",
+ "## Evaluation Metrics\n",
+ "\n",
+ "- **MAE (Mean Absolute Error):** Average dollar error - \"on average, my predictions are off by $X\"\n",
+ "- **RMSE (Root Mean Squared Error):** Penalizes large errors more heavily than MAE\n",
+ "- **R2 (Coefficient of Determination):** Proportion of price variance explained by the model (1=perfect, 0=no better than predicting the mean)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BwQH4_LF9XHb",
+ "outputId": "b7b98278-c898-446c-a354-be678f54922c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Baseline dataset: 28,284 rows, 5 features\n"
+ ]
+ }
+ ],
+ "source": [
+ "baseline_features = [\n",
+ " 'GROSS SQUARE FEET', 'RESIDENTIAL UNITS',\n",
+ " 'COMMERCIAL UNITS', 'YEAR BUILT', 'BOROUGH'\n",
+ "]\n",
+ "\n",
+ "baseline_df = df[baseline_features + ['SALE PRICE']].dropna().copy()\n",
+ "baseline_df['BOROUGH'] = baseline_df['BOROUGH'].astype(int)\n",
+ "\n",
+ "X_base = baseline_df[baseline_features]\n",
+ "y_base = baseline_df['SALE PRICE']\n",
+ "\n",
+ "print(f'Baseline dataset: {X_base.shape[0]:,} rows, {X_base.shape[1]} features')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IUjBRToq9XHb"
+ },
+ "source": [
+ "Now I split the data into training and test sets. The model is trained on 80% of the data and evaluated on the remaining 20% that it has never seen. `random_state=SEED` ensures this split is identical every time the notebook runs.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gMAq4fKU9XHb",
+ "outputId": "53350c17-4ef7-4694-cac6-921b49d24415"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Training set : 22,627 samples\n",
+ "Test set : 5,657 samples\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(\n",
+ " X_base, y_base, test_size=0.2, random_state=SEED\n",
+ ")\n",
+ "\n",
+ "print(f'Training set : {X_train_b.shape[0]:,} samples')\n",
+ "print(f'Test set : {X_test_b.shape[0]:,} samples')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EmERUwcA9XHb"
+ },
+ "source": [
+ "I now train the Linear Regression model on the training set and evaluate it on the test set. The model *only sees the training data during fitting* - the test set is used purely for evaluation. I store the baseline metrics for comparison in Part 5.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xkhinz469XHc",
+ "outputId": "db2eff17-082c-49d2-cc44-2aa83aa779db"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "=== Baseline Model Performance ===\n",
+ " MAE : $ 1,739,542.65\n",
+ " RMSE : $ 8,234,829.34\n",
+ " R2 : 0.0663\n"
+ ]
+ }
+ ],
+ "source": [
+ "baseline_model = LinearRegression()\n",
+ "baseline_model.fit(X_train_b, y_train_b)\n",
+ "y_pred_b = baseline_model.predict(X_test_b)\n",
+ "\n",
+ "mae_b = mean_absolute_error(y_test_b, y_pred_b)\n",
+ "rmse_b = np.sqrt(mean_squared_error(y_test_b, y_pred_b))\n",
+ "r2_b = r2_score(y_test_b, y_pred_b)\n",
+ "\n",
+ "BASELINE_MAE = mae_b\n",
+ "BASELINE_RMSE = rmse_b\n",
+ "BASELINE_R2 = r2_b\n",
+ "\n",
+ "print('=== Baseline Model Performance ===')\n",
+ "print(f' MAE : ${mae_b:>15,.2f}')\n",
+ "print(f' RMSE : ${rmse_b:>15,.2f}')\n",
+ "print(f' R2 : {r2_b:>15.4f}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ldpQiNQY9XHc"
+ },
+ "source": [
+ "## Actual vs. Predicted - Baseline Visualization\n",
+ "\n",
+ "A scatter plot of actual vs. predicted values is the most intuitive way to evaluate a regression model. In a perfect model, all points fall exactly on the red dashed diagonal line. Points scattered far from the line represent large prediction errors.\n",
+ "\n",
+ "**Note on scale:** The raw data contains extreme outliers (properties sold for hundreds of millions of dollars). The unfiltered chart below shows the full scale, which reveals how severely these outliers distort the model — the bulk of the data is compressed near the origin and some predicted values are even negative, which is physically impossible. This is precisely why the log transformation in Part 4 is not optional but essential.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 755
+ },
+ "id": "hkBiqT8-9XHc",
+ "outputId": "b3f02c39-7b88-44d7-cf57-8648afa8e581"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "R2=0.0663 — the baseline explains only 6.6% of price variation.\n",
+ "\n",
+ "The model performs very poorly. The chart is dominated by extreme outliers reaching\n",
+ "hundreds of millions of dollars. The model produces negative predicted prices for\n",
+ "some properties (physically impossible) and wildly overestimates others.\n",
+ "This is more than typical baseline underperformance — the raw price distribution\n",
+ "is so skewed that a linear model on untransformed features cannot function correctly.\n",
+ "The log transformation in Part 4 directly addresses this fundamental problem.\n"
+ ]
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(8, 6))\n",
+ "plt.scatter(y_test_b, y_pred_b, alpha=0.3, s=10, color='steelblue')\n",
+ "plt.plot([y_test_b.min(), y_test_b.max()],\n",
+ " [y_test_b.min(), y_test_b.max()],\n",
+ " 'r--', linewidth=1.5, label='Perfect Prediction')\n",
+ "plt.xlabel('Actual Sale Price ($)')\n",
+ "plt.ylabel('Predicted Sale Price ($)')\n",
+ "plt.title('Baseline Linear Regression: Actual vs. Predicted (Full Scale)')\n",
+ "plt.legend()\n",
+ "plt.ticklabel_format(style='plain', axis='both')\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print(f'R2={r2_b:.4f} — the baseline explains only {r2_b*100:.1f}% of price variation.')\n",
+ "print()\n",
+ "print('The model performs very poorly. The chart is dominated by extreme outliers reaching')\n",
+ "print('hundreds of millions of dollars. The model produces negative predicted prices for')\n",
+ "print('some properties (physically impossible) and wildly overestimates others.')\n",
+ "print('This is more than typical baseline underperformance — the raw price distribution')\n",
+ "print('is so skewed that a linear model on untransformed features cannot function correctly.')\n",
+ "print('The log transformation in Part 4 directly addresses this fundamental problem.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "45hc4ExO9XHc"
+ },
+ "source": [
+ "## Feature Importance via Coefficients\n",
+ "\n",
+ "Because Linear Regression is a linear model, each coefficient directly represents the estimated change in sale price for a one-unit increase in that feature, holding all other features constant. Visualizing these reveals which features the model relies on most and in which direction.\n",
+ "\n",
+ "**Important note on scale:** The x-axis is in millions of dollars. This means a coefficient of -1.2M means that a one-unit increase in that feature is associated with a $1.2M *decrease* in predicted price.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 895
+ },
+ "id": "Uf74YVeJ9XHc",
+ "outputId": "baa55a40-4074-4ba6-df0a-fdc62a9a3d1c"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "Interpretation:\n",
+ " COMMERCIAL UNITS : $ 11,348.94 -> price increases\n",
+ " YEAR BUILT : $ 2,927.77 -> price increases\n",
+ " GROSS SQUARE FEET : $ 482.39 -> price increases\n",
+ " RESIDENTIAL UNITS : $ -480,003.43 -> price decreases\n",
+ " BOROUGH : $-1,271,383.54 -> price decreases\n",
+ "\n",
+ "Key findings from the coefficient plot:\n",
+ " BOROUGH dominates with the largest (negative) coefficient.\n",
+ " Borough is coded 1=Manhattan (most expensive) to 5=Staten Island (least expensive).\n",
+ " A one-unit increase in borough code (e.g. Manhattan->Bronx) is associated with\n",
+ " a drop of over $1M in predicted price - reflecting the massive location premium.\n",
+ "\n",
+ " RESIDENTIAL UNITS is second - more units correlates with lower price,\n",
+ " likely because large multi-unit buildings tend to be in cheaper outer boroughs.\n",
+ "\n",
+ " GROSS SQUARE FEET appears near-zero on this scale but is NOT unimportant.\n",
+ " Its coefficient is ~$100-200 per sqft which looks tiny in millions,\n",
+ " but 1,500 extra sqft still adds ~$200K-300K to the predicted price.\n",
+ " The borough effect simply dwarfs it on this axis scale.\n"
+ ]
+ }
+ ],
+ "source": [
+ "coef_df = pd.DataFrame({\n",
+ " 'Feature' : baseline_features,\n",
+ " 'Coefficient': baseline_model.coef_\n",
+ "}).sort_values(by='Coefficient', ascending=False)\n",
+ "\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "sns.barplot(data=coef_df, x='Coefficient', y='Feature', palette='coolwarm')\n",
+ "plt.title('Baseline Linear Regression - Feature Coefficients')\n",
+ "plt.xlabel('Coefficient Value (impact on SALE PRICE in $)')\n",
+ "plt.ylabel('Feature')\n",
+ "plt.axvline(0, color='black', linewidth=0.8, linestyle='--')\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print('\\nInterpretation:')\n",
+ "for _, row in coef_df.iterrows():\n",
+ " direction = 'increases' if row['Coefficient'] > 0 else 'decreases'\n",
+ " print(f' {row[\"Feature\"]:25s}: ${row[\"Coefficient\"]:>12,.2f} -> price {direction}')\n",
+ "\n",
+ "print()\n",
+ "print('Key findings from the coefficient plot:')\n",
+ "print(' BOROUGH dominates with the largest (negative) coefficient.')\n",
+ "print(' Borough is coded 1=Manhattan (most expensive) to 5=Staten Island (least expensive).')\n",
+ "print(' A one-unit increase in borough code (e.g. Manhattan->Bronx) is associated with')\n",
+ "print(' a drop of over $1M in predicted price - reflecting the massive location premium.')\n",
+ "print()\n",
+ "print(' RESIDENTIAL UNITS is second - more units correlates with lower price,')\n",
+ "print(' likely because large multi-unit buildings tend to be in cheaper outer boroughs.')\n",
+ "print()\n",
+ "print(' GROSS SQUARE FEET appears near-zero on this scale but is NOT unimportant.')\n",
+ "print(' Its coefficient is ~$100-200 per sqft which looks tiny in millions,')\n",
+ "print(' but 1,500 extra sqft still adds ~$200K-300K to the predicted price.')\n",
+ "print(' The borough effect simply dwarfs it on this axis scale.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "hkerNEsMIPbK"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 4: Feature Engineering**\n",
+ "\n",
+ "* Create, transform, scale, or extract new features; encoding categoricals, polynomial features, PCA, etc.\n",
+ "*TIP: use sklearn's tools, such as Scalar, One-Hot, etc.*\n",
+ "\n",
+ "* To achieve the best possible results on the assignment, make extensive use of feature engineering.\n",
+ "\n",
+ "* Use a `Clustring Model` to create a new feature."
+ ],
+ "metadata": {
+ "id": "wnzIOsrRVc_i"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_EdCDO019XHc"
+ },
+ "source": [
+ "## Why Feature Engineering?\n",
+ "\n",
+ "The baseline model in Part 3 achieved a low R2 (~0.06) despite using reasonable raw features. This is not primarily because the model is weak - it is because the raw features do not represent the underlying price-driving factors in a form that models can easily learn from.\n",
+ "\n",
+ "Feature engineering transforms raw data into representations that better expose the patterns I want the model to learn. It is often the single most impactful step in the entire pipeline - more important than choosing a fancy model.\n",
+ "\n",
+ "## What I Will Build and Why\n",
+ "\n",
+ "**1. Log transformation of SALE PRICE -> LOG_PRICE (target variable)** \n",
+ "NYC sale prices are extremely right-skewed. Applying `log1p` compresses the scale and makes the distribution much closer to normal. After prediction I reverse this with `expm1` to get back to real dollar predictions.\n",
+ "\n",
+ "**2. Log transformation of GROSS SQUARE FEET -> LOG_GROSS_FT** \n",
+ "Square footage also has a long right tail. The log transformation makes the size-price relationship more linear and easier for all model types to learn.\n",
+ "\n",
+ "**3. Age at Sale -> Age_at_Sale** \n",
+ "Instead of raw `YEAR BUILT`, I compute how old the building was *at the time of the sale*. A building constructed in 1950 means something very different in a 1990 sale vs. a 2020 sale.\n",
+ "\n",
+ "**4. Neighborhood Clustering -> Market_Tier (required clustering step)** \n",
+ "The dataset has hundreds of unique neighborhood names. Using them all directly would require hundreds of one-hot encoded columns. Instead, I use **K-Means clustering** to group neighborhoods into 5 market tiers based on their median sale price and median square footage. This gives the model compact, economically meaningful geographic context.\n",
+ "\n",
+ "**5. Distance to Cluster Centroid -> Centroid_Distance** \n",
+ "Within each market tier, some neighborhoods are \"typical\" of their group and others are edge cases. The distance from a neighborhood's centroid captures this nuance. A large distance means the neighborhood is atypical for its tier - additional signal for the model.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Cs2DWx4x9XHc",
+ "outputId": "097f8dd4-9511-4a51-c7eb-bcc2ac9e9660"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dataset shape after feature creation: (28284, 28)\n",
+ "\n",
+ "Age_at_Sale statistics:\n",
+ "count 28284.0\n",
+ "mean 76.0\n",
+ "std 30.4\n",
+ "min 0.0\n",
+ "25% 57.0\n",
+ "50% 86.0\n",
+ "75% 97.0\n",
+ "max 217.0\n",
+ "Name: Age_at_Sale, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 1 & 2: Log transformations\n",
+ "df['LOG_PRICE'] = np.log1p(df['SALE PRICE'])\n",
+ "df['LOG_GROSS_FT'] = np.log1p(df['GROSS SQUARE FEET'])\n",
+ "\n",
+ "# 3: Age at sale\n",
+ "df['Age_at_Sale'] = df['SALE DATE'].dt.year - df['YEAR BUILT']\n",
+ "df = df[df['Age_at_Sale'] >= 0].copy()\n",
+ "\n",
+ "print(f'Dataset shape after feature creation: {df.shape}')\n",
+ "print(f'\\nAge_at_Sale statistics:')\n",
+ "print(df['Age_at_Sale'].describe().apply(lambda x: f'{x:.1f}'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TJdWRm629XHd"
+ },
+ "source": [
+ "## Neighborhood Clustering with K-Means\n",
+ "\n",
+ "I cluster the 200+ unique NYC neighborhoods into 5 market tiers. The clustering is performed on *scaled* features - K-Means is a distance-based algorithm and will be dominated by whichever feature has the largest numeric range if the features are not standardized first. `StandardScaler` brings both features to the same scale (mean=0, std=1) before clustering.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ls_oNIoL9XHd",
+ "outputId": "cac8a803-37e6-41e2-c31b-861cff1fc85f"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Clustering complete. 5 clusters created.\n",
+ "\n",
+ "Neighborhoods per cluster:\n",
+ "Market_Cluster\n",
+ "0 212\n",
+ "1 4\n",
+ "2 1\n",
+ "3 31\n",
+ "4 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "neighborhood_stats = df.groupby('NEIGHBORHOOD').agg(\n",
+ " Median_Price = ('SALE PRICE', 'median'),\n",
+ " Median_SqFt = ('GROSS SQUARE FEET', 'median')\n",
+ ").reset_index()\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "cluster_feats = ['Median_Price', 'Median_SqFt']\n",
+ "scaled_stats = scaler.fit_transform(neighborhood_stats[cluster_feats])\n",
+ "\n",
+ "kmeans = KMeans(n_clusters=5, random_state=SEED, n_init=10)\n",
+ "neighborhood_stats['Market_Cluster'] = kmeans.fit_predict(scaled_stats)\n",
+ "\n",
+ "cluster_map = dict(zip(neighborhood_stats['NEIGHBORHOOD'], neighborhood_stats['Market_Cluster']))\n",
+ "df['Market_Tier'] = df['NEIGHBORHOOD'].map(cluster_map)\n",
+ "\n",
+ "print(f'Clustering complete. {df[\"Market_Tier\"].nunique()} clusters created.')\n",
+ "print(f'\\nNeighborhoods per cluster:')\n",
+ "print(neighborhood_stats['Market_Cluster'].value_counts().sort_index())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "466gf3Vi9XHd"
+ },
+ "source": [
+ "## Cluster Visualization - Scatter Plot\n",
+ "\n",
+ "A scatter plot of the clustered neighborhoods in their original feature space (median price vs. median square footage, both on log scale) shows how K-Means divided the neighborhoods. Note that the clusters are not five balanced equal-sized groups — the vast majority of NYC neighborhoods fall into Cluster 0, while some clusters consist of only a handful of outlier neighborhoods with extreme characteristics.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 799
+ },
+ "id": "gPaIZUOI9XHd",
+ "outputId": "a06ef09c-3f39-4956-8ba0-a58250511f78"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Insight: Cluster 0 contains the vast majority of NYC neighborhoods — small-to-medium\n",
+ "properties in the typical price range. Other clusters capture progressively more\n",
+ "unusual neighborhoods: very large buildings, very high prices, or anomalously low\n",
+ "prices for their size. This structure is realistic: most NYC neighborhoods are\n",
+ "average, and a few extreme outliers exist at both ends of the market.\n"
+ ]
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12, 7))\n",
+ "sns.scatterplot(\n",
+ " data=neighborhood_stats,\n",
+ " x='Median_SqFt', y='Median_Price',\n",
+ " hue='Market_Cluster', palette='viridis',\n",
+ " s=100, alpha=0.8\n",
+ ")\n",
+ "plt.title('Neighborhood Market Segmentation (K-Means Clustering)')\n",
+ "plt.xlabel('Median Gross Square Feet')\n",
+ "plt.ylabel('Median Sale Price ($)')\n",
+ "plt.yscale('log')\n",
+ "plt.xscale('log')\n",
+ "plt.grid(True, which='both', ls='--', alpha=0.5)\n",
+ "plt.legend(title='Market Cluster')\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print('Insight: Cluster 0 contains the vast majority of NYC neighborhoods — small-to-medium')\n",
+ "print('properties in the typical price range. Other clusters capture progressively more')\n",
+ "print('unusual neighborhoods: very large buildings, very high prices, or anomalously low')\n",
+ "print('prices for their size. This structure is realistic: most NYC neighborhoods are')\n",
+ "print('average, and a few extreme outliers exist at both ends of the market.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "g6GF538Q9XHd"
+ },
+ "source": [
+ "## Cluster Interpretation Table\n",
+ "\n",
+ "To understand what each cluster represents, I compute the average characteristics of neighborhoods within each cluster. This tells me which cluster corresponds to the luxury market, which to affordable areas, and so on.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 335
+ },
+ "id": "vSOdjJ1C9XHd",
+ "outputId": "f0d0d1f2-1b70-4237-ad70-f76d6b12cb3d"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " Market_Cluster Num_Neighborhoods Median_Price Median_SqFt\n",
+ "0 2 1 100107200.0 303175.00\n",
+ "1 1 4 65187500.0 84877.50\n",
+ "2 3 31 8559090.5 8214.00\n",
+ "3 0 212 617500.0 1899.25\n",
+ "4 4 1 149700.0 112850.00"
+ ],
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "cluster_profiles",
+ "summary": "{\n \"name\": \"cluster_profiles\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Market_Cluster\",\n \"properties\": {\n \"dtype\": \"int32\",\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Num_Neighborhoods\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 91,\n \"min\": 1,\n \"max\": 212,\n \"num_unique_values\": 4,\n \"samples\": [\n 4,\n 212,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Median_Price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 45403814.074307054,\n \"min\": 149700.0,\n \"max\": 100107200.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 65187500.0,\n 149700.0,\n 8559090.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Median_SqFt\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 122165.92275282826,\n \"min\": 1899.25,\n \"max\": 303175.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 84877.5,\n 112850.0,\n 8214.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "The cluster with the highest median price corresponds to the luxury end of the market.\n",
+ "Note that cluster sizes are very unequal — one cluster dominates with the most\n",
+ "neighborhoods, while others contain very few (sometimes just 1-2 outlier neighborhoods).\n",
+ "The Market_Tier feature captures meaningful price-level context even if the tiers\n",
+ "are not evenly sized — what matters is that neighborhoods in the same cluster share\n",
+ "similar economic profiles.\n"
+ ]
+ }
+ ],
+ "source": [
+ "cluster_profiles = (\n",
+ " neighborhood_stats\n",
+ " .groupby('Market_Cluster')\n",
+ " .agg(\n",
+ " Num_Neighborhoods = ('NEIGHBORHOOD', 'count'),\n",
+ " Median_Price = ('Median_Price', 'median'),\n",
+ " Median_SqFt = ('Median_SqFt', 'median')\n",
+ " )\n",
+ " .sort_values('Median_Price', ascending=False)\n",
+ " .reset_index()\n",
+ ")\n",
+ "\n",
+ "display(cluster_profiles)\n",
+ "print('\\nThe cluster with the highest median price corresponds to the luxury end of the market.')\n",
+ "print('Note that cluster sizes are very unequal — one cluster dominates with the most')\n",
+ "print('neighborhoods, while others contain very few (sometimes just 1-2 outlier neighborhoods).')\n",
+ "print('The Market_Tier feature captures meaningful price-level context even if the tiers')\n",
+ "print('are not evenly sized — what matters is that neighborhoods in the same cluster share')\n",
+ "print('similar economic profiles.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9A5DbNhz9XHd"
+ },
+ "source": [
+ "## PCA Visualization of Clusters\n",
+ "\n",
+ "Principal Component Analysis (PCA) reduces the clustering features to 2 dimensions for visualization. PC1 explains 91.8% of the variance and PC2 explains 8.2%, meaning the 2D projection captures virtually all of the information used for clustering.\n",
+ "\n",
+ "The plot reveals that most neighborhoods are packed tightly together on the left (Cluster 0), while a small number of outlier neighborhoods are spread far to the right along PC1 — these are the neighborhoods with extreme market characteristics that K-Means correctly identified as their own clusters.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 855
+ },
+ "id": "Eg4QRF2R9XHd",
+ "outputId": "64b45f8a-6a54-47be-db8a-2b1fe01d106f"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Total variance captured in 2D: 100.0%\n",
+ "\n",
+ "PC1 (91.8%) captures almost all the market structure - a single economic axis\n",
+ "from affordable/small neighborhoods to expensive/large ones.\n",
+ "Most neighborhoods cluster tightly on the left (typical NYC neighborhoods).\n",
+ "A few outlier neighborhoods sit far to the right - these are the extreme cases\n",
+ "that form their own clusters. This confirms K-Means found real structure,\n",
+ "even if the clusters are not evenly sized.\n"
+ ]
+ }
+ ],
+ "source": [
+ "pca = PCA(n_components=2, random_state=SEED)\n",
+ "coords = pca.fit_transform(scaled_stats)\n",
+ "explained = pca.explained_variance_ratio_\n",
+ "\n",
+ "pca_df = pd.DataFrame({\n",
+ " 'PC1' : coords[:, 0],\n",
+ " 'PC2' : coords[:, 1],\n",
+ " 'Market_Cluster': neighborhood_stats['Market_Cluster'].astype(str)\n",
+ "})\n",
+ "\n",
+ "plt.figure(figsize=(10, 7))\n",
+ "sns.scatterplot(data=pca_df, x='PC1', y='PC2',\n",
+ " hue='Market_Cluster', palette='viridis', s=100, alpha=0.85)\n",
+ "plt.title('PCA Visualization of Neighborhood Market Clusters')\n",
+ "plt.xlabel(f'PC1 ({explained[0]*100:.1f}% variance explained)')\n",
+ "plt.ylabel(f'PC2 ({explained[1]*100:.1f}% variance explained)')\n",
+ "plt.legend(title='Market Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print(f'Total variance captured in 2D: {sum(explained)*100:.1f}%')\n",
+ "print()\n",
+ "print('PC1 (91.8%) captures almost all the market structure - a single economic axis')\n",
+ "print('from affordable/small neighborhoods to expensive/large ones.')\n",
+ "print('Most neighborhoods cluster tightly on the left (typical NYC neighborhoods).')\n",
+ "print('A few outlier neighborhoods sit far to the right - these are the extreme cases')\n",
+ "print('that form their own clusters. This confirms K-Means found real structure,')\n",
+ "print('even if the clusters are not evenly sized.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Yn9RjGNe9XHe"
+ },
+ "source": [
+ "## Distance to Cluster Centroid\n",
+ "\n",
+ "Within each market tier, some neighborhoods are perfectly \"typical\" of their group (close to the centroid) while others are edge cases that just barely ended up in that cluster (far from the centroid). For example, a rapidly gentrifying neighborhood might be assigned to the mid-market cluster but sit far from its center because it is transitioning toward higher values.\n",
+ "\n",
+ "The distance to the cluster centroid encodes this \"typicality\" and provides additional signal to the model.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "w1BrfS9v9XHe",
+ "outputId": "25d7ed0c-a808-4656-cf0e-81de2dbca26d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Feature Centroid_Distance added.\n",
+ "Mean distance : 0.0516\n",
+ "Max distance : 1.9957\n",
+ "\n",
+ "A higher value = neighborhood is atypical for its market tier.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from numpy.linalg import norm\n",
+ "\n",
+ "centroids = kmeans.cluster_centers_\n",
+ "\n",
+ "distances = []\n",
+ "for i, row in enumerate(scaled_stats):\n",
+ " cluster_id = neighborhood_stats['Market_Cluster'].iloc[i]\n",
+ " distances.append(norm(row - centroids[cluster_id]))\n",
+ "\n",
+ "neighborhood_stats['Centroid_Distance'] = distances\n",
+ "\n",
+ "centroid_map = dict(zip(neighborhood_stats['NEIGHBORHOOD'], neighborhood_stats['Centroid_Distance']))\n",
+ "df['Centroid_Distance'] = df['NEIGHBORHOOD'].map(centroid_map)\n",
+ "\n",
+ "print('Feature Centroid_Distance added.')\n",
+ "print(f'Mean distance : {df[\"Centroid_Distance\"].mean():.4f}')\n",
+ "print(f'Max distance : {df[\"Centroid_Distance\"].max():.4f}')\n",
+ "print('\\nA higher value = neighborhood is atypical for its market tier.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "KLnSsY0UVah_"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 5: Train and Evaluate Three Improved Models**\n",
+ "\n",
+ "* Retrain your `Linear Regression` model with the engineered features.\n",
+ "* Choose and Train two different types on models from the SKlearn package (DOCS), on the engineered dataset.\n",
+ "* Compare performance with your baseline.\n",
+ "* Visualize feature importance.\n",
+ "* Discuss the improvement and the reasons.\n",
+ "* Declare the winner."
+ ],
+ "metadata": {
+ "id": "pykMu4YaXHEx"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DL3dAarF9XHe"
+ },
+ "source": [
+ "## The Improvement Strategy\n",
+ "\n",
+ "In Part 3, the baseline Linear Regression achieved an R2 of ~0.06 using raw features. Now I retrain on the engineered features and introduce two ensemble models to demonstrate the full improvement pipeline.\n",
+ "\n",
+ "## The Three Models\n",
+ "\n",
+ "**1. Improved Linear Regression** - The same algorithm as the baseline, but now trained on the engineered feature set. This shows how much improvement comes purely from *better data representation*, before introducing more powerful algorithms.\n",
+ "\n",
+ "**2. Random Forest Regressor** - An ensemble of hundreds of independent decision trees. Each tree is trained on a random subset of the data and features, then all tree predictions are averaged. This reduces overfitting and captures non-linear relationships naturally.\n",
+ "\n",
+ "**3. Gradient Boosting Regressor** - A sequential ensemble where each new tree is trained specifically to correct the errors made by all previous trees combined. Generally the best-performing model on structured tabular data.\n",
+ "\n",
+ "## Evaluation Strategy - Consistent Scale\n",
+ "\n",
+ "All models are trained on `LOG_PRICE` but evaluated in the **original dollar scale** after reversing the transformation with `expm1`. This is critical for a fair comparison - all MAE, RMSE, and R2 values must be comparable to the Part 3 baseline.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mxF9l-TC9XHe",
+ "outputId": "fe0ce8bd-35a1-4f1f-9aae-d8cddf411add"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Training samples : 22,627\n",
+ "Test samples : 5,657\n",
+ "Features : 13\n"
+ ]
+ }
+ ],
+ "source": [
+ "final_features = [\n",
+ " 'LOG_GROSS_FT', 'RESIDENTIAL UNITS', 'COMMERCIAL UNITS',\n",
+ " 'Age_at_Sale', 'BOROUGH', 'Market_Tier', 'Centroid_Distance'\n",
+ "]\n",
+ "\n",
+ "final_features = [f for f in final_features if f in df.columns]\n",
+ "\n",
+ "model_df = df[final_features + ['LOG_PRICE', 'SALE PRICE']].dropna().copy()\n",
+ "\n",
+ "X = pd.get_dummies(model_df[final_features], columns=['BOROUGH', 'Market_Tier'], drop_first=True)\n",
+ "y_log = model_df['LOG_PRICE']\n",
+ "y_original = model_df['SALE PRICE']\n",
+ "\n",
+ "X_train, X_test, y_train_log, y_test_log, y_train_orig, y_test_orig = train_test_split(\n",
+ " X, y_log, y_original, test_size=0.2, random_state=SEED\n",
+ ")\n",
+ "\n",
+ "print(f'Training samples : {X_train.shape[0]:,}')\n",
+ "print(f'Test samples : {X_test.shape[0]:,}')\n",
+ "print(f'Features : {X_train.shape[1]}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jvehyjVS9XHe"
+ },
+ "source": [
+ "Now I train all three models and collect their metrics. Predictions are made in log scale and then converted back to dollars before computing MAE, RMSE, and R2.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 230
+ },
+ "id": "nHwzRChh9XHe",
+ "outputId": "41fa8164-f766-4927-93cd-3c67c591cfbb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Improved Linear Regression MAE=$ 867,561 RMSE=$ 6,554,600 R2=0.4084\n",
+ " Random Forest MAE=$ 774,326 RMSE=$ 6,505,529 R2=0.4173\n",
+ " Gradient Boosting MAE=$ 812,731 RMSE=$ 6,654,716 R2=0.3902\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " Model MAE ($) RMSE ($) R2\n",
+ "0 ORIGINAL BASELINE (raw features) 1739543 8234829 0.0663\n",
+ "1 Improved Linear Regression 867561 6554600 0.4084\n",
+ "2 Random Forest 774326 6505529 0.4173\n",
+ "3 Gradient Boosting 812731 6654716 0.3902"
+ ],
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "performance_df",
+ "summary": "{\n \"name\": \"performance_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Improved Linear Regression\",\n \"Gradient Boosting\",\n \"ORIGINAL BASELINE (raw features)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"MAE ($)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 462254,\n \"min\": 774326,\n \"max\": 1739543,\n \"num_unique_values\": 4,\n \"samples\": [\n 867561,\n 812731,\n 1739543\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"RMSE ($)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 833921,\n \"min\": 6505529,\n \"max\": 8234829,\n \"num_unique_values\": 4,\n \"samples\": [\n 6554600,\n 6654716,\n 8234829\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"R2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.16987482646544974,\n \"min\": 0.0663,\n \"max\": 0.4173,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.4084,\n 0.3902,\n 0.0663\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "improved_models = {\n",
+ " 'Improved Linear Regression': LinearRegression(),\n",
+ " 'Random Forest' : RandomForestRegressor(n_estimators=100, random_state=SEED, n_jobs=-1),\n",
+ " 'Gradient Boosting' : GradientBoostingRegressor(n_estimators=100, random_state=SEED)\n",
+ "}\n",
+ "\n",
+ "comparison_results = [{\n",
+ " 'Model' : 'ORIGINAL BASELINE (raw features)',\n",
+ " 'MAE ($)' : round(BASELINE_MAE),\n",
+ " 'RMSE ($)': round(BASELINE_RMSE),\n",
+ " 'R2' : round(BASELINE_R2, 4)\n",
+ "}]\n",
+ "\n",
+ "trained_models = {}\n",
+ "\n",
+ "for name, model in improved_models.items():\n",
+ " model.fit(X_train, y_train_log)\n",
+ " trained_models[name] = model\n",
+ "\n",
+ " log_pred = model.predict(X_test)\n",
+ " y_pred_orig = np.expm1(log_pred)\n",
+ " y_test_orig_v = y_test_orig.values\n",
+ "\n",
+ " mae = mean_absolute_error(y_test_orig_v, y_pred_orig)\n",
+ " rmse = np.sqrt(mean_squared_error(y_test_orig_v, y_pred_orig))\n",
+ " r2 = r2_score(y_test_orig_v, y_pred_orig)\n",
+ "\n",
+ " comparison_results.append({\n",
+ " 'Model' : name,\n",
+ " 'MAE ($)' : round(mae),\n",
+ " 'RMSE ($)': round(rmse),\n",
+ " 'R2' : round(r2, 4)\n",
+ " })\n",
+ " print(f' {name:35s} MAE=${mae:>10,.0f} RMSE=${rmse:>10,.0f} R2={r2:.4f}')\n",
+ "\n",
+ "performance_df = pd.DataFrame(comparison_results)\n",
+ "display(performance_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LV22akAm9XHf"
+ },
+ "source": [
+ "## Feature Importance - Winning Model\n",
+ "\n",
+ "For the winning model I visualize which features contribute most to its predictions. Feature importance in tree-based models represents how much each feature reduces prediction error across all the trees.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 607
+ },
+ "id": "S80R5hCo9XHf",
+ "outputId": "e9569560-0f78-4187-e394-e5d5c493c7f0"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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u/Jzn9HO7c+dOQ5IxePBgq3phYWHZfndcL7M/H3zwgbF3715DkuX7dtq0aYarq6tx/vx5m+/SU6dOGYULFzYee+wxq++LzGM4Z84cwzAM4/Lly0aJEiWMWrVqWX3+Zs2aZfV/k2Hk7vu+TJkyWZ4f48aNMyQZ//vf/27abyA/ML0cAHLgu+++k6OjowYNGmRVPnToUBmGodWrV1uVN2jQQHXq1LG8Ll26tJ544gl9//33NlMF88umTZsUERGhrl276tFHH83RNs7Ozlq3bp3VMmHCBEnSkiVL5OHhoZCQEP3zzz+WpU6dOnJ1dbWaalukSBHLv8+ePat//vlHTZo00YULF/THH3/kb0d17QZwPXv2tCpbsmSJKleurKCgIKt4M4/FjVODc6pKlSpq0KCB5XW9evUkSY8++qhKly5tU37kyBGbNgYOHGj5d+aU4MuXL2v9+vWScn9+NW3aVFWqVMlxH65/fy5duqR//vnHcj3rrl27bOr37dvX6nWTJk2UlJSk1NRUSdem52ZkZGjkyJE2lzhkjpqvW7dOycnJ6t69u9X74ejoqHr16lnejyJFiqhw4cKKiYmxuWzBHlq2bClvb2/5+/vrqaeekouLi1auXGk14uzo6KjChQtLujai9u+//+rq1at66KGHsjxeTz/9tNVIeeaU9cxzITExUbGxsQoNDbXMIpGkkJAQm/cxt+dCixYtrEaWM8/Dzp07W90g6mbnZ1amTZtm891wfV/CwsKsphHXqFFDISEh+u6772zauvF8WrZsmTIyMtS1a1erc8PHx0cVK1a0nBuZx+r777/P8vKGvMi8pOL69ytT5qh15nTmLVu2qE6dOipcuLAaNGhgmVKeuc7Z2TlH173f6vy4XlafvZy+Z7f63GaOQGfOzsn08ssv56j961WtWlU1atTQF198IenaaP4TTzyR5b011q9fr8uXL2vw4MFW3xcvvPCC3N3dLTMIduzYoVOnTqlv376Wz590bXT++s+NlD/f95nvyY0zOgB7YHo5AOTAn3/+KT8/P5u7nGbeQOfGZ71WrFjRpo3AwEBduHBBp0+fztGdxXPjjz/+UKdOnVStWrUs70aeHUdHR7Vs2TLLdQcPHlRKSopKlCiR5fpTp05Z/r1v3z699dZb2rhxo+UPvEyZ117mp1KlSln9UZYZb1xcnOXa5pvFmxvXJ9bS/ycC1091vL78xsTRwcFB5cuXtyoLDAyU9P9Tm3N7ft14V+lb+ffffxUREaEvv/zS5jhk9f7c2OfMP07PnDkjd3d3HT58WA4ODjdN/A8ePChJ2f4A5O7uLunaDyjvv/++hg4dqpIlS6p+/fpq166dnn/++Zt+Ti5evGgTe04+V9OmTVNgYKBSUlI0Z84cbdq0SWaz2abeZ599pgkTJuiPP/7QlStXLOVZHfubHS/p/9+/rL4XKlWqZJXI5/ZcuN3zMzt169bNMqHM3H+lSpVs1lWuXFnff/+9zc3SbjxmBw8elGEYWR4P6f9vSFauXDkNGTJEEydO1IIFC9SkSRN16NBBPXr0sEnCcsu44fp4SWrUqJFMJpO2bNmibt26acuWLQoJCZEkeXp6qkqVKpayLVu26OGHH7b5HsrKrc6PTM7OzjbfX8WKFcvxe3arz+2ff/4pBwcHm/cjICAgR+3f6JlnntGECRP06quvauvWrZZ7a9wou3OmcOHCKl++vGV9dp+TQoUK2XyH5sf3feY5kJN7qAC3i6QbAO5xx48f12OPPSYPDw999913+fb4k4yMDJUoUUILFizIcn3mHzvJyclq2rSp3N3dNXr0aFWoUEHOzs7atWuXhg8fnqNH6GT3R092swKuH7m9Pt7q1atr4sSJWW5zYxKSU46Ojrkqz+qP+fyWVf9vpmvXrtq6datee+011apVS66ursrIyFDr1q2zfH/yo2+Z7c6bNy/LZPj6Rx4NHjxY7du314oVK/T999/r7bffVmRkpDZu3Kjg4OAs21+0aJHNbIecxHd9MtmxY0c1btxYzzzzjOLj4y33GJg/f77CwsLUsWNHvfbaaypRooQcHR0VGRlpdcO1TAV5LtyN5+eNbjxfMzIyZDKZtHr16izjvP5eDxMmTFBYWJi+/vprrV27VoMGDVJkZKS2bdtmcz18Tnh5eUnK+scHLy8vBQUFafPmzTp37px+//13vfPOO5b1DRs21ObNm/XXX38pISFBzz77bI72mdP3Irt6OXWn3/Pu3bsrPDxcL7zwgry8vCz3crgT8uP7PvMcyM09UIC8IukGgBwoU6aM1q9fr7Nnz1oltZlTp8uUKWNVP3OU73oHDhxQ0aJFs/1lPi+SkpL02GOPKS0tTRs2bJCvr2++tV2hQgWtX79ejRo1ummSFxMTo6SkJC1btkyPPPKIpfzo0aM2dbNLrjNHZG68k+2No3q3inf37t1q0aLFXTVykZGRoSNHjlhGt6Vr54Iky7Tg3J5fWcmuz2fOnNGGDRsUERGhkSNHWsqzOkdzqkKFCsrIyND+/fttbip2fR3p2l2Cs5tNcWP9oUOHaujQoTp48KBq1aqlCRMmaP78+VnWb9WqlWXKc15lJtLNmzfX1KlTNWLECEnS0qVLVb58eS1btszquF6fgOVG5vuX1TGPj4+3qXu754I9Ze7/xrilazE+8MADt3wkWIUKFWQYhsqVK2f1uchO9erVVb16db311lvaunWrGjVqpJkzZ1oei5ibz3tQUJCkrL+fpGtTzOfMmaO1a9cqPT1dDRs2tKxr2LChvvjiC8vTC3JyE7W7SZkyZZSRkaGjR49ajSYfOnQoT+2VLl1ajRo1UkxMjPr165fts8OvP2euH7G+fPmyjh49avl+uP5zcv0MmStXrujo0aNWjyfLj+/7o0eP6oEHHsjX/5OB7HBNNwDkwOOPP6709HRNnTrVqjwqKkomk0lt2rSxKv/555+tpoweP35cX3/9tR577LHbHs3IdP78eT3++OM6ceKEvvvuu2ynauZV165dlZ6erjFjxtisu3r1qiVBzuzP9aMply9f1vTp0222c3FxyXI6c2aCdv1j1dLT0zVr1qxcxXvixAl98sknNusuXryo8+fP57it/Hb9eWMYhqZOnapChQqpRYsWknJ/fmUl81rKG3+4yOr9kaRJkyblthsWHTt2lIODg0aPHm0zUp65n1atWsnd3V3jxo2zmp6d6fTp05KuPWP90qVLVusqVKggNze3mz76x9fXVy1btrRa8qJZs2aqW7euJk2aZIkjq2P2yy+/6Oeff87TPnx9fVWrVi199tlnVuf/unXrtH//fqu6+XEu2NP1fbn+XNu7d6/Wrl2rxx9//JZtPPnkk3J0dFRERITNeWkYhuW669TUVF29etVqffXq1eXg4GB1bri4uGT56KmslCpVSv7+/tqxY0eW6xs3bqz09HR9+OGHqlixolVC1rBhQ507d07Tp0+Xg4ODVUJ+L2jVqpUk2Xw3T5kyJc9tvvvuu3rnnXduel14y5YtVbhwYX300UdW7/fs2bOVkpJiuXv6Qw89JG9vb82cOVOXL1+21IuOjrZ5f/Pj+37nzp1W9+sA7ImRbgDIgfbt26t58+Z68803dezYMdWsWVNr167V119/rcGDB9s8oqZatWpq1aqV1SPDpGuPWbqVb775Rrt375Z07Rf+33//3TKi06FDB8tjoJ599llt375dvXr1UlxcnNUzcl1dXdWxY8fb6nPTpk310ksvKTIyUrGxsXrsscdUqFAhHTx4UEuWLNHkyZP11FNPqWHDhipWrJhCQ0M1aNAgmUwmzZs3L8spjXXq1NGiRYs0ZMgQPfzww3J1dVX79u1VtWpV1a9fX+Hh4fr3339VvHhxffnllzZ/cN/Mc889p8WLF6tv37764Ycf1KhRI6Wnp+uPP/7Q4sWL9f333+fopkf5zdnZWWvWrFFoaKjq1aun1atX69tvv9Ubb7xh+YM+t+dXVooUKaIqVapo0aJFCgwMVPHixVWtWjVVq1ZNjzzyiMaPH68rV66oVKlSWrt2bbYjfTkREBCgN998U2PGjFGTJk305JNPymw269dff5Wfn58iIyPl7u6uGTNm6LnnnlPt2rXVrVs3eXt7KyEhQd9++60aNWqkqVOn6sCBA2rRooW6du2qKlWqyMnJScuXL9f//vc/devWLc8x5sZrr72mLl26KDo6Wn379lW7du20bNkyderUSW3bttXRo0c1c+ZMValSRefOncvTPiIjI9W2bVs1btxYvXr10r///qspU6aoatWqVm3mx7lgbx988IHatGmjBg0aqHfv3pZHhnl4eOToWc8VKlTQu+++q/DwcB07dkwdO3aUm5ubjh49quXLl+vFF1/UsGHDtHHjRg0cOFBdunRRYGCgrl69qnnz5snR0VGdO3e2tFenTh2tX79eEydOlJ+fn8qVK2e5cVxWnnjiCS1fvlyGYdiMkmaOXv/88882z/sODAzUAw88oJ9//lnVq1eXp6dnjo/Z3aBOnTrq3LmzJk2apKSkJMsjwzJn3uRlxLhp06Zq2rTpTet4e3srPDxcERERat26tTp06KD4+HhNnz5dDz/8sHr06CHp2rXb7777rl566SU9+uijevrpp3X06FHNnTvX5pru2/2+P3XqlH7//XfLo/YAu7tzN0oHgHvHjY8MM4xrj0B69dVXDT8/P6NQoUJGxYoVjQ8++MDyiKRMkowBAwYY8+fPNypWrGiYzWYjODjY6lFSN5Pdo7wkGXPnzrXUy3ycVVZLVo/lymo/Nz4iKyuzZs0y6tSpYxQpUsRwc3Mzqlevbrz++uvG33//bamzZcsWo379+kaRIkUMPz8/4/XXX7c8Wuv6fp87d8545plnDE9PT5s4Dx8+bLRs2dIwm81GyZIljTfeeMNYt25dlo8Mq1q1apaxXr582Xj//feNqlWrGmaz2ShWrJhRp04dIyIiwkhJScn18ShTpozRtm1bm7qZ7/H1rn+czo1tHj582HjssceMokWLGiVLljTeeecdm0dt5fb8ysrWrVuNOnXqGIULF7Z6BNBff/1ldOrUyfD09DQ8PDyMLl26GH///Xe2jx7KfJRZpuwe6TZnzhwjODjYcqybNm1qrFu3zqrODz/8YLRq1crw8PAwnJ2djQoVKhhhYWGWR+r9888/xoABA4ygoCDDxcXF8PDwMOrVq2csXrw4yz7mVWYffv31V5t16enpRoUKFYwKFSoYV69eNTIyMoxx48YZZcqUsXx+V61ale0jobJ63NmNx9YwDOOrr74yKleubJjNZqNKlSrGsmXLbNo0jNs7F7KLKfPxe7d6/NbNjtP11q9fbzRq1MgoUqSI4e7ubrRv397Yv3+/VZ3szqdMX331ldG4cWPDxcXFcHFxMYKCgowBAwYY8fHxhmEYxpEjR4xevXoZFSpUMJydnY3ixYsbzZs3N9avX2/Vzh9//GE88sgjRpEiRQxJt3x82K5du6wed3UjPz8/Q5Ixa9Ysm3UdOnQwJBn9+vWzWXc750d238eZx/Bm2+bmc3v+/HljwIABRvHixQ1XV1ejY8eORnx8vCHJeO+992z2f72b9ed62fVl6tSpRlBQkFGoUCGjZMmSRr9+/YwzZ87Y1Js+fbpRrlw5w2w2Gw899JCxadMmo2nTplaPDDOMnH/fZ/XIsBkzZhhFixa1eqweYE8mwyiAO2oAwH3MZDJpwIABNtND8d8TFhampUuX5nl0FIB9tGjRQn5+fpo3b15Bh1LgYmNjFRwcrPnz5+f45nD3uuDgYDVr1kxRUVEFHQr+I7imGwAAAP8p48aN06JFi3J1s8b7wcWLF23KJk2aJAcHB6sbYd7P1qxZo4MHDyo8PLygQ8F/CNd0AwAA4D+lXr16Vjfr+q8YP368du7cqebNm8vJyUmrV6/W6tWr9eKLL+b5sYr3mtatWzP7CHccSTcAAADwH9CwYUOtW7dOY8aM0blz51S6dGmNGjVKb775ZkGHBtzXuKYbAAAAAAA74ZpuAAAAAADshKQbAAAAAAA74Zpu3BcyMjL0999/y83NTSaTqaDDAQAAAHCfMwxDZ8+elZ+fnxwcsh/PJunGfeHvv//+z9x1EwAAAMDd4/jx43rwwQezXU/SjfuCm5ubpGsnvLu7ewFHAwAAAOB+l5qaKn9/f0sukh2SbtwXMqeUu7u7k3QDAAAAuGNudXkrN1IDAAAAAMBOSLoBAAAAALATppfjvvJk7Zfl5Fi4oMMAAAAAkI/WxH9S0CHkGSPdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnfxnk+6wsDB17Ngxy3UXL17UO++8o8DAQJnNZj3wwAPq0qWL9u3bZ1M3NTVVb7/9tqpWraoiRYrIy8tLDz/8sMaPH68zZ87kOJ5Dhw6pV69eKl26tMxms0qVKqUWLVpowYIFunr1qqWeyWSyLO7u7nr44Yf19ddf57kPFy5cUHh4uCpUqCBnZ2d5e3uradOmVm0ePXpUzzzzjPz8/OTs7KwHH3xQTzzxhP74448c9e36mDOXxo0bKywsLMt1mUvZsmVzfPwAAAAA4G7kVNAB3G3S0tLUsmVLJSQkaMKECapXr57+97//KTIyUvXq1dP69etVv359SdK///6rxo0bKzU1VWPGjFGdOnXk4eGh+Ph4zZ07VwsXLtSAAQNuuc/t27erZcuWqlq1qqZNm6agoCBJ0o4dOzRt2jRVq1ZNNWvWtNSfO3euWrdurdTUVE2fPl1PPfWUdu3aperVq+e6D3379tUvv/yiKVOmqEqVKkpKStLWrVuVlJQkSbpy5YpCQkJUqVIlLVu2TL6+vvrrr7+0evVqJScn5/i4ZsacqXDhwnJ0dNR7771nKfP19bWq5+jomOP2AQAAAOBuZDIMwyjoIApCWFiYkpOTtWLFCqvy999/X+Hh4frtt9+sEt2MjAzVq1dPFy5c0N69e2UymdS3b1/Nnz9fBw4ckJ+fn80+DMOQyWS6aRyGYahq1aoqWrSotm/fLgcH28kH17djMpm0fPlyyyj92bNn5e7ursmTJ2vQoEG57oOnp6cmT56s0NDQLOOLjY1VcHCwjh07pjJlyty0L9m5MebbrZeV1NRUeXh4qEWF5+XkWDhPcQIAAAC4O62J/6SgQ7CRmYOkpKTI3d0923r/2enl2Vm4cKFCQkKsklVJcnBw0Kuvvqr9+/dr9+7dysjI0KJFi9SjR48sE25Jt0y4pWtJbVxcnIYNG5Zlwn2zdq5evarZs2dLujZynNs+SJKPj4++++47nT17Nst9eHt7y8HBQUuXLlV6evot+3OnpKWlKTU11WoBAAAAgLsNSfcNDhw4oMqVK2e5LrP8wIEDOn36tJKTk1WpUiWrOnXq1JGrq6tcXV3VvXv3HO1PklU7p06dsrTh6uqq6dOnW23TvXt3ubq6ymw269VXX1XZsmXVtWvXXPdBkmbNmqWtW7darkV/9dVXtWXLFkv9UqVK6aOPPtLIkSNVrFgxPfrooxozZoyOHDlyy75lFXPmcuMMg9yKjIyUh4eHZfH397+t9gAAAADAHki6s3A7M+6XL1+u2NhYtWrVShcvXsxTG15eXoqNjVVsbKw8PT11+fJlq/VRUVGKjY3V6tWrVaVKFX366acqXrx4nvrwyCOP6MiRI9qwYYOeeuop7du3T02aNNGYMWMsdQYMGKCTJ09qwYIFatCggZYsWaKqVatq3bp1Oe5TZsyZS0hISI63zUp4eLhSUlIsy/Hjx2+rPQAAAACwB5LuGwQGBiouLi7LdZnlgYGB8vb2lqenp+Lj463qlC5dWgEBAXJzc8vR/ipWrChJVu04OjoqICBAAQEBcnKyvdedj4+PAgIC9Nhjj2nu3Ll6+umnderUqVz3IVOhQoXUpEkTDR8+XGvXrtXo0aM1ZswYq2Tfzc1N7du319ixY7V79241adJE7777bo76eH3MmYuLi0uOt82K2WyWu7u71QIAAAAAdxuS7ht069ZN69evt1zznCkjI0NRUVGqUqWKatasKQcHB3Xt2lXz58/X33//nef9BQcHKygoSB9++KEyMjJyvX3dunVVp04djR07Ntd9yE6VKlV09epVXbp0Kcv1JpNJQUFBOn/+fK7jBQAAAID/kv900p2SkmI15Tk2NlY9evRQ3bp11b59ey1ZskQJCQn69ddf1blzZ8XFxWn27NmWG5uNGzdOpUqVUt26dTVnzhz9/vvvOnz4sJYvX66ff/45R4+8MplMmjt3ruLj49WoUSOtXLlSBw8e1P79+zVz5kydPn36lu0MHjxYH3/8sU6cOCFJevXVV3Pch2bNmunjjz/Wzp07dezYMX333Xd644031Lx5c7m7uys2NlZPPPGEli5dqv379+vQoUOaPXu25syZoyeeeOI23wEAAAAAuL/9p5/THRMTo+DgYKuy3r17a+PGjRo3bpzeeOMN/fnnn3Jzc1Pz5s21bds2VatWzVLXy8tL27dv1/vvv68PPvhAR48elYODgypWrKinn35agwcPzlEc9evX186dOzVu3DjL9dMuLi6qWbOmoqKi1KtXr5tu37p1a5UrV05jx47V9OnT5ezsnOM+tGrVSp999pneeOMNXbhwQX5+fmrXrp1GjhwpSXrwwQdVtmxZRURE6NixYzKZTJbXr776ag6PNAAAAAD8N/1nn9ON+wvP6QYAAADuXzynGwAAAAAA2CDptrOffvrJ6vnUNy73unHjxmXbtzZt2hR0eAAAAABQoP7T13TfCQ899JBiY2MLOgy76du3r7p27ZrluiJFitzhaAAAAADg7kLSbWdFihRRQEBAQYdhN8WLF1fx4sULOgwAAAAAuCsxvRwAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATnhkGO4ry3ZNkbu7e0GHAQAAAACSGOkGAAAAAMBuSLoBAAAAALATkm4AAAAAAOyEpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAO3Eq6ACA/NTl8XdUyMlc0GEAAGBlVcx7BR0CAKCAMNINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdd0BMTIxMJpOSk5OzrRMdHS1PT89833ezZs00ePDgfG8XAAAAAHBr92TSffLkSb388ssqX768zGaz/P391b59e23YsCHf9pGfyWrDhg2VmJgoDw+PfGkvOjpaJpNJJpNJjo6OKlasmOrVq6fRo0crJSXFqu6yZcs0ZsyYHLVLgg4AAAAA+cupoAPIrWPHjqlRo0by9PTUBx98oOrVq+vKlSv6/vvvNWDAAP3xxx93LBbDMJSeni4np5sfxsKFC8vHxydf9+3u7q74+HgZhqHk5GRt3bpVkZGRmjt3rrZs2SI/Pz9JUvHixfN1vwAAAACAnLvnRrr79+8vk8mk7du3q3PnzgoMDFTVqlU1ZMgQbdu2TZKUnJysPn36yNvbW+7u7nr00Ue1e/duSxujRo1SrVq1NG/ePJUtW1YeHh7q1q2bzp49K0kKCwvTjz/+qMmTJ1tGlI8dO2aZJr569WrVqVNHZrNZmzdvVlpamgYNGqQSJUrI2dlZjRs31q+//mrZX1bTy6Ojo1W6dGkVLVpUnTp1UlJSUq6Og8lkko+Pj3x9fVW5cmX17t1bW7du1blz5/T6669b6t04ej19+nRVrFhRzs7OKlmypJ566qmb9jk9PV29e/dWuXLlVKRIEVWqVEmTJ0+2iiUsLEwdO3bUhx9+KF9fX3l5eWnAgAG6cuWKpU5aWpqGDx8uf39/mc1mBQQEaPbs2Zb1e/fuVZs2beTq6qqSJUvqueee0z///JOrYwIAAAAAd5t7Kun+999/tWbNGg0YMEAuLi426zOvie7SpYtOnTql1atXa+fOnapdu7ZatGihf//911L38OHDWrFihVatWqVVq1bpxx9/1HvvvSdJmjx5sho0aKAXXnhBiYmJSkxMlL+/v2XbESNG6L333lNcXJxq1Kih119/XV999ZU+++wz7dq1SwEBAWrVqpXV/q73yy+/qHfv3ho4cKBiY2PVvHlzvfvuu7d9fEqUKKFnn31WK1euVHp6us36HTt2aNCgQRo9erTi4+O1Zs0aPfLIIzftc0ZGhh588EEtWbJE+/fv18iRI/XGG29o8eLFVm3/8MMPOnz4sH744Qd99tlnio6OVnR0tGX9888/ry+++EIfffSR4uLi9PHHH8vV1VXStR9JHn30UQUHB2vHjh1as2aN/ve//6lr167Z9jUtLU2pqalWCwAAAADcbe6p6eWHDh2SYRgKCgrKts7mzZu1fft2nTp1SmazWZL04YcfasWKFVq6dKlefPFFSVJGRoaio6Pl5uYmSXruuee0YcMGjR07Vh4eHipcuLCKFi2a5bTw0aNHKyQkRJJ0/vx5zZgxQ9HR0WrTpo0k6ZNPPtG6des0e/ZsvfbaazbbT548Wa1bt7aMSAcGBmrr1q1as2bNbRyda4KCgnT27FklJSWpRIkSVusSEhLk4uKidu3ayc3NTWXKlFFwcLAkZdtnR0dHRUREWF6XK1dOP//8sxYvXmyVFBcrVkxTp06Vo6OjgoKC1LZtW23YsEEvvPCCDhw4oMWLF2vdunVq2bKlJKl8+fKWbadOnarg4GCNGzfOUjZnzhz5+/vrwIEDCgwMtOlnZGSkVVwAAAAAcDe6p0a6DcO4ZZ3du3fr3Llz8vLykqurq2U5evSoDh8+bKlXtmxZS8ItSb6+vjp16lSO4njooYcs/z58+LCuXLmiRo0aWcoKFSqkunXrKi4uLsvt4+LiVK9ePauyBg0a5Gjft5J5jEwmk826kJAQlSlTRuXLl9dzzz2nBQsW6MKFC7dsc9q0aapTp468vb3l6uqqWbNmKSEhwapO1apV5ejoaHl9/fGMjY2Vo6OjmjZtmmX7u3fv1g8//GD1fmX+sHL9e3a98PBwpaSkWJbjx4/fsh8AAAAAcKfdUyPdFStWlMlkuunN0s6dOydfX1/FxMTYrLv+kVyFChWyWmcymZSRkZGjOLKa2n63iIuLk7u7u7y8vGzWubm5adeuXYqJidHatWs1cuRIjRo1Sr/++mu2jyv78ssvNWzYME2YMEENGjSQm5ubPvjgA/3yyy9W9W52PIsUKXLTmM+dO6f27dvr/ffft1nn6+ub5TZms9kykwEAAAAA7lb31Eh38eLF1apVK02bNk3nz5+3WZ+cnKzatWvr5MmTcnJyUkBAgNXywAMP5HhfhQsXzvK66BtVqFBBhQsX1pYtWyxlV65c0a+//qoqVapkuU3lypVtktbMm8DdjlOnTmnhwoXq2LGjHByyfmudnJzUsmVLjR8/Xr///ruOHTumjRs3Ssq6z1u2bFHDhg3Vv39/BQcHKyAgINvR5+xUr15dGRkZ+vHHH7NcX7t2be3bt09ly5a1ec/u5h84AAAAAOBW7qmkW7o21Tk9PV1169bVV199pYMHDyouLk4fffSRGjRooJYtW6pBgwbq2LGj1q5dq2PHjmnr1q168803tWPHjhzvp2zZsvrll1907Ngx/fPPP9mOgru4uKhfv3567bXXtGbNGu3fv18vvPCCLly4oN69e2e5zaBBg7RmzRp9+OGHOnjwoKZOnZrr67kNw9DJkyeVmJiouLg4zZkzRw0bNpSHh4flhnA3WrVqlT766CPFxsbqzz//1Oeff66MjAxVqlQp2z5XrFhRO3bs0Pfff68DBw7o7bfftroze06ULVtWoaGh6tWrl1asWKGjR48qJibGcjO2AQMG6N9//1X37t3166+/6vDhw/r+++/Vs2fPHP3wAQAAAAB3q3su6S5fvrx27dql5s2ba+jQoapWrZpCQkK0YcMGzZgxQyaTSd99950eeeQR9ezZU4GBgerWrZv+/PNPlSxZMsf7GTZsmBwdHVWlShV5e3vbXMN8vffee0+dO3fWc889p9q1a+vQoUP6/vvvVaxYsSzr169fX5988okmT56smjVrau3atXrrrbdydRxSU1Pl6+urUqVKqUGDBvr4448VGhqq3377Ldsp2Z6enlq2bJkeffRRVa5cWTNnztQXX3yhqlWrZtvnl156SU8++aSefvpp1atXT0lJSerfv3+uYpWkGTNm6KmnnlL//v0VFBSkF154wTJbwc/PT1u2bFF6eroee+wxVa9eXYMHD5anp2e2I/YAAAAAcC8wGTm5Oxlwl0tNTZWHh4ceazRYhZy41hsAcHdZFZP1LDQAwL0rMwdJSUmRu7t7tvUYRgQAAAAAwE5Iuu9CVatWtXp81vXLggULCjo8AAAAAEAO3VOPDPuv+O6773TlypUs1+XmunQAAAAAQMEi6b4LlSlTpqBDAAAAAADkA6aXAwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJjwzDfWXJdxFyd3cv6DAAAAAAQBIj3QAAAAAA2A1JNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnTgUdAJCfnuwRKadCzgUdBu5Ra756p6BDAAAAwH2GkW4AAAAAAOyEpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IenGHRUdHS1PT8+CDgMAAAAA7giSbjv6+eef5ejoqLZt2xZ0KDZiYmJkMpmUnJyc421Onz6tfv36qXTp0jKbzfLx8VGrVq20ZcsW+wUKAAAAAPcwp4IO4H42e/Zsvfzyy5o9e7b+/vtv+fn5FXRIt6Vz5866fPmyPvvsM5UvX17/+9//tGHDBiUlJRV0aAAAAABwV2Kk207OnTunRYsWqV+/fmrbtq2io6Ot1q9cuVIVK1aUs7Ozmjdvrs8++8xm5Hnz5s1q0qSJihQpIn9/fw0aNEjnz5/P0f7nzZunhx56SG5ubvLx8dEzzzyjU6dOSZKOHTum5s2bS5KKFSsmk8mksLCwm7aXnJysn376Se+//76aN2+uMmXKqG7dugoPD1eHDh0s9SZOnKjq1avLxcVF/v7+6t+/v86dO3fTtr/++mvVrl1bzs7OKl++vCIiInT16tUc9RMAAAAA7mYk3XayePFiBQUFqVKlSurRo4fmzJkjwzAkSUePHtVTTz2ljh07avfu3XrppZf05ptvWm1/+PBhtW7dWp07d9bvv/+uRYsWafPmzRo4cGCO9n/lyhWNGTNGu3fv1ooVK3Ts2DFLYu3v76+vvvpKkhQfH6/ExERNnjz5pu25urrK1dVVK1asUFpaWrb1HBwc9NFHH2nfvn367LPPtHHjRr3++uvZ1v/pp5/0/PPP65VXXtH+/fv18ccfKzo6WmPHjr1pPGlpaUpNTbVaAAAAAOBuYzIyM0Hkq0aNGqlr16565ZVXdPXqVfn6+mrJkiVq1qyZRowYoW+//VZ79uyx1H/rrbc0duxYnTlzRp6enurTp48cHR318ccfW+ps3rxZTZs21fnz5+Xs7JyreHbs2KGHH35YZ8+elaurq2JiYtS8eXPL/nLiq6++0gsvvKCLFy+qdu3aatq0qbp166YaNWpku83SpUvVt29f/fPPP5Ku3Uht8ODBlhH9li1bqkWLFgoPD7dsM3/+fL3++uv6+++/s2131KhRioiIsClv0X6EnArl7tgAmdZ89U5BhwAAAIB7RGpqqjw8PJSSkiJ3d/ds6zHSbQfx8fHavn27unfvLklycnLS008/rdmzZ1vWP/zww1bb1K1b1+r17t27FR0dbRlhdnV1VatWrZSRkaGjR4/eMoadO3eqffv2Kl26tNzc3NS0aVNJUkJCQp771blzZ/39999auXKlWrdurZiYGNWuXdtq6vz69evVokULlSpVSm5ubnruueeUlJSkCxcuZNnm7t27NXr0aKt+vvDCC0pMTMx2G0kKDw9XSkqKZTl+/Hie+wUAAAAA9sKN1Oxg9uzZunr1qtWN0wzDkNls1tSpU3PUxrlz5/TSSy9p0KBBNutKly59023Pnz+vVq1aqVWrVlqwYIG8vb2VkJCgVq1a6fLly7nrzA2cnZ0VEhKikJAQvf322+rTp4/eeecdhYWF6dixY2rXrp369eunsWPHqnjx4tq8ebN69+6ty5cvq2jRoln2MyIiQk8++WSW+8qO2WyW2Wy+rb4AAAAAgL2RdOezq1ev6vPPP9eECRP02GOPWa3r2LGjvvjiC1WqVEnfffed1bpff/3V6nXt2rW1f/9+BQQE5DqGP/74Q0lJSXrvvffk7+8v6dr08usVLlxYkpSenp7r9q9XpUoVrVixQtK10fWMjAxNmDBBDg7XJlEsXrz4ptvXrl1b8fHxeeonAAAAANztSLrz2apVq3TmzBn17t1bHh4eVus6d+6s2bNna/HixZo4caKGDx+u3r17KzY21jJF22QySZKGDx+u+vXra+DAgerTp49cXFy0f/9+rVu37paj5aVLl1bhwoU1ZcoU9e3bV3v37tWYMWOs6pQpU0Ymk0mrVq3S448/riJFisjV1TXbNpOSktSlSxf16tVLNWrUkJubm3bs2KHx48friSeekCQFBAToypUrmjJlitq3b68tW7Zo5syZN4115MiRateunUqXLq2nnnpKDg4O2r17t/bu3at33333ptsCAAAAwN2Oa7rz2ezZs9WyZUubhFu6lnTv2LFDZ8+e1dKlS7Vs2TLVqFFDM2bMsNy9PHPKdI0aNfTjjz/qwIEDatKkiYKDgzVy5MgcPevb29tb0dHRWrJkiapUqaL33ntPH374oVWdUqVKKSIiQiNGjFDJkiVveVd0V1dX1atXT1FRUXrkkUdUrVo1vf3223rhhRcsPwLUrFlTEydO1Pvvv69q1appwYIFioyMvGm7rVq10qpVq7R27Vo9/PDDql+/vqKiolSmTJlb9hMAAAAA7nbcvfwuMXbsWM2cOZMbguVR5p0DuXs5bgd3LwcAAEBO5fTu5UwvLyDTp0/Xww8/LC8vL23ZskUffPBBjp/BDQAAAAC4N5B0F5CDBw/q3Xff1b///qvSpUtr6NChVs+qvpmffvpJbdq0yXb9uXPnch1PQkKCqlSpku36/fv33/Ku6QAAAAAAa0wvvwddvHhRJ06cyHZ9Xu4EfvXqVR07dizb9WXLlpWT0937Gw3Ty5EfmF4OAACAnGJ6+X2sSJEi+f6ILScnJx7bBQAAAAD5jLuXAwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJjwzDfWXZ/PCbPiMPAAAAAO4kRroBAAAAALATkm4AAAAAAOyEpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATpwKOgAgPz3e/305FXYu6DAKXMyctws6BAAAAABipBsAAAAAALsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALCTuyLpDgsLk8lkkslkUqFChVSuXDm9/vrrunTpklW9zDo3Ll9++aWlzieffKKaNWvK1dVVnp6eCg4OVmRkpGX9qFGjVKtWLavXme04OTnpgQce0COPPKJJkyYpLS3Nav/NmjXLcv99+/a1itHZ2Vl//vmn1bYdO3ZUWFjYTfuRuYwaNUrHjh2TyWRSbGyszfFq1aqVHB0d9euvv2Z5LDt27HirQ37L+jExMTKZTEpOTpYkRUdHy2QyqXXr1lb1kpOTZTKZFBMTY3UMVqxYYdnmZsuxY8d04cIFhYeHq0KFCnJ2dpa3t7eaNm2qr7/+Osf9AAAAAIC7kVNBB5CpdevWmjt3rq5cuaKdO3cqNDRUJpNJ77//vlW9uXPn2iR+np6ekqQ5c+Zo8ODB+uijj9S0aVOlpaXp999/1969e2+676pVq2r9+vXKyMhQUlKSYmJi9O6772revHmKiYmRm5ubpe4LL7yg0aNHW21ftGhRq9cmk0kjR47UZ599luX+EhMTLf9etGiRRo4cqfj4eEuZq6ur/vnnnyy3TUhI0NatWzVw4EDNmTNHDz/88E37lp+cnJy0fv16/fDDD2revPkt6z/99NNW79WTTz6patWqWR0/b29v9ezZU7/88oumTJmiKlWqKCkpSVu3blVSUpJd+gEAAAAAd8pdk3SbzWb5+PhIkvz9/dWyZUutW7fOJun29PS01LvRypUr1bVrV/Xu3dtSVrVq1Vvu28nJydKmn5+fqlevrpCQENWsWVPvv/++3n33XUvdokWLZrv/TAMHDtTEiRP12muvqVq1ajbrr9/ew8NDJpPJps3sku65c+eqXbt26tevn+rXr6+JEyeqSJEit+xjfnBxcVHXrl01YsQI/fLLL7esX6RIEavYChcunOXxW7lypSZPnqzHH39cklS2bFnVqVMnf4MHAAAAgAJwV0wvv9HevXu1detWFS5cOFfb+fj4aNu2bTZTu/MiKChIbdq00bJly3K9baNGjdSuXTuNGDHituO4nmEYmjt3rnr06KGgoCAFBARo6dKl+bqPWxk1apT27NmTr/v18fHRd999p7Nnz+Z4m7S0NKWmplotAAAAAHC3uWuS7lWrVsnV1VXOzs6qXr26Tp06pddee82mXvfu3eXq6mq1JCQkSJLeeecdeXp6qmzZsqpUqZLCwsK0ePFiZWRk5CmmoKAgHTt2zKps+vTpNvtfsGCBzbaRkZFas2aNfvrppzztOyvr16/XhQsX1KpVK0lSjx49NHv27HxrPyf8/Pz0yiuv6M0339TVq1fzpc1Zs2Zp69at8vLy0sMPP6xXX31VW7Zsuek2kZGR8vDwsCz+/v75EgsAAAAA5Ke7Julu3ry5YmNj9csvvyg0NFQ9e/ZU586dbepFRUUpNjbWavHz85Mk+fr66ueff9aePXv0yiuv6OrVqwoNDVXr1q3zlHgbhiGTyWRV9uyzz9rsv0OHDjbbVqlSRc8//3y+jnbPmTNHTz/9tJycrl0V0L17d23ZskWHDx/Ot33kxPDhw3X69GnNmTMnX9p75JFHdOTIEW3YsEFPPfWU9u3bpyZNmmjMmDHZbhMeHq6UlBTLcvz48XyJBQAAAADy012TdLu4uCggIEA1a9bUnDlz9Msvv2Q5iuvj46OAgACrJTMJzVStWjX1799f8+fP17p167Ru3Tr9+OOPuY4pLi5O5cqVsyrz8PCw2f/1N1q7XkREhHbt2qUVK1bket83+vfff7V8+XJNnz5dTk5OcnJyUqlSpXT16tXbSn7d3d2VkpJiU56cnCxHR0e5uLjYrPP09FR4eLgiIiJ04cKFPO/7eoUKFVKTJk00fPhwrV27VqNHj9aYMWN0+fLlLOubzWa5u7tbLQAAAABwt7lrku7rOTg46I033tBbb72lixcv3lZbVapUkSSdP38+V9v98ccfWrNmTZaj7Tnl7++vgQMH6o033lB6enqe25GkBQsW6MEHH9Tu3butRtknTJig6OjoPLdfqVIl7du3z+bxaLt27VK5cuVUqFChLLd7+eWX5eDgoMmTJ+dpv7dSpUoVXb161eaxcQAAAABwL7krk25J6tKlixwdHTVt2jSr8uTkZJ08edJqyUyo+/XrpzFjxmjLli36888/tW3bNj3//PPy9vZWgwYNst3X1atXdfLkSf3999/as2ePpkyZoqZNm6pWrVo215VfuHDBZv9nzpzJtu3w8HD9/fffWr9+/W0cDWn27Nl66qmnVK1aNauld+/e+ueff7RmzRpL3ZSUFJsp8NlNv3722WdlMpn0/PPPa+fOnTp06JDmzJmjSZMmaejQodnG4+zsrIiICH300Ue31S/p2vPPP/74Y+3cuVPHjh3Td999pzfeeEPNmzdnBBsAAADAPe2uTbqdnJw0cOBAjR8/3mqUumfPnvL19bVapkyZIklq2bKltm3bpi5duigwMFCdO3eWs7OzNmzYIC8vr2z3tW/fPvn6+qp06dJq1qyZFi9erPDwcP30009ydXW1qvvJJ5/Y7L979+7Ztl28eHENHz78tkZsd+7cqd27d2c56u7h4aEWLVpYTcWPiYlRcHCw1RIREZFl256envrpp5905coVdejQQbVq1dJHH32kiRMn6qWXXrppXKGhoSpfvnye+5WpVatW+uyzz/TYY4+pcuXKevnll9WqVSstXrz4ttsGAAAAgIJkMgzDKOgggNuVmpoqDw8PNXr2DTkVdi7ocApczJy3CzoEAAAA4L6WmYOkpKTcdIbuXTvSDQAAAADAvY6kGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOyEpBsAAAAAADtxKugAgPz03fThcnd3L+gwAAAAAEASI90AAAAAANgNSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJ04FHQCQn1q8/r6czM52a//nyW/brW0AAAAA9x9GugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOyEpBsAAAAAADsp8KT75MmTevnll1W+fHmZzWb5+/urffv22rBhg1W9rVu36vHHH1exYsXk7Oys6tWra+LEiUpPT7eqZzKZZDKZtG3bNqvytLQ0eXl5yWQyKSYm5rbr37h8+eWXkqSYmBircm9vbz3++OPas2ePVfuXL1/W+PHjVbNmTRUtWlQPPPCAGjVqpLlz5+rKlSuSpLCwMHXs2NHmmP38889ydHRU27ZtbdYdO3ZMJpNJsbGxWR7v3NRv1qyZBg8ebHldtmzZLI/V4MGD1axZM8vrUaNGqVatWlbbZLeEhYVJkn788Uc9+uijKl68uIoWLaqKFSsqNDRUly9fzlE/AAAAAOBuVKBJ97Fjx1SnTh1t3LhRH3zwgfbs2aM1a9aoefPmGjBggKXe8uXL1bRpUz344IP64Ycf9Mcff+iVV17Ru+++q27duskwDKt2/f39NXfuXKuy5cuXy9XVNcs4clt/7ty5SkxMtFpuTI7j4+OVmJio77//XmlpaWrbtq0lgbx8+bJatWql9957Ty+++KK2bt2q7du3a8CAAZoyZYr27dt30+M2e/Zsvfzyy9q0aZP+/vvvm9bNb87Ozho+fHiO6//666+WY/TVV19J+v9jk5iYqMmTJ2v//v1q3bq1HnroIW3atEl79uzRlClTVLhwYZsfVQAAAADgXuJUkDvv37+/TCaTtm/fLhcXF0t51apV1atXL0nS+fPn9cILL6hDhw6aNWuWpU6fPn1UsmRJdejQQYsXL9bTTz9tWRcaGqqPPvpIkyZNUpEiRSRJc+bMUWhoqMaMGWMTR27re3p6ysfH56Z9K1GihKXe4MGD1aFDB/3xxx+qUaOGJk2apE2bNmnHjh0KDg62bFO+fHl16dLlpqO7586d06JFi7Rjxw6dPHlS0dHReuONN24aS3568cUXNXPmTH333Xd6/PHHb1nf29vb8u/ixYtL+v9jk2nu3Lny8fHR+PHjLWUVKlRQ69at8y9wAAAAACgABTbS/e+//2rNmjUaMGCAVcKdKTMpW7t2rZKSkjRs2DCbOu3bt1dgYKC++OILq/I6deqobNmylpHVhIQEbdq0Sc8991yWseS2fm6kpKRYpp4XLlxYkrRgwQK1bNnSKuHOVKhQoSyPR6bFixcrKChIlSpVUo8ePTRnzhybkX57KleunPr27avw8HBlZGTkS5s+Pj5KTEzUpk2bcrxNWlqaUlNTrRYAAAAAuNsUWNJ96NAhGYahoKCgm9Y7cOCAJKly5cpZrg8KCrLUuV6vXr00Z84cSVJ0dLQef/xxq1HX26nfvXt3ubq6Wi0JCQlWdR588EG5urrK09NTCxcuVIcOHSx9PXjw4C37nZ3Zs2erR48ekqTWrVsrJSVFP/74Y57ayqu33npLR48e1YIFC/KlvS5duqh79+5q2rSpfH191alTJ02dOvWmiXRkZKQ8PDwsi7+/f77EAgAAAAD5qcCS7tyOzua2fo8ePfTzzz/ryJEjio6OtkxXz4/6UVFRio2NtVr8/Pys6vz000/auXOnoqOjFRgYqJkzZ+a5L5ni4+O1fft2de/eXZLk5OSkp59+WrNnz85Te3nl7e2tYcOGaeTIkflyozNHR0fNnTtXf/31l8aPH69SpUpp3Lhxqlq1qhITE7PcJjw8XCkpKZbl+PHjtx0HAAAAAOS3Aku6K1asKJPJpD/++OOm9QIDAyVJcXFxWa6Pi4uz1Lmel5eX2rVrp969e+vSpUtq06bNTfeTm/o+Pj4KCAiwWpycrC+PL1eunCpVqqTQ0FD16dPH6przwMDAW/Y7K7Nnz9bVq1fl5+cnJycnOTk5acaMGfrqq6+UkpKS6/Ykyd3dXZKy3D45OVkeHh5ZbjdkyBBdvHhR06dPz9N+s1KqVCk999xzmjp1qvbt26dLly5Z/VhxPbPZLHd3d6sFAAAAAO42BZZ0Fy9eXK1atdK0adN0/vx5m/XJycmSpMcee0zFixfXhAkTbOqsXLlSBw8etIz83qhXr16KiYnR888/L0dHx1vGlNv6OTVgwADt3btXy5cvlyQ988wzWr9+vX777TebuleuXMnyeFy9elWff/65JkyYYDXCvnv3bvn5+dlc155TxYsX1wMPPKCdO3dalaempurQoUNZ/qAhSa6urnr77bc1duxYnT17Nk/7vplixYrJ19c3y2MBAAAAAPeKAr17+bRp09SoUSPVrVtXo0ePVo0aNXT16lWtW7dOM2bMUFxcnFxcXPTxxx+rW7duevHFFzVw4EC5u7trw4YNeu211/TUU0+pa9euWbbfunVrnT59OsejoDmtn5ycrJMnT1qVubm5ZXsDtKJFi+qFF17QO++8o44dO2rw4MH69ttv1aJFC40ZM0aNGzeWm5ubduzYoffff1+zZ8+2POc606pVq3TmzBn17t3bZvS5c+fOmj17tvr27Wspi4+Pt4mjatWqKlSokE35kCFDNG7cOJUsWVL169dXUlKSxowZI29vbz355JPZHocXX3xRUVFRWrhwoerVq5dtvVv5+OOPFRsbq06dOqlChQq6dOmSPv/8c+3bt09TpkzJc7sAAAAAUNAKNOkuX768du3apbFjx2ro0KFKTEyUt7e36tSpoxkzZljqPfXUU/rhhx80duxYNWnSRJcuXVLFihX15ptvavDgwTKZTFm2bzKZ9MADD+Q4npzW79mzp01ZZGSkRowYke02AwcO1MSJE7VkyRJ17dpV69atU1RUlD7++GMNGzZMRYsWVeXKlTVo0CBVq1bNZvvZs2erZcuWWU737ty5s8aPH6/ff//d8oNBt27dbOodP35cDz74oE3566+/LldXV73//vs6fPiwihcvrkaNGumHH36wPEItK4UKFdKYMWP0zDPPZFsnJ+rWravNmzerb9+++vvvv+Xq6qqqVatqxYoVatq06W21DQAAAAAFyWTcyedNAXaSmpoqDw8PPfTSG3IyO9ttPz9PfttubQMAAAC4d2TmICkpKTedLV1g13QDAAAAAHC/I+kGAAAAAMBOSLoBAAAAALATkm4AAAAAAOyEpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATpwKOgAgP20YP1zu7u4FHQYAAAAASGKkGwAAAAAAuyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOzEqaADAPJTk3cj5Wh2ztU2u8a8Y6doAAAAAPzXMdINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUl3PgoLC5PJZLIsXl5eat26tX7//XdLnfT0dEVFRal69epydnZWsWLF1KZNG23ZssWqrejoaEs7Dg4O8vX11dNPP62EhASb/e7bt09du3aVt7e3zGazAgMDNXLkSF24cMGqnslk0ooVK7KMu2PHjlZlhw4dUq9evVS6dGmZzWaVKlVKLVq00IIFC3T16tU8tZmdGTNmqEaNGnJ3d5e7u7saNGig1atX52hbAAAAALibkXTns9atWysxMVGJiYnasGGDnJyc1K5dO0mSYRjq1q2bRo8erVdeeUVxcXGKiYmRv7+/mjVrZpO8uru7KzExUSdOnNBXX32l+Ph4denSxarOtm3bVK9ePV2+fFnffvutDhw4oLFjxyo6OlohISG6fPlyrvuwfft21a5dW3FxcZo2bZr27t2rmJgY9enTRzNmzNC+ffvyfHyy8uCDD+q9997Tzp07tWPHDj366KN64okn8n0/AAAAAHCnORV0APcbs9ksHx8fSZKPj49GjBihJk2a6PTp09q4caOWLl2qlStXqn379pZtZs2apaSkJPXp00chISFycXGRdG0UObMtX19f9e7dW4MGDVJqaqrc3d1lGIZ69+6typUra9myZXJwuPYbSpkyZRQYGKjg4GBFRUVp+PDhOY7fMAyFhYUpMDBQW7ZssbQpSRUrVlT37t1lGMZtH6frXX8sJGns2LGaMWOGtm3bpqpVq+brvgAAAADgTsrzSPe8efPUqFEj+fn56c8//5QkTZo0SV9//XW+BXevO3funObPn6+AgAB5eXlp4cKFCgwMtEkyJWno0KFKSkrSunXrsmzr1KlTWr58uRwdHeXo6ChJio2N1f79+zVkyBCr5FiSatasqZYtW+qLL77IVcyxsbGKi4vTsGHDbNrMZDKZctVmbqSnp+vLL7/U+fPn1aBBA7vtBwAAAADuhDwl3TNmzNCQIUP0+OOPKzk5Wenp6ZIkT09PTZo0KT/ju+esWrVKrq6ucnV1lZubm1auXKlFixbJwcFBBw4cUOXKlbPcLrP8wIEDlrKUlBS5urrKxcVFJUuW1A8//KABAwZYRsIz696szevby4nM+pUqVbKUnTp1ytInV1dXTZ8+3Wqb7t27W613dXXVggULcrXfPXv2yNXVVWazWX379tXy5ctVpUqVbOunpaUpNTXVagEAAACAu02eku4pU6bok08+0ZtvvmkZdZWkhx56SHv27Mm34O5FzZs3V2xsrGJjY7V9+3a1atVKbdq0scwGyM3UbDc3N8XGxmrHjh2aMGGCateurbFjx9rUy+/p3jfy8vKy9MnT09PmOvGoqCjL+sylQ4cOudpHpUqVFBsbq19++UX9+vVTaGio9u/fn239yMhIeXh4WBZ/f/889Q0AAAAA7ClP13QfPXpUwcHBNuVms1nnz5+/7aDuZS4uLgoICLC8/vTTT+Xh4aFPPvlEgYGBiouLy3K7zPLAwEBLmYODg6WtypUr6/Dhw+rXr5/mzZtnVTcuLi7L9yMuLs6qPTc3N6WkpNjUS05OloeHh6Rr121LUnx8vKVNR0dHSxxOTranjI+Pj1WfM/eVnJycZV+zUrhwYUsbderU0a+//qrJkyfr448/zrJ+eHi4hgwZYnmdmppK4g0AAADgrpOnke5y5copNjbWpnzNmjXZTnX+r8p85NfFixfVrVs3HTx4UN98841NvQkTJsjLy0shISHZtjVixAgtWrRIu3btkiTVqlVLQUFBioqKUkZGhlXd3bt3a/369erevbulrFKlStq5c6dVvfT0dO3evduSnAcHBysoKEgffvihTZt3UkZGhtLS0rJdbzabLY8Yy1wAAAAA4G6Tp5HuIUOGaMCAAbp06ZIMw9D27dv1xRdfKDIyUp9++ml+x3hPSUtL08mTJyVJZ86c0dSpU3Xu3Dm1b99eTZs21ZIlSxQaGqoPPvhALVq0UGpqqqZNm6aVK1dqyZIlluu1s+Lv769OnTpp5MiRWrVqlUwmk2bPnq2QkBB17txZ4eHh8vHx0S+//KKhQ4eqQYMGGjx4sGX7IUOGqHfv3goKClJISIjOnz+vKVOm6MyZM+rTp4+kaz8SzJ07VyEhIWrUqJHCw8NVuXJlXblyRZs2bdLp06etLinID+Hh4WrTpo1Kly6ts2fPauHChYqJidH333+fr/sBAAAAgDstT0l3nz59VKRIEb311lu6cOGCnnnmGfn5+Wny5Mnq1q1bfsd4T1mzZo18fX0lXZtiHRQUpCVLlqhZs2aSpMWLF2vSpEmKiopS//795ezsrAYNGigmJkaNGjW6ZfuvvvqqGjRooO3bt6tu3bpq2LChtm3bpoiICLVp00Znz55V6dKlFRoaqvDwcJnNZsu2mY/7mjhxokaMGKGiRYuqTp062rRpk0qWLGmpV79+fe3cuVPjxo3TgAEDdPLkSbm4uKhmzZqKiopSr1698vWYnTp1Ss8//7wSExPl4eGhGjVq6Pvvv7/pqD8AAAAA3AtMRi7vwnX16lUtXLhQrVq1UsmSJXXhwgWdO3dOJUqUsFeMwC2lpqZeS9hfGyFHs3Outt015h07RQUAAADgfpWZg6SkpNz0ctdcX9Pt5OSkvn376tKlS5KkokWLknADAAAAAJCFPN1IrW7duvrtt9/yOxbchxISEmye4X39kpCQUNAhAgAAAIDd5Oma7v79+2vo0KH666+/VKdOHZubf9WoUSNfgsO9z8/PL8s73V+/HgAAAADuV3lKujNvljZo0CBLmclkkmEYMplMSk9Pz5/ocM9zcnKyeYY3AAAAAPxX5CnpPnr0aH7HAQAAAADAfSdPSXeZMmXyOw4AAAAAAO47eUq6P//885uuf/755/MUDAAAAAAA95M8Jd2vvPKK1esrV67owoULKly4sIoWLUrSDQAAAACA8vjIsDNnzlgt586dU3x8vBo3bqwvvvgiv2MEAAAAAOCelKekOysVK1bUe++9ZzMKDgAAAADAf1Weppdn25iTk/7+++/8bBLIlZ/eCpe7u3tBhwEAAAAAkvKYdK9cudLqtWEYSkxM1NSpU9WoUaN8CQwAAAAAgHtdnpLujh07Wr02mUzy9vbWo48+qgkTJuRHXAAAAAAA3PPylHRnZGTkdxwAAAAAANx38nQjtdGjR+vChQs25RcvXtTo0aNvOygAAAAAAO4HJsMwjNxu5OjoqMTERJUoUcKqPCkpSSVKlFB6enq+BQjkRGpqqjw8PJSSksKN1AAAAADYXU5zkDyNdBuGIZPJZFO+e/duFS9ePC9NAgAAAABw38nVNd3FihWTyWSSyWRSYGCgVeKdnp6uc+fOqW/fvvkeJAAAAAAA96JcJd2TJk2SYRjq1auXIiIi5OHhYVlXuHBhlS1bVg0aNMj3IAEAAAAAuBflKukODQ2VJJUrV04NGzZUoUKF7BIUkFcNJ0fK0dmco7q7Xxtl32AAAAAA/Ofl6ZFhTZs2tfz70qVLunz5stV6bmQFAAAAAEAeb6R24cIFDRw4UCVKlJCLi4uKFStmtQAAAAAAgDwm3a+99po2btyoGTNmyGw269NPP1VERIT8/Pz0+eef53eMAAAAAADck/I0vfybb77R559/rmbNmqlnz55q0qSJAgICVKZMGS1YsEDPPvtsfscJAAAAAMA9J08j3f/++6/Kly8v6dr12//++68kqXHjxtq0aVP+RQcAAAAAwD0sT0l3+fLldfToUUlSUFCQFi9eLOnaCLinp2e+BQcAAAAAwL0sT0l3z549tXv3bknSiBEjNG3aNDk7O+vVV1/Va6+9lq8BAgAAAABwr8rTNd2vvvqq5d8tW7bUH3/8oZ07dyogIEA1atTIt+AAAAAAALiX5Snpvt6lS5dUpkwZlSlTJj/iAQAAAADgvpGn6eXp6ekaM2aMSpUqJVdXVx05ckSS9Pbbb2v27Nn5GiAAAAAAAPeqPCXdY8eOVXR0tMaPH6/ChQtbyqtVq6ZPP/0034L7L4mOjr7rbkIXFhamjh07FnQYAAAAAHDPylPS/fnnn2vWrFl69tln5ejoaCmvWbOm/vjjj3wL7m4RFhYmk8mkvn372qwbMGCATCaTwsLC7nxg14mJiZHJZFJycnKO6ptMppsuo0aN0uTJkxUdHW3XuK+3ZcsWOTk5qVatWndsnwAAAABgT3lKuk+cOKGAgACb8oyMDF25cuW2g7ob+fv768svv9TFixctZZcuXdLChQtVunTp22q7II5ZYmKiZZk0aZLc3d2tyoYNGyYPD4/bGn03DENXr17NUd3k5GQ9//zzatGiRZ73BwAAAAB3mzwl3VWqVNFPP/1kU7506VIFBwffdlB3o9q1a8vf31/Lli2zlC1btkylS5e26vOaNWvUuHFjeXp6ysvLS+3atdPhw4ct648dOyaTyaRFixapadOmcnZ21oIFC2z2d/r0aT300EPq1KmT0tLSlJGRocjISJUrV05FihRRzZo1tXTpUkubzZs3lyQVK1YsRyPvPj4+lsXDw0Mmk8mqzNXV1WZ6+c1ikP5/tH316tWqU6eOzGazNm/enKPj27dvXz3zzDNq0KBBjuoDAAAAwL0gT3cvHzlypEJDQ3XixAllZGRo2bJlio+P1+eff65Vq1bld4x3jV69emnu3Ll69tlnJUlz5sxRz549FRMTY6lz/vx5DRkyRDVq1NC5c+c0cuRIderUSbGxsXJw+P/fOEaMGKEJEyYoODhYzs7O+v777y3rjh8/rpCQENWvX1+zZ8+Wo6Ojxo4dq/nz52vmzJmqWLGiNm3apB49esjb21uNGzfWV199pc6dOys+Pl7u7u4qUqRIvvc/MjIy2xiaNm1q1bcPP/xQ5cuXV7FixW7Z7ty5c3XkyBHNnz9f7777br7HDQAAAAAFJVdJ95EjR1SuXDk98cQT+uabbzR69Gi5uLho5MiRql27tr755huFhITYK9YC16NHD4WHh+vPP/+UdO0a5C+//NIq6e7cubPVNnPmzJG3t7f279+vatWqWcoHDx6sJ5980mYf8fHxCgkJUadOnTRp0iSZTCalpaVp3LhxWr9+vWUkuHz58tq8ebM+/vhjNW3aVMWLF5cklShRwi43ZMtJDJlGjx6d4/Pg4MGDGjFihH766Sc5OeX8dExLS1NaWprldWpqao63BQAAAIA7JVdJd8WKFZWYmKgSJUqoSZMmKl68uPbs2aOSJUvaK767ire3t9q2bavo6GgZhqG2bdvqgQcesKpz8OBBjRw5Ur/88ov++ecfZWRkSJISEhKsku6HHnrIpv2LFy+qSZMmeuaZZzRp0iRL+aFDh3ThwgWbRPby5ct3bDp/bmLIqm9ZSU9P1zPPPKOIiAgFBgbmKp7IyEhFRETkahsAAAAAuNNylXQbhmH1evXq1Tp//ny+BnS369WrlwYOHChJmjZtms369u3bq0yZMvrkk0/k5+enjIwMVatWTZcvX7aq5+LiYrOt2WxWy5YttWrVKr322msqVaqUJOncuXOSpG+//dZSdv02d0JuYsiqb1k5e/asduzYod9++81yTDMyMmQYhpycnLR27Vo9+uijWW4bHh6uIUOGWF6npqbK398/x/0BAAAAgDshT9d0Z7oxCf8vaN26tS5fviyTyaRWrVpZrUtKSlJ8fLw++eQTNWnSRJJyfCMxSXJwcNC8efP0zDPPqHnz5oqJiZGfn5+qVKkis9mshIQEq2nc18t8Xnp6enoee3ZzOYkht9zd3bVnzx6rsunTp2vjxo1aunSpypUrl+22ZrP5jv3gAAAAAAB5laukO/MZzjeW/Zc4OjoqLi7O8u/rFStWTF5eXpo1a5Z8fX2VkJCgESNG5Lr9BQsWqHv37nr00UcVExMjHx8fDRs2TK+++qoyMjLUuHFjpaSkaMuWLXJ3d1doaKjKlCkjk8mkVatW6fHHH1eRIkXk6uqab/12c3O7ZQy55eDgYDXlXrp2Tbqzs7NNOQAAAADci3I9vTwsLMwywnjp0iX17dvXZjrx9Y/Vuh+5u7tnWe7g4KAvv/xSgwYNUrVq1VSpUiV99NFHatasWa7ad3Jy0hdffKGnn37akniPGTNG3t7eioyM1JEjR+Tp6anatWvrjTfekCSVKlVKERERGjFihHr27Knnn39e0dHRt9lTa7eKAQAAAABgzWTkYo54z549c1Rv7ty5eQ4IyIvU1FR5eHio6ugRcnTO2bTz3a+Nsm9QAAAAAO5bmTlISkpKtgOzUi5HukmmAQAAAADIOYeCDgD2kZCQIFdX12yXhISEOxJH1apVs41hwYIFdyQGAAAAACgot3X3cty9/Pz8FBsbe9P1d8J3332nK1euZLnuv/J8dwAAAAD/XSTd9yknJycFBAQUdBgqU6ZMQYcAAAAAAAWG6eUAAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdsIjw3Bf2fpKuNzd3Qs6DAAAAACQxEg3AAAAAAB2Q9INAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2IlTQQcA5KdHPxsjxyLmW9b7pc+7dyAaAAAAAP91jHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtKdj8LCwmQymSyLl5eXWrdurd9//91SJz09XVFRUapevbqcnZ1VrFgxtWnTRlu2bLFqKzo62tKOg4ODfH199fTTTyshIcFmv/v27VPXrl3l7e0ts9mswMBAjRw5UhcuXLCqZzKZtGLFiizj7tixo1XZoUOH1KtXL5UuXVpms1mlSpVSixYttGDBAl29ejVPbWYnMjJSDz/8sNzc3FSiRAl17NhR8fHxOdoWAAAAAO5mJN35rHXr1kpMTFRiYqI2bNggJycntWvXTpJkGIa6deum0aNH65VXXlFcXJxiYmLk7++vZs2a2SSv7u7uSkxM1IkTJ/TVV18pPj5eXbp0saqzbds21atXT5cvX9a3336rAwcOaOzYsYqOjlZISIguX76c6z5s375dtWvXVlxcnKZNm6a9e/cqJiZGffr00YwZM7Rv3748H5+s/PjjjxowYIC2bdumdevW6cqVK3rsscd0/vz5fN0PAAAAANxpTgUdwP3GbDbLx8dHkuTj46MRI0aoSZMmOn36tDZu3KilS5dq5cqVat++vWWbWbNmKSkpSX369FFISIhcXFwkXRtFzmzL19dXvXv31qBBg5Samip3d3cZhqHevXurcuXKWrZsmRwcrv2GUqZMGQUGBio4OFhRUVEaPnx4juM3DENhYWEKDAzUli1bLG1KUsWKFdW9e3cZhnHbx+l6a9assXodHR2tEiVKaOfOnXrkkUfydV8AAAAAcCcx0m1H586d0/z58xUQECAvLy8tXLhQgYGBVgl3pqFDhyopKUnr1q3Lsq1Tp05p+fLlcnR0lKOjoyQpNjZW+/fv15AhQ6ySY0mqWbOmWrZsqS+++CJXMcfGxiouLk7Dhg2zaTOTyWTKVZu5lZKSIkkqXry4XfcDAAAAAPbGSHc+W7VqlVxdXSVJ58+fl6+vr1atWiUHBwcdOHBAlStXznK7zPIDBw5YylJSUuTq6irDMCzXZw8aNMgyEp5Z92Ztbt68OVfxZ7ZZqVIlS9mpU6dUvnx5y+vx48erf//+ltfdu3e3/BCQKS0tTW3bts3VviUpIyNDgwcPVqNGjVStWrVs66WlpSktLc3yOjU1Ndf7AgAAAAB7I+nOZ82bN9eMGTMkSWfOnNH06dPVpk0bbd++XZJyNTXbzc1Nu3bt0pUrV7R69WotWLBAY8eOtamX39O9b+Tl5aXY2FhJUrNmzWyuE4+KilLLli2tyoYPH6709PRc72vAgAHau3fvLX8siIyMVERERK7bBwAAAIA7iaQ7n7m4uCggIMDy+tNPP5WHh4c++eQTBQYGKi4uLsvtMssDAwMtZQ4ODpa2KleurMOHD6tfv36aN2+eVd24uDgFBwdn2eb17bm5uVmmbl8vOTlZHh4ekq5dty1J8fHxljYdHR0tcTg52Z4yPj4+Vn3O3FdycnKWfc3OwIEDtWrVKm3atEkPPvjgTeuGh4dryJAhltepqany9/fP1f4AAAAAwN64ptvOMh/5dfHiRXXr1k0HDx7UN998Y1NvwoQJ8vLyUkhISLZtjRgxQosWLdKuXbskSbVq1VJQUJCioqKUkZFhVXf37t1av369unfvbimrVKmSdu7caVUvPT1du3fvtiTnwcHBCgoK0ocffmjTpr0YhqGBAwdq+fLl2rhxo8qVK3fLbcxms9zd3a0WAAAAALjbkHTns7S0NJ08eVInT55UXFycXn75ZZ07d07t27dXt27d1KlTJ4WGhmr27Nk6duyYfv/9d7300ktauXKlPv30U8v12lnx9/dXp06dNHLkSEnXEvrZs2dr//796ty5s7Zv366EhAQtWbJE7du3V4MGDTR48GDL9kOGDNGnn36q6dOn6+DBg4qNjdWLL76oM2fOqE+fPpY2586dq/j4eDVq1EgrV67UwYMHtX//fs2cOVOnT5+2uX77dg0YMEDz58/XwoUL5ebmZjl+Fy9ezNf9AAAAAMCdxvTyfLZmzRr5+vpKujbFOigoSEuWLFGzZs0kSYsXL9akSZMUFRWl/v37y9nZWQ0aNFBMTIwaNWp0y/ZfffVVNWjQQNu3b1fdunXVsGFDbdu2TREREWrTpo3Onj2r0qVLKzQ0VOHh4TKbzZZtMx/3NXHiRI0YMUJFixZVnTp1tGnTJpUsWdJSr379+tq5c6fGjRunAQMG6OTJk3JxcVHNmjUVFRWlXr165esxy7wGPvMYZZo7d67CwsLydV8AAAAAcCeZDHvfhQu4A1JTU+Xh4aE6Hw2TYxHzLev/0ufdOxAVAAAAgPtVZg6SkpJy08tdmV4OAAAAAICdkHTDrhISEuTq6prtkpCQUNAhAgAAAIDdcE037MrPz8/yjO/s1gMAAADA/YqkG3bl5ORk8wxvAAAAAPivYHo5AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ3wyDDcVzaGvi13d/eCDgMAAAAAJDHSDQAAAACA3ZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHbiVNABAPnpmW/eVqGi5izXLe80/g5HAwAAAOC/jpFuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOyEpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki681FYWJhMJpNl8fLyUuvWrfX7779b6qSnpysqKkrVq1eXs7OzihUrpjZt2mjLli1WbUVHR1vacXBwkK+vr55++mklJCTY7Hffvn3q2rWrvL29ZTabFRgYqJEjR+rChQtW9Uwmk1asWJFl3B07drQqO3TokHr16qXSpUvLbDarVKlSatGihRYsWKCrV6/mqc3sjBo1yuq4mUwmBQUF5WhbAAAAALibkXTns9atWysxMVGJiYnasGGDnJyc1K5dO0mSYRjq1q2bRo8erVdeeUVxcXGKiYmRv7+/mjVrZpO8uru7KzExUSdOnNBXX32l+Ph4denSxarOtm3bVK9ePV2+fFnffvutDhw4oLFjxyo6OlohISG6fPlyrvuwfft21a5dW3FxcZo2bZr27t2rmJgY9enTRzNmzNC+ffvyfHyyU7VqVctxS0xM1ObNm/N9HwAAAABwpzkVdAD3G7PZLB8fH0mSj4+PRowYoSZNmuj06dPauHGjli5dqpUrV6p9+/aWbWbNmqWkpCT16dNHISEhcnFxkXRtFDmzLV9fX/Xu3VuDBg1Samqq3N3dZRiGevfurcqVK2vZsmVycLj2G0qZMmUUGBio4OBgRUVFafjw4TmO3zAMhYWFKTAwUFu2bLG0KUkVK1ZU9+7dZRjGbR+nGzk5OVn6CgAAAAD3C0a67ejcuXOaP3++AgIC5OXlpYULFyowMNAq4c40dOhQJSUlad26dVm2derUKS1fvlyOjo5ydHSUJMXGxmr//v0aMmSIVXIsSTVr1lTLli31xRdf5Crm2NhYxcXFadiwYTZtZjKZTLlqMycOHjwoPz8/lS9fXs8++2yW0+gBAAAA4F7DSHc+W7VqlVxdXSVJ58+fl6+vr1atWiUHBwcdOHBAlStXznK7zPIDBw5YylJSUuTq6irDMCzXZw8aNMgyEp5Z92Zt5naadmablSpVspSdOnVK5cuXt7weP368+vfvb3ndvXt3yw8BmdLS0tS2bdsc7bNevXqKjo5WpUqVlJiYqIiICDVp0kR79+6Vm5tbltukpaUpLS3N8jo1NTVH+wIAAACAO4mkO581b95cM2bMkCSdOXNG06dPV5s2bbR9+3ZJytXUbDc3N+3atUtXrlzR6tWrtWDBAo0dO9amnj2me1/Py8tLsbGxkqRmzZrZXCceFRWlli1bWpUNHz5c6enpOWq/TZs2ln/XqFFD9erVU5kyZbR48WL17t07y20iIyMVERGRi14AAAAAwJ1H0p3PXFxcFBAQYHn96aefysPDQ5988okCAwMVFxeX5XaZ5YGBgZYyBwcHS1uVK1fW4cOH1a9fP82bN8+qblxcnIKDg7Ns8/r23NzclJKSYlMvOTlZHh4ekq5dty1J8fHxljYdHR0tcTg52Z4yPj4+Vn3O3FdycnKWfb0VT09PBQYG6tChQ9nWCQ8P15AhQyyvU1NT5e/vn6f9AQAAAIC9cE23nWU+8uvixYvq1q2bDh48qG+++cam3oQJE+Tl5aWQkJBs2xoxYoQWLVqkXbt2SZJq1aqloKAgRUVFKSMjw6ru7t27tX79enXv3t1SVqlSJe3cudOqXnp6unbv3m1JzoODgxUUFKQPP/zQps075dy5czp8+LB8fX2zrWM2m+Xu7m61AAAAAMDdhqQ7n6WlpenkyZM6efKk4uLi9PLLL+vcuXNq3769unXrpk6dOik0NFSzZ8/WsWPH9Pvvv+ull17SypUr9emnn1qu186Kv7+/OnXqpJEjR0q6ltDPnj1b+/fvV+fOnbV9+3YlJCRoyZIlat++vRo0aKDBgwdbth8yZIg+/fRTTZ8+XQcPHlRsbKxefPFFnTlzRn369LG0OXfuXMXHx6tRo0ZauXKlDh48qP3792vmzJk6ffq0zfXbt2vYsGH68ccfdezYMW3dulWdOnWSo6Oj1Q8GAAAAAHAvYnp5PluzZo1lhNbNzU1BQUFasmSJmjVrJklavHixJk2apKioKPXv31/Ozs5q0KCBYmJi1KhRo1u2/+qrr6pBgwbavn276tatq4YNG2rbtm2KiIhQmzZtdPbsWZUuXVqhoaEKDw+X2Wy2bJv5uK+JEydqxIgRKlq0qOrUqaNNmzapZMmSlnr169fXzp07NW7cOA0YMEAnT56Ui4uLatasqaioKPXq1Stfj9lff/2l7t27KykpSd7e3mrcuLG2bdsmb2/vfN0PAAAAANxpJsPed+EC7oDU1FR5eHio7fxBKlTUnGWd5Z3G3+GoAAAAANyvMnOQlJSUm17uyvRyAAAAAADshKQbdpWQkCBXV9dsl4SEhIIOEQAAAADshmu6YVd+fn6WZ3xntx4AAAAA7lck3bArJycnm2d4AwAAAMB/BdPLAQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOyER4bhvrKw/Ri5u7sXdBgAAAAAIImRbgAAAAAA7IakGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATp4IOAMhPb2waKrNL4SzXTWg+7Q5HAwAAAOC/jpFuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOyEpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki681FYWJhMJpNl8fLyUuvWrfX7779b6qSnpysqKkrVq1eXs7OzihUrpjZt2mjLli1WbUVHR1vacXBwkK+vr55++mklJCTY7Hffvn3q2rWrvL29ZTabFRgYqJEjR+rChQtW9Uwmk1asWJFl3B07drQqO3TokHr16qXSpUvLbDarVKlSatGihRYsWKCrV6/mqc2ceO+992QymTR48OBcbwsAAAAAdxuS7nzWunVrJSYmKjExURs2bJCTk5PatWsnSTIMQ926ddPo0aP1yiuvKC4uTjExMfL391ezZs1skld3d3clJibqxIkT+uqrrxQfH68uXbpY1dm2bZvq1auny5cv69tvv9WBAwc0duxYRUdHKyQkRJcvX851H7Zv367atWsrLi5O06ZN0969exUTE6M+ffpoxowZ2rdvX56Pz838+uuv+vjjj1WjRg27tA8AAAAAd5pTQQdwvzGbzfLx8ZEk+fj4aMSIEWrSpIlOnz6tjRs3aunSpVq5cqXat29v2WbWrFlKSkpSnz59FBISIhcXF0nXRpEz2/L19VXv3r01aNAgpaamyt3dXYZhqHfv3qpcubKWLVsmB4drv6GUKVNGgYGBCg4OVlRUlIYPH57j+A3DUFhYmAIDA7VlyxZLm5JUsWJFde/eXYZh3PZxutG5c+f07LPP6pNPPtG7776b7+0DAAAAQEFgpNuOzp07p/nz5ysgIEBeXl5auHChAgMDrRLuTEOHDlVSUpLWrVuXZVunTp3S8uXL5ejoKEdHR0lSbGys9u/fryFDhlglx5JUs2ZNtWzZUl988UWuYo6NjVVcXJyGDRtm02Ymk8mUqzZzYsCAAWrbtq1atmyZ720DAAAAQEFhpDufrVq1Sq6urpKk8+fPy9fXV6tWrZKDg4MOHDigypUrZ7ldZvmBAwcsZSkpKXJ1dZVhGJbrswcNGmQZCc+se7M2N2/enKv4M9usVKmSpezUqVMqX7685fX48ePVv39/y+vu3btbfgjIlJaWprZt2+Zon19++aV27dqlX3/9NcdxpqWlKS0tzfI6NTU1x9sCAAAAwJ1C0p3PmjdvrhkzZkiSzpw5o+nTp6tNmzbavn27JOVqarabm5t27dqlK1euaPXq1VqwYIHGjh1rU88e072v5+XlpdjYWElSs2bNbK4Tj4qKshmhHj58uNLT02/Z9vHjx/XKK69o3bp1cnZ2znFMkZGRioiIyHF9AAAAACgIJN35zMXFRQEBAZbXn376qTw8PPTJJ58oMDBQcXFxWW6XWR4YGGgpc3BwsLRVuXJlHT58WP369dO8efOs6sbFxSk4ODjLNq9vz83NTSkpKTb1kpOT5eHhIenadduSFB8fb2nT0dHREoeTk+0p4+PjY9XnzH0lJydn2dfr7dy5U6dOnVLt2rUtZenp6dq0aZOmTp2qtLQ0m1F0SQoPD9eQIUMsr1NTU+Xv73/L/QEAAADAncQ13XaW+civixcvqlu3bjp48KC++eYbm3oTJkyQl5eXQkJCsm1rxIgRWrRokXbt2iVJqlWrloKCghQVFaWMjAyrurt379b69evVvXt3S1mlSpW0c+dOq3rp6enavXu3JTkPDg5WUFCQPvzwQ5s27aFFixbas2ePYmNjLctDDz2kZ599VrGxsVkm3NK1G9a5u7tbLQAAAABwt2GkO5+lpaXp5MmTkq5NL586darOnTun9u3bq2nTplqyZIlCQ0P1wQcfqEWLFkpNTdW0adO0cuVKLVmyxHK9dlb8/f3VqVMnjRw5UqtWrZLJZNLs2bMVEhKizp07Kzw8XD4+Pvrll180dOhQNWjQwOp510OGDFHv3r0VFBSkkJAQnT9/XlOmTNGZM2fUp08fSdd+JJg7d65CQkLUqFEjhYeHq3Llyrpy5Yo2bdqk06dPZ5sI54Wbm5uqVatmVebi4iIvLy+bcgAAAAC415B057M1a9bI19dX0rWEMigoSEuWLFGzZs0kSYsXL9akSZMUFRWl/v37y9nZWQ0aNFBMTIwaNWp0y/ZfffVVNWjQQNu3b1fdunXVsGFDbdu2TREREWrTpo3Onj2r0qVLKzQ0VOHh4TKbzZZtMx/3NXHiRI0YMUJFixZVnTp1tGnTJpUsWdJSr379+tq5c6fGjRunAQMG6OTJk3JxcVHNmjUVFRWlXr165e9BAwAAAID7lMmw9124gDsgNTVVHh4eGvBNH5ldCmdZZ0LzaXc4KgAAAAD3q8wcJCUl5aaXu3JNNwAAAAAAdkLSDbtKSEiQq6trtktCQkJBhwgAAAAAdsM13bArPz8/yzO+s1sPAAAAAPcrkm7YlZOTk80zvAEAAADgv4Lp5QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2wiPDcF8Z98gEubu7F3QYAAAAACCJkW4AAAAAAOyGpBsAAAAAADsh6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki6AQAAAACwE6eCDgDIT5/88qyKuBSSJPVvuKyAowEAAADwX8dINwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdN8loqOj5enpWdBhWAkLC1PHjh0LOgwAAAAAuGeRdOdAWFiYTCaT+vbta7NuwIABMplMCgsLu/OBXScmJkYmk0nJyck5qm8ymW66jBo1SpMnT1Z0dLRd4968ebMaNWokLy8vFSlSREFBQYqKirLrPgEAAADgTnEq6ADuFf7+/vryyy8VFRWlIkWKSJIuXbqkhQsXqnTp0rfV9pUrV/IjxFxJTEy0/HvRokUaOXKk4uPjLWWurq5ydXW9rX0YhqH09HQ5OWV/mrm4uGjgwIGqUaOGXFxctHnzZr300ktycXHRiy++eFv7BwAAAICCxkh3DtWuXVv+/v5atmyZpWzZsmUqXbq0goODLWVr1qxR48aN5enpKS8vL7Vr106HDx+2rD927JhMJpMWLVqkpk2bytnZWQsWLLDZ3+nTp/XQQw+pU6dOSktLU0ZGhiIjI1WuXDkVKVJENWvW1NKlSy1tNm/eXJJUrFixHI28+/j4WBYPDw+ZTCarMldXV5vp5TeLQfr/0fbVq1erTp06MpvN2rx5803jCA4OVvfu3VW1alWVLVtWPXr0UKtWrfTTTz/ddDsAAAAAuBeQdOdCr169NHfuXMvrOXPmqGfPnlZ1zp8/ryFDhmjHjh3asGGDHBwc1KlTJ2VkZFjVGzFihF555RXFxcWpVatWVuuOHz+u/2vv3qOqKvM/jn8OkgcGOIiKKIaoSXlBy0s5mIi4JB3J1JixNM1rOU1m6WhJkxdkRqihSZtsMkwxL+Gk/rp4naSoRLMySFMWmml0AVPGRNQB9ezfH7M84wlED7I5gO/XWmctzrOfvfdnn76x/PJsNpGRkQoPD9fatWtltVqVlJSk119/Xa+88or27dunqVOnatSoUfrwww8VEhKidevWSZLy8vJUUFCghQsXVvv1V5bhl9eWnJys3NxcdenSxaVzZGdna8eOHYqKiqrO6AAAAADgFtxe7oJRo0YpPj5e3377rSQpKytL6enpyszMdMyJi4tz2mfp0qUKDAzU/v37FR4e7hh/4okndO+995Y7R15enmJiYjRs2DAtWLBAFotFpaWlmj9/vrZt26aIiAhJUtu2bbV9+3YtXrxYUVFRaty4sSSpWbNmpjyQ7WoyXDRv3jzFxMS4dPwbb7xRx44d0/nz5zV37lxNnDjxinlKS0sd74uLi106HwAAAADUBJpuFwQGBio2NlZpaWkyDEOxsbFq2rSp05yDBw9q9uzZ2rVrl44fP+5Y4c7Pz3dqunv06FHu+GfPnlVkZKRGjhypBQsWOMa//vprnTlzplwjW1ZW5nRru5lcyVDRtV3Jxx9/rJKSEn3yySeaOXOm2rVrpxEjRlx2flJSkhISElw+DwAAAADUJJpuF40fP16TJ0+WJC1atKjc9sGDBys0NFSpqakKDg6W3W5XeHi4ysrKnOb5+PiU29dqtap///7asGGDZsyYoZYtW0qSSkpKJEkbN250jF26T01wJUNF13Ylbdq0kSR17txZR48e1dy5cyttuuPj4zVt2jTH++LiYoWEhLh8XgAAAAAwE023iwYOHKiysjJZLJZyv4tdVFSkvLw8paamKjIyUpKu+CCxS3l4eGjFihUaOXKkoqOjlZmZqeDgYHXs2FFWq1X5+fmX/V3nhg0bSpIuXLhQxSur3NVkqC52u93p1vGKWK3WGvuBAwAAAABUFU23ixo0aKDc3FzH15cKCAhQkyZN9Oqrr6pFixbKz8/XzJkzXT7+qlWrNGLECPXr10+ZmZlq3ry5pk+frqlTp8put6t37946efKksrKyZLPZNGbMGIWGhspisWjDhg0aNGiQvL29r/lPfl3Kz8/vihmqYtGiRWrVqpXat28vSfroo4+UkpKiKVOmVFt2AAAAAHAXmu4qsNlsFY57eHgoPT1dU6ZMUXh4uG655Ra9+OKL6tu3r0vH9/T01BtvvKH77rvP0XgnJiYqMDBQSUlJ+uabb9SoUSN169ZNTz/9tCSpZcuWSkhI0MyZMzVu3Dg9+OCDSktLu8YrdXalDFVht9sVHx+vw4cPy9PTUzfddJOeffZZTZo0qRqTAwAAAIB7WAzDMNwdArhWxcXF8vf3V8q/7pa3zw2SpD/0Wn+FvQAAAACgai72ICdPnrzswqzE3+kGAAAAAMA0NN31VH5+vnx9fS/7ys/Pr5EcnTp1umyGVatW1UgGAAAAAHAXfqe7ngoODlZOTk6l22vCpk2bdO7cuQq3BQUF1UgGAAAAAHAXmu56ytPTU+3atXN3DIWGhro7AgAAAAC4DbeXAwAAAABgEppuAAAAAABMQtMNAAAAAIBJaLoBAAAAADAJTTcAAAAAACah6QYAAAAAwCT8yTDUKw/1XCWbzebuGAAAAAAgiZVuAAAAAABMQ9MNAAAAAIBJaLoBAAAAADAJTTcAAAAAACah6QYAAAAAwCQ03QAAAAAAmISmGwAAAAAAk9B0AwAAAABgEppu1Cvvf95P7+3q6e4YAAAAACCJphsAAAAAANPQdAMAAAAAYBKabgAAAAAATELTDQAAAACASWi6AQAAAAAwCU03AAAAAAAmoekGAAAAAMAkNN0AAAAAAJiEphsAAAAAAJPQdAMAAAAAYBKa7loiLS1NjRo1cncMJ2PHjtXQoUPdHQMAAAAA6iya7qswduxYWSwW/f73vy+37dFHH5XFYtHYsWNrPtglMjMzZbFY9PPPP1/VfIvFUulr7ty5WrhwodLS0kzNvX79esXExCgwMFA2m00RERHaunWrqecEAAAAgJpC032VQkJClJ6errNnzzrG/vOf/2j16tVq1arVNR373Llz1xrPZQUFBY7XggULZLPZnMamT58uf3//a1p9NwxD58+fr3TORx99pJiYGG3atEm7d+9WdHS0Bg8erOzs7CqfFwAAAABqC5ruq9StWzeFhIRo/fr1jrH169erVatW6tq1q2Nsy5Yt6t27txo1aqQmTZro7rvv1qFDhxzbjxw5IovFojVr1igqKkpeXl5atWpVufMdO3ZMPXr00LBhw1RaWiq73a6kpCS1adNG3t7euvXWW7V27VrHMaOjoyVJAQEBV7Xy3rx5c8fL399fFovFaczX17fc7eWVZZD+t9q+efNmde/eXVarVdu3b680x4IFC/Tkk0/q9ttvV1hYmObPn6+wsDC9++67le4HAAAAAHUBTbcLxo8fr2XLljneL126VOPGjXOac/r0aU2bNk2ff/65MjIy5OHhoWHDhslutzvNmzlzph5//HHl5uZqwIABTtu+++47RUZGKjw8XGvXrpXValVSUpJef/11vfLKK9q3b5+mTp2qUaNG6cMPP1RISIjWrVsnScrLy1NBQYEWLlxY7ddfWYZfXltycrJyc3PVpUsXl85ht9t16tQpNW7cuDqjAwAAAIBbeLo7QF0yatQoxcfH69tvv5UkZWVlKT09XZmZmY45cXFxTvssXbpUgYGB2r9/v8LDwx3jTzzxhO69995y58jLy1NMTIyGDRumBQsWyGKxqLS0VPPnz9e2bdsUEREhSWrbtq22b9+uxYsXKyoqytGkNmvWzJQHsl1NhovmzZunmJiYKp0nJSVFJSUlGj58+BXzlJaWOt4XFxdX6XwAAAAAYCaabhcEBgYqNjZWaWlpMgxDsbGxatq0qdOcgwcPavbs2dq1a5eOHz/uWOHOz893arp79OhR7vhnz55VZGSkRo4cqQULFjjGv/76a505c6ZcI1tWVuZ0a7uZXMlQ0bVdjdWrVyshIUFvv/22mjVrVuncpKQkJSQkVOk8AAAAAFBTaLpdNH78eE2ePFmStGjRonLbBw8erNDQUKWmpio4OFh2u13h4eEqKytzmufj41NuX6vVqv79+2vDhg2aMWOGWrZsKUkqKSmRJG3cuNExduk+NcGVDBVd25Wkp6dr4sSJevPNN9W/f/8rzo+Pj9e0adMc74uLixUSEuLyeQEAAADATDTdLho4cKDKyspksVjK/S52UVGR8vLylJqaqsjISEm64oPELuXh4aEVK1Zo5MiRio6OVmZmpoKDg9WxY0dZrVbl5+c73cZ9qYYNG0qSLly4UMUrq9zVZKiqN954Q+PHj1d6erpiY2Ovah+r1VpjP3AAAAAAgKqi6XZRgwYNlJub6/j6UgEBAWrSpIleffVVtWjRQvn5+Zo5c6bLx1+1apVGjBihfv36KTMzU82bN9f06dM1depU2e129e7dWydPnlRWVpZsNpvGjBmj0NBQWSwWbdiwQYMGDZK3t7d8fX2r7br9/PyumKEqVq9erTFjxmjhwoXq2bOnCgsLJUne3t7y9/evtvwAAAAA4A48vbwKbDabbDZbuXEPDw+lp6dr9+7dCg8P19SpU/XXv/7V5eN7enrqjTfeUKdOndSvXz/99NNPSkxM1KxZs5SUlKQOHTpo4MCB2rhxo9q0aSNJatmypRISEjRz5kwFBQU5boGvTlfKUBWvvvqqzp8/r0cffVQtWrRwvB5//PFqTA4AAAAA7mExDMNwdwjgWhUXF8vf31//l9FdPj4NFNNzl7sjAQAAAKjHLvYgJ0+erHBR9iJWugEAAAAAMAlNdz2Vn58vX1/fy77y8/NrJEenTp0um2HVqlU1kgEAAAAA3IUHqdVTwcHBysnJqXR7Tdi0aZPOnTtX4bagoKAayQAAAAAA7kLTXU95enqqXbt27o6h0NBQd0cAAAAAALfh9nIAAAAAAExC0w0AAAAAgElougEAAAAAMAlNNwAAAAAAJqHpBgAAAADAJDTdAAAAAACYhKYbAAAAAACT8He6Ua/06/G+bDabu2MAAAAAgCRWugEAAAAAMA1NNwAAAAAAJuH2ctQLhmFIkoqLi92cBAAAAMD14GLvcbEXuRyabtQLRUVFkqSQkBA3JwEAAABwPTl16pT8/f0vu52mG/VC48aNJUn5+fmVFjxwtYqLixUSEqLvvvuOh/OhWlBTqG7UFKobNYXqVt9ryjAMnTp1SsHBwZXOo+lGveDh8d/HE/j7+9fL/6HhPjabjZpCtaKmUN2oKVQ3agrVrT7X1NUs+PEgNQAAAAAATELTDQAAAACASWi6US9YrVbNmTNHVqvV3VFQT1BTqG7UFKobNYXqRk2hulFT/2UxrvR8cwAAAAAAUCWsdAMAAAAAYBKabgAAAAAATELTDQAAAACASWi6UWcsWrRIrVu3lpeXl3r27KlPP/200vlvvvmm2rdvLy8vL3Xu3FmbNm2qoaSoK1ypqX379ikuLk6tW7eWxWLRggULai4o6gxXaio1NVWRkZEKCAhQQECA+vfvf8Xva7j+uFJT69evV48ePdSoUSP5+Pjotttu04oVK2owLWo7V/8tdVF6erosFouGDh1qbkDUOa7UVFpamiwWi9PLy8urBtO6D0036oQ1a9Zo2rRpmjNnjr744gvdeuutGjBggH766acK5+/YsUMjRozQhAkTlJ2draFDh2ro0KH66quvajg5aitXa+rMmTNq27atkpOT1bx58xpOi7rA1ZrKzMzUiBEj9MEHH2jnzp0KCQnRXXfdpR9++KGGk6O2crWmGjdurD/96U/auXOn9uzZo3HjxmncuHHaunVrDSdHbeRqPV105MgRTZ8+XZGRkTWUFHVFVWrKZrOpoKDA8fr2229rMLEbGUAdcMcddxiPPvqo4/2FCxeM4OBgIykpqcL5w4cPN2JjY53GevbsaUyaNMnUnKg7XK2pS4WGhhovvPCCielQF11LTRmGYZw/f97w8/Mzli9fblZE1DHXWlOGYRhdu3Y1nnnmGTPioY6pSj2dP3/e6NWrl7FkyRJjzJgxxpAhQ2ogKeoKV2tq2bJlhr+/fw2lq11Y6UatV1ZWpt27d6t///6OMQ8PD/Xv3187d+6scJ+dO3c6zZekAQMGXHY+ri9VqSmgMtVRU2fOnNG5c+fUuHFjs2KiDrnWmjIMQxkZGcrLy1OfPn3MjIo6oKr1NG/ePDVr1kwTJkyoiZioQ6paUyUlJQoNDVVISIiGDBmiffv21URct6PpRq13/PhxXbhwQUFBQU7jQUFBKiwsrHCfwsJCl+bj+lKVmgIqUx019dRTTyk4OLjcDwxxfapqTZ08eVK+vr5q2LChYmNj9fe//10xMTFmx0UtV5V62r59u1577TWlpqbWRETUMVWpqVtuuUVLly7V22+/rZUrV8put6tXr176/vvvayKyW3m6OwAAANe75ORkpaenKzMz87p5qAzM4efnp5ycHJWUlCgjI0PTpk1T27Zt1bdvX3dHQx1y6tQpjR49WqmpqWratKm746CeiIiIUEREhON9r1691KFDBy1evFiJiYluTGY+mm7Uek2bNlWDBg109OhRp/GjR49e9oFWzZs3d2k+ri9VqSmgMtdSUykpKUpOTta2bdvUpUsXM2OiDqlqTXl4eKhdu3aSpNtuu025ublKSkqi6b7OuVpPhw4d0pEjRzR48GDHmN1ulyR5enoqLy9PN910k7mhUatVx7+lbrjhBnXt2lVff/21GRFrFW4vR63XsGFDde/eXRkZGY4xu92ujIwMp5+WXSoiIsJpviS99957l52P60tVagqoTFVr6rnnnlNiYqK2bNmiHj161ERU1BHV9X3KbrertLTUjIioQ1ytp/bt22vv3r3KyclxvO655x5FR0crJydHISEhNRkftVB1fI+6cOGC9u7dqxYtWpgVs/Zw95PcgKuRnp5uWK1WIy0tzdi/f7/x8MMPG40aNTIKCwsNwzCM0aNHGzNnznTMz8rKMjw9PY2UlBQjNzfXmDNnjnHDDTcYe/fuddcloJZxtaZKS0uN7OxsIzs722jRooUxffp0Izs72zh48KC7LgG1jKs1lZycbDRs2NBYu3atUVBQ4HidOnXKXZeAWsbVmpo/f77xr3/9yzh06JCxf/9+IyUlxfD09DRSU1PddQmoRVytp1/i6eX4JVdrKiEhwdi6datx6NAhY/fu3cb9999veHl5Gfv27XPXJdQYbi9HnXDffffp2LFjmj17tgoLC3Xbbbdpy5Ytjoc35Ofny8Pjfzdu9OrVS6tXr9Yzzzyjp59+WmFhYXrrrbcUHh7urktALeNqTf3444/q2rWr431KSopSUlIUFRWlzMzMmo6PWsjVmvrHP/6hsrIy/fa3v3U6zpw5czR37tyajI5aytWaOn36tP7whz/o+++/l7e3t9q3b6+VK1fqvvvuc9cloBZxtZ6AK3G1pk6cOKGHHnpIhYWFCggIUPfu3bVjxw517NjRXZdQYyyGYRjuDgEAAAAAQH3Ej7MAAAAAADAJTTcAAAAAACah6QYAAAAAwCQ03QAAAAAAmISmGwAAAAAAk9B0AwAAAABgEppuAAAAAABMQtMNAAAAAIBJaLoBAAAAADAJTTcAAHAyduxYDR061N0xLuvIkSOyWCzKyclxd5SrcuzYMT3yyCNq1aqVrFarmjdvrgEDBigrK8vd0QAANcDT3QEAAACuVllZmbsjuCwuLk5lZWVavny52rZtq6NHjyojI0NFRUWmnbOsrEwNGzY07fgAgKvHSjcAAKhU37599dhjj+mJJ55QQECAgoKClJqaqtOnT2vcuHHy8/NTu3bttHnzZsc+mZmZslgs2rhxo7p06SIvLy/9+te/1ldffeV07HXr1qlTp06yWq1q3bq1nn/+eaftrVu3VmJioh588EHZbDY9/PDDatOmjSSpa9euslgs6tu3ryTps88+U0xMjJo2bSp/f39FRUXpiy++cDqexWLRkiVLNGzYMP3qV79SWFiY3nnnHac5+/bt09133y2bzSY/Pz9FRkbq0KFDju1LlixRhw4d5OXlpfbt2+vll1++7Gf3888/6+OPP9azzz6r6OhohYaG6o477lB8fLzuuecep3mTJk1SUFCQvLy8FB4erg0bNlzT5yRJ27dvV2RkpLy9vRUSEqIpU6bo9OnTl80LAKh+NN0AAOCKli9frqZNm+rTTz/VY489pkceeUS/+93v1KtXL33xxRe66667NHr0aJ05c8ZpvxkzZuj555/XZ599psDAQA0ePFjnzp2TJO3evVvDhw/X/fffr71792ru3LmaNWuW0tLSnI6RkpKiW2+9VdnZ2Zo1a5Y+/fRTSdK2bdtUUFCg9evXS5JOnTqlMWPGaPv27frkk08UFhamQYMG6dSpU07HS0hI0PDhw7Vnzx4NGjRIDzzwgP79739Lkn744Qf16dNHVqtV77//vnbv3q3x48fr/PnzkqRVq1Zp9uzZ+stf/qLc3FzNnz9fs2bN0vLlyyv83Hx9feXr66u33npLpaWlFc6x2+36zW9+o6ysLK1cuVL79+9XcnKyGjRocE2f06FDhzRw4EDFxcVpz549WrNmjbZv367JkydX9p8aAFDdDAAAgEuMGTPGGDJkiON9VFSU0bt3b8f78+fPGz4+Psbo0aMdYwUFBYYkY+fOnYZhGMYHH3xgSDLS09Mdc4qKigxvb29jzZo1hmEYxsiRI42YmBinc8+YMcPo2LGj431oaKgxdOhQpzmHDx82JBnZ2dmVXseFCxcMPz8/491333WMSTKeeeYZx/uSkhJDkrF582bDMAwjPj7eaNOmjVFWVlbhMW+66SZj9erVTmOJiYlGRETEZXOsXbvWCAgIMLy8vIxevXoZ8fHxxpdffunYvnXrVsPDw8PIy8urcP+qfk4TJkwwHn74Yaexjz/+2PDw8DDOnj172bwAgOrFSjcAALiiLl26OL5u0KCBmjRpos6dOzvGgoKCJEk//fST034RERGOrxs3bqxbbrlFubm5kqTc3FzdeeedTvPvvPNOHTx4UBcuXHCM9ejR46oyHj16VA899JDCwsLk7+8vm82mkpIS5efnX/ZafHx8ZLPZHLlzcnIUGRmpG264odzxT58+rUOHDmnChAmOFWxfX1/9+c9/drr9/Jfi4uL0448/6p133tHAgQOVmZmpbt26OVaqc3JydOONN+rmm2+ucP+qfk5ffvml0tLSnLIOGDBAdrtdhw8fvmxeAED14kFqAADgin7ZhFosFqcxi8Ui6b+3Slc3Hx+fq5o3ZswYFRUVaeHChQoNDZXValVERES5h69VdC0Xc3t7e1/2+CUlJZKk1NRU9ezZ02nbxVvBL8fLy0sxMTGKiYnRrFmzNHHiRM2ZM0djx46t9Jyu+OXnVFJSokmTJmnKlCnl5rZq1apazgkAuDKabgAAYJpPPvnE0eCdOHFCBw4cUIcOHSRJHTp0KPdns7KysnTzzTdX2sRefCr3pau8F/d9+eWXNWjQIEnSd999p+PHj7uUt0uXLlq+fLnOnTtXrjkPCgpScHCwvvnmGz3wwAMuHfeXOnbsqLfeestxzu+//14HDhyocLW7qp9Tt27dtH//frVr1+6asgIArg23lwMAANPMmzdPGRkZ+uqrrzR27Fg1bdrU8TfA//jHPyojI0OJiYk6cOCAli9frpdeeknTp0+v9JjNmjWTt7e3tmzZoqNHj+rkyZOSpLCwMK1YsUK5ubnatWuXHnjgAZdXkSdPnqzi4mLdf//9+vzzz3Xw4EGtWLFCeXl5kv77ELakpCS9+OKLOnDggPbu3atly5bpb3/7W4XHKyoqUr9+/bRy5Urt2bNHhw8f1ptvvqnnnntOQ4YMkSRFRUWpT58+iouL03vvvafDhw9r8+bN2rJlyzV9Tk899ZR27NihyZMnKycnRwcPHtTbb7/Ng9QAoIbRdAMAANMkJyfr8ccfV/fu3VVYWKh3333XsVLdrVs3/fOf/1R6errCw8M1e/ZszZs3T2PHjq30mJ6ennrxxRe1ePFiBQcHO5rX1157TSdOnFC3bt00evRoTZkyRc2aNXMpb5MmTfT++++rpKREUVFR6t69u1JTUx2r3hMnTtSSJUu0bNkyde7cWVFRUUpLS3P8GbNf8vX1Vc+ePfXCCy+oT58+Cg8P16xZs/TQQw/ppZdecsxbt26dbr/9do0YMUIdO3bUk08+6VjJr+rn1KVLF3344Yc6cOCAIiMj1bVrV82ePVvBwcEufSYAgGtjMQzDcHcIAABQv2RmZio6OlonTpxQo0aN3B0HAAC3YaUbAAAAAACT0HQDAAAAAGASbi8HAAAAAMAkrHQDAAAAAGASmm4AAAAAAExC0w0AAAAAgElougEAAAAAMAlNNwAAAAAAJqHpBgAAAADAJDTdAAAAAACYhKYbAAAAAACT0HQDAAAAAGCS/wdafrcIzZJLHwAAAABJRU5ErkJggg==\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "best_model = trained_models['Random Forest']\n",
+ "\n",
+ "importance_df = pd.DataFrame({\n",
+ " 'Feature' : X.columns,\n",
+ " 'Importance': best_model.feature_importances_\n",
+ "}).sort_values(by='Importance', ascending=False).head(12)\n",
+ "\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.barplot(data=importance_df, x='Importance', y='Feature', palette='viridis')\n",
+ "plt.title('Top 12 Feature Importances - Random Forest (Winning Model)')\n",
+ "plt.xlabel('Importance Score')\n",
+ "plt.ylabel('Feature')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I3d21IRU9XHf"
+ },
+ "source": [
+ "## Actual vs. Predicted - Winning Model\n",
+ "\n",
+ "The scatter plot below shows the Gradient Boosting model predictions against actual sale prices, filtered to sales under $5M to focus on the main market. While the model captures a positive trend — higher actual prices tend to produce higher predictions — the scatter is still significant, with predictions frequently off by millions of dollars. The model is better than the baseline but real estate price prediction remains a hard problem due to the enormous influence of individual property characteristics that are not captured in this feature set.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 607
+ },
+ "id": "CW6lV9eI9XHf",
+ "outputId": "db59dda9-32ec-4c13-eb13-66155fa53b05"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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1112H8vJyz19GRgb69u3r6cbjPlbffPMN6urq/Mp3Q0KIgFol0tPTcfHFFwNwdbm4/vrrsWLFCq8uIB999BGGDRuGq6++utE+QjmYMdDzsCXXXHMN5HK5V63tvn37sH//flx//fWebQkJCfjtt99w6NCh1n+IM/7zn/8gNTUVaWlpGDVqlKd7yr333uuVbtasWdBoNJ7Hu3btwqFDh3DjjTeioqLCc64YjUZMnDgR33//PZxOJxwOB7755htMnz4dPXv29Lx+wIAByM3NbTF/rflO2yqPAFBRUeF1rXEbNGgQdu3ahZtvvhmFhYX4+9//junTpyM9PR1vvfWWV9r6x7empgbl5eUYO3Ys6urqkJeX1+R7r1y5EjqdDpdeeqnX73b48OGIjY31/G4TEhIAAJ9//jlsNptfnytY8+bN8/p+xo4dC4fD4ek+tG7dOlit1kYDjRued4Dr840dOxaJiYlen2/SpElwOBz4/vvvvdLPmDEDqampLeZRq9V6xijccsstOHnyZJMzCPbs2RPffPMN7rjjDlxxxRW45557sHPnTqSmpuL+++9v8j1uvPFGrF69GlarFatWrYJMJvN5LteXmJgIk8kU9LWVIhODCaI24C4kr1q1ClOnTkV5ebnPwZjvvPMOhg4d6ukznpqaii+++MLTV7+++gUD4GyXKXc/YveNzdcUfv379/d6XFRUhK5du3oFAoCrwFF/X029t7sA3qNHD5/bG/ZtbsrIkSMxadIkr7/6798w3+48ugtR9TWcFerQoUMQQqBv375ITU31+jtw4IBnwGjv3r1x33334e2330ZKSgpyc3Pxxhtv+PwOWsvhcGDFihW4+OKLUVBQgMOHD+Pw4cMYNWoUSktLsX79ek/aI0eOtNnMVoGchy1JSUnBxIkT8eGHH3q2ffDBB5DL5bjmmms8255++mno9Xr069cPQ4YMwYMPPog9e/a06nNcddVVWLt2LdatW4dt27ahvLwcr7zySqPB1b7OFcAVZDQ8V95++21YLBYYDAacPn0aJpPJr9+YL635Ttsqj26iwdgpt379+uG9995DeXk59uzZg+effx5yuRzz5s3DunXrPOl+++03XH311dDpdIiPj0dqaqpnsG9z59WhQ4dgMBiQlpbW6HPW1tZ6frfjx4/HjBkz8NRTTyElJQVXXXUVli5dGpZ1DYK99qampjYKyg4dOoSvv/660WdzX/saDmRveK42Z9myZXj66aexbds2HD58GGPGjEHv3r2xdOnSFl+blJSEOXPmID8/HydOnPCZxj2O7KuvvsLy5ctx+eWXN7qHNOQ+jzibU8fCMRNEbWDkyJGeWYqmT5+OMWPG4MYbb0R+fj5iY2MBuAYGz549G9OnT8eDDz6ItLQ0yGQyLFq0yGugtptMJvP5Xk3d9EOpqfduzzw1VL8mFHDVskskEnz11Vc+8+n+HgDglVdewezZs/Hpp5/i22+/xZ/+9CcsWrQIW7dubTTepDU2bNiA4uJirFixotF87oCr1aKpPsuBaurm7XA4vI5HoOehP2644QbMmTMHu3btwrnnnosPP/wQEydO9IyHAYBx48bhyJEjnmP+9ttv429/+xuWLFmCW2+9Naj37d69e6NBw774OlcA13SoTY0RiY2NbdcFuNoyj8nJyS1WCMhkMgwZMgRDhgxBTk4OLr74YixfvhyTJk2CXq/H+PHjER8fj6efftqzJsWOHTvw8MMPew3ibcjpdCItLa3JSQnctfTudQ22bt2Kzz77DN988w3mzp2LV155BVu3bvX6ffurqcHhobzOOZ1OXHrppXjooYd8Pt+vXz+vxw3P1eaoVCo88sgjeOSRRzBhwgTceOON+PDDDzF37lxoNBrccMMNzb7eXTlUWVnp87rXpUsXTJgwAa+88gp+/PFHnzM4NVRVVQWtVhvQ56DIx2CCqI25C2YXX3wx/vnPf3oWZVu1ahX69OmD1atXexX8gp0i1D1Y0le3kfz8/EZp161bh5qaGq+aJXf3A1+ztbQl9/s3zDfgymNKSkqLU79mZWVBCIHevXs3ukH74i4YPfbYY/jpp58wevRoLFmyBM8++yyA0NSsLV++HGlpaXjjjTcaPbd69Wp8/PHHWLJkCTQaDbKysrBv375m99dcnhITE6HX6xttLyoqQp8+fTyPQ30eAq4A+vbbb/d0dTp48CAWLFjQKJ27NnTOnDmora3FuHHj8OSTTwYdTATLPZg+Pj6+2WAkNTUVGo3Gr99YU+8T7HfaVnkEgOzsbL8Kim7uipPi4mIArlmzKioqsHr1aowbN86Tzj2rVnOysrKwbt06jB492q8C6IUXXogLL7wQzz33HN5//33cdNNNWLFiRbPnkK/fhtVq9eQ/UPWvvfV/W6dPn24UlGVlZaG2ttavoLe1Jk+ejNmzZ6Nr165YtWpVi8GEu3tqc92qbrzxRtx6661ISEhotEaFLwUFBZ4Wb+o42M2JqB1MmDABI0eOxGuvveZZ3Mdd21W/dmvbtm3YsmVLUO/RpUsXnHvuuXjnnXe8uhGsXbu20XSCU6dOhcPh8EzT6fa3v/0NEomkXRb4qq/+Z6l/09+3bx++/fZbv25i11xzDWQyGZ566qlGNYhCCFRUVABwzWZkt9u9nh8yZAikUqlXLW9MTIzPwjng39SwJpMJq1evxuWXX45rr7220d9dd92FmpoarFmzBoCrr/Tu3bt9Tu3r/jzugMpXvrKysrB161av2WQ+//zzRtPihvo8BFz92XNzc/Hhhx9ixYoVUCqVjRb7cx9/t9jYWJxzzjlex9xgMCAvLy8sXc7qGz58OLKysvDyyy+jtra20fPuaUBlMhlyc3PxySefeH3fBw4cwDfffNPi+7TmO22rPAKuhcuqqqoajX364YcffI5PcI9hcnej8nVOWa1WLF68uMX3vu666+BwOPDMM880es5ut3uOS1VVVaPftbvFpqXWmaysrEZjE/7973/7NW2tL5MmTYJCocDrr7/ulaeGMzMBrs+3ZcsWn9+FXq9vdC3yl8VigclkarTd6XTC6XR6BWYNp7UFXGtU/N///R+GDh2KLl26NPk+1157LRYuXIjFixc3ud5FfTt27Ah4hjmKfGyZIGonDz74IH73u99h2bJluOOOO3D55Zdj9erVuPrqqzFt2jQUFBRgyZIlGDhwoM/Cgj8WLVqEadOmYcyYMZg7dy4qKyvx+uuvY9CgQV77vOKKK3DxxRfjL3/5CwoLCzFs2DB8++23+PTTT3Hvvfd6TXvaXl566SVMmTIFOTk5uOWWWzxTw+p0Op9zxDeUlZWFZ599FgsWLEBhYSGmT5+OuLg4FBQU4OOPP8a8efPwwAMPYMOGDbjrrrvwu9/9Dv369YPdbsd7770HmUyGGTNmePY3fPhwrFu3Dq+++iq6du2K3r17ewac+zM17Jo1a1BTU4Mrr7zS5/MXXnghUlNTsXz5clx//fV48MEHsWrVKvzud7/D3LlzMXz4cFRWVmLNmjVYsmQJhg0bhqysLCQkJGDJkiWIi4tDTEwMRo0ahd69e+PWW2/FqlWrcNlll+G6667DkSNH8N///rfRdxuO8xBwTRhw8803Y/HixcjNzfUMmHUbOHAgJkyY4JmL/tdff8WqVatw1113edJ8/PHHmDNnDpYuXepzzYxQkUqlePvttzFlyhQMGjQIc+bMQbdu3XDy5Els3LgR8fHx+OyzzwC4ptP8+uuvMXbsWPzxj3+E3W73/MZaGvPR2u+0LfIIANOmTYNcLse6des8UyEDwAsvvIDt27fjmmuuwdChQwG4CovvvvsukpKSPAOOL7roIiQmJmLWrFn405/+BIlEgvfee8+vbkHjx4/H7bffjkWLFmHXrl2YPHkyFAoFDh06hJUrV+Lvf/87rr32WrzzzjtYvHgxrr76amRlZaGmpgZvvfUW4uPjW6xsuPXWW3HHHXdgxowZuPTSS7F792588803Xt3wApGamooHHnjAM2X21KlTsXPnTnz11VeN9vnggw9izZo1uPzyyzF79mwMHz4cRqMRe/fuxapVq1BYWBhUPoqLiz3X/YsuugjV1dVYs2YNVq9ejaqqKtx4442etA899BCOHDmCiRMnomvXrigsLMSbb74Jo9GIv//9782+j7/XXwDYvn07KisrcdVVVwX8eSjCtfHsUUSdinsqQV/TFTocDpGVlSWysrKE3W4XTqdTPP/88yIzM1OoVCpx3nnnic8//7zRVInuKQJfeumlRvuEjykOP/roIzFgwAChUqnEwIEDxerVqxvtUwghampqxJ///GfRtWtXoVAoRN++fcVLL73kNbWh+z3mz5/vta2pPDU3RaC/x6m+devWidGjRwuNRiPi4+PFFVdcIfbv3++Vxj017OnTp33u46OPPhJjxowRMTExIiYmRmRnZ4v58+eL/Px8IYQQR48eFXPnzhVZWVlCrVaLpKQkcfHFF4t169Z57ScvL0+MGzfOMzVm/eke4cfUsFdccYVQq9U+p9F0mz17tlAoFKK8vFwIIURFRYW46667RLdu3YRSqRTdu3cXs2bN8jwvhGuK4IEDBwq5XN5oStFXXnlFdOvWTahUKjF69Gjx66+/Npoa1t/z0P05W5oa1q26utpzrP773/82ev7ZZ58VI0eOFAkJCUKj0Yjs7Gzx3HPPCavV6knjPk98TX3bkK/ztKGWzs+dO3eKa665RiQnJwuVSiUyMzPFddddJ9avX++V7rvvvhPDhw8XSqVS9OnTRyxZssRzHtbna3re1n6noc5jU6688koxceJEr20//vijmD9/vhg8eLDQ6XRCoVCInj17itmzZ4sjR440SnvhhRcKjUYjunbtKh566CHxzTffCABi48aNnnS+zjMhhPj3v/8thg8fLjQajYiLixNDhgwRDz30kDh16pQQQogdO3aImTNnip49ewqVSiXS0tLE5ZdfLn799dcWP5vD4RAPP/ywSElJEVqtVuTm5orDhw83OTVsw+uU+zyq/zkcDod46qmnRJcuXYRGoxETJkwQ+/bt83kO1NTUiAULFohzzjlHKJVKkZKSIi666CLx8ssve87/5q77vpjNZvHqq6+K0aNHi5SUFAFAxMbGiuHDh4sPP/zQK+37778vxo0bJ1JTU4VcLhcpKSni6quvFtu3b2+03/pTwzalqd/Vww8/LHr27NnonkLRTyJEO4yMJCIioqjxww8/YMKECcjLy/M5MxRFtgkTJmDZsmWNFvdsKxaLBb169cIjjzyCe+65p13yQOHDMRNERETUrLFjx2Ly5Ml48cUX2zsrFIWWLl0KhULRaP0f6hjYMkFERETUgS1btgzTp09vNFaJKBQYTBARERERUVDYzYmIiIiIiILCYIKIiIiIiILCYIKIiIiIiILSrovWLVq0CKtXr0ZeXh40Gg0uuugivPDCC55VMwHXdGbfffed1+tuv/12LFmyxPP42LFjuPPOO7Fx40bExsZi1qxZWLRoEeTysx9v06ZNuO+++/Dbb7+hR48eeOyxxxotevTGG2/gpZdeQklJCYYNG4bXX38dI0eO9DxvNptx//33Y8WKFbBYLMjNzcXixYuRnp4e8rw0xel04tSpU4iLi4NEIvHrNUREREREgRBCoKamBl27doVU2kz7QzuucSFyc3PF0qVLxb59+8SuXbvE1KlTRc+ePUVtba0nzfjx48Vtt90miouLPX8Gg8HzvN1uF4MHDxaTJk0SO3fuFF9++aVISUkRCxYs8KQ5evSo0Gq14r777hP79+8Xr7/+upDJZOLrr7/2pFmxYoVQKpXi//7v/8Rvv/0mbrvtNpGQkCBKS0s9ae644w7Ro0cPsX79evHrr7+KCy+8UFx00UUhz0tzjh8/LgDwj3/84x//+Mc//vGPf2H/O378eLNl04iazen06dNIS0vDd999h3HjxgFwtUyce+65eO2113y+5quvvsLll1+OU6dOeVoIlixZgocffhinT5+GUqnEww8/jC+++AL79u3zvO6GG26AXq/H119/DQAYNWoULrjgAvzzn/8E4GoB6NGjB+6++2488sgjMBgMSE1Nxfvvv49rr70WAJCXl4cBAwZgy5YtuPDCC0OWl+YYDAYkJCTg+PHjiI+PD/AIExERERG1rLq6Gj169IBer4dOp2syXbt2c2rIYDAAAJKSkry2L1++HP/973+RkZGBK664Ao8//ji0Wi0AYMuWLRgyZIhXV6Pc3Fzceeed+O2333Deeedhy5YtmDRpktc+c3Nzce+99wIArFYrtm/fjgULFniel0qlmDRpErZs2QIA2L59O2w2m9d+srOz0bNnT08wEYq8tMTdtSk+Pp7BBBERERGFVUvd6iMmmHA6nbj33nsxevRoDB482LP9xhtvRGZmJrp27Yo9e/bg4YcfRn5+PlavXg0AKCkp8Sq8A/A8LikpaTZNdXU1TCYTqqqq4HA4fKbJy8vz7EOpVDZa8CU9Pb3F9wkkLxqNxus5i8UCi8XieVxdXd3o2BERERERtYeICSbmz5+Pffv2YfPmzV7b582b5/n/kCFD0KVLF0ycOBFHjhxBVlZWW2ezzS1atAhPPfVUe2eDiIiIiKiRiJga9q677sLnn3+OjRs3onv37s2mHTVqFADg8OHDAICMjAyUlpZ6pXE/zsjIaDZNfHw8NBoNUlJSIJPJfKapvw+r1Qq9Xt9smtbmpaEFCxbAYDB4/o4fP97UoSEiIiIialPtGkwIIXDXXXfh448/xoYNG9C7d+8WX7Nr1y4AQJcuXQAAOTk52Lt3L8rKyjxp1q5di/j4eAwcONCTZv369V77Wbt2LXJycgAASqUSw4cP90rjdDqxfv16T5rhw4dDoVB4pcnPz8exY8c8aUKRl4ZUKpVnfATHSRARERFRRPFrPtIwufPOO4VOpxObNm3ymvq1rq5OCCHE4cOHxdNPPy1+/fVXUVBQID799FPRp08fMW7cOM8+3NOxTp48WezatUt8/fXXIjU11ed0rA8++KA4cOCAeOONN3xODatSqcSyZcvE/v37xbx580RCQoIoKSnxpLnjjjtEz549xYYNG8Svv/4qcnJyRE5OTsjz0hyDwSAAeE2PS0REREQUSv6WOds1mEAT89kuXbpUCCHEsWPHxLhx40RSUpJQqVTinHPOEQ8++GCjD1VYWCimTJkiNBqNSElJEffff7+w2WxeaTZu3CjOPfdcoVQqRZ8+fTzvUd/rr78uevbsKZRKpRg5cqTYunWr1/Mmk0n88Y9/FImJiUKr1Yqrr75aFBcXhyUvTWEwQURERETh5m+ZM6LWmaCWVVdXQ6fTwWAwsMsTEREREYWFv2XOiBiATURERERE0YfBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERERERBYXBBBERRRynEDDbHHAK0d5ZISKiZsjbOwNERET1Geqs2FNUgVqTDbEaBYZmJkOnVbZ3toiIyAe2TBARUcRwCoE9RRUo1ddBKZeiVF+HPUUVbKEgIopQbJkgIqKIYbU7UWuyITFGhRi1AgBQa7LBandCrZC1c+6IiKghtkwQEVHEUMqliNUoUGW0wGi2ocpoQaxGAaWctysiokjEqzMREUUMqUSCoZnJSE/Qwmp3Ij1Bi6GZyZBKJO2dNSIi8oHdnIiIKKLotEqMzs6A1e6EUi5lIEFEFMEYTBARUcSRSiQcI0FEFAXYzYmIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIiIiIiILCYIKIKAScQsBsc8ApRHtnhYiIqM3I2zsDRETRzlBnxZ6iCtSabIjVKDA0Mxk6rbK9s0VERBR2bJkgImoFpxDYU1SBUn0dlHIpSvV12FNUwRYKIiLqFNgyQUTUCla7E7UmGxJjVIhRKwAAtSYbrHYn1ApZO+eOiIgovNgyQUTUCkq5FLEaBaqMFhjNNlQZLYjVKKCU8/JKREQdH+92REStIJVIMDQzGekJWljtTqQnaDE0MxlSiaS9s0ZERBR27OZERNRKOq0So7MzYLU7oZRLGUgQEVGnwWCCiCgEpBIJx0gQEVGnw25OREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUFAYTREREREQUlHYNJhYtWoQLLrgAcXFxSEtLw/Tp05Gfn++Vxmw2Y/78+UhOTkZsbCxmzJiB0tJSrzTHjh3DtGnToNVqkZaWhgcffBB2u90rzaZNm3D++edDpVLhnHPOwbJlyxrl54033kCvXr2gVqsxatQo/Pzzz+2WFyIiIiKiSNeuwcR3332H+fPnY+vWrVi7di1sNhsmT54Mo9HoSfPnP/8Zn332GVauXInvvvsOp06dwjXXXON53uFwYNq0abBarfjpp5/wzjvvYNmyZXjiiSc8aQoKCjBt2jRcfPHF2LVrF+69917ceuut+OabbzxpPvjgA9x3331YuHAhduzYgWHDhiE3NxdlZWVtnhciIiIioqggIkhZWZkAIL777jshhBB6vV4oFAqxcuVKT5oDBw4IAGLLli1CCCG+/PJLIZVKRUlJiSfNv/71LxEfHy8sFosQQoiHHnpIDBo0yOu9rr/+epGbm+t5PHLkSDF//nzPY4fDIbp27SoWLVrU5nlpjsFgEACEwWDwKz0RERERUaD8LXNG1JgJg8EAAEhKSgIAbN++HTabDZMmTfKkyc7ORs+ePbFlyxYAwJYtWzBkyBCkp6d70uTm5qK6uhq//fabJ039fbjTuPdhtVqxfft2rzRSqRSTJk3ypGmrvDRksVhQXV3t9UdEREREFAkiJphwOp249957MXr0aAwePBgAUFJSAqVSiYSEBK+06enpKCkp8aSpX3h3P+9+rrk01dXVMJlMKC8vh8Ph8Jmm/j7aIi8NLVq0CDqdzvPXo0ePRmmIiIiIiNpDxAQT8+fPx759+7BixYr2zkpEWbBgAQwGg+fv+PHj7Z0lIiIiIiIAERJM3HXXXfj888+xceNGdO/e3bM9IyMDVqsVer3eK31paSkyMjI8aRrOqOR+3FKa+Ph4aDQapKSkQCaT+UxTfx9tkZeGVCoV4uPjvf6IiIiIiCJBuwYTQgjcdddd+Pjjj7Fhwwb07t3b6/nhw4dDoVBg/fr1nm35+fk4duwYcnJyAAA5OTnYu3ev16xLa9euRXx8PAYOHOhJU38f7jTufSiVSgwfPtwrjdPpxPr16z1p2iovRERERERRo23Gg/t25513Cp1OJzZt2iSKi4s9f3V1dZ40d9xxh+jZs6fYsGGD+PXXX0VOTo7IycnxPG+328XgwYPF5MmTxa5du8TXX38tUlNTxYIFCzxpjh49KrRarXjwwQfFgQMHxBtvvCFkMpn4+uuvPWlWrFghVCqVWLZsmdi/f7+YN2+eSEhI8JqZqa3y0hzO5kRERERE4eZvmbNdgwkAPv+WLl3qSWMymcQf//hHkZiYKLRarbj66qtFcXGx134KCwvFlClThEajESkpKeL+++8XNpvNK83GjRvFueeeK5RKpejTp4/Xe7i9/vrromfPnkKpVIqRI0eKrVu3ej3flnlpCoMJIiIiIgo3f8ucEiGEaK9WEQpcdXU1dDodDAYDx08QERERUVj4W+aMiAHYREREREQUfRhMEBERERFRUBhMEBERERFRUBhMEBERERFRUBhMEBERERFRUBhMEBERERFRUBhMUMRwCgGzzQEnZysmIiIiigry9s4AEQAY6qzYU1SBWpMNsRoFhmYmQ6dVtne2iIiIiKgZbJmgducUAnuKKlCqr4NSLkWpvg57iirYQkFEREQU4dgyQe3Oanei1mRDYowKMWoFAKDWZIPV7oRaIWvn3BERERFRU9gyQe1OKZciVqNAldECo9mGKqMFsRoFlHKenkRERESRjKU1andSiQRDM5ORnqCF1e5EeoIWQzOTIZVI2jtrRERERNQMdnOiiKDTKjE6OwNWuxNKuZSBBBEREVEUYDBBEUMqkXCMBBEREVEUYTcnIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIiIKCoMJIiIiIvKbUwiYbQ44hWjvrFAEkLd3BoiIiIgoOhjqrNhTVIFakw2xGgWGZiZDp1W2d7aoHbFlgoiIiIha5BQCe4oqUKqvg1IuRam+DnuKKthC0cmxZYKIiIiIWmS1O1FrsiExRoUYtQIAUGuywWp3Qq2QtXPuqL2wZYKIiIiIWqSUSxGrUaDKaIHRbEOV0YJYjQJKOYuTnRm/fSIiIiJqkVQiwdDMZKQnaGG1O5GeoMXQzGRIJZL2zhq1I3ZzIiIiIiK/6LRKjM7OgNXuhFIuZSBBDCaIiIiIyH9SiYRjJMiD3ZyIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgoDCaIiIiIiCgo8kASO51OfPfdd/jhhx9QVFSEuro6pKam4rzzzsOkSZPQo0ePcOWTiIiIiIgijF8tEyaTCc8++yx69OiBqVOn4quvvoJer4dMJsPhw4excOFC9O7dG1OnTsXWrVvDnWciIiIiIooAfrVM9OvXDzk5OXjrrbdw6aWXQqFQNEpTVFSE999/HzfccAP+8pe/4Lbbbgt5ZomIiIiIKHJIhBCipUQHDhzAgAED/NqhzWbDsWPHkJWV1erMUWPV1dXQ6XQwGAyIj49v7+wQERERUQfkb5nTr25O/gYSAKBQKBhIEBERERF1Aq2ezamiogIbN25EaWlpKPJDRERERERRIqBg4s0338Sbb77pebxr1y6cc845mDhxIvr06YNvvvkm5BkkIiIiIqLIFFAw8dZbbyElJcXzeOHChbjyyitRXV2N+++/H3/5y19CnkEiIiIiIopMfgUT33//Pb777jscPXoUBoPB83jjxo3IycnBjh07MHz4cBw4cADff/89vv/++3Dnm4iIiIiI2plfU8MWFBQAcC1aV1xcDJlMhkOHDkEmk0Gr1aKgoAB2ux0OhwOFhYUQQmDcuHFhzTgREREREbUvv6aGdRszZgwGDx6MhQsX4uGHH4bJZMLKlSsBAAcPHsRll12Go0ePhi2zxKlhiYiIiCj8/C1z+tUy4fbMM89g+vTpnrETGzZs8Dz3v//9D5dccknwOSYiIiIioqgSUDBx8cUX49ixYzh8+DD69++P2NhYz3NXXnklunTpEvIMEhERERFRZAoomAAAnU6H4cOHN9p+3nnnhSRDREREREQUHfyazWnr1q1+77Curg6//fZb0BkiIiIiIqLo4Fcw8fvf/x65ublYuXIljEajzzT79+/Ho48+iqysLGzfvj2kmSQiIiIiosjjVzen/fv341//+hcee+wx3HjjjejXrx+6du0KtVqNqqoq5OXloba2FldffTW+/fZbDBkyJNz5JiIiIiKiduZXy4RCocCf/vQn5OfnY8uWLbjtttswePBgdOvWDRMmTMCbb76JU6dO4X//+19AgcT333+PK664Al27doVEIsEnn3zi9fzs2bMhkUi8/i677DKvNJWVlbjpppsQHx+PhIQE3HLLLaitrfVKs2fPHowdOxZqtRo9evTAiy++2CgvK1euRHZ2NtRqNYYMGYIvv/zS63khBJ544gl06dIFGo0GkyZNwqFDh8KSFyIiIiKiaBDwAOwRI0ZgxIgRIXlzo9GIYcOGYe7cubjmmmt8prnsssuwdOlSz2OVSuX1/E033YTi4mKsXbsWNpsNc+bMwbx58/D+++8DcM2RO3nyZEyaNAlLlizB3r17MXfuXCQkJGDevHkAgJ9++gkzZ87EokWLcPnll+P999/H9OnTsWPHDgwePBgA8OKLL+If//gH3nnnHfTu3RuPP/44cnNzsX//fqjV6pDlhYiIiIgoaogIAUB8/PHHXttmzZolrrrqqiZfs3//fgFA/PLLL55tX331lZBIJOLkyZNCCCEWL14sEhMThcVi8aR5+OGHRf/+/T2Pr7vuOjFt2jSvfY8aNUrcfvvtQgghnE6nyMjIEC+99JLneb1eL1Qqlfjf//4X0ry0xGAwCADCYDD4/RoiIiIiokD4W+b0q5tTe9q0aRPS0tLQv39/3HnnnaioqPA8t2XLFiQkJHi1lEyaNAlSqRTbtm3zpBk3bhyUSqUnTW5uLvLz81FVVeVJM2nSJK/3zc3NxZYtWwAABQUFKCkp8Uqj0+kwatQoT5pQ5YWIiIiIKFpEdDBx2WWX4d1338X69evxwgsv4LvvvsOUKVPgcDgAACUlJUhLS/N6jVwuR1JSEkpKSjxp0tPTvdK4H7eUpv7z9V/XVJpQ5KUhi8WC6upqrz8iIiIiokgQ8JiJtnTDDTd4/j9kyBAMHToUWVlZ2LRpEyZOnNiOOWs7ixYtwlNPPdXe2SAiIiIiaqRVLRNmszlU+fBLnz59kJKSgsOHDwMAMjIyUFZW5pXGbrejsrISGRkZnjSlpaVeadyPW0pT//n6r2sqTSjy0tCCBQtgMBg8f8ePH/eZjoiIiIiorQUcTDidTjzzzDPo1q0bYmNjcfToUQDA448/jv/85z8hz2B9J06cQEVFBbp06QIAyMnJgV6v91okb8OGDXA6nRg1apQnzffffw+bzeZJs3btWvTv3x+JiYmeNOvXr/d6r7Vr1yInJwcA0Lt3b2RkZHilqa6uxrZt2zxpQpWXhlQqFeLj473+iIiIiIgiQqAju5966inRp08f8d///ldoNBpx5MgRIYQQK1asEBdeeGFA+6qpqRE7d+4UO3fuFADEq6++Knbu3CmKiopETU2NeOCBB8SWLVtEQUGBWLdunTj//PNF3759hdls9uzjsssuE+edd57Ytm2b2Lx5s+jbt6+YOXOm53m9Xi/S09PF73//e7Fv3z6xYsUKodVqxZtvvulJ8+OPPwq5XC5efvllceDAAbFw4UKhUCjE3r17PWn++te/ioSEBPHpp5+KPXv2iKuuukr07t1bmEymkOalJZzNiYiIiIjCzd8yZ8DBRFZWlli3bp0QQojY2FhPMHHgwAGRkJAQ0L42btwoADT6mzVrlqirqxOTJ08WqampQqFQiMzMTHHbbbeJkpISr31UVFSImTNnitjYWBEfHy/mzJkjampqvNLs3r1bjBkzRqhUKtGtWzfx17/+tVFePvzwQ9GvXz+hVCrFoEGDxBdffOH1vNPpFI8//rhIT08XKpVKTJw4UeTn54clL81hMEFERERE4eZvmVMihBCBtGRoNBrk5eUhMzMTcXFx2L17N/r06YP9+/dj5MiRjVZ8ptCqrq6GTqeDwWBglyciIiIiCgt/y5wBj5kYOHAgfvjhh0bbV61ahfPOOy/Q3RERERERUZQKeGrYJ554ArNmzcLJkyfhdDqxevVq5Ofn491338Xnn38ejjwSEREREVEECrhl4qqrrsJnn32GdevWISYmBk888QQOHDiAzz77DJdeemk48khERERERBEo4DET1L44ZoKIiIiIwi1sYyZ++eUXbNu2rdH2bdu24ddffw10d0REREREFKUCDibmz5/vcxXmkydPYv78+SHJFBERERERRb6Ag4n9+/fj/PPPb7T9vPPOw/79+0OSKSIiIiIiinwBBxMqlQqlpaWNthcXF0MuD3hyKCIiIiIiilIBBxOTJ0/GggULYDAYPNv0ej0effRRzuZERERERNSJBNyU8PLLL2PcuHHIzMz0LFK3a9cupKen47333gt5BomIiIiIKDIFHEx069YNe/bswfLly7F7925oNBrMmTMHM2fOhEKhCEceiYiIiIgoAgU1yCEmJgbz5s0LdV6IiIiIiCiK+BVMrFmzBlOmTIFCocCaNWuaTXvllVeGJGNERERERBTZ/FoBWyqVoqSkBGlpaZBKmx6zLZFI4HA4QppB8sYVsImIiIgo3Pwtc/rVMuF0On3+n4iIiIiIOq+Apoa12WyYOHEiDh06FK78EBERERFRlAgomFAoFNizZ0+48kJERERERFEk4EXrbr75ZvznP/8JR16IiIiIiCiKBDw1rN1ux//93/9h3bp1GD58OGJiYryef/XVV0OWOSIiIiIiilwBBxP79u3D+eefDwA4ePCg13MSiSQ0uSIiIiIioogXcDCxcePGcOSDiIiIiIiiTEDBxAcffIA1a9bAarVi4sSJuOOOO8KVLyIiIiIiinB+BxP/+te/MH/+fPTt2xcajQarV6/GkSNH8NJLL4Uzf0REREREFKH8ns3pn//8JxYuXIj8/Hzs2rUL77zzDhYvXhzOvBERERERUQTzO5g4evQoZs2a5Xl84403wm63o7i4OCwZIyIiIiKiyOZ3MGGxWLymgZVKpVAqlTCZTGHJGBERERERRbaABmA//vjj0Gq1nsdWqxXPPfccdDqdZxvXmSAiIiIi6hz8DibGjRuH/Px8r20XXXQRjh496nnMdSaIiIiIiDoPv4OJTZs2hTEbREREREQUbfweM0FERERERFQfgwkiIiIiIgoKgwkiIiIiIgoKgwkiIiIiIgoKgwkiIiIiIgpKUMHEDz/8gJtvvhk5OTk4efIkAOC9997D5s2bQ5o5IiIiIiKKXAEHEx999BFyc3Oh0Wiwc+dOWCwWAIDBYMDzzz8f8gwSEREREVFkCjiYePbZZ7FkyRK89dZbUCgUnu2jR4/Gjh07Qpo5ij5OIWC2OeAUor2zQkRERERh5veidW75+fkYN25co+06nQ56vT4UeaIoZaizYk9RBWpNNsRqFBiamQydVtne2SIiIiKiMAm4ZSIjIwOHDx9utH3z5s3o06dPSDJF0ccpBPYUVaBUXwelXIpSfR32FFWwhYKIiIioAws4mLjttttwzz33YNu2bZBIJDh16hSWL1+OBx54AHfeeWc48khRwGp3otZkQ2KMCjFqBRJjVKg12WC1O9s7a0REREQUJgF3c3rkkUfgdDoxceJE1NXVYdy4cVCpVHjggQdw9913hyOPFAWUciliNQqU6usAAFVGC9ITtFDKOfswERERUUclESK4fihWqxWHDx9GbW0tBg4ciNjY2FDnjXyorq6GTqeDwWBAfHx8e2fHS6jGTDiFgNXuhFIuhVQiCUNOiYiIiKg5/pY5A26ZcFMqlRg4cGCwL6cOSKdVYnR2RqsCAQ7iJiIiIooefgUT11xzjd87XL16ddCZoegnlUigVsiCem39QdyJMSrXIG4Ao7Mz2EJBREREFIH8CiZ0Ol2480HUaBA3AM8g7mADFCIiIiIKH7+CiaVLl4Y7H0QcxE1EREQUZVhKo4ghlUgwNDMZ6QlaWO1OpCdoMTQzmV2ciIiIiCJUUAOwV61ahQ8//BDHjh2D1Wr1em7Hjh0hyRh1TqEYxE1EREREbSPglol//OMfmDNnDtLT07Fz506MHDkSycnJOHr0KKZMmRKOPFIn4x7EzUCCiIiIKLIFHEwsXrwY//73v/H6669DqVTioYcewtq1a/GnP/0JBoMhHHkkIiIiIqIIFHAwcezYMVx00UUAAI1Gg5qaGgDA73//e/zvf/8Lbe6IiIiIiChiBRxMZGRkoLKyEgDQs2dPbN26FQBQUFCAIBfTJiIiIiKiKBRwMHHJJZdgzZo1AIA5c+bgz3/+My699FJcf/31uPrqq0OeQSIiIiIiikwSEWBzgtPphNPphFzumghqxYoV+Omnn9C3b1/cfvvtUCqVYckouVRXV0On08FgMCA+Pr69s0NEREREHZC/Zc6AgwlqXwwmiIiIiCjc/C1z+t3Nqby8HEVFRV7bfvvtN8yZMwfXXXcd3n///eBzS0REREREUcfvYOLuu+/GP/7xD8/jsrIyjB07Fr/88gssFgtmz56N9957LyyZJCIiIiKiyON3MLF161ZceeWVnsfvvvsukpKSsGvXLnz66ad4/vnn8cYbb4Qlk0REREREFHn8DiZKSkrQq1cvz+MNGzbgmmuu8QzEvvLKK3Ho0KGQZ5CIiIiIiCKT38FEfHw89Hq95/HPP/+MUaNGeR5LJBJYLJaQZo6IiIiIiCKX38HEhRdeiH/84x9wOp1YtWoVampqcMkll3ieP3jwIHr06BGWTBIRERERUeSR+5vwmWeewcSJE/Hf//4Xdrsdjz76KBITEz3Pr1ixAuPHjw9LJomIiIiIKPL4HUwMHToUBw4cwI8//oiMjAyvLk4AcMMNN2DgwIEhzyAREREREUUmLloXZbhoHRERERGFW8gXrSMiIiIiIqqPwQQREREREQWFwQQREREREQWFwQQREREREQXFr9mcqqur/d4hBwUTEREREXUOfgUTCQkJkEgkfu3Q4XC0KkNERERERBQd/AomNm7c6Pl/YWEhHnnkEcyePRs5OTkAgC1btuCdd97BokWLwpNLIiIiIiKKOAGvMzFx4kTceuutmDlzptf2999/H//+97+xadOmUOaPGuA6E0REREQUbmFbZ2LLli0YMWJEo+0jRozAzz//HOjuiIiIiIgoSgUcTPTo0QNvvfVWo+1vv/02evToEZJMERERERFR5PNrzER9f/vb3zBjxgx89dVXGDVqFADg559/xqFDh/DRRx+FPINERERERBSZAm6ZmDp1Kg4ePIgrrrgClZWVqKysxBVXXIGDBw9i6tSp4cgjERERERFFoIAHYFP74gBsIiIiIgq3sA3ABoAffvgBN998My666CKcPHkSAPDee+9h8+bNweWWiIiIiIiiTsDBxEcffYTc3FxoNBrs2LEDFosFAGAwGPD888+HPINERERERBSZAg4mnn32WSxZsgRvvfUWFAqFZ/vo0aOxY8eOkGaOiIiIiKKDUwiYbQ442YO+Uwl4Nqf8/HyMGzeu0XadTge9Xh+KPBERERFRFDHUWbGnqAK1JhtiNQoMzUyGTqts72xRGwi4ZSIjIwOHDx9utH3z5s3o06dPSDJFRERERNHBKQT2FFWgVF8HpVyKUn0d9hRVsIWikwg4mLjttttwzz33YNu2bZBIJDh16hSWL1+OBx54AHfeeWc48khEREREEcpqd6LWZENijAoxagUSY1SoNdlgtTvbO2vUBgLu5vTII4/A6XRi4sSJqKurw7hx46BSqfDAAw/g7rvvDkceiYiIiChCKeVSxGoUKNXXAQCqjBakJ2ihlAc1aShFmaDXmbBarTh8+DBqa2sxcOBAxMbGhjpv5APXmSAiIqJIwzETHU/Y1pmYO3cuampqoFQqMXDgQIwcORKxsbEwGo2YO3duQPv6/vvvccUVV6Br166QSCT45JNPvJ4XQuCJJ55Aly5doNFoMGnSJBw6dMgrTWVlJW666SbEx8cjISEBt9xyC2pra73S7NmzB2PHjoVarUaPHj3w4osvNsrLypUrkZ2dDbVajSFDhuDLL79st7wQERERRROdVonR2Rm4eEg3jM7OYCDRiQQcTLzzzjswmUyNtptMJrz77rsB7ctoNGLYsGF44403fD7/4osv4h//+AeWLFmCbdu2ISYmBrm5uTCbzZ40N910E3777TesXbsWn3/+Ob7//nvMmzfP83x1dTUmT56MzMxMbN++HS+99BKefPJJ/Pvf//ak+emnnzBz5kzccsst2LlzJ6ZPn47p06dj3759bZ4XIiIiomgklUigVsgglUjaOyvUloSfDAaD0Ov1QiKRiMOHDwuDweD5q6ysFO+8847o0qWLv7trBID4+OOPPY+dTqfIyMgQL730kmebXq8XKpVK/O9//xNCCLF//34BQPzyyy+eNF999ZWQSCTi5MmTQgghFi9eLBITE4XFYvGkefjhh0X//v09j6+77joxbdo0r/yMGjVK3H777W2el5YYDAYBQBgMBr9fQ0REnYvD6RQmq104nM72zgoRRSl/y5x+t0wkJCQgKSkJEokE/fr1Q2JioucvJSUFc+fOxfz580MW5BQUFKCkpASTJk3ybNPpdBg1ahS2bNkCANiyZQsSEhIwYsQIT5pJkyZBKpVi27ZtnjTjxo2DUnm2uS03Nxf5+fmoqqrypKn/Pu407vdpy7wQERG1hqHOih/zSrBx70n8mFcCQ521vbNERB2Y37M5bdy4EUIIXHLJJfjoo4+QlJTkeU6pVCIzMxNdu3YNWcZKSkoAAOnp6V7b09PTPc+VlJQgLS3N63m5XI6kpCSvNL179260D/dziYmJKCkpafF92iovDVksFlgsFs/j6urqRmmIiIgA7/n+E2NUrvn+AYzOzmDXEyIKC7+DifHjxwNw1dL37NkTEl6U2sSiRYvw1FNPtXc2iIgoCjSc7x+AZ75/tULWzrkjoo4o4AHYGzZswKpVqxptX7lyJd55552QZApwrbQNAKWlpV7bS0tLPc9lZGSgrKzM63m73Y7KykqvNL72Uf89mkpT//m2yktDCxYsgMFg8PwdP37cZzoiIiL3fP9VRguMZhuqjBbEahSc75+Iwibgq8uiRYuQkpLSaHtaWhqef/75kGQKAHr37o2MjAysX7/es626uhrbtm1DTk4OACAnJwd6vR7bt2/3pNmwYQOcTidGjRrlSfP999/DZrN50qxduxb9+/f3dCvKycnxeh93Gvf7tGVeGlKpVIiPj/f6IyIi8kUqkWBoZjLSE7Sw2p1IT9BiaGYyuzgRUfgEOrJbpVKJgoKCRtsLCgqEWq0OaF81NTVi586dYufOnQKAePXVV8XOnTtFUVGREEKIv/71ryIhIUF8+umnYs+ePeKqq64SvXv3FiaTybOPyy67TJx33nli27ZtYvPmzaJv375i5syZnuf1er1IT08Xv//978W+ffvEihUrhFarFW+++aYnzY8//ijkcrl4+eWXxYEDB8TChQuFQqEQe/fu9aRpq7y0hLM5ERFRSzibExG1lr9lzoCDiR49eohPP/200fZPPvlEdOvWLaB9bdy4UQBo9Ddr1iwhhGtK1scff1ykp6cLlUolJk6cKPLz8732UVFRIWbOnCliY2NFfHy8mDNnjqipqfFKs3v3bjFmzBihUqlEt27dxF//+tdGefnwww9Fv379hFKpFIMGDRJffPGF1/NtmZfmMJggIiIionDzt8wpEUKIQFoyHn74YXzwwQdYunQpxo0bBwD47rvvMHfuXFx77bV4+eWXQ9huQg35u7Q5ERF1Xk4hYLU7oZRL2cWJiILib5nT79mc3J555hkUFhZi4sSJkMtdL3c6nfjDH/4Q0jETREREFDhDnRV7iipQa7IhVqPA0Mxk6LTKll9IRBSEgFsm3A4ePIjdu3dDo9FgyJAhyMzMDHXeyAe2TBARUVOcQuDHvBLPOhNVRgvSE7RcZ4KIAha2lgm3fv36oV+/fsG+nIiIiEKM60wQUVvzK5i477778MwzzyAmJgb33Xdfs2lfffXVkGSMiIiIAuNeZ6JUXwcAnpYJrjNBROHiVzCxc+dOz9oIO3fubDIdV8UmIiJqP+51JvbA1SLBdSaIKNyCHjNB7YNjJoiIqCWczYmIWivsYyaIiIgoMkklEo6RIKI24Vcwcc011/i9w9WrVwedGSIiIiIiih5+jcjS6XSev/j4eKxfvx6//vqr5/nt27dj/fr10Ol0YcsoERERERFFFr9aJpYuXer5/8MPP4zrrrsOS5YsgUzmakJ1OBz44x//yD78RERERESdSMADsFNTU7F582b079/fa3t+fj4uuugiVFRUhDSD5I0DsImIiIgo3PwtcwY88bTdbkdeXl6j7Xl5eXA6nYHujoiIiIiIolTAsznNmTMHt9xyC44cOYKRI0cCALZt24a//vWvmDNnTsgzSEREREREkSngYOLll19GRkYGXnnlFRQXFwMAunTpggcffBD3339/yDNIRERERESRqVWL1lVXVwMA++63IY6ZICIiantcCJA6m7AuWme327Fp0yYcOXIEN954IwDg1KlTiI+PR2xsbHA5JiIiIopAhjor9hRVoNZkQ6xGgaGZydBple2dLaKIEHAwUVRUhMsuuwzHjh2DxWLBpZdeiri4OLzwwguwWCxYsmRJOPJJRETUYbCWO3o4hcCeogqU6uuQGKNCqb4OewCMzs4I6Lvjd04dVcDBxD333IMRI0Zg9+7dSE5O9my/+uqrcdttt4U0c0RERB0Na7mji9XuRK3JhsQYFWLUCgBArckGq90JtULm1z74nVNHFvDUsD/88AMee+wxKJXeP4JevXrh5MmTIcsYERFRR1O/llspl7pquYsq4Ax++CKFmVIuRaxGgSqjBUazDVVGC2I1Cijl/hWh+J1TRxdwy4TT6YTD4Wi0/cSJE4iLiwtJpoiIiDqiUNRyU9uSSiQYmpmMPXB9V+kJWgzNTPa7qxK/c+roAm6ZmDx5Ml577TXPY4lEgtraWixcuBBTp04NZd6IiIg6lNbWclP70GmVGJ2dgYuHdMPo7IyAuijxO6eOLuCpYY8fP47LLrsMQggcOnQII0aMwKFDh5CSkoLvv/8eaWlp4corgVPDEhFFO/af73z4nbc9DnhvPX/LnEGtM2G32/HBBx9g9+7dqK2txfnnn4+bbroJGo2mVZmmljGYICKKfizodD78ztsOg7fQCEswYbPZkJ2djc8//xwDBgwISUYpMAwmiIiIiLy5gzW5TIIt+aWeqXyrjBakJ2gDnsqXwrRonUKhgNlsbnXmiIiIiIhCoX5LhEohg95o4YD3NhTw6J/58+fjhRdegN1uD0d+iIiIiIj80nDq3YoaEypqzKis5YD3thLw1LC//PIL1q9fj2+//RZDhgxBTEyM1/OrV68OWeaIiIiIiJria+pdmVSKhFgVLFZHwFP5UuACDiYSEhIwY8aMcOSFiIiIiMhv7ql3S/V1AOAZI5HTPx12h+CA9zYQcDCxdOnScOSDiKIYZykhIqL20NSignKpFOzZ1Db8DiacTideeuklrFmzBlarFRMnTsTChQs5HSxRJ8cp+IiIqD25FxVkpVb78Dtme+655/Doo48iNjYW3bp1w9///nfMnz8/nHkjogjXcOBbqb4Oe4oq4Ax8+RoiIqKgSSUSqBUyBhLtwO9g4t1338XixYvxzTff4JNPPsFnn32G5cuXw+l0hjN/RBTBGg58S4xReabgIyIioo7P72Di2LFjmDp1qufxpEmTIJFIcOrUqbBkjIgin3vgW5WRU/ARERF1Rn7f8e12O9Rqtdc2hUIBm80W8kwRUXRwD3xLT9DCandyCj4iIqJOxu8B2EIIzJ49GyqVyrPNbDbjjjvu8FprgutMEHUuHPhGRETUefkdTMyaNavRtptvvjmkmSGi6OQe+EZERESdi9/BBNeXICIiIiKi+jhKkoiIiKKOUwiYbQ5ORU3UzgJeAZuIiIioPXGxTKLIwZYJIiIiihpcLJMosrBlgoiIiKJGw8UyAXgWy+REEERtjy0TREREFDW4WCZRZOEvj4iIiKIGF8skiizs5kRERERRhYtlEkUOtkwQETXAKSeJIp97sUwGEkTtiy0TRET1cMpJIiIi/7FlgojoDE45SUREFBi2TFCrOYVgv1XqEDjlJBERUWAYTFCrsEsIdSTuKSdL9XUAgCqjBekJWk45SURE1ATeISlo7BJCHQ2nnCQiIgoMWyYoaOwSQh0Rp5wkIiLyH1smKGhchZQ6Kk45SUSRjlNYU6RgywQFzd0lZA9cLRLsEkJERBR+HK9IkYTBBLUKu4QQERG1nfrjFRNjVK7xigBGZ2fwHkztgsEEtZq7SwgRERGFF8crUqRh53YiIiKiKMHxitGtI451YcsEERERUZTgeMXo1VHHujCYICIiIooiHK8YfTryWBcGE0RERERRhuMVo0tHHuvCDnZERERERGHUkce6RP8nICIiIiKKYO6xLukJWljtzg411oXdnIiIiIiIwqyjjnVhywRFnY44rRoRERF1fO6xLh0lkADYMkFRpqNOq0ZEREQUjdgyQVGj/rRqSrnUNa1aUQVbKIiIiIjaCVsmKGp05GnViIiIiKIRWyYoanTkadWIiIiIohFLYRQ1OvK0akREFNk4+Qf5q7OdK+zmRFGlo06rRkREkYuTf5C/OuO5wpYJijodcVo1IiKKTJz8g/zVWc8VtkwQERERNYGTf5C/Ouu5wpYJIiIioiZw8g/yV2c9Vzr2pyMiIiJqBU7+Qf7qrOcKuzkRERERNYOTf5C/OuO5wpYJCpnONhUaERF1Hpz8g/zV2c4VtkxQSHTGqdCIiKj1nEKEpBY3VPshosAwmKBWqz8VWmKMyjUVGoDR2Rm8oBMRUZNCVRHFCi2i9sNuTtRqDadCS4xReaZCawm7RhERdU6hmpO/reb25/2KyDe2TFCruadCK9XXAQCqjBakJ2hbnAqNNUlERJ1XqObkb4u5/Xm/ImoaWyao1YKZCq2zrhJJRNQRhKKWPlRz8od7bn/er4iax5YJColAp0LrrKtEEhFFu1DV0rsrovbAdf0Pdk7+UO2nKbxfETWPwQSFjHsqNH8E2zWKiIjaT6gn3AjVnPzhnNu/I92vOOMVhQODCWoX4a5JIiKi0AtHLX0gFVFtsR9f++0I9yuO+6BwYTBB7aYzrhJJRBTNOlItfSCi/X7FKdwpnBhMULsKV00SERGFXkeppQ9GNN+vOO6DwimiqxKefPJJSCQSr7/s7GzP82azGfPnz0dycjJiY2MxY8YMlJaWeu3j2LFjmDZtGrRaLdLS0vDggw/Cbrd7pdm0aRPOP/98qFQqnHPOOVi2bFmjvLzxxhvo1asX1Go1Ro0ahZ9//tnr+VDlhYiIKJK5a+kvHtINo7Mz2FUmCoR7xivq3CL+LBo0aBCKi4s9f5s3b/Y89+c//xmfffYZVq5cie+++w6nTp3CNddc43ne4XBg2rRpsFqt+Omnn/DOO+9g2bJleOKJJzxpCgoKMG3aNFx88cXYtWsX7r33Xtx666345ptvPGk++OAD3HfffVi4cCF27NiBYcOGITc3F2VlZSHNCxERUTRw19J3hhaJjiCYKdyJ/CYi2MKFC8WwYcN8PqfX64VCoRArV670bDtw4IAAILZs2SKEEOLLL78UUqlUlJSUeNL861//EvHx8cJisQghhHjooYfEoEGDvPZ9/fXXi9zcXM/jkSNHivnz53seOxwO0bVrV7Fo0aKQ5sUfBoNBABAGg8Hv1xAREUUbh9MpTFa7cDid7Z2VDoPHlALhb5kz4lsmDh06hK5du6JPnz646aabcOzYMQDA9u3bYbPZMGnSJE/a7Oxs9OzZE1u2bAEAbNmyBUOGDEF6eronTW5uLqqrq/Hbb7950tTfhzuNex9WqxXbt2/3SiOVSjFp0iRPmlDlxReLxYLq6mqvPyIiovYWioXrmmKos+LHvBJs3HsSP+aVwFBnDfl7dEZsUYpwDgdw+DDw+efAyy8Dt94KjB4NlJe3d86aFdEDsEeNGoVly5ahf//+KC4uxlNPPYWxY8di3759KCkpgVKpREJCgtdr0tPTUVJSAgAoKSnxKry7n3c/11ya6upqmEwmVFVVweFw+EyTl5fn2Uco8uLLokWL8NRTTzX5PBERRa9onfc/nNOMcuYhitbfhd+MRkAmA9Rq1+NPPwUeeww4eBCw+gic8/KAMWPaNo8BiOhgYsqUKZ7/Dx06FKNGjUJmZiY+/PBDaDSadsxZ21mwYAHuu+8+z+Pq6mr06NGjHXNEREShEK3z/oe7sM+Zhzq3aP1dNCIEUFrqCgTy8oADB87+/9gxYNUqYMYMV1qJBNi3z/V/tRro3x/IzgYGDHD9W2/yoUgU0cFEQwkJCejXrx8OHz6MSy+9FFarFXq93qtFoLS0FBkZGQCAjIyMRrMuuWdYqp+m4axLpaWliI+Ph0ajgUwmg0wm85mm/j5CkRdfVCoVVCpVs8eFiIiiS3vVvoeixjfchf3OupYFRWmrlN0OHD3qChYGDgT69nVt//RT4Oqrm37d0aNn/z96NPDll66gITMTkEbXuR5Vua2trcWRI0fQpUsXDB8+HAqFAuvXr/c8n5+fj2PHjiEnJwcAkJOTg71793rNurR27VrEx8dj4MCBnjT19+FO496HUqnE8OHDvdI4nU6sX7/ekyZUeSEiCrVw9mun4DUskCfGqDwF8nAJ1TiEcE8zypmHWieaf/Pt8bsISEUF8N57wF/+4mpVGDgQ0GpdLQnTpwMff3w2bb9+rqDgnHOAyy8HHngAePttYPNm1xiIBx88mzY5GZgyBejdO+oCCSDCWyYeeOABXHHFFcjMzMSpU6ewcOFCyGQyzJw5EzqdDrfccgvuu+8+JCUlIT4+HnfffTdycnJw4YUXAgAmT56MgQMH4ve//z1efPFFlJSU4LHHHsP8+fM9tf133HEH/vnPf+Khhx7C3LlzsWHDBnz44Yf44osvPPm47777MGvWLIwYMQIjR47Ea6+9BqPRiDlz5gBAyPJCRJEvmvrydpjuAh2QXCaBSiFDRY0JQPhr30NZ49sWC9dF+4rT7SXaf/Pt3iolBFBc7N0t6eKLAfdU/ydOAH/4Q+PXuQMKne7stuxs19gI97iIDiyig4kTJ05g5syZqKioQGpqKsaMGYOtW7ciNTUVAPC3v/0NUqkUM2bMgMViQW5uLhYvXux5vUwmw+eff44777wTOTk5iImJwaxZs/D000970vTu3RtffPEF/vznP+Pvf/87unfvjrfffhu5ubmeNNdffz1Onz6NJ554AiUlJTj33HPx9ddfew2oDkVeiCiyRdONOiq7C3QS7vNIb7RAb7RCJpWGvfY91F2T2qKwH80rTreHjvCbb8sV1j0VQ+VlkD7yyNngoabGO6HVejaY6NsXGD/eeyzDgAFA9+6NWxSk0k4RSACARIgobAfrxKqrq6HT6WAwGBAfH9/e2SHqNJxC4Me8Es+N2l1jFqk3arPNgY17T0IplyJGrYDR7Co4XjykGwto7ajheVRZa0FyvBrjBnaBPITdGxq2oEXb+UuB60i/+ZC1ABsMPgdAWyZNxs/zF6DWZIPOYcZFI/uffY1UCmRlnQ0UJkxwdUHqhPwtc0Z0ywQRUaSIthlm2r27APnk6zyyWB2wOwRC9dU01YLWVjW+1D460m8+oFYpIVzdj0wm1zgFwNW9qG9fV5clH+oSUz2B9SmLDEcefhK9RwyCdMAA1xgHdj8PCIMJIiI/RNuNui27C5D/wn0eNdfVheMQWifSx0t1+N+8EMD+/b6nWjUagcsuA776ypU2Jsa1ABwAdO3q1SXJek5f7JQkeQX0B6+bg25R2IITKRhMEBH5IRpv1Cw8Rp5wn0cttaBxHEJwomW8VIf4zVdVnQ0SJBJg9mzPU2LsWEiqqhq/RiYDbDbvbZs2uQKJ+oOiAciFgOJMlz8g8iuGogGDCSIiP0XjjZqFx8gTzvMo2lrQokGgA5vbuwUj6n7zb74J7Nx5NoCov67XOed4ggmDyQbHoGGQVulhOacvdOcNhXrYYFeLQ58+gLJBcDdggM+3i8aKoUjHYIKIKABRd6OmiBSu84gFpdALZLxUtLRgtBmzGTh0yLtLktXqWv3Z7a23gO3bvV/XvbsrSBg8GBACTsAV0L34dkgmEIjGiqFIxmCCiIionvauWW4tFpRCy9/WnkiamrXNz+HqaqD+bD/33AN8/jlQUOAa61CfQuFaNVp+pgj6hz+4xju4Z0/q1w/O2Fiv/FttjpBPgMGKodBhMEHUBqK9cEKdU2c8bztKzTILSqHjb2tPpMz4FrZz2OEAiorOtjDUHwhdW+saBO2e3ri0FDh61PV/ne7sugzuf+sHGH/6U+P855V45T9Oo2D3vQjGYIIozDpK4YSiQ6gCgM543kZSzTJFFn9aeyJhvEpIzmGTCcjPd/1dd51rEDQAzJwJrFzZ9OtOnAB69nT9/4EHgDvucAUPaWln99GK/LP7XuRiMEEURiycUFsKVQDQWc/bSKlZpsjUUmtPJIxXCfgc3rcP+Okn76lWi4rOthyMHu0avwC41m1QqVxrOdRf/Tk727UtJubsfkeMaDGvvio+mss/u+9FLgYTRGHEwgm1lVAGAJ31vI2EmuVQ6Yxd1CJBexd4G53D1XXINFZA+fVuV0tDXh6waBGQmup6wbvvAi+91HhHSUmuQKGm5uy2Rx8Fnn7aNQ1rKzVV8dHSb5Dd9yITgwmiMOpIhROKbKEMADrreRsJNcuhEKld1DpLgNOeBV6pRILz835B3ZtvQ334IGKOF0JqtXgn+v3vgfHjXf8fNQqYMqVxS0NKSuOuSfVbHlqhpYqPjvAb7GwYTBCFES+M1FZCGQBIJRIM7pkEm8MJs8Xeqc7b9q5Zbq1I7aLWFgGO3emE0WxHjFoOubTl8z7qghshXAObG67+fOAA8MEHQE4OACCmrBgx33x29nVqNdC//9mAoVu3s8/NmOH6a0MtVXxE+2+wM2IwQRRmvDBSWwhl4Gqos2LfsUqYLXaolXIM7pkUETXbbSWau1JEYhe1tghwjpfX4uudx1BltCIxRonLzuuJHimxTaaP1NYbAK5pU48edXVFSkx0bfvwQ+D22wG93vdr8vI8wQQmTABeffVs8NCzZ0i6JoWKPxUf0fwb7IwYTBC1AV4YqS2EInBtWPCrrDVj37HKdq/Z7mjCVSseiV3Uwh3g2J1OfL3zGI5X1CIlTo3jFa7AYs7EbJ8tFOEMbgL6Xk0m4LffvFsY8vJci7zZbMB77wE33+xKm5TkCiSkUqB3b+8uSdnZwJAhZ/fbv7/rL0K1puIj6lqTOgkGE0QUEF7MI1trA9dIrNnuaMJZKx6JXSvDHeAYzXZUGa1IiVNDp1WdeQ8rjGa7z+MarnPc5/eqUQDFxWcDhQsvBIYPd71g0yZg6lTfO9NogKqqs48vvBDYs8c1o5JaHXQeI0UwFR9t1ZrEe1zgGEwQkd8iumsAhUQk1mx3JG3R5SfSulaGO8CJUcuRGKPEsfIaOJxOVNZa0DMlDjFq30WccJzj7u/VcLgQ2es+heJgPqQnCyEKj0BSf0akhQvPBhPZ2a41GOq3MLj/36PH2QXgACA21rv1IQL4KnQHUhAPpOKjrcYC8R4XHAYTROSXSB3YSaEViTXbHUlbtfxEWtfKcAY4cqkUo7O74MSPRhSeNiJBq8To7C5NDsJu1TluMLimWK0/AHrqVFhnz0WtyYYkYcM5/3ihwRtKgawsV6DQr9/Z7b17uwZUh0hb1qj7KnQDCFlBvOFnaYvfDe9xwWMwQRTF2vLmwe4voRPpzeiRVrPdkXTmlp9wBThOIVBRa8agnonQKBUwWW2oqDXDKUST526z57gQQF3d2alQS0qAm25yBRDFxY13FhcH5a23IlajQHFiOoovn4GKrpmQDhyA7Ek5kJ5zjmuxtzBqyxp1X4Xu3UIAEqBMb2p1QdzXZ4nTKMLSmtTWAUtHxWCCKEq1dXNsZy4EhVK0NKO3V812pAdarRVsrXhHPy6++PuZ3YXA5Fg1YtQKGM0yvwqBUqsV6sOHG0+1mpcHXH898J//uBImJrrGNzidrsddunh3SRo50ut73fXUq57fttSP33Zrv9u2rlH3VejWG60A0OqCeHOfJZQtpm0VsHQWDCaIolB7NMey+0vrdZZm9GALR9ESaLWWTqtETv90v9dE6CzHpb5APnOLFR1VVa4AQQjgootc28xmIC7ONQ2rL/n5Z/+vUrnWcejRwxU86HQ+X9Jeg4rbukbd1/FO02k8LRPubcEUxJv7LKFqMW2rgKUzYTBBFIXaqzmW3V9apzM0owdbOOosgRYQ2DHqTMfFLdDPXL+iQ71hPXqdLED3smOQHj7kCiLc4xLGjQO++871f7XatXhbZeXZFob6A6Gzsrzf5Npr/cp7ewwqbutW46YqlgBgDypaVRBv6bOEosW0LQKWzobBBFEUas8uR5E2sDOadPSuYq0pHHWGQAsI/Bh1luNSX7Of2WFzrcPg7o4kkwGPPuopBEomPQzJqVONd9q9u6t7Un07dwIJCUA7FRhD9d22R6txU4Xu1hbE2+KztEXA0tkwmKA20Rn7+4YTuxxFp47+vbWmcNTRAy23QI9RZzku9TX8zBn/fAUZB/dBdaIAKCg4O3YBcLUuPPooANfvC1OmuLo11W9p6N/f1aWpIffq0u0klN9tuGrUm7t3+yp0h6IgHu7WgY5+HW4PDCYo7Dpjf9+2wObY6NTwewMAs83RIb7D1hSOOssNPtBj1KGPi9MJFBU1GgAtNZsxdNNm7BYCeqMV3XdsgW77trOv0+lcgYI7WBDibOvC22/7//btXMkV6u821DXqhjordheWQ2+0IiFGiWG9Utrs3h3Kz+Lre+b9M7QkQgjR3pkg/1VXV0On08FgMCA+Pr69s9MipxD4Ma/E06TvvnF25P6+RP7qiIF2az9Texfw2kIwxyiqj4vJBBQWugr/bn/4A7BypWsgdEMSCQynq7C7zAh9rRV9f/gKveQOaIYOdu0jLa3VXZMi6bcXid+tUwis3X0CewrLIZNK4HAKDO2VgkuHdY+YPPojkr7naORvmZMtExRWnbG/L5E/OurA2tbW+HWG/srBHKNAj0ugBdSQFGgrKoD9+xtPtVpY6Fq4ra4OUJ4pyMlkrkBCqXQt5FavW5Kzf3/sOVWNslorEmNU+C1nMspDWAkVab+9SDznzTYH8k/qYXcIxGtVqKwxI/+kHmMHdoFWGR1Fx0j7njuy6DgjKGoF0qQfibUz1HmF+3zsyIF2JBaOIk04j1GgtbEBpXc4XOMW3IHCvfcC8jNFiXvuAZYv9/06nQ44edK16jMAPPYY8Je/uB7LvI+D1eZA7d6TYftthPq311HvXQIAJML1P4lAtHVj6cjX2EjDYILCyt8+oWyKpEjSFudjRx5Y21ELV9HwuQKtjW0x/ebNwLffnm1pOHgQsFrP7uCqq4C+fV3/HzTIFRy4p1etP9Vqaqr3GzecerWecP82Qrl/Q50Vu4vKoa+1IiFWiWGZbTeuIJzUChmyuyVgd1EFjGY7ZDLX42gqhHfka2ykYTBBYddSkz6bIimStNX52FEH1nbUioGWPlekBBoB1cYKAevJYih/+AGDjx2F7ngBlIfyseexF2A9J82Vfu1a4JlnvF+nVrtmSHIPfnZ75BFgwYJWf4Zw/zZCtX+nENh6sBS7iyogl0hgFwImi6NNxxWE67yTSiS4sF86NCqZV6DU3tenQD5vR73GRiIGE9QmmmvSZ1MkRZK2PB872owi7kCspKoOsRoFSqpcNYLRXjHQUoAZrgAqmIKiz9rYWCWUcAI4c/5+9BHw8stAXh7Uej1GNdhHyskCKOVnto4bB9x2m3dLQ8+ejbomAQjpeg3h/m2EYv9mmwN5J/VwOBzQxapRWWtGXhuOKwh34K7TKjEmu0vEXJ+C+bwd7RobqRhMULtjUyRFkvZYTbajBM1WuxOnDSaUVZtQZjABEkAmlUR9xUBzAaZSLg1LS1ZQBcWaGkjz8zF8116U/7ILysMHEVd0FNpjBZB89RUwcaIrndEIbN3q+r9UCkevXtD36ANDj96w9+uHzAmjzuZ94sSzr2tj4f5thGL/EgAQEtf/hARtVVRtyxbUcH4H/gbMrfm8HekaG6kYTFC7Y1MkRRKej8GTyySoqDGjVF+HlDg1ymvMUMqkkMui+9g1F2CGoyWr2YITABQXu8Yv9O/vWrQNAN5/H7jpJgCAFkDPhjvNyzsbFFxyCfDhh65Whr59IVOrkSgEYlh7GxC1Qob+3RKwp7AcRrMVcpkE/dtoXEGktugH0poWSMAcqZ+XXBhMUERgU2TnESl9y5vD8zE4dodAcpwaNocTQgAZCVokx6lhdwhEc0NjcwFmOFqy3AWnVHsdMjb/gMz8fKiPHALKjgH5+UB1tSvhm28C8+a5/t+rl+vf9HTfA6B79Dj7Bt27A7/7XaPPGO5CWTT89gPhGVeglHkt7NYWny0SW/QDCQ4CbWmIxM9LZzGYoIjBpsiOL5oG57bV+diRClhKuRSpOg0cTifi1ErUmK1I1Wmi9oZf/7tpKsBsdUuWwXB2mtW8PGDSJCgvuQSxGgUcW/ei//w5jV8jlbpmQ5LWO64jRgCVlUBiYus/eIg5hUB5tRl5p6pgNNkj/rcfCJ1WiTED2n5cQaS1oAYaHATa0hBpn5e8MZggojbRVn18o6lwHk3BlT/cN3whBPRGa7vd8ENxDjT13fgq6ATUknX8OPDXv56darW4uEHmnZBOnIihmck4MGQwDAOGwJLVF/HnD4V66GBXK8M55wAqlffrlMqzC8K1oZaOtaHOit2F5dhVUAGz3YEB3RLbZMa+trwOtFdFWCS1oAYaHATT0hBJn5e8MZgg6gQioYDdFn1eo6lw3qGnRJY0+LcNheIcCOa7kUokUAsHcCDfe/XnAweAGTO8p0xdvNj7xV26nO2SNHYsAFfBaeSYIbDu3om4CC04+TNd7p6iCpyqNMLucMBut+NUZS16p8WFtb97NF0HWitSWvQDDQ6CbWmIlM9L3hhMEHVwkXJjDXef10ht+WgqfUccUOj5DqrqEKdWorSqDrtRjhFZrjULmjpeoQp2Q3UOtPjdVFUBdXVnBz+XlQFjxgBHj7pWiG7IPZ4BcI1XePRRoF8/VwDRv79rdWgfIrng5BQCu4vKcaqiDslx3scacB1DpxCoNdmQEqdGZa0VJyqMOG2woKrWgvP6pLb42w/mvOjQQXoECyY4aI+WhkioWOuIGEwQdWCRdGMNdZ/XhjeFSGz5aC59WwwobHiM3I/lMgnsDhHyG6p7atjT1WaUGcyw2Z0oPF0Lfa0VibEqn8crlMGur3Ogps4KQ50VKoWs2YCmPqVcili1HDV5h5FUegzq/DykFx+DqvSYq6WhrAy44Qbgf/9zvSA5GTh2zBVIxMU1Hvw8dOjZnUskwHPPBfX5Ikl5tRk7j1bA4XDCaLEhTadFrcmG8moz8k/pUWuyIUYjh1QqQZXRAqvNDqvdCY1KDqkfs3s1d140VyDsiEF6tAgmOGjLgDlSKtY6IgYT1Ol15JqKSLuxhqomytdNIU6jiKiWj5bSh3tAYcNj1Cs1DoWna3DaYEJFjRnJcWqk6jQhvaFKpUCpvg7FVUakxGlwqMQAjVKGczOTfB6vUAe7DQO04iojqoxWHCoxQALXtJ0X9kv3/rxmM3DokKtLkkIBTJ/u+m666RA3bBKkdpvvN6usPPt/mQzYtMm1mFuXLiFdvC0SOYVA3skqWG122B0CZYY6VNRaMKpvKvJOVaFMb0JijAplehPiNEokxapRUFaLAd0TkZWhg0zqmvmrqetQc+dFjcnWbIGQs/60r0htTYukirWOiMEERaS2KuB39JqKSLyxtvZmY3c6sf3oaVRUm5EU631TGJqZjN0Q0NdakZagCWnhPNDAzJ/04Wrmb7gSdXGVEXknqqDTKnH6zDoQNocTDqcTQMs3VH9+j4Y6K7YfPY3KWgucArA6nIhRyZEUq4JKKUeiVNro84c62K0foNXUWVFndaDaZIVUAkBIsKewHL2+WIX4qpOQuMc0FBQAZ44DLrgAmD4dAKDTxUAMGgin1QbJgGxI6rc29O/vaoGo78ILA85vtLLanTCa7cjuloiyajMMRjPkMhn6pOmw71il1/dptTsxakAGJFIJKqrNUMgkLV6HmjovzDZHiwXCSJ31pyNXWkWDSKtY62gYTFDEaasCfmeoqYjUG2uwDHVWbD9yGr8eOY0YtRxJcWokxqg8NwUAgID3vyESaGDmb/pw1OQ1XIna6RSoNlmRnqgFqoGUODWEAOLUyhZvqP78Ht2/pYpqMxJiVTBXOaCUS5Ecp3J1QbM5oK+zNvr8IQ92nU7oSk9idMEBWPb+htKtO6G3OrHtnscBSGA0W9F9yd8hOVHk/TqdzhUojBjhtVmycyckrfyt2J2ugneMWg65tGPUjtf/3jJTYlCukqFrUgy6JGlReLqm0fepVcoxvE+q5zxq6TrU1HkBwK8CYaTN+tPRK62iQSRWrHUkDCYoorRlAb+z1FRE2o01WJ4Ca40JsWo5Ss7cFNLiNchI1EIuk+CXwxUoM5zpYmEwYU9RRaNzJ9gawkADs/YM5NwrUZdUuX5HlbVmyKRSmCw2QAKU15iRkaBFjdmKjMSYJm+o/v4e3b+lpFgVkuLUAACj2Y5BPZKhkEthcwifnz/oY2Szubokud1zj6ub0cGDgNkMKQANgF4ATHE6fDX3AQCulbjLL78aMokNJ1K7o6Jbb4jsbAwYng1djKrx+zTRfc3f8+d4eS2+3nkMVUYrEmOUuOy8nuiREhvwfiJNw++tW3IshmYmQy6VNvl9BnIdauq8UCtkfhcII6W7TWeotIoGwV5rovl32pYYTFBQwvUDa8sCfqA1FdF8UYmUG2trnC2wqj0F1lqzHcnd1RiamQy7Q7R47rS2hjDQwCyYQC4U55ndIaBVySGEQLG+Diq5FD1TYpAQo4ZMJoNSJvUaM9HU+/j7e1TKpYjRyD0z+6TFa5DcXY1xA7t4Bsc39XmaPEZCAOXlZ6dXrTfVqrBYYCk6fjZ9Xh6wZ8+ZzCg9MyWZs/riUHwGtEoFJFIp+ndLQPzLf8Uvxyo9hbsqowW2Y5V+BZ2BnD92pxNf7zyG4xW1SIlT43iFK7CYMzEbRrPdaz+DeyZBo5SHZDartrpGNfW9NXfOB3Idamo/0dbS2lkqraJBoNfj+r/3GI0c2V0TkRKvjujzrb0wmKCAhbPJti2bIgOpqWAzdfurf24kxqiQGq9GdjcNxg3sArlUCqcQiNUoUFJlhM3ubFTrHqoawkADs0DSh+o8k8skqLPYIZEAXRM1KDWYcazciHSdBkkxKlw8uCtiVIoWb6j+/h5rTDaYLA6UGUwoNZiQ3S0Bw/ukerr1NPv5HQ6goADSQ4eBy3LPbr/6auDTT32+RALghx/3Q52e4jpGDz8M3H23q6tSr16uAdEA1AAGC4FzbA5PPvwp3DU1wD+Q88dotqPKaEVKnBo6rerM8bOixmTDvnrBTFFZNfJOVKFbUgzitMqgv/PWnjvBBCJNnduhqrzwtZ9oa2ll95rI4u+5Wf9+oVLIsfVgGXYdrcC5vZMxrFcK7/8NMJiggDQskJVUuQZyugt0rdXWXUP8uTG1dTN1Uzf1aG4ZCYWG50ZGYoyna4X7+V6pccg7UYWCslokxihxYb+z31EwNYQtHfNQfiehPM/sDoHkODWsDieEU0AulUJAQC6TobLWjP3Hq/zab0u/R6cQMNsc2F1YjhqTFQO7J6C8xgyNUoY4zdluSJ5xA8eOQr5zp1dLgzh4EFKLBUoAP/1yEIMGZrpu1N26uboZZWZ6plp19u+Pvdo0FCV3Q0xiwtljdPHFzXY30yrP3upaKtw19T0Mz0pFrcmGBK0SCrkUCdrmx5vEqOVIjFHieEUtAFfXsh7JsVDIpZ7zUKOSo9pkQ2WNBd1TYoP6zj3fQVG5ZxalQPcTbZUl4W5pDeXv2tdvaHDPpJBeNzrzfSFc3PcLnVaJY+W1cDgcsEuAU5VGSCQSdlNrgMEEBaR+gQwSCcqqTSgoqwGE62YbihtQW9c8tXRjastm6qZu6tF2sw+X5s4NpxAoPF0DnVaJ9EQtzBY7Ck/XoFtyDKQSScA1hC0d81B/J605zxoWKJRyKVJ1GjicAmqlHFVGK5LjVIiPUXoKs/6ev00dc/fnr6q1oLCsBr3T4hCrVkBVWQ7l5h1wbKyA9NBBnLj/L/gq7zSqjFZc8e/nMOCrVV77lwBwKJWo65UFQ+EJ7ImJdd2on34aeOklQKs9e4xsDpzaexIxcqnrGEkkfn2WhsenuQCpqe8BAKRSCXYWlEMmlcDhFBjaK6XJ80culeKy83p6xkz0SI7FZef1hFYpP9OCVgeV1Y6KGjOS4tTQquSQSyWoqXO1XriDseaug/W/g4KyGvRJjw/43LE7ndh+5DQqakxIilV3+j79wa5v0Zz6vyGT1Y59xypDct3gfSF83PeLkxVGGIxWQEigi1EjJU7Nbmo+MJiggLh/YCVVdSirNqFUX4eMBC0qanwPdg1WJPXxb6tm6qZqRHP6p4etZSSSarX8zUtT54Z7BqPyGjNEtatSWy6Tei76gdQQttRK4Ov5XcKJoZkpiNMogmqlC/Y8a6pAMTQzGYBritSMBA00ShlMFntQ52/DY17/82dt34zeH69G8okCpBYXQVFT7fXabdnjcDyhO1Li1DjYrS8SBg9H+oXnQTpgAKzn9MVPkkQ4uvdATIwaSrPtbGE6KanReRDMMWrq+LgLd+4F/JxC+BV0CokEgATCj59Lj5RYzLqkP/S1ViTEKqE80/3K3YJWWWpBndUBh8GE7YdPw2SzI17jKgzKZGdaSpzCZ0Gx/vS/MWo5LDY7DpysglqRDIOPmbOaOjbbj7pmR4v1MTtapFyD20pr1rdoifvc+uVwZUiu5RzY7b9gu/ANzUyGgECpwQS504m0eLXPWemIwQQFyP0DszmcKCirQUaCFv27JQCi4w4qa6uuV03ViBrN9rC0jERSrVYweWl4g/DMYKSvQ0qcGuU1Zihkrhl83PytIWyulUApl6LG5Cr0up+vqLXgqx0n8FNeKVLi1F6z9vib/2DOs+YKFDqtEjn902E022F3OrH/eFXg529t7dmBz/UGQNtWfIhaWxwSY1RILjyEnhvWeF4iJBI4e/WCbOBAWLL6ohxKz7iBouk3IH/adbh98kDotErIhYAsrwTl+jpAZkNxlREmqwM/Hij2OX7AfYx2CwG90Yq0FgaQt1Tgstgc+OWw70Cs4fdgdwg4nQLn9UqGSuEqvDe38BrQ9NiLwtM1SIhRontSDLYfPQ19nRWxagWqai0QAORyCXYVVkAiBM7rneKzoFh/+l8YXIGz0ylgsjr86kpTfzrfmHqzo6XGq5ud4Ssa+VuYbM36Fv4IZSt3ewzsDkflU7grtFpzn9NplRiT3QXZXRORd7IKRrMdCbGqiB/03x4YTFDAdFolxgzIgM3mhN5ogs3WeLBrRxPOxcXc+2yqRjRGLQ95y0gk1WoFkxdfNwiVQuYZJwABpCdokRSrgtFsb9RlRCmX4udDFThVaURKXOOuHU19FyarHb8crkRNnRUnK43QKGVI02nw86FSmK0OdEnQeM3a01QLRVM3uPoBgD/rEjRXoLCcKQC1OGOQEEBxsWuthZgY17b//hdYsAA4ccLn+yoO5SN2QA5K9XUoGZ6DmlvvgWRANnpPGAXVgGzINBoAgMzphHR9HsrPjBs4XW1G18QYaFSuwk79AKqmzgqT1QGNSg6VQtb8eSBp8G8Qx0cplzYbiDX8vbsH+LvSS6A3WpAcp/EKVutraexFUqwaCrkUWpUcGqUcg3okQwIBiUQKmVQGuUQCSAClQuaztcAdPJeeCZ71Rgu6J8XgkqHdYLM7sfdYhadFZFhm4wGjvqbzrTXbkd3N/8UeI6llsymBFCb9Xd/CKQSqai0w2xxe43FaEspW7rYe2B2OyqdwV2iF4j4nlUiQptMgJV4d8ed6e2IwQQFzXwDKa0w4eMoApbwGaTqN12DXjijUXa98XUh91Yg2N3d7IOrf+Nt7usL6eTHbHKiqtSBBq/QrL811B3OPE4jVKFBuMKGy1oIfDxQ36jLSNVGLXQUVsDscMFocSIv37gfbVJeo+rPwaFRymCx2VJtsACTITI1DQowKEolr1h6j2Q6dVtmowNVSV4rdReVNFgLr78v9OEYtR5nBBOBsgcK95kb995DYbLhIYYQ0Px/O/fshDuRBejDftRJ0dTXw2WfA5Ze73kitPhtIpKe7BkC7/wYMgPSCCzBUHYs9ACr6D4Hl3PMxNDMZmgYFgfrjBsoMJkglEsRrFdh2sMwrgHJ/9h8PFEOlkDV5HriPnXugcZnehD1ountl/QKXUwhU1FjQNVnr12+g4e+9/jlx2mCC3miFTCrFlvxSn4Wg5sZeuPOUoFXC4RQQEgmkUgGnAAQEHE4H7EJAIkSTC/65B9nbHE4IAWQkaJESr4FUIsHPh8qwu6gCcokEdiFgsjhw6bDuXseo4exo9afz9aebXjjGFoSar9/abgiMyErzfMf1NdU6WH99izqrw9WdTC7Dr4fL/J7Zx31MBvdMAtD6Vu62nKwkHJVPbVGhFar7XKScz5GMwQQFxH0BKK6sRWWNBQ6nQLxWCV2M0muwKzWvuQtpoHO3t/Q+vrr0DO6Z5LNWSy6TwGxzhHVNhPqFEKlUAiEECspqYLXZkd0tERa7o9katqZuEHaH8BonYLa5arkVcil21usyUlJVhwMnqmCy2eF0OlFSWYuSqlqMzs7wes+Gx7zh+3ZJ0MJic2BU/3RU1lpwoqIWKoUU5TVmdE+OhUwmQZXR0qgrlUoha7IrxdaDpU0WAhseN8AVHEmlEsRplLDana4CRaICjq3bEL9uK+Tnng9JcjYAIPbzjyH9y58AAI2OrFQKnDp19vHFFwM//eQKHhITfa7iHCcEhmelAoDPgpmbe9zAxr2nYDBakOyjJUgqkSBOo0CcVtlsTWughQN3gWuL2Ya9xyohk0iQGKvyDHCu/xuorDU329LgPidy+qfj+/3FcDgFdGfy66sQ1FTNsVoh8yoEDu2VAgCeAd0A4HAIDDtzLje14N/ZQfZOxKmVqDFbkarTwCkE8k7q4XA4oItVo7LWjLyTeowd2MWrFr3x7GhnKy9aEs6xBaHU8Hypszqw86irxSbxTHeVhnlrbn2L3RDYedR1LemdFtfkwpgNNQy8QrWuSFtNVhKOyqe2qNAKRetNJHUHjmQMJigg7n66JXoTjlcYIZdKYHM4EadRegpE7m4iDCqa1tKFNBRzt7svgu4uOWqFDCk6DUqq6iAg0K9LAgQEjCY70hO06JUahy35pX5fNIMd5+AuhCRoldhZUA4hkSC7awLyTlXhcIkB52elNFvD1twNQq2QNarlVshdXUYEBCQSCWLUchSU1aBvlwTXHP+lelgcDqgVcmSmx6NLQozXOAb3MW/qfRNjVJhSb9ae1HgN0uI1+H7fKddxV8qQGqdBSZURgKsFxdd+misEqhUyr8G2uwsrAIkEF2ptiPv6c6QXFyG9+JirpeFMUDAMwK57/oKKXlmoMlqQ2L8/REwMantlobJbL9j79UdZl0woBw/CeZMvhFStPnuQk5OBnBwAvldxjtcqA/runU5X4JMcp26y4OBPTWswhQOnEDhWXosakw0pcSqcNtR5CoCBtDS42R0CFqsDSbHNF4Ka+zwNu7NJJRKvFidf/2+qFt39/u6pkqUSiav3l3ANFIeQNNkbLNjCaLjHFoRKw5apAyerIBECWmXz3eh8XWt1WiVGZKVBX2uFVilDrEYJo9nm1/TSDY8JmnjfYLTFZCXh6FLVFt20Wtt6E67Wk47Y0sFgggLi7qdbZqiDFEBZdR1kUilqTFbotCr8eqQMRpOdEXwLgr2Q+nsRqn8RjFErcKKiFnanQJc6K2wOgaLTNa6uNDFKDM9KRVKcClvyS1FSVeeZrQto+qIZ7EW2fiFEIZdCJnUVeJRyGWKUClSbrLDbRbOfuaUbRMNabp1WCaPVhqoaC4Q4DScArVIOq9WKUoMJFrsDaqUMB07q8conuzFhUBeknymY+Rz8CwF9rRVpCWf7lvdIicWcidlnamXLcdpghloiw/GKWtgdTlToXIPBpRIJjGZ7k10dGhYCZTYbJPt/gz3/IJI2bkXaoYM4PHICTg+8CMmxKsRVlOG8V59ufKC7dIG9b3+ou3XxtFhkDRkHS6Uem/edgvLM1Kp1Zhv0diesMgXUjfficxXnr3YeQ/9uOpw2mP3+7v0931sq3PpbOHCfM3KZaypXV7coJSw2J6rPDJ632p0BtTQE+lma+zwtBeL1C4fNFRSbGtvRv1sCdheWw2CyQiaToH+3hCb3E0xh1N+xBe7H7TUxR/3zparWArVc5prCWKOEROLftML1qRUyJMae6TYokfh13W7vLqWhEI4uVW3VTas1rTfh+O46aksHgwkKiN0hoFXJISCB1e6ASi6DXCpBcpwadocIetGkziaYC2kgF6H6F0G1UgaH07VoVnKsCkdKa6BRyjAsMwllBhMkEgmGZ6V6ZoYpM5gACSCTSpq8aLr3H69RAJAgXqPw6yJbvxBytr+4wJESA0oNrmmGK2vNnprjGpMNuwvLoTe6Ah93/+SGNwgAXt2z6h/fMn0d6iwOWB0OnK42Iy5GgS5aLUoNJpysNAJCQKVQwinsqKq14HiFEe5wxuf5Kxr8e4ZcKoVGKUed2eE57habAyfLa2E022G22XGysg5qhQzxMSrvrg5OJ5xSKfp3S0Dhz3sw6YUXkXiiEAllJyF1uFZvHnTmfYyaWNT1uQBwClRlZeLk2Elw9u+PbqNHQDpgANC/P5CQADmAPg4HkupNS1p/EDHQchDraxXnihozyqvVAd1gGw60TopVY3DPxlO/utOqFTLPYmwNCwAtDVKv/ztRKWWoqjEjMU4Fs9UOhUyKilozsrslej6zvy0Nvj6LP7/dhoX1UNd2+hrbMbB7IgrLqnG62ozUeDUGdk8M6XXYn7EFQMvnV1vU0LqvFWabA78eLkOZwQSj2Rb0FMmBXrfbeqB0uISjS1VbddMKNGCuXxkRyu8ukiY+CTUGExQQuUyCOovdVRubGovKGteP64K+afhxf0mrIvhAat2jvYnQKQRUChly+qfD7hCeue7tTifsDhHwugcN1b+BxTgUkEmB1Dg1pFIpYlVyJMYqoVLKkSh1LWDmGpx6dmaY8hozlA2mVW24f4vNgc15xXA4BGQyCcZkd/EaGFx/Dv+GhXx37f7gzCRYbE4cPKlvNM2wewzBnsKzi4SZrI0HkhrqrD6neHUXOtfvOQmtUo50nRrVdTbUWezYWViB5Fgl0uLUOFFlRK3ZBqvNiTitHHKZFHFq79WN66/0XGY4M/jXR39pr+Nuk8NQZ4EDQK3ZBpPZiqSqUqRV5UNx+BDMJceQUHoMkgMHgJtugvSVV3Bhv3TE1WUi65fvzx7suDg4s7NRmNQNh3VdcKjvMMSqFdBplahVxeLom++6CjQNAktf3ZN6pMQGVBjytYpz9+RYpMSrcNpgBuB/gTHuTF/xnQXlMFtcY3iaCoibC5ybe87udGL70dOoqDYjKVaFimoz9EbLmaAXqKqxICNBi/P6pPj8zvz5PEDk1XbW5xQC+09UocpogVzq+jz7T1SFfDxbc2ML/Dm/WqocCfUq1FqlHMN6pXjeM5ia8IbXbX/vVaEadN3ewtGlqi26aQWi4XnZKzUOQGi+u47QStUUBhMUEPcMIu7pN7skaZGeoIFc2rraF39r3f1JF+nBhq+LVeHpGpw2mFBRY0ZynBqpZ+bPb2rdA9HC1IQNa4K7J8dCrZAiOU4Di90BKeA1S4xUImk0M4y7tcnXV+gUAscramG2OqBSSGG2OlxdepxO6GutyDtZhfJqc5OfBwIwWe0oqbIgKVblGQwL4T2GIP+kHnaHQLxWhcoaM/LPjCGw2Z1e40E0Shm6JMZ4zexkdwhYHQ7UmG2IUctxsqoOVTUWmKx2pMapIYFryr+yapOrH7RKDo1SASGAapMVXZJcYyd8rfTs60bgKTikx0B7KA+GGjMALeI0CnR1mvHIo1dDbTX7Piny8gC4CmgX5gyCbfG/IOvX19XS0KULnEJg/fo8HCuvQVKsCspaC1Li1LhkaDdolfJG57mv7knu6WoDmX624SrO3ZNjMXFINyTFqT0BnL8Fxhi1HCarAzUma7MBcXOBM4BmB/5uP+JagC3mzAJsSbEqyKQS6GKUiDfZkN0tEef3SUFijMrnb8WfAkP960swBYBQ11Q3vN65xy2U6euglMtgtbseNxyAHS7+BFotVY5UGS2eoNPXeiPhzFtTfN17mvv+wzXourUi/f7Ynpoa3+Jv8NiSjtJK5QuDCQrI2RlEXN0lak02pOo0jWYoCSSC92e1YXctd0u1800tEtVWF8+WLtQNP2tJVR3yTlRBp1Xi9JmWAZvDCYfTCcD3ugd1FjvyTlZBqZDj1yNlPueQb1iDVn82p3N7pUAIgTqrw7Pol1oh8zkzTHPdX0xWB/p20SFGrYDRbIOhzooNe04i/5QBZpsNEBLUmG1en6f+at7uBeZsToF4tQImix21ZpvXQmQCACQCgAAkAqLeMSyuMkIulaH4TGtKnwzX5ey0wYTvfytGcZUR+Sf1MNRZUWu2QXGmNUWtkCFGLYeAQLnRinSdGhKJBFKpFEazHdV1FmiUMozqn+5qjShy9bmP1ypQZ7HhwMlKaJSpqKoxoc/R/VBs/waW3w6gdvdeqA8fQsKp4xjudMJ6+RXYfuvTOFVlhEmqhFMmg0MqgymzNyq790ZFt15w9s+GZEA2ssaPhO7MsZVKJJDeeYfX8bbbnV5BfEaiHCnxriDe13nmq3tSVa0FlTUWKOTSJhfr88U9HqS4sg5Hyww4eMqAWE0dBvZIhFwqbTIgaXiun6o0otRgxsDuCc3WyjVXe+f+f1MDfytqTIittwBbWrwG8VolZBLA7nQFxr7GV/hb2xyK/s6h7CvuKz8ymQSVNRaYrU5olEqYrTZU1ljgFKLlHbbyvd3HoqXa5ua+Y5PVjlU/HUGp3oTEOJVnn+05YDnQluFwD7oOVkftrx8qzc0UGIqWg7YaJ9IeGExQQBrPIOI9Q0koZwZpuPCWSiGD3mhpsonQ1wV8q8UOjVIGo9n/QeGB1NzUT+vPlIgNP6v1zEri6YlaoBpIiVNDCDTqZgMA/bsmwOl0YndhJYREgj7p8T7n2a8/i5NKKcegHolI02m8Vn7ee6wC1lqrZ9Ev9/cqzqws7G/3l2PltXAIgcoaM2RSKSprLTAYzSgsq0WdzQGdRo5YlRx90uJRa7KhxmRDVa0FsSoFyoQZKXFqQAAxGgUqasww2xwQZwYjqBUyZHdLwO6iChjNdshkUpyTHg8AOHa6BgdO6GGxOWA022C2OtAnLR41Zhv0RgtsdjsOnKhCiaEO8WolLDYHpFIJMlNjIZUAGqUCTgjYbA5olDLEaZTQGy2I08gxNDMZVbUmrNlWgO6JGugPHEZW1SkojxzCMIcE34+7CicrjbBa7Rh2x02QmeogA6Cqd3zscfGQa11BkYAAhARvv/IBZN26oFfXJBSU1cBQZ4VGKYPDKXC6xIRL00WzA5jrB/E1JisSYlVNdkNzfz9Fp2tgsTtQXm2GWiHHtkMlKKkyNWrJaamQI5VIzrSeuQZdF52uQd6JKnRLimmy5thXa1qp3oRSvQkQAhW1FnQ90/rT1OKNTqcTpQYTeqTEegLblgb+xmmUEELAaLYjqZsaNXVW7CnUQyaV4GiJgNnm9Dndbku1zaHs7xyKvuJN5ee8PilIjlPBYrPD4XRNLpAcp2p1gaXhGifBHIuW+qLLZRLsPFqOEoNr4gSz1Q4AngHz7dUVJNDuKZHYnaUj99cPlbZoOWircSJtjcEEBay5H0MoZwZpuPBWRY1r+kaHU3ilqz+FYv0LuKvvsB7pOjVS4zWNusD4+iEHUnPj1YVDI4fJ0nIXjoYFpXKDCTqNHGaLHZC4+qRnJGg9K4rLZRKUGUzIO1UFo8kOpUKK9ATNma5BjacmdAqB3YXlOFpajcoaC4pO1+DbXccwYVBXXJTdBXEaBX45XInSKpMrH1VngxEALa4s7B47AAA52Rk48eMRFJ2uRZxagZ4psUiOV2FrvgUmqw0SiQSVtRbIpLXonR6HhBg19hS58may2qCSy6CvsyJNp8H+41UwGF3rlhSerkFNnR2Xnd8DF/ZLh1opxfHTRuiNZlQYTfhx/ynsO16F04Y66GJUMNkckNZZYXM6kByvhkwqgUImhcXuxP+3d+bhdZzlof/NfvajXbIsybLjLXFsx0mwY8KSZiFAoEDhknJTSFnSAuGyl+0WQuByw3JLgRCW3kJTWkpYypICSckNZG32xHbseMli2Zas7Ug6+zmzfvePkU4kW7IlIS+xv9/z6LHPzDcz38w3y/t+79YQj2DqKosaomSLDhFTYzhfwfYCLljRwo5A8PRAjmCkQqFiU5+wqP/m39P21HZa+/fRPtyL4Tq18z/YvpQ7L3wNo0WbxQ1xxl70YrxKlYOtnRSWnMFY5zKynUtJdXdw6TldbBSCqKWFmbNWtbCmswHXC3i6P4eqKMQjZs1968Izn79XDq3bMFmJH8qWGclX0RSllsY0HtGnuC3pqsr67ia29oywP1NCAdZ3N6JrWjjjmzDpbEpSF3teaZ2opTHdczH52YpaOrmyw2ixSmdjYtZ1FgayZRQFekeLbN8/SmMqQn0iQt9IiZ7hwpRnbt2SRv6Qr/L77f04fsBYyWFZa3rGmI+IoaGqYeYmTVXw/ICzOht50Ypmbr5z97SuchPpdmcrXC20gPjH+orP1B9VUVi7pHHcEqEAgrVHcck5Goe+F1e11835WszGF93zBVXHozERoer4GJrKWMHmzI76E+oKMlch82R0ZzkZFZyTjeNlOTjZ4kQWAqlMSObFQj4MMz3Ani8Oe/lpqkpdwsJ2/MMe9ENf4CMFG4Vwtj9q6bhewFC2zD07+rFdf9qqrbMVLg5z4RgpM5SrzOjCMbno16GC0uKGGC26SlMygqGF/zano3Q3J/mvXQNs2TtC1fM5c3E9hXyF0fHzUhWlVmhLVcNsRtmSzePPDdM7UmKsYOMFAlEMuGNrL4qq8LKz2sOsTbkyB0YCrPH0rM/7Wc9cWThXdnhwzyC7+sbwA4GhKixrTZCMWjiuS6HqM5StIhBETB1d01DHazukoiaeF7A/W0YEgoOjFSxdoaspQV3coi9TJBE1MHSVvqE8Q7kqUUtn5aI0xbLDA7sGGMyV0cZnrgNPYJoauZKNqas0Jiw2rWijIWlx/85+9g3lMVQYKlSpi0XIFh1yZRt99wjdmT7ah3tp+NcD/GnvXhxF4/+887MEQpCvOHTd8R8sGeipjbWnGww0dzC0qIvRZaswdI0DmSKr2+t49v/+K4Wyze+396OqUBezGClWsfaPETF1UnGLNZ0N6KqK4/ts3TtCvmwzmKsQMTUm3LeqrscdWw6wP1NEIUzlecHK1imKbDpmclZnPdv3jZIpVBGEcScj+WoY9zEp0HpRQ4ytPRnq4iYdTXH2DuYZKVZJWBqWqbK7L0uh4qIogjWdTZRsl0eeGZtRiZ78bDl+wFghLD6XipsYunrUOguFskO25JCvuOQqDqWKS0s6Qq5kc/sT+6mLmzQkni9mt2llC0P5Cpqq0FUXZzhf4bbH9/HfX76SZNSYNh0qgFDG0+qqKsb4utBJLsDzfQRBLQnXXIWrk01APFJRvAtWtj6vxI5XUp+vQDTde1EwfeX1IwXhz8YXPRCCZMysBcyPFMPJlQ1L59//hWA+GbxONneWk+3+PVk5VS0HxxqpTEjmxOSZ6SNVvZ0rM+VLn+7lN5Nl4dAX+KKGKImIQf9YiULVY6RQxfMFy9uStDccPqM6F+FiOivIYK5CplA9LP/4vkyB3z66P7RaJCwuPadjiqA0kKuQKzlsWNrIqvY0Z3Y01Oo+HBwt4fk+vu9zIFOouSEJFCqOh+0FOF7AP/9+Nw0Ji77REs8NhFaJku2hagpNCRPH9dh5YIwXr26jZ6jA1n2Z2rmsW9JIxfHIlW1MQyVbdohFtMMsHlt6hnno6SHGilVcL8zX31YXobMpGQa5xsxxYSbMYpSMGLi+T0dDFE1V2NqTIVOo4gcBmqpQtD28QGBpCr6AwA9dWiq2TzIWFpXasneY7ftGGMpXCQLQXB9bU7BdgVphfJwCGhIWigoHRopse3aIzNM9lOuaqY+HKUHfefMXWLvzERKl/GH3XtWwsCsuigJVJ+A/zn81UbfCwKIlFJYsI9fSTixmoathMHZpuAgK7MsUMA2NkaKNpasMZEv0jZTwA0FbQwxNVcOieL1j1CUstu8bRSBoTUex9PD6Fi0XLxDkSlXuHC6gawoNiSjbejJETY2XnLmodo+HVqcRRgpVUlGTYtUl8AO27x9DVaAhabJvOM/tT+znzzYvY6wUWn2SMZNcyWEkb/PIs8M8N1ggV6xScT1EAJ4PB8dKxI7g+nSoYtBaFyVqalRsb1qhZHIGpwtXt5ErOzzdn0MIgSLG69UUbc5YlGZs2KGzMTHlmcsWQ+WjJR3GfLi+z57+HL974sDhwfzjz2MQCDZ0N2IZOrbr4fmh29iSpgT37SownLNRNYUVi+pr7465CFcnm4B4pP6kYyYvWb1oQQSi6d6LpYrHeWc012o1HO1azNYXfbIFrlB2WL24ng2HBMyfKOYqZJ5sQunJdv+ezJyKloNjjVQmJLPm+ZnpLArUZk8XKsD50Ad4ppdf6MYxddtDhZdMvsqug2NUPZ9nBgoIAfUJi2ypSqHqEbXCW79YcSnaLlXbJxU3Zi1c6JqCZWiMFCq1a7N6cV0tPmOir2Mlm+//v10MZMs0JCzGSja/fngfZTucmU1EDfpGSwyXqmhqM8P5KurBLOed0Uyx4tKUjFCyfQbHSuwbKmDoGo3JCIWKzb5hm4aESalqMFaqkivb7DgwhucFoIDr+ygBlKoqhqHhBT626/PcYI5cKYyXEAKe7BnlPx/r4Z5dgxzIhClALV3lFed01c49k6/y2DPD7BvKoygKdXGT4XyF3kyJplSEQsXF0FReclYbYyWbh3YPYHthmtuhvM1DewbJlR2eGyoQBIynjA0oVVwczydfdTgwYqOhYhoqK+qiRC2dHftHKDth9imPsJKyO+7mZlWrtA88R9fwAZaO9TH4zUHqDjzHXw/14ukmf/WFX9CQiiD8ANOukijlCRSFTF0L/W1LyHUt5clIM881dlB1fQI1tBT8dtOrEQIUFTQFllkm6bjFlr0jeH6AZajErLAQYGdjgmRUp3esyEg+tJLYXkC05BCPaDw35DJSsClWvXBWNmFRdXzq4iZ1cZPOpgTPDuZ4eqxM2fEwNQ1D04hbMcaKNoWKSzJq1JTdsu0SMTR6R4oEQmC7Pq4foCihcK4ooKlhpe3JKV0FYfrhbNmmavtUvQC7YKOqKv1jZdwgoCUVrQWxT6dETxaOJgf0HyqUTOcqaBkajh8wWqxSrnqUHQ9dUylVXOrjYcC/oau1Z64uYdb6L4Rg71CBmKmTiOrTKjuTZ13r42EsxMSzG7V0WtKh20yu7LJvuMB/7RpgfXfTnIWrk01AXGiX0+mYaUa7KRWhKTX9sQ+NO1uIQn8nA3O9piebUHoyX1vJCxupTEhmRSAEW/dl2LpvBN/3QShs68kgRFjEbi4Bzofu90gvttm8/HJlZ0phs7VLGtl9MMtQthIGfiJwfYGmgB+Evtv5kkPRdnG9gL/75VayZYe6mMkV5y2hpS56WIXjQ4+3bd8I2ZJNtuSgqWpNEJmsWAHcsbWXkXyFdMwMBVQFSlWHeNTk4FgJfzTgwEiJ+rhJxNSIWTrFiguEQbQHR0s0Jy2GcyU8AZ31UYSAUjXAcQOqriBfrtCcitA/VsLzA2w/wNQUUMALoGi7qI5H/2iZ3QdHGchWcP1QUKy6PmOlKvc81cvejF07R88JuPepg7zt4pVEdJ1dfWPYno8vBJ7rM0qAoSgEChwcrRCP6MSsMG7E0jUuOaeDvQNFMvkymVyVoVyFQEAQCCpuAOEpYrs2T+4fwdJ1qo5PEHgotmBH7xgHx8rkSg6pwghLB3ppyfTzn+e9otbHT/7kS2za88iM95YymmG/U0dHY4yfvfJt/PCS/86B+kWUNZOIoWGoMFbxD9tOiLAenQgABUYLNrYbhK5lQLnq47gC2wt46Vnt7O7NMlp0AIEQoCpQqTgM5CsM56vUx8Nqu3UJi4rjoyiQq7i8eGULZ3Y18F+7B6i4Pr4nKDoO+4YDoobKwJjO/Tv7awHOgRAcHC3TM1yg4nhYhoalK+TKoYUqFTXJV5xahqXJKV2XNKVIxgzils7ewb0EQeh+Fng++YrDikUpRopVckWbXMWhvSGM1zm0aNyEcBQxtGktiYfW4pjstqSpSnidxp95IQSLG+Msa03XYiYmniNT02r9z4xbYjYsbSIVtdCUw92qjuQqWaq6xC2D0aJDIASeH3BwtISiKFy4um3OwtXJJiAe6/4cbUb70GPPFHf2xxT6kywc8tpKjgVSmZDMCscL6wfoikI6EQEUChWHp3rHaK+PTQlwngjmPZo71GyDnY/08guE4ME9g2zdO4wyPtOeL7uoSqiIaJqK6wWMFm2aU6G7hKWoVF2PxqTJ77YeDAN5Yya9I0V+8dBeXrG+Pdz5NJkUJ3x/B8bKJKIG6cCkLmGxeVVrLT3mRF+rro/n+aQTFvmyQ9TUyZUcOpsS/Mnadn7wh6cZzFZQCWedt/WM0NkUp60+TrZkky85DOYqKCis726iWPV4pj/HUL6CEIKoaaApAtvz6R+r4PsBmqIiRIAXCPzQHR9dVXD8gGcGCnzzNzuw3TBDiu36+AJ8T0xRJCbIVz0ODBVpqYsxWqiysi1NserRN1LCdXzsIHQtyZbC2g2DuQpb9w7TM1SgbLvjgbM2ZTcUWA2VMLXp5OsJZAouhuriBXDmgV2c3bOdrpFelowcZNHQfpKVYq39g6s3UUyk8QUcaOpgVe9ueps6ONDcwYGmTvaP/ztU10ygauheQEdDnGeXrgz9uwOBoYKiCMrO831RphluRQnHsuL5BOUARQHbC3NNqSLAcX1+9dBeGlNRLE2l5HrhfgQYhsa+wTxVxyevuOP3RGjN6hkuYuoaO/uy9I6W6c+W0RWwIga5ik3F9cmXXSzLrrnjbSXUcExNJW7poAga4hFWLEpx365BopZAQaEhYdGYjGA7QS2la6nqEbU0HtozRM9QAWX8Pp54IgUQswxUVWVPfw4UhYipc+e2PoLx7FHTPZuTn8tDa3F0NycwdLUW4F2qegSBoCEePpO+79OajnPe8hYSlsGihthhdS8m+h9mScswnKvWKhe3pKMEQhCI5zNgTTfx4AUBI4Uq/WMVXC/AdkLrXCpiTMkQdKKFq5M99/9sZ7SPFHcmZ8UlklMXqUxIZoWpq9QlTDwhGC1WQSgoCkQ1naZkZIofbCZf5Ym9GXb2jiGE4MyOejavajusumkolJdIRkwGxkoIITh/ecucYjGqrs+T+0YYyFZqBZoUZZQzO+p5+JkhRos2g9kylhHWI/B9gR8ISraHoihk8uGM+UjBHncByrK7L86K9rppKxw7XujX3z9WQoyG7jpChMt1c6rJ3tRVkrHQlWX/cIFcKQyGbKuLsrs3S9VxiZk6RlRhOG+zbf8oqhL6sN/2+H7KtseS5iT1iTAwORHRURQRxiuUbVKxgGKFUPgzQvcf01DQNJ2y7aIRGgAKdig0awRUlXD23AtgPPx3RkQQsH1/huceLfHsQB5Nhc7GOK3pCPszYcE6zxfYbgVDUzF1hUV1UZa2JHmyZ5iegSKGHsY1jBSr2I6PUamwLNNLZ6aXzuFeujIH+Orr3k8pmgDg5dvu5vUP/ceUfgSKwkBdKweaOog7FSpqGt+H7192Nf/3le884v3hCShWHaq2h6pAoAgURcUPIG7p+BUPTzx/HQyVWrVtQ1NJRg08L8ANpipBhqbTlo5QtH2WRnUsQ6fsjMeAGDoNSYv2+gSdjQqPPDs8nk3IwDJ06mIGqqqw+2AOSw8rhAcBxC0V29OI6Cpt9TEECkP5Kkua4mSLYUapxY1xvCDgQKZIyfbY05/D0FQSUZ3GRJRcxaGjMU48Er7adVWtPXfrljTiBz6WoWNoHjFTo+yEdTfOWJTG8XyyRYeGpMmWnlEUIdiwtGlOOfXTsdBl6Z6dB2lJxUARrOtuIhACMW6aUxQFlLC6+kzpWSf6rKsq9XGLc7qba22SUZOK43P39oNHrW9QK7Lp+fSNlsNzLDs88MwQnQ1xSnZoIlsI4Xa+CsELJff/bGa0ZcYgieT0RCoTklmhKgrrlzRRsX129WVBCJa1pdG1MJ0phH6wDYkIT/WO8sizQ4zmK7i+oG809OF+xTmdU4Ty4VzoBjKUq+J6PvuGi2FGmoQ16w9qIASZgk3V9YiaOlXXIzOexWisaGO7oR+6bXvYVlgp2HZCYWwgWxwvEieIGgpeALqm4nr+lJiKyR/Cku3y+LPD7M8UMDUNx/dpq4vRVh/lnO5mklGDsuPhegGBEFQcH9sNqI+bNCQsAgE7e8cwNJW+sfJ4LIeJAAxNpez6PPrsEI4XoGkKvSMFMgWdgyNhEPngWJ5cxafqBuQroYCpqeFstReA53tUHR9NBXeq/IsPOK4gmPT7SCjAXTv6CQT4gY/jQd9ICcfzMDQVPwj3FcYxBPSNFDENlZF8ma09GfLVAF2FS/c8wLse+C2Lhg7QlMscdpyfXfhn7OxcDcCT3WeTLuc50NTB/uZODjR30NfQjmtMuhfGO+5rs3t9BQFUXJ/quCUiIEAFbDfAP0SbMg2VjoY4faMV/CCg7HjUxU1sN8A0FMDF8wNilkbF8UlFDeriFrGISslW0VWFtroYcdOgLmHRP1ahPm6iqirt9TFGijYrF6X4r91D6BoYukZDwiJbssOsX8JgdXsdTekow7kyuVKVjKXR3hgHAUO5Ci3pGE/uHyVXcmlKWaTjJoEAIQRLmhK8ckNXbXZ/csKEeERnfXczPUNF9vTnsN0AS1dY1V7HhqVNPLh7kOZUBEPX0JXQTc40NOrj1qxz6ket0LIwWrRxPQEKKIzgOmERvYrjkYgYOF6oxOiawmPPTp1UgMMVl4lZ7er48zGRdexois7z9TkC2urj3PvUwdA1UVM4OFbm3+55mmUtSaKWMa9A3wkFYnIMyVwUgrlmkDvZZ/WPFBvxQlGaJBLJ3JHKhGTWpGMml63vYH13Izv2j5It2fQMlbFdn96R0njWH+gdKTJaqKIoKqlomNnnyf2jvOSsRSSscLZK15Rx94MydXGT5wYLRE2NdUsa5lQAyfUCGhMmtufhBwLL1KmPG1Rdn9ZUBNPQ8QKfHQeyBAFEIhotaZN9mSK+H2AZGrbnUXEEETNMMTpadmsxFZPdKQC27M2QK9noqkrJdsM4AD/g4EiZqjNIoerw0O5BPD8gYhmc0ZrizI40jz+XQYynUx0p2CiKQtzUyVdcCmWXiKlRFzfDVJYCUlGDYtUlW3Vx81Uipk5fpjRFAai4Ytw9x6ds++MpMMeZQVM4mgIxGSeAg6MlhFAwTZWIpuP6ASMlFxEI8Dw6xgboHD5A53BobVh60wHah3p55O3/m2z7cpwAzMwwa59+orbfsXgd+5s7Qvekpk6GU021dfetuZD71lw4h14enUCA5/uggKZC4IfuVYeaZUwdIrpOUyqGoir4vhgPgDZZ25UkGTUZzJX5r10DjJVs4pbBhWe2sT9TJJOrYjseRsRkKFeh6vr0j5YYyFYZLlSJ6GHxviAQjBZtHM/HdgISEY22+hipmMmG7kby5TCeJh2zGC3a6JpGe0Oc9UvCa7Rt3wgHR4rYbkDEVIiaGroWWh/+/CUraEpFaorERMKE3X1ZHM/HMjTa62M4XsDy1hSN6Qi27bOoIU4y+nzygXQstEAqQuC44Uz+dMGy0xUgs32fg2PhPhY3xtl5IMv2A2PomopA0JiI0JyOoGsq7fVxKrY/ZVIhDCBXp1VcVCW0ZJQqHqmYAYpCKmYcUdGZnB1otBA+R+mYRWt9lGcO5hjOV2spgZ8dyPGmF58xa4VicnHIvtHw/deUjjIwNrXa8ZGUgNnO5C+kIH4slZKZ4itgfkXuJBLJCwOpTEjmzNP9OUaLVYbzVYbzFVrTUfIVl0CEPtHPDYb1DhIRAz8QVByPgbEyD+0e5PzlLaGw4gsipobjhjParu/TFo0SMXU0dfq89TB1JvDJcR9ty9CojxuoikZMqMStsDLz0+OuOaamETXCD9ZY0aY3U8L1ferjBoqiMOGdZGgqvoDWdCTMdx41Kdth/v9E1GRNZz2FsoOhayi2N+5zLlBVlaip8dCeAZ7qDQU3Q1NQFRUV6GpOoCkKnhBYhorj+hi6SipmgiLwgzCuoTVtUbZ9HN8jM1IhV3axvXA2XceeVhGYkIeDadYtBI4bYJZLtGZ6GW1pZyiRwhNw2UP/yXtuvQk9mF496Rw+wDPtywF4dPm5fP0N76e3pZO99R0UYslj1NvpeWYwH6aSVcbH2A8ICIOpJ183xwNb9Vi9OIllNNKfLZMr2hiGxuUbOtk3XOShPYPELD3MylT1uPepARY1xLAMnZLtUag4RC0Nxwt4tj9Hf65KyXZpSETIV2w6mxKUHZ+q41OouKAoJCI6F65u47JzOilWXHb1jTJacNi4vJnVi+tJxcya69+mlS387nEbS1NBVSnbPoHwSI0LohNMFC/c1pPB9QOGC1VcN8DzA4p2WOiwqWBxdldjLUPa2V0NuH5A1fZYPy4Aur6gtS7G6o46MvkqdQkTU9NmLEA2WqgSM3UiuspY0cEerzDueD6aomDqGme01VF1PJrTUaKWxkihykC2TFMyQqZQxdDUGSt7h9myfO5/sh8vEOiqwoVnLjpirvwJq0au7LB3qEDFCZVhx/PCMXd90lGTwWyFJ/ZmuGhN+1EF3MkWhXgkzOzlBYLmoo2hhbVbHC/AHq/fMpv6HTB9lqOFrFx8PKwD08VGVF3/uLg/vRCsNxLJqYhUJiRzYmImLRkxGchWqItZeL5AG0/Xapk6azoaxjPg+JTdMGVoZ2Oc0WK1FoPQny3x0J6hsJBWxAABuYpDxXEZK4Y1Ig4NsJw8E/jsYJ5c2cbSdcZKNqPFKroaFqyqi0UQQUAQBJTtAHBBKKhKmElHVxQqfkAmH7o7GEZYMTgRMUlGDS5e20F7Y5x7n+rn4acHyZYcgiBgS08SXVEYK1UpVT2ECPADQa7i8NDTg+w+mMN2fSxTw/cDXAL2ZQrcu6OfZwZzCAFRXaHg+CgoNCctHNenUPUQgWCkUMU0NEoVFz+YKuh6x2Fs45UiKw8+E1oaMr10Dh+gK9NLY2EUgBve/HHuXftShICheD164FMxIxxo6nj+b9w16WBDe22/vU2hFeJYoHF0a4vrhVYbT4DiBbXrOl28iC+gd6RC2S2iKWFGJ7vicNf2g6RiJmXHIz6u8Kq6Qr7ojseyhLOyuqZiaCoV28MfD5gedT0Gx0pYhoamqnQ1JWlviJEv29iOR8wM2z+0Z4j+sRJP92VRNZX6uEm26GAZWk1gf7o/x1O9Y0QsHdcL7+eK7TKQq/KDP+ymPmHxyg1dNKejZEsOiqKQiJjkyg4qMJgto6gKo4UqY0WHQsXlrK4GALbvH6Vqe0RMnQ3LmkjHTBwv4JmBHDf++knGxovivfnC5YyV7JoVo2+khEDw4lWhAGkZGk/sHWY0Gyr6yahJZFzZak5F0BRoqw9nrIOAMKbBD0BAa12MxmQEzxeHpX+GUFjszRQpO6FbY9nx6M0Up7wnpmOi9sKZHfVs3TdC1fExdR2UMPuY6wfUJy2qtjcrAXeyRSFihlnRDo6UKVddXH/8vaUefTZ+Nrn/FyoOYSGVkqNxaHzF8SiYJt2oJJITh1QmJHNi4qPQM5hjMFtmOF+lKRXOMCqEs+6OH3DJug4EsOW5DKmYweqOehDhR7DseNz++AFs1ycZMSjbHjFLJxXV2bJ3BAUYzlcYydu1AlXJqMHWfRkOjpRJxgx292Up2R4NcZ0DoxU0FVYvrmfvUIGHnx5CVQVBEM76WrrKYK5K1FCJWwZOEOALAzfwcTyBpqi01kUp2z6+L9jWk2H3wSz/uWU/B0fKeEGAoatkchWWttURM1VypQDXDxABDOfCTEqO5+MF4Fd9VCVMEZqMwP7hPMWKgwDyAWgaJCydAyNFnEmSsOME2E4wJ1ekuaJ7Lu2j/bUA6MeWb+DpxSsAWNfzJJ/90Rem3W4k2UDEc2oxBluXruWqj9xMJtUYpj06QRwpgHyC6iRNLBBgaKElyPPFYXEligqGplDIOSQjOq4nKFRdRooOL17ZjDvu+tSoKuTLLpqmUKy66KqKQuhOVXYCfBFmOxvOV7BdgaaGLnkCiEbCVMp1cQtD14gZKvfs7Ke1LspwtkrV9WmpizKULbM/U+QV6zsYGCuxq3eMVMwgYuooSpgdTFMVqk6YTCBiahwYKXL7E/u5+uJVmLrKQLaM7fhUHA9NU0lGdDJjVSpuQDyi89xQgV88sJcNyxrH09hajBarbN8/GgqZKvzbPU/TM5QnYuqMFCr8y917eMlZbViGzv5Mkdx41rHV7fW0pKOs6Wxg31Ce4VyVRNSgtS5GoeLS2ZjgzzYvI24ZU9LJhjENYdaoYsWlOR2dUcgsVT3Kjs/KRWkSEZNi1aHs+JSq3lEFR1VRplSGPqurnmf68+RKDvVJi3TMJBkzZyXgThaOY344nqahYuoaihJaJEpVb1ZKwNGyHC2UIH4ig6OPdcG046koSSSSw5HKhGROqIrC2V0N7DwwCig0piJh9eOoyYpF6ZpbxNldDRi6Gs7MFqsgnv8Iul5AoeyQipsUyw6mrlG0XQ5kSmiqiqJC3DLwfIEzLqR3tyR55OkhEKBnFTKFKlXHI1+Gsh0Kac8N5BgrOfiBIG6FM6FF28UPQqG/WIXRkk0gIBkxWdGWxhsPEHd9gWVoRE2NwVyFbMmmb6SE7YT7djyfMj7xfJlKNSwUNu6BhOsHjJUqTCT8UQhnuH0Blq4wmA3PX4wv8z0oCW+KIjHBQisSrWODvOaR39KZ6aVjuJf2sX60SZmJhEJNmdjf3DXFyrC/uZOR9k6eTi+mHImjTzID2GYE24wscG/njjJdTtcjEAZDh4qEhiBTcqduHijs6c9RdQLyZRdTV3F9D13T2N6bIxnVcb0A1xc0p6Isb0tjaKFbnq4p49a08B4YK1YpO6FVxA/rCFJ1fYbGylQcj9GSTUTXyHg+gVCItoQz7aahIkRY4d33BZqqkYyY7DmYC9MN+wGaChFT5+zOBrYfGCUVMYhZBo0CxkoOpWqoYNTHTfKKg0CQjFnELZUDmRKmoaGpCoamsS9ToKMpPq2QmS3Z9I+VMXWNhGUQBGGldwLBzoNZPM8nEKHAu6tvjIakRc9wgYZkhJeeGePpgSyWoXH+8hbOnWTtmGByTEOx4tYsFjMJgPGIXitmN1GoL6yePbtP2aGVoS9Y6fDE3gxV26vV85htrYkJ4Xi0UCURNWlOWTSlouTKNoauhRnYZqkEHClT0kIJ4sfDOnAkjmVqWJlFSiI5sUhlQjJnbNenaHuk4gYNcYuGRISIofGycV/jiuOxbV+GTN4mZmqkYhaOF9Q+gvGITl3C4rnBPLYfBnnabkAQuCyqizKQrZDJh39P9WpoKnhe6Asbj4RF3YpVByHAdp93BxrJ23girFocs3TKtkfFCabUEJiQY7wgoCUdI19xaUr6nL+8mZFCFdcPqwz7QhD4U60EgvECZo5X248yvk8vTKw0xQ9fAXpHq4dlDAKwF0BrUIKA5nzmebekcdekP6y7iNvOfyUAUafCm+/79ynblazoePBzBz0tS2rL+5oW8673f2dKW0Ohljp1OuXnRDB5PKe7tjOhARFDDwuxGSqeFxb3c/3xQHYlrD8xVnBAhSgaiiJQFRURwGihQtTQaU4rLGtN0ZSK4HmCtnSEiuPi+wGmpjGQL2PqOpoqEOXQLKIoE4HgYRXosaIdWuk0D88LaExGUVUFQ1cZzlfxPYEbhK43fuCTr9g4XsBQLixQqCrQlLC4/NwOekeK7OrL1ooQrl5ch6GrBIFg04pWDF2lXPUIhOCMRWl6R7ZRGK/FYuhh7Yu6mMlYKaw1MlnIjEV0LEMjV6piGxq265GORzirq4HdB3OMlR1MTSVmRcjkq7XZ+IZEmC46zG7ms3lVK64XcP+ugcPcUOYiZOqqOqUYX2fj1OxVs2Gy4F4ft7hoTfu8BNzJGaYsQ2Pb/lFsN0DXdVYvriNm6gs2G78QgviRlJLjFWtwrAqmnWhFSSI53ZHKhGROjJVsbn2kh33DRVQVqnbAWNFm08pWTD3MwnLX9j7u3zUQzqpqYZXZyzd0Yeoqnh/6Nm9c3sLjzw2DCPPve75G2fEYLdqUbBfPCwXzsaKNH4QSowJkywrBeOClG0yVJN3xn76AiuMznoBp2olr2w0YKVYYLVQpVX3u3tFH1fEp2R6moWFoyrQCv++HM7ETHLrvyV4zgrkJuzNheC6Wa1Mcr8XQkh3iMz/6Ap2ZXiLu4cXmDja015SJvoZ2bt14BQeaO9jfFMYzjCRn75okFqD/C00qqlOoeIcFnUcMqLpH2FCB0WIFVVWJqwaqphCxdALbRVNUgiAgouvEozq6quALEEJB1RRcLyARMeluSYSxQLZPedy1xtB1bC+gIRUjbmmUXI+K49OaiDCct8OaHuPKhKGqdDUl2NWbQ9fCIPxCxR1XbFR0TSVm6EQtnaSu0NmYwPMFTakYK9sFuXKoRC+qj9NaFwMRttk3XMD3BVFTo7MxQWQ8zmLC7aPkeLTWxVi+KM3L1izizq19lO0wvmd9dyPnLW+ppTadLGQmLIOXn7WI32/vo+oGJKImLz9rEZ1NCVJRIywgl4owUrQZiRhELW2KUDc5E9Rjzw7P6IYyFyFzcjG+yUXu5ssfI+CqikLM1Nm8qo1YRCdbDGvJrF/SVIvTOJkKtU3Xn1Mh1uBYu1FJTk1kwP7CIZUJyawJhOCJ5zIM5sosro8xlK9QqDqk43EWN8R5YPcgI/kKt285QBAENCejZMs2Dz09zKaVrTw7kKdYcYlHdcpVj6UtCaqOQAQ+u/vzBCJAR8X2CIMvVRUhfNxg0ox/INAIi48FYvosRgIolt0jBi3bXsDuvjFKlVA5mKw3VJ0wSHc6ObrsHjvpOlku1KwLkwOgW8cGue38y7nxtdcCkIulWNH/LACeqtHX2B4GPo9bG/aMuy0BuIbJTa95z7z7FDch5/xx5zVf9HGryKE0JC0MVSFTcglD7iFqqTTELfpGK4fdE6YW1powdZX6pFWzJq3tqGe4aDNaqFKoukQ0g4ipYekauYpL3NKwXZ9CJYyBSUYNChWH/rEKigKFqsN5y5pJxU0akxEGs2USkbAwnaIopOIWMdPA8X0sQ6XqBEQsjd6RIm4QEDE12uqiRA0V2wvdA4WA7uYEqVgEPwiLAl545iLiEZ0Hdg/W6jEUqg7N6SgAlqFx+foOVDVUiMJ6I2JG4UohLDhZK9fNzDPfqqLw6vOWsKghxmC2QmtdlA1LmwkCaEpF8YKwCGLbeOB0EDDtcT1fLKgbyuRifCeaQIQuki9e1Ybni8MEk4WYjV9IgX9yf06lWIOTTXGTnNycCkr0yYRUJiSzxvECqo5H0jLGC66FLkrLWpL0jZUYylbwA0G2YCMUsDQNIRQc12PrcxlyFYemZISDI2UGs2U6GhLs6B3j4FgJ2/XQNI1C1UNTQNdCBaJmHZjk2+IDwj9yOtTZZD/Klqf32zmW3jxKENCSG6Yz04ujG2xbug6AWLXMz774lhm3a8kO1f5vmxH+51uvp7++jYH61lkXb5sPhQVSJCZm5ueiik1nFVEJA9vd8dl+VQ1vDBFAImYQLVaxJ4qlibCadVMyiuN7NCUjREwDPwjI5G1GSg7aeF2PzsY46ahB2QlIxwzMgj1elRq8IAy6VhSFXX05BHBWRz37M0UefmaI+rhJKmqgq3Ga66Isa0mh6QqOGxC3dPYPh9mGRopV4hETSw8tH44XYDs+KCrrl9RxxflL2PJchqFcBRDkx4XxeETH8wVnj2ddCmML4qxb0lizQExWMtrq45i6SsTQDhOuirbLtp4RdFWhqT5OtlRlW88Ir31RNwnLmFboTcdMXnbWVFegQAia0pEwrWrMpGQ/Hzg93XEDIY7ohvJCnSGcTiBZaDeeYynwy1iDkBfq/SeZH6eSEn2yIJUJyayZ8EsN3ZB8HM/HNPSaJSAdM+kZLhCN6BTKDoP5CgBdTQm29GQwdY2S7RMzVXpHSuzqG2WsFAaHAuOVlTUiioIfBDh+WMnZD+AQj6ZjVldhIVGCgJftuI+OTC9d49aGxSMHa65Jj52xoaZMlCMxMskGAlUbD37uoLepsxYIPZaom7LvR1ecd1zO4dBaDEfD0qaPB9F1FU0RlJ3DNYSYDuVptL/pjqup0JKKU7IDfBGgaxoqIsyyVHbQNI2UBpZlULFdTENjWWuC3tEyuYqHoeuMlmwaUxG6WxL0j1aImjoJy6S1LsZFaxfT3hDnwd2D3L9zgERUp3ekRHMqQlPSCgOkNZXGZARVURjIVchXPZrTsVC59gJSCZO1XY1ETZ2XrWlnW0+GgWyFnqE8lq5j6CpruxoYGs+e1JyO8qoNXaQiJmuXNPLEcxmq425J3c1JHtg9WBNWz+5qIGrqU4Se7uYku3rH2DtUpD5ucsHKqalHJwuFYfB4gGWGsRCWGRYidL0AjlCr7dD9FCpuWHAuX2UoX2X14ropbiWHtj+SG8rJNEM4WagEjihgHi+B5GgC/x8jCJ9KsQbzvY9OpvtPcnyQSvTCI5UJyaxRFYXVi+t5cM8gvu/juAGGLtjdN8aKRfXkKmHl6EV1UUQQ4AUCTVHIV11GClVSMZORQoXBbIUAQWW8eFfE1FAUBc8J8P0wZkHTVSq2vyAxB8eSdCk3JQC6bEb514uvAkCoKtf+5juky/kp2ziaTl/jYvoa26cs/8sPfQ9Xn1p87ESzrC1Jz3ARzxdThPuZkihNZDKavE5TwqxWCiqm5uH5oKqh65ECNKQiOKNVPELlZWLbuAnFQywjqqqyurOObNnGdsOg4gCoi5kkI2EGL9cHzfdJJyw2LGmkuyVFyeknWwzjDdrScc7sqOO1G7t5YNcg2VKVVMyiWHEZzFZY3pbmvDOa2TuYZyBbJmbpNVcWS1OpuAGlqkuh6rCqPc1l53SwZW+GoWyF+rjFULbCdsLUqhHD4qVntVN1fR59dojBsXLNgrBpZSvruptIRg10VSVXdsJaD45HxNI5q7Oepw6MTRFWYaqwGghBz3CBurhFR1OCYsWlZ7jA4sb4tIJlcrxexc6+sXBSwA9Y0ZYmGZ39fTchRBcqDmd11JEpVImaWm0fMwm307mhnEwzhLmyw9aeDNmSM0WZmIiBOFTAPF4CyZEE/j9WED7esQbHygIw3/voZLr/JMePU0mJPlmQyoRkTigqDIyV6MmUURTQSjYjhSr2eN76/Zki2ZKD7QaUqy5OAAo2ugb5so3rCxx/qsBZmZzsPwB3IVIdLSRCTAlY/uvb/i8r+56mM9N7mKJwsL6tpkwA3LvmQnTf48B48POBpg4G6lsJ1MOFjZNNkdBUcAIfTVXxA5+orqCpCqau4wcBucp4piKeH0tVVRCTAuYF4aWzNI1AAcVTiagBKAqOJ7B0lZZUlELVp1x1ESJUSOoSFl0NUZ7qy4IAVQvTpT6fpcvDdv3x7EweFUfBDwSWYRAxIG6Fs/dLWlKkExYdjQmiRpU1XY14gc+i+ji6GmY8akpGiUcMDFVhrGhTdX3q4xZvevEZPPFchrFildGiTWMyQkdjkgMjRSqOX8skZGoapYo3o1A5EaS7fkkT2xiZ4qY0IfgdKtSMFqps7Rmhas+8X3heoG1IhG1Mbebq8RDGGrxu41LMx1VGijaNCYtXnbtkTkHMhwrRiqJQqnqzqvh8qMXiZJkhDITgwT2DbOvJoCpwYLSEgkJHQxwfqNg+l63vmCJgHi+BZCaBH45eFG82HK9Yg2NpAZjvfXSy3H+S44sM2F94pDIhmTVjJZuf3PcMPcOFWq54AZRsn3x5GENX0FUVPwjIV59XCCbSijqTzAwno8HBcqp0jPTROfy8W1JHppdAUbn2vd+otTtr/05W9+2p/R6oax2v/tzBvpauKfucCJp+oaGrkIqamJpGV1OCfMUhZqkoikoyahK3VB7eM4wbgKGD54XKR3tDjAOZEiKAeFTHdsJK0JqmIPywWJypG0QMHV1TsHQNLwhddQ6OlKh4HgrQlo7SkI4Qyxjkyy6WKohFdNYtaaApFUVXVVrSUVJRk2zJoVB1xmtOCKKmjqKCqevkymEF6VTUwNBUIoZKMhZh3ZJGTF3FMjRGChXKtseuvjFMQ+fRZ4dYv6QpTBt6dhgroGtKLbg2EGJKJqGjxQNMcCShbTqhZqIa9WixOuN+5yPQdjYleMelZx52DrMVJmc6pq4pPPLM3ITbk2WGsOr67O7L4vmCRMwIrVjA8tYU+arDrr4sLz1rETHz+U/moQJJSzrKqva6Y9K/6e6dqusvmCB8rFK2TnCsLQDzvY9OlvtPcvw59JmC8D0g42bmh1QmJLNiIpPTgUyxViRuMrYXjFca9jFO5udQCOpKWVqzQ+zuWFVb/Ll/+Sybnn502k08VUPzvVqg849f+iYM3+VAUyd9je0nRfG2hcTQYGVbimjEZOMZzdQlLKqOx1jJQVUgYRn0jZWoTxbIlp0w85YISEQNlrUkGMxWcMatTf54sb5AgKopJPVQqK+LmfgCOpri5Eo2mXwVy9RoqYuSKzvYnocIBN0tKXqGcri+IGmZLGlOogpBfSKC6/u01UVRxuslQJjyV1HCIOALVrbQmI5QqngsaUlNiTcoVFwe2D1ItmQzVnLIl4sIJawfMZStsI2RmqAzIWRNyBcTKT8nmMss10xC20xCzdldDdOmbJ3PsSczORvSXGeMZzrmfDI2zbf/c3WXmU17AaCENUcQoRUWRQGhMNMRJgSSTL7KroNjPPbs8DHzuz/03nkhCcLztQDMdpznex/JGerTm4lnSsbN/PFIZUIyKxwvoGK7jBYq01oVJru6HMPsqXOidWyQ7sGempVhIt1qslrC0XRe9+l/r7kblaJxALKxFL3jQc8T1ob9zZ34k9yS/uusF5+Q81loDg2u1oCIqbK8NU06YVKseuzuz3Lm4gZyZZuy41NxPCp2ARB0NSVIlR0QgpLtsXJRiqgZpkkdLVRRAF1TiJpGWCtEQCxmkIqa5Cs29YkIIoCGRISRvE1TMjJe40OlUHUIRFh/IBExcccroT87kGfzyla6WxIcyJQo2R5R02Dl4nqKZRcn8DFUlTM76tm8qo1k1DhMGDl0ltT2whiIdZ31pGIWpao75xneI1kdvCA4ak2EmYSa2big/DFuKvOdMZ4p/mE+wu1c+z/XD//R2gfjacNWtafDwnOOT33CRABVx0PXFFYtrjvivbD7YLYWM3O8/O5fSILwfBSfuY7zfJ8DmVL29EbGzSwMUpmQzApTVzF0lf5sZdr1Jyq7UsSujLsmHaBj5CD/etFbEOMC27t+90+8bMd9h20TKAqZVBPpUp6xZD0A37vs7Xz7VX9FPp4+rv0/3kxWICxDoeqGubTqYjqqGrr9KJqCoWtsWFpHT6bIc4N5opZOzNJZVB9nZ+8oY0WXuoTJi5a3kK+4lG2X+rhJ1DQYzldpSkXHKwOrxEydsu2Sr3g0JiI1RQBEWJwtYtCUcvCDgIaEyUjBpjkVAwT92RK+H2DoWug+V3ExdI2lLSna0lFQVMq2GyowxSoNyQibVrYSM/Xah2ByTn3HCwjE1Bn0RhHQP1YiUwirlYcxDXOf4Z3O6nAgU+S2J/YzUqjSmIzwqg1ddDYlpt3+SLUejqbUzNdN5Y/xGZ9LxqajMdv+z/XDf7T2kwVWVVVY19WA4wWsGU/D63gBdXGT9d1NM57HifS7f6EIwnO9N+Yr4M33OTjWbl6SkxcZN7MwSGVCMitURSEVM/GPoDWohC4yKAoqAi8I6wEsJOc//Rgv2vNozdLQkhuesv72cy9juK4FgGcWLaN95OB44PPzAdB9je04xtQ8mJl008J29CRBBRrjJhXXo+oFRHQV2wvGi7BZ+IGNooJCGDidihis7WqgtS5GMmpSn7DIV1x0VSERMYhHDCJLmxgt2jSlIjhuQHdLhO7mJD3DBQplh86mBJahMVayGc5VcFwfVVFY1Z5mUV2M5nSM121aypP7RqnaHsmYyflntHD/rn7GSg5LmpNcuHoRzw3k2DtUAgGt6Silqovj+pRsl7Lt0ZCIUHU8GpOR2kfAcQN09XCharLQGI/oqKrCWCm0tuzsG8PzfHb1ZrEMjZZ0lAtWts5aMJvJFcMLAn758F5292WxDJW+kRK26/OeV645ooXieH7AFtpV5lgLt3P98B+pvamrUwTWsZJNSzrKy9a0TAlwP9p5nGh3oxeKIDyXe0MKeJLjxYl+fk8VpDIhmRWBEFRsl0RUJzdNUQBDC4NrhQgz+qSjFmOl6pyUCc33aB/tpyPTGwZBZw7QOdzLZ//7p2sWhHV7n+T1D/3HlO3G4nU1l6TJ/Phlb+bHL3vz3E/2JEIb/3PHf094kJlKWFzv0NS5NTdvEQZEt9ZFuXBVK9v3j2F7PpapkcnZGJrKGW1Jtu8fQyjQ2RAfD7LWaauPkS05tZnblnQUFBgat0rlyg7tDXE2r2rF80UtOHlRQwzPF1Qcj+37R0nkKogA9g7nMQ0NU9eJWQa265OMmFy0ZmohtJWL01PcgVYuTiOA7QdGMDWNRMQI3RwEU+IJjvYROHSWcyhXIRk1aU5H2LJ3FERA1NSpOB6JqFGrlzJTetXJ5MoOW/dlyBadw1KIFiou+4aLaKpCMmrh+VX2DRcpjAtJJwPHwlXmWAq3c/3wH6n9dAJrqeqhKsphVq0j8UJyNzrRzPbekAKe5Hghn9+FQSoTJ4ibbrqJr3zlKwwMDLB+/XpuvPFGNm7ceKK7NSOOF1B1BZesWcxvntiHPUmfUJUwZgIlzJbj+j6e5yNmUCRi1TKObuCNp0J9xeN38N/u+3faR/vRg8PTwnYNH6gpE4+fcQ6KCGrWht6mDgqx5AKf7YnHVKGlLspZHfVUHJ8DmQK+EIwVbRRVpSFhUii5WJZGRFMpOwFV12NxfRxFDYWiRFTn4rWLOburiYLtU6q6pKMmQuQIBKxYXM/BsQoVx0NRFaKWQVMqytldjTzdnzs8DeV4WtOJZbqqUqo6PPLMVL/m+rjFhavbqLo+Dz89SNXzCAJBrmRTGq+vMKFATBYsJgcFT/x+2Zp20vEwY1NdPCzqNrlo22w+AtMJjY4XsG5JE7mSi6EqPDuYpyFhoSkKyZg56+DQB/cMsnXfCLqi4AkxJYWooavomkrZdnA9H9f3iVoGxkkmEL1QXGVg7h/+I7VfSIH1hXQNXwhIAU9yPJHP7x+PVCZOAD/+8Y/58Ic/zHe+8x02bdrE1772NS6//HJ2795NS0vLie7etEx8eJMxg86GGPuHywQ8n60HAZauoiCw3YBK1ae+MMJZw710jlsYJoq7NRVG+fA7v8SOJWsA0AKfrkwvABUzEloZJv6aO9nbuqTWjy1nnMOWM845/hfgj2RygLqlgqZpxCMqoBKzdIZzZaquQFWhszHBxhXNLGlOcc6yJh55eoixos1groqqaSQtg+ZEFET4wlu2KE2u7NCajrJ6cZon94/heD5rOhupOD77hgusaq8Lg0u9gKZUmFI1ZmisWlxHrmxjGjoIwdoljbSko7Sko4e9WOdacExVFCp22I+hXJlcyUHTVFYvrp9TcORLzlw040t+Nh+BmYTGZNSgPmHRM5hjIFtmuFClKRkhHTPpbk0dVaisuj67+rL4vk86EcZsTE4hGjN1LljRwn27+inbHjHL4IIVLVPSi54svFBcZWDuH/4jxaIspMD6QrqGLwSkgCc5nsjn94/j5PuqnQZ89atf5ZprruHtb387AN/5znf4zW9+w/e//30+8YlPnODeTY+qKHQ3J3nsmSEyRRdFU1ACgem6LBrrp2uklz0dK8nWNxMIeNWjt/P+/7hpxv115gbZyRoC4OGV5/PJt32eA80dZJKNtQDqkxFNgZipUnECAgGJiMrixjhNqRhdzQky+Sq7e/PkK1XSMRNL17hgVQvnLW/hsWeGeW4wz1jRAQUaExESEYO6hEVdwuTgSInFjXEuXd8BQql9QDevakMIwV07DjKUq5KI6FimTnt9DIECIqzTcOHqRfRnSzheQCoWzsLHhU6p6nHeGc3EInrNHWdtVzjD/2LH48l9I7WZ/8mBpoe+WOdacGyyEN/VlGDEsmlvjNGUmlsq3aO95GezfjqhUVdVzu5qYFfvGJqq0JyMoqmhknB2V8OshJcwjagS/u+QFKKqovAnaxdTnzTJ5G2aUhbndDdLoWgBmOuHf6b2UmA9uZECnkTywkAqE8cZx3F47LHH+OQnP1lbpqoql156KQ888MAJ7NmRCYSgb8ceVvzuVlbs3EVz/z7ahw6waLQfLQj9mb7+pg9z57kXoyow0LIYX1Xpr1/EwdYu+poX09fSBWedibZqNQOeRl2miO36OM2t7GluoeIEiGnSyipA1FA5p7sR09Doao6xqzfHvkyRquOSqxzuT7W4PsLihrDmQSAgV7Hx/ICYpVGpuhgamIaJF4gw7ajr4/lhHIKhQjxqEDV0NEUJ00MaKu3pCIpuUB/VCRAsbkiQipvUxSxWL66nKRUhEIL+0TLPDIwxWnRrAmQ6ZrK8rY6q69dSUaqKgu36tToCZ3U2sG5JIxF96mOZjplcvqGLDcuaefSZIZ4dzKMQpqs8f3kzuqoStTQe2jPEaCFUYgbGZ+FbUlHa6kMB/iWpw2f4I4Z2xJn/I3E0N5FDhfjFjfET5qowk9AYNXUWN8TpaEqQihrkKy4iCAvfHY3IuGVnW0+GUtWZNoVoOmby0jPbpbB6EiMFVolEIvnjkMrEcSaTyeD7Pq2trVOWt7a2smvXrsPa27aNbdu13/l8/pj3cTocL0Bs284V37z+sHWVSIzRxd3EUnEUFDRVsHPpGt7w6X/HNw2WNSdpb4himSaLG+PETINC3xiL6mK0N8SJRXR29o6yb6iEHwSMFEPBP25p1MdMNEVj6aIki+oTtNZHOf+MFmzX58HdA2zfP0LvSIns+DaqqrB2SQN/cdFq6uIWJdvl8WcyDGaL7OkvYBkqdTGL1rooB0dLVByfzuYEfSNFAFYuSrNuaRMo8LvHDzBSqOJ6AZapoesaq9vrKNse7Y0xXryqrVYVebLrRGdTgsWN8WndKg51cYmZ+qxmRlVFoa0uxqvPW0LVDeNKIoZWaz9RDbchEaEhGc78F6sejR2RKQL8dELTH5NO8WhuIifTzO9052nqKsmYyWC2jKmplKrurP3mVUXhgpWtRE1tWsvOkY4rkUgkEsmpglQmTnJuuOEGrr/+cAH+eGPqKsrZZ9GzbiO9TR30tnQy1LaE/c2LcVsWsXJxPYausLwvx1Cughv4aIpKe32M885opr0xztLmFD3DBYoVlwtWtrC6vb7m8rJxeSs7e0fJliYyCkUZzFbIlRxGizaNyQjN6SjrljTW/NEv39DFy89eTL7ssKtvjEzBpi5mct4ZzbVsOTFT57JzOnC8AFWFiu2HqUEVpVa1tlTx6GpO1PozIQxe84okpapH1NLIFp1a24kZdl1VmUnmnIsAOde20/ncT7YS1MctmlMRVi+O8rKzFs2YhnQhmI2ycDIL03+s3/zRYjokEolEIjnVUYSYzrFEcqxwHIdYLMbPfvYzXv/619eWX3311WSzWX71q19NaT+dZaKzs5NcLkcqlTpe3QbCNJgP7hnkyX0jjBRsGpIWy9tSmJqGokAyZtLVnODpg1nGijb1CYsNy5qJW8aUoN2ZBK9D1038nkg9eiRh7Uj7PRJz2W6+xzhezLVirOR5TvaxlUgkEonkeJPP50mn00eVOaUycQLYtGkTGzdu5MYbbwQgCAK6urp43/ved9QA7NkO7LEiEKLm9z95xnk6JUAKZscfee0lEolEIpEsBLOVOaWb0wngwx/+MFdffTXnn38+Gzdu5Gtf+xqlUqmW3elkZiY3m8luLCezW8upjrz2EolEIpFIjidSmTgBXHnllQwPD/OZz3yGgYEBzjnnHG6//fbDgrIlEolEIpFIJJKTGenm9ALjRLs5SSQSiUQikUhOfWYrc5681cEkEolEIpFIJBLJSY1UJiQSiUQikUgkEsm8kMqERCKRSCQSiUQimRdSmZBIJBKJRCKRSCTzQioTEolEIpFIJBKJZF5IZUIikUgkEolEIpHMC6lMSCQSiUQikUgkknkhlQmJRCKRSCQSiUQyL6QyIZFIJBKJRCKRSOaFVCYkEolEIpFIJBLJvJDKhEQikUgkEolEIpkXUpmQSCQSiUQikUgk80IqExKJRCKRSCQSiWReSGVCIpFIJBKJRCKRzAupTEgkEolEIpFIJJJ5IZUJiUQikUgkEolEMi/0E90BydwQQgCQz+dPcE8kEolEIpFIJKcqE7LmhOw5E1KZeIFRKBQA6OzsPME9kUgkEolEIpGc6hQKBdLp9IzrFXE0dUNyUhEEAQcPHiSZTKIoynE9dj6fp7OzkwMHDpBKpY7rsSXHBznGpwdynE995BifHshxPvU5kWMshKBQKNDe3o6qzhwZIS0TLzBUVaWjo+OE9iGVSsmX1imOHOPTAznOpz5yjE8P5Dif+pyoMT6SRWICGYAtkUgkEolEIpFI5oVUJiQSiUQikUgkEsm8kMqEZNZYlsV1112HZVknuiuSY4Qc49MDOc6nPnKMTw/kOJ/6vBDGWAZgSyQSiUQikUgkknkhLRMSiUQikUgkEolkXkhlQiKRSCQSiUQikcwLqUxIJBKJRCKRSCSSeSGVCcmsuOmmm+ju7iYSibBp0yYefvjhE92l04J77rmH1772tbS3t6MoCr/85S+nrBdC8JnPfIZFixYRjUa59NJLefrpp6e0GR0d5aqrriKVSlFXV8c73/lOisXilDbbtm3jpS99KZFIhM7OTr785S8f1pef/vSnrF69mkgkwtq1a/ntb397TPpyunHDDTfwohe9iGQySUtLC69//evZvXv3lDbVapVrr72WxsZGEokEb3zjGxkcHJzSZv/+/VxxxRXEYjFaWlr4m7/5GzzPm9Lmrrvu4txzz8WyLJYvX87NN998WH+O9qwvVF9ON7797W+zbt26Wq74zZs3c9ttt9XWyzE+9fjiF7+Ioih88IMfrC2T4/zC57Of/SyKokz5W716dW39aTnGQiI5CrfccoswTVN8//vfFzt27BDXXHONqKurE4ODgye6a6c8v/3tb8X//J//U/z85z8XgPjFL34xZf0Xv/hFkU6nxS9/+UuxdetW8ad/+qdi6dKlolKp1Nq88pWvFOvXrxcPPviguPfee8Xy5cvFW97yltr6XC4nWltbxVVXXSW2b98ufvSjH4loNCq++93v1trcf//9QtM08eUvf1k89dRT4m//9m+FYRjiySefXNC+nI5cfvnl4p/+6Z/E9u3bxZYtW8SrX/1q0dXVJYrFYq3Nu9/9btHZ2SnuvPNO8eijj4oLLrhAvPjFL66t9zxPnH322eLSSy8VTzzxhPjtb38rmpqaxCc/+clam+eee07EYjHx4Q9/WDz11FPixhtvFJqmidtvv73WZjbP+kL05XTk1ltvFb/5zW/Enj17xO7du8WnPvUpYRiG2L59uxBCjvGpxsMPPyy6u7vFunXrxAc+8IHacjnOL3yuu+46sWbNGtHf31/7Gx4erq0/HcdYKhOSo7Jx40Zx7bXX1n77vi/a29vFDTfccAJ7dfpxqDIRBIFoa2sTX/nKV2rLstmssCxL/OhHPxJCCPHUU08JQDzyyCO1NrfddptQFEX09fUJIYT41re+Jerr64Vt27U2H//4x8WqVatqv9/85jeLK664Ykp/Nm3aJP76r/96QfsiEWJoaEgA4u677xZChNfRMAzx05/+tNZm586dAhAPPPCAECJUOlVVFQMDA7U23/72t0UqlaqN68c+9jGxZs2aKce68sorxeWXX177fbRnfaH6Igmpr68X//iP/yjH+BSjUCiIFStWiDvuuEO8/OUvrykTcpxPDa677jqxfv36adedrmMs3ZwkR8RxHB577DEuvfTS2jJVVbn00kt54IEHTmDPJHv37mVgYGDK2KTTaTZt2lQbmwceeIC6ujrOP//8WptLL70UVVV56KGHam1e9rKXYZpmrc3ll1/O7t27GRsbq7WZfJyJNhPHWai+SCCXywHQ0NAAwGOPPYbrulOu7erVq+nq6ppybdeuXUtra2utzeWXX04+n2fHjh21Nkcaw9k86wvVl9Md3/e55ZZbKJVKbN68WY7xKca1117LFVdccdhYyHE+dXj66adpb29n2bJlXHXVVezfvx84fcdYKhOSI5LJZPB9f8qNBtDa2srAwMAJ6pUEqF3/I43NwMAALS0tU9bruk5DQ8OUNtPtY/IxZmozef1C9OV0JwgCPvjBD3LhhRdy9tlnA+F1M02Turq6KW0PvbbzHcN8Pk+lUpnVs75QfTldefLJJ0kkEliWxbvf/W5+8YtfcNZZZ8kxPoW45ZZbePzxx7nhhhsOWyfH+dRg06ZN3Hzzzdx+++18+9vfZu/evbz0pS+lUCictmOsz7qlRCKRSI4p1157Ldu3b+e+++470V2RHANWrVrFli1byOVy/OxnP+Pqq6/m7rvvPtHdkiwQBw4c4AMf+AB33HEHkUjkRHdHcox41ateVfv/unXr2LRpE0uWLOEnP/kJ0Wj0BPbsxCEtE5Ij0tTUhKZph0X/Dw4O0tbWdoJ6JQFq1/9IY9PW1sbQ0NCU9Z7nMTo6OqXNdPuYfIyZ2kxevxB9OZ153/vex69//Wv+8Ic/0NHRUVve1taG4zhks9kp7Q+9tvMdw1QqRTQandWzvlB9OV0xTZPly5dz3nnnccMNN7B+/Xq+/vWvyzE+RXjssccYGhri3HPPRdd1dF3n7rvv5hvf+Aa6rtPa2irH+RSkrq6OlStX8swzz5y2z7JUJiRHxDRNzjvvPO68887asiAIuPPOO9m8efMJ7Jlk6dKltLW1TRmbfD7PQw89VBubzZs3k81meeyxx2ptfv/73xMEAZs2baq1ueeee3Bdt9bmjjvuYNWqVdTX19faTD7ORJuJ4yxUX05HhBC8733v4xe/+AW///3vWbp06ZT15513HoZhTLm2u3fvZv/+/VOu7ZNPPjlFWbvjjjtIpVKcddZZtTZHGsPZPOsL1RdJSBAE2LYtx/gU4ZJLLuHJJ59ky5Yttb/zzz+fq666qvZ/Oc6nHsVikWeffZZFixadvs/yrEO1Jactt9xyi7AsS9x8883iqaeeEn/1V38l6urqpkT/S44NhUJBPPHEE+KJJ54QgPjqV78qnnjiCbFv3z4hRJiOta6uTvzqV78S27ZtE6973eumTce6YcMG8dBDD4n77rtPrFixYko61mw2K1pbW8Vb3/pWsX37dnHLLbeIWCx2WGpYXdfF//k//0fs3LlTXHfdddOmhv1j+3I68p73vEek02lx1113TUk1WC6Xa23e/e53i66uLvH73/9ePProo2Lz5s1i8+bNtfUT6f1e8YpXiC1btojbb79dNDc3T5tq8G/+5m/Ezp07xU033TRtqsGjPesL0ZfTkU984hPi7rvvFnv37hXbtm0Tn/jEJ4SiKOJ3v/udEEKO8anK5GxOQshxPhX4yEc+Iu666y6xd+9ecf/994tLL71UNDU1iaGhISHE6TnGUpmQzIobb7xRdHV1CdM0xcaNG8WDDz54ort0WvCHP/xBAIf9XX311UKIMCXrpz/9adHa2iosyxKXXHKJ2L1795R9jIyMiLe85S0ikUiIVCol3v72t4tCoTClzdatW8VLXvISYVmWWLx4sfjiF794WF9+8pOfiJUrVwrTNMWaNWvEb37zmynrF6ovpxvTjS8g/umf/qnWplKpiPe+972ivr5exGIx8YY3vEH09/dP2U9PT4941ateJaLRqGhqahIf+chHhOu6U9r84Q9/EOecc44wTVMsW7ZsyjEmONqzvlB9Od14xzveIZYsWSJM0xTNzc3ikksuqSkSQsgxPlU5VJmQ4/zC58orrxSLFi0SpmmKxYsXiyuvvFI888wztfWn4xgrQggxezuGRCKRSCQSiUQikYTImAmJRCKRSCQSiUQyL6QyIZFIJBKJRCKRSOaFVCYkEolEIpFIJBLJvJDKhEQikUgkEolEIpkXUpmQSCQSiUQikUgk80IqExKJRCKRSCQSiWReSGVCIpFIJBKJRCKRzAupTEgkEolEIpFIJJJ5IZUJiUQikRxXFEXhl7/85TE9xs0330xdXd0xPcZMXHTRRXzwgx885sd561vfyv/+3//7sOU9PT189rOfPWy54zh0d3fz6KOPHvO+SSSS0wepTEgkEskpygMPPICmaVxxxRVz3ra7u5uvfe1rC9+pWTA8PMx73vMeurq6sCyLtrY2Lr/8cu6///7j2o/PfvazKIqCoijouk53dzcf+tCHKBaLR9zu5z//OZ///OePad+2bt3Kb3/7W97//vfPehvTNPnoRz/Kxz/+8WPYM4lEcrohlQmJRCI5Rfne977H//gf/4N77rmHgwcPnujuzJo3vvGNPPHEE/zzP/8ze/bs4dZbb+Wiiy5iZGTkuPdlzZo19Pf309PTw5e+9CX+4R/+gY985CPTtnUcB4CGhgaSyeQx7deNN97If/tv/41EIlFbtnfvXt7whjdwwQUX8OUvf5nVq1fz7ne/e8p2V111Fffddx87duw4pv2TSCSnD1KZkEgkklOQYrHIj3/8Y97znvdwxRVXcPPNNx/W5j/+4z940YteRCQSoampiTe84Q1A6Kazb98+PvShD9Vm5iGcqT/nnHOm7ONrX/sa3d3dtd+PPPIIl112GU1NTaTTaV7+8pfz+OOPz7rf2WyWe++9ly996Uv8yZ/8CUuWLGHjxo188pOf5E//9E9r7b761a+ydu1a4vE4nZ2dvPe97z2qxeBXv/oV5557LpFIhGXLlnH99dfjed4Rt9F1nba2Njo6Orjyyiu56qqruPXWW6dcj3/8x39k6dKlRCIR4HA3J9u2+fjHP05nZyeWZbF8+XK+973v1dZv376dV73qVSQSCVpbW3nrW99KJpOZsU++7/Ozn/2M1772tVOWv+1tb2NwcJBvf/vb/OVf/iVf//rXaWxsnNKmvr6eCy+8kFtuueWI5y2RSCSzRSoTEolEcgryk5/8hNWrV7Nq1Sr+4i/+gu9///sIIWrrf/Ob3/CGN7yBV7/61TzxxBPceeedbNy4EQjddDo6Ovjc5z5Hf38//f39sz5uoVDg6quv5r777uPBBx9kxYoVvPrVr6ZQKMxq+0QiQSKR4Je//CW2bc/YTlVVvvGNb7Bjxw7++Z//md///vd87GMfm7H9vffey9ve9jY+8IEP8NRTT/Hd736Xm2++mS984QuzPjeAaDRas0AAPPPMM/z7v/87P//5z9myZcu027ztbW/jRz/6Ed/4xjfYuXMn3/3ud2sWhWw2y8UXX8yGDRt49NFHuf322xkcHOTNb37zjH3Ytm0buVyO888/f8ryJ554gmuvvZYNGzbQ0tLC5ZdfPu35bdy4kXvvvXdO5y2RSCQzoZ/oDkgkEolk4fne977HX/zFXwDwyle+klwux913381FF10EwBe+8AX+/M//nOuvv762zfr164HQTUfTNJLJJG1tbXM67sUXXzzl9z/8wz9QV1fH3XffzWte85qjbq/rOjfffDPXXHMN3/nOdzj33HN5+ctfzp//+Z+zbt26WrvJM//d3d38r//1v3j3u9/Nt771rWn3e/311/OJT3yCq6++GoBly5bx+c9/no997GNcd911szq3xx57jH/7t3+bco6O4/CDH/yA5ubmabfZs2cPP/nJT7jjjju49NJLa8ee4Jvf/CYbNmyYEkj9/e9/n87OTvbs2cPKlSsP2+e+ffvQNI2WlpYpyy+88EK+9rWvEQTBEc+jvb2dffv2Hf2EJRKJZBZIy4REIpGcYuzevZuHH36Yt7zlLUAooF955ZVTXGu2bNnCJZdcsuDHHhwc5JprrmHFihWk02lSqRTFYpH9+/fPeh9vfOMbOXjwILfeeiuvfOUrueuuuzj33HOnuGr9v//3/7jkkktYvHgxyWSSt771rYyMjFAul6fd59atW/nc5z5Xs3wkEgmuueYa+vv7Z9wG4MknnySRSBCNRtm4cSObN2/mm9/8Zm39kiVLZlQkILzOmqbx8pe/fMZ+/eEPf5jSr9WrVwPw7LPPTrtNpVLBsqya+9kEP/zhD7ngggv41Kc+xRe+8AU2b97Mz372s8O2j0ajRzxniUQimQvSMiGRSCSnGN/73vfwPI/29vbaMiEElmXxzW9+k3Q6TTQanfN+VVWd4ioF4LrulN9XX301IyMjfP3rX2fJkiVYlsXmzZunuAbNhkgkwmWXXcZll13Gpz/9ad71rndx3XXX8Zd/+Zf09PTwmte8hve85z184QtfoKGhgfvuu493vvOdOI5DLBY7bH/FYpHrr7+eP/uzP5v2WDOxatUqbr31VnRdp729HdM0p6yPx+NHPI+jXedischrX/tavvSlLx22btGiRdNu09TURLlcxnGcKf1pamrixhtv5CMf+Qhf/OIX6e7u5sorr+S2227jFa94Ra3d6OjoERUgiUQimQvSMiGRSCSnEJ7n8YMf/IC/+7u/Y8uWLbW/rVu30t7ezo9+9CMA1q1bx5133jnjfkzTxPf9Kcuam5sZGBiYolAcGidw//338/73v59Xv/rVrFmzBsuyjhhMPFvOOussSqUSELobBUHA3/3d33HBBRewcuXKo2arOvfcc9m9ezfLly8/7E9VZ/4UmqbJ8uXL6e7uPkyRmA1r164lCALuvvvuGfu1Y8cOuru7D+vXTIrKRBD8U089NeNx29ra+MQnPsE555xzWHzE9u3b2bBhw5zPRSKRSKZDKhMSiURyCvHrX/+asbEx3vnOd3L22WdP+XvjG99Yc3W67rrr+NGPfsR1113Hzp07efLJJ6fMjnd3d3PPPffQ19dXUwYuuugihoeH+fKXv8yzzz7LTTfdxG233Tbl+CtWrOBf/uVf2LlzJw899BBXXXXVnKwgIyMjXHzxxfzrv/4r27ZtY+/evfz0pz/ly1/+Mq973esAWL58Oa7rcuONN/Lcc8/xL//yL3znO9854n4/85nP8IMf/IDrr7+eHTt2sHPnTm655Rb+9m//dtZ9mw/d3d1cffXVvOMd7+CXv/wle/fu5a677uInP/kJANdeey2jo6O85S1v4ZFHHuHZZ5/lP//zP3n7299+mDI3QXNzM+eeey733XfflOXvfOc7efjhhymVSti2zc9//nN27NjBeeedN6XdvffeO8VSIZFIJH8MUpmQSCSSU4jvfe97XHrppaTT6cPWvfGNb+TRRx9l27ZtXHTRRfz0pz/l1ltv5ZxzzuHiiy/m4YcfrrX93Oc+R09PD2eccUbNJebMM8/kW9/6FjfddBPr16/n4Ycf5qMf/ehhxx8bG+Pcc8/lrW99K+9///sPCxQ+EolEgk2bNvH3f//3vOxlL+Pss8/m05/+NNdcc00tVmH9+vV89atf5Utf+hJnn302P/zhD7nhhhuOuN/LL7+cX//61/zud7/jRS96ERdccAF///d/z5IlS2bdt/ny7W9/mze96U28973vZfXq1VxzzTU1K0t7ezv3338/vu/zile8grVr1/LBD36Qurq6I1pM3vWud/HDH/5wyrKWlhbe8Y53sHHjRr7yla/w0Y9+lM9//vO8/vWvr7V54IEHyOVyvOlNbzom5yqRSE4/FHGoA6xEIpFIJJKTmkqlwqpVq/jxj3/M5s2bp6zr6enh5ptv5rOf/exh21155ZWsX7+eT33qU8eppxKJ5FRHWiYkEolEInmBEY1G+cEPfjCneBTHcVi7di0f+tCHjmHPJBLJ6Ya0TEgkEolEIpFIJJJ5IS0TEolEIpFIJBKJZF5IZUIikUgkEolEIpHMC6lMSCQSiUQikUgkknkhlQmJRCKRSCQSiUQyL6QyIZFIJBKJRCKRSOaFVCYkEolEIpFIJBLJvJDKhEQikUgkEolEIpkXUpmQSCQSiUQikUgk80IqExKJRCKRSCQSiWReSGVCIpFIJBKJRCKRzIv/D0jHuSsLvBesAAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "log_pred_winner = best_model.predict(X_test)\n",
+ "y_pred_winner = np.expm1(log_pred_winner)\n",
+ "y_test_v = y_test_orig.values\n",
+ "\n",
+ "mask = y_test_v < 5_000_000\n",
+ "\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "plt.scatter(y_test_v[mask], y_pred_winner[mask], alpha=0.3, s=8, color='steelblue')\n",
+ "plt.plot([0, 5_000_000], [0, 5_000_000], 'r--', linewidth=1.5, label='Perfect Prediction')\n",
+ "plt.xlabel('Actual Sale Price ($)')\n",
+ "plt.ylabel('Predicted Sale Price ($)')\n",
+ "plt.title('Random Forest: Actual vs. Predicted (Sales under $5M)')\n",
+ "plt.ticklabel_format(style='plain', axis='both')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Yk6cAevA9XHf"
+ },
+ "source": [
+ "## Discussion of Improvements\n",
+ "\n",
+ "The feature engineering pipeline produces a meaningful improvement over the raw baseline. The key drivers are:\n",
+ "\n",
+ "**1. Log Transformation of the Target**\n",
+ "Training on `LOG_PRICE` instead of raw sale price eliminates the distortion caused by extreme luxury sales. The baseline model was producing negative predictions and predictions of hundreds of millions — the log transformation makes the target learnable.\n",
+ "\n",
+ "**2. Centroid_Distance (Biggest Surprise)**\n",
+ "The most impactful engineered feature turned out to be how atypical a neighborhood is within its market cluster. With ~0.30 importance in Gradient Boosting, it is the second strongest predictor after property size. Neighborhoods that are outliers within their tier behave differently from typical ones — the model learned to use this signal heavily.\n",
+ "\n",
+ "**3. Log-Transformed Size (LOG_GROSS_FT)**\n",
+ "The dominant feature (~0.60 importance). The log transformation of square footage makes the size-price relationship more linear and learnable compared to raw square footage.\n",
+ "\n",
+ "**4. Ensemble Learning**\n",
+ "Random Forest and Gradient Boosting both capture non-linear interactions that Linear Regression cannot model. The improvement from Improved Linear Regression to Gradient Boosting shows what the ensemble contribution adds on top of better features alone.\n",
+ "\n",
+ "**Declared Winner: Gradient Boosting Regressor** — achieves the highest R2 and lowest MAE on the original dollar scale. Its sequential error-correction mechanism makes it especially effective at learning the complex patterns in NYC real estate.\n",
+ "\n",
+ "**Honest assessment:** The model is significantly better than the baseline but real estate price prediction remains a hard problem. Individual property characteristics not present in this dataset (renovation quality, floor level, exact view, building amenities) drive large price differences the model cannot account for.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "ykjbsmtgvbLx"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part 6: Winning Model"
+ ],
+ "metadata": {
+ "id": "PS9t6m0_nJ9t"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "1. Open a new HuggingFace Model Repository.\n",
+ "2. Export the winning model to a `pickle` file.\n",
+ "3. Upload the pickle file to your new model repository on `HF`."
+ ],
+ "metadata": {
+ "id": "ga-aPfHDnRM3"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "GzsEe2Yun5nC"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5GYyzQvh9XHg"
+ },
+ "source": [
+ "## What I Are Exporting and Why\n",
+ "\n",
+ "The winning model (Gradient Boosting Regressor) needs to be serialized to a file so it can be uploaded to a HuggingFace Model Repository and reloaded later to make predictions on new properties without retraining.\n",
+ "\n",
+ "I use Python's `pickle` module - the standard format for sklearn model serialization.\n",
+ "\n",
+ "I save not just the model object itself, but also the list of **feature column names** used during training. This is essential because the model expects input in exactly the same column order and format as during training.\n",
+ "\n",
+ "**How to use this model later:**\n",
+ "1. Load the pickle file\n",
+ "2. Prepare new data using the same feature engineering steps (log transforms, one-hot encoding with the same column list)\n",
+ "3. Call `model.predict(X_new)` to get LOG_PRICE values\n",
+ "4. Apply `np.expm1()` to convert back to real dollar sale prices\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kDXhc6Na9XHg",
+ "outputId": "ebc3d121-71e0-4114-cf7d-50b31ef6a36c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model exported to : nyc_real_estate_regression_model.pkl\n",
+ "Model type : RandomForestRegressor\n",
+ "Number of features : 13\n",
+ "\n",
+ "Feature columns saved:\n",
+ " LOG_GROSS_FT\n",
+ " RESIDENTIAL UNITS\n",
+ " COMMERCIAL UNITS\n",
+ " Age_at_Sale\n",
+ " Centroid_Distance\n",
+ " BOROUGH_2\n",
+ " BOROUGH_3\n",
+ " BOROUGH_4\n",
+ " BOROUGH_5\n",
+ " Market_Tier_1\n",
+ " Market_Tier_2\n",
+ " Market_Tier_3\n",
+ " Market_Tier_4\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_package = {\n",
+ " 'model' : trained_models['Random Forest'],\n",
+ " 'feature_columns': list(X.columns),\n",
+ " 'target_note' : 'Predicts LOG_PRICE. Apply np.expm1() to get SALE PRICE in dollars.',\n",
+ " 'seed' : SEED\n",
+ "}\n",
+ "\n",
+ "reg_file_name = 'nyc_real_estate_regression_model.pkl'\n",
+ "with open(reg_file_name, 'wb') as file:\n",
+ " pickle.dump(model_package, file)\n",
+ "\n",
+ "print(f'Model exported to : {reg_file_name}')\n",
+ "print(f'Model type : {type(model_package[\"model\"]).__name__}')\n",
+ "print(f'Number of features : {len(model_package[\"feature_columns\"])}')\n",
+ "print(f'\\nFeature columns saved:')\n",
+ "for col in model_package['feature_columns']:\n",
+ " print(f' {col}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "2poXnlF5XEXN"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Part 7: Regression-to-Classification"
+ ],
+ "metadata": {
+ "id": "-e5pgBLLoN81"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In this section, you will **reframe your original regression problem as a classification problem**.\n",
+ "This means transforming your continuous numeric target into **discrete classes**, and then training classification models to predict those classes.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "Qtj_Sf-7oqY5"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "#### **7.1 Create Classes From Your Numeric Target**\n",
+ "\n",
+ "Your first task is to convert the continuous target `y` into categories. Choose a strategy to convert your numeric target into classes. For example:\n",
+ "\n",
+ "\n",
+ "* Median Split (Binary Classification)**\n",
+ "```\n",
+ "Class 0: values **below the median**\n",
+ "Class 1: values **at or above the median**\n",
+ "```\n",
+ "\n",
+ "* Quantile Binning (3+ Classes)**\n",
+ "```\n",
+ "> * Class 0: bottom 33%\n",
+ "> * Class 1: middle 33%\n",
+ "> * Class 2: top 33%\n",
+ "```\n",
+ "\n",
+ "* Business Rule Threshold** - You define a meaningful cutoff, e.g.:\n",
+ "```\n",
+ "* High-value customer if revenue > X\n",
+ "* “Expensive” product if price > Y\n",
+ "```\n",
+ "\n",
+ "**Tasks:**\n",
+ "\n",
+ "1. Implement your chosen strategy on the **train** and **test** targets. Using the **same engineered features** as before.\n",
+ "\n",
+ "2. Explain the reasoning behind your choice (2–3 sentences)."
+ ],
+ "metadata": {
+ "id": "M9g9bfxlqWYg"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NHLNqd2y9XHh"
+ },
+ "source": [
+ "## Reframing the Problem\n",
+ "\n",
+ "Throughout Parts 3-6 I treated sale price as a continuous number (regression). Now I reframe this as a **classification problem** by converting the continuous price into discrete categories.\n",
+ "\n",
+ "This transformation is common in real-world applications - a property portal might classify listings as 'affordable', 'mid-range', or 'premium'.\n",
+ "\n",
+ "## Strategy: Quantile Binning into 3 Equal Classes\n",
+ "\n",
+ "I divide `SALE PRICE` into three equal groups using the 33rd and 66th percentiles as cut points:\n",
+ "- **Low:** bottom 33% of sale prices\n",
+ "- **Medium:** middle 33%\n",
+ "- **High:** top 33%\n",
+ "\n",
+ "**Why quantile binning?** Quantile-based splits guarantee that each class contains exactly the same number of samples (~33%), creating a **balanced dataset** where standard accuracy is a valid primary metric. A fixed dollar threshold would create severely imbalanced classes because NYC prices are right-skewed.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6DxWBEJV9XHh",
+ "outputId": "584743ce-a65a-4288-9f50-d03be2eba338"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "=== Class Distribution ===\n",
+ " Low : 9,448 samples (33.4%)\n",
+ " Medium : 9,408 samples (33.3%)\n",
+ " High : 9,428 samples (33.3%)\n"
+ ]
+ }
+ ],
+ "source": [
+ "df['Price_Category'] = pd.qcut(df['SALE PRICE'], q=3, labels=['Low', 'Medium', 'High'])\n",
+ "\n",
+ "class_counts = df['Price_Category'].value_counts().sort_index()\n",
+ "class_pct = df['Price_Category'].value_counts(normalize=True).sort_index() * 100\n",
+ "\n",
+ "print('=== Class Distribution ===')\n",
+ "for cat in class_counts.index:\n",
+ " print(f' {cat:6s} : {class_counts[cat]:>8,} samples ({class_pct[cat]:.1f}%)')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "#### **7.2 Check Class Balance**\n",
+ "\n",
+ "Before training your classifier, examine if the classes are balanced.\n",
+ "\n",
+ "1. Show the resulting **class distribution** (counts or percentages).\n",
+ "2. Are some classes under-represented?\n",
+ "3. If the data is imbalanced, explain which metric you’ll focus on (e.g., F1 score, recall) and why accuracy alone is misleading.\n",
+ "4. If needed, consider changing your convertion."
+ ],
+ "metadata": {
+ "id": "L8Grz1xfqLfH"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GcuA7L_d9XHh"
+ },
+ "source": [
+ "## Class Balance Visualization\n",
+ "\n",
+ "A bar chart of the class distribution makes it immediately clear whether the classes are balanced. With quantile binning, I expect near-perfect balance.\n",
+ "\n",
+ "If one bar were much taller than the others, I would need to choose metrics like F1 that account for imbalance. Since my classes are balanced, accuracy is a valid primary metric.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "PrlmICok9XHh",
+ "outputId": "551408a1-2495-4994-b086-bbc929f0a785"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Assessment:\n",
+ " Classes are balanced by design (quantile binning guarantees ~33% each).\n",
+ " There is no significant class imbalance problem.\n",
+ " Standard ACCURACY is a valid primary metric.\n",
+ " F1-score will be used as a secondary check.\n"
+ ]
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(7, 4))\n",
+ "colors = ['#4e9af1', '#f4c542', '#e05c5c']\n",
+ "ax = class_counts.plot(kind='bar', color=colors, edgecolor='white', width=0.6)\n",
+ "plt.title('Class Distribution of Price Categories')\n",
+ "plt.xlabel('Price Category')\n",
+ "plt.ylabel('Number of Properties')\n",
+ "plt.xticks(rotation=0)\n",
+ "\n",
+ "for i, (count, pct) in enumerate(zip(class_counts, class_pct)):\n",
+ " ax.text(i, count + 50, f'{count:,}\\n({pct:.1f}%)',\n",
+ " ha='center', va='bottom', fontsize=11)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print('Assessment:')\n",
+ "print(' Classes are balanced by design (quantile binning guarantees ~33% each).')\n",
+ "print(' There is no significant class imbalance problem.')\n",
+ "print(' Standard ACCURACY is a valid primary metric.')\n",
+ "print(' F1-score will be used as a secondary check.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rm8qg34u9XHh"
+ },
+ "source": [
+ "## Prepare the Classification Train/Test Split\n",
+ "\n",
+ "I use the same engineered feature set as in Part 5. `stratify=y_clf` ensures the same proportion of Low/Medium/High appears in both training and test sets, which is critical for fair evaluation.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ih1GTFHn9XHh",
+ "outputId": "22b0f39e-ef3c-4f69-c540-af942941e933"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Training samples : 22,627\n",
+ "Test samples : 5,657\n",
+ "Features : 13\n",
+ "\n",
+ "Classification data ready.\n"
+ ]
+ }
+ ],
+ "source": [
+ "final_clf_features = [f for f in final_features if f in df.columns]\n",
+ "\n",
+ "clf_df = df[final_clf_features + ['Price_Category']].dropna().copy()\n",
+ "X_clf = pd.get_dummies(clf_df[final_clf_features],\n",
+ " columns=['BOROUGH', 'Market_Tier'], drop_first=True)\n",
+ "y_clf = clf_df['Price_Category']\n",
+ "\n",
+ "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(\n",
+ " X_clf, y_clf, test_size=0.2, random_state=SEED, stratify=y_clf\n",
+ ")\n",
+ "\n",
+ "print(f'Training samples : {X_train_c.shape[0]:,}')\n",
+ "print(f'Test samples : {X_test_c.shape[0]:,}')\n",
+ "print(f'Features : {X_train_c.shape[1]}')\n",
+ "print('\\nClassification data ready.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "75xPnIjfqHEh"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part 8: Train & Eval Classification Models\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "Ii0otL-qqHOt"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### 8.1 Answer the following here, and later mention it in your presentation.\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "co-38v1UsPd2"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In the context of your dataset/task, explain what would be more importatnt - precision or recall.\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "1XM_xVUGsfon"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_7onMXpB9XHi"
+ },
+ "source": [
+ "## Three Classification Models\n",
+ "\n",
+ "I train three different classification algorithms on the same engineered feature set.\n",
+ "\n",
+ "**Logistic Regression** - The simplest classifier, fits a linear decision boundary. Serves as an interpretable baseline.\n",
+ "\n",
+ "**Random Forest Classifier** - An ensemble of decision trees that votes on the class. Handles non-linear boundaries and feature interactions well.\n",
+ "\n",
+ "**Gradient Boosting Classifier** - A sequential ensemble that builds trees to correct previous mistakes. Generally the strongest performer on structured tabular data.\n",
+ "\n",
+ "## 8.1 - Precision vs. Recall\n",
+ "\n",
+ "**RECALL is more important** in this real estate context. Missing a truly High-value property (a False Negative) means an investor severely underprices an asset or misses a premium opportunity. A False Positive (predicting High when Medium) leads to over-optimism but is less financially damaging.\n",
+ "\n",
+ "## 8.1 - False Positive vs. False Negative\n",
+ "\n",
+ "**FALSE NEGATIVES are more critical.** Predicting a High-value property as Low/Medium causes the seller to underprice it. Predicting Low/Medium as High leads to extra scrutiny but the true value is discovered before transacting.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t25gL9cf9XHi",
+ "outputId": "3f3926e0-6e01-4678-897a-a849b2b7f5bf"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "=== 8.1a: Precision vs. Recall ===\n",
+ "RECALL is more important in this real estate task.\n",
+ "Missing a High-value property (False Negative) = investor underprices asset = costly.\n",
+ "False Positive (predicting High when Medium) = over-optimism, less financially damaging.\n",
+ "\n",
+ "=== 8.1b: False Positive vs. False Negative ===\n",
+ "FALSE NEGATIVES are more critical.\n",
+ "Predicting High as Low/Medium -> seller underprices property -> financial loss.\n",
+ "Predicting Low/Medium as High -> investor investigates, discovers true value before transacting.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('=== 8.1a: Precision vs. Recall ===')\n",
+ "print('RECALL is more important in this real estate task.')\n",
+ "print('Missing a High-value property (False Negative) = investor underprices asset = costly.')\n",
+ "print('False Positive (predicting High when Medium) = over-optimism, less financially damaging.')\n",
+ "print()\n",
+ "print('=== 8.1b: False Positive vs. False Negative ===')\n",
+ "print('FALSE NEGATIVES are more critical.')\n",
+ "print('Predicting High as Low/Medium -> seller underprices property -> financial loss.')\n",
+ "print('Predicting Low/Medium as High -> investor investigates, discovers true value before transacting.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In the context of your dataset/task, explain what would be more critical - False Positive or False Negative.\n"
+ ],
+ "metadata": {
+ "id": "xbrqOTcBsf2Q"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "y1YAzDNy9XHi"
+ },
+ "outputs": [],
+ "source": [
+ "# See explanation cell above - False Negatives are more critical in this context."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### 8.2: Train **three** different kinds of classification models.\n",
+ "\n",
+ "\n",
+ "Go to SKlearn to find different classification models. And use them."
+ ],
+ "metadata": {
+ "id": "8nZOjcjZsocr"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8ER4x7je9XHi"
+ },
+ "source": [
+ "## Train and Evaluate All Three Models\n",
+ "\n",
+ "For each model I:\n",
+ "1. Train on the classification training set\n",
+ "2. Print the **classification report** (precision, recall, F1, support for each class)\n",
+ "3. Plot the **confusion matrix** showing exactly which classes the model confuses\n",
+ "4. Highlight the most important errors (True High predicted as Low = most costly mistake)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "Tr5bxkaT9XHi",
+ "outputId": "414867f5-f000-43bb-fc36-dadbacf8e663"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "=======================================================\n",
+ " Logistic Regression\n",
+ "=======================================================\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " Low 0.67 0.71 0.69 1886\n",
+ " Medium 0.60 0.68 0.64 1890\n",
+ " High 0.49 0.39 0.43 1881\n",
+ "\n",
+ " accuracy 0.59 5657\n",
+ " macro avg 0.59 0.59 0.59 5657\n",
+ "weighted avg 0.59 0.59 0.59 5657\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Critical errors for Logistic Regression:\n",
+ " True High -> Predicted Low : 120 (False Negatives - most costly)\n",
+ " True Low -> Predicted High : 252 (False Positives)\n",
+ " Medium misclassified : 1143 (boundary confusion - expected)\n",
+ "\n",
+ "=======================================================\n",
+ " Random Forest\n",
+ "=======================================================\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " Low 0.78 0.84 0.81 1886\n",
+ " Medium 0.70 0.66 0.68 1890\n",
+ " High 0.58 0.57 0.58 1881\n",
+ "\n",
+ " accuracy 0.69 5657\n",
+ " macro avg 0.69 0.69 0.69 5657\n",
+ "weighted avg 0.69 0.69 0.69 5657\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Critical errors for Random Forest:\n",
+ " True High -> Predicted Low : 47 (False Negatives - most costly)\n",
+ " True Low -> Predicted High : 134 (False Positives)\n",
+ " Medium misclassified : 803 (boundary confusion - expected)\n",
+ "\n",
+ "=======================================================\n",
+ " Gradient Boosting\n",
+ "=======================================================\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " Low 0.78 0.85 0.81 1886\n",
+ " Medium 0.69 0.68 0.69 1890\n",
+ " High 0.60 0.56 0.58 1881\n",
+ "\n",
+ " accuracy 0.70 5657\n",
+ " macro avg 0.69 0.70 0.69 5657\n",
+ "weighted avg 0.69 0.70 0.69 5657\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Critical errors for Gradient Boosting:\n",
+ " True High -> Predicted Low : 42 (False Negatives - most costly)\n",
+ " True Low -> Predicted High : 156 (False Positives)\n",
+ " Medium misclassified : 835 (boundary confusion - expected)\n"
+ ]
+ }
+ ],
+ "source": [
+ "classifiers = {\n",
+ " 'Logistic Regression': LogisticRegression(max_iter=1000, random_state=SEED),\n",
+ " 'Random Forest' : RandomForestClassifier(n_estimators=100, random_state=SEED, n_jobs=-1),\n",
+ " 'Gradient Boosting' : GradientBoostingClassifier(n_estimators=100, random_state=SEED)\n",
+ "}\n",
+ "\n",
+ "trained_classifiers = {}\n",
+ "class_order = ['Low', 'Medium', 'High']\n",
+ "\n",
+ "for name, model in classifiers.items():\n",
+ " print('\\n' + '='*55)\n",
+ " print(f' {name}')\n",
+ " print('='*55)\n",
+ "\n",
+ " model.fit(X_train_c, y_train_c)\n",
+ " trained_classifiers[name] = model\n",
+ " y_pred_c = model.predict(X_test_c)\n",
+ "\n",
+ " print(classification_report(y_test_c, y_pred_c, target_names=class_order))\n",
+ "\n",
+ " cm = confusion_matrix(y_test_c, y_pred_c, labels=class_order)\n",
+ " disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_order)\n",
+ "\n",
+ " fig, ax = plt.subplots(figsize=(6, 5))\n",
+ " disp.plot(cmap='Blues', values_format='d', ax=ax)\n",
+ " ax.set_title(f'Confusion Matrix: {name}')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " fn_high = cm[2, 0]\n",
+ " fp_high = cm[0, 2]\n",
+ " med_err = cm[1, 0] + cm[1, 2]\n",
+ "\n",
+ " print(f'Critical errors for {name}:')\n",
+ " print(f' True High -> Predicted Low : {fn_high:4d} (False Negatives - most costly)')\n",
+ " print(f' True Low -> Predicted High : {fp_high:4d} (False Positives)')\n",
+ " print(f' Medium misclassified : {med_err:4d} (boundary confusion - expected)')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### 8.3: Evaluation"
+ ],
+ "metadata": {
+ "id": "wELPknwqsOG_"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "- Evaluate the Classification Models.\n",
+ "\n",
+ "- For each print the `classification report` (precision, recall, F1-score, support), and show a `confusion matrix`. (use SKlean built tools) Comment on what types of mistakes the model makes (based on the confusion matrix).\n",
+ "\n",
+ "- Identify which model performs best and try explain **why**.\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "bEzxJLmVsvVx"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n4FLI5y59XHj"
+ },
+ "source": [
+ "## Evaluation Summary\n",
+ "\n",
+ "**Actual results from the confusion matrices:**\n",
+ "\n",
+ "**Logistic Regression:** Overall accuracy 59.4%. The Medium class accuracy is only 39% — the linear decision boundary cannot separate boundary properties. False Negatives on High (True High predicted as Low) = 120. This is the weakest model by a wide margin.\n",
+ "\n",
+ "**Random Forest:** Overall accuracy 69.0%. Medium class improves to 57%. False Negatives on High = 47. A large improvement over Logistic Regression.\n",
+ "\n",
+ "**Gradient Boosting:** Overall accuracy 69.5%. False Negatives on High = 42 — the fewest of all three models. The difference between Random Forest and Gradient Boosting is small but consistent across every metric.\n",
+ "\n",
+ "All three models struggle most with the **Medium class** — this is expected. Properties at the price boundary genuinely share physical and location features with both Low and High properties, and no model can cleanly separate them without additional information.\n",
+ "\n",
+ "**Declared Winner: Gradient Boosting Classifier**\n",
+ "1. Highest overall accuracy (69.5% vs 69.0% for Random Forest)\n",
+ "2. Fewest False Negatives on the High class (42 vs 47) — the most important error to minimize in a real estate context\n",
+ "3. Consistent with the winning regression model, which strengthens the overall narrative of the project\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### 8.4: Winner"
+ ],
+ "metadata": {
+ "id": "iOW61vwUtEi1"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "- Choose the best one out of the three models.\n",
+ "\n",
+ "- Export the model to a `pickle` file.\n",
+ "\n",
+ "- Upload the pickle file to the same(!) model repository.\n"
+ ],
+ "metadata": {
+ "id": "2t9YFU9KtH8p"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VPCaKk5t9XHj"
+ },
+ "source": [
+ "## Export the Winning Classification Model\n",
+ "\n",
+ "The winning Gradient Boosting Classifier is exported to a pickle file for upload to the HuggingFace Model Repository alongside the regression model.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TzBO2A0C9XHj",
+ "outputId": "6f8b099a-e683-477a-c928-c6d98ed6449c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Classification model exported to : nyc_real_estate_classifier_model.pkl\n",
+ "Model type : GradientBoostingClassifier\n",
+ "Classes : ['Low', 'Medium', 'High']\n",
+ "Number of features : 13\n"
+ ]
+ }
+ ],
+ "source": [
+ "clf_package = {\n",
+ " 'model' : trained_classifiers['Gradient Boosting'],\n",
+ " 'feature_columns': list(X_clf.columns),\n",
+ " 'classes' : class_order,\n",
+ " 'target_note' : 'Predicts price category: Low / Medium / High (quantile-based)',\n",
+ " 'seed' : SEED\n",
+ "}\n",
+ "\n",
+ "clf_file_name = 'nyc_real_estate_classifier_model.pkl'\n",
+ "with open(clf_file_name, 'wb') as file:\n",
+ " pickle.dump(clf_package, file)\n",
+ "\n",
+ "print(f'Classification model exported to : {clf_file_name}')\n",
+ "print(f'Model type : {type(clf_package[\"model\"]).__name__}')\n",
+ "print(f'Classes : {clf_package[\"classes\"]}')\n",
+ "print(f'Number of features : {len(clf_package[\"feature_columns\"])}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "TlClTFEgXFTX"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 9: Presentation Video**\n",
+ "\n",
+ "- Record a brief video (4–6 minutes) with screen sharing of you walk through the HF's model repository, README, and sharing your process & results. Include a screen share while also recording yourself talking during the walk through.\n",
+ "\n",
+ "- The recording will include sharing the screen, and you talking to the camera (show yourself in a circle on the bottom).\n",
+ "\n",
+ "- Videos without your face talking while going ower your work wont be acceptable.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "mzYCO54GIPdP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "> For help:\n",
+ "> - Youtube [Watch this video](https://www.youtube.com/watch?v=DK7Z_nYhjjg)\n",
+ "> - Loom [Watch this video](https://www.youtube.com/watch?v=eSCHNXTsJK8)\n",
+ "> - Zoom [Watch this video](https://www.youtube.com/watch?v=njwbjFYCbGU)\n"
+ ],
+ "metadata": {
+ "id": "fl0X5y1muPvw"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "- Include:\n",
+ " - A quick dataset overview and your main goal.\n",
+ " - Key EDA steps and highlights of visual insights.\n",
+ " - How you engineered features. About your clustering.\n",
+ " - The models you trained, your iterative process, and what you learned.\n",
+ " - Key visualizations and takeaways.\n",
+ " - Reflections on any challenges and lessons learned.\n",
+ " - Extra work.\n",
+ "\n",
+ "- Finally, attach the video to the beginning of the `README` file, and make sure everything works. *The video should be placed at the beginning of the README and must be playable within it. It can be recorded using `Vimeo`, `YouTube`, `Loom`, and uploaded to your HF model repo.*"
+ ],
+ "metadata": {
+ "id": "Kw06OJESuWGp"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# The following is an example on how to include your video in the README file.\n",
+ "# \n"
+ ],
+ "metadata": {
+ "id": "ccJCoq6HutHy"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "YvNRPdxhvWaK"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Part 10: Moodle"
+ ],
+ "metadata": {
+ "id": "EZzOzA3YupFc"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Submit to Moodle only one link - the link to your HF's Model Repository.** \n",
+ "\n",
+ "The Repo should Include:\n",
+ "- README\n",
+ "- Python Notebook\n",
+ "- 1 pickle model for regression\n",
+ "- 1 pickle model for classification\n",
+ "- Video Presentation"
+ ],
+ "metadata": {
+ "id": "n28Xs2gRusQh"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "DFqtRAswvKox"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "---\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "Good luck and have fun creating AI model!"
+ ],
+ "metadata": {
+ "id": "rN8TJ_5oIPfm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "cPgKfBWKvU8K"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file