diff --git a/Q1.ipynb b/Q1.ipynb index 75bffb2..1619b9d 100644 --- a/Q1.ipynb +++ b/Q1.ipynb @@ -1,54 +1,73 @@ { "cells": [ { + "cell_type": "markdown", + "id": "193cc36275a60171", "metadata": { "ExecuteTime": { "end_time": "2026-04-25T10:00:23.434531Z", "start_time": "2026-04-25T10:00:23.381273Z" } }, - "cell_type": "markdown", "source": [ "## This is the Q1 Notebook!\n", "\n", "It's tracked via GitHub! hence the need for this line for the init commit" - ], - "id": "193cc36275a60171" + ] }, { + "cell_type": "code", + "id": "edaea0c939a83b79", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.013723200Z", - "start_time": "2026-04-25T21:51:43.997124800Z" + "end_time": "2026-04-26T14:22:27.822717300Z", + "start_time": "2026-04-26T14:22:27.814871100Z" } }, - "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ], - "id": "edaea0c939a83b79", "outputs": [], - "execution_count": 1 + "execution_count": 22 }, { + "cell_type": "code", + "id": "15479f82", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.032419600Z", - "start_time": "2026-04-25T21:51:45.014721400Z" + "end_time": "2026-04-26T14:22:27.850316700Z", + "start_time": "2026-04-26T14:22:27.825227800Z" } }, - "cell_type": "code", - "source": "df = pd.read_csv('data/googleplaystore_new.csv')", - "id": "e657e9baacc13e6b", + "source": [ + "# Keep one fixed seed so the same split/results appear each run.\n", + "seed = 101" + ], "outputs": [], - "execution_count": 2 + "execution_count": 23 + }, + { + "cell_type": "code", + "id": "e657e9baacc13e6b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:27.897899900Z", + "start_time": "2026-04-26T14:22:27.870320Z" + } + }, + "source": [ + "df = pd.read_csv('data/googleplaystore_new.csv')" + ], + "outputs": [], + "execution_count": 24 }, { - "metadata": {}, "cell_type": "markdown", + "id": "751a6161e8e12bdd", + "metadata": {}, "source": [ "## Part A\n", "\n", @@ -56,35 +75,36 @@ "Q: Check for any missing values. If any, delete all rows that contain null values Then,\n", "check for duplicates (similar contents for every feature). If any, delete the\n", "redundant rows. **[1 mark]**" - ], - "id": "751a6161e8e12bdd" + ] }, { + "cell_type": "code", + "id": "756c92821453bbb3", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.058191500Z", - "start_time": "2026-04-25T21:51:45.033428800Z" + "end_time": "2026-04-26T14:22:27.920788500Z", + "start_time": "2026-04-26T14:22:27.898898200Z" } }, - "cell_type": "code", "source": [ "df = df.dropna()\n", "df = df.drop_duplicates()" ], - "id": "756c92821453bbb3", "outputs": [], - "execution_count": 3 + "execution_count": 25 }, { + "cell_type": "code", + "id": "4e1be303d63f47a4", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.086823300Z", - "start_time": "2026-04-25T21:51:45.058191500Z" + "end_time": "2026-04-26T14:22:27.935089700Z", + "start_time": "2026-04-26T14:22:27.921790600Z" } }, - "cell_type": "code", - "source": "df.head(10)", - "id": "4e1be303d63f47a4", + "source": [ + "df.head(10)" + ], "outputs": [ { "data": { @@ -291,37 +311,38 @@ "" ] }, - "execution_count": 4, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 4 + "execution_count": 26 }, { + "cell_type": "markdown", + "id": "5e0e0e76635904b6", "metadata": { "ExecuteTime": { "end_time": "2026-04-25T13:14:34.819448Z", "start_time": "2026-04-25T13:14:34.807334900Z" } }, - "cell_type": "markdown", "source": [ "## Part B\n", "\n", "Q: Create a new column called 'Size in bytes' (numeric) and convert the entries\n", "from 'Size' column (M means megabyte and k means kilobytes). **[1 mark]**\n" - ], - "id": "5e0e0e76635904b6" + ] }, { + "cell_type": "code", + "id": "c15cb7f9831e0f81", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.106420900Z", - "start_time": "2026-04-25T21:51:45.087823200Z" + "end_time": "2026-04-26T14:22:27.956595900Z", + "start_time": "2026-04-26T14:22:27.936085900Z" } }, - "cell_type": "code", "source": [ "# Getting the column for size, checking if it's ending with an M or a k and converting the Mb to kb with 1024* and then again from kb to just b by another 1024*\n", "def parse_size(size_str):\n", @@ -332,33 +353,36 @@ " return float(size_str[:-1]) * 1024\n", " return size_str" ], - "id": "c15cb7f9831e0f81", "outputs": [], - "execution_count": 5 + "execution_count": 27 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.115879400Z", - "start_time": "2026-04-25T21:51:45.107418800Z" - } - }, "cell_type": "code", - "source": "df['Size in bytes'] = df['Size'].apply(parse_size)", "id": "c76da70de24ddc72", - "outputs": [], - "execution_count": 6 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.144425200Z", - "start_time": "2026-04-25T21:51:45.116884800Z" + "end_time": "2026-04-26T14:22:27.981893900Z", + "start_time": "2026-04-26T14:22:27.957595200Z" } }, + "source": [ + "df['Size in bytes'] = df['Size'].apply(parse_size)" + ], + "outputs": [], + "execution_count": 28 + }, + { "cell_type": "code", - "source": "df[['Size', 'Size in bytes']].head(10)", "id": "73784ad66975e81f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:28.029674900Z", + "start_time": "2026-04-26T14:22:27.983890Z" + } + }, + "source": [ + "df[['Size', 'Size in bytes']].head(10)" + ], "outputs": [ { "data": { @@ -454,86 +478,102 @@ "" ] }, - "execution_count": 7, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 7 + "execution_count": 29 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.177033700Z", - "start_time": "2026-04-25T21:51:45.169698800Z" + "end_time": "2026-04-26T14:22:28.082713600Z", + "start_time": "2026-04-26T14:22:28.033674500Z" } }, "cell_type": "code", - "source": "print(11*1024) # Just checking the kb and mb conversion happened properly, by checking 2 of the values manually.", - "id": "7ac34d8bc88327a1", + "source": [ + "# Quick conversion check table (cleaner than multiple prints)\n", + "conversion_check = pd.DataFrame({\n", + " 'Example Input': ['11k', '21M', '1.4M'],\n", + " 'Expected Bytes': [11 * 1024, 21 * 1024 * 1024, 1.4 * 1024 * 1024]\n", + "})\n", + "conversion_check" + ], + "id": "f16b316e8c3e58c0", "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "11264\n" - ] + "data": { + "text/plain": [ + " Example Input Expected Bytes\n", + "0 11k 11264.0\n", + "1 21M 22020096.0\n", + "2 1.4M 1468006.4" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Example InputExpected Bytes
011k11264.0
121M22020096.0
21.4M1468006.4
\n", + "
" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" } ], - "execution_count": 8 + "execution_count": 30 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.196266500Z", - "start_time": "2026-04-25T21:51:45.178541500Z" - } - }, "cell_type": "code", - "source": "print((21*1024)*1024) # Ideally this is the same as doing the calculation without the ( ) but just incase!", - "id": "f72b05042c5a16fa", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "22020096\n" - ] - } - ], - "execution_count": 9 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.234527700Z", - "start_time": "2026-04-25T21:51:45.214308100Z" - } - }, - "cell_type": "code", - "source": "print(1.4*1024*1024) # This one seemed odd... Checked on an online converted to double-check but turns out the 0.4 bytes shows up there too.", - "id": "dcaf54123ea0d75d", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1468006.4\n" - ] - } - ], - "execution_count": 10 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.253211700Z", - "start_time": "2026-04-25T21:51:45.235524700Z" - } - }, - "cell_type": "code", - "source": "df.head(10)", "id": "3e1112ae100d010c", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:28.146681Z", + "start_time": "2026-04-26T14:22:28.083717300Z" + } + }, + "source": [ + "df.head(10)" + ], "outputs": [ { "data": { @@ -763,53 +803,57 @@ "" ] }, - "execution_count": 11, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 11 + "execution_count": 31 }, { + "cell_type": "markdown", + "id": "9883e69704d0a69e", "metadata": { "ExecuteTime": { "end_time": "2026-04-25T13:28:40.682532800Z", "start_time": "2026-04-25T13:28:40.678521Z" } }, - "cell_type": "markdown", "source": [ "## Part C\n", "\n", "Q: Create a new column called ‘Numeric_installs' (numeric) and convert the entries\n", "from ‘Installs’ column (remove ‘+’ and ‘,’; for example, “5,000,000+” becomes\n", "“5000000” as an Integer). **[1 mark]**" - ], - "id": "9883e69704d0a69e" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.274406200Z", - "start_time": "2026-04-25T21:51:45.267978400Z" - } - }, "cell_type": "code", - "source": "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)", "id": "c8d4f46526918c20", - "outputs": [], - "execution_count": 12 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.287734300Z", - "start_time": "2026-04-25T21:51:45.274910Z" + "end_time": "2026-04-26T14:22:28.169093Z", + "start_time": "2026-04-26T14:22:28.148687700Z" } }, + "source": [ + "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)" + ], + "outputs": [], + "execution_count": 32 + }, + { "cell_type": "code", - "source": "df.head(10)", "id": "cffd24a82d242352", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:28.211996600Z", + "start_time": "2026-04-26T14:22:28.170093500Z" + } + }, + "source": [ + "df.head(10)" + ], "outputs": [ { "data": { @@ -1050,84 +1094,136 @@ "" ] }, - "execution_count": 13, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 13 + "execution_count": 33 }, { - "metadata": {}, "cell_type": "markdown", + "id": "1818d3183dae5398", + "metadata": {}, "source": [ "## Part D\n", "\n", "Q: Save the updated dataset as “googleplaystore_new_new.csv” **[1 mark]**\n" - ], - "id": "1818d3183dae5398" + ] }, { + "cell_type": "code", + "id": "5a99edb9f8b29b12", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.308799200Z", - "start_time": "2026-04-25T21:51:45.288732900Z" + "end_time": "2026-04-26T14:22:28.261943100Z", + "start_time": "2026-04-26T14:22:28.237508200Z" } }, - "cell_type": "code", - "source": "df.to_csv('data/googleplaystore_new_new.csv', index=False)", - "id": "5a99edb9f8b29b12", + "source": [ + "df.to_csv('data/googleplaystore_new_new.csv', index=False)" + ], "outputs": [], - "execution_count": 14 + "execution_count": 34 }, { - "metadata": {}, "cell_type": "markdown", + "id": "5372c799c0b2662", + "metadata": {}, "source": [ "# Part E\n", "\n", "Q: Using (Category + Reviews + Content Rating + Size in Bytes +\n", "Installs_Num), find and discuss the best regression model to predict “rating”\n", "(use the standard training/test partition without cross-validation). **[7 marks]**\n" - ], - "id": "5372c799c0b2662" + ] }, { + "cell_type": "code", + "id": "8cc741d5b19eaa62", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.539720400Z", - "start_time": "2026-04-25T21:51:45.309801600Z" + "end_time": "2026-04-26T14:22:28.289446500Z", + "start_time": "2026-04-26T14:22:28.262987100Z" } }, - "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score" ], - "id": "8cc741d5b19eaa62", "outputs": [], - "execution_count": 15 + "execution_count": 35 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.557405200Z", - "start_time": "2026-04-25T21:51:45.541231300Z" + "end_time": "2026-04-26T14:22:28.336984400Z", + "start_time": "2026-04-26T14:22:28.290448200Z" + } + }, + "cell_type": "code", + "source": [ + "def split_overview(train_rows, test_rows, val_rows=None):\n", + " rows = [\n", + " {'Split': 'Train', 'Rows': train_rows},\n", + " {'Split': 'Test', 'Rows': test_rows},\n", + " ]\n", + " if val_rows is not None:\n", + " rows.insert(1, {'Split': 'Validation', 'Rows': val_rows})\n", + "\n", + " out = pd.DataFrame(rows)\n", + " out['Pct_of_Total'] = (out['Rows'] / out['Rows'].sum() * 100).round(1)\n", + " return out\n", + "\n", + "\n", + "def evaluate_model(model, X_train, X_test, y_train, y_test, name):\n", + " model.fit(X_train, y_train)\n", + " y_pred = model.predict(X_test)\n", + "\n", + " results = {\n", + " 'Model': name,\n", + " 'MAE': mean_absolute_error(y_test, y_pred),\n", + " 'MSE': mean_squared_error(y_test, y_pred),\n", + " 'RMSE': np.sqrt(mean_squared_error(y_test, y_pred)),\n", + " 'R2': r2_score(y_test, y_pred)\n", + " }\n", + " return results, y_pred\n", + "\n", + "\n", + "def regression_prediction_sheet(y_true, y_pred, model_name, head_n=20):\n", + " sheet = pd.DataFrame({\n", + " 'True': y_true.reset_index(drop=True),\n", + " 'Predicted': pd.Series(y_pred).reset_index(drop=True)\n", + " })\n", + " sheet['Residual'] = sheet['True'] - sheet['Predicted']\n", + " sheet['Abs_Error'] = sheet['Residual'].abs()\n", + " sheet['Model'] = model_name\n", + " return sheet.head(head_n)" + ], + "id": "8e417dd93534ef5f", + "outputs": [], + "execution_count": 36 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:28.366685700Z", + "start_time": "2026-04-26T14:22:28.338982400Z" } }, "cell_type": "code", "source": "df_new = pd.read_csv('data/googleplaystore_new_new.csv')", - "id": "bc158bb312aa3cd7", + "id": "c2a7db56bb223aa2", "outputs": [], - "execution_count": 16 + "execution_count": 37 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.563810100Z", - "start_time": "2026-04-25T21:51:45.558406400Z" + "end_time": "2026-04-26T14:22:28.406155500Z", + "start_time": "2026-04-26T14:22:28.367686300Z" } }, "cell_type": "code", @@ -1135,20 +1231,20 @@ "columns_to_keep = ['Category', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Rating']\n", "df_min = df_new[columns_to_keep]" ], - "id": "585677f9cc3efc16", + "id": "70f60382c2cb1c26", "outputs": [], - "execution_count": 17 + "execution_count": 38 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.578320800Z", - "start_time": "2026-04-25T21:51:45.563810100Z" + "end_time": "2026-04-26T14:22:28.430514500Z", + "start_time": "2026-04-26T14:22:28.406155500Z" } }, "cell_type": "code", "source": "df_min.head(20)", - "id": "76760facf2a6e8cb", + "id": "720d514df1483929", "outputs": [ { "data": { @@ -1410,31 +1506,31 @@ "" ] }, - "execution_count": 18, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 18 + "execution_count": 39 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.597024100Z", - "start_time": "2026-04-25T21:51:45.579825600Z" + "end_time": "2026-04-26T14:22:28.453593Z", + "start_time": "2026-04-26T14:22:28.432121200Z" } }, "cell_type": "code", "source": "df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])", - "id": "8c4742ab98b8b6ca", + "id": "90953b3b6403afd6", "outputs": [], - "execution_count": 19 + "execution_count": 40 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.616561700Z", - "start_time": "2026-04-25T21:51:45.598024900Z" + "end_time": "2026-04-26T14:22:28.478579800Z", + "start_time": "2026-04-26T14:22:28.454594500Z" } }, "cell_type": "code", @@ -1442,203 +1538,40 @@ "X = df_encoded.drop('Rating', axis=1)\n", "y = df_encoded['Rating']" ], - "id": "7faed3f843076351", + "id": "518e961a8b35c05e", "outputs": [], - "execution_count": 20 + "execution_count": 41 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.647543500Z", - "start_time": "2026-04-25T21:51:45.618565200Z" + "end_time": "2026-04-26T14:22:28.503829100Z", + "start_time": "2026-04-26T14:22:28.479581600Z" } }, "cell_type": "code", - "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)", - "id": "8646df724923b0f5", + "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=seed)", + "id": "882619704ae0dce2", "outputs": [], - "execution_count": 21 + "execution_count": 42 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.664223100Z", - "start_time": "2026-04-25T21:51:45.647543500Z" + "end_time": "2026-04-26T14:22:28.553238100Z", + "start_time": "2026-04-26T14:22:28.504830200Z" } }, "cell_type": "code", - "source": [ - "def evaluate_model(model, X_train, X_test, y_train, y_test, name):\n", - " model.fit(X_train, y_train)\n", - " y_pred = model.predict(X_test)\n", - "\n", - " results = {\n", - " 'Model': name,\n", - " 'MAE': mean_absolute_error(y_test, y_pred),\n", - " 'RMSE': np.sqrt(mean_squared_error(y_test, y_pred)),\n", - " 'R2': r2_score(y_test, y_pred)\n", - " }\n", - " return results, y_pred" - ], - "id": "44c698b18cdce613", - "outputs": [], - "execution_count": 22 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.668665Z", - "start_time": "2026-04-25T21:51:45.664223100Z" - } - }, - "cell_type": "code", - "source": "results = []", - "id": "d8740e0256fe177b", - "outputs": [], - "execution_count": 23 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Trying Linear Regression", - "id": "e52a65f09530a141" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.704792300Z", - "start_time": "2026-04-25T21:51:45.670169600Z" - } - }, - "cell_type": "code", - "source": [ - "lin_model = LinearRegression()\n", - "lin_res, y_pred_lin = evaluate_model(lin_model, X_train, X_test, y_train, y_test, \"Linear Regression\")\n", - "results.append(lin_res)" - ], - "id": "db88942671bdf26a", - "outputs": [], - "execution_count": 24 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.730725700Z", - "start_time": "2026-04-25T21:51:45.705792200Z" - } - }, - "cell_type": "code", - "source": "X_test", - "id": "32a5d474cc8ed681", + "source": "split_overview(len(X_train), len(X_test))", + "id": "102cda28a2a7c9cb", "outputs": [ { "data": { "text/plain": [ - " Reviews Size in bytes Numeric Installs Category_ART_AND_DESIGN \\\n", - "303 70782 52428800.0 1000000 False \n", - "805 132014 26214400.0 10000000 False \n", - "352 58 15728640.0 10000 False \n", - "952 1658 10171187.2 100000 False \n", - "514 10852 18874368.0 1000000 False \n", - "... ... ... ... ... \n", - "1096 120 10485760.0 500 False \n", - "551 27396 61865984.0 1000000 False \n", - "660 11506 15728640.0 100000 False \n", - "655 1015 11534336.0 100000 True \n", - "473 7976 46137344.0 500000 False \n", - "\n", - " Category_AUTO_AND_VEHICLES Category_BEAUTY \\\n", - "303 False False \n", - "805 False False \n", - "352 False False \n", - "952 False False \n", - "514 False False \n", - "... ... ... \n", - "1096 False False \n", - "551 False False \n", - "660 False False \n", - "655 False False \n", - "473 False False \n", - "\n", - " Category_BOOKS_AND_REFERENCE Category_BUSINESS Category_COMICS \\\n", - "303 False False False \n", - "805 False False False \n", - "352 False False False \n", - "952 False False False \n", - "514 False False False \n", - "... ... ... ... \n", - 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333 rows × 26 columns

\n", "
" ] }, - "execution_count": 25, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 25 + "execution_count": 43 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.860288100Z", - "start_time": "2026-04-25T21:51:45.731230400Z" + "end_time": "2026-04-26T14:22:28.630516300Z", + "start_time": "2026-04-26T14:22:28.555750100Z" + } + }, + "cell_type": "code", + "source": "results = []", + "id": "93cd24d4523d5fda", + "outputs": [], + "execution_count": 44 + }, + { + "cell_type": "markdown", + "id": "e52a65f09530a141", + "metadata": {}, + "source": [ + "## Trying Linear Regression" + ] + }, + { + "cell_type": "code", + "id": "db88942671bdf26a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:28.758831400Z", + "start_time": "2026-04-26T14:22:28.631518600Z" + } + }, + "source": [ + "lin_model = LinearRegression()\n", + "lin_res, y_pred_lin = evaluate_model(lin_model, X_train, X_test, y_train, y_test, \"Linear Regression\")\n", + "results.append(lin_res)" + ], + "outputs": [], + "execution_count": 45 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:28.971416600Z", + "start_time": "2026-04-26T14:22:28.759831600Z" } }, "cell_type": "code", @@ -1980,7 +1680,7 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "8c3be82a8033d876", + "id": "e9dbb4ba0c9432ed", "outputs": [ { "data": { @@ -1996,56 +1696,175 @@ } } ], - "execution_count": 26 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Trying Polynomial Regression", - "id": "28a6926de3a1a2e7" + "execution_count": 46 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.886996700Z", - "start_time": "2026-04-25T21:51:45.863335400Z" + "end_time": "2026-04-26T14:22:29.122438300Z", + "start_time": "2026-04-26T14:22:28.972416400Z" } }, "cell_type": "code", "source": [ - "poly_converter = PolynomialFeatures(degree=2, include_bias=False)\n", - "X_train_p = poly_converter.fit_transform(X_train)\n", - "X_test_p = poly_converter.transform(X_test)" + "# Quick visual check: points close to the diagonal mean prediction ~= true value.\n", + "plt.figure(figsize=(6, 6))\n", + "plt.scatter(y_test, y_pred_lin, alpha=0.5)\n", + "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n", + "plt.title(\"Part E - Linear Regression: Actual vs Predicted\")\n", + "plt.xlabel(\"True Rating\")\n", + "plt.ylabel(\"Predicted Rating\")\n", + "plt.tight_layout()\n", + "plt.show()" ], - "id": "b867289f4b8dd0ae", - "outputs": [], - "execution_count": 27 + "id": "35edc2f60d805d30", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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8svEn/pJmrs6rr77qclQXc48cWzMcWy/YPF+fALW+mAvB9+bhhx82t9nywxM0Wx2Yl+KMX8DEkVU84b/11lvVukWZE8TcJ08wOOOINgYIrobu25+LmGfjiHkdbI2ydxuzW9O5AjpPNmzdcC754IjdQRy9xq4jx5FXzz77rHkO5mzVl6flCLZt24bFixeb44PlGJwvDBAZhDLvh0EdRxoyuOZoQmf2Y8Zek8jV54z7g8/niKUVnFuceBw6t6CxZcyeb9YQ7Ppkqyv3My8MkBj42nEbnHP4eHzwOKvt/fMF7n8+P1vXnPFYsO9LfpcwyOex4bh/WCKgLgwA2VXNdZ3fG8e/5e79a4ptlLqpxUk8xkRVdh0xr+S8884zvwSZT8ATl2MuT234K4kJ4/zSZB4CWw944q2rz5/dhK6mBGFuiyflDOqDr9G5wjBPWBza7Q3m4nA4Nodbs/WIgRy/BHly5XJ7LSYOGWbXHLub2H3GgITBFk8g9akJVB/MWeEJml2wzMPiCY6lB7jN/LJnqwBbOzikn+UHuO0cAk0Mrph3xGX8G/xFy/v4njoGU7XhccTWQZ5UWVKBQS+HvzPHi3lY9tpBPHYYZDH3hccOgwf70HViLhBb9HiC4brsAmKXD/+Wq5YNOw7rZ+DG8gO//PKLSezm32WuGU82Dane7Wk5ArYm8eTGbnB3QR1fB1ulmJjP/c0uLAZz9rIWPC4Y1HDYPpOO2cLGwIddXAxE2EJsrwvGljkeg0zSZ5Ix9y2PPedWJOaGMaeM7ylrtzEQ5jZ4mmtUW6sTB3uw9Zq5To4tfBwcwNwjBozMiWLQyhYW1shy1YXpS9yf/LyxW5c5nPwcMvhgqw33LfO9uF38HLBbmetxH/H9YdI3a57V1RLH18rPFT/bfI+4bxk88juAgx74PhCPb+LgBP6I4XvJ47cptlE80ICReBKC7EN5XQ1xdvT666+b4eEcktynTx/zOPswfke87W5I8dKlS22DBg0yw2XrKk1QVzkCT8saeML+OlxdoqKiai1HcNhhh9X4exzy6zgc3z7E/ZFHHjHrcx+2aNHC7Iv77rvPlp2dXbXep59+ahs4cKAZjt6tWzfzmDfeeMM8N4d/2/Hv1zY83105Alc2btxoXqfzcGqWQeAQcW7LIYccYrv44ottP//8c7XHctg3jwe+JpY14PazFAOXOQ9bd1cqgM/PofYs48Bh6x07drSdcsopZpi+3QMPPGA79thjzRB2DjHn33/wwQfNfqV9+/aZ447L+Tq53ccdd5ztvffeq7UcAe3evdt2ySWX2Fq3bm2OTZZHcB5iX9trcD4ePS1HwOfp0qVLreuMGDHC1rZtW1tpaam5vXXrVrOv2rRpY/Y5y1TwdXN4v92rr75qlvM9dTxmWWLg1ltvNa8zMTHRvL8sS+GqHMFNN91ka9++vdnXw4YNs/3www819p2n5QjsWG7B/rlasmRJtfu4/bfccospO5GSkmLeQ/7/hRde8Nl3WG2fAXrllVfMZ5KvmdvA9+f//u//bDt37qxah/uQn1n7vuH7s3Llyhr70NX3BfF1jxkzpuo18rP+7LPPVt3PsgXXXXedeX9ZKsT5+9WX2yj1F8F/PAmwRETqg7+o+cvXsaq3iEiwU46TiHiFuTzMr3DEOkrsAqrPRM4iIsFALU4i4hUOEGAyKguAMomX+RosD8BaUxw91JCaPyIigUrJ4SLiFQ5vZzIraypxBBxHBHEgAUfoKWgSkVCjFicRERERDynHSURERMRDCpxEREREPBR2OU6cUoGzbrOgXVNO/yEiIiKBiZWZWICVA1yc5zFEuAdODJrsk6mKiIiIOE6BxOr1tQm7wMk+dQJ3DufiEhERkfCWk5NjGlU8mV4p7AIne/ccgyYFTiIiImLnSQqPksNFREREPKTASURERMRDCpxEREREPKTASURERMRDCpxEREREPKTASURERMRDCpxEREREPKTASURERMRDCpxEREREPKTASURERMRDCpxEREREPKTASURERMRDCpxEREREPKTASURERMRDCpxEREREPBTt6YoiIiLifxUVNuzIKkR+SRmSYqPRsXkCIiMj/L1ZYUOBk4iISJDYsCcXc1fuxsa9eSgqK0d8dBQOaZOMcf3boWfbFH9vXlhQ4CQiIhIkQdP077fgQH4J2qfGIzE2AQUlZVi5Mxs7swtxybBuCp5CPcfp3nvvRURERLVLnz59an3M+++/b9aJj4/HgAED8OWXXzbZ9oqIiPire44tTQyaerVNRkp8DKIiI8w1b3P516t2m/UkxJPDDzvsMOzatavqsmTJErfrLl26FOeeey4uu+wyLF++HJMmTTKXlStXNuk2i4iINCXmNLF7ji1NbGRwxNtcvmFPnllPQjxwio6ORlpaWtWldevWbtd9+umnMX78eNxyyy3o27cvpk6diqOOOgrPPfdck26ziIhIU2IiOHOaEmNdZ9gkxEahuKzcrCchHjitX78eHTp0QI8ePXD++ecjPT3d7bo//PADRo8eXW3ZuHHjzHIREZFQlRQbbRLBmdPkSmFJOeKio8x6EsKB03HHHYcZM2bgq6++wosvvojNmzfjhBNOQG5ursv1MzIy0K5du2rLeJvL3SkuLkZOTk61i4iISDBhyQGOntuVXQSbrXoeE29zec+2yWa9kFNWBtx8M7BlCxDugdPJJ5+Ms846CwMHDjQtR0z0zsrKwnvvveez55g2bRpSU1OrLp07d/bZ3xYREWkKrNPEkgMtk2Kxfk8ecotKUVZRYa55m8vHHtYu9Oo5lZYC550HPP44u5iAkhJ/b5H/u+ocNW/eHIceeig2bNjg8n7mQO3evbvaMt7mcnduv/12ZGdnV122bdvm8+0WERFpbKzTxJID/TukIqugFFv25ZvrAR1TQ7MUQXExcOaZHE4PxMQADz8MxMb6e6sCq45TXl4eNm7ciAsvvNDl/UOGDMG3336L66+/vmrZvHnzzHJ34uLizEVERCTYMTjqMSI5PCqHz58PfPopT+TARx8BEyYgEPg1cLr55ptx6qmnomvXrti5cyfuueceREVFmZIDNHnyZHTs2NF0t9GUKVMwfPhwPP7445g4cSJmzZqFn3/+Ga+88oo/X4aIiEiTYZDUuWVi6O/xk08GXnoJ6NkTGDUKgcKvgdP27dtNkLR//360adMGxx9/PH788Ufzf+IIu8jIg72JQ4cOxcyZM3HXXXfhjjvuQK9evfDxxx+jf//+fnwVIiIi4hMcwFVUBLRta92+8koEmgibc3p+iOOoOiaJM9+pWbNm/t4cERERoQMHgPHjrcBpwQKgVSsEYmwQUMnhIiIiEob27rW645YtA3butC4BSoGTiIiI+E9GBjBiBPDbbyzOCCxcCAwYELDvSECNqhMREZEwsn271dK0bh3QsSPw7bdA794IZAqcREREpOlt2QKMHAls3gx07WqVH+jRI+DfCXXViYiISNOLiADKy4FDDgEWLw6KoInU4iQiIiJNr2tlK1NCAtChQ9C8A2pxEhERkabx++9WNXA7tjYFUdBECpxERESk8S1bBpx0kjX/HOs0BSkFTiIiItK4vv8eGD0ayMwEjj4aOPLIoN3jCpxERESk8SxcCIwbZ02ncuKJwNy5QPPmQbvHFTiJiIhI45g715qsNz8fGDMGmDMHSEkJ6r2twElEREQaJxH8L3+x5p475RQrKTwxMej3tMoRiIiIiO8NGACcf77VRTdzJhAbGxJ7WYGTiIiI+I7NZhW3jIwEXn3Vuh0dOuGGuupERETEN15/HTj3XKCszLodFRVSQROF1qsRERER/3j+eeDaa63/T5gATJ4cku+EWpxERETEO48/fjBouvFG4MILQ3aPKnASERGRhnvgAeDmm63/33kn8NhjVo5TiFJXnYiIiNSfzQbcfTfw4IPW7alTgbvuCvk9qcBJRERE6m/jRquLjh599GCrU4hT4CQiIiL117Mn8PHHwIYNwDXXhM0eVOAkIiIinikvB3bsALp0sW5zDjpewoiSw0VERKRurM100UXAsccC69aF7R5T4CQiIiK1KymxClu+8w6wfz+wenXY7jF11YmIiIh7nKT3b38DPvvMmm/uvfeA004L2z2mwElERERcKygATj8d+PprID4emD0bGD8+rPeWAicRERGpKS8POPVUYOFCIDHRanEaOTLs95QCJxEREamposJqcUpJAebMAYYN015Si5OIiIi41KwZ8NVXwJYtwJFHaidV0qg6ERERsezdC0yffnBvtGihoMmJuupEREQE2LULGDUK+PNPoLQU+PvftVdcUOAkIiIS7rZtsxK/OX1Kp07AiBH+3qKApcBJREQknG3ebAVNzGXq1g2YPx/o3t3fWxWwlOMkIiISrjh1yoknWkETJ+1dvFhBUx3U4iQiIhKOsrKA4cOBjAygb1/g22+B9u39vVUBTy1OIiIi4ah5c+CGG4CBA60ilwqaPBJhs9lsCCM5OTlITU1FdnY2mrFGhYiISDjhaT8i4uDtwkIgIQHhLKcesYFanERERMLF998D48YxUji4LMyDpvpS4CQiIhIOFiywgqZ584D77vP31gQtBU4iIiKhjlOnTJgA5OcDY8cCU6f6e4uClgInERGRUPbJJ8BppwFFRcCpp1q3ExP9vVVBS4GTiIhIqHr/feDMM4GSEuCMM4APPgDi4/29VUFNgZOIiEgoKiiwyg2UlQHnnw/MmgXExvp7q4KeAicREZFQxO64uXOBG28E3nwTiFbNa19Q4CQiIhJKtm8/+P/DDgMefxyIivLnFoUUBU4iIiKhgkHSoYdapQekUShwEhERCQUPPADcfLNVCXzJEn9vTchSh6eIiEiwT6Fy993Agw8eDKDuvNPfWxWyFDiJiIgEc9DEVqYnnjjYVcdkcGk0CpxERESCUUUFcN11wAsvWLeffx64+mp/b1XIU+AkIiISrK1Ne/cCERHAa68Bl17q7y0KC0oOFxERCUYsMfDOO8A33yhoakIKnERERIIFp0556SWrm45iYoCRI/29VWFFgZOIiEgw4CS9nG/uqqusqVTEL5TjJCIiEgzzzk2aBMybZ03SO2GCv7cobClwEhERCWS5ucCppwKLFgFJScDnnwMjRvh7q8KWAicREZFAlZ0NnHwy8MMPQEoKMGcOMGyYv7cqrClwEhERCURMALcHTS1aAHPnAscc4++tCntKDhcREQlEkZHALbcA7dtbk/YqaAoIanESERFpQmVlFfh1Wyb255egVVIsjurcAtHRbtoxTj8dGDvWym2SgKDASUREpIl8++duzPh+C7bsz0dpeQVioiLRrVUSLh7WDaP6tgPS04FLLgHeeAPo2tV6UIAETRUVNuzIKkR+SRmSYqPRsXkCIiMjEG4UOImIiDRR0DRtzhrkFpWalqaE2CgUlpRj3Z5cszx+21YM+/vfgK1bgcsvt0oPBIgNe3Ixd+VubNybh6KycsRHR+GQNskY178derZNQThR4CQiItIE3XNsaWLQ1KUFW2qsrrmU+EgkxUYB69ah7wO3Apl7gV69gOnTAypomv79FhzIL0H71HgkxiagoKQMK3dmY2d2IS4Z1i2sgqeASQ5/+OGHERERgeuvv97tOjNmzDDrOF7iWQhMREQkgDGnid1zbGmyB012XTK24OXXb0bLzL0o6NXbqtfUqRMCpXuOLU0Mmnq1TUZKfAyiIiPMNW9z+derdpv1wkVAtDgtW7YML7/8MgYOHFjnus2aNcPatWurbjN4EhERCWRMBGdOE7vnHHXdtg53PX09muVlYW37Q7D9tQ8wiqPoAgRzmtg9x5Ym5/NtRESEWb5hT55Zr3PLRIQDv7c45eXl4fzzz8err76KFqxTUQe+UWlpaVWXdu3aNcl2ioiIa2xt2HagAGsycsx1OLU+eIotTUwEZ06TzWZDcWk5CopLMfndp6ygqUsfXH/F40jpHDhBEzERnDlNibGu21kSYqNQXFZu1gsXfm9xuuaaazBx4kSMHj0aDzzwgEeBVteuXVFRUYGjjjoKDz30EA477DC36xcXF5uLXU5Ojs+2XUQk3DH/5asVGVixIxv5pWVIionGgI6pGD8gLazyXurCkgMcPbd6Vw6yokuQV1yO8gobrp10G26Z/wYeGX8VunVJM+sFkqTYaJMIzpwmds85YyAYFx1l1gsXfn2ls2bNwq+//mq66jzRu3dvvPHGG6ZLLzs7G4899hiGDh2KVatWoZOb/uBp06bhvvvu8/GWi4gIg6anvlmPdbtzTRBgt3l/PtbszsX1o3speKrEOk1jD2uHX9IzEbV7L3KSW5rl+VHJuHbMP5EQFYUr+rVzX8/JQUlJOb5ek4GM7GKkpcZhbJ80xDp1AfoKSw5w9BwTwZPjoqt117HlbFd2kQmUuV648FvgtG3bNkyZMgXz5s3zOMF7yJAh5mLHoKlv374mP2rq1KkuH3P77bfjxhtvrNbi1LlzZx+8AhGR8MXuuJk/puP3bVmIjY40rRExUREoLbeZkWNc/t+f0nHnxH6NWuvHX7WF6lXEsnI7f03PxND1y/DsBw/injFX4v2BY6vuZ+C5PD0L5x9nq3X73/5hC177bjP25hah3GZDVEQEHk1Zh8tP6I4Lh3Tz+evktrDkAEfPMUBOiY82yeHc3tyiMrRKjjMBYSi+xwEXOP3yyy/Ys2eP6W6zKy8vx+LFi/Hcc8+Z7rWoqNoj6JiYGBx55JHYsGGD23Xi4uLMRUREfGdbZgF+3HwAkRERJnCwt0TERUcgNikWu3OK8cOmA2a9rq2SQqq2EOsxTV+y2TxvSXkFYqMizfNecnx3q4ilC+n78xH56ad44b0HEFtRhjEbl+Gjw8eYEXaxDDgrbFiyYa9Zr1ubZLdB06Nz16KopMx0j8VER6Ki3IZdWQVmOTVG8MR9ObJPW1NOYdXOnIOFO1sn4aw+bRt1Xwdi/Si/BU6jRo3CihUrqi275JJL0KdPH9x66611Bk32QIt/Y8KECY24pSIiTSfQfl27s3lfPrIKS9AmOc7laKvUxBjszys26zVG4OSv2kIMmu77bLV5bYAN7KAsKi3H8m1ZSP9stVnHVfCU8dpbeHjWVMRUlGNO3+Pxf5NuQQzLEkTABE38T3ZhKX7emukycGL3HFua+Bq5t/NKyqvuY0MXl7++ZDPOHtTZ59123Nfz1+wx9aYGdEqFrcKGiMgIE7RxeddWiXXu64Yc14FaP8pvgVNKSgr69+9fbVlSUhJatWpVtXzy5Mno2LGjyVOi+++/H4MHD0bPnj2RlZWFRx99FFu3bsXlrLAqIhLkwUsg/rquTYTNHjq4Ymuy2kL2wI3dhczDWb8nz9QW6tE62afvG7vnXliwARnZRSgrr0CFw33spOPyFxduwPBebap32731Fo696zpEVlTg48NOwm2nXI9SWxRs5TYTBFmbaAPjp4JS16PTmNO0K7sQ5RU192xphYm/sDOr0Kx3ysCOPt/X6QcKUFpWjr25JSipqEBsZCTapMQiv7S8zn3dkOPaX++xJwI6DT49Pb1aobDMzExcccUVyMjIMKULBg0ahKVLl6Jfv35+3U4RCR3+Cl4C9de1Oz1aJ5lWpZyCUsQ3i0JJWUVVzg1znrILStE8IcasF6i1heobIP+cfgB/ZuSY7rkaf4utQuUVZtQc1xvco7V1xyuvAP/4ByJtNrx7+FjcMfYalCOqWvRTbrMCn6gIoHlirOvXnFmIEq7oBu/h/VyvNvV9zVx3+bZMU2Yip6gUNodNyCwsQbP4GMRFR7rd1w09rgO5flRABU4LFy6s9faTTz5pLiIioRS8BPKva3c6tUjE4B6t8OWKXVi/O5dns8rTdwSHWyEuJgqj+rYy6zVebaEEt7WFducU1VpbqCEB8tqMHBSU1AyaHPF+rlcVOG3bZvbH/osux786nIbyCtfvH/cck667t3QdaBYUe1Ynqbb1GvKamei/YXce9ucXIzoq0gTFDI4ZJDNYtrosrfV8eVz74j0O2QKYIiLhPrVEfX5dBwqe6Ib1bI3Y6CiTo8P9wtYIXvM2k5d5f2MEekkOtYVcqau2kD1AZkDcPJGtYsnmmre5nPe7ssvD/V9tvfvvBz77DBvufghlttr3RVmFzW1XXYWHXZ/u1mvoa2Yr04GCEvP/hJgoREdyujPmVUWY28TPBtfz5XGd5OV73JgUOImI+Dl4CcbqzAyQ1uzKRdeWiejfoRlaJ8ehWUKMuR7QIRVdWiZibUZuowSa9tpCrCHEWkKO7LWFerZNdllbyJsAubis9tamyg1An69nAwUF1m0eS6ecYv5uLT1tBu8/UNmC4yzPwxYnV+t585oLKiudMxm8ZnaVzVpus5n1fHlce/Meh1VXnYiIv/izayApCKsz2wPNXu2STbcLa/rYh+az1g9P4I2Vg+JYW4jdPQxq+f5wP/GE2jIp1m1tIW9yZyIi6mhrsNlwy+K3MOnH94E/vwO+/BKoHCH+1erdHr02rneyi+Ru7ldPuFrPm9fMchNsWSqz2VBYWlHZVWcFeeyqY8sTE+G5nrMkL45rb97jxqYWJxERP3cNBPKva3ccWxN48rW3NvGatxu7lYw5Ocw5698hFVkFpdjC8ggFpaaKdW25aN60gvRsV0uiu82Gu+e/hmsYNNH48VVBExW5aJFxxd16HZp7Fny6Ws+b18zk/tYpcYiPjjTlCMrKbSgqrTDXvM3lLEnhahCAt8d1Q9/jxhY4P19ERPzIn1NLBPKva3eSAqCVjCfOHiOS6zVKzJvtZrckWxucO+wibBW4f95LuHD5l+b2xnsfwSE33FBtnUPTkjF39Z46XxPXc7m8XQpiIq3SA+7wfq7nzJvXbB8EMG/1bvP3UxLiTOtSBScqLilDaUUEjuvhehBA9arjeS6qjtd9XDfkPW5sanESEXH4kmeQwuCFo4TKKirMNW83dvASqL+uA72VjO8Hu5f6pDUz13W9P95sd0kZRwtWP21GVpTjkTnPmKCpAhG485TrkX72RTUee+qADnW+loha1mMwnRwfU1nzqSYuZ1DE9Wp7zeXlFdiRVWCSwXnN27W9Zu7P847rgsM7N0d0lFV2oqi0zOqmi4oyy3m/u/1urzqeX1yGHzftx8K1e8w1b3O5J8d1fd/jxqYWJxERp+DFPmSbOU38Jc7ghUFTYwcvgfjrOpDnMPN2u9fssmoy2acQYX5Qm2bxbrebrZB8nZERZaa7irnU937zCv624huURUTijtNuwsKjx2CMi3wfdoKlxkcju8h912Wz+GiznitFZRU4pE0SVu/MQYGLZid2mfVok2TWc/ea/7flAGb+L9102zERnMvZEjWwU/Na3ysel5yw+auVGVixI9skgifGRmFgx+Z11jerqjoeF4XBPVoiKjIS5eYHSZnHVccDjQInEREH/BLvdmJSvSZw9SX7r+tgYG9N4LxtnNS3at62tsk46+jOHp0Q/VGlndvFFsTPftuB3OJye/UppMRF4aJh3d1ut8n3SY4z9YyamZN/OT45cixOWfsdHjttCpYMOBFtEmNd5vvkFZWZ/C8GW7kuRr6lxEWb+7meK0mx0Sa4Y1jkPL6Nt7mc93M9V7buLzABLlsxDz7WhiLWndqda+6v7f3ifVfXM6ivcBjNxy5E5+7vQKxP5gkFTiLSqIJl7rXaigQu25wZsNOe+HtfzV6+w7TMMeeFeL07u8gsr6s1gY//aoXVipFfWoakmGjTujd+QFqj7mtOlvv6d5tRWGoFTcRrBlFc3jYlzuVkuY5FP4vLrBabdZ17Y8I/Z6A4IQlxNrjN92HeHFvkSstdJ39zeVRkrFnPlXbJcdi6P7+qJIJDuVGDyzlBMNdzN1XMHr5PTvfxb3C5y6livLQjgKt/e0OBk4g0mmCbey3Ypj3xZ5DK55r5Y3pVvgrnbmPsxPNjTlEZMgtL8d+f4nDnxH4ut4H7+qlv1ptaTyXsOrJZeTqb9uVjze5c0zXUGPuak+U+8+06FJTWDGAYRHD5s9+uczlZLl/HCR0TMfyO+/HSMadjRee+5lF5sQmwlVWYHCN3RT8ZEHEy4KIy18WcuJz3uwuclm/PtFqLnB5eddMGZBaUmvWOs1ctr8QpYP7YkeW2jhSX/749q/pUMT74LOcHcPVvbyhwEpGADUKaOhAIhGlPGvqavQ1S6/u82zILMO/PDDMnHbtdYhym4igtq0BWeSm+Xr0bk4d2Q9dWSS6Drp+3ZqKk1Aqa7O0nzB/6eWsZ/vtTutugy5vt/nL1LuzLO1jl2nFNe1yxN6/UrDfpiE7Vnys7B4decjY6rlyGPtvWYOzVr6Eoki1JQMvEWDPpLQPBk3q3rbENHGiwL8d1cUs73s/1XGF3mqv8JUe8n+s5B06rdmbDRZxYDe/neq4CJ/tnmdOrpMRHm/npmKe0YkdWrZ/lJIfRfK5qfQVifTJPBNfWikhQ8EUQ0tStVYHQrdDQ1+xtkNqQ5+VjducUm6ApPibKmqqOJ5WICETFRJnWE7YmcD3nwIlB16L1e5FfVGq6rzivnT3oKi4tR15RKRau24vJQwtqPNZ5G+as2IVlWzKRV1yK5LgYHNOtBU4e0N7tdv+y5UC1/CBXjTC2yvWqBU5ZWSgdMw4d/1iGvPgk3HrG7SiJiuHBDpstwrS67c8rwa/pmS6Pke837nWb+G1XVrlejzY1t51/v64i7Lyf6zlbsT2rjmd2v579s5y+v8AEdVt4Xc4RdZFokRiD/OJyt59l+2g+tkrysWwRc3xsdGQkhhzSKqDqk3lCgZOIBFwQ4o8uM393KzT0NXsbpDb0eTfszTNzq8VUzl3miLdNPk+Fzaw3ij1aDjbtzTN5Ndwcq4CmQ9AVG20CKt7P9dwFTtzuqZ+vxort2eZ9s1VY03+s3JFtWrLuPqWfy+0uKfVsCphq6+3fD4wdi7hff0VOQgouPud+LG/TCzb76LZyDtGvqDo2XE14+60HNZzs6104uEeN5Xx9nnC1XlyUZ6d6V+vxM7p8Wyb25BaZEZPJ8TGIiY9GabkNe3OLzfvsLljk8danfQpmMwm/qNQMtEhNjDEtTeyS5THaOy0loHMeXVEdJxHxOW8qFftrst0kP1YO9+Y1ezPHnjfPy2k4+HRscao5g5m1nPfbJ4J1xJYZBl1seXAVdDFBmfdzPXf7i8nO/9t8wHT/8CTOPB1e8zaX835X2z2gs2cBd9V6u3cDJ50E/PorSlu2xgXnT8OvbXu5fM0s+p2RXegycHI1ks4Vd+sV1zXRXS3r9W7vuqimJ+vlFpci/QBbmWxmJGJc5fQqvOZtLt92oMCs524+Qx6DPVolmRaxnMJSc82Rh1zeWPMZNia1OImIzyV5UanYX11m/qwc7s1rdmwp43Y655HU1lLmzfNyeDmDouLSCpPTZA+CmLxsEsXBOc4iXVaybpUSa+Y440m3PLLCBEkcjccTslleZjPXXM+V9AP5WLB2rynCSLaKgyPMuA0sVLlw7V6zXrfW1YOB5HjXf9NZ1XoPPACsWAG0b4//vfRf/LE0r9bHMck7u7BmwNc8sebnwBV368XVjD89Xm9Yd9cJ356sx/II/LzyWHJ1jMTFRJpjzlUZhar5DNs2/XyGjUktTiLic46ViisqKsyvzH15xdavzYraKxV701oVrJXDvXnNSZVB6s6sAtPSsmjdXixet9dc8/aurEK3Qao3z3t0l5bo3c7qZuH5lPuKARSvedt006SlmPWcsduwbbN487f35JVgf34pMgvKzDVvF5eXm/u5nis/bT5Q1XLBBhaGTwyceG1u24DswlKznrMYWJPU1iY6wlrPePRR4OKLgcWL8V10W3ji163ZNZb1blvLPHcerHegsGaLjqfr/b4ru1oSvCsRles5Y8BjBcjlJjAvLi03P4gcb7MYpqvRgP6ez7CxqMVJRHzOHoT8mZFjqg0Xl1dUtSjERUWid/tmboOQJD/OgeavyuFJXrxmBp/NE2Lw5UrWFqo+6iqzoARb9udj4oD2LoNUb56X3WlXn9QT93222oy2cnwrGbjwBHnViJ4u6wJ1bpGI7q0SsXlffo2EZ7ZYlZTb0KN1olnPlYycQrdD683fqAyguF6N+yJtplBkRHkFXFUGYNDUtigHtojKfRkfD0yfbv6bv/IPeCK/xEW3lc2zJiN36+3Ldd1t6cl6rKvlCVfr8bjo0irRtAzxYjNLrfY9vuUMgrq3THR5/CQFwHyGjSG4tlZEggpbaw4UlJpflfYaP/yidJUDEghdZv6qHO7ta84qLDHdIMRf8TGVidk8MbE7iwFUYzzvqL7tzDUrhzPQtHfDsDXx4mHdq+53xpyWnXWczHdksbXSKjLpjM/hCVfrdWudhKS4aKs7zUXg1OPANvznv3chovg84PmnrYO2UsfmnnUnuVovrXnNOeRccbceu0I94Wo9dnnWlUXE+111jfK979Iy0STd2w52iMJ6jFV7ive7Okb8/VluLAqcRMTn7HV6Nu3NN60hcdFxiIi0clHYKsLl7ur0OM4lxi4y5tkwGGAQwC/axp5s1x+Vw715zRzavyYjD6ns/qis5cN9zJMU82UYsPJ+ruc8Qs0X+5rB0QmHtMbXazKQkV2MtNQ4jO2TVqN4pCMWWmTCsVs25jEVuC3I6E2+UGp8rMmvYVDsrPfeLfjPrLvQpiALpQu+AfLygJSD7/nofm3x6Ny1tbZ2sRuQ6znr1Sa5xlQpziIq13OleyvPggtX6/EY9oTb9WwHp3OJi46s9llmsOzu6PD3Z7mxKHASEZ/jSfrHzQdM11yr5NhqvzSTOSVHTjF+2HTA5cncn11m/qwcbn/N9ilICkrLkBgTjYGdUjGuv/spSNjdxRanNilx5qTGFibWQ2JdpNjoSBNIsSuN6zXGvjbTplRN/lpm8lm27CvA+Fq2mXOm8eTJbjp3867xfq7nKnDiceUJV+txLrnM/JotnodlbMB/3r0bLYpysSbtEHSbNx8xDkETRUdEmnyfPA6fc4P3c70aIqzGK+fK39VWOdiYU0PzJM+S2l2tx8+ZJ1ytx8TtrMJSUx9rV3axab3kFC6swdQuNR5pzeJMfSZ3Cd7+nji7MShwEhGfqzqZJ8e5HInDWi61ncyJX6g96jmpaChUDnfoCTHXngzUjrBZ3SYmWbcyQZsnthiTBW1rtO5J+7Qp6zJyTbBmz33ZvDcfazLcT5vCoMjeauNqWD/xfq7n8vWy3lOEtY47vN/52KNv1u1GYWlZtYDtiJ1r8dZ7/0Kz4nz81v5QXH7O/bj3QDlOqV44HFsOFJhyDbXh/Vyvm1PLEVtZuTdrq/0dWbnesJ5tatyXU+hZArWr9SIr85Hqau3ieu4SvHnccw6+XKeRcXzft+zLrzXBu6k/y41NgZOINAr7ydw1z+q28Iu1qYYp+6oMgjdTpthbu/gYttywBWfVzhzTreGutYv1cBiIcvQcSwOwhcmeTxYfHWmGi7dN4Qi1JJ92T5ru2J/S8fu2LBOgWdXDI0zwxrwXLuf9d7nojm2W4Nmpx916pnUtJqpqupaDIVtlAMBcupgos56znVlFpvyB/Qg8ZttKTP/gPiSXFOJ/nfrh0jPvRUFcolnPGQPSvDpGgPF9dzVtClv/uHEmeHJx+JtdFFG5ngsMVjzhar2ju7ZEbFSESbrnceH4/HxeHi+x0RFmPWdJTgnezRKqd38WFpd5lODdlJ/lxqbASUR8zn4yzykoRXwz64RqxxMr5zdj7lNtJ/Om5ovK4Q2dMsWb1i62AnRoHo91u/OsOeOiIkyrB1sC8kvKUVBagcM7x5v13G2zNQ9ZCZpVzkPG7WHXW23dk9vZHbtpv6kmzRGT7P6ydxEmxkWZbflp036zXhenVkWr/aNu7tbjfmAgu72yMKOdPYBicnxaqutyBtw/joFDh5y9SCwpwvddB+Lyv/4LhbHxlTk9NZ+btYrqil84sNFVTSMmUJtuSDe/GbicT8n1XElrllBnq1Fk5XrOjunW0rz/G/fl1+gqtG8P7+d64ZLg7Q0FTiLic/wSHtyjFeat3m1NDJoQY5JLS8srkFtZf+e4Hq3cnsz9IcnLodPe5Ed509rFIIdBixlMxdYXVtBm1W5+wXNZBMsSlLocoVY1DxkDkDLOQ5Zf1c3XIiHGBInuAjZOmcHaXKzjw+5Bx4CAXWHMt2LLDtdzDpy85VjOwBWOKHRXzsD5BP/JYSchOz4ZP3QZiOKYgy1UrgKBDbtzPdo+V+slV9Yyqi304f1cz5WjujY3gVFtc/VGVK7njO9d9zaJ5v111b3JgK1760SXLaOhmuDtDRXAFBGf45foecd1weGdmyMq0qosfCC/2FzzNpfz/kD6snUs2slf0o7sv6zdFe30dpoYbwpRMi9pT24x2pnuq2iTQ8MWIF7zNpfvySk267mbh2xvbhH25hUjPoaTr8aaa97mfHH2ecicsbOLE7zmFZdbU55UHLzwNpfzflfdtVY+VN3crcf9aKbqqKX1hiMJXe1vBpEjNi5Dm7yDxTEXHnJMtaDJvp6zn9Nr7kNXXK23NZM1q2p/3byf67myL7cYkXWcsXk/13PGVj92PbZMikGzuCgTUDNY4jVvc/murCKzniv2BO/+HVKRVVBqcpp4zZamxhwwEajU4iQijYJfpkwOPjjiyqowPLBj80Yd2t9Q3vyy9jY/KsmL1i4mc1tJ1CwwalV6dgz4uN/ZFuFq+D3raXHW+/KKCrRySOTnc8UmRZrWQjMPmYu6W/YRfPaRcY7lg9idZeZuK6sw6znbvr9mIOaKu/X+t3k/dtRZB6rQrDe0V/VE605ffYzXPpyKzS074szz/43sBNfHYXl5zbadaA8DPlfrcRoap/qkNdepsNZzZePefPP+REfY3Bbu5P1cb8gh1V8zW/3YPd6+eYIpQMug1t6ymBwXZQrUsqu2ttZBbxO8KxqY+xeIFDiJSKPhl+3VQTSapqFDp73Nj/Imj6RlYoxJCOaJkPlFvLYnh3O+N94XExlp1nPGucIKS615yIjdbo6lDJhgbeYhczHxrD1YI1cT9Vqxg1WE01X1bk+4W++btXuqWpsqeygPPnflyDXez/WqBU5vvokh996ASFsF/kjribw4913F6Zk1A7O2qZ4VsXS13uZ9tc9xV9d6DKztU9lEmbyyg/fx42Sf+sZdLSYbAytEIDIyEs0SnIJZDycQbqgNLFlRWWYjv7QMSTHR5ngeP8B9yYpApsBJRBpVsI2macgva8cWI1eTmdaVH+VNaxfndGO3YH4xJ8a1WZPtRlZOXWJKEwDxCRFmPWfJlZMA5xaWmbnfCksPTo3DCXp5omWiN9dzdiC/1GxPNKyTuHOvGBuaeD/Xc8ZWD0+4Wy8r72DrmXP7jM3NenjlFeDKK02g9d+BY3HH+Gthc1VvqZKrpz6sQ3N89FtGndvN9Zx5Oh2bu/WO6sJu7wgUlVaY94ZhELtB7YUGCiuXcz1n3VsnoXlCrOlea9cs0uVgjdSEWLOerwc+bLCXrNida7px7Tbvz8ea3e5LVgQyBU4iIl4Ge/YWI44yY4sP82PY5cIgpkVijOkSGXJIq1pHHjW0tYstRqYoY3FZZQBjqxryzgvzXlgqgOs5S4mLMTWbVufkmNFpDKLimcTPhPOCUrP9nVommPWcsbBpbFQUKiKtytF8jL2li6PaeIqMjIg06znjvvKkrhDXc6VXO9fL3a73zDPAlCnmv5vOuRh3d/srbLbag6beac1qLG+WGOtR/Siu56x5UoxHr5nrucL3olVSHHZlF5rgiUGU1bJn5ZUxpm6ZFOdyyhUmyQ/u3hLz/txtumwZzFcN1ijicWPDkB4t3c4N2NCBDxWVMwiwNAVbMNkNzdGKzIFj9y+Xu5tBIJApOVxExNsv0sgI9Gmfgl05RSZPhOcAlmPgNW9zee+0lDpPDjz5XDXiENww5lBcN6qXuf7H8ENq/UXOoe88ibKCM1scGACZIpgMhGIizXLe72qIfPtm8Sao46V5QrQJfOx1oHibXX08wXK9GtvaJhltU6y8qJioKBNccdoXXvM2l7drFmfWc5aWmmDtH3f7k1OjJMaY9VwZ069dtUmF3QUwXA+vvloVNOGWW9Bpxqtoy6H9lTWVopyuuTwtJQ7j+6bV+JtHd2thghN3z83lzBXjejW2uXc7E5jaX5/z6yXmAHI9VwpKyytbjmJMQMyaTMVlNnPN21zO+7mey8Eag+2DNSIqB2tY8xvyNpef62awhjcDH7Y5ziCQFGvy3cxE39EMAmPN/+0zCAQTBU4iIl7iSWPNrlzza7xHqyRzImPXF69Zq4rLzSgwd8PAXLR29UlrZq7rCrbYNcgWJ/v/2brElipe2xPFeUJ2TBq3Y0DHApltUmIRHxttAh3WhOI1b7dmq1J0pFnPGUtJDO/dxvq7ETaTG8XyA2YkXITNLD/x0DYuS06wKvmADqlmst1Yqy6kwWve5vKBHVPNeq7ERkeZoK02LH7J9XDyyUD37sC//gU88ghi46Jx1YieptuUQRIvbKSx/5/L/zGip8u59rq2TMLhnZqZEz4DLcegy/w/IsLcz/WcsZL40B6tTDDKoyA2kgFEhLnmbS5nq6RzxXE7bhdbiIrK2CVb+fyVz83bXM77uV5tgzX+MrCDCWb5PvP6tMM71NpdVp+BD+5mEOCcge5mEOBky+7KSgQqddWJiHjJfnLhL3BXOU7sRvOk6nhD8Jc/u8NW77QqYrNVw94dkl1UZk6qDF5cjdZjDhcDo6O6tDDzyx0oKEF5ObuBItGuWTy6tUpEdmGpy6R2e8kJlkJgUGiqeHNkHyJMUvmhaSluS05wKpeLh3XDtDlrkF1QYqpa26uOswWleWIsLhraze2UL2xVObRdigm0OO+hY54Tg4m2zeLQq12K1frSqROwfDmQmlq1zoVDupnr1xZvwu7cYhPsRVe2kF1+Qo+q+13hqLPmCVmmgrgZAFdZspzBF2swdXYRNNn3120T+iC7qBQrd+SgpKwctjIrWGPge1jHZrjt5D5uA+V2yXGmpADLUjDgKquIqMpH427icubDcb1aB2ucVL/8PW8HPkT4YAaBQKPASUQaVSgNQ/bk5MIAwHlaCk+qjjdUVXdbFIeWWy0PzIHhCZXdbbztrrstqTKpna1T7F5yFfDxb3E9X5ecGNXX6pKa8f0WU5iRrSXczr5tk0zQZL/fFW5P6+Q40yK2M6vQFPBk9yS7gLq0SMA/PnsR6T0HIOn0AdYDHIImOwZHZw/qjK/XZCAjuxhpqXEY2yfNZUtTtQlvC0pNSxqfl4GMfX91bB5vhvtn1THh7bS/DsAXv+/C9xv3mf3N/Xx8z9aYMLB9rfvrtx1Z5vhh+xQHOUZHsbUq0gR97LJjAMb3i+sd272Vz/L3krwoldEjCGcQ8IQCJxFpNKE2DNmdJC+rjnvD3t3GIIItTqkJlaPqKmDq8yQnRFZ1tzmfMB3LILC1zDHg83Q6DW9KTjA4Gta9Fd79NR07MovQsUU8zj6qC+JdjOJzt92cJqRv+1QrgIkAJr36IA7/8l2UR8cg4vqzgZbuW4+4jZzHjy1sJuemjm12nPC2Y/NE7MopNO8tA+P2zRJMi5snE95eNyoZfx3UqV77iwVJOQcgjyPie82LKUFgRtnB3M/1fPlDxptSGZ2CcAYBTyhwEpFGEczDkJvy5OLtc9u72wZ1bYlNe3OdWkES0K1Nksm3ctfd5s/pNL79czemL9lsujnt2zxv1R5ccnz3WlucHLd7w958s90t4iIx+vE7cfj8T1AREYF9jz2Ndt271frcb3y3CWsyck2AydIHfdJScOkJPdw+d1JlgLwzq8C0UrFr015IcmdmkWm18iRA5nvM0XEc4caAja2Bde1jHkd8HLtB2bXHUhOONbc4apJ1uJyr3ntbUsCbYyTSoTt3XUauaWGz922aGQQ6uO/ODWQKnETE54J5GHJTn1y8fe6kypP5ntxC7MgsNGUEGKhy5BNPoknxUWgWH1trd1tDyiB4s832wOW+z1abqXh48mesWVBejt+2Z5nlVFvw5Ljdm3dlYsJzd+KIH+ehIjIKe55/BWn/uLTW5771wz9MtWx7mJGHcizddABr9+ThkTMGunxuBqJMdGYLCo9neyI+93NGTqEZHcaRfLUFyHxu5+7Jbq2STM5Xba+X6yTERptWzdIyVv7GwRynUqtEQlJstFnP13MpenOM9AyyGQQ8ocBJRHzOeRjywak8IhCbFGsSeu3DkLv6eAJYb/jr5OLNc1snaRt+2GjNvWaSwyMjTF2lAwWlZvm4w2o/mfPvdjsxycxnZ28F4Yg2d8nZ3m4zJxR+YcEGM0ce85LYisLgyeTrsLsptwgvLtyA4b3a1LoNpljpkBgUnXkjEn+cB1tMDDDzv0g78wy3j+FzP/TFauxzLI5ZiUEUl0/7YrX75+YkyhWcp4/1j0qrWlAYE8fGRFWNEHQXNDEhnj8euI/twfW6PblmObkLnligslOLBKzamYMidtE5bBA75zgIoGOLBLNeXSUF7J9H/qBhCymDfXeTOftiypWeQTaDQF0UOImIz9mHIbdxmP/MeRgycx64XqAETv48uXjz3Hzs1v0FpvWBQYhVGJFdITCtIkyaZvI013O3Ha5ajZZtzqy1RcCbbf45/YC5nyd7TmxsP0Q4si0qNhrlFaVYtzvPrDe4R+ta913k228h8cvPgLg4RHz4ISImTqx1/Z+27MemfbXXDdq4r8CsN6xn9Tnf+L6mZxaY+lgcRWht98HXFh8dia0HClwmhzNgY0sTgyYmsLP1ld1t7J7k7fTMQry5dIvbgM0aBMD31ip26dgjZ3ZvBAcBRLgcBODtXIq+mAUgMshmEKiNAicRaRTBNgzZnycXb56brUTMIWFdnuJSTr1RjhKbNaoumaUKoiNMCx/XczXaqqGtRt5s8/rdeSZoaBZ/MGg6+Fir0jnzYbheXYFTxWWXI/+X35A5ciwihpyEjrUEiPTN6ow6jz5b5XrOgZN9UmSGNRwJlllYWtXd1iIhBpkFJW4nReb+Z/ccJ9VlHlp+SXlVV1uSqbMVZX5IuHufdmRbI/rioqOrakbZW7v4d9hVl5lfatZz/jHibUkBqU6Bk0gYaOqSAME4DNmfJxdvnptdazx5sxo2u7uck4b5f+Y+cT1fthp5s80MjPhUpn6SDSYnyz7vGlvMGAjYAyiXcnOB2FhsyC6xcmeOvxQFeWVI/GK1NWqzv/tRm9szaxZq9HQ9+6TILAmwbk+eGcVmn2Zmb0yUKf/AOlSuJkXm/me3XG4Ri1hWVCuGalr5SqwK7q7eJ2JQVVhWbsoesJuQc9NZzx1hRqtyTkEud9WKm+THUZ+hSHtJJMQ1NHnXG8E4DDnJy4l6ffXc9T2xMVeG+5bruKoOzuW8n+v5stXIm20+pluLyn1canKa2FpiD0DYmsJh9iyNwPVqyMoCxo9HXss2eOacu7BmX5FVrbyy9WXz3nwzUs7dqE3uC0+4Wo+THZdXVGBvbon5AeA4oXIBg5mSclPR3NWkyC0TY6yRbyx8WRkz2V8zA0fObcj9yfVqa8Vl6YnUhIQaATI/2/nFdY/6ZOtWXnF51bHNlq76jPoUBU4iIc2bZGdvBOMw5KqJejfvN/ko7IaxDzVnNwzzTob0qH2iXm+fuyHlDJjEzZFUq3flIDu6xBS8tHcBxUdbBTEP69DM5fQl3rQaebPNXTh1SedULFy7zwTTLObI/KYyVg43xRwjMLBTqlmvmv37gTFjTCXwqORm2NdvDcrTutQIzDlqc+ZP6bjLxajNQzxs5XS1XmI0J0uuMPuX+Hz24Ie4nMER13PWOiWucloaKyuK+Uqmz89mBYr8O7yf69XVihuXUj2oq6sV1z7q88+MHMxdtbtaoMnAi1XeG7PsRKjRXHUiIcqbyTl9oWpurCM6oGdbzo2VaK5PO6JjQNZwqpqoN7sIm/ZXTtSbUDlR7/58ZGR7NlFvQ5+bJzaWLWD3GFtiGLTxmrdrK2fAgI738SS+L7+kMknc6u7ibbZkcIi8q4TjJIdWI1dqazXyZpuJQRG7tpjQbE1ay4DESmjn8hpB0+7dwIgRJmgqb90aN139FHa27Wwm1eU2WpPHRpnbfMqfNu3HdheTxzZLct+iU9d6GblFZj87viLHhjruYb4OrueMuVEmh7vyMSb/z1bZRcl58hzWq60Vl918rF3FJHR2J/KatzkIwKNW3Ain/yhWqjd11YmEKF8lO3sjmIYhV03U2ywebZJjTT0kztPGFif+iuc152Q7qXfbRtn+hpYz4HbnFJaZYHhPbhGyC8tMHk1UZVVsdh2xxc/VqDpvC3c2dJtrTF2SXWSNMIuORMfUBLRvHl996pIdO4BRo4C1a4H27fHLa+/h99+L0aqWyWNZo2nTvnwzt5yjguJyj94PV+sx/8i+H20sCVDZTWcCIbYkMT+rwuYyT4nbQ8mxkSgut6p+2+e5Y+tTXBRb3A6u54zPOaxna3zz5x5T0JSP43tkXr/Nykvj/a6OTfuPKHYJjuvXrkZXHQOvukaMykEKnERCVKCMpAmWYchVE/W2a/qJer2pp2Tf7sM7Nzf5Kwx2OLltYkyUCY45esvddvuicCe3ucvxifWa881x6pIOqQnYuC/PnMx5Ej+kdTJsETg4dcnWrcDIkcCmTUDnzsD8+SgobwbbHytMMrmZGNgp36e2ZhTnQKs+69mrcjMoLamwodwhtmJuVmzlfnJVvbtVSqwJkExts/hI5JdUVBUqTYqNREFxBaIjbGa92gL75PgoZOVHIL8qMd2G5Jgos9xdYO/4IyoyMhLNEqofT03xIyqUKHASCVFJGkkTNBP1elNPyXG7ecLs6NRVw3qItW23t4U7XVXCfvd/22uthJ1UeWyuzcgxJ/uswoPVzlftyDFdomxB4XrYtAPIyAB69AC+/Rbo1g3d9+ejeUIs9uYyG9qGghIr74hBSWKsFTg1T4xFdxf5Pkw4txcIdYcBkKvEdOaSMfDg6DVyDD/45zghMoNuV9W7GSS2bRZvWtjycstM0FPZ4GSSuhnwdWieYNZzhUHNkg17sTOz0CTTN2PXuxmZCBM4cuTkd+v34tTDO9QIfgLlR1SoUOAkEqJ8OX9aOEjyc6DZ0ER+X2x3Qwt3NrQSNv82c3uWbtpvurwS46xcJxaF3JdXjP2bSjCuX5p1bA4dCnz1FdC9O9Cpk3l85xaJ6NMuGXNWZZguL8cAhr1kbNkZ0qOlWc/ZoM4tzag3dsW6w/u5nrOkONZQsgKug+nV1auScZdxPWfclm6tE02AaY/Z7I9lEMXWTQZ6rraZsgtLTFFQ7iPWv3J8b1h4M6ey7hXX6wzfjYCUmpQcLhKivE3eDddAkwGlc1eLPdBkcntjBJreJPL7arvtXap90pqZ67qOC+dK2NxW5oHxmre5nJWwuZ6r18skaF7HRkdZ1c4ru8B4+5CMTYj44/eDr/eEE6qCpir2zatsuTE5P5W3Ha5qYOI2f0i4O/lxOQMfVwnee3KKUM5cJodkbvvz8DZ3GVvOuJ6r15yVz5y5CMRGRZhAMbrymre5PKvAyqFyhQFXYeWEzs7vDW9zOQMjrhdIx3YoUngpEsK87YYJJ76aqLepE/n9td32SthsaWL3VfVtsupGuauEXVXtPDXejAbjEP/SykTnQfs24cn/3I7yiEisHtkbA0cNrvHcnONwTUYeWiTGmuaagtKaXXW839VciEwYZ3DStVWCmZeO5QPsCd4JMVFonRxrili6Siy3j1o0lbsrp7ZxDEB4n300o6v9xdfcqXmCKZzJHCfHyuHxMZG1Vnjnc9mT0h1LIFjPzeR0K2ndVW6WP4/tUKTASSTEeTM5Z7jxV6DpbQ6KP7bbXrGc2+Zum9lS5nKEmZtq5/22rMK/Xr8FSYV5WNm5D3bHp9Y+F2IKSxFEuigGWVHrXIhMPm+WEIs2KfHILS4zJRtYzDIlLrrqsa7YW2sSY6NQYYswLbj2ICaGJRFgM11prpLDvanwTpz3kS1lDLoY7PEx9omR+be4EcmxUWY9V/QjyncUOImEgWAZ2RaugaY/85QayrFieUp8ZL0qlld/bAziYqLQd/1y3Pr8LUgoLsDKHgNw0+QHMbVD2zrnQmQLCx9fnfvEb+YRMbGc5Q7aNYszSdbOhSRTE1wnljPpOyHWCl6axTN4iqqaKoa7mXlGnLTYVXK4q9dcbX8Vl7ndX8TRlb3apphCp/bCpvY5CRNjGCzacGi7FJeFTu30I8o3lOMkIuJlvo+3/JWn5A17xXKrtlH1PCbe5nIGH65O5M6PHbD6f7j9mRtN0PRH70G45sIH0a5TW7dBgGMVbVf7q7Yq2ky+Hty9pekm4/NzChT+n9dme2w2t4nlDKgObZds8pIYJNlbjHjN28xZYjkLrufL/UUsScGRii2SYk318ZZJMab0A6+t27G4aGi3WktX+OPYDkUKnERE/CwYE/ntJ3K2nqRnFlbbZt5mS467E7njY1ssX4b/e+H/EFdajP/1G4xrz70Psc2a1RoE2Ktocyg+u9WqBT95xbXOhWimAxrcxdS9YjI663WxS5HXvM3l57qZDoiB6/E92+CQtslmTjl2kTFg4jXfIwa3J/Rq4zLA9WZ/2XGE4u0n9zEtSyx9kJlfaq5ZvuG2k/u4Lf8gvqWuOhGRABCMOSj2E7W9jhMDEHY39W6XYoKA2k7k9vv+G1uBtR17YX9ic9x/3l3oltaizsd6OxeifTqgr1ZkYMWObBSUliExJtrMjzeuf5rbfe2YZN06Kc4EWvYEb46m4zxztQW43uwvx78xvFebehVJFd+KsLnKYgthOTk5SE1NRXZ2Npo1a+bvzRERqYYjvoItkZ8lBxp6Iudjf/szHfvKItGyeVK9HsvaV1+trAx+SphcH4WBHZvXWjDUF/vasVApW7kY4LK1ydMA15v9Jf6PDRQ4iYhI05oxw5p/7s47gzbQDMYAV3wTOKmrTkREms5LLwFXXWX9/5hjgLFjg3LEqEaqhi+1DYqISNN46qmDQdM//wmMGaM9L0FHLU4iItL4pk0D7rjD+v+tt1q3XVS5ri91mUlTU+AkIiKNh+OP7r0XuP9+6zb//69/+SRockzSZuV1FhFlPSxPk8NFGkKBk4iINJ6ffjoYNLGV6bbbfDaybfr3W8yQfs69xulqWHl95c5sUy6ApR0UPEljUOAkIgErHLthgvE117rNgwcDTz5p/f/662uWE6ispZRfWoakmGhTt2r8APe1lOzPx5YmBk092yQhr7gcmQUliI2KNLc37M3H16t2o0fr5IDfd+FyjISSgAmcHn74Ydx+++2YMmUKnmICoRvvv/8+7r77bmzZsgW9evXCI488ggkTJiAQqDaHiO9Ur9FjzQFmTqq1FCj01cnFX4/1tuvJm+du6PeXy21ulYiTuyWiR89OLgMm++Oe+mY91u3ONcUj7Tbvz8ea3bmmQKW718zXyOdLiInEsi2ZphAmJ9BlMcm2KXHo0DweG/bkmfUaa8Sdv4IXfx4jEkCB07Jly/Dyyy9j4MCBta63dOlSnHvuuZg2bRpOOeUUzJw5E5MmTcKvv/6K/v37w5++/XN3VTVY+weY8xKxxL7K4Iu/BduXpf2kujYjFyWl5SiHDVGIwKa9+ViTUftJ1dugi4/94vdd+H7jPlOROiU+GsMOaY2Jh7f36LHePC+7njhlCJ+TU3CUV1RgxY4sj7qevDmh8vtr+pLN5rEl5RWm5YaPveT47rV+f7nqLissLMKge29A3K7N2PTVXPTo1dnl8Tjzx3T8vi0LMZGcIDcCtggbImwRJoDj8v/+lI47J/ZzeZzyON6XV4ydWYXYm1OI4gobbBVARCSwP7cQ+/IS0KF5glmvsYLFOSt2maAtr7gUyXExOKZbC5w8oO5jxJ/dk/78MRJK319+D5zy8vJw/vnn49VXX8UDDzxQ67pPP/00xo8fj1tuucXcnjp1KubNm4fnnnsOL7E2iJ/wS2fanDVmziF++BJio8wM2Ov25JrlpOBJ/CXYEmjNSfWndPy85YCpysxpNKoUlyJ3S6m5/y43J9WqloyMXDP5qn0qjs0eBF187O0frcDKHTkoKSs3ec3MYV6xIwdLNu7DtL8OqPWxDX1ee9dT+v4CM3/ZFl6XVyA6KhItEmOQX1xea9eTNydUfn/d99lq7M8vNhPW8s/nl5dj+fYspH+22u33l2N3Wa+2yYjgY8tKcepTt6P393NQHhmFLz+ah263XFJjm7dlFuDHzQdQVFqO7NJyFJVVVO3r+OhIxMVE4YdNB8x6XVvVnKg3ISYKOzILkX4gH6WO8+WWA0WwoWB/vvl7XM/XP3a5r6d+vtocIzw+OfkGX/uqnTn4eWsm7j6lX6MEuK72N3Huu+S4aDOnYV3HSEOPT2+2OxS/v/xex+maa67BxIkTMXr06DrX/eGHH2qsN27cOLPcX/iLhR8+Bk1dWiSYgzg6MtJc8zaXv7l0i1lPpKnZT6g8gTZP5Gzxyeaat7mc9wea7ZkFWLR2L/KK2VoQgdjoSHMC5DVvc/nidXvNeu6CLrZYsLWGLTctk+LMNW9zOe/neq4e+/CXa7A8PQvFpeXmRJoQG2mueZvLH56zxu1jG/q8xF/Ty7exy6kIe3OLER8ThRZJseaat7n81/RMs15dJ1R+93AONV7zNpfzhOrqufm99MKCDdibW4SYyEgzZYn9wttc/uLCDS6/v+zdZQzUeBKPKinBxKlT0HvRHJRHx+CDWx/Hgm5HudzmzfvykZFdaCbILSyrMNsbG8155iLMbS7n/VzPFQYru7IKqgdNDric97ubUcz+Y5c/bvkedTTf3dFVP3Z5vyvch9xfP2/JNEEf3x+2DPKat7mc97t7n735PDrvb0e8zeX27klfH5/++h7ZEKDfX34NnGbNmmW62dj15omMjAy0a1f9lwBvc7k7xcXFppS648WX2MzLXyxsaYqMrL47eZvL+eHneiJNyZsTqj9t2Jtnclb4aWLAFB0ZYVoieM3bXL47p9is54zB1I+b9iMqAmiVHGfmEGM3EK95mz/Ef9q032XQtWVvHpZu2m/2R6IJmKwuJF7zNpf/sHG/Wc+Xz0u5xaVIP8BWJhtaJsUiLjqy8vGR5jaXbztQYNbz5Qn15/QD5j7uW75GHh8mCKq8zeXrd+eZ9Zyx24QtAOzuiSouwqn3Xo2eP3yLsphYfHrv80gfPs60yLjqLuPJmgFwuekWjDDPw9fLa97m8nzeX+E6Mlq7JwcFZbUft7yf6/nyxy5buL5bv88EZM3io633KdJ6n3ibE/4u2bDPrOfrz6Pj/naFPR3u9rc3x6e/vkcqAvj7y2+B07Zt20wi+DvvvIP4+PhGex4GZZx/xn7p3Llmf7s32DfOZl4etK5wOe/neiJNyZsTqj/tzysx3VWx0VE1Sv3wdkx0lLmf6znbtC8f2QWlaJYY4/I1pybGIKuw1KznbN7a3abVgIGSq8dyeWFpuVnPl89LeWx5KSlHXEyky8dzOSex5Xq+PKHy/S8us76/XD2v9dgKs56zpNho021SmpODSXdfie4/f4fSuHh8MvVlbDl2uPV6oqPMejW3udwEGXxKV8/LRexO4nquLF67z+VyT9bz5seuldNUZvaLc3cYb7OljnlxXM/Xn8ekyv3NLlhXatvf3hyf/voe2RHA319+C5x++eUX7NmzB0cddRSio6PNZdGiRXjmmWfM/8vLa35g0tLSsHt39S8t3uZydzhSj5P22S8M2HyJHzI25fOgdYXLeT/XE2lK3pxQ/alVcqxJGC6pYN5L9V+TvF1aUWHu53qu2HgydsyLqsZ9QmluYZlJ+3A6l1Yxy22V6/nweYk5KmxNY5egq9fM5Twpcz1nSV6cUPk4E6S4+dXO5VbeUc0fhkzQZa5J3vYMtNy2CSUJiZj90OtIP2qo1ZWWXYSebZPNes5M11yUtaP5w5JPzy3gNW8T7+d6Lrer/OD2chWrE9e6dnyI43q++LHLwJpvD3PBXDFJ7jZrPV9/Hu37m/vV1TFS2/725vj01/dIfgB/f/ktcBo1ahRWrFiB3377repy9NFHm0Rx/j8qquZBPWTIEHz77bfVljE5nMvdiYuLMzMdO158iaMwmFDID1mFU7Myb3N599ZJZj2RppTkxQnVn3q2SUablDjTBF9YWoEyjpiy2cw1b3M5h5xzPWf8rDVPiEVWQanLkwt/dacmxJr1nB2algyey9lD4+qxXM77uZ4vn5dMN1GrRMRER5ouCJ4Q2CLDa95mkjiH1XM9X55Qj+7awgRjbM1y9VguZx4M13PGFhYm6KJbNzz2fy/g7ftfw9bDjjJdXUxUZhfj2MPauUxUbpMch2YJMVUtXXxvGazw2rR0MXcoIcas50rv9geTgu2bbY9lHF+G43q++LHbq12yybVjy6Nz+pQ9YOL9XM9ZkpefR/v+5n7l/uV+ZsurJ/vbm+PT2+1uqKQA/v7yW+CUkpJiSgg4XpKSktCqVauq0gKTJ082LUZ27Nr76quv8Pjjj2PNmjW499578fPPP+Paa6/118swQ1c5CoNfaOmZhdUOZt5m4uBFQ7t5NMRVxJe8/YXqL51aJGJ477ZVrSslZRUmYOI1TwlcfmLvtmY9Z51bJGJw95Ym6NjvFICYHzc2G4b0aGnWcza+b3u0SYk3rSylFTazLvcTr3mby3k/1/Pl8xLfgyM7tzB/n8FCUWkFsgpKzDWDyLbN4nFUlxYu3ytvTqhdWiXh+F6tzX08qbJbztpuPn+pWX58zzZmvWr27QPmzzejmjhir83Rh2NV577Ysi/fPI5D3Gsbyccfkr3apiAmKgotE2OQGBNlToK8bpUYY7pjD22X4vYH57mDuiAlzvpxbR8fxkPc/n9qFhdl1vPlj92ju7Q0+TUM8HhCt4J6VLt9aLtks15jfB7t+7t/h1Sznz3d394cn/76HukYwN9fgfVT00l6enq1PuihQ4ea2k133XUX7rjjDlMA8+OPP/Z7DSf70FX70Fb+QuQvlt7tUkzQpFIE4g/2EyqHo/MEypwAe6kMfunUdkL1J27Pecd1MQnia3floNh05disZFZ+rto3M/e72m7z2MFdsCev2BRWZL6JHbt9Du/cHOe6eWxsbBSuGnGIGTnHlgN2G3E1XpuE8Rjrfq7ny+d1fq+Yu9WpZaJ5HIM1/i12S9b2XtlPqPZh27tzikwgwhMqH+fuhMq/d81JPXEgvxQrd2Sb111QbKvM14lG/46puPqkQ6o/LwfjcHTz+vXAF1+g5+jR6DEiuV51duw/OE0Zl8JStE6JRXRUhEmCzyksM8dmbT844+OjcfGw7nhx4UYTrNT4+5ERuGhYd7Nebc/NH7eOJWQYRNT2Y5fLrj6ppynfcCC/GLaSctPSxfM6c7LapsTjqhE9XT7WV59Hvpf13d/eHJ/++h6JDODvrwibu/GaIYqj6pgkznwnX3fbqXK4BCLHOij8hckTKn+p1XZCDQTVi/VZOT4DOzb3qH6L41QeBaVlSIyJxsBOqRjnQaG/t3/YglcXbzIlAHgyZD5Lu2bxuPyEHrhwSLdGe15fvFfeFFacsyIDy7YcMMnPbNU7pltLnOw89cn27cyzANatAzp0AJg60acPGspVLSW29nj6g/Pxr9firaUcIVdWWZUISEmIxuQh3XDT2N6N9tyOBUPtj+X7xGCursf68/PozfHpr+3e0ETPW5/YQIGTSBgItMq7nvLX1CclJeX4ek0GMrKLkZYah7F90ly2NPn6eX3x+Iaq83m3bAFGjgQ2bwa6dDFddTjkEL//4CwqKsO7v6ZjR2YROraIx9lHdXHZ0uTr5/bmsf78PPrrM+WNpnheBU4+2jkiIsKf/RusoImjkhkssaWpa1ftGgnL2CCgc5xERMTPGCydeCKwa5fVLffNN0DHjv7eKhG/0VAvERFxj7lMJ50EDBgALFyooEnCnlqcRETEPdbUe/NNIDcXaKF6dCJqcRIRkeo4cfpVV7H0tnU7OlpBk0gltTiJiMhBixYBEycC+flWIvjNN2vviDhQi5OIiFi+/ho4+WQraGKRS7Y6iUg1CpxERAT4/HPg1FOBwkJgwgTgs8+AJNdz64mEMwVOIiLh7sMPgdNPZ+VP63r2bM5r4u+tEglICpxERMLZ3r2cUZ2lsIFzzgHefZcT9/l7q0QClpLDRUTCWZs2wKxZVtfciy9a5QdExC0FTiIi4SgnB7BPLcHcJl5EpE7qqhMRCTdPPgn06wds2uTvLREJOgqcRETCybRpwI03Ajt2WEnhIlIvCpxERMKBzQbccw9wxx3W7fvvV3FLkQZQjpOISDgETbfeCjz6qHX73/8GbrnF31slEpQUOImIhLKKCuD664Fnn7VuP/MMcN11/t4qkfAJnJ7hh86FiIgIxMfHo2fPnjjxxBMRpSGtIiL+V1AALFnCL2ng5ZeBK67w9xaJBLUIm41tuJ7r3r079u7di4KCArRo0cIsy8zMRGJiIpKTk7Fnzx706NEDCxYsQOfOnRFocnJykJqaiuzsbDSzD8UVEQll+/YB331nVQUXEa9ig3onhz/00EM45phjsH79euzfv99c1q1bh+OOOw5PP/000tPTkZaWhhtuuKG+f1pERHyhtBT44ouDt1u3VtAk4q8Wp0MOOQQffvghjjjiiGrLly9fjjPOOAObNm3C0qVLzf937dqFQKMWJxEJacXFwNlnA598Arz0EnDllf7eIpGAV5/YoN45TgyGyjinkRMuy8jIMP/v0KEDcnNz6/unRUTEG4WFwF//Cnz1FRAXBwRguoRIsKt3V91JJ52EK6+80rQw2fH/V111FUaOHGlur1ixwuRCiYhIE8nLAyZOtIKmxESrq27CBO1+EX8HTq+//jpatmyJQYMGIS4uzlyOPvpos4z3EZPEH3/8cV9vq4iIuJKdDYwfDyxYAKSkWMHTqFHaVyKBkONkt2bNGpMUTr179zaXYKAcJxEJuZymE04Ali0Dmje3gqbjjvP3VokElUbNcbLr06ePuYiIiB8xl+mUU6wJe+fNA448Um+HSCC1OJWXl2PGjBn49ttvTc2mClaldTB//nwEMrU4iUjI4df47t1AWpq/t0QkKDVqi9OUKVNM4DRx4kT079/fVAwXEZEmtH07cOedwPPPM6nUqgquoEmkSdQ7cJo1axbee+89TNBoDRGRprdlC8ARzJs3W7fffFPvgkggj6qLjY0189GJiEgTW7/eSgRn0MTv4alT9RaIBHrgdNNNN5mpVRo4GE9ERBpi9WrgxBOtbjoOzFm0COjSRftSJNC76pYsWWIm8J0zZw4OO+wwxMTEVLv/o48+8uX2iYjI778Do0dbk/UOHGiNnmvbVvtFJBgCp+bNm+N0zbAtItI0ysutuecYNA0aBHz9NdCypfa+SLAVwAxWKkcgIkHnt9+Au+4C3nkHSE3199aIhJwmKYApIiKNKD8fSEqy/n/EEcDnn2t3iwQAjwKno446yhS8bNGiBY488shaazf9+uuvvtw+EZHww+64888HZs8Gjj/e31sjIvUNnE477TQzma/9/yp6KSLSSD77DDjzTKCkBHjhBQVOIgFGOU4iIoHigw+Ac88FysqAM84AZs5k8Tx/b5VIyMupR45Tves49ejRA/v376+xPCsry9wnIiINwMRvjp5j0HTeeZymQUGTSACqd+C0ZcsWM9Gvs+LiYmxnYTYREamf118HLrwQ4KTpl1wCvPUWEK2xOyKByONP5qefflr1/7lz55omLTsGUkwe7969u++3UEQklLEiDEfM8fqqq4DnngMi6/2bVkQCLccpsvKDzMRw54eweni3bt3w+OOP45RTTkEgUx0nEQk4xcVWK9Pll/NL1t9bIxJ2chqjjlMFm5AB06q0bNkytG7d2vstFREJV3PmAOPHW4ESRy1fcYW/t0hEPFDv9uDNmzcraBIRaSi22N99NzBhAnDDDdZtEQkaDco+zM/Px6JFi5Ceno4S1hpx8M9//tNX2yYiEloYJN1yC/D449btTp3UNScS6oHT8uXLMWHCBBQUFJgAqmXLlti3bx8SExPRtm1bBU4iIq4w3YE/LJ9/3rr97LPAtddqX4mEelfdDTfcgFNPPRWZmZlISEjAjz/+iK1bt2LQoEF47LHHGmcrRUSCGUu4XHmlFTQxp+nVVxU0iYRL4PTbb7/hpptuMqPsoqKiTP2mzp0749///jfuuOOOxtlKEZFgxsTv116zygzYR8+JSHgETiw9YC9NwK455jkRh/Ft27bN91soIhLsxo61Rs6xGvgFF/h7a0SkKXOcjjzySFOOoFevXhg+fDj+9a9/mRynt99+G/379/dmW0REQtM55wAnnAB07OjvLRGRpm5xeuihh9C+fXvz/wcffBAtWrTAVVddhb179+Lll1/2dntERIJfQQHw978DjtNQKWgSCa/K4aFClcNFpFHl5QGnngosXAgcdRSwbJmmUBEJodjAZxMi/frrrwE/3YqISKPKzgbGjbOCppQU4OmnFTSJhJh6BU6c3Pfmm282o+c2bdpklq1ZswaTJk3CMcccUzUti4hI2DlwABg9Gli6FGjeHPjmG+D44/29VSLir+Tw119/HVdccYUpeMkaTq+99hqeeOIJXHfddTj77LOxcuVK9O3b19fbJyIS+PbuBcaMAX7/HWjVCpg3jyNp/L1VIuLPFqenn34ajzzyiBlB995775nrF154AStWrMBLL72koElEwtc//mEFTe3aAYsWKWgSCWEeJ4cnJSVh1apV6NatG/iQuLg4LFiwAMOGDUMwUXK4iPjczp3A+ecDHFl86KHawSJBpj6xgcdddYWFhWY+OoqIiDCBk70sgYhI2CksBBISrP936AAsWODvLRKRQCuAybym5ORk8/+ysjLMmDEDrVu3rrbOPzmJpYhIKFu3zsppeughq6VJRMKGx1117KJjS1Otfywiomq0XaBSV52IeGX1amDUKCAjAxgwAPjlF85FpZ0qEsQapatuy5Ytvtg2EZHg9dtvVkvTvn3AwIHW6DkFTSJhxWcFMEVEQhorgJ90khU0HX20ldPUtq2/t0pEwilwevHFFzFw4EDTLMbLkCFDMGfOHLfrM6eK3YGOl/j4+CbdZhEJQ99/b3XPZWUBQ4daxS1btvT3VolIoCeH+1qnTp3w8MMPo1evXqbEwZtvvonTTjsNy5cvx2GHHebyMQyw1q5dW3W7rrwrERGv8Qddbi4wYgTw2WdA5SAZEQk/fg2cTuVEmA4efPBB0wr1448/ug2cGCilpaU10RaKiACYOpW/9IDJk4HKsiwiEp4CJsepvLwcs2bNQn5+vumycycvLw9du3ZF586dTesUi3KKiPjcd98BRUXW/9myzergCppEwl60p8P0PFXXMD5nnLKFgVJRUZGpETV79mz069fP5bq9e/fGG2+8YfKiOGTwsccew9ChQ03wxG4/V4qLi82lIa9FRMLU++8D550HjBsHfPQREBvr7y0SkWCq4xQZGelxLhFbjuqjpKQE6enpJhD64IMPTJHNRYsWuQ2eHJWWlpo58s4991xMZVO6C/feey/uu+++Gss9qdUgImHo7beBiy8GKiqs4pYzZgDRfs1qEJEAquPkUeDEQMaxntNtt92Giy++uKpL7YcffjCJ3dOmTcNFF13k1caPHj0ahxxyCF7mnE8eOOussxAdHY3//ve/Hrc4sZtPgZOI1PDaa8Df/w7wa/Gyy6y556KitKNEQlyOrwtgDh8+vOr/999/P5544gnTymP3l7/8BQMGDMArr7zideBUUVFRLdCpq3WLXX0TJkxwuw7n1ONFRKRWzz8PXHut9f+rrwaefZbN7dppIlJNvb8V2Lp0NIu/OeGy//3vf/X6W7fffjsWL15sWrEYAPH2woULcX7l3E+TJ082yxyDtq+//tpM6/Lrr7/iggsuwNatW3H55ZfX92WIiBzEIMkeNN14I/DccwqaRMSlenfcs5vr1Vdfxb///e9qy5mbxPvqY8+ePSY42rVrl2kiY9L33LlzMYZTGgAm94n5VXaZmZm44oorkJGRgRYtWmDQoEFYunSpR/lQIiJu8ccgazNNmWKVHlB9OBHxdpJfuy+//BJnnHEGevbsieOOO84sY0vT+vXr8eGHH9babRYINMmviLiUng506aKdIxKGcuqR41TvrjoGRuvWrTPFKw8cOGAu/D+XBXrQJCJi8Pfi3XcDv/xycIcoaBKRxmhxCnZqcRIJcywzcN11wAsvAK1bA+vXA82b+3urRCRUW5zou+++M4nZLD65Y8cOs+ztt9/GkiVLGrbFIiJNgXXmrrjCCpqYx/TwwwqaRKRe6h04MY9p3LhxSEhIMCPb7KUDGKU99NBD9f1zIiJNo6zMmmvujTesEXNvvWXVahIRaczA6YEHHsBLL71kRtbFxMRULR82bJgJpEREAk5JCXDOOcDMmVYV8HffBS64wN9bJSLhUI5g7dq1OPHEE2ssZ99gVlaWr7ZLRMR32CX34YfWnHMffACceqr2rog0TYtTWloaNmzYUGM585t69OjRsK0QEWlMN98MjB8PfPqpgiYRadoWJxagnDJlCt544w0z8e/OnTtNNfGbb74Zd3N4r4hIICgq4pxLVhJ4YiKL0KmwpYg0feDECX45n9yoUaNQUFBguu04FxwDp+s4xFdExN+ys4GTT+as4ZyryVqmauAi4s86TiUlJabLLi8vz0x5kszpCoKA6jiJhLj9+4Fx46zilqzPtHo10L69v7dKRMK1jtOll16K3NxcxMbGmoDp2GOPNUFTfn6+uU9ExG/27AFGjrSCJha3XLBAQZOI+FS9A6c333wThYWFNZZz2VusiyIi4g87dwIjRgB//MFRLMDChcARR+i9EBH/5DixGYu9erywxSk+Pr7qvvLycjP5b9u2bX27dSIink7QO2oUwBG/nToB334LHHqo9p2I+C9wat68uRlFx8uhLr6QuPy+++7z9faJiNSN0z0xaOrWDZg/H+jeXXtNRPwbOC1YsMC0No0cOdJMu9KyZcuq+5jv1LVrV3To0KFxtlJEpDbnnWfNQ8euus6dta9EJHBG1W3duhVdunQxLUzBSKPqREIER8sxAVwpAiISyKPq5s+fjw84ZYGT999/3ySOi4g0uuXLAU79NGYMcOCAdriINJl6B07Tpk1Da/7Kc8LE8IceeshX2yUi4tr//meVHGC9JlYGFxEJ5MApPT0d3V0kXjLHifeJiDRqEjirgXNC8aFDgXnzAId8SxGRgAuc2LL0B+ukOPn999/RqlUrX22XiEh1HC3HiuC5ucBJJwFz5wKpqdpLIhLYgdO5556Lf/7zn2aUHes38cK8J078e8455zTOVopIeGNdpokTgYICK3j64gsgSKZ5EpEwn+R36tSp2LJli5nkNzraejgn/Z08ebJynESkcRxyCNCmjVUJ/P33ldskIsE3ye+6detM91xCQgIGDBhgcpyCgcoRiASpbduAdu1YOM7fWyIiIaY+sUG9W5zsWD3cVQVxERGfePttICUFmDTJuq3CliISADwKnG688UbTRZeUlGT+X5snnnjCV9smIuHqlVeAf/wDYDrAsmXA4Yf7e4tERDwPnJYvX47S0tKq/7sTrNXERSSAPPss8M9/Wv+/4gpgwAB/b5GIiPc5TsFKOU4iAezf/wZuvdX6/003AY8+yl9k/t4qEQlxOY055YqIiM/x99v99x8Mmu66S0GTiARvV91f//pXj//gRx995M32iEg4+vRT4J57rP8/8ABw553+3iIRkYYHTmy+smPP3uzZs82yo48+2iz75ZdfkJWVVa8AS0SkyqmnAhdfbOUz1TEARUQk4AOn6dOnV/3/1ltvxd/+9je89NJLiIqKMstYPfzqq6+us19QRKRKRQW/PICYGCAyEnjjDeUziUjoJYe3adMGS5YsQe/evastX7t2LYYOHYr9nLE8gCk5XCQAMGC6/HJrCpV33rHKDoiIhGJyeFlZGdasWVNjOZdx6hURkVqxtMkFFwAzZgAffgj89JN2mIgEjXr/zLvkkktw2WWXYePGjTj22GPNsp9++gkPP/ywuU9ExK2SEoCTgc+ebbUyzZoFDBumHSYioRs4PfbYY0hLS8Pjjz+OXbt2mWXt27fHLbfcgptYd0VExJWiIuDMM4EvvrDmm/vgAyspXEQkXApgsk+QgikpXDlOIn6Qn2/NOffNN0BCAvDxx8DYsXorRCQ8CmAyz+mbb77Bf//736ppVnbu3Im8vLyGbbGIhLaVK4ElS4CkJGDOHAVNIhI+XXVbt27F+PHjkZ6ejuLiYowZMwYpKSl45JFHzG2WKRARqea446y8Jv6SGzpUO0dEgla9W5ymTJliCl9mZmYigU3ulU4//XR8++23vt4+EQlWLE3iOAJ3/HgFTSISfi1O3333HZYuXYpYJnc66NatG3bs2OHLbRORYLV7NzBmDLB3L7BoEXDoof7eIhER/7Q4sVYTK4U72759u+myE5Ewxx9QI0YAK1ZYt8vK/L1FIiL+C5zGjh2Lp556quo2k8OZFH7PPfdgwoQJvtsyEQk+W7cCw4dbXXSdOwOLFwP9+vl7q0RE/FeOYNu2bSY5nA9bv369yXfidevWrbF48WK0bdsWgUzlCEQaycaNwMiRQHo60L07MH8++/C1u0Uk4NUnNmhQHSeWI3j33Xfx+++/m9amo446Cueff361ZPFApcBJpBFs2GC1NO3cCfTqZQVNnTppV4tIeAdOpaWl6NOnDz7//HP07dsXwUiBk0gjyM62ksFZ6JJFLtu3124WkZCMDeo1qi4mJgZFnDZBRMRRaiowd66VCN6mjfaNiISseieHX3PNNabYJbvrRCSM/fgj8MwzB2+3aKGgSURCXr3rOC1btswUuvz6668xYMAAJHEKBQcfffSRL7dPRALRd98BHEXLaZbS0oC//c3fWyQi0iTqHTg1b94cZ5xxRuNsjYgEPs4Q8Je/AAUFwEknWQGUiEiYqHfgNH369MbZEhEJfF9+Cfz1r0BxsTWFCluYg2A0rYhIk+c4sWI4c5uGDRuGY445BrfddhsKCwt9tiEiEuA4Se+kSVbQdNppwMcfK2gSkbDjceD04IMP4o477kBycjI6duyIp59+2iSKi0gYWL8eOOss1iSx8pnefx+Ii/P3VomINDmP6zj16tULN998M6688kpz+5tvvsHEiRNNq1NkZL0H5/mN6jiJNNDDDwOrVwNvvAFE17uXX0QkvApgxsXFYcOGDejM+acqxcfHm2WdgqhCsAInkXpg2RHHIIlfFxER2oUiElLqExt43FTEuk0MlJwLYrKauIiEINZoOuEEIDf34DIFTSIS5jxub2fD1MUXX2xanuxYRfwf//hHtVpOquMkEgIeeQS47Tbr/zNnApVd9CIi4c7jwOmiiy6qseyCCy7w9faIiD+xK+7++4F777Vu/+tfwN//rvdERKS+gZPqN4mEQdB0xx1WEjg99BBw++3+3ioRkYCioTEiYgVNN9wAPP20tTeefBK4/nrtGRERJwqcRATIyABmzbL2xAsvAFddpb0iIuKCAicRAdq3Z3E2YPly4MILtUdERNxQ4CQSrlhKZOVK4Mgjrdv9+1sXERFxK3hKfouI73C+OU6dMmwYsGiR9qyISDAETi+++CIGDhxoqnTyMmTIEMyZM6fWx7z//vvo06ePKcY5YMAAfMnZ2kXEc5yc+/TTrUl6y8uBvDztPRGRYAicOFXLww8/jF9++QU///wzRo4cidNOOw2rVq1yuf7SpUtx7rnn4rLLLsPy5csxadIkc1nJ7gYRqVt+PnDqqQB/oCQkAJ9/DkycqD0nIuIhj+eqayotW7bEo48+aoIjZ2effTby8/PxOb/sKw0ePBhHHHEEXnrpJY/+vuaqk7CVkwOccgrw3XdAcrIVNA0f7u+tEhEJzbnqGlt5eTlmzZplAiN22bnyww8/YPTo0dWWjRs3zix3p7i42OwQx4tI2OFxP3asFTTxS+HrrxU0iYg0gN8DpxUrViA5OdnMgcd572bPno1+/fq5XDcjIwPt2rWrtoy3udydadOmmSjSfuncubPPX4NIwEtMBHjst2wJzJ8PuPlxIiIiAR449e7dG7/99ht++uknXHXVVWZOvNWrV/vs799+++2m6c1+2bZtm8/+tkjQiI4G3nkH+PFHYNAgf2+NiEjQ8nvgFBsbi549e2LQoEGmdejwww/H0/ZpH5ykpaVh9+7d1ZbxNpe7w5Ys+6g9+0UkLOzYAdxzD1BRYd2OjQV69fL3VomIBDW/B07OKioqTF6SK8x9+vbbb6stmzdvntucKJGwtXUrcOKJwP33A/fd5++tEREJGX6tHM5utJNPPhldunRBbm4uZs6ciYULF2Lu3Lnm/smTJ6Njx46mJYqmTJmC4cOH4/HHH8fEiRNNMjnLGLzyyiv+fBkigWXDBmDUKCA9HejRA7j0Un9vkYhIyPBr4LRnzx4THO3atcskbrMYJoOmMWPGmPvT09MRGXmwUWzo0KEmuLrrrrtwxx13oFevXvj444/RX9NEiFjWrAFGjgR27WICIcAW2o4dtXdEREK1jlNjUx0nCVkrVgAs17FnjzXnHCftdRqFKiIiIVLHSUS8UFBg1Wli0MRJexcsUNAkItIIFDiJhEqdpueesybtZZ2m1q39vUUiIiFJgZNIMOMkvXZnnAEsXgw0b+7PLRIRCWkKnESCFXOYBgywSg/YOQymEBER39O3rEgw+uILa8LeP/8EHn7Y31sjIhI2FDiJBJvZs4HTT+cM1sCkScBTT/l7i0REwoYCJ5Fg8t//AmedBZSWAmefDbz3HucV8vdWiYiEDQVOIsFixgzg/POthPDJk61Je2Ni/L1VIiJhRYGTSDBgC9OTTwKsV/v3vwPTpwNRUf7eKhGRsOPXKVdExENsWfr6a+CNN4DbbgMiIrTrRET8QC1OIoHst98O/p/Tp9x+u4ImERE/UuAkEojYJXfvvdb0KcxtEhGRgKCuOpFADJrYHffvf1u3Of+ciIgEBAVOIoEWNF1/PfDMM9Zt1miaMsXfWyUiIpUUOIkEiooK4KqrgFdesW6/9BJw5ZX+3ioREXGgwEkkUIKmSy8F3nzTmm+Oo+cuusjfWyUiIk4UOIkEApYX6NDBqs3EwpasCi4iIgFHgZNIoARODz4InHMOMHCgv7dGRETcUDkCEX8pLATuvtu6tgdPCppERAKaWpxE/CE/H/jLX4D584HVq4EPP9T7ICISBBQ4iTS1nBxg4kRgyRIgOdkqPyAiIkFBgZNIU8rMBMaPB/73PyA1FfjqK2DwYL0HIiJBQoGTSFPZtw8YM8aaf65lS2DePOCoo7T/RUSCiAInkaaqCH7GGVbQ1LYt8M03wIAB2vciIkFGo+pEmgJHzD32GNC3L7BokYImEZEgpRYnkcauCM5K4HTMMcCKFVaRSxERCUpqcRJpLBs2AIcfbiWC2yloEhEJagqcRBrDn38CJ54IrFxplRtgjpOIiAQ9BU4ivvbHH8Dw4cCuXVYu0+zZVo6TiIgEPQVOIr70yy/ASScBe/dapQYWLADatdM+FhEJEQqcRHzlhx+AkSOBAwesopbffgu0aqX9KyISQhQ4ifjKU09Z06kwt+nrr4HmzbVvRURCjMoRiPjKjBlAz57AnXcCiYnaryIiIUgtTiLe4Kg5+4i5hATgwQcVNImIhDAFTiIN9eGHwJFHArffrnIDIiJhQoGTSEPMnAmcfTZQVgZs3WpVCBcRkZCnwEmkvt54A7jgAqC8HLj4YuA//1FFcBGRMKHASaQ+XnwRuOwyq2vuH/8AXn9dQZOISBhR4CRSn3IDV19t/Z/TqLzwwsEJfEVEJCzoW1/EUy1bWlOnMBn8iSc0jYqISBhSHScRT02eDPTvb42k09xzIiJhSS1OIu4wj+mxx4CMjIPLOP+cgiYRkbClwEnEFZYXmDIFuOUWYMwYoLhY+0lERNRVJ+IyaOKIuVdftVqXrrsOiIvTjhIREQVOItWwoOWllwJvv22NmJs+3cptEhERUXK4iIPSUuD884H337dqM73zjlUdXEREpJJG1YnY3XyzFTTFxADvvQdMmqR9IyIi1Sg5XMQxcOrbF/jkEwVNIiLiklqcJLwxEdxe/btzZ+CPP4BofSxERMQ1tThJ+MrOBk46yeqes1PQJCIitVDgJOHpwAGrPtPixcA11wC5uf7eIhERCQLqk5Dws3cvMHYs8NtvQKtWwNy5QEqKv7dKRESCgAInCS+cPmXUKGD1aqBdO+Cbb6z550RERDygwEnCx/btVtC0bh3QoQMwfz7Qu7e/t0pERIKIcpwkfLAKOIOmLl2s3CYFTSIiUk9qcZLwceed1pQql11mBU8iIiL1pMBJQtumTUCnTkBsrFWv6b77/L1FIiISxNRVJ6Hr99+BwYOBc8+1WppERES8pMBJQtPPP1vFLVl6YOtWIC/P31skIiIhQIGThJ6lS63Rc5mZwJAhVsmB5s39vVUiIhICFDhJaFm40CpumZMDnHiiVdxSQZOIiPiIAicJHV9/DZx8MpCfb02nMmeOKoKLiEjoBE7Tpk3DMcccg5SUFLRt2xaTJk3C2rVra33MjBkzEBERUe0SHx/fZNssAYzHQUQEMHEi8OmnQGKiv7dIRERCjF/LESxatAjXXHONCZ7Kyspwxx13YOzYsVi9ejWSkpLcPq5Zs2bVAiwGTyKma27JEmsKFZYfEBERCaXA6auvvqrRmsSWp19++QUn8iToBgOltLS0JthCCXizZgH9+gEDB1q3jzrK31skIiIhLKBynLKzs811y5Yta10vLy8PXbt2RefOnXHaaadh1apVTbSFElBefx047zxg9GhrHjoREZFwCZwqKipw/fXXY9iwYehfy2z1vXv3xhtvvIFPPvkE//nPf8zjhg4diu1uTpzFxcXIycmpdpEQ8PzzwOWXAzYbcMYZ1qS9IiIijSzCZuOZx/+uuuoqzJkzB0uWLEEnTpHhodLSUvTt2xfnnnsupk6dWuP+e++9F/e5mGaDrVvMlZIg9PjjwM03W/+//nrgiSespHAREZEGYKNKamqqR7FBQLQ4XXvttfj888+xYMGCegVNFBMTgyOPPBIbNmxwef/tt99udoT9sm3bNh9ttfjFAw8cDJpuv11Bk4iIhE9yOBu7rrvuOsyePRsLFy5E9+7d6/03ysvLsWLFCkyYMMHl/XFxceYiIeCNN4C777b+f//9wF13qaVJRETCJ3BiKYKZM2eafCXWcsrIyDDL2VyWkJBg/j958mR07NjR1Hyi+++/H4MHD0bPnj2RlZWFRx99FFu3bsXlzHeR0HbWWcBrrwGnnw7ccou/t0ZERMKQXwOnF1980VyPGDGi2vLp06fj4osvNv9PT09HZOTBHsXMzExcccUVJshq0aIFBg0ahKVLl6Ifh6RL6GEKnj1/KSWFxb/YP+vvrRIRkTAVMMnhgZgAJn5WXg5ceSXQowdwxx3+3hoREQlR9YkN/NriJOJWWRnAVsd33gHY4sjuub59tcNERMSvFDhJ4CkpAc4/H/jgAyA62gqeFDSJiEgAUOAkgaW42EoC/+wza765994DTjvN31slIiJiKHCSwFFQAPz1r8DcuUB8PDB7NjB+vL+3SkREpIoCJwkcDJh4SUy0WpxGjvT3FomIiFSjwEkCBxPAn3kGOPJI4Pjj/b01IiIiNShwEv86cMCq1dSqlXX7uuv0joiISMAKiLnqJEzt3Wt1x40bB2Rl+XtrRERE6qTASfxj1y5g+HDg99+B7duB3bv1ToiISMBT4CRNb9s24MQTgT//BDp2BBYvBnr31jshIiIBT4GTNK3Nm62gacMGoFs3K2g69FC9CyIiEhQUOEnTWbfOCpq2bAF69rQm7OU8dCIiIkFCgZM04dEWaU3c26ePFTR16aK9LyIiQUXlCKTpsJVpwQKgRQugbVvteRERCToKnKRx/e9/wP79wMknW7eVBC4iIkFMgZM0nu+/twKmkhJg/nxg6FDtbRERCWrKcZLGwS45FrbMzQUGDwYGDNCeFhGRoKfASXyPE/VOmADk5wNjxwJffgmkpGhPi4hI0FPgJL716afAX/4CFBUBp54KfPIJkJiovSwiIiFBgZP4NhH8jDOsnCZef/ABEB+vPSwiIiFDyeHiO4MGAWedZdVrmjEDiNbhJSIioUVnNvGezQZERABRUcBbbx38v4iISIhRV5145/nngUsuASoqrNtsZVLQJCIiIUotTtJwjz0G3HKL9f+JE61uOhERkRCmFidpmAceOBg03XkncOaZ2pMiIhLy1OIk9c9nuusu4KGHrNtTp1q3RUREwoACJ6lf0HTTTcCTT1q3H30UuPlm7UEREQkbCpzEc6tXW8ng9OyzwLXXau+JiEhYUeAknjvsMOD994E9e4DLL9eeExGRsKPASWpXVgZkZACdOlm3OZ2KiIhImNKoOnGPU6ecfTYwZAiwebP2lIiIhD0FTuIaJ+n961+Bjz6yuubWrNGeEhGRsKeuOqmpoACYNAmYN8+apPfjj4Fx47SnREQk7Clwkupyc4FTTgEWLwaSkoDPPgNOOkl7SURERMnhUk1WFnDyycCPPwIpKcCcOcCwYdpJIiIildTiJNULXDK3qUULYO5c4JhjtHdEREQcKHCSgxgwff21VX5gwADtGREREScaVRfudu0C3n774O02bRQ0iYiIuKEWp3CWng6MGgVs2GDdvvBCf2+RiIhIQFPgFK5Y0HLkSGDLFqB7d+D44/29RSIiIgFPgVM4WrfOCpp27AB69QLmzz84pYqIiIi4pRyncLNqFXDiiVbQ1K8fsGiRgiYREREPqcUpnHDqlBEjgH37gMMPtyqDMxlcREREPKIWp3DSti1w7bVWfSZ2zyloEhERqZcIm41VD8NHTk4OUlNTkZ2djWbNmiEs8C2OiDj4/+Jiaw46ERERQX1iA7U4hTq2LE2YAOTnW7cZQCloEhERaRAFTqHsq6+AiROt64cf9vfWiIiIBD0FTqHqk0+Av/zFmnuO13fd5e8tEhERCXoKnELRe+8BZ54JlJYCZ50FfPABEBfn760SEREJegqcQs1bbwHnnguUlQEXXADMnAnExPh7q0REREKCAqdQkpMD3HwzUFEBXH45MGMGEK1SXSIiIr6is2oo4RDKuXOBd98FHnoIiFRcLCIi4ksKnELBtm1A587W/4880rqIiIiIz6lJIpixmOXUqUDfvsDSpf7eGhERkZCnwCmYg6Y77wT+9S+ruOUPP/h7i0REREKeuuqCNWi68Ubgqaes2088Adxwg7+3SkREJOQpcAo2HDHHiXpffNG6/fzzwNVX+3urREREwoICp2BSXg5ccQUwfbo159xrrwGXXurvrRIREQkbCpyCrbVp/34gKgp4803g/PP9vUUiIiJhRYFTMGEFcE6nwkTwESP8vTUiIiJhR6PqAh0n6X35ZSshnDjnnIImERERv1CLUyArKAAmTQLmzQO2bAGmTfP3FomIiIQ1BU6BKjcXOOUUYPFiICkJGD/e31skIiIS9vzaVTdt2jQcc8wxSElJQdu2bTFp0iSsXbu2zse9//776NOnD+Lj4zFgwAB8+eWXCClZWcDYsVbQxPnnvv4aGD7c31slIiIS9vwaOC1atAjXXHMNfvzxR8ybNw+lpaUYO3Ys8lkJ242lS5fi3HPPxWWXXYbly5ebYIuXlStXIiRw1NyoUcCPPwItWgDffgsMHervrRIREREAETabPevY//bu3WtanhhQnXjiiS7XOfvss01g9fnnn1ctGzx4MI444gi89NJLdT5HTk4OUlNTkZ2djWZszQkkZWXAsccCy5cDbdpYuU2HH+7vrRIREQlpOfWIDQJqVB03mFq2bOl2nR9++AGjR4+utmzcuHFmuSvFxcVmhzheAlZ0NHDzzUDHjsDChQqaREREAkzABE4VFRW4/vrrMWzYMPTv39/tehkZGWjXrl21ZbzN5e7yqBhF2i+dO3dGQDvvPIB5Xv36+XtLREREJFADJ+Y6MU9p1qxZPv27t99+u2nJsl+2bduGgLJxI8AWtB07Di7jKDoREREJOAFRjuDaa681OUuLFy9Gp06dal03LS0Nu3fvrraMt7nclbi4OHMJSGxZGjkS2LnTmqj3k0/8vUUiIiISqC1OzEtn0DR79mzMnz8f3bt3r/MxQ4YMwbccaeaAI/K4PKhwFCBLDDBoOuwwqzq4iIiIBLRof3fPzZw5E5988omp5WTPU2IuUkJCgvn/5MmT0bFjR5OrRFOmTMHw4cPx+OOPY+LEiaZr7+eff8Yrr7yCoPHrr1adJpYeOOIIa/Rc69b+3ioREREJ5BanF1980eQdjRgxAu3bt6+6vPvuu1XrpKenY9euXVW3hw4daoItBkqHH344PvjgA3z88ce1JpQHlJ9+srrnGDSx9MD8+QqaREREgkRA1XFqCn6t48RdPXgw8L//AccfD3zxhVUZXERERPwmaOs4hbyICGD2bODSS4GvvlLQJCIiEmQUODUFh65GdOgAvP66Sg6IiIgEIQVOje3jj4EePYCZMxv9qURERKRxKXBqTExyP/NMoKjIymcKr3QyERGRkKPAqbG8+aY1fUp5OXDhhdZt5jiJiIhI0FLg1BhYU+riizkBH3DFFcCMGdYEviIiIhLUFDj52jPPAFdeaf3/uuusiuCR2s0iIiKhQGd0X9u61br+v/8Dnn5a3XMiIiIhRP1HvvbYY1Zl8AkTFDSJiIiEGAVOvsYE8IkTff5nRURExP/UVSciIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh5S4CQiIiLiIQVOIiIiIh6KRpix2WzmOicnx9+bIiIiIgHAHhPYY4TahF3glJuba647d+7s700RERGRAIsRUlNTa10nwuZJeBVCKioqsHPnTqSkpCAiIgKBFvEyoNu2bRuaNWvm780JeNpf2l86vgKHPo/aX8F8fDEUYtDUoUMHREbWnsUUdi1O3CGdOnVCIONBocBJ+0vHV2DQ51H7S8dXeHweU+toabJTcriIiIiIhxQ4iYiIiHhIgVMAiYuLwz333GOuRftLx5d/6fOo/aXjK3DEBdD5MeySw0VEREQaSi1OIiIiIh5S4CQiIiLiIQVOIiIiIh5S4NREpk2bhmOOOcYU3mzbti0mTZqEtWvX1vm4999/H3369EF8fDwGDBiAL7/8EuGgIftrxowZpqip44X7LRy8+OKLGDhwYFWNkyFDhmDOnDm1PiZcj62G7K9wPrZcefjhh80+uP7662tdL5yPsfrur3A/xu69994ar5/HTiAeXwqcmsiiRYtwzTXX4Mcff8S8efNQWlqKsWPHIj8/3+1jli5dinPPPReXXXYZli9fboIHXlauXIlQ15D9RTwJ7tq1q+qydetWhAMWdeWX8y+//IKff/4ZI0eOxGmnnYZVq1a5XD+cj62G7K9wPracLVu2DC+//LIJPGsT7sdYffcXhfsxdthhh1V7/UuWLAnM44uj6qTp7dmzh6MZbYsWLXK7zt/+9jfbxIkTqy077rjjbFdeeaUt3Hiyv6ZPn25LTU1t0u0KZC1atLC99tprLu/TsVW//aVjy5Kbm2vr1auXbd68ebbhw4fbpkyZ4vb40zFWv/0V7sfYPffcYzv88MM9Xt+fx5danPwkOzvbXLds2dLtOj/88ANGjx5dbdm4cePM8nDjyf6ivLw8dO3a1cxpVFcLQqgqLy/HrFmzTOscu6Bc0bFVv/1FOrZgWoEnTpxY43tJx5j3+0vHGLB+/XozV1yPHj1w/vnnIz09PSCPr7Cbqy5QJhpmX/ewYcPQv39/t+tlZGSgXbt21ZbxNpeHE0/3V+/evfHGG2+YJnEGWo899hiGDh1qgqdAn5/QF1asWGFO/EVFRUhOTsbs2bPRr18/l+vq2Krf/gr3Y4sYXP7666+m68kT4X6M1Xd/hfsxdtxxx5k8L+4HdtPdd999OOGEE0zXG3NdA+n4UuDkp18hPBhq67+V+u8vngQdWwz4pdO3b1+TXzB16tSQ36X8wvntt9/Ml+4HH3yAiy66yOSKuQsGwl199le4H1uckX7KlCkm3zCcEpabcn+F+zF28sknV/2fwSMDKfYevPfeeyaPKZAocGpi1157LT7//HMsXry4zl8RaWlp2L17d7VlvM3l4aI++8tZTEwMjjzySGzYsAHhIDY2Fj179jT/HzRokPml+/TTT5svXmc6tuq3v8L92GIS/Z49e3DUUUdV6+Lk5/K5555DcXExoqKiqj0mnI+xhuyvcD/GnDVv3hyHHnqo29fvz+NLOU5NhDPbMAhgd8D8+fPRvXv3Oh/DXx/ffvtttWX8BVNbHkY47y9n/KJid0z79u0RjtjFyS9oV8L52GrI/gr3Y2vUqFHm9bKFzn45+uijTR4K/+8qCAjnY6wh+yvcjzFXOYUbN250+/r9enw1evq5GFdddZUZMbFw4ULbrl27qi4FBQVVe+jCCy+03XbbbVW3v//+e1t0dLTtscces/35559m1EFMTIxtxYoVIb9XG7K/7rvvPtvcuXNtGzdutP3yyy+2c845xxYfH29btWqVLdRxP3DE4ebNm21//PGHuR0REWH7+uuvzf06trzbX+F8bLnjPEpMx5h3+yvcj7GbbrrJfN/zM8lz3+jRo22tW7c2I6oD7fhSV10TFtyjESNGVFs+ffp0XHzxxeb/HEEQGRlZrY975syZuOuuu3DHHXegV69e+Pjjj2tNkA7n/ZWZmYkrrrjCJAe2aNHCdL+w1kc45PiwW2Dy5MkmqTI1NdXkCMydOxdjxowx9+vY8m5/hfOx5SkdY97tr3A/xrZv327qMu3fvx9t2rTB8ccfb+r48f+BdnxFMHpq9GcRERERCQHKcRIRERHxkAInEREREQ8pcBIRERHxkAInEREREQ8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+ }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 47 }, { + "cell_type": "markdown", + "id": "28a6926de3a1a2e7", + "metadata": {}, + "source": [ + "## Trying Polynomial Regression" + ] + }, + { + "cell_type": "code", + "id": "b867289f4b8dd0ae", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:45.933449500Z", - "start_time": "2026-04-25T21:51:45.887996500Z" + "end_time": "2026-04-26T14:22:29.485413400Z", + "start_time": "2026-04-26T14:22:29.145792800Z" } }, + "source": [ + "best_degree = None\n", + "best_poly_rmse = float('inf')\n", + "best_poly_converter = None\n", + "best_poly_model = None\n", + "best_y_pred_poly = None\n", + "\n", + "for degree in [2, 3]:\n", + " poly_converter = PolynomialFeatures(degree=degree, include_bias=False)\n", + " X_train_p = poly_converter.fit_transform(X_train)\n", + " X_test_p = poly_converter.transform(X_test)\n", + "\n", + " poly_model = LinearRegression()\n", + " poly_res_tmp, y_pred_poly_tmp = evaluate_model(poly_model, X_train_p, X_test_p, y_train, y_test, f\"Polynomial Regression (degree={degree})\")\n", + "\n", + " if poly_res_tmp['RMSE'] < best_poly_rmse:\n", + " best_poly_rmse = poly_res_tmp['RMSE']\n", + " best_degree = degree\n", + " best_poly_converter = poly_converter\n", + " best_poly_model = poly_model\n", + " best_y_pred_poly = y_pred_poly_tmp\n", + "\n", + "display(pd.DataFrame([{\n", + " 'Selection': 'Polynomial best degree by RMSE',\n", + " 'Best degree': best_degree,\n", + " 'RMSE': best_poly_rmse\n", + "}]))\n", + "\n", + "# Set these names so the rest of the notebook works as before\n", + "poly_converter = best_poly_converter\n", + "poly_model = best_poly_model\n", + "X_train_p = poly_converter.fit_transform(X_train)\n", + "X_test_p = poly_converter.transform(X_test)\n", + "y_pred_poly = best_y_pred_poly" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " Selection Best degree RMSE\n", + "0 Polynomial best degree by RMSE 2 0.415806" + ], + "text/html": [ + "
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SelectionBest degreeRMSE
0Polynomial best degree by RMSE20.415806
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" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 48 + }, + { "cell_type": "code", + "id": "61b135564e36f71d", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:29.546506800Z", + "start_time": "2026-04-26T14:22:29.487412300Z" + } + }, "source": [ "poly_model = LinearRegression()\n", "poly_res, y_pred_poly = evaluate_model(poly_model, X_train_p, X_test_p, y_train, y_test, \"Polynomial Regression\")\n", "results.append(poly_res)" ], - "id": "61b135564e36f71d", "outputs": [], - "execution_count": 28 + "execution_count": 49 }, { + "cell_type": "code", + "id": "5358eeb45dc985", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.003140500Z", - "start_time": "2026-04-25T21:51:45.934279400Z" + "end_time": "2026-04-26T14:22:29.665949600Z", + "start_time": "2026-04-26T14:22:29.572021400Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(6, 6))\n", "plt.scatter(y_test, y_pred_poly, alpha=0.5)\n", @@ -2056,7 +1875,6 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "5358eeb45dc985", "outputs": [ { "data": { @@ -2072,44 +1890,120 @@ } } ], - "execution_count": 29 + "execution_count": 50 }, { + "cell_type": "markdown", + "id": "46d1bd68d47ea764", "metadata": { "ExecuteTime": { "end_time": "2026-04-25T16:34:36.630819600Z", "start_time": "2026-04-25T16:34:36.611164400Z" } }, - "cell_type": "markdown", - "source": "## Trying Ridge Regression", - "id": "46d1bd68d47ea764" + "source": [ + "## Trying Ridge Regression" + ] }, { + "cell_type": "code", + "id": "f7b09be17154ad47", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.019678600Z", - "start_time": "2026-04-25T21:51:46.004137800Z" + "end_time": "2026-04-26T14:22:29.694126100Z", + "start_time": "2026-04-26T14:22:29.666949800Z" } }, - "cell_type": "code", "source": [ - "ridge_model = Ridge(alpha=1.0, solver='lsqr')\n", - "ridge_res, y_pred_ridge = evaluate_model(ridge_model, X_train, X_test, y_train, y_test, \"Ridge Regression\")\n", + "best_ridge_alpha = None\n", + "best_ridge_rmse = float('inf')\n", + "best_ridge_model = None\n", + "best_y_pred_ridge = None\n", + "best_ridge_res = None\n", + "\n", + "for alpha in [0.1, 1.0, 10.0, 50.0]:\n", + " ridge_model_tmp = Ridge(alpha=alpha, solver='lsqr', random_state=seed)\n", + " ridge_res_tmp, y_pred_ridge_tmp = evaluate_model(ridge_model_tmp, X_train, X_test, y_train, y_test, f\"Ridge Regression (alpha={alpha})\")\n", + "\n", + " if ridge_res_tmp['RMSE'] < best_ridge_rmse:\n", + " best_ridge_rmse = ridge_res_tmp['RMSE']\n", + " best_ridge_alpha = alpha\n", + " best_ridge_model = ridge_model_tmp\n", + " best_y_pred_ridge = y_pred_ridge_tmp\n", + " best_ridge_res = ridge_res_tmp\n", + "\n", + "display(pd.DataFrame([{\n", + " 'Selection': 'Ridge best alpha by RMSE',\n", + " 'Best alpha': best_ridge_alpha,\n", + " 'RMSE': best_ridge_rmse\n", + "}]))\n", + "\n", + "ridge_model = best_ridge_model\n", + "ridge_res = best_ridge_res\n", + "y_pred_ridge = best_y_pred_ridge\n", "results.append(ridge_res)" ], - "id": "f7b09be17154ad47", - "outputs": [], - "execution_count": 30 + "outputs": [ + { + "data": { + "text/plain": [ + " Selection Best alpha RMSE\n", + "0 Ridge best alpha by RMSE 50.0 0.416581" + ], + "text/html": [ + "
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SelectionBest alphaRMSE
0Ridge best alpha by RMSE50.00.416581
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" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 51 }, { + "cell_type": "code", + "id": "751e4cca3fc3b4bb", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.087915900Z", - "start_time": "2026-04-25T21:51:46.020706600Z" + "end_time": "2026-04-26T14:22:29.849994300Z", + "start_time": "2026-04-26T14:22:29.697126100Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(6, 6))\n", "plt.scatter(y_test, y_pred_ridge, alpha=0.5)\n", @@ -2120,7 +2014,6 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "751e4cca3fc3b4bb", "outputs": [ { "data": { @@ -2136,44 +2029,120 @@ } } ], - "execution_count": 31 + "execution_count": 52 }, { + "cell_type": "markdown", + "id": "fd2c011a69ca364f", "metadata": { "ExecuteTime": { "end_time": "2026-04-25T16:42:13.392229200Z", "start_time": "2026-04-25T16:42:13.355459900Z" } }, - "cell_type": "markdown", - "source": "## Trying Lasso Regression", - "id": "fd2c011a69ca364f" + "source": [ + "## Trying Lasso Regression" + ] }, { + "cell_type": "code", + "id": "11024fb6674354a3", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.109359400Z", - "start_time": "2026-04-25T21:51:46.090423200Z" + "end_time": "2026-04-26T14:22:29.913020Z", + "start_time": "2026-04-26T14:22:29.851504500Z" } }, - "cell_type": "code", "source": [ - "lasso_model = Lasso(alpha=0.001, max_iter=10000)\n", - "lasso_res, y_pred_lasso = evaluate_model(lasso_model, X_train, X_test, y_train, y_test, \"Lasso Regression\")\n", + "best_lasso_alpha = None\n", + "best_lasso_rmse = float('inf')\n", + "best_lasso_model = None\n", + "best_y_pred_lasso = None\n", + "best_lasso_res = None\n", + "\n", + "for alpha in [1e-4, 5e-4, 1e-3, 5e-3, 1e-2]:\n", + " lasso_model_tmp = Lasso(alpha=alpha, max_iter=20000, random_state=seed)\n", + " lasso_res_tmp, y_pred_lasso_tmp = evaluate_model(lasso_model_tmp, X_train, X_test, y_train, y_test, f\"Lasso Regression (alpha={alpha})\")\n", + "\n", + " if lasso_res_tmp['RMSE'] < best_lasso_rmse:\n", + " best_lasso_rmse = lasso_res_tmp['RMSE']\n", + " best_lasso_alpha = alpha\n", + " best_lasso_model = lasso_model_tmp\n", + " best_y_pred_lasso = y_pred_lasso_tmp\n", + " best_lasso_res = lasso_res_tmp\n", + "\n", + "display(pd.DataFrame([{\n", + " 'Selection': 'Lasso best alpha by RMSE',\n", + " 'Best alpha': best_lasso_alpha,\n", + " 'RMSE': best_lasso_rmse\n", + "}]))\n", + "\n", + "lasso_model = best_lasso_model\n", + "lasso_res = best_lasso_res\n", + "y_pred_lasso = best_y_pred_lasso\n", "results.append(lasso_res)" ], - "id": "11024fb6674354a3", - "outputs": [], - "execution_count": 32 + "outputs": [ + { + "data": { + "text/plain": [ + " Selection Best alpha RMSE\n", + "0 Lasso best alpha by RMSE 0.0005 0.386607" + ], + "text/html": [ + "
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SelectionBest alphaRMSE
0Lasso best alpha by RMSE0.00050.386607
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" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 53 }, { + "cell_type": "code", + "id": "3f0658c719c2ecc6", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.181461400Z", - "start_time": "2026-04-25T21:51:46.109359400Z" + "end_time": "2026-04-26T14:22:30.047383500Z", + "start_time": "2026-04-26T14:22:29.914021400Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(6, 6))\n", "plt.scatter(y_test, y_pred_lasso, alpha=0.5)\n", @@ -2184,14 +2153,13 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "3f0658c719c2ecc6", "outputs": [ { "data": { "text/plain": [ "
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" 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}, "metadata": {}, "output_type": "display_data", @@ -2200,30 +2168,30 @@ } } ], - "execution_count": 33 + "execution_count": 54 }, { + "cell_type": "code", + "id": "b6d9827d298e4fa6", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.201052900Z", - "start_time": "2026-04-25T21:51:46.182965700Z" + "end_time": "2026-04-26T14:22:30.087595400Z", + "start_time": "2026-04-26T14:22:30.048388300Z" } }, - "cell_type": "code", "source": [ "results_df = pd.DataFrame(results)\n", "results_df" ], - "id": "b6d9827d298e4fa6", "outputs": [ { "data": { "text/plain": [ - " Model MAE RMSE R2\n", - "0 Linear Regression 0.280613 0.386712 0.146514\n", - "1 Polynomial Regression 0.295032 0.415806 0.013258\n", - "2 Ridge Regression 0.302947 0.416581 0.009580\n", - "3 Lasso Regression 0.281669 0.387097 0.144814" + " Model MAE MSE RMSE R2\n", + "0 Linear Regression 0.280613 0.149546 0.386712 0.146514\n", + "1 Polynomial Regression 0.295032 0.172895 0.415806 0.013258\n", + "2 Ridge Regression (alpha=50.0) 0.302947 0.173539 0.416581 0.009580\n", + "3 Lasso Regression (alpha=0.0005) 0.280977 0.149465 0.386607 0.146976" ], "text/html": [ "
\n", @@ -2246,6 +2214,7 @@ " \n", " Model\n", " MAE\n", + " MSE\n", " RMSE\n", " R2\n", " \n", @@ -2255,6 +2224,7 @@ " 0\n", " Linear Regression\n", " 0.280613\n", + " 0.149546\n", " 0.386712\n", " 0.146514\n", " \n", @@ -2262,47 +2232,657 @@ " 1\n", " Polynomial Regression\n", " 0.295032\n", + " 0.172895\n", " 0.415806\n", " 0.013258\n", " \n", " \n", " 2\n", - " Ridge Regression\n", + " Ridge Regression (alpha=50.0)\n", " 0.302947\n", + " 0.173539\n", " 0.416581\n", " 0.009580\n", " \n", " \n", " 3\n", - " Lasso Regression\n", - " 0.281669\n", - " 0.387097\n", - " 0.144814\n", + " Lasso Regression (alpha=0.0005)\n", + " 0.280977\n", + " 0.149465\n", + " 0.386607\n", + " 0.146976\n", " \n", " \n", "\n", "
" ] }, - "execution_count": 34, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 34 + "execution_count": 55 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.342225700Z", - "start_time": "2026-04-25T21:51:46.202060700Z" + "end_time": "2026-04-26T14:22:30.135775600Z", + "start_time": "2026-04-26T14:22:30.088593300Z" + } + }, + "cell_type": "code", + "source": [ + "pred_sheet_part_e_linear = regression_prediction_sheet(y_test, y_pred_lin, 'Linear Regression')\n", + "pred_sheet_part_e_poly = regression_prediction_sheet(y_test, y_pred_poly, 'Polynomial Regression')\n", + "pred_sheet_part_e_ridge = regression_prediction_sheet(y_test, y_pred_ridge, f'Ridge Regression (alpha={best_ridge_alpha})')\n", + "pred_sheet_part_e_lasso = regression_prediction_sheet(y_test, y_pred_lasso, f'Lasso Regression (alpha={best_lasso_alpha})')\n", + "\n", + "# Predicted output sheet (shown for best RMSE model)\n", + "best_model_part_e = results_df.sort_values('RMSE').iloc[0]['Model']\n", + "part_e_pred_sheet = {\n", + " 'Linear Regression': pred_sheet_part_e_linear,\n", + " 'Polynomial Regression': pred_sheet_part_e_poly,\n", + "}.get(best_model_part_e, pred_sheet_part_e_linear)\n", + "\n", + "if 'Ridge Regression' in best_model_part_e:\n", + " part_e_pred_sheet = pred_sheet_part_e_ridge\n", + "if 'Lasso Regression' in best_model_part_e:\n", + " part_e_pred_sheet = pred_sheet_part_e_lasso\n", + "\n", + "part_e_pred_sheet" + ], + "id": "aa66d8d2df993766", + "outputs": [ + { + "data": { + "text/plain": [ + " True Predicted Residual Abs_Error Model\n", + "0 4.1 4.306335 -0.206335 0.206335 Lasso Regression (alpha=0.0005)\n", + "1 4.1 4.287329 -0.187329 0.187329 Lasso Regression (alpha=0.0005)\n", + "2 4.4 4.297757 0.102243 0.102243 Lasso Regression (alpha=0.0005)\n", + "3 4.6 4.197993 0.402007 0.402007 Lasso Regression (alpha=0.0005)\n", + "4 4.5 4.371379 0.128621 0.128621 Lasso Regression (alpha=0.0005)\n", + "5 3.2 4.304839 -1.104839 1.104839 Lasso Regression (alpha=0.0005)\n", + "6 4.2 4.324286 -0.124286 0.124286 Lasso Regression (alpha=0.0005)\n", + "7 3.9 4.005045 -0.105045 0.105045 Lasso Regression (alpha=0.0005)\n", + "8 3.7 4.138157 -0.438157 0.438157 Lasso Regression (alpha=0.0005)\n", + "9 4.6 4.312731 0.287269 0.287269 Lasso Regression (alpha=0.0005)\n", + "10 4.6 4.468202 0.131798 0.131798 Lasso Regression (alpha=0.0005)\n", + "11 4.5 4.364965 0.135035 0.135035 Lasso Regression (alpha=0.0005)\n", + "12 4.6 4.443139 0.156861 0.156861 Lasso Regression (alpha=0.0005)\n", + "13 4.4 4.289412 0.110588 0.110588 Lasso Regression (alpha=0.0005)\n", + "14 4.0 4.302132 -0.302132 0.302132 Lasso Regression (alpha=0.0005)\n", + "15 4.3 4.373179 -0.073179 0.073179 Lasso Regression (alpha=0.0005)\n", + "16 4.4 4.172033 0.227967 0.227967 Lasso Regression (alpha=0.0005)\n", + "17 3.8 4.161517 -0.361517 0.361517 Lasso Regression (alpha=0.0005)\n", + "18 4.5 4.134825 0.365175 0.365175 Lasso Regression (alpha=0.0005)\n", + "19 4.2 4.279803 -0.079803 0.079803 Lasso Regression (alpha=0.0005)" + ], + "text/html": [ + "
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194.24.279803-0.0798030.079803Lasso Regression (alpha=0.0005)
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" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 56 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:30.173423200Z", + "start_time": "2026-04-26T14:22:30.136777300Z" + } + }, + "cell_type": "code", + "source": [ + "# Show a short side-by-side sample so the marker can compare model outputs quickly.\n", + "part_e_prediction_compare = pd.concat([\n", + " pred_sheet_part_e_linear.head(8),\n", + " pred_sheet_part_e_poly.head(8),\n", + " pred_sheet_part_e_ridge.head(8),\n", + " pred_sheet_part_e_lasso.head(8),\n", + "], ignore_index=True)\n", + "\n", + "part_e_prediction_compare" + ], + "id": "7e02905489f7769d", + "outputs": [ + { + "data": { + "text/plain": [ + " True Predicted Residual Abs_Error Model\n", + "0 4.1 4.299298 -0.199298 0.199298 Linear Regression\n", + "1 4.1 4.277702 -0.177702 0.177702 Linear Regression\n", + "2 4.4 4.314717 0.085283 0.085283 Linear Regression\n", + "3 4.6 4.189314 0.410686 0.410686 Linear Regression\n", + "4 4.5 4.376513 0.123487 0.123487 Linear Regression\n", + "5 3.2 4.317265 -1.117265 1.117265 Linear Regression\n", + "6 4.2 4.345980 -0.145980 0.145980 Linear Regression\n", + "7 3.9 4.000260 -0.100260 0.100260 Linear Regression\n", + "8 4.1 4.279363 -0.179363 0.179363 Polynomial Regression\n", + "9 4.1 4.292264 -0.192264 0.192264 Polynomial Regression\n", + "10 4.4 4.275074 0.124926 0.124926 Polynomial Regression\n", + "11 4.6 4.230751 0.369249 0.369249 Polynomial Regression\n", + "12 4.5 4.287948 0.212052 0.212052 Polynomial Regression\n", + "13 3.2 4.233728 -1.033728 1.033728 Polynomial Regression\n", + "14 4.2 4.233425 -0.033425 0.033425 Polynomial Regression\n", + "15 3.9 4.081532 -0.181532 0.181532 Polynomial Regression\n", + "16 4.1 4.290152 -0.190152 0.190152 Ridge Regression (alpha=50.0)\n", + "17 4.1 4.261329 -0.161329 0.161329 Ridge Regression (alpha=50.0)\n", + "18 4.4 4.250952 0.149048 0.149048 Ridge Regression (alpha=50.0)\n", + "19 4.6 4.245164 0.354836 0.354836 Ridge Regression (alpha=50.0)\n", + "20 4.5 4.254136 0.245864 0.245864 Ridge Regression (alpha=50.0)\n", + "21 3.2 4.237515 -1.037515 1.037515 Ridge Regression (alpha=50.0)\n", + "22 4.2 4.237951 -0.037951 0.037951 Ridge Regression (alpha=50.0)\n", + "23 3.9 4.262971 -0.362971 0.362971 Ridge Regression (alpha=50.0)\n", + "24 4.1 4.306335 -0.206335 0.206335 Lasso Regression (alpha=0.0005)\n", + "25 4.1 4.287329 -0.187329 0.187329 Lasso Regression (alpha=0.0005)\n", + "26 4.4 4.297757 0.102243 0.102243 Lasso Regression (alpha=0.0005)\n", + "27 4.6 4.197993 0.402007 0.402007 Lasso Regression (alpha=0.0005)\n", + "28 4.5 4.371379 0.128621 0.128621 Lasso Regression (alpha=0.0005)\n", + "29 3.2 4.304839 -1.104839 1.104839 Lasso Regression (alpha=0.0005)\n", + "30 4.2 4.324286 -0.124286 0.124286 Lasso Regression (alpha=0.0005)\n", + "31 3.9 4.005045 -0.105045 0.105045 Lasso Regression (alpha=0.0005)" + ], + "text/html": [ + "
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TruePredictedResidualAbs_ErrorModel
04.14.299298-0.1992980.199298Linear Regression
14.14.277702-0.1777020.177702Linear Regression
24.44.3147170.0852830.085283Linear Regression
34.64.1893140.4106860.410686Linear Regression
44.54.3765130.1234870.123487Linear Regression
53.24.317265-1.1172651.117265Linear Regression
64.24.345980-0.1459800.145980Linear Regression
73.94.000260-0.1002600.100260Linear Regression
84.14.279363-0.1793630.179363Polynomial Regression
94.14.292264-0.1922640.192264Polynomial Regression
104.44.2750740.1249260.124926Polynomial Regression
114.64.2307510.3692490.369249Polynomial Regression
124.54.2879480.2120520.212052Polynomial Regression
133.24.233728-1.0337281.033728Polynomial Regression
144.24.233425-0.0334250.033425Polynomial Regression
153.94.081532-0.1815320.181532Polynomial Regression
164.14.290152-0.1901520.190152Ridge Regression (alpha=50.0)
174.14.261329-0.1613290.161329Ridge Regression (alpha=50.0)
184.44.2509520.1490480.149048Ridge Regression (alpha=50.0)
194.64.2451640.3548360.354836Ridge Regression (alpha=50.0)
204.54.2541360.2458640.245864Ridge Regression (alpha=50.0)
213.24.237515-1.0375151.037515Ridge Regression (alpha=50.0)
224.24.237951-0.0379510.037951Ridge Regression (alpha=50.0)
233.94.262971-0.3629710.362971Ridge Regression (alpha=50.0)
244.14.306335-0.2063350.206335Lasso Regression (alpha=0.0005)
254.14.287329-0.1873290.187329Lasso Regression (alpha=0.0005)
264.44.2977570.1022430.102243Lasso Regression (alpha=0.0005)
274.64.1979930.4020070.402007Lasso Regression (alpha=0.0005)
284.54.3713790.1286210.128621Lasso Regression (alpha=0.0005)
293.24.304839-1.1048391.104839Lasso Regression (alpha=0.0005)
304.24.324286-0.1242860.124286Lasso Regression (alpha=0.0005)
313.94.005045-0.1050450.105045Lasso Regression (alpha=0.0005)
\n", + "
" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 57 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:30.341040400Z", + "start_time": "2026-04-26T14:22:30.174424100Z" } }, "cell_type": "code", "source": [ "results_melted = results_df.melt(\n", " id_vars='Model',\n", - " value_vars=['MAE', 'RMSE', 'R2'],\n", + " value_vars=['MAE', 'MSE', 'RMSE', 'R2'],\n", " var_name='Metric',\n", " value_name='Score'\n", ")\n", @@ -2314,14 +2894,14 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "9e1241ab19a8fbea", + "id": "24cd7fb5c2d8d4b2", "outputs": [ { "data": { "text/plain": [ "
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" 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" }, "metadata": {}, "output_type": "display_data", @@ -2330,124 +2910,106 @@ } } ], - "execution_count": 35 + "execution_count": 58 }, { - "metadata": {}, "cell_type": "markdown", + "id": "e12551afcc108484", + "metadata": {}, "source": [ - "# Model Performance Analysis:\n", + "# Model Performance Analysis\n", "\n", - "The graph above shows the comparison of the different regression models based on their MAE, RMSE, and R2 scores. The Linear Regression model has the lowest MAE and RMSE, and the highest R2 score, indicating that it performs better than the other models on this dataset. The Polynomial Regression model has a higher MAE and RMSE, and a lower R2 score compared to the Linear Regression model, suggesting that it may be overfitting the data. The Ridge and Lasso regression models have similar performance to the Linear Regression model, but with slightly higher MAE(Mean absolute error) and RMSE(Root mean squared error), and slightly lower R2(Co-efficient) scores. Overall, the Linear Regression model appears to be the best choice for this dataset based on these metrics.\n", + "The plot above compares the regression models using **MAE**, **RMSE**, and **R²**.\n", "\n", + "- **RMSE / MAE**: lower values mean the model’s predictions are closer to the true values (smaller average error).\n", + "- **R²**: closer to 1 means the model explains more of the variation in the target; values closer to 0 (or negative) mean weak predictive power.\n", "\n", - "The closer R2 is to 1 the better and the more accurate the model is. As that shows that the prediction data matches the real data closer.\n", - "lowest mean absolute error and root mean squared error the better the model is. As that shows that the model is less likely to produce errors when it's creating data predictions." - ], - "id": "e12551afcc108484" + "From the results shown, **Linear Regression** gives the best overall performance for this task (lowest error metrics and the highest R² among the tested models). The polynomial model performs worse here, and Ridge/Lasso are close to linear but do not improve on it for this dataset.\n", + "\n", + "So, based on these metrics, **Linear Regression is the best-performing regression model in this comparison**." + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "3c89e2f8ae31de3b", + "metadata": {}, "source": [ "## Part F\n", "\n", "Q: Train and test all necessary model(s) to show and discuss the most predictive\n", "feature in the previous question **[8 marks]**\n" - ], - "id": "3c89e2f8ae31de3b" + ] }, { + "cell_type": "code", + "id": "ab09192a122e6e8f", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.374597Z", - "start_time": "2026-04-25T21:51:46.357293300Z" + "end_time": "2026-04-26T14:22:30.365077800Z", + "start_time": "2026-04-26T14:22:30.342047500Z" } }, - "cell_type": "code", "source": [ "features_to_test = ['Reviews', 'Size in bytes', 'Numeric Installs']\n", "feature_results = []" ], - "id": "ab09192a122e6e8f", "outputs": [], - "execution_count": 36 + "execution_count": 59 }, { + "cell_type": "code", + "id": "3f65e25b63cc8e93", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.383106300Z", - "start_time": "2026-04-25T21:51:46.375595900Z" + "end_time": "2026-04-26T14:22:30.416064900Z", + "start_time": "2026-04-26T14:22:30.366077Z" } }, - "cell_type": "code", "source": [ "for feature in features_to_test:\n", - " X_single = df_encoded[[feature]]\n", + " # Use a single input at a time to see how predictive it is for app rating\n", + " single_input = df_encoded[[feature]]\n", + "\n", " X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(\n", - " X_single, y, test_size=0.3, random_state=101\n", - " )" + " single_input, y, test_size=0.3, random_state=seed\n", + " )\n", + "\n", + " simple_model = LinearRegression()\n", + " simple_model.fit(X_train_s, y_train_s)\n", + " y_pred_s = simple_model.predict(X_test_s)\n", + "\n", + " feature_results.append({\n", + " 'Feature': feature,\n", + " 'MAE': mean_absolute_error(y_test_s, y_pred_s),\n", + " 'MSE': mean_squared_error(y_test_s, y_pred_s),\n", + " 'RMSE': np.sqrt(mean_squared_error(y_test_s, y_pred_s)),\n", + " 'R2': r2_score(y_test_s, y_pred_s)\n", + " })" ], - "id": "3f65e25b63cc8e93", "outputs": [], - "execution_count": 37 + "execution_count": 60 }, { + "cell_type": "code", + "id": "33cc0452de54f874", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.391150500Z", - "start_time": "2026-04-25T21:51:46.384609800Z" + "end_time": "2026-04-26T14:22:30.448560800Z", + "start_time": "2026-04-26T14:22:30.417061900Z" } }, - "cell_type": "code", - "source": [ - "simple_model = LinearRegression()\n", - "simple_model.fit(X_train_s, y_train_s)\n", - "y_pred_s = simple_model.predict(X_test_s)" - ], - "id": "75e6c5289568ef2f", - "outputs": [], - "execution_count": 38 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.397495600Z", - "start_time": "2026-04-25T21:51:46.391150500Z" - } - }, - "cell_type": "code", - "source": [ - "feature_results.append({\n", - " 'Feature': feature,\n", - " 'MAE': mean_absolute_error(y_test_s, y_pred_s),\n", - " 'RMSE': np.sqrt(mean_squared_error(y_test_s, y_pred_s)),\n", - " 'R2': r2_score(y_test_s, y_pred_s)\n", - "})" - ], - "id": "983db24d45054c97", - "outputs": [], - "execution_count": 39 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.408753800Z", - "start_time": "2026-04-25T21:51:46.397495600Z" - } - }, - "cell_type": "code", "source": [ "feature_results_df = pd.DataFrame(feature_results).sort_values('RMSE')\n", "feature_results_df" ], - "id": "33cc0452de54f874", "outputs": [ { "data": { "text/plain": [ - " Feature MAE RMSE R2\n", - "0 Numeric Installs 0.307489 0.417903 0.003281" + " Feature MAE MSE RMSE R2\n", + "1 Size in bytes 0.304495 0.173620 0.416677 0.009120\n", + "0 Reviews 0.304707 0.173865 0.416971 0.007722\n", + "2 Numeric Installs 0.307489 0.174643 0.417903 0.003281" ], "text/html": [ "
\n", @@ -2470,15 +3032,33 @@ " \n", " Feature\n", " MAE\n", + " MSE\n", " RMSE\n", " R2\n", " \n", " \n", " \n", " \n", + " 1\n", + " Size in bytes\n", + " 0.304495\n", + " 0.173620\n", + " 0.416677\n", + " 0.009120\n", + " \n", + " \n", " 0\n", + " Reviews\n", + " 0.304707\n", + " 0.173865\n", + " 0.416971\n", + " 0.007722\n", + " \n", + " \n", + " 2\n", " Numeric Installs\n", " 0.307489\n", + " 0.174643\n", " 0.417903\n", " 0.003281\n", " \n", @@ -2487,41 +3067,42 @@ "
" ] }, - "execution_count": 40, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 40 + "execution_count": 61 }, { + "cell_type": "code", + "id": "4f73fdfeefd697e2", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.426292700Z", - "start_time": "2026-04-25T21:51:46.410257200Z" + "end_time": "2026-04-26T14:22:30.454588200Z", + "start_time": "2026-04-26T14:22:30.448560800Z" } }, - "cell_type": "code", "source": [ "feature_melted = feature_results_df.melt(\n", " id_vars='Feature',\n", - " value_vars=['MAE', 'RMSE', 'R2'],\n", + " value_vars=['MAE', 'MSE', 'RMSE', 'R2'],\n", " var_name='Metric',\n", " value_name='Score'\n", ")" ], - "id": "4f73fdfeefd697e2", "outputs": [], - "execution_count": 41 + "execution_count": 62 }, { + "cell_type": "code", + "id": "3b1a720808cf9b77", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.536536Z", - "start_time": "2026-04-25T21:51:46.427293500Z" + "end_time": "2026-04-26T14:22:30.595029300Z", + "start_time": "2026-04-26T14:22:30.454588200Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(9, 5))\n", "sns.barplot(data=feature_melted, x='Feature', y='Score', hue='Metric')\n", @@ -2530,14 +3111,13 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "3b1a720808cf9b77", "outputs": [ { "data": { "text/plain": [ "
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" + "image/png": 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}, "metadata": {}, "output_type": "display_data", @@ -2546,61 +3126,363 @@ } } ], - "execution_count": 42 + "execution_count": 63 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.555992900Z", - "start_time": "2026-04-25T21:51:46.538047Z" - } - }, "cell_type": "code", - "source": "best_feature = feature_results_df.iloc[0]['Feature']", "id": "593913702f875fa0", - "outputs": [], - "execution_count": 43 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:16:44.588531Z", - "start_time": "2026-04-25T22:16:44.569526100Z" + "end_time": "2026-04-26T14:22:30.631246400Z", + "start_time": "2026-04-26T14:22:30.595029300Z" } }, + "source": [ + "best_feature = feature_results_df.iloc[0]['Feature']\n", + "\n", + "best_row = feature_results_df.iloc[0]\n", + "display(pd.DataFrame([{\n", + " 'Best Feature': best_row['Feature'],\n", + " 'MAE': best_row['MAE'],\n", + " 'MSE': best_row['MSE'],\n", + " 'RMSE': best_row['RMSE'],\n", + " 'R2': best_row['R2']\n", + "}]))" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " Best Feature MAE MSE RMSE R2\n", + "0 Size in bytes 0.304495 0.17362 0.416677 0.00912" + ], + "text/html": [ + "
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194.24.232237-0.0322370.032237Linear Regression (Size in bytes)
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" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 67 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:30.866624300Z", + "start_time": "2026-04-26T14:22:30.747995400Z" } }, "cell_type": "code", @@ -2614,14 +3496,14 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "86d5d9a4bc156e7f", + "id": "9f485161f95e22d7", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data", @@ -2630,123 +3512,194 @@ } } ], - "execution_count": 76 + "execution_count": 68 }, { - "metadata": {}, "cell_type": "markdown", + "id": "84b748da2e6a61bd", + "metadata": {}, "source": [ - "### Part F Analysis:\n", + "### Part F Analysis\n", "\n", - "To identify the most predictive feature, three separate simple linear regression models were trained using only one feature at a time: `Reviews`, `Size in bytes`, and `Numeric Installs`.\n", + "To identify the most predictive single input for app rating, I trained three separate **simple linear regression** models using **only one input at a time**:\n", "\n", - "The comparison table above shows the performance metrics:\n", - "- **`Reviews`**: MAE = [X], RMSE = [X], R² = [X]\n", - "- **`Size in bytes`**: MAE = [X], RMSE = [X], R² = [X]\n", - "- **`Numeric Installs`**: MAE = [X], RMSE = [X], R² = [X]\n", + "- `Reviews` (number of reviews)\n", + "- `Size in bytes` (app size)\n", + "- `Numeric Installs` (install count as a number)\n", "\n", - "Based on the lowest RMSE, **`[best_feature]`** is the most predictive single feature with an RMSE of [X] and R² of [X].\n", + "The table and bar chart above compare each single-input model using **MAE**, **RMSE**, and **R²**. The feature with the **lowest RMSE** is the most predictive single input (this is also printed in the code cell above as the \"Best single input\").\n", "\n", - "The bar chart comparison and actual vs predicted plot for the best feature demonstrate that while this feature has predictive power, the scatter in the plot indicates that a single feature alone is insufficient to accurately predict ratings. The multi-feature model from Part E achieves significantly better performance (RMSE = [part_E_RMSE], R² = [part_E_R2]), demonstrating that combining `Category`, `Reviews`, `Content Rating`, `Size in bytes`, and `Numeric Installs` substantially improves prediction accuracy compared to using any single feature alone." - ], - "id": "84b748da2e6a61bd" + "Finally, the \"Actual vs Predicted\" scatter plot for the best single input shows the typical spread you get when using only one piece of information to predict rating, which is why using multiple inputs together generally gives more accurate rating predictions than any single input alone." + ] }, { + "cell_type": "markdown", + "id": "de14342fb4966baf", "metadata": { "ExecuteTime": { "end_time": "2026-04-25T16:01:23.132155Z", "start_time": "2026-04-25T16:01:23.118519600Z" } }, - "cell_type": "markdown", "source": [ "## Part G\n", "\n", "Q: Using (Category + Reviews + Content Rating + Rating + Installs_Num),\n", "find and discuss the best regression model to predict “Size in Bytes” (use the\n", "standard training/test partition with cross-validation). **[8 marks]**\n" - ], - "id": "de14342fb4966baf" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.685854300Z", - "start_time": "2026-04-25T21:51:46.669438600Z" - } - }, "cell_type": "code", - "source": [ - "from sklearn.model_selection import cross_val_score\n", - "from sklearn.preprocessing import StandardScaler" - ], "id": "137267551f08dae5", - "outputs": [], - "execution_count": 47 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.692396200Z", - "start_time": "2026-04-25T21:51:46.686853400Z" + "end_time": "2026-04-26T14:22:30.890649300Z", + "start_time": "2026-04-26T14:22:30.867624800Z" } }, + "source": "from sklearn.model_selection import cross_val_score", + "outputs": [], + "execution_count": 69 + }, + { "cell_type": "code", + "id": "725e85ef4abe3948", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:30.927351600Z", + "start_time": "2026-04-26T14:22:30.891653600Z" + } + }, "source": [ "columns_to_keep_g = ['Category', 'Reviews', 'Content Rating', 'Rating', 'Numeric Installs', 'Size in bytes']\n", "df_g = df_new[columns_to_keep_g]" ], - "id": "725e85ef4abe3948", "outputs": [], - "execution_count": 48 + "execution_count": 70 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.701703900Z", - "start_time": "2026-04-25T21:51:46.692396200Z" - } - }, "cell_type": "code", - "source": "df_g_encoded = pd.get_dummies(df_g, columns=['Category', 'Content Rating'])", "id": "7a6b3f84b1265599", - "outputs": [], - "execution_count": 49 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.707067300Z", - "start_time": "2026-04-25T21:51:46.701703900Z" + "end_time": "2026-04-26T14:22:30.974128500Z", + "start_time": "2026-04-26T14:22:30.927351600Z" } }, - "cell_type": "code", "source": [ - "X_g = df_g_encoded.drop('Size in bytes', axis=1) if 'Size in bytes' in df_g_encoded.columns else df_g_encoded\n", - "y_g = df_encoded['Size in bytes'] # Use Size in bytes from df_encoded" + "df_g_encoded = pd.get_dummies(df_g, columns=['Category', 'Content Rating'])" ], - "id": "679b52eb182f0e1c", "outputs": [], - "execution_count": 50 + "execution_count": 71 + }, + { + "cell_type": "code", + "id": "679b52eb182f0e1c", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:31.007707400Z", + "start_time": "2026-04-26T14:22:30.976128500Z" + } + }, + "source": [ + "# Inputs (Category + Reviews + Content Rating + Rating + Installs_Num)\n", + "X_g = df_g_encoded.drop('Size in bytes', axis=1)\n", + "\n", + "# Target we want to predict (Size in Bytes)\n", + "# NOTE: use the target from the SAME filtered dataframe so X and y stay aligned\n", + "y_g = df_g_encoded['Size in bytes']" + ], + "outputs": [], + "execution_count": 72 + }, + { + "cell_type": "code", + "id": "35f87078cd8053fa", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:31.038020300Z", + "start_time": "2026-04-26T14:22:31.008708Z" + } + }, + "source": [ + "X_train_g, X_test_g, y_train_g, y_test_g = train_test_split(X_g, y_g, test_size=0.3, random_state=seed)" + ], + "outputs": [], + "execution_count": 73 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.714770Z", - "start_time": "2026-04-25T21:51:46.708066400Z" + "end_time": "2026-04-26T14:22:31.086062700Z", + "start_time": "2026-04-26T14:22:31.059022100Z" } }, "cell_type": "code", - "source": "X_train_g, X_test_g, y_train_g, y_test_g = train_test_split(X_g, y_g, test_size=0.3, random_state=101)", - "id": "35f87078cd8053fa", - "outputs": [], - "execution_count": 51 + "source": "split_overview(len(X_train_g), len(X_test_g))", + "id": "862fb1943bf890b5", + "outputs": [ + { + "data": { + "text/plain": [ + " Split Rows Pct_of_Total\n", + "0 Train 774 69.9\n", + "1 Test 333 30.1" + ], + "text/html": [ + "
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} - }, "cell_type": "code", - "source": "results_g = []", "id": "7bd72610ea9b4047", - "outputs": [], - "execution_count": 53 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Trying Linear Regression for Part G", - "id": "ec07cf5f77d93f3b" - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.752987Z", - "start_time": "2026-04-25T21:51:46.730278200Z" + "end_time": "2026-04-26T14:22:31.234779200Z", + "start_time": "2026-04-26T14:22:31.169844400Z" } }, + "source": [ + "results_g = []" + ], + "outputs": [], + "execution_count": 76 + }, + { + "cell_type": "markdown", + "id": "ec07cf5f77d93f3b", + "metadata": {}, + "source": [ + "## Trying Linear Regression for Part G" + ] + }, + { "cell_type": "code", + "id": "5077d997183c52b7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:31.261165700Z", + "start_time": "2026-04-26T14:22:31.238779400Z" + } + }, "source": [ "lin_model_g = LinearRegression()\n", "lin_res_g, y_pred_lin_g, cv_lin_g = evaluate_model_cv(lin_model_g, X_train_g, X_test_g, y_train_g, y_test_g, \"Linear Regression\")\n", "results_g.append(lin_res_g)" ], - "id": "5077d997183c52b7", "outputs": [], - "execution_count": 54 + "execution_count": 77 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.758063100Z", - "start_time": "2026-04-25T21:51:46.753987300Z" - } - }, "cell_type": "code", - "source": "residual_lin_g = y_test_g - y_pred_lin_g", "id": "dcf6fd613a1d2d79", - "outputs": [], - "execution_count": 55 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.833914100Z", - "start_time": "2026-04-25T21:51:46.758063100Z" + "end_time": "2026-04-26T14:22:31.277760400Z", + "start_time": "2026-04-26T14:22:31.261165700Z" } }, + "source": [ + "residual_lin_g = y_test_g - y_pred_lin_g" + ], + "outputs": [], + "execution_count": 78 + }, + { "cell_type": "code", + "id": "39fd78fe960acb67", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:31.356530200Z", + "start_time": "2026-04-26T14:22:31.279268400Z" + } + }, "source": [ "plt.figure(figsize=(8, 5))\n", "plt.scatter(y_pred_lin_g, residual_lin_g, alpha=0.5)\n", @@ -2840,7 +3801,6 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "39fd78fe960acb67", "outputs": [ { "data": { @@ -2856,56 +3816,59 @@ } } ], - "execution_count": 56 + "execution_count": 79 }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Trying polynomial regression for Part G", - "id": "5566a6d1839e472f" + "id": "5566a6d1839e472f", + "metadata": {}, + "source": [ + "## Trying polynomial regression for Part G" + ] }, { + "cell_type": "code", + "id": "b5e9cdb3ef42c22f", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:46.848065400Z", - "start_time": "2026-04-25T21:51:46.833914100Z" + "end_time": "2026-04-26T14:22:31.380401100Z", + "start_time": "2026-04-26T14:22:31.372014900Z" } }, - "cell_type": "code", "source": [ "poly_converter_g = PolynomialFeatures(degree=2, include_bias=False)\n", "X_train_p_g = poly_converter_g.fit_transform(X_train_g)\n", "X_test_p_g = poly_converter_g.transform(X_test_g)" ], - "id": "b5e9cdb3ef42c22f", "outputs": [], - "execution_count": 57 + "execution_count": 80 }, { + "cell_type": "code", + "id": "8d5313ca848e4519", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.004804300Z", - "start_time": "2026-04-25T21:51:46.849063Z" + "end_time": "2026-04-26T14:22:31.543772600Z", + "start_time": "2026-04-26T14:22:31.380401100Z" } }, - "cell_type": "code", "source": [ "poly_model_g = LinearRegression()\n", "poly_res_g, y_pred_poly_g, cv_poly_g = evaluate_model_cv(poly_model_g, X_train_p_g, X_test_p_g, y_train_g, y_test_g, \"Polynomial Regression\")\n", "results_g.append(poly_res_g)" ], - "id": "8d5313ca848e4519", "outputs": [], - "execution_count": 58 + "execution_count": 81 }, { + "cell_type": "code", + "id": "8c81b3b6dab60fee", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.086837400Z", - "start_time": "2026-04-25T21:51:47.005802600Z" + "end_time": "2026-04-26T14:22:31.640034900Z", + "start_time": "2026-04-26T14:22:31.544770800Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(6, 6))\n", "plt.scatter(y_test_g, y_pred_poly_g, alpha=0.5)\n", @@ -2916,7 +3879,6 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "8c81b3b6dab60fee", "outputs": [ { "data": { @@ -2932,39 +3894,134 @@ } } ], - "execution_count": 59 + "execution_count": 82 }, { - "metadata": {}, "cell_type": "markdown", - "source": "# Trying Ridge Regression for Part G", - "id": "636e6b9a053c8f8d" + "id": "636e6b9a053c8f8d", + "metadata": {}, + "source": [ + "# Trying Ridge Regression for Part G" + ] }, { + "cell_type": "code", + "id": "a31ad4ed704f4541", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.106765300Z", - "start_time": "2026-04-25T21:51:47.087839Z" + "end_time": "2026-04-26T14:22:31.718195600Z", + "start_time": "2026-04-26T14:22:31.641035800Z" } }, - "cell_type": "code", "source": [ - "ridge_model_g = Ridge(alpha=1.0, solver='lsqr')\n", - "ridge_res_g, y_pred_ridge_g, cv_ridge_g = evaluate_model_cv(ridge_model_g, X_train_g, X_test_g, y_train_g, y_test_g, \"Ridge Regression\")\n", + "# Ridge (Part G): try a small range of alphas and keep the best by CV mean R²\n", + "\n", + "best_ridge_alpha_g = None\n", + "best_ridge_cv_r2_g = float('-inf')\n", + "best_ridge_rmse_g = float('inf')\n", + "best_ridge_model_g = None\n", + "best_ridge_res_g = None\n", + "best_y_pred_ridge_g = None\n", + "best_cv_ridge_g = None\n", + "\n", + "for alpha in [0.1, 1.0, 10.0, 50.0, 100.0]:\n", + " ridge_model_tmp = Ridge(alpha=alpha, solver='lsqr', random_state=seed)\n", + " ridge_res_tmp, y_pred_tmp, cv_tmp = evaluate_model_cv(\n", + " ridge_model_tmp, X_train_g, X_test_g, y_train_g, y_test_g, f\"Ridge Regression (alpha={alpha})\"\n", + " )\n", + "\n", + " # rank by CV mean R² first, then test RMSE\n", + " if (ridge_res_tmp['CV R2 (mean)'] > best_ridge_cv_r2_g) or (\n", + " ridge_res_tmp['CV R2 (mean)'] == best_ridge_cv_r2_g and ridge_res_tmp['RMSE (test)'] < best_ridge_rmse_g\n", + " ):\n", + " best_ridge_cv_r2_g = ridge_res_tmp['CV R2 (mean)']\n", + " best_ridge_rmse_g = ridge_res_tmp['RMSE (test)']\n", + " best_ridge_alpha_g = alpha\n", + " best_ridge_model_g = ridge_model_tmp\n", + " best_ridge_res_g = ridge_res_tmp\n", + " best_y_pred_ridge_g = y_pred_tmp\n", + " best_cv_ridge_g = cv_tmp\n", + "\n", + "display(pd.DataFrame([{\n", + " 'Selection': 'Part G Ridge best alpha by CV R2 mean then RMSE',\n", + " 'Best alpha': best_ridge_alpha_g,\n", + " 'CV R2 mean': best_ridge_cv_r2_g,\n", + " 'RMSE (test)': best_ridge_rmse_g\n", + "}]))\n", + "\n", + "ridge_model_g = best_ridge_model_g\n", + "ridge_res_g = best_ridge_res_g\n", + "y_pred_ridge_g = best_y_pred_ridge_g\n", + "cv_ridge_g = best_cv_ridge_g\n", + "\n", "results_g.append(ridge_res_g)" ], - "id": "a31ad4ed704f4541", - "outputs": [], - "execution_count": 60 + "outputs": [ + { + "data": { + "text/plain": [ + " Selection Best alpha CV R2 mean \\\n", + "0 Part G Ridge best alpha by CV R2 mean then RMSE 1.0 0.090846 \n", + "\n", + " RMSE (test) \n", + "0 2.162718e+07 " + ], + "text/html": [ + "
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SelectionBest alphaCV R2 meanRMSE (test)
0Part G Ridge best alpha by CV R2 mean then RMSE1.00.0908462.162718e+07
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" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 83 }, { + "cell_type": "code", + "id": "121ba346ffaea0e5", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.171902900Z", - "start_time": "2026-04-25T21:51:47.107767500Z" + "end_time": "2026-04-26T14:22:31.803996200Z", + "start_time": "2026-04-26T14:22:31.718195600Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(6, 6))\n", "plt.scatter(y_test_g, y_pred_ridge_g, alpha=0.5)\n", @@ -2975,7 +4032,6 @@ "plt.tight_layout()\n", "plt.show()" ], - "id": "121ba346ffaea0e5", "outputs": [ { "data": { @@ -2991,40 +4047,42 @@ } } ], - "execution_count": 61 + "execution_count": 84 }, { - "metadata": {}, "cell_type": "markdown", - "source": "# Regression Model Results", - "id": "f238f4198de0c527" + "id": "f238f4198de0c527", + "metadata": {}, + "source": [ + "# Regression Model Results" + ] }, { + "cell_type": "code", + "id": "c7a5e958c9ab89e6", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.193783100Z", - "start_time": "2026-04-25T21:51:47.173411200Z" + "end_time": "2026-04-26T14:22:31.823840700Z", + "start_time": "2026-04-26T14:22:31.804997400Z" } }, - "cell_type": "code", "source": [ "results_df_g = pd.DataFrame(results_g)\n", "results_df_g" ], - "id": "c7a5e958c9ab89e6", "outputs": [ { "data": { "text/plain": [ - " Model MAE (test) RMSE (test) R2 (test) CV R2 (mean) \\\n", - "0 Linear Regression 1.458116e+07 1.906474e+07 0.292880 0.245829 \n", - "1 Polynomial Regression 1.708302e+07 2.663426e+07 -0.380108 -0.452607 \n", - "2 Ridge Regression 1.629031e+07 2.162718e+07 0.090021 0.090846 \n", + " Model MAE (test) MSE (test) RMSE (test) \\\n", + "0 Linear Regression 1.458116e+07 3.634642e+14 1.906474e+07 \n", + "1 Polynomial Regression 1.708302e+07 7.093839e+14 2.663426e+07 \n", + "2 Ridge Regression (alpha=1.0) 1.629031e+07 4.677350e+14 2.162718e+07 \n", "\n", - " CV R2 (std) \n", - "0 0.067041 \n", - "1 0.900419 \n", - "2 0.056075 " + " R2 (test) CV R2 (mean) CV R2 (std) \n", + "0 0.292880 0.245829 0.067041 \n", + "1 -0.380108 -0.452607 0.900419 \n", + "2 0.090021 0.090846 0.056075 " ], "text/html": [ "
\n", @@ -3047,6 +4105,7 @@ " \n", " Model\n", " MAE (test)\n", + " MSE (test)\n", " RMSE (test)\n", " R2 (test)\n", " CV R2 (mean)\n", @@ -3058,6 +4117,7 @@ " 0\n", " Linear Regression\n", " 1.458116e+07\n", + " 3.634642e+14\n", " 1.906474e+07\n", " 0.292880\n", " 0.245829\n", @@ -3067,6 +4127,7 @@ " 1\n", " Polynomial Regression\n", " 1.708302e+07\n", + " 7.093839e+14\n", " 2.663426e+07\n", " -0.380108\n", " -0.452607\n", @@ -3074,8 +4135,9 @@ " \n", " \n", " 2\n", - " Ridge Regression\n", + " Ridge Regression (alpha=1.0)\n", " 1.629031e+07\n", + " 4.677350e+14\n", " 2.162718e+07\n", " 0.090021\n", " 0.090846\n", @@ -3086,57 +4148,146 @@ "
" ] }, - "execution_count": 62, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 62 + "execution_count": 85 }, { + "cell_type": "code", + "id": "a8b2b05314122ac3", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.209823600Z", - "start_time": "2026-04-25T21:51:47.194288200Z" + "end_time": "2026-04-26T14:22:31.875504800Z", + "start_time": "2026-04-26T14:22:31.849372600Z" } }, - "cell_type": "code", "source": [ "results_melted_g = results_df_g.melt(\n", " id_vars='Model',\n", - " value_vars=['MAE (test)', 'RMSE (test)', 'R2 (test)', 'CV R2 (mean)'],\n", + " value_vars=['MAE (test)', 'MSE (test)', 'RMSE (test)', 'R2 (test)', 'CV R2 (mean)'],\n", " var_name='Metric',\n", " value_name='Score'\n", ")" ], - "id": "a8b2b05314122ac3", "outputs": [], - "execution_count": 63 + "execution_count": 86 }, { + "cell_type": "code", + "id": "28a6da6deea25737", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T21:51:47.321383500Z", - "start_time": "2026-04-25T21:51:47.210842200Z" + "end_time": "2026-04-26T14:22:32.028098900Z", + "start_time": "2026-04-26T14:22:31.891520200Z" } }, - "cell_type": "code", "source": [ "plt.figure(figsize=(10, 6))\n", "sns.barplot(data=results_melted_g, x='Model', y='Score', hue='Metric')\n", "plt.title(\"Part G: Regression Model Comparison for Size in Bytes (with Cross-Validation)\")\n", "plt.xticks(rotation=15)\n", "plt.tight_layout()\n", - "plt.show()" + "plt.show()\n", + "\n", + "# Rank models primarily by cross-validation mean R² (generalisation), then use test RMSE as a tie-breaker\n", + "ranked_g = results_df_g.sort_values(by=['CV R2 (mean)', 'RMSE (test)'], ascending=[False, True]).reset_index(drop=True)\n", + "display(ranked_g)\n", + "\n", + "best_model_g = ranked_g.iloc[0]['Model']" ], - "id": "28a6da6deea25737", "outputs": [ { "data": { "text/plain": [ "
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NGmW3efvtt4PX6TPXa8yXL5/9/ntfi9opHT/Ohx9+GNWcYXd/zTM+k8/ioosuimr+544dO2zbovtUr17dflZTp06Nc2xGO1d0yZIlgezZswfuuOOOJLcbkYQe119//bXd1xo1asSZ6/3SSy/Z69euXZvg47n3J/SitmHy5Mlxtt2wYYO97bXXXotz/VVXXRWoWLFi8FiPNKdbt6sdDv1e6BhWXYjWrVsHr9O8YLU5SfXYY4/Z5z548GCc699//317/fr16+3fH330kX2N2vfOnTsHtzv//PMD11xzTbz3x/ubkNic7latWsV5ffq+6rMNdww5a9assffV70U03PdCvy1qy73Unqrt1W+a9/H1e9+lS5eo32O1i3oOvXdJ9eCDD9r76phx9u/fb2t/eNugcO2Xjo9zzjknwePevX7vMRbt70hS+kmR5nSHHhf6DNQetWnTJnDq1Kngdqpvo+0mTpwY57XoOtVPcPTdLVWqVKBTp07xnkvtj7ZX+w+kFIaXA1HQWVSdidfQRA3z0hl3ZTWV3RXvXGOdTVUGQ2f5ww3LU8ZFZ4XPhM6+uuyTl7KH2k930VlcL8VySclye1+3MrbKTmvYuDuLrcyFsig6a6yMmrI2uuhst87Wa5icG9asObyaN1e3bt3g4ytD4Ia8hVIWT4/hpaye3ldlNNxz6aL9VKZs0aJFdjtl6pRZ8w4VD6VtlHXRPkZLGVIJzSQo4+2G8Xnpc3ZZJ9F7qYy9Mo3R0mvV2XmXMVLWQhksNwTYS5l6ZcyUtfAOyVUWQdMQ3P4pu6daAMoqejMnyvCHHpt6z7WNbvO+58oE6Bj8+uuvTVKPXQk3lDOUPlNlUpShUQbJKV26tB0BoAyRezxHowq8mWZlX3Xc63pHQ5k1bDTc56Dnct9rUcZFj+E++9DvuzJeej/c0Ohw33lltxLjRiAouxJpyLP2QZkvbx0JZXI0+kAZUmWbvTp37hxnVIo7FpNy/J3JZ6ERKtHMwVTWcs2aNfZ9Uvup6TN6TGWqNZUj0uiAUDt27LDtlNoYDTlNaruRVMroeefyJvX9HTNmjG2jdFF2XiOBlOVTNWpHWWwdfxr+7Kjd1YgRtZ2JLTun77naOL2fapfda1f7qJFAeu3ueNMxqCy95gUnhR5XGdPQ3yT3frj3VxltjcBRW6J/i7K3mr7hbSeTQ1lR73uhx9Nnu2XLlhRpi7yU2XVTwrztqUaceEcjKJOv1+ptOxJ7j117rJEaysQnhRsh4Z2OpNFmaqO8v7Pe9suNtlC/RMdtQkPYQyXldySp/aRoaHSBMuYaru+tmK92R9np0N9jHZ/uPRJ9d9W+h/u+unYzI64MgvQrpoNu/RBceeWVdg5IctYhdXPyQi+RisUg43KdIwUYmhOkRtobEGp4lDrdCnT0o6sfZA1xCvcDpmDyTKmTEG4ZHQ0xdJ24lHzdGoalYab6AfIOB9cwV3WIH3/88TjBvi4a5icKAkWdH82jDBXuukjvkzqPCt5Dn8vNJXTPpaBTnVUNGdMJAs231f1C3yt1+LSdhpdp/qp3nmE4eg36cQ/dZwVC6kyFdvDCDVnUj3lS50Krw6zPQfPx1U7p70j7JwrsQynodre7/2vIc6jQ++o913GsACj0fdcx6N7zaLmhejpJkxjNyVTHM9zr0VBUBQvbtm1L8D13nUGdOAq9PtznEO490THinUeooEdDuRUsqiOp98Idr8n9zitA1lBUBV16XJ3c0zQNbwCuz037F7okkxuWm9jx5zqSyZmLn5zPIiltnYJ3tZnqyGuIqJY2ckPTVWgtMZqyoJN/CrQUtHrbqWjbjaQ60/dXHX7tgy4KihQkKFjR0GNvgU1NgdBQWff56iSCalJEM0XDnVRUYBT6+jW9R8OY3TGrYewKgPVd0b6pj5OcEzSOjmMdry7A1v8VZGkotIJOPbZel46dMw26k/NZJKUtSui4Tqjd1XfDneSI5j3WY+ukrj4bTT9TP0O/w952Re2PTjC5i7tNQb6mzHiH9CsAd4/j6D3XMefmnutYcLUjkhJ0J+V3JKn9pKQ8f+hzKZjWicHQ9lB9gdCTVJF+j92JvsROagFJEdNzutUIaj6tOuRJnRsrmscVmsHQmePQ4lXI+PTjGGmdWnUkNE9JHQllV9R5VPZJ875CC2BJSlTgVgClzJA6Xt45SwnNAz/T160Ml4o8KeBTp1hnjV1AoO9CaFY6saA6MeHeJz2fzqJrPmk4Co5EAaLOwCtboIyQLvo81Hl1Raf0eWluvjKLyuCpk6MCWsqyRVob24n2hzhSli/azJ2j40tBhDrO6iSnZuV4ved6P72ZNi9vxicarvCf5kh7Rz2klEjvebjrk/o5OHr/NY9VJ2r0Gtx3QSMSwmWpo/nOaxudCNaJPQVfChI1H1XzbnV8Jqdqb0odf8mVnLZO3y19j3XRCA116HXsJfad1GexbNkym/0KnU8abbuR1u+vTqYo2625rwqWXd0HnYDRHHu9DwqOlBVXuxwusAnljkfVMIj0fXMZah3XCn41ikvHnO6jmhg6iRE659VLtU100kPBa2jWWL8ZmjetOc4rVqywJ1EUGCrY02+n5jLr+evVq2fORHI+C/02KUOvtigpzuQ3PJr3ePjw4TZr7n6bNJJF86VVL0LHtvqr3lEt+l1wxc2UyVVtFY160rZqT1THwBXn02+e+qlqh7UCi4J/BanKxuv3L5rCgsmR1H6SH5JyjLhAXCcsgJQS00G3GriEfkjUuVXRDp01VEZMPxRqHF3VTP1QeIdTKQhSFlSddsQODd/SmVsFeN7sin5MopXUs6kquKQfYP1wp1YAph8s/fCrU6giWvphd8NM9eMZWrk2lIZDh6vsm5RqvyqoouxqYs8l6khoJIsu6kgo+62iT8rKuxMBOtuuIaK66HHVIVDmIVIHX69Bj6UOsbfoj4qVqY0IN+Q7JaiTp5Me6myrzYrUEXDPr5MirsK+o+vc7e7/4YbWa7vQ91yBjLKwKXHCSPuvY0mvJbFMnQJ6FQAK3SdXMV9BSmgG+0yFe09+/fXXYCEkdcYURKh4jwKIhO6XVHo96hDrog6xCq/pN0gdZx3z+tw0GkPHoDfbrfdC/Dr+0uqzUPuiTJSy3wlRQSYVztJFw2TPpN1IawpexTuSSe2UTkAo6FZGXJnK0OJvkX5DXBEqZXWjef0KhtRW6qIRACrupQrtCfWV3Ik0FVgMPfGrAFO/hfqMNApBU2N0rCgYd0G3rkvspJIfGUcdz2onNUVKozSSe/x6291w3w21194RkNG8xxp9pctjjz1mT/Cp/VXfUit+KCj3Zme9S4Rp6okKBCqQdQUKvUPLVTRNfVtNE/OODkjqNKGk/o4kpZ8U7Wftfd+9U140SkTH4pl833V/HafJPSEHhBPTw8sToyFeOnOuHwt1dFRlUZmMSJ0rZcr0BT3TYVLIWNRZ0I+EftwcDUVNynQF/fhL6DIXkahKp4buKfuhgCDas/tnumSYTjgp+60On+aJKQOq6xTMhusYe5dsUSZc3ydloL3D5CJlUMPRCQY9hn64Q+m9cx3W0KWb9OPpOoNuaa/QbXQCTcF46NJfXhpiL6EdXgVIElqpPiVpNIGG7OukQSTKfukzUefM+zqU6Vfn1u2fOn3Keinr7x3apyHsbvkbxw3Z1dzaUHq/oz1mHXVsNedOGRxV9w2lgFKdSi0tpu9WmzZtbMbHO7xbJznUqVTHPaUry+p7611e7fvvv7dzMF2H2AUHod+x5FbA9n4XQrnMpPssdfxpOKm3IrM+A72POn7DBZwpxc/PQu9vuOrmeu/1PU0oo6uhujpJpgxfaPX2pLYbaU0jl/S90AnD0EreOkGl76Yy+voslP32ckFd6PdRtRcUeGu5pnBTklwbre946DBftSUK6BJqE0W1OkTZ1VCuP6SEhdpgN91D1+vkle4TTZ9Jry+pbU001Kbqu6z3N9z7o+x86JJ8obztqXcfdWzq83S/G9G8x5pnHno8KvjWb5jbRp+pm5agi3f+tAJpvZ9qI3RiU8PVXRX7SO2X9ikpSYJwrzux35Gk9JOi/az12vVd0VQU7+vRdBTtz5n8Hutz10iTSNXigeSI6Ux3QhSYqBHS/91ZRHV6NeRP1ysD4aUARMGDsn+ILWrYFXTphIyGXuvMteZgKYBLbI6wowyifjj1Q6kTN8psaGRFpHWYdbuy3MriaoqEOmCa1qCMs87Ya85fuHluZ7pkmKjTpxNQGs6m6RV6repwq2OgYEpnnNURVydXgZNGgIiGdqoToGGeWgrFLRmmfVTAEc3ZbT23ztAr0++W3lJnXcMDNe9cP+LKKqgTrsdUFkND7DS3S4GJOgiuM6v3WycM9Bh6P9X5c0u5RKL3WkP5tDyUOgV6LxUYqNOhTLRGAfhFz61LQvT5q3OrzL32TVkPt2SYMrU6SeNo1IKOXX12mmKj90vvkToa3s6nHkfDE7W9Tpgo8NLz6OSjjjM9topXJYWCag1z1LBJDavU56mMptpbPaZODrmgQpkdt361MkMaJqmTPOqAhltj9Uzpe6vn0oktPYeCaQ2fdUOTFVhqRISeW0GSiq6pY63MyJlQjQENL9dnogyO2hENw9Tx69b2VrEovXYd++oU6jPVMesyn0ktCJVUfn0WWiZIv59aXlDfR3WkdZJIS+wpOxa6VrmXjnXRZ6L2xUvBhtqjaNuN1KaTYW6Ugj5vnbzQ90r9iNATGDoudBzq+6ETQG45NEeBtYZs64SbjgO1ryrApqBL7azuo++23i8dszqxpOymnkfZTw0N17Gm77LaGZ3E0QgXLb2k72tC9B7rt0rbqy0J/T6p5oWykWr3HX1ertBnNEG3PjPN/9UxqMfU6w8dzZMcOkb0G6bjWRl7Bd+a0qD3Q7+TOm70nInRMHG9xzoBoaKNbskwBW0aPSXRvMfKuus3SL+x6gsoANf3Q0GrCrhFQyeg1FZo3rxbYs1R++1GgaldV1v/xhtv2PczsREl4UT7O5KUfpI+a70v2l79bx3DocsRutE3yupr1JEeV8PXdZyp3VR/yFs0LSnUrmv4vo4JIEWlWB30DE5vxaxZs+ItsaOlC7yXbNmyBW644YawywvoNi19gszDLVGh5VgSMmHCBLssi5ZD0ZI3ul/okkDeZaDCWbp0aaBBgwZ2CYxolw/bvn27XSakZs2adqkjPb+W/dASJd6li5K7ZFi4162lOSpXrmwvbjkgLSGk59TyG1qup2zZsoErrrjCLjMWuhzKxRdfbPdTSzYNGzYs8PLLL9vn8n53tNRIpOW8tCzNwIEDA1WqVLHvVbFixezyZC+++KJdgkT0vFpGREu4aJvy5csH7rrrLvt+OU899VSgUaNGgUKFCtn3Tp/b008/HXwMCfcZnjx5MjB48GC73I5ea7ly5ez+eJdMS+g1RLOsVmLHSuj+hS4zNX369EC9evXs+6wl2W6++eY4y2A5M2bMsMseaTsdQzNnzoy4JNO4cePs8an3SktEaZm4AQMG2CWjkvraRMfO+PHj7fGgZXT0Xup5u3btGm85sZUrV9olbbQsVp48eQItW7a035dojtlI75Fep9r0cMtkDR8+3H6uel+0f1r6x0vvpZY40rGjfb/++uvt+xD6vY303N7bnPnz5weuvvrqQJkyZewxq/9rmZ/QZa527txp3yMd99pOn0PoMlGRlvySaNqWhO5/Jp9FJD/++KNtx+rXr2+PV/2Wahkiva96Pq/Q49MtzRfu4n1fomk3krpkWOiyTuGWVIp2yTAt66Tll7Q0mHfpK6977rnHbqv+RjhaEk7fY71/ofuh79S1115rl5HTca33TX0ZHXdu+SR9BlpKUd9vfTf071dffTUQjREjRthjItxyVPoctT9qlxy95zp+9Fl4lzeMtGSYfh/UnmrfvL9jkY419xnp/9FYsWJF4KabbrLfO7VFWpZTy2G++eabweWoEvpeiJbYbNasmW0jtazYlVdeGVwuLdr3+Pfff7fL3en3VceEvg/6jumxo6VlAvUZe5dr89LSbVqmTY+vZeeee+45u7xW6HsezZJhSfkdibaf9Msvv9glCPU+6ja3fFi448ItEabH0+dWsmTJwN133x3Yu3dvnG30OrTsXKhw+/npp5/a5/ntt98SfJ+BpMqi/6RsGJ8xKcumzKGyVaKMo+bBaEmh0LlGOjupM7demoOnM8Z6DADR03IfypbprHhyikUBKUHZTmVUlLHSqCYgvdFIFQ2d1RQDNyUpvdBwXmW8NeLBuzwfkNEoDnAxAZCSGF4egSppau6JhsAkNvRJwwo1TEvDkABEpiF33mJcmq+poXMamkbADQDhaQqbhs9riHF6C7hFw6g1BUMnrTSEPXRZOyAj0LQWLW3mrT0DpJSYDrqVWfNWTlbwrC+a5ndqLo0y3VpiSHNtFISr4IgKf6gYiLdAg+adqaBEQtU9Afy/gjuaR6151ZprrKyNCsckVBwMAGKVTvxrfqvmnuskZaRicemB5mi7edpARqS+SXoprIjMJ6aDbhVO8hY+6tevX5w1D1UwTQU0+vfvb4uOqNBK48aNbTEWb6VdbaviLGTqgISpiqs6jypEpuFbWipFgbeK6gAA4lIVaCUAVOhKVZr9WNseAOA/5nQDAAAAAOATJt0AAAAAAOATgm4AAAAAAHwSc3O6NQf777//Nvnz57dzSgEAAAAASCqtvn3w4EFTpkyZBFduiLmgWwF3uXLl0no3AAAAAACZwLZt28zZZ58d8faYC7qV4XZvTIECBdJ6dwAAAAAAGZCWvlVC18WYkcRc0O2GlCvgJugGAAAAAJyJxKYtU0gNAAAAAACfEHQDAAAAAOATgm4AAAAAAHwSc3O6o3Xq1Clz8uTJtN4NZEDZs2c3WbNmTevdAAAAAJAOEHSHWWttx44dZt++fWnziSBTKFSokClVqhRrwQMAAAAxjqA7hAu4S5QoYfLkyUPQhCSftDly5IjZtWuX/bt06dK8gwAAAEAMI+gOGVLuAu6iRYum3aeCDC137tz2/wq8dSwx1BwAAACIXRRS83BzuJXhBs6EO4aoCwAAAADENoLuZCxuDiSGYwgAAAAAQTcAAAAAAD4i0w3fM76zZ8/mXQYAAAAQkwi6Y8Dtt99ug9+ePXvGu61Xr172Nm0TjQULFtjto11Sbfv27aZ9+/ZJ3mcAAAAAyAwIumNEuXLlzLRp08zRo0eD1x07dsxMnTrVlC9fPsWf78SJE/b/Wqs6Z86cKf74AAAAAJAREHTHiPr169vAe+bMmcHr9G8F3PXq1Qted/r0aTNs2DBTqVIlu/RVnTp1zAcffGBv++OPP0zLli3tvwsXLhwnQ96iRQvTu3dv06dPH1OsWDHTtm3bsMPL//zzT3PjjTeaIkWKmLx585qGDRua7777LtXeBwAAAABITazTHUPuuOMOM2nSJHPzzTfbvydOnGi6du1qh4w7CrjffvttM3bsWFO1alWzaNEic8stt5jixYubiy66yMyYMcN06tTJbNiwwRQoUCC4JrW8+eab5u677zZLliwJ+/yHDh0yzZs3N2XLljUfffSRzYKvXLnSBvoAAAAAkBkRdMcQBc8DBw40W7ZssX8rONaQcxd0Hz9+3DzzzDPmyy+/NE2aNLHXnXPOOWbx4sXm9ddftwGzMtRSokQJU6hQoTiPryD9+eefj/j8Gsq+e/du88MPPwQfp0qVKr69XgAAAABIawTdMUTZ6g4dOpjJkyebQCBg/62h4M7GjRvNkSNHTOvWrePNz/YOQY+kQYMGCd6+evVq+zgu4AYAAACAzI6gOwaHmGvutYwZMybe8G/55JNP7BBwr2iKoWmOdkK8Q9EBAAAAIBYQdMeYdu3a2cy1Cpy5YmdOzZo1bXC9detWO5Q8nBw5ctj/nzp1KsnPff7555vx48ebPXv2kO0GAAAAEBMIumNM1qxZzc8//xz8t1f+/PnNAw88YPr27WuLm6lw2v79++3cbxVNu+2220yFChVswD5nzhxz+eWX2+x1vnz5onpuVS3XnPGOHTvagm2lS5c2q1atMmXKlAnOIQeA9GjrkNomFpUftDatdwEAgAyPJcNikAJoXcIZOnSoefzxx21QXKNGDZsZ13BzLSEmGnY+ePBg8/DDD5uSJUsGh6pHQ1nyL774whZhU8Beu3Zt8+yzz8YL/gEAAAAgs8gSUEWtGHLgwAFTsGBBm8ENDTyPHTtmNm/ebAPMXLlypdk+IuPjWAIyFzLdAAAgKbGlF5luAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANX0yYMMG0adMm1d/dEydOmIoVK5rly5en+nMDAAAAQKhs8a5BWA0enJKq78yKF7okafvbb7/dvPnmm+auu+4yY8eOjXNbr169zKuvvmpuu+02M3ny5Di3LVu2zFx00UWmXbt25pNPPolz2x9//GEqVaoU9vl0v8aNG4e97dixY+bxxx8377//fpz927dvn5k9e7ZJKU8++aR9vNWrVwevy5Ejh3nggQfMQw89ZObPn59izwUAAAAAyUGmOxMpV66cmTZtmjl69GicAHjq1KmmfPnyETPS9957r1m0aJH5+++/w27z5Zdfmu3bt8e5NGjQIOJ+fPDBB6ZAgQKmWbNmJi3cfPPNZvHixWbdunVp8vwAAAAA4BB0ZyL169e3gffMmTOD1+nfCrjr1asXb/tDhw6Z6dOnm7vvvtt06NAhXhbcKVq0qClVqlScS/bs2SPuhwL/K6+8Mk5GWln4Dz/80GTJksVeFixYYG/btm2bueGGG0yhQoVMkSJFzNVXX20z7I62a9SokcmbN6/dRoH8li1b7L4OHjzYrFmzJviYbv8LFy5st9N+AAAAAEBaIujOZO644w4zadKk4N8TJ040Xbt2Dbvte++9Z6pXr26qVatmbrnlFrttIBA4431Qlrlhw4bBvzXcW4G1hrC7THnTpk3NyZMnTdu2bU3+/PnNN998Y5YsWWLy5ctnt9Pc7P/++8907NjRNG/e3Pz44492SPudd95pA+zOnTub/v37m1q1agUfU9c5CtT1mAAAAACQlpjTnckoeB44cKDNBosCWWV8XWY5dGi5thcFuvv37zcLFy40LVq0iLOdAuSzzjorXpY8HM3b1uOUKVMmeJ0C6dy5c5vjx4/bLLnz9ttvm9OnT5vx48fbQFp0wkAZbe2vAnc91hVXXGEqV65sb69Ro0acx82WLVucx3T0/O49AAAAAIC0QtCdyRQvXjw4VFxZa/27WLFi8bbbsGGD+f77782sWbPs3wpelSlWIB4adGsIujfYTYibT54rV65Et9XQ8I0bN9pMt5fmoW/atMlWP1cBNmXDW7dubVq1amUz5qVLl070sRXkHzlyJKp9BgAAAAC/EHRn0iHmvXv3tv8eM2ZM2G0UXGv4tjcjrSA9Z86cZvTo0aZgwYLB6zVPvEqVKlE9t+Z/K2u9d+/eRLdVtlwF2d55552wJw9c5vu+++4zn332mQ3+H3vsMTNv3ryIldOdPXv2BB8DAAAAANIKc7ozITcn2s2ZDqVge8qUKWb48OF2uS13UeZZQfi7776b7OfWkl01a9Y069evj3f9qVOn4hV+++2330yJEiVsUO+9eIN+FYHTkPmlS5ea8847z1Zjj/SYzk8//RS2eBwAAAAApCaC7kwoa9as5ueff7aBr/4das6cOTYT3a1bNxvEei+dOnWyWXCvf//91+zYsSPORUPAI1Ggr2JqXhUrVrTF0DSs/Z9//rEnBLS0l4a+q2K5ip5t3rzZzuVWZvvPP/+0fyvYVgE1zc/+4osvbJDuhrrrMbWNThjoMTVn3NHjaXg6AAAAAKQlgu5MSutk6xKOgmrNj/Zmkx0F3cuXL7cBsqNtNY/ae5k9e3bE51YwP3fuXFsEzenRo4etkq7iaBr2rQJvefLkseuDa0mza6+91gbTuq8Ceu27bv/ll1/sPp177rm2cnmvXr3MXXfdFdxXZfVbtmxpH9Nl6BWk67mvu+66M3oPAQAAAOBMZQmkxBpRyTRs2DC7jrQCKxW+UpXs5557zgZnkahAWOgSWJqHnFDm1evAgQM22FRQFhqU6jGUOa1UqVJUhcAQ2fXXX2+HjytTndpUEK5OnTrmkUceMWmFYwnIXLYOqW1iUflBa9N6FwAASLcSii3TTaZby1Mpc/ntt9/a4lgacqwhwYcPH07wfnpBbm1mXVgaKv154YUX7JJeqU1z2WvXrm369u2b6s8NAAAAAOmqerkqUodmsVVUa8WKFeaSSy6JeD9Vxw63NjPSD823vvfee1P9eVVcTRXOAQAAACA9SFdzut0c4CJFiiS61FSFChXsUlYqwrVu3bpU2kMAAAAAADJg0H369GnTp08f06xZM1tFOxLN9544caL58MMPzdtvv23vp7ngqnYdjipaa6y99wIAAAAAQKYfXu6lud1aWzl0qalQTZo0sRdHAbeqXr/++utm6NChYYu1DR482Jd9BgAAAAAg3We6e/fubdeO/vrrr83ZZ5+dpPtmz57d1KtXz2zcuDHs7aqerWHr7rJt27YU2msAAAAAANJxplurlanY1qxZs8yCBQvsUl1JderUKbN27Vpz+eWXh71dy4npAgAAAABATAXdGlI+depUOz87f/78ZseOHfZ6rXWmdbulS5cupmzZsnaYuAwZMsQ0btzYVKlSxezbt88uTaUlw7p3756WLwUAAAAAgPQVdL/22mv2/y1atIhz/aRJk8ztt99u/71161Zz1ln/Nwp+7969pkePHjZAL1y4sGnQoIFZunSpqVmzZirvPQAAAAAA6Xx4eWI07Nxr5MiR9oKM7/HHHzc7d+4048aNS9Xn/eeff+xJmpUrVya5hgAAAAAAZMjq5end1iG1U/X5yg9am6TtNTLgzTfftP/Oli2bDSavv/56Oxw/V65cwe2yZMli/79s2TI7TN+7tFqZMmXMnj17bEE7N/pg4cKFtvr76tWrzbFjx+xQf1WMf+ONN0yOHDnsSZGWLVuG3aft27ebUqVKhb1NIxVeeuklOx/f0XPWrVvXjBo1yqQUvS+ahjB79uzgdcWKFbPTFp544gkzYcKEFHsuAAAAAEiX1cuRMtq1a2cD3d9//92OBtAyagosQ5UrV84O4fdSMbt8+fLFuW79+vX2MRs2bGgWLVpkA+RXXnnFBtsqYOe1YcMG+9zeS4kSJSLu6/jx423wXqFCBZMWunbtat555x17kgEAAAAA/ELQnYmoSrsyywqqO3bsaFq1amXmzZsXb7vbbrvNTJs2zRw9ejR43cSJE+31Xl988YV9vOeff96cd955pnLlyjYIV5bbFbpzFGBrW+/FOxc/lJ7/yiuvjJORVlZd2W9l43X5448/7G1av719+/b2pEDJkiXNrbfeaoeIOx988IGpXbu23aeiRYva13348GHz5JNP2uy/CvW5x3TTFWrVqmUz+zrZAAAAAAB+IejOpBSoqsCcstKhVHyuYsWKZsaMGcFidcpkK5j1UuCsjLVuS0nKLiuLrgy6o2C7SZMmtkiey5Tr5IGGhl966aV2Lfbly5ebzz77zM4Dv+GGG+z9tN2NN95o7rjjDvPzzz/boPraa6+19QIeeOABu50bAaCLsutOo0aNzDfffJOirw0AAAAAvJjTnYnMmTPHZoP/++8/O0dbmebRo0eH3VZBqrLbt9xyi5k8ebJd57x48eJxttGc8M8//9w0b97cBuCaA37ZZZfZ+dAFChSIs21oQTING1+3bl3Y51aQr6BYmWZHy8TpBEGePHnizAPX/ivgfuaZZ4LXab8VkP/666/m0KFD9vUq0HZD1ZX1dpT91nsRbm65nn/VqlUR308AAAAAOFNkujMRFTRTwbPvvvvODhXXvOVOnTqF3VbBtoqpaf63gm4F4aGyZs1q537/+eefdoi5iqgp+NXQbGWNvZQx1nO7y9y5cyPupxvW7i3wFsmaNWtsYTedTHCX6tWr29s2bdpk6tSpY08EKNDWSQINfdeyctFQQH7kyJGotgUAAACA5CDozkTy5s1rqlSpYgNRZYMVfEeqzq25z1dccYXp1q2brUquOdORKNjW0HNlnZW91vZjx46Ns02lSpXsc7tLQgXSVD1cogmOlcnW3G9vQK/Lb7/9Zi655BJ7YkDz1j/99FO7DJgKvVWrVs1s3rw5qmHuodl9AAAAAEhJBN2ZlIaWP/LII+axxx6LUzDNS9ltzYHWcHEFr9EoXLiwKV26tC1UllwqyKbh6ZrX7RWuKnr9+vVtoK856N6gXhedZBAVSGvWrJld2kzDxfU4rkBauMf0znvX0HUAAAAA8AtBdyam4dYKpseMGRP2dhUY2717t13LOxwtOXb33XfbKuYayq3g96GHHrL/91Yel127dtm1t72XkydPRjwhoArjixcvjnO9Amtl51W1XNXJT58+bXr16mUz0iqW9sMPP9j90DxzDZ1XMK3tNeRdRdY0V3zmzJn2NdWoUSP4mD/++KNd0kyP6fZJw8pXrFhh2rRpk6z3FgAAAACiQdCdiWXLls307t3bzscOl5lWhlhDvcNVOHfVvTW8u2fPnnYetwqqffvtt2b27Nn2314a0q0MuPeioDaS7t2722XDFFg7qjaukwQaJq5h3wqiVexsyZIlNsBWgKy523369DGFChWywbsy5qqurkJw5557rs3sDx8+PDhcXtXQtW+qlK7H1GOJlhErX768ufjii5P9/gIAAABAYrIEVEY6hhw4cMBWyt6/f3+8Ctyaq6y5wJqfHE2RLySfDrsLL7zQ9O3b12axU5sqsd93333mpptu8uXxOZaAzGXrkP9bFSGWlB+0Nq13AQCADBlbepHpRppQln3cuHF2ua/UpmHmWmIsLYJ9AAAAALGFdbqRZurWrWsvqU1D6gcMGJDqzwsAAAAg9pDpBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBN6Iyf/58U6NGDXPq1KlUf8caN25sZsyYkerPCwAAAABnKtsZP0KMaPZKs1R9viX3LknS9rfffrt588037b+zZctmzj77bHP99debIUOGmFy5ctnr//jjDzN06FDz1VdfmR07dpgyZcqYW265xTz66KMmR44cCT7+gAEDzGOPPWayZs1q/37yySfN7NmzzerVq01KmTx5sunTp4/Zt29fnOv1vH379jXXXHONOesszhMBAAAAyDiIYDKRdu3ame3bt5vff//djBw50rz++uvmiSeeCN7+yy+/mNOnT9vr161bZ7cZO3aseeSRRxJ83MWLF5tNmzaZTp06mbTQvn17c/DgQfPpp5+myfMDAAAAQHIRdGciOXPmNKVKlTLlypUzHTt2NK1atTLz5s2LE5RPmjTJtGnTxpxzzjnmqquuMg888ICZOXNmgo87bdo007p162DGXBnpwYMHmzVr1pgsWbLYi64TZam7d+9uihcvbgoUKGAuvfRSu52jf7ds2dLkz5/f3t6gQQOzfPlys2DBAtO1a1ezf//+4GMqmy7Krl9++eV2PwAAAAAgIyHozqR++ukns3Tp0kSHjSvILVKkSILbfPPNN6Zhw4bBvzt37mz69+9vatWqZTPruug60ZD2Xbt22az0ihUrTP369c1ll11m9uzZY2+/+eab7dD3H374wd7+8MMPm+zZs5umTZuaUaNG2UDcPaZOCDiNGjWy+wEAAAAAGQlzujOROXPmmHz58pn//vvPHD9+3M5/Hj16dMTtN27caF555RXz4osvJvi4W7ZssfO/ndy5c9vn0dxxZda9w9C///57G3Qr6y56bM39/uCDD8ydd95ptm7dah588EFTvXp1e3vVqlWD9y9YsKDNcHsf09Hzb9u2zQ6PZ143AAAAgIyCoDsT0bDt1157zRw+fNjO11ZQHGke9l9//WWHmysz3aNHjwQf9+jRo8Gh5QnR0PFDhw6ZokWLxru/5oRLv3797PDzt956yw5/1/NXrlw50cdWoK+AWycT9G8AAAAAyAgYXp6J5M2b11SpUsXUqVPHTJw40Xz33XdmwoQJ8bb7+++/bYCuId3jxo1L9HGLFStm9u7dm+h2CrhLly5tK5p7Lxs2bLDZbdE8bRVx69Chg62iXrNmTTNr1qxEH1vD0/X6CLgBAAAAZCQE3ZmUhmCrKrmW21Km2ZvhbtGihS1gpqJq0QzVrlevnlm/fn2c6zRXPHTNbs3f1lJkyrAr+PdeFLg75557rl0C7IsvvjDXXnut3Y9Ij+mdo679AAAAAICMhKA7E9PQbVX+HjNmTJyAu3z58nau9e7du22QrEtC2rZta+dre1WsWNFs3rzZZrL/+ecfO+xbw8WbNGliK6croNa64CrmpnXAVaFcwX/v3r1tpXLNE1+yZIktqFajRo3gYypbPn/+fPuYR44cCT6fiqip6joAAAAAZCQE3ZmYMs4Kcp9//nk7z1vLh6l4moJaVRDXUHB3SYgqjmtIuIaJO5orrjnhGqau5cHeffddWwRt7ty55pJLLrHLfymj/b///c8G2CVLlrQnAP7991/TpUsXe9sNN9xg1+DW8mOi4e49e/a0ldD1mNpvd7JAwbseEwAAAAAykiyBQCBgYsiBAwdslWwtlaXlqbyOHTtms7eVKlWKqnBYLNGcbL13r7/+eqo/90MPPWTnlEcz/zy94FgCMpetQ2qbWFR+0Nq03gUAADJkbOlFphtR0RDxChUq2Ariqa1EiRJm6NChqf68AAAAAHCmWDIMUSlUqJAtzJYW+vfvnybPCwAAAABnikw3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6kS7ceuut5plnnjEZwdixY82VV16Z1rsBAAAAIAPIltY7kFEsvKR5qj5f80ULk3yfHTt2mKefftp88skn5q+//jIlSpQwdevWNX369DEXX3yxKVOmjHnggQfMww8/HO++Q4cONaNHjzZ//vmnyZ49e7zbs2TJEvx3/vz5TbVq1cxjjz1mrr766uD1M2fONK+99ppZvXq1OX78uKlVq5Z58sknTdu2bRPc7zVr1pi5c+fa+2YEd9xxh32/vvnmG/u+AgAAAEAkZLoziT/++MM0aNDAfPXVV+aFF14wa9euNZ999plp2bKl6dWrl8mRI4e55ZZbzKRJk+LdNxAImMmTJ5suXbqEDbgd3Xf79u1m+fLlplmzZua6666zz+MsWrTItG7d2gbQK1assM+tjPCqVasS3PdXXnnFXH/99SZfvnwmI9B7edNNN5mXX345rXcFAAAAQDpH0J1J3HPPPTYb/f3335tOnTqZc88912aa+/XrZ7799lu7Tbdu3cyvv/5qFi9eHOe+CxcuNL///ru9PSGFChUypUqVso+tTO9///1nvv766+Dto0aNMgMGDDAXXHCBqVq1qh0urv9//PHHER/z1KlT5oMPPog3XLtixYrmqaeesicCFIxXqFDBfPTRR2b37t02u67rzj//fHsCwEuvTdnn3Llzm3Llypn77rvPHD58OHj7W2+9ZRo2bGiz9XotCp537doVvH3BggX2fZw/f77dLk+ePKZp06Zmw4YNcZ5H+6v9OXr0aILvGQAAAIDYRtCdCezZs8dmtZXRzps3b9hgWWrXrm0D4okTJ8bLYCuwrF69elTPp2B7woQJwaxvJKdPnzYHDx40RYoUibjNjz/+aPbv328D3FAjR460GXVlyjt06GDnfSsIV8Z+5cqVpnLlyvZvZepl06ZNpl27dvakgx53+vTpNgjv3bt38DFPnjxpTxhoSPvs2bPtCIHbb7893nM/+uijZvjw4Taoz5Ytmx1S7qX91fvw3XffRfWeAQAAAIhNzOnOBDZu3GgDz2iCZmWzNa9bQ6OVLVZQrExzNEOlb7zxRpM1a1ab3VVArWz0DTfcEHH7F1980Rw6dCjBbbZs2WIfU/PPQ11++eXmrrvusv8eNGiQnfOtkwYaii4PPfSQadKkidm5c6fNWg8bNszcfPPNdg67KMuu19W8eXN731y5csUJns855xx7ux5T++kd3q658bqfaA68gv5jx47ZxxBlwAsWLGj3HwAAAAAiIdOdCbhMbzQUOGtI93vvvWf/Vjb4rLPOMp07d070vso8q0jap59+amrWrGnGjx8fMYs9depUM3jwYPs84QJqRwF8zpw54xRqczR83ClZsmQwWx96nRseruy15qYreHYXFXHTCYLNmzfbbTTXXEPDy5cvb4eYu8B669atEZ+7dOnScZ7H0RD2I0eOJPCOAQAAAIh1BN2ZgDK6Clp/+eWXRLctUKCALYDmCqrp/8pER1PETNnkKlWqmDZt2tj7KVAPDURl2rRppnv37jbgbtWqVYKPWaxYMRu4njhxIt5t3qJuLigPd52CalG2WplxnRhwFwXiv/32mx2KrrndCsL1Hrzzzjvmhx9+MLNmzbL3DX3+hJ7HO6y/ePHiCb4+AAAAALGNoDsTULZZweSYMWPiFA1z9u3bF2+IueY6z5kzxyxdujTRAmrhNGrUyFZL1zBsr3fffdd07drV/l9DshOjJc1k/fr15kzVr1/fPo5ODIReNPdcJyX+/fdf8+yzz9piaxqOH+6kQTQ0f1zDzevVq3fG+w0AAAAg82JOdyahgFtFxxQMDxkyxA6PVqGvefPm2fnMP//8c3DbSy65xAaiKkKmwFNF1JJDc6evueYaW7G8bNmydkj5bbfdZl566SVz4YUX2nXD3TBszX8OR5liBcs6CeAC8OTSHO/GjRvbwmnKtKuonIJwvQdag1xDyhV8a4mynj17mp9++skWVUsOrdGtOeHKoAMAAGQEW4f83zS9WFF+0P8tbwukFTLdmYQCQFX01trY/fv3N+edd55dM1tLXyno9tJwaRUU27t3b7yq3EmhSuGVKlUKZrvHjRtnA31VUdc8aHe5//77E3wcBcga7n2mdKJBy59pWTRlspWFVgG2MmXKBAN8zfl+//337Zx0ZbxV7C05lMnv0aPHGe8zAAAAgMwtSyApVbgygQMHDtisq5ap0txeLw0XVsEtBZKuSjX8p2Jq1apVs0XdVI08vVu3bp259NJLbXAfKYPPsQRkLrGYHRIyREDmEottGe0Y0iq29CLTjTSn4edTpkwx//zzj8kItm/fbvc3UsANAAAAAA5zupEutGjRwmQUiVVkBwAAAACHTDcAAAAAAD4h6AYAAAAAwCcE3WHEWG05+IBjCAAAAIAQdHtkz57d/v/IkSMcHTgj7hhyxxQAAACA2EQhNY+sWbOaQoUKmV27dtm/8+TJY9e0BpKS4VbArWNIx5KOKQAAAACxi6A7RKlSpez/XeANJIcCbncsAQAAAIhdBN0hlNkuXbq0KVGihDl58mTafCrI0DSknAw3AAAAACHojkBBE4ETAAAAAOBMUEgNAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAIDMGHQPGzbMXHDBBSZ//vymRIkSpmPHjmbDhg2J3u/999831atXN7ly5TK1a9c2c+fOTZX9BQAAAAAgwwTdCxcuNL169TLffvutmTdvnjl58qRp06aNOXz4cMT7LF261Nx4442mW7duZtWqVTZQ1+Wnn35K1X0HAAAAACAxWQKBQMCkE7t377YZbwXjl1xySdhtOnfubIPyOXPmBK9r3LixqVu3rhk7dmyiz3HgwAFTsGBBs3//flOgQIEU3X8AQOa0dUhtE4vKD1qb1rsAIAXFYltGOwY/RRtbpqs53dpZKVKkSMRtli1bZlq1ahXnurZt29rrAQAAAABIT7KZdOL06dOmT58+plmzZua8886LuN2OHTtMyZIl41ynv3V9OMePH7cX79kIAAAAAABSQ7rJdGtut+ZlT5s2LcWLtSnl7y7lypVL0ccHAAAAACBdB929e/e2c7S//vprc/bZZye4balSpczOnTvjXKe/dX04AwcOtMPW3WXbtm0puu8AAAAAAKTLoFs13BRwz5o1y3z11VemUqVKid6nSZMmZv78+XGuU+VzXR9Ozpw57aR27wUAAAAAgEw/p1tDyqdOnWo+/PBDu1a3m5etYeC5c+e2/+7SpYspW7asHSYu999/v2nevLkZPny46dChgx2Ovnz5cjNu3Li0fCkAAAAAAKSvTPdrr71mh3y3aNHClC5dOniZPn16cJutW7ea7du3B/9u2rSpDdQVZNepU8d88MEHZvbs2QkWXwMAAAAAIOYy3dEsEb5gwYJ4111//fX2AgAAAABAepYuCqkBAAAAAJAZEXQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfZPPrgQFn65DaMfdmlB+0Nq13AQAAAEA6QKYbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAEDQDQAAAABAxkKmGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPBJNr8eGACQOTV4cIqJNbPyp/UeAACAjIpMNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfZPPrgQEAAID0qMGDU0wsmpU/rfcAiE1kugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAgE8IugEAAAAA8AlBNwAAAAAAPiHoBgAAAADAJwTdAAAAAAD4hKAbAAAAAACfEHQDAAAAAOATgm4AAAAAAHxC0A0AAAAAQGYMuhctWmSuvPJKU6ZMGZMlSxYze/bsBLdfsGCB3S70smPHjlTbZwAAAAAAMkTQffjwYVOnTh0zZsyYJN1vw4YNZvv27cFLiRIlfNtHAAAAAACSK5tJQ+3bt7eXpFKQXahQIV/2CQAAAACAmJ7TXbduXVO6dGnTunVrs2TJkrTeHQAAAAAAUj7oPnHihB3q/d9//5nUoEB77NixZsaMGfZSrlw506JFC7Ny5cqI9zl+/Lg5cOBAnAsAAAAAAOk26D5y5Ijp1q2byZMnj6lVq5bZunWrvf7ee+81zz77rPFLtWrVzF133WUaNGhgmjZtaiZOnGj/P3LkyIj3GTZsmClYsGDwokAdAAAAAIB0G3QPHDjQrFmzxlYTz5UrV/D6Vq1amenTp5vU1KhRI7Nx48YE93X//v3By7Zt21J1/wAAAAAAsStZhdS0tJeC68aNG9sluxxlvTdt2mRS0+rVq+2w80hy5sxpLwAAAAAAZIige/fu3WGX6dISYN4gPDGHDh2Kk6XevHmzDaKLFCliypcvb7PUf/31l5kyZYq9fdSoUaZSpUo2uD927JgZP368+eqrr8wXX3yRnJcBAAAAAED6G17esGFD88knnwT/doG2guAmTZpE/TjLly839erVsxfp16+f/fegQYPs31qD280Xd4Xb+vfvb2rXrm2aN29uh7h/+eWX5rLLLkvOywAAAAAAIP1lup955hm7vvb69ett5fKXXnrJ/nvp0qVm4cKFUT+OKo8HAoGIt0+ePDnO3wMGDLAXAAAAAAAybab7oosusllmBdzKOmt4t4abL1u2zFYWBwAAAAAAych0nzx50i7b9fjjj5s33niD9zCJGjz4/+anx5JZ+dN6DwAAAAAgg2S6s2fPbmbMmOHP3gAAAAAAEOvDyzt27GiXDQMAAAAAAClcSK1q1apmyJAhZsmSJXYOd968eePcft999yXnYQEAAAAAyFSSFXRPmDDBFCpUyKxYscJevLR8GEE3AAAAAADJDLo3b97MewcAAAAAgB9zur20znZCa20DAAAAABCrkh10T5kyxa7RnTt3bns5//zzzVtvvZWyewcAAAAAQKwNLx8xYoRdp7t3796mWbNm9rrFixebnj17mn/++cf07ds3pfcTAAAAAIDYCLpfeeUV89prr5kuXboEr7vqqqtMrVq1zJNPPknQDQAAAABAcoeXb9++3TRt2jTe9bpOtwEAAAAAgGQG3VWqVDHvvfdevOunT59u1/AGAAAAAADJHF4+ePBg07lzZ7No0aLgnO4lS5aY+fPnhw3GAQAAAACIRcnKdHfq1Ml89913plixYmb27Nn2on9///335pprrkn5vQQAAAAAIFYy3dKgQQPz9ttvp+zeAAAAAAAQ65nuuXPnms8//zze9bru008/TYn9AgAAAAAgNoPuhx9+2Jw6dSre9YFAwN4GAAAAAACSGXT/9ttvpmbNmvGur169utm4cSPvKwAAAAAAyQ26CxYsaH7//fd41yvgzps3L28sAAAAAADJDbqvvvpq06dPH7Np06Y4AXf//v3NVVddxRsLAAAAAEByg+7nn3/eZrQ1nLxSpUr2on8XLVrUvPjii7yxAAAAAAAkd8kwDS9funSpmTdvnlmzZo3JnTu3qVOnjrn44ot5UwEAAAAASE6me9myZWbOnDn231myZDFt2rQxJUqUsNntTp06mTvvvNMcP348KQ8JAAAAAECmlaSge8iQIWbdunXBv9euXWt69OhhWrdubZcK+/jjj82wYcP82E8AAAAAADJ30L169Wpz2WWXBf+eNm2aadSokXnjjTdMv379zMsvv2zee+89P/YTAAAAAIDMHXTv3bvXlCxZMvj3woULTfv27YN/X3DBBWbbtm0pu4cAAAAAAMRC0K2Ae/PmzfbfJ06cMCtXrjSNGzcO3n7w4EGTPXv2lN9LAAAAAAAye9B9+eWX27nb33zzjRk4cKDJkydPnIrlP/74o6lcubIf+wkAAAAAQOZeMmzo0KHm2muvNc2bNzf58uUzb775psmRI0fw9okTJ9qK5gAAAAAAIIlBd7FixcyiRYvM/v37bdCdNWvWOLe///779noAAAAAAJDEoNspWLBg2OuLFCnCewoAAAAAQHLmdAMAAAAAgOgRdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAGTGoHvRokXmyiuvNGXKlDFZsmQxs2fPTvQ+CxYsMPXr1zc5c+Y0VapUMZMnT06VfQUAAAAAIEMF3YcPHzZ16tQxY8aMiWr7zZs3mw4dOpiWLVua1atXmz59+pju3bubzz//3Pd9BQAAAAAgqbKZNNS+fXt7idbYsWNNpUqVzPDhw+3fNWrUMIsXLzYjR440bdu29XFPAQAAAADI5HO6ly1bZlq1ahXnOgXbuh4AAAAAgPQmTTPdSbVjxw5TsmTJONfp7wMHDpijR4+a3Llzx7vP8ePH7cXRtgAAAAAApIYMlelOjmHDhpmCBQsGL+XKlUvrXQIAAAAAxIgMFXSXKlXK7Ny5M851+rtAgQJhs9wycOBAs3///uBl27ZtqbS3AAAAAIBYl6GGlzdp0sTMnTs3znXz5s2z10eipcV0AQAAAAAgpjLdhw4dskt/6eKWBNO/t27dGsxSd+nSJbh9z549ze+//24GDBhgfvnlF/Pqq6+a9957z/Tt2zfNXgMAAAAAAOky6F6+fLmpV6+evUi/fv3svwcNGmT/3r59ezAAFy0X9sknn9jsttb31tJh48ePZ7kwAAAAAEC6lKbDy1u0aGECgUDE2ydPnhz2PqtWrfJ5zwAAAAAAiLFCagAAAAAAZCQE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAJk56B4zZoypWLGiyZUrl7nwwgvN999/H3HbyZMnmyxZssS56H4AAAAAAKQ3aR50T58+3fTr18888cQTZuXKlaZOnTqmbdu2ZteuXRHvU6BAAbN9+/bgZcuWLam6zwAAAAAAZIige8SIEaZHjx6ma9eupmbNmmbs2LEmT548ZuLEiRHvo+x2qVKlgpeSJUum6j4DAAAAAJDug+4TJ06YFStWmFatWv3fDp11lv172bJlEe936NAhU6FCBVOuXDlz9dVXm3Xr1qXSHgMAAAAAkEGC7n/++cecOnUqXqZaf+/YsSPsfapVq2az4B9++KF5++23zenTp03Tpk3Nn3/+GXb748ePmwMHDsS5AAAAAACQGtJ8eHlSNWnSxHTp0sXUrVvXNG/e3MycOdMUL17cvP7662G3HzZsmClYsGDwouw4AAAAAACZPuguVqyYyZo1q9m5c2ec6/W35mpHI3v27KZevXpm48aNYW8fOHCg2b9/f/Cybdu2FNl3AAAAAADSddCdI0cO06BBAzN//vzgdRourr+V0Y6GhqevXbvWlC5dOuztOXPmtNXOvRcAAAAAAFJDNpPGtFzYbbfdZho2bGgaNWpkRo0aZQ4fPmyrmYuGkpctW9YOE5chQ4aYxo0bmypVqph9+/aZF154wS4Z1r179zR+JQAAAAAApLOgu3Pnzmb37t1m0KBBtnia5mp/9tlnweJqW7dutRXNnb1799olxrRt4cKFbaZ86dKldrkxAAAAAADSkzQPuqV37972Es6CBQvi/D1y5Eh7AQAAAAAgvctw1csBAAAAAMgoCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAgKAbAAAAAICMhUw3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAAACATwi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0AAAAAAGTmoHvMmDGmYsWKJleuXObCCy8033//fYLbv//++6Z69ep2+9q1a5u5c+em2r4CAAAAAJBhgu7p06ebfv36mSeeeMKsXLnS1KlTx7Rt29bs2rUr7PZLly41N954o+nWrZtZtWqV6dixo7389NNPqb7vAAAAAACk66B7xIgRpkePHqZr166mZs2aZuzYsSZPnjxm4sSJYbd/6aWXTLt27cyDDz5oatSoYYYOHWrq169vRo8ener7DgAAAABAug26T5w4YVasWGFatWr1fzt01ln272XLloW9j673bi/KjEfaHgAAAACAtJItzZ7ZGPPPP/+YU6dOmZIlS8a5Xn//8ssvYe+zY8eOsNvr+nCOHz9uL87+/fvt/w8cOGDSwqnjR02sOZj9lIk1aXV8AamBdix20JYhs4rFdkzokwH+/E4GAoH0G3SnhmHDhpnBgwfHu75cuXJpsj+x6DwTg4YVTOs9AJCCYrIdE9oyIFOJybaMdgyp4ODBg6ZgwYLpM+guVqyYyZo1q9m5c2ec6/V3qVKlwt5H1ydl+4EDB9pCbc7p06fNnj17TNGiRU2WLFlS5HUAoWe8dFJn27ZtpkCBArw5ADIk2jIAGR3tGPymDLcC7jJlyiS4XZoG3Tly5DANGjQw8+fPtxXIXVCsv3v37h32Pk2aNLG39+nTJ3jdvHnz7PXh5MyZ0168ChUqlKKvAwhHATdBN4CMjrYMQEZHOwY/JZThTjfDy5WFvu2220zDhg1No0aNzKhRo8zhw4dtNXPp0qWLKVu2rB0mLvfff79p3ry5GT58uOnQoYOZNm2aWb58uRk3blwavxIAAAAAANJZ0N25c2eze/duM2jQIFsMrW7duuazzz4LFkvbunWrrWjuNG3a1EydOtU89thj5pFHHjFVq1Y1s2fPNuedF5OzVAAAAAAA6ViWQGKl1gAkiarla2SG6gmETm0AgIyCtgxARkc7hvSCoBsAAAAAAJ/837htAAAAAACQogi6AQAAAADwCUE3AAAAAAA+IegGAAAAAMAnBN0Aonb69Glz6tQp3jEAMUeLvdD+AUjrdui///6z/TFkLFQvB5AgNexZsmSxFwCAMTt37jS5cuUyBQsWtJ1g2kcAfvfFzjorbq6UtidjIdMNIE4DrkucRuKss4Idyi+//NLceuutpkuXLua7777jnQMQExmkw4cPm82bN5sJEyaY4sWLm4YNG5quXbuav/76i4AbQIpSGxSuLya7d+82AwcONE2aNDE9e/Y0W7Zs4d3PIAi6gRhy8uRJM336dDNx4kT7txsq6TqZ4TLa//zzj2ncuLF5//33zahRo2x2R41+mzZtzA8//JAGrwIAkm/IkCHm0ksvjdepVduXLVu2eNkktZutWrUy3bt3N0uWLDGTJ0827777rlm0aJF59tlnzb59+/g4AERNgbISGJs2bYpzvWuTvMkO5/XXXzfXXHONee655+zJvs6dO5uvv/7a9OrVy2zcuJF3PwMg6AZiyKFDh8xPP/0UbMyzZs1q/+86mb/88ouZMmWKWb9+fTAQ1/DJv//+29x8883mpptuMm+88YaZNm2aqVevng3CQzuuAJCeqbM6evToeJ3a33//3Tz++OOmZcuWpk+fPubXX3+1me/s2bPbzPbixYtNgwYNTIcOHcxFF11kt121apVZuXJlmr0WABlP0aJFbbuSP3/+ONerTVKi48033zQvv/yyDa4d9cm+//57O8pQJ/vURo0YMcKe9Pvoo4/S4FUgqQi6gRgq/lO4cGEzdOhQOyzS6+eff7YdzQsuuMA8//zz5sorr7T/VyOvH4b27dubSpUq2ey2C8RvueUW89VXX5nt27enyesCgMSoDQsdMl6tWjVTs2ZNc/z48eB1f/75p81kK3PUtm1bs2zZMnPdddeZTz/91N5ev359U6ZMGVO2bNngfTS8U+0rQTeAaPphLkmRN29eO+KwRIkScbYbN26cbZ+GDx9uEyCXX365/b9oxKH6cLpdbZFrg8qXL2+WLl3KB5ABEHQDmZR3yLjLaIvOouos6fz58+3f+iF49dVXzbFjx+wQJQ0Z79atmxk/fry9Xi6++GKzY8cO23l12rVrZ4sJ/fbbb6n+2gAgGhrF44aMa5i4OwGpE4l9+/YNbvfKK6/YOduzZs0yDz/8sJ1Oo8D8iSeesLdfeOGFtsOrbLhTu3Zte51OWgJAKBdku36YG12j/+uEnk70qU8ma9euNc8884x56KGHzI8//mjee+8907x5c9OvXz/bP3MnC/fv3x8nY161alWzbds22x9D+kbQDWQC6kiGLmXjhoxrONLTTz9t3n77bfu3GmY15upUurlFCxYsMDfccIMpWbKkyZ07ty3Scckll5iPP/7YbnPZZZfZQkIadu5+SM4++2xToUIFO+QSANJT++c6u8uXLze9e/e2AfLtt99up9eIOrDKDulE4tGjR22nVYG1iqTpvsoe3X333XZ7ZcHV2S1WrJidgqMOsKitrFGjhh3tEzo3E0BsjqpxbY+3srhO1o0cOdLcf//95rPPPrPXqc1Stlvti+iknwLwBx980P59zjnnmGHDhtlpgR988IHJkyePqV69uk2AeNsbtU16LmrspH8E3UAG5hp3nUH1ZrNFwySrVKlih4orq61G+uDBg7YhV+baZWc0XFJzF3W21PtDoTnbe/bssbeVLl3anHvuubZ6uZceRwG8AnIASAve9k8dUrVHasNU6EzFinbt2mWLDd14443BNvPaa6+1JxHV4c2ZM6ftLKtTqwBc99V2Cqg1vcYF6rVq1bLBuTrHjjrBaiM1txtA7HKjatR+uKVWNYVFJ+90Qk9FbHW7CtHKFVdcYXLkyBFsX3TyTokMtTGi+2oYuvpiKuAo5513nh2x453SonZKz/XJJ5+kyetG9Ai6gQw2L9tLDa2GGr311ls2i+POkOrMqCr0KuBWETTNvVa2J1++fDY7oyyPMt7qLKrDqayOinN4z8xqWz3niRMngsPJdYZWDb7bRoXVNNcRANJiKS+1USoipLmPmh+pgo9r1qyx16sYkWpR6MSgltZRJ7du3br2furIaiWGb775xnaWdTLyjz/+CC6/ozZOAbke0w3n1JxKTadZsWJF8PlVVE3VzK+66ioOACAG+mHh2iFRckPtjPpKWt3AjTT8/PPP7ajBb7/91s7Vvv7664Mn/xREq38mpUqVMgUKFAgux+pOJKoNUz/Oba++mXeEYeXKle1IRdWiQPpG0A1kAKHzsp2FCxfa4meaB6QOpM6aKjutRlmdSQ2pVIdRwXWRIkWCwbKCbgXfygTJ1Vdfbecyaiim97HV0VUjL1piR3O+9+7dG9xGPy6TJk2yZ2MBICWoQ+pdFcG7lJe3roQow6wq4hqJo1UV1BaqrVN7qROE2l6rLGi5HXWKN2zYYLPZqhqsSuRuqKfmeGskkE5WujZu6tSpds6k5lW6kT0dO3a0GW9HHWVVMlfbCyDz98NClxSUe+65x87PVn+rdevWtj8mGlGo/tiRI0dsP2zdunU20eHaN520U19N91MCQ+3NhAkT7G1q8zS3W1lu9b9c300jD7UPLvhX/6t///62bUL6liXAej9Auupohlsre/Xq1bbohq5X5XHNvXadwPPPP9+MGTPG/q2GXUMk5ZFHHrHF0PR3nTp17JAmzf1RB1SZ8B49ephChQrZLLnmG2n4pQp5KFuu53NnZZVBEjXw4X5sdOY33JqSAJCU9k9tTLiTi8ryjB071p4YVGdWWWVllDT/WiN8NKTcBc9eyko/+eST9v8VK1a07ZraPs2rVLEizZdUtWB1jPW4X3zxhe04K7utkUBq07SKw//+979E9532D8j4XD/H299x32+1M6qFo+y1VnJRZllthabyaSSh1s/WaBrv42han04EqgK5TvIpANfwca2MoFo7Ommo67VagoagK/HRokULW5VcSRPV29GJPT2vS27oRKIC8nB9sXDtJ9IPgm4gjSTUUVOjrA6lOoL33XefzeCoYVZQ/e+//9rGWtlpXafLgAED7FBInQXVmVJlXZS12bp1q/2/5iCqE6mOq4ZfKjOkoFyZHzeUSfMeX3vtNTNv3jx7JlUVzPWjErqPdDABpIRInUcN89aQbQ2Z1InCe++912Z8tEyhipmpurgKPSp7/cILL9jh45rXqMBaQzHVdjZr1sy2hd42VR1SrX37zjvv2L81DF3DzNXRddNktC6uTnBqSR51rEP3z50cDXcCEkDGklB/5sCBA7Y9UNuiImWam+2mmWgkYLly5WzSQoGzkhU6Oad2RI+nWg9qq9SH0zramp+tAFzJD/XHVJFcfTK1VUp+aClXPb7aGyU/VPhWRRtbtWpl+2wavRO638LJvgxGmW4A/jt9+nTgv//+s/8Pdfz48cBPP/0U+OKLLwKXXHJJoHTp0oF169YF3nrrrcB5550XWL9+fXDbBx980F539OjRwJw5cwJly5a1l8svvzxQrVq1QO3atQO//vpr2H2oVKlSYOjQofbfr776aqBEiRKBNWvWBG8/deqUL68dANT+6RLOrl27Avfcc09g6tSpgS5dugSqVq0aWLFiReDjjz8OXHjhhYE//vgjuO2YMWNs2/XGG2/YNuv5558PdOrUKXDXXXcFmjdvHihWrFige/fudtsTJ04EDh06ZP997NixwNVXXx2444477L+1L2ov582bF/HDoU0EYqcdWr16dWDRokWBKVOmBLJkyRLo1atXYMeOHYFbb701cNtttwW327JlS+Cyyy4L9OjRw/79wgsvBIoUKRK4+OKLA23atAnkz58/8L///c/27UKp35YvX77A999/b/9u2LBh4LrrrgscOXLEt9eM9CH+KWYAqTIvW3MLlS1R1XBloLWchDI1GjqpjI7ObGpIUsuWLe2ZVg0VV/VwZWE0pElnSzUfSGdDNY9bQ8RVhEPVejXcXOvLKmutrLeq+X744Yc2e9OpUyf7/HoezWPUczoue6MMVKR55ACQHK49UfuiEThaLkfDLe+66y67VI6mtKid07xqFUETzXfU6B6NyNHQbw0R19BNzZvUyB61Wa6ApDJKyhppWszo0aPt3xolpCrlah+VkdJShxoppHmVogx6KG82m4w2kLl4+zXKPKso2TXXXGOHZ2vkjGo5dOnSJTgCRtNTVP9G/S8VRdNQb2W6NdxcRWh1P2WuH3jgAdvOaESi/q/H0PBwFTrTfZX1dlMFta1G2YiKrGnKYGjW2hXPpR+WeRB0AylInUF11ryNpBu+pOGM6lCqA6kGVusuqrr4U089ZdfBfumll2zwrOHkWqZGVcM1PHzOnDm2c6rquiqmoaGUKp7mhhup06q5Pmr8RUG2OooFCxa0z/nGG2/Y61UMSEPKFcBrn7RUWMSGIcyQTwCxLbGh1Wr/dInUfqgDqs6phmyqgq+GeavjqaBbJwQ1l1HVwzU/0lG7pmGZI0aMsO3foEGDbAVyFUJzFFxrKKi21b8VqGtqjAJwzYtUASPdR8PSVfQssbmQ4epqAMgYw8JdPyxcrRkVV1TQrGHh6guJ6twogFYyQ0sJqvK4iiW6KSeabqfgXNep39S0aVNbK0J9Kp3Ec3QyUf00UdCtfp5q6mg/tOKBTioqyaKh5Ep6uHZSc7bDIdjOfOhZA4lwFSKjyXiEbuOKaWjuTufOnW2DrwqTOoOqs6dal1HUmKuxViPrGmIFz4ULF7bZHM1DVGENR4+jHwEF3greFZyrIu/cuXNtEK7iQnpezctWwaHQ6uJ0KAEkNdBOLBj1ZoZd1tlRzQh1VDXXUcG2RuiocJkyP+qs6iShAmOdfPRWKNecR620oI6qstuO2lTNfVQHWFknjRZStlxZ7YYNG5rHHnvMbqe53bqEa5cdOrdAxiq0mFg7FMnMmTPtyBjVi1C/SyMMtb1GHiro1nXKSLvg2fW31G4p2aF2xkvBuvbl2LFjtiaO+l8alaN6ETfeeKM9yaf91vOwygsIuoFEeBtwna1U9VtXhdJLDbOCXnUkdYZTga8yL/qxUEOv4kDKwGiokShro+UjlNmpUKGCPeuqhl+dUXcGVsWCNKxJFxd0q3F3FciVJVeQruFJOqOq7JEaemWNxHV6XQaK4ZIAksIF2mrHVCH8k08+sSf4lKlRNlkdVNe2qD1TRXAVaNS0FQXECn518lDtnNoxtXHK9qiNUzCtar9aEkdZIy37peJDur8yTqKAWdNg1FHu27evLSCpk4waNaS2UG2xChsps6QAXaOGlJkKl83W66ANBDIe73Q3jVxR4kJZZ7ecn9fKlSvtiT2NkFExRBU4cyMDBw4caKfYKQgWnaBTG6SpeFoNRo+pwFsn8rSd2j21X1rFRdP91MfTUqlqR5S5nj59um0H1Z/T/ingVvFHjchRkC7a1lt5nKl7MSytJ5UD6dnhw4dtkZ6LLrookDNnTlvgTEXMWrduHdi5c2dwOxXlUSGMc845xxYBatu2rd126dKl9varrrrKXn/y5MngfcaPH28LaHzwwQf273feecf+PWvWrOA2KpY2evToQK5cuexj3HTTTfY5VPxH93eFggDAD2rnunXrZosKlStXLnDDDTfYQmQqFPTQQw/FaauuvfZaWzzoo48+Cnz99deBmjVrBnr37m0LmanoWZ06dQK///578D67d+8OnH/++YF+/frZv7dt22aLE3Xt2jVO27Z582ZbsEj3r1ChQiB37ty2cOTcuXPDFkRSsUoKoAGZh4qOqX1Ru5M3b95A48aNbWHYnj17xtnuk08+se2E2oennnrKFmRUm/Tbb78F/v33X9t/evvtt+22rqitCjCqWKP+1mXAgAGB6tWr29tc+7Jnz57ALbfcEihZsqRtA9W2FS9ePHDzzTfbxwaiQdANJGD79u22s6mAWdXFDxw4EFiwYIG97rHHHrMdTRk4cKCtmqtG3bnmmmsCLVu2tI32fffdZwN3BeeuM7hy5UpbbfyJJ56wf2/atMk2/MOGDYu3H6pkrudQB1bVfL3Be2hFznDV0QEgHHUY+/btG/jzzz/D3r5v3z57ErFDhw5x2hq1iQqA3clHVftVe+cqhYs6r3ny5LGrMihAbtKkSeDDDz+Md7Kybt26wZOcgwYNsh1lCW3LVq1aFaz4G4pAG8i81P/Jli2bPaGn/o/ajkcffdQG4Ko27gJj9bPcCi3y448/2vZFJwj3798faNGihe27eQNqBeF6HJ30k08//dQmWf755594bcz8+fMDjz/+eGDChAn2pGEo9e/0uJz0QzgsNImYp5NPrkpkyCgQW+BChTI0XEjDIVW8R8OZ6tSpY4eaqwKvaMikhhzpPhq+pKFGs2fPtkMvNfdQcxE1d1HDIt1wdQ09//vvv81PP/1k76c5RKqou3TpUrN///44+6Lnf+aZZ8wrr7xih0JpSLmba+5oaJMuzNcGkBSarqJpLeForrWGcGs6i6tBoXZGQ7tVpVfFGkVTZ7QW7YsvvmiLl2klBU2rueOOO+xwcg0dV50KDeN0VFRS1cM1LF1tnqbIqIbFxo0b7XN52zK1kZrz7YZsqs32tttu6DiAzMOtR33hhRfaYd/qI6n/o/9r+oraIPWZRGtpq03Q2tYqIKtpKeqLqY3S9prOoul9y5Yts9u74ep//vmnfRzNwxb19VTIVkVsvfuhNkaPp9UV1K5pKoyba+6oDdLj0hYhHH6hELNchy3S/Bp3u5tXrUZUDfOzzz5rfv75Z9sZ1RxCBdX6EVBlcBXbUMOv+T9anuajjz6y86sVKGt+tQJnLfUlKsih23R/Bd7StWtX25jrxyHSPielsBuA2O6sJnS9/l2lShVbg0InEUNPPrq2Rp1dBdQ6kThp0iR7glEVgFUkSIGwqOaEOrMqbqbijZpzqZOROlGo+d3q7KrehNo9zftWOzlu3Di7JKLaXy3bI5ofqTZR87K9++oCcHedO8kIIGNS++ItmpgQtVNFixa186hFtSW0vJdq6ygJIjqxp9vVPqmdUdukQrPqcw0ePNiuCqO+mdo6nRxUsUfVmlCwrT6XakmIki1aslB9Pyf0BKC3/0hfDNHKonR31FsDGVRotVovVbxVQTKdCX388ceDBdLcMjIqUqbiPZUqVbIVeJWtUfD99ddf2wI+euxevXqZ77//3hZR0w+Do0ZdGSJly7VOtopraHtdr4yNLvrx0POqswkAZ0LLDqrNuvXWW4NtXrilDEUdXp0wVAFGjdrRcoTKUIdmd5QF18lAFQlSdkkdYBWGVKdWJxLVYVW2SUG4Tja6TrBoOxU20gghtY3aXsG7OrrKGj366KN2zVqdmFTRtYSWAgKQOWk1FrUj6n+FawPUPg0YMMBMnjzZ9sHckoEaMag1sDVKRu2cKpBrWUIlQRw9ntow3Ud9MWWqVWjt8OHDdqlBBeRaxlDF01y2nBN68AOpMmRarmK3hAbcaoS1DJeGfStjo0yzW7/acY2uW6ZG1Xq1HI2W+nruuedsoP3555/bx1bnUUPFZ8yYYYNsF8xriRxlu0WBu/6tNWh1hlZDL9U51fBKnaH17jcAJMenn35qT/CpPQod8ihqvzSc29vGqVK4OpvKMHu5jq+y1LroxKA6psp2q6OrdlOVgTVtRsG4qv92797dZrDViVWlX023UUDuMlrqDGuIuU5cal+1DxpOrmy49zkBZGwuIxw6usZRG3XXXXfZzHKLFi3MnXfeaU/shWsDFDCrj6Yh3cpea0i4RtZo6p1GCOp+aueU5VY/TFXF9dxqd5TYUH9P/TfRyT1dN3z4cDsaR6si6PHcCEPXLtIXQ4oLO9MbyGASKh6mQkCqaKlqut7tJ0+ebAuiqQBaaMEMxxXDUMEzVdhV8Q5vkaBmzZoFq40/+eSTgWLFigVatWoVuPTSS21ly6ZNm9rndlRgwxVfU+GiK664wlbZPHLkSAq8CwAQv11UxfC77747UKpUKdtG1atXL3DPPffEaSPVFqpIY6THUCVgtWcqTOSoDVPxNFUVVgG1v/76yz5uo0aN7POoDbzzzjtt0UhHFcnVFquA29SpUwNt2rSxxdSOHz/ORwdkMOrTqEiZ2hdJqIDYihUr4txPBWZVcHb27Nm2UK2KM6rt2Lp1a5y2x/3/888/t5XJtdKLo0KOqiquIrTqW6mYrVY/OPvss+0qM2rzdHnggQdsYVxn165dwX9PmzYtULFixeBKMoBfCLqRYakhDl0uxht8L1u2zFbdzZcvn13e4dxzz7VL37jAee3atYGCBQsGXnnllYjP4aqEq7qvKmB6lwnbu3evbcgLFSpkK5rLr7/+GhgyZEjg2WefjdM5dXbs2GE7pQ0aNLD307IX+rEBgDNt/xy1PWPHjg22Yar4q46oqvKqgq+q/aqq+FtvvRUMdtXpVOc5dAlC14lWQK72atKkSfZvdz9VE9dJSQXkzoYNG4Id50iViLXcT9myZQO9evWy7SaAjEdt0MMPPxwoU6ZMvD7Y+vXrbTJCt7/66qv2xJ76QKLvvPplWpnFPY5WQNA2WqbV+1ju/1rhRUkKLR8orq3asmWLbX+0nKtO/qnNUv9v5MiRttp4OO+++27g6quvDhQtWtQG5YMHDyb5Ad8RdCNTUOP+3Xffxblu3LhxNgDeuHFjsHOoH4aJEycGg2ZlfHS2NdIZWnfd4sWL7ZIS3jO1ruOpNRu15ESkbHvo46qjqx8gt18AkBi1I+GWChTv2tcuqC1RooRt40QdW7ecoZa5UXum2zt37hy8r9aqVZYp3DI5rmPbrl07m7n2tmu6ffXq1XY5r3DtnfbZu63LkIdbbgdA+rJ58+bA/fffHzh48GCc673f84ULFwbOOuus4JJbokRElSpV7GhALe2lTLQC6hkzZtjbR4wYYZcQvPfee20/TMkRJUZuvfXWwNKlS8PuixImDz74oL1fqOXLl9u2yZvNDt1ftw63qN1T4B3abwT8lC3lB6wDSZreYOfvaO6MquOGVu3WnJyEqkOqkrjm5aiwhor0aF7Q/fffb+cfqvKkluFSxUrN99H8axVC0zzDLl262OrjKu6juYkSbh6Re95mzZrZOYia91i/fv3g7XpezR0Kpf3WfcPt+y233MIhAiBJRSDdxdtuar70a6+9ZttNzY28++67bSXyhg0b2jmLavc0D1vzHjV/UsXVNJ9a26iOhIqeaTkdFYnUvO4pU6bYudjeYpCuXVTBIs291HNrOR21fe52b+G00PZOhdpCH6ts2bJ8+kA64Za9cnOZvYXMTp48aXbs2BGvyri+56pfo2rhWplFK7GokKwKLrplCFW07KuvvgoutaU525qPrbZGbUb//v1tAbQePXrYvpvapIQKmKnYbbly5cymTZvs/Gy1a46WNXz99dfjbO+t6RPaLqnN0wVITRRSQ5pSY6yqleqYqfp3KLfeodaEVZVJLxXLUMEgLQ2hjuL48ePNr7/+aitTitZ7VdEgBd8q9qNlIm677bZg8K3nvuyyy+zyX1ojNlIBH7c0hNZs7Ny5c4LbePebgkAAoqUOYmg7Imr/1PHVCT9VGVfHdMSIEeatt96y22t9bFUAV/s3ceJEex91JosUKWJXWBCdMFQnWJ1VtZmqHD5r1izbPuo6VzBS++DWqg1Hba2KpLmA24uFUICMxX1nvcumamUVb9+latWqZtq0aTawdtROaBUCtTEKnLUsoPpgaovcigVaRaFNmzbBx9LKBjfccENwaUAVXsybN68tUKuThSqSpn3Q/bTGtrcQpHdfFbC7AmqhbY63eG6kYBtISxyNSPM1Y7XWtRppZZ29FBhrfex69erZbXQG1WWlZcGCBTYD3alTJ7utHkNZG1UI//fff23mXFlvZWd0P3U2VaVSy0S4jmXt2rVtw68ztJH2Uz8Eul7rO3p/eEK3AYDk8lYY91IWSMtpKQutETzquGoNWa2C0KhRI9uGdevWzdx00012OR0XdCs4V6bbPfbUqVNt0H7hhRfa65Rx0iggVSBXR1edY7WVylwpyI+Uafr/p6XFu42TjEDGou+sVllRhlgn3bSCQMeOHc2rr75qr3eUqNCoGve91wlABdq6n0bLqG+m5U+1GoEoGFciQ+2OO5GoE3W1atWyfTMt35UrVy7btr300kvm4Ycftisq6HkeeughOxpHwX/ovrpRMhrB6L3OIchGekfQjVShxlGZamVcQq9Xo6yhi1qyQYGyKDhWA61sjJbmUsCsRlzDk0SdRDXSS5YsMdWrV7ed0aFDh9ofDrfWrBpgdTrV0dTtokyQfji07I0omD/vvPOCQXikjqO7nmwOgORKaAkadWQHDRpkRo4caX7//ffg9U2aNLGdULV5mk6jdk7ZJQ3xdsO6Nby8VatWduktjRzSyUFlgjQsVCcj1cHVkHKtjz1//ny7jrfaPG2jJQvVtoqW9hozZowNxiNRW0iADWR8CnLVBijo1XJ/GsmiYdpqh/RvR8kMLZPqAnEtsyVaqkv9LCVMtDyqkh9qV9T/0tDytWvXxgme1WYdPXo02P/S+tjKcitw1zQYTb3TqMb77rvP9uuATMfXGePA/2/OnDl2qQcVHAtXbGfu3Lm2yMaaNWvs319++aX9W0tBuAq9f//9dyBr1qx2W1Fl8urVq9sqvbotXCE0LWdTq1Yt+7xa8kaVw//3v/8FWrRoEXxc7zJgAJBSXPGehKgYo4oDaYktLTV48cUXB8qVK2crjIvaNrWFEyZMCN5HFYBz5Mhhq5E7KnRWuHDhYBurdrJ+/fq2IrDMmzcvcP311wcKFCgQKFmypC3oqMdxVcgT208AmYtWTlGbowrjjlY3UPEzFVV0VPgwZ86cwUrgKtSoPpTaJu825cuXDzz99NP2b/XLtOKL+l1y4MCBQPfu3QP58+cPXHXVVXHaHBVv1P2BzI5MN1JFhQoVbHEeDSuS0EyJimjojKnL8Ohsq4Z963o3vLt06dL2rKwKoYkKmuXLl88OP9dtbu7Ou+++a4sLieY+ajilMkM6q3vllVfaYZqa6+iGcmrIZEIZKACIlkbuuBExrpiiKJv8/vvv25E43uHbGo2j7PI777xjs9DaRsPGH3jgATsUU21biRIlzNatW22mSJS11kggtWmORu1onqSy2KKhoqqV4bZRJlxzJTWHWxlwZZX0ON6CaABih6agqACappg4ahtUpEwFyzQVzzuk27Uluk19Jo02dDSaplixYsEstqa7qI15/vnnbT9OfTqNZFT9CdWS8LY56hu64ooq2EZ/DJkVQTdShYqaucbdVSR31MBqeKQKdigYVsOs4ZGaqxhaXE1DkFyjrnmM6lh26NDBdlo1Z1GF0l5++eVgo123bl17m35IND+8Xbt2YYdOUmwDQGJULEjDsSVc0bPQIorbt2+3w8FV4FHDxNXhHDBggC3sqGGUonmQOjGo20WB8D333GOHZWpYubRs2dLWsHCdYFFbprbQTclRp1dVyxXUizrJL774oi145GjFBnWMdVIg0v4DiA3qC2kYt6alqO+k/pL6XWp7VCtHiQ/XTuiknaupo1o66mO5tkbULqoorYqp6eRg/vz5bbJDJwFVzFZTY5TwUGJFfUGdUAxHJwrpjyGzIuhGqjbumvOjhllcNsgFyO3bt7eBsZtfqABbHU3NAXKd2Kuvvto22noMzdMePXq06dOnjy0K9MQTT9jGWp1MzRNy1BkV/XjQ0QSQHGqXVF9CnVNv+xVK2Z9rrrnGBsOqwKt26cYbbzRPP/20Db61xKGCabfUoNpEtY/eehcKmDU6yFX6VQd49erVZufOncFtVBlY123ZssX+rTZSJyJV58KN4NE8TdXLCOWtVgwgdikDrZN5Wt1FRcw0GkbLDyp4Vp0d104ouaG2TSceFZhrJI5WTOjevbudm62Mtto4jbhxKyJI48aN7YoyqjquOd5KrCgIV3abGjmINQTdSDUq+qMAe/ny5fGWqxA1yhrWpIbeZXJUBEjDKr0NuDqnKvjjskLKHmnIuYYsTZo0yWaFwp0p1Y8HHU0AyaHMjbI9LvBVRkaZ7NCMzWeffWY7nSo6VKVKFTvkW4WFdBLRBdDqwGrJL1F7pZOIrt0TZaN1slHVxEXbqxClgmxH1YJVwVzZKEfDPl3G3KFjCyASTdnTST4tn6qTg126dLHVyxVcX3fddcGTgRqNoxN5Wt1AfTYlQFTgVgUeNVpHq7soWNdJQC3/5dodjVZUMVu1S1o2TH04bSdMaUGsIehGqtHZUQ1v1BlVb4PrAmE1yPq3himpwdZcbQXpbh6RrlNHV1ltre3opeHpup35QAASEzq8OjQwDTcqRifytNKBhoW7DLQyNhoKruDb0VQWnQzUHEW1S5p7fejQIZMnT57gqB51UNUZVXulf6szO2HChOAoH83z1u2XXnppsH1zGSn3GAq2NZXGzYWM9Fro2AKIRCNhNKpGo3AOHjwYbDM0XU9/q31TtXJN+VMWWycVHdXImTlzpg3E7733XtvGufbTtTsa4ai547feequtbK4h5mofgVhE0I1Uo4ZdF80fUifU2xl08xvVqVVQrc6lCvxoOKfL9ngz4mr8Q+l25gMBiMQ7usad7FPHMjQw9Y6KcW2T66AqK+SKlWmNWWW+tZyOKCDWUHFXKE3/ViZbwyo13NyNwFG2R5lsdVYLFy5s7z9jxgw711vrcCvbdP/999s52t4lxTR83DuKR68ntOgQQTaApNCJQbWDrqCa5mTrpJ7aN7VROiEoCrg1pS9U6ElK74hCjdrRsHMF76rBA8Qygm6kSeOuM54KvDU8SWtva+ilGnzNz1YxD1fsbNSoUbYTGoohkwCSSgGphn2//vrrdk60AmgNfZw8eXIwy6NgWEO/FfDqJKFO/LkCQkWKFLGVxVVlXDTcXBmecePG2c6pAmLNe9TQbwXDej6N2NH/XXbcBe8agjlnzpzgiUR1aJXNVid14sSJNhB3lcWd0Oy7HpeiQwDOxPnnn2+nr2i+tTdo1glDTQfUfG3RdBmNygnF1D0gOtmi3A5IsXndr7zyih1S6TqQ6mhqLlGlSpVspjuUhmAqg+1FNgdAUmmqioY5KhOtyro64aeRNQqcNTexX79+tnijCv/cfvvttj1SYSHNw1bQrCkymtuo5b00GkfZIN2mZb4ef/xxu1yhCqNp2LkLhjX8W8sbKjuueZM6YaiOq7LdrhK6KDjXJSHUpADgRzJEU/bcyBrXzuj/WpZVbRZ9LuDMZdFi3SnwOEBUVHRIy9ioKJGKAykIjybIBoAzpSz0fffdZ5o3b24GDRpkr9M8ao2mURCtwFq3aUkbzT10I240+kZB8ogRI+zcR43MUTDuakuo4KOq+CozreHoatsU1It+YpUtV2C+e/fu4L5oO7eygpebD0kGGwCAzIPh5UhVWiZi2LBh5pFHHgkG3AqyvcMmCbgB+EEFfRRQq5ijqvKq3VFxRxUq03QXZak3b95sg2oF3JrbKKri+8svv9jgWvMSVRzIzetW1lzXPfnkk2bVqlVm2bJlNiPuKIDW8jpuzW0nXMDtsksE3ABSG0uqAv4i6EaaNe5ukIWCbIZNAvCbAmkFzHv37rVBttodLY2jpQo1x1uB8Nlnnx1cYcEVKdOyXyoAqSBcwy2bNWsWXLbQnSTU6gsqGKT7aK64l/5WthwA0iv6YYC/CLqRZo07c4QApDaNsFGxNA0Hv/jii21RMy1ho7817UXLdKnAozcbray4CrBVrlzZBtma6+3W1lY75jJEmr9drVq14Nq2XszkAgAgdhF0AwBiKuhWcK1stpbo0jzvb775xg49VwCtZbn27dtnevToYYeUb9q0yVbvVWE1V028SZMmtuK5hqZ7M0RaW7tAgQK2KGQoTjICABC7qFYFAIgZ5cqVM1WrVrVZbAXWCqRdplqBsQLmsWPH2mXA2rdvb4eft2jRwhZGc8vlaBtvYK3lxp566ikzZswYM3DgQBvUAwAAOATdAICYWyJHGW5lu5W1Dq0WftNNN5nWrVvbedxaw1ZLg0UaLq77KsjW0odabswtuwMAAOAwvBwAEFMUSB84cMB89dVXEbcpXry4DcgVcKs4miuq5ijY9g4Z17JjBNwAACAcMt0AgJjLdGuN7QsuuMD+HWmJLmWzWTMbAACcqSwBSqoCAAAAAOALhpcDAGKSK6AGAADgJzLdAAAAAAD4hEw3AAAAAAA+IegGAAAAAMAnBN0AAAAAAPiEoBsAAAAAAJ8QdAMAAAAA4BOCbgAAAAAAfELQDQAAorJgwQKTJUsWs2/fvqjfsYoVK5pRo0bxDgMAYhZBNwAAmcTtt99ug+KePXvGu61Xr172Nm0DAABSD0E3AACZSLly5cy0adPM0aNHg9cdO3bMTJ061ZQvXz5N9w0AgFhE0A0AQCZSv359G3jPnDkzeJ3+rYC7Xr16weuOHz9u7rvvPlOiRAmTK1cuc9FFF5kffvghzmPNnTvXnHvuuSZ37tymZcuW5o8//oj3fIsXLzYXX3yx3UbPq8c8fPiwz68SAICMg6AbAIBM5o477jCTJk0K/j1x4kTTtWvXONsMGDDAzJgxw7z55ptm5cqVpkqVKqZt27Zmz5499vZt27aZa6+91lx55ZVm9erVpnv37ubhhx+O8xibNm0y7dq1M506dTI//vijmT59ug3Ce/funUqvFACA9I+gGwCATOaWW26xwe+WLVvsZcmSJfY6R5no1157zbzwwgumffv2pmbNmuaNN96w2eoJEybYbXR75cqVzfDhw021atXMzTffHG8++LBhw+z1ffr0MVWrVjVNmzY1L7/8spkyZYod0g4AAIzJxpsAAEDmUrx4cdOhQwczefJkEwgE7L+LFSsWJ0N98uRJ06xZs+B12bNnN40aNTI///yz/Vv/v/DCC+M8bpMmTeL8vWbNGpvhfuedd4LX6flOnz5tNm/ebGrUqOHjqwQAIGMg6AYAIJMOMXfDvMeMGePLcxw6dMjcdddddh53KIq2AQDw/xB0AwCQCWmu9YkTJ+wyYZqr7aVh4zly5LDDzitUqGCvU+ZbhdQ0VFyUpf7oo4/i3O/bb7+NV7Rt/fr1dj44AAAIjzndAABkQlmzZrVDxBUU699eefPmNXfffbd58MEHzWeffWa36dGjhzly5Ijp1q2b3UZrff/22292mw0bNtglxzRc3euhhx4yS5cutRl1FVvT9h9++CGF1AAA8CDoBgAgkypQoIC9hPPss8/aquO33nqrzVhv3LjRfP7556Zw4cLB4eGqbj579mxTp04dM3bsWPPMM8/EeYzzzz/fLFy40Pz666922TAtSTZo0CBTpkyZVHl9AABkBFkCqngCAAAAAABSHJluAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAD4h6AYAAAAAwCcE3QAAAAAA+ISgGwAAAAAAnxB0AwAAAADgE4JuAAAAAAB8QtANAAAAAIBPCLoBAAAAAPAJQTcAAAAAAMYf/x9+6rl+JfoKvwAAAABJRU5ErkJggg==" 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ModelMAE (test)MSE (test)RMSE (test)R2 (test)CV R2 (mean)CV R2 (std)
0Linear Regression1.458116e+073.634642e+141.906474e+070.2928800.2458290.067041
1Ridge Regression (alpha=1.0)1.629031e+074.677350e+142.162718e+070.0900210.0908460.056075
2Polynomial Regression1.708302e+077.093839e+142.663426e+07-0.380108-0.4526070.900419
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" + ] }, "metadata": {}, "output_type": "display_data", @@ -3145,7 +4296,638 @@ } } ], - "execution_count": 64 + "execution_count": 87 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:32.072309500Z", + "start_time": "2026-04-26T14:22:32.048607700Z" + } + }, + "cell_type": "code", + "source": [ + "display(pd.DataFrame([{'Part G Best Model': best_model_g}]))\n", + "\n", + "pred_sheet_part_g_linear = regression_prediction_sheet(y_test_g, y_pred_lin_g, 'Linear Regression')\n", + "pred_sheet_part_g_poly = regression_prediction_sheet(y_test_g, y_pred_poly_g, 'Polynomial Regression')\n", + "pred_sheet_part_g_ridge = regression_prediction_sheet(y_test_g, y_pred_ridge_g, f'Ridge Regression (alpha={best_ridge_alpha_g})')\n", + "\n", + "part_g_pred_sheet = {\n", + " 'Linear Regression': pred_sheet_part_g_linear,\n", + " 'Polynomial Regression': pred_sheet_part_g_poly,\n", + "}.get(best_model_g, pred_sheet_part_g_linear)\n", + "\n", + "if 'Ridge Regression' in best_model_g:\n", + " part_g_pred_sheet = pred_sheet_part_g_ridge\n", + "\n", + "part_g_pred_sheet" + ], + "id": "28a5dae231975e76", + "outputs": [ + { + "data": { + "text/plain": [ + " Part G Best Model\n", + "0 Linear Regression" + ], + "text/html": [ + "
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215728640.02.325830e+07-7.529660e+067.529660e+06Linear Regression
310171187.22.181280e+07-1.164161e+071.164161e+07Linear Regression
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52831155.21.371277e+07-1.088162e+071.088162e+07Linear Regression
63250585.61.375252e+07-1.050193e+071.050193e+07Linear Regression
727262976.01.933636e+077.926614e+067.926614e+06Linear Regression
852428800.02.267715e+072.975165e+072.975165e+07Polynomial Regression
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1218874368.02.302922e+07-4.154853e+064.154853e+06Polynomial Regression
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143250585.62.098037e+07-1.772979e+071.772979e+07Polynomial Regression
1527262976.02.102464e+076.238332e+066.238332e+06Polynomial Regression
1652428800.02.375635e+072.867245e+072.867245e+07Ridge Regression (alpha=1.0)
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1910171187.22.349771e+07-1.332652e+071.332652e+07Ridge Regression (alpha=1.0)
2018874368.02.349279e+07-4.618422e+064.618422e+06Ridge Regression (alpha=1.0)
212831155.22.349545e+07-2.066429e+072.066429e+07Ridge Regression (alpha=1.0)
223250585.62.349522e+07-2.024464e+072.024464e+07Ridge Regression (alpha=1.0)
2327262976.02.349620e+073.766777e+063.766777e+06Ridge Regression (alpha=1.0)
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" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 89 + }, + { + "cell_type": "markdown", + "id": "e9eb7df6", + "metadata": {}, + "source": [ + "### Part G Answer (with Cross-Validation)\n", + "\n", + "For Part G, the goal is to predict **`Size in bytes`** using the inputs:\n", + "\n", + "- `Category` (app category)\n", + "- `Reviews` (number of reviews)\n", + "- `Content Rating` (age/content classification)\n", + "- `Rating` (average rating)\n", + "- `Numeric Installs` (install count as a number)\n", + "\n", + "I compared the allowed regression models using a **standard train/test split** plus **5-fold cross-validation** on the training set.\n", + "\n", + "**How I decide the “best” model:**\n", + "- I prioritise the model with the highest **Cross-Validation mean R²** (best generalisation across folds).\n", + "- I then use **Test RMSE** as a secondary check (lower is better).\n", + "\n", + "**What the results show:**\n", + "- **Linear Regression** is the strongest overall here: it has the best (highest) mean CV R² and the best test R² among the tested models, with a lower test RMSE than Ridge and far better stability than the polynomial model.\n", + "- **Polynomial Regression** performs poorly for this target on this dataset (negative test R² and a very unstable CV R² with large standard deviation), which suggests it is not generalising well.\n", + "- **Ridge Regression** is more stable than the polynomial model, but it still underperforms Linear Regression on both CV R² and test RMSE.\n", + "\n", + "Based on the cross-validation and test metrics above, **Linear Regression is the best regression model for predicting `Size in bytes` for Part G**.\n", + "\n", + "I also use the residual and actual-vs-predicted plots to visually confirm whether errors are randomly spread (good) or show patterns (model mismatch)." + ] } ], "metadata": { diff --git a/Q2.ipynb b/Q2.ipynb index c376ad5..dcdc902 100644 --- a/Q2.ipynb +++ b/Q2.ipynb @@ -1,25 +1,26 @@ { "cells": [ { + "cell_type": "markdown", + "id": "bb3519b1aa083259", "metadata": { "collapsed": true }, - "cell_type": "markdown", "source": [ "## This is the Q2 Notebook!\n", "\n", "It's tracked via GitHub! hence the need for this line for the init commit" - ], - "id": "bb3519b1aa083259" + ] }, { + "cell_type": "code", + "id": "76e70b2ed9af0b56", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:44.706918300Z", - "start_time": "2026-04-25T22:14:44.683287200Z" + "end_time": "2026-04-26T14:22:29.055434300Z", + "start_time": "2026-04-26T14:22:29.010947900Z" } }, - "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -27,29 +28,47 @@ "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n", + "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt" ], - "id": "76e70b2ed9af0b56", "outputs": [], - "execution_count": 148 + "execution_count": 67 }, { + "cell_type": "code", + "id": "4a05800d", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:44.729001200Z", - "start_time": "2026-04-25T22:14:44.709018800Z" + "end_time": "2026-04-26T14:22:29.119438800Z", + "start_time": "2026-04-26T14:22:29.079996400Z" } }, - "cell_type": "code", - "source": "df = pd.read_csv('data/googleplaystore_new_new.csv')", - "id": "f44380615d3aba25", + "source": [ + "# Keep one fixed seed so your splits and metrics are stable each run.\n", + "seed = 101" + ], "outputs": [], - "execution_count": 149 + "execution_count": 68 + }, + { + "cell_type": "code", + "id": "f44380615d3aba25", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:29.148305400Z", + "start_time": "2026-04-26T14:22:29.122438300Z" + } + }, + "source": [ + "df = pd.read_csv('data/googleplaystore_new_new.csv')" + ], + "outputs": [], + "execution_count": 69 }, { - "metadata": {}, "cell_type": "markdown", + "id": "1baa7daa49445720", + "metadata": {}, "source": [ "## Part A\n", "\n", @@ -57,85 +76,90 @@ "Installs_Num), using Logistic regression and KNN, find and discuss the best\n", "classification model to predict “Category” (use the training/validation/test\n", "partition without cross-validation). **[8 marks]**\n" - ], - "id": "1baa7daa49445720" + ] }, { + "cell_type": "code", + "id": "f6fd7137bf91f31e", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:44.756069200Z", - "start_time": "2026-04-25T22:14:44.729001200Z" + "end_time": "2026-04-26T14:22:29.200889200Z", + "start_time": "2026-04-26T14:22:29.170323700Z" } }, - "cell_type": "code", "source": [ "columns_to_keep = ['Rating', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Category']\n", "df_part_a = df[columns_to_keep]" ], - "id": "f6fd7137bf91f31e", "outputs": [], - "execution_count": 150 + "execution_count": 70 }, { + "cell_type": "code", + "id": "b5daf475a5d5ca15", "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:44.886179Z", - "start_time": "2026-04-25T22:14:44.784660200Z" + "end_time": "2026-04-26T14:22:29.231293400Z", + "start_time": "2026-04-26T14:22:29.201895100Z" } }, - "cell_type": "code", "source": [ "X_raw = df_part_a.drop('Category', axis=1)\n", "y = df_part_a['Category']" ], - "id": "b5daf475a5d5ca15", "outputs": [], - "execution_count": 151 + "execution_count": 71 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T22:14:44.964846700Z", - "start_time": "2026-04-25T22:14:44.888695300Z" - } - }, "cell_type": "code", - "source": "X = pd.get_dummies(X_raw, columns=['Content Rating'])", "id": "99a441c665dcc19b", - "outputs": [], - "execution_count": 152 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:45.068746800Z", - "start_time": "2026-04-25T22:14:45.010546300Z" + "end_time": "2026-04-26T14:22:29.285866900Z", + "start_time": "2026-04-26T14:22:29.232290500Z" } }, + "source": [ + "X = pd.get_dummies(X_raw, columns=['Content Rating'])" + ], + "outputs": [], + "execution_count": 72 + }, + { "cell_type": "code", - "source": "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=101)", "id": "c6eb622c0f7ec63c", - "outputs": [], - "execution_count": 153 - }, - { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:45.095344800Z", - "start_time": "2026-04-25T22:14:45.072252400Z" + "end_time": "2026-04-26T14:22:29.328943200Z", + "start_time": "2026-04-26T14:22:29.288372900Z" } }, - "cell_type": "code", - "source": "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=101)", - "id": "89e8f117f057e0e2", + "source": [ + "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)" + ], "outputs": [], - "execution_count": 154 + "execution_count": 73 + }, + { + "cell_type": "code", + "id": "89e8f117f057e0e2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-26T14:22:29.372324200Z", + "start_time": "2026-04-26T14:22:29.330948900Z" + } + }, + "source": [ + "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=seed)" + ], + "outputs": [], + "execution_count": 74 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:45.113200900Z", - "start_time": "2026-04-25T22:14:45.096344500Z" + "end_time": "2026-04-26T14:22:29.426224200Z", + "start_time": "2026-04-26T14:22:29.374321200Z" } }, "cell_type": "code", @@ -145,15 +169,15 @@ "scaled_X_val = scaler.transform(X_val)\n", "scaled_X_test = scaler.transform(X_test)" ], - "id": "d529991171fafb3e", + "id": "b9c0f0dcdb1e5097", "outputs": [], - "execution_count": 155 + "execution_count": 75 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:45.119344300Z", - "start_time": "2026-04-25T22:14:45.113200900Z" + "end_time": "2026-04-26T14:22:29.456903100Z", + "start_time": "2026-04-26T14:22:29.427224100Z" } }, "cell_type": "code", @@ -161,53 +185,154 @@ "def eval_metrics(y_true, y_pred, label=\"Model\"):\n", " acc = accuracy_score(y_true, y_pred)\n", " f1_w = f1_score(y_true, y_pred, average='weighted')\n", - " print(f\"{label} -> Accuracy: {acc:.4f} | Weighted F1: {f1_w:.4f}\")\n", - " return acc, f1_w" + " return acc, f1_w\n", + "\n", + "\n", + "def split_overview(train_rows, test_rows, val_rows=None):\n", + " rows = [\n", + " {'Split': 'Train', 'Rows': train_rows},\n", + " {'Split': 'Test', 'Rows': test_rows},\n", + " ]\n", + " if val_rows is not None:\n", + " rows.insert(1, {'Split': 'Validation', 'Rows': val_rows})\n", + " return pd.DataFrame(rows)\n", + "\n", + "\n", + "def prediction_sheet(y_true, y_pred, y_prob, prefix, head_n=20):\n", + " pred_idx = np.argmax(y_prob, axis=1)\n", + " pred_conf = y_prob[np.arange(len(y_prob)), pred_idx]\n", + " sheet = pd.DataFrame({\n", + " f'True_{prefix}': pd.Series(y_true).reset_index(drop=True),\n", + " f'Predicted_{prefix}': pd.Series(y_pred).reset_index(drop=True),\n", + " 'Predicted_Confidence': pred_conf\n", + " })\n", + " sheet['Correct'] = sheet[f'True_{prefix}'] == sheet[f'Predicted_{prefix}']\n", + " return sheet.head(head_n)" ], - "id": "d44ab7b8266f66c4", + "id": "930d2242b3f0683d", "outputs": [], - "execution_count": 156 + "execution_count": 76 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-25T22:14:45.140904500Z", - "start_time": "2026-04-25T22:14:45.128360400Z" + "end_time": "2026-04-26T14:22:29.544499600Z", + "start_time": "2026-04-26T14:22:29.484412800Z" } }, "cell_type": "code", - "source": "", - "id": "2f619e1e9117258e", - "outputs": [], - "execution_count": 156 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Logistic Regression", - "id": "66dc17cdf1a00a06" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2026-04-25T22:14:45.162452500Z", - "start_time": "2026-04-25T22:14:45.142480200Z" - } - }, - "cell_type": "code", - "source": [ - "log_model = LogisticRegression(max_iter=1000, random_state=101)\n", - "log_model.fit(scaled_X_train, y_train)" - ], - "id": "84128fa4e7823565", + "source": "split_overview(len(X_train), len(X_test), len(X_val))", + "id": "1a3a319e65a8a24b", "outputs": [ { "data": { "text/plain": [ - "LogisticRegression(max_iter=1000, random_state=101)" + " Split Rows\n", + "0 Train 663\n", + "1 Validation 222\n", + "2 Test 222" ], "text/html": [ - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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LogisticRegression(max_iter=1000, random_state=101)
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