mirror of
https://github.com/mudabbir-ahmad/UNI-PROG3-CW2-MLWP.git
synced 2026-10-07 20:10:20 +00:00
4914 lines
734 KiB
Plaintext
4914 lines
734 KiB
Plaintext
{
|
||
"cells": [
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||
{
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||
"cell_type": "markdown",
|
||
"id": "193cc36275a60171",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2026-04-25T10:00:23.434531Z",
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"start_time": "2026-04-25T10:00:23.381273Z"
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}
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||
},
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"source": [
|
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"## This is the Q1 Notebook!\n",
|
||
"\n",
|
||
"It's tracked via GitHub! hence the need for this line for the init commit"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
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||
"id": "edaea0c939a83b79",
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||
"metadata": {
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"ExecuteTime": {
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"end_time": "2026-04-26T14:22:27.822717300Z",
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"start_time": "2026-04-26T14:22:27.814871100Z"
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}
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||
},
|
||
"outputs": [],
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||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 23,
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||
"id": "15479f82",
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||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2026-04-26T14:22:27.850316700Z",
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"start_time": "2026-04-26T14:22:27.825227800Z"
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||
}
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},
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||
"outputs": [],
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||
"source": [
|
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"# Keep one fixed seed so the same split/results appear each run.\n",
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"seed = 101"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 24,
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||
"id": "e657e9baacc13e6b",
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||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2026-04-26T14:22:27.897899900Z",
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"start_time": "2026-04-26T14:22:27.870320Z"
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}
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||
},
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||
"outputs": [],
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"source": [
|
||
"df = pd.read_csv('data/googleplaystore_new.csv')"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"id": "751a6161e8e12bdd",
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||
"metadata": {},
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||
"source": [
|
||
"## Part A\n",
|
||
"\n",
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||
"\n",
|
||
"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]**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
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||
"id": "756c92821453bbb3",
|
||
"metadata": {
|
||
"ExecuteTime": {
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||
"end_time": "2026-04-26T14:22:27.920788500Z",
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||
"start_time": "2026-04-26T14:22:27.898898200Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df = df.dropna()\n",
|
||
"df = df.drop_duplicates()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "4e1be303d63f47a4",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:27.935089700Z",
|
||
"start_time": "2026-04-26T14:22:27.921790600Z"
|
||
}
|
||
},
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||
"outputs": [
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{
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"data": {
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"text/html": [
|
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"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
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||
" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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||
" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>App</th>\n",
|
||
" <th>Category</th>\n",
|
||
" <th>Rating</th>\n",
|
||
" <th>Reviews</th>\n",
|
||
" <th>Size</th>\n",
|
||
" <th>Installs</th>\n",
|
||
" <th>Type</th>\n",
|
||
" <th>Price</th>\n",
|
||
" <th>Content Rating</th>\n",
|
||
" <th>Genres</th>\n",
|
||
" <th>Android Ver</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Market Update Helper</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>20145</td>\n",
|
||
" <td>11k</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>SuperLivePro</td>\n",
|
||
" <td>BUSINESS</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>46353</td>\n",
|
||
" <td>21M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Business</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Wifi Connect Library</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>58055</td>\n",
|
||
" <td>41k</td>\n",
|
||
" <td>5,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Apk Installer</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>7750</td>\n",
|
||
" <td>292k</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>English speaking texts</td>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>1619</td>\n",
|
||
" <td>3.0M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Education</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Eternal life</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>2.5M</td>\n",
|
||
" <td>1,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Dresses Ideas & Fashions +3000</td>\n",
|
||
" <td>BEAUTY</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>473</td>\n",
|
||
" <td>8.2M</td>\n",
|
||
" <td>100,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Mature 17+</td>\n",
|
||
" <td>Beauty</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>GO Notifier</td>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>124346</td>\n",
|
||
" <td>695k</td>\n",
|
||
" <td>10,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Communication</td>\n",
|
||
" <td>2.0 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Prosperity</td>\n",
|
||
" <td>EVENTS</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>2.3M</td>\n",
|
||
" <td>100+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Events</td>\n",
|
||
" <td>2.0 and up</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>NSE Mobile Trading</td>\n",
|
||
" <td>FINANCE</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>13868</td>\n",
|
||
" <td>1.4M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Finance</td>\n",
|
||
" <td>2.1 and up</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" App Category Rating Reviews Size \\\n",
|
||
"0 Market Update Helper LIBRARIES_AND_DEMO 4.1 20145 11k \n",
|
||
"1 SuperLivePro BUSINESS 4.3 46353 21M \n",
|
||
"2 Wifi Connect Library LIBRARIES_AND_DEMO 3.9 58055 41k \n",
|
||
"3 Apk Installer LIBRARIES_AND_DEMO 3.8 7750 292k \n",
|
||
"4 English speaking texts EDUCATION 4.4 1619 3.0M \n",
|
||
"5 Eternal life LIBRARIES_AND_DEMO 5.0 26 2.5M \n",
|
||
"6 Dresses Ideas & Fashions +3000 BEAUTY 4.5 473 8.2M \n",
|
||
"7 GO Notifier COMMUNICATION 4.2 124346 695k \n",
|
||
"8 Prosperity EVENTS 5.0 16 2.3M \n",
|
||
"9 NSE Mobile Trading FINANCE 4.1 13868 1.4M \n",
|
||
"\n",
|
||
" Installs Type Price Content Rating Genres Android Ver \n",
|
||
"0 1,000,000+ Free 0 Everyone Libraries & Demo 1.5 and up \n",
|
||
"1 1,000,000+ Free 0 Everyone Business 1.5 and up \n",
|
||
"2 5,000,000+ Free 0 Everyone Libraries & Demo 1.5 and up \n",
|
||
"3 1,000,000+ Free 0 Everyone Libraries & Demo 1.6 and up \n",
|
||
"4 1,000,000+ Free 0 Everyone Education 1.6 and up \n",
|
||
"5 1,000+ Free 0 Everyone Libraries & Demo 1.6 and up \n",
|
||
"6 100,000+ Free 0 Mature 17+ Beauty 1.6 and up \n",
|
||
"7 10,000,000+ Free 0 Everyone Communication 2.0 and up \n",
|
||
"8 100+ Free 0 Everyone Events 2.0 and up \n",
|
||
"9 1,000,000+ Free 0 Everyone Finance 2.1 and up "
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5e0e0e76635904b6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-25T13:14:34.819448Z",
|
||
"start_time": "2026-04-25T13:14:34.807334900Z"
|
||
}
|
||
},
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "c15cb7f9831e0f81",
|
||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2026-04-26T14:22:27.956595900Z",
|
||
"start_time": "2026-04-26T14:22:27.936085900Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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",
|
||
" if isinstance(size_str, str):\n",
|
||
" if size_str.endswith('M'):\n",
|
||
" return float(size_str[:-1]) * 1024 * 1024\n",
|
||
" elif size_str.endswith('k'):\n",
|
||
" return float(size_str[:-1]) * 1024\n",
|
||
" return size_str"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "c76da70de24ddc72",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:27.981893900Z",
|
||
"start_time": "2026-04-26T14:22:27.957595200Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df['Size in bytes'] = df['Size'].apply(parse_size)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "73784ad66975e81f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.029674900Z",
|
||
"start_time": "2026-04-26T14:22:27.983890Z"
|
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|
||
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|
||
{
|
||
"data": {
|
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"<div>\n",
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"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
||
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|
||
"\n",
|
||
" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Size</th>\n",
|
||
" <th>Size in bytes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>11k</td>\n",
|
||
" <td>11264.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>21M</td>\n",
|
||
" <td>22020096.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>41k</td>\n",
|
||
" <td>41984.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>292k</td>\n",
|
||
" <td>299008.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>3.0M</td>\n",
|
||
" <td>3145728.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2.5M</td>\n",
|
||
" <td>2621440.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>8.2M</td>\n",
|
||
" <td>8598323.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>695k</td>\n",
|
||
" <td>711680.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>2.3M</td>\n",
|
||
" <td>2411724.8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>1.4M</td>\n",
|
||
" <td>1468006.4</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Size Size in bytes\n",
|
||
"0 11k 11264.0\n",
|
||
"1 21M 22020096.0\n",
|
||
"2 41k 41984.0\n",
|
||
"3 292k 299008.0\n",
|
||
"4 3.0M 3145728.0\n",
|
||
"5 2.5M 2621440.0\n",
|
||
"6 8.2M 8598323.2\n",
|
||
"7 695k 711680.0\n",
|
||
"8 2.3M 2411724.8\n",
|
||
"9 1.4M 1468006.4"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['Size', 'Size in bytes']].head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "f16b316e8c3e58c0",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.082713600Z",
|
||
"start_time": "2026-04-26T14:22:28.033674500Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Example Input</th>\n",
|
||
" <th>Expected Bytes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>11k</td>\n",
|
||
" <td>11264.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>21M</td>\n",
|
||
" <td>22020096.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1.4M</td>\n",
|
||
" <td>1468006.4</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Example Input Expected Bytes\n",
|
||
"0 11k 11264.0\n",
|
||
"1 21M 22020096.0\n",
|
||
"2 1.4M 1468006.4"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "3e1112ae100d010c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.146681Z",
|
||
"start_time": "2026-04-26T14:22:28.083717300Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>App</th>\n",
|
||
" <th>Category</th>\n",
|
||
" <th>Rating</th>\n",
|
||
" <th>Reviews</th>\n",
|
||
" <th>Size</th>\n",
|
||
" <th>Installs</th>\n",
|
||
" <th>Type</th>\n",
|
||
" <th>Price</th>\n",
|
||
" <th>Content Rating</th>\n",
|
||
" <th>Genres</th>\n",
|
||
" <th>Android Ver</th>\n",
|
||
" <th>Size in bytes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Market Update Helper</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>20145</td>\n",
|
||
" <td>11k</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" <td>11264.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>SuperLivePro</td>\n",
|
||
" <td>BUSINESS</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>46353</td>\n",
|
||
" <td>21M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Business</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" <td>22020096.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Wifi Connect Library</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>58055</td>\n",
|
||
" <td>41k</td>\n",
|
||
" <td>5,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" <td>41984.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Apk Installer</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>7750</td>\n",
|
||
" <td>292k</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>299008.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>English speaking texts</td>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>1619</td>\n",
|
||
" <td>3.0M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Education</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>3145728.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Eternal life</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>2.5M</td>\n",
|
||
" <td>1,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>2621440.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Dresses Ideas & Fashions +3000</td>\n",
|
||
" <td>BEAUTY</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>473</td>\n",
|
||
" <td>8.2M</td>\n",
|
||
" <td>100,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Mature 17+</td>\n",
|
||
" <td>Beauty</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>8598323.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>GO Notifier</td>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>124346</td>\n",
|
||
" <td>695k</td>\n",
|
||
" <td>10,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Communication</td>\n",
|
||
" <td>2.0 and up</td>\n",
|
||
" <td>711680.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Prosperity</td>\n",
|
||
" <td>EVENTS</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>2.3M</td>\n",
|
||
" <td>100+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Events</td>\n",
|
||
" <td>2.0 and up</td>\n",
|
||
" <td>2411724.8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>NSE Mobile Trading</td>\n",
|
||
" <td>FINANCE</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>13868</td>\n",
|
||
" <td>1.4M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Finance</td>\n",
|
||
" <td>2.1 and up</td>\n",
|
||
" <td>1468006.4</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" App Category Rating Reviews Size \\\n",
|
||
"0 Market Update Helper LIBRARIES_AND_DEMO 4.1 20145 11k \n",
|
||
"1 SuperLivePro BUSINESS 4.3 46353 21M \n",
|
||
"2 Wifi Connect Library LIBRARIES_AND_DEMO 3.9 58055 41k \n",
|
||
"3 Apk Installer LIBRARIES_AND_DEMO 3.8 7750 292k \n",
|
||
"4 English speaking texts EDUCATION 4.4 1619 3.0M \n",
|
||
"5 Eternal life LIBRARIES_AND_DEMO 5.0 26 2.5M \n",
|
||
"6 Dresses Ideas & Fashions +3000 BEAUTY 4.5 473 8.2M \n",
|
||
"7 GO Notifier COMMUNICATION 4.2 124346 695k \n",
|
||
"8 Prosperity EVENTS 5.0 16 2.3M \n",
|
||
"9 NSE Mobile Trading FINANCE 4.1 13868 1.4M \n",
|
||
"\n",
|
||
" Installs Type Price Content Rating Genres Android Ver \\\n",
|
||
"0 1,000,000+ Free 0 Everyone Libraries & Demo 1.5 and up \n",
|
||
"1 1,000,000+ Free 0 Everyone Business 1.5 and up \n",
|
||
"2 5,000,000+ Free 0 Everyone Libraries & Demo 1.5 and up \n",
|
||
"3 1,000,000+ Free 0 Everyone Libraries & Demo 1.6 and up \n",
|
||
"4 1,000,000+ Free 0 Everyone Education 1.6 and up \n",
|
||
"5 1,000+ Free 0 Everyone Libraries & Demo 1.6 and up \n",
|
||
"6 100,000+ Free 0 Mature 17+ Beauty 1.6 and up \n",
|
||
"7 10,000,000+ Free 0 Everyone Communication 2.0 and up \n",
|
||
"8 100+ Free 0 Everyone Events 2.0 and up \n",
|
||
"9 1,000,000+ Free 0 Everyone Finance 2.1 and up \n",
|
||
"\n",
|
||
" Size in bytes \n",
|
||
"0 11264.0 \n",
|
||
"1 22020096.0 \n",
|
||
"2 41984.0 \n",
|
||
"3 299008.0 \n",
|
||
"4 3145728.0 \n",
|
||
"5 2621440.0 \n",
|
||
"6 8598323.2 \n",
|
||
"7 711680.0 \n",
|
||
"8 2411724.8 \n",
|
||
"9 1468006.4 "
|
||
]
|
||
},
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9883e69704d0a69e",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-25T13:28:40.682532800Z",
|
||
"start_time": "2026-04-25T13:28:40.678521Z"
|
||
}
|
||
},
|
||
"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]**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "c8d4f46526918c20",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.169093Z",
|
||
"start_time": "2026-04-26T14:22:28.148687700Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "cffd24a82d242352",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.211996600Z",
|
||
"start_time": "2026-04-26T14:22:28.170093500Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>App</th>\n",
|
||
" <th>Category</th>\n",
|
||
" <th>Rating</th>\n",
|
||
" <th>Reviews</th>\n",
|
||
" <th>Size</th>\n",
|
||
" <th>Installs</th>\n",
|
||
" <th>Type</th>\n",
|
||
" <th>Price</th>\n",
|
||
" <th>Content Rating</th>\n",
|
||
" <th>Genres</th>\n",
|
||
" <th>Android Ver</th>\n",
|
||
" <th>Size in bytes</th>\n",
|
||
" <th>Numeric Installs</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Market Update Helper</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>20145</td>\n",
|
||
" <td>11k</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" <td>11264.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>SuperLivePro</td>\n",
|
||
" <td>BUSINESS</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>46353</td>\n",
|
||
" <td>21M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Business</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" <td>22020096.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Wifi Connect Library</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>58055</td>\n",
|
||
" <td>41k</td>\n",
|
||
" <td>5,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.5 and up</td>\n",
|
||
" <td>41984.0</td>\n",
|
||
" <td>5000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Apk Installer</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>7750</td>\n",
|
||
" <td>292k</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>299008.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>English speaking texts</td>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>1619</td>\n",
|
||
" <td>3.0M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Education</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>3145728.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Eternal life</td>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>2.5M</td>\n",
|
||
" <td>1,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Libraries & Demo</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>2621440.0</td>\n",
|
||
" <td>1000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Dresses Ideas & Fashions +3000</td>\n",
|
||
" <td>BEAUTY</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>473</td>\n",
|
||
" <td>8.2M</td>\n",
|
||
" <td>100,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Mature 17+</td>\n",
|
||
" <td>Beauty</td>\n",
|
||
" <td>1.6 and up</td>\n",
|
||
" <td>8598323.2</td>\n",
|
||
" <td>100000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>GO Notifier</td>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>124346</td>\n",
|
||
" <td>695k</td>\n",
|
||
" <td>10,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Communication</td>\n",
|
||
" <td>2.0 and up</td>\n",
|
||
" <td>711680.0</td>\n",
|
||
" <td>10000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Prosperity</td>\n",
|
||
" <td>EVENTS</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>2.3M</td>\n",
|
||
" <td>100+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Events</td>\n",
|
||
" <td>2.0 and up</td>\n",
|
||
" <td>2411724.8</td>\n",
|
||
" <td>100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>NSE Mobile Trading</td>\n",
|
||
" <td>FINANCE</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>13868</td>\n",
|
||
" <td>1.4M</td>\n",
|
||
" <td>1,000,000+</td>\n",
|
||
" <td>Free</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>Finance</td>\n",
|
||
" <td>2.1 and up</td>\n",
|
||
" <td>1468006.4</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" App Category Rating Reviews Size \\\n",
|
||
"0 Market Update Helper LIBRARIES_AND_DEMO 4.1 20145 11k \n",
|
||
"1 SuperLivePro BUSINESS 4.3 46353 21M \n",
|
||
"2 Wifi Connect Library LIBRARIES_AND_DEMO 3.9 58055 41k \n",
|
||
"3 Apk Installer LIBRARIES_AND_DEMO 3.8 7750 292k \n",
|
||
"4 English speaking texts EDUCATION 4.4 1619 3.0M \n",
|
||
"5 Eternal life LIBRARIES_AND_DEMO 5.0 26 2.5M \n",
|
||
"6 Dresses Ideas & Fashions +3000 BEAUTY 4.5 473 8.2M \n",
|
||
"7 GO Notifier COMMUNICATION 4.2 124346 695k \n",
|
||
"8 Prosperity EVENTS 5.0 16 2.3M \n",
|
||
"9 NSE Mobile Trading FINANCE 4.1 13868 1.4M \n",
|
||
"\n",
|
||
" Installs Type Price Content Rating Genres Android Ver \\\n",
|
||
"0 1,000,000+ Free 0 Everyone Libraries & Demo 1.5 and up \n",
|
||
"1 1,000,000+ Free 0 Everyone Business 1.5 and up \n",
|
||
"2 5,000,000+ Free 0 Everyone Libraries & Demo 1.5 and up \n",
|
||
"3 1,000,000+ Free 0 Everyone Libraries & Demo 1.6 and up \n",
|
||
"4 1,000,000+ Free 0 Everyone Education 1.6 and up \n",
|
||
"5 1,000+ Free 0 Everyone Libraries & Demo 1.6 and up \n",
|
||
"6 100,000+ Free 0 Mature 17+ Beauty 1.6 and up \n",
|
||
"7 10,000,000+ Free 0 Everyone Communication 2.0 and up \n",
|
||
"8 100+ Free 0 Everyone Events 2.0 and up \n",
|
||
"9 1,000,000+ Free 0 Everyone Finance 2.1 and up \n",
|
||
"\n",
|
||
" Size in bytes Numeric Installs \n",
|
||
"0 11264.0 1000000 \n",
|
||
"1 22020096.0 1000000 \n",
|
||
"2 41984.0 5000000 \n",
|
||
"3 299008.0 1000000 \n",
|
||
"4 3145728.0 1000000 \n",
|
||
"5 2621440.0 1000 \n",
|
||
"6 8598323.2 100000 \n",
|
||
"7 711680.0 10000000 \n",
|
||
"8 2411724.8 100 \n",
|
||
"9 1468006.4 1000000 "
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1818d3183dae5398",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part D\n",
|
||
"\n",
|
||
"Q: Save the updated dataset as “googleplaystore_new_new.csv” **[1 mark]**\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "5a99edb9f8b29b12",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.261943100Z",
|
||
"start_time": "2026-04-26T14:22:28.237508200Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df.to_csv('data/googleplaystore_new_new.csv', index=False)"
|
||
]
|
||
},
|
||
{
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "8cc741d5b19eaa62",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.289446500Z",
|
||
"start_time": "2026-04-26T14:22:28.262987100Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "8e417dd93534ef5f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.336984400Z",
|
||
"start_time": "2026-04-26T14:22:28.290448200Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"id": "c2a7db56bb223aa2",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.366685700Z",
|
||
"start_time": "2026-04-26T14:22:28.338982400Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_new = pd.read_csv('data/googleplaystore_new_new.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"id": "70f60382c2cb1c26",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.406155500Z",
|
||
"start_time": "2026-04-26T14:22:28.367686300Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"columns_to_keep = ['Category', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Rating']\n",
|
||
"df_min = df_new[columns_to_keep]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"id": "720d514df1483929",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.430514500Z",
|
||
"start_time": "2026-04-26T14:22:28.406155500Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Category</th>\n",
|
||
" <th>Reviews</th>\n",
|
||
" <th>Content Rating</th>\n",
|
||
" <th>Size in bytes</th>\n",
|
||
" <th>Numeric Installs</th>\n",
|
||
" <th>Rating</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>20145</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>11264.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>BUSINESS</td>\n",
|
||
" <td>46353</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>22020096.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>58055</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>41984.0</td>\n",
|
||
" <td>5000000</td>\n",
|
||
" <td>3.9</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>7750</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>299008.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>3.8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>1619</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>3145728.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>2621440.0</td>\n",
|
||
" <td>1000</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>BEAUTY</td>\n",
|
||
" <td>473</td>\n",
|
||
" <td>Mature 17+</td>\n",
|
||
" <td>8598323.2</td>\n",
|
||
" <td>100000</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>124346</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>711680.0</td>\n",
|
||
" <td>10000000</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>EVENTS</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>2411724.8</td>\n",
|
||
" <td>100</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>FINANCE</td>\n",
|
||
" <td>13868</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>1468006.4</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>32254</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>5767168.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>125232</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>2831155.2</td>\n",
|
||
" <td>10000000</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>430</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>538624.0</td>\n",
|
||
" <td>10000</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>275</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>2411724.8</td>\n",
|
||
" <td>50000</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>BOOKS_AND_REFERENCE</td>\n",
|
||
" <td>1778</td>\n",
|
||
" <td>Mature 17+</td>\n",
|
||
" <td>5138022.4</td>\n",
|
||
" <td>500000</td>\n",
|
||
" <td>3.9</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>BUSINESS</td>\n",
|
||
" <td>2287</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>1572864.0</td>\n",
|
||
" <td>1000000</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>LIBRARIES_AND_DEMO</td>\n",
|
||
" <td>126862</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>638976.0</td>\n",
|
||
" <td>10000000</td>\n",
|
||
" <td>3.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>COMMUNICATION</td>\n",
|
||
" <td>255</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>1677721.6</td>\n",
|
||
" <td>10000</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>LIFESTYLE</td>\n",
|
||
" <td>360</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>4823449.6</td>\n",
|
||
" <td>10000</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>EDUCATION</td>\n",
|
||
" <td>656</td>\n",
|
||
" <td>Everyone</td>\n",
|
||
" <td>569344.0</td>\n",
|
||
" <td>10000</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Category Reviews Content Rating Size in bytes \\\n",
|
||
"0 LIBRARIES_AND_DEMO 20145 Everyone 11264.0 \n",
|
||
"1 BUSINESS 46353 Everyone 22020096.0 \n",
|
||
"2 LIBRARIES_AND_DEMO 58055 Everyone 41984.0 \n",
|
||
"3 LIBRARIES_AND_DEMO 7750 Everyone 299008.0 \n",
|
||
"4 EDUCATION 1619 Everyone 3145728.0 \n",
|
||
"5 LIBRARIES_AND_DEMO 26 Everyone 2621440.0 \n",
|
||
"6 BEAUTY 473 Mature 17+ 8598323.2 \n",
|
||
"7 COMMUNICATION 124346 Everyone 711680.0 \n",
|
||
"8 EVENTS 16 Everyone 2411724.8 \n",
|
||
"9 FINANCE 13868 Everyone 1468006.4 \n",
|
||
"10 COMMUNICATION 32254 Everyone 5767168.0 \n",
|
||
"11 COMMUNICATION 125232 Everyone 2831155.2 \n",
|
||
"12 EDUCATION 430 Everyone 538624.0 \n",
|
||
"13 EDUCATION 275 Everyone 2411724.8 \n",
|
||
"14 BOOKS_AND_REFERENCE 1778 Mature 17+ 5138022.4 \n",
|
||
"15 BUSINESS 2287 Everyone 1572864.0 \n",
|
||
"16 LIBRARIES_AND_DEMO 126862 Everyone 638976.0 \n",
|
||
"17 COMMUNICATION 255 Everyone 1677721.6 \n",
|
||
"18 LIFESTYLE 360 Everyone 4823449.6 \n",
|
||
"19 EDUCATION 656 Everyone 569344.0 \n",
|
||
"\n",
|
||
" Numeric Installs Rating \n",
|
||
"0 1000000 4.1 \n",
|
||
"1 1000000 4.3 \n",
|
||
"2 5000000 3.9 \n",
|
||
"3 1000000 3.8 \n",
|
||
"4 1000000 4.4 \n",
|
||
"5 1000 5.0 \n",
|
||
"6 100000 4.5 \n",
|
||
"7 10000000 4.2 \n",
|
||
"8 100 5.0 \n",
|
||
"9 1000000 4.1 \n",
|
||
"10 1000000 4.4 \n",
|
||
"11 10000000 4.2 \n",
|
||
"12 10000 4.0 \n",
|
||
"13 50000 4.0 \n",
|
||
"14 500000 3.9 \n",
|
||
"15 1000000 4.4 \n",
|
||
"16 10000000 3.5 \n",
|
||
"17 10000 4.1 \n",
|
||
"18 10000 4.1 \n",
|
||
"19 10000 4.3 "
|
||
]
|
||
},
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_min.head(20)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"id": "90953b3b6403afd6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.453593Z",
|
||
"start_time": "2026-04-26T14:22:28.432121200Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"id": "518e961a8b35c05e",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.478579800Z",
|
||
"start_time": "2026-04-26T14:22:28.454594500Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = df_encoded.drop('Rating', axis=1)\n",
|
||
"y = df_encoded['Rating']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"id": "882619704ae0dce2",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.503829100Z",
|
||
"start_time": "2026-04-26T14:22:28.479581600Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=seed)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"id": "102cda28a2a7c9cb",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.553238100Z",
|
||
"start_time": "2026-04-26T14:22:28.504830200Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Split</th>\n",
|
||
" <th>Rows</th>\n",
|
||
" <th>Pct_of_Total</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Train</td>\n",
|
||
" <td>774</td>\n",
|
||
" <td>69.9</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Test</td>\n",
|
||
" <td>333</td>\n",
|
||
" <td>30.1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Split Rows Pct_of_Total\n",
|
||
"0 Train 774 69.9\n",
|
||
"1 Test 333 30.1"
|
||
]
|
||
},
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"split_overview(len(X_train), len(X_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"id": "93cd24d4523d5fda",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.630516300Z",
|
||
"start_time": "2026-04-26T14:22:28.555750100Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results = []"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e52a65f09530a141",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Trying Linear Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"id": "db88942671bdf26a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.758831400Z",
|
||
"start_time": "2026-04-26T14:22:28.631518600Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"id": "e9dbb4ba0c9432ed",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:28.971416600Z",
|
||
"start_time": "2026-04-26T14:22:28.759831600Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"residual_linear = y_test - y_pred_lin\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"plt.scatter(y_pred_lin, residual_linear, alpha=0.5)\n",
|
||
"plt.axhline(y=0, color='r', linestyle='--')\n",
|
||
"plt.title(\"Part E - Linear Regression: Residual Plot\")\n",
|
||
"plt.xlabel(\"Predicted Rating\")\n",
|
||
"plt.ylabel(\"Residual (y - y_hat)\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"id": "35edc2f60d805d30",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.122438300Z",
|
||
"start_time": "2026-04-26T14:22:28.972416400Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 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()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "28a6926de3a1a2e7",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Trying Polynomial Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"id": "b867289f4b8dd0ae",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.485413400Z",
|
||
"start_time": "2026-04-26T14:22:29.145792800Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Selection</th>\n",
|
||
" <th>Best degree</th>\n",
|
||
" <th>RMSE</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Polynomial best degree by RMSE</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.415806</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Selection Best degree RMSE\n",
|
||
"0 Polynomial best degree by RMSE 2 0.415806"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"id": "61b135564e36f71d",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.546506800Z",
|
||
"start_time": "2026-04-26T14:22:29.487412300Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"id": "5358eeb45dc985",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.665949600Z",
|
||
"start_time": "2026-04-26T14:22:29.572021400Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(6, 6))\n",
|
||
"plt.scatter(y_test, y_pred_poly, alpha=0.5)\n",
|
||
"plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
|
||
"plt.title(\"Part E - Polynomial Regression: Actual vs Predicted\")\n",
|
||
"plt.xlabel(\"True Rating\")\n",
|
||
"plt.ylabel(\"Predicted Rating\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "46d1bd68d47ea764",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-25T16:34:36.630819600Z",
|
||
"start_time": "2026-04-25T16:34:36.611164400Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Trying Ridge Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"id": "f7b09be17154ad47",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.694126100Z",
|
||
"start_time": "2026-04-26T14:22:29.666949800Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Selection</th>\n",
|
||
" <th>Best alpha</th>\n",
|
||
" <th>RMSE</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Ridge best alpha by RMSE</td>\n",
|
||
" <td>50.0</td>\n",
|
||
" <td>0.416581</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Selection Best alpha RMSE\n",
|
||
"0 Ridge best alpha by RMSE 50.0 0.416581"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"id": "751e4cca3fc3b4bb",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.849994300Z",
|
||
"start_time": "2026-04-26T14:22:29.697126100Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(6, 6))\n",
|
||
"plt.scatter(y_test, y_pred_ridge, alpha=0.5)\n",
|
||
"plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
|
||
"plt.title(\"Part E - Ridge Regression: Actual vs Predicted\")\n",
|
||
"plt.xlabel(\"True Rating\")\n",
|
||
"plt.ylabel(\"Predicted Rating\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fd2c011a69ca364f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-25T16:42:13.392229200Z",
|
||
"start_time": "2026-04-25T16:42:13.355459900Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Trying Lasso Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"id": "11024fb6674354a3",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:29.913020Z",
|
||
"start_time": "2026-04-26T14:22:29.851504500Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Selection</th>\n",
|
||
" <th>Best alpha</th>\n",
|
||
" <th>RMSE</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Lasso best alpha by RMSE</td>\n",
|
||
" <td>0.0005</td>\n",
|
||
" <td>0.386607</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Selection Best alpha RMSE\n",
|
||
"0 Lasso best alpha by RMSE 0.0005 0.386607"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"id": "3f0658c719c2ecc6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.047383500Z",
|
||
"start_time": "2026-04-26T14:22:29.914021400Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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4qsmsK0fneTbNVnW8+dng94aj9sf1WHnzOsyXVhVPx66htlG8pz5OUqtf3vyQO/9aZQ1OTTIWO3L41CVLsrd4UeQXjuNWVfNNTfaBa00Df1G7pgdgnwTXiwP7e/G5TETJfejab4S/vtks4/gFyZo/rmNw4/yrkr/K2dTlOorRW/xCZvMBR2px1BPfE5cZGLv7kmdTngP7TPC98gLrwCCAo9u8xZo2/g13z2GTA/twEIMFV45f3o79Ud1+dofnAsv95z//qXAsGQzzmNR2v3qbjoDNdDw3OaqSzY7ON476Yi0Cm+uIx4Xb6K72zHnbGTy6+0zxWPM9OTcrckQkA2d3tWrOf5PPq8mPIncYzHL0H9NasH+OczOdu+PHQNlRu1mfNSlsGue+Ya20c3O+6znP/cJznt9xPL4O/PwxoK0Ojx9/JLjub+d97WhadD1+DbWN4j3VOEmN8YLCX/2samc/EtaQsE8UfxV629+Dv4x4YeOvUPal4q9oDsXmrSpsJuQF2hW/ZGvbjOQJ36NrkkZ+obO/BofHc/gwOzxzyDCHAfMi51xzQeyrwWHYDNbYV4xfYs8884zZh6zd4Zckmxl4sWTnUV4s2VeHnZxnzJhh/gabQdl0x9diJ112HnakI2BOGOaGqS0OeWfTCRMFsgaKN9YusIMtm/F4jBi4sFM4t8sRxFx11VXmfXBbOFycNWZ8/46aHW9qFJnJm6/NwIGvyX3EQJJBB9fzy55BI/czm+q4z1hLxPONzXDcb46axOr2s6df6KyNYjDCc5nNz/xlzr/NDvPOHcFrwpt0BBwIwPfMFArusDaGF0EG4wzsWLvJ/cX/s0aB28taGX4e+BhrchwXWR4nnrsMvlnbw2PJAR7sB8bOyXxNNtOypoufPefBDNyPjpofHmNeqBnYMnBnoFVbDJIZDPLGz7przQn7r/HcYo0qjytrwpiXiwEym2HrCz/P7D/Hml32i+JnjH2sGNDz+LCWh7VwxPOEzcBsVmdtGIf6O3KHVfe9x88Za6mZvoTN+zxOfL/84cEfRPzsMzhiJ3ku85xlIMVjx2PYENsoNVCHEXkShBxDtjk0tiqvvPKKGSLL4fccgs7nuRvazGVPQ+2XLVtmHzhwoBkSXl1qgurSEXib1sAbjvfh7sZhvY50BDfffLMZus80DUOHDrV///33lYZov/DCC/Zhw4aZIefcV127drXfeuut9oyMjPIh7Vzm8PPExEQznJz/f/bZZytt13vvvWc/+uijzd9p2rSpfeLEieXpIari2HcffPCB28dHjBhhhoIfOnTILO/du9ccs/bt29sjIyPNkHsO737xxRcrPG/Tpk32sWPHmvffokULsz8++ugj81o//PBDeTnuD0+pAjgc/5FHHjGP832lpKSYc2LatGnl+2jRokUm3UCbNm3MucL7CRMm2NevX+/1fnaXjsA5/QDPYb7XVq1a2a+55hp7enp6hTKe3oO7Yf7epCNwpHPge/Nk5syZpsynn35qljlU/9FHHzXbyv3AfX7aaafZV6xYUf6ctWvXmv3AY8LnOg+Jnz9/vklpwef27NnT/tZbb7n9zH722Wf2/v37m2HtnTp1MseHqRlc95036Qic8TPCv3H55ZdXeuzDDz80aSGYdoPb16FDB/tVV13lVZqRqr5jvP0MrFy50n722WeXnz88puedd16l47NkyZLy7yymNmDqDHf70DUdAR04cMB+3XXX2du2bWue365dO1PGOfUHj3WfPn3sERERlVIT+HobpfZs/KcmgZaIiCesuWINGFMDOFJXiIgEEwVOIlIr7Ifk3NGeTahHH320aW5j5m8RkWCkPk4iUisctcaRV+yHwg7E7OzM/kmODs0iIsFIgZOI1Ao7L7PTKgMl1jKxIzmTarqOmBIRCSZqqhMRERHxkvI4iYiIiHhJgZOIiIiIl0KujxOTxjH5HBOMNeS0HyIiIhKYmJkpKyvLJI51nvzcnZALnBg0OSYPFREREXHYvn27yV5flZALnBzTL3DnMFW9iIiIhLbMzExTqeJpiqaQDpwczXMMmhQ4iYiIiIM3XXjUOVxERETESwqcRERERLykwElERETESwqcRERERLykwElERETESwqcRERERLykwElERETESwqcRERERLykwElERETESwqcRERERLykwElERETESwqcRERERLykwElERETESwqcRERERLykwElERETESxHeFhQRERH/Ky21Y+ehPOQUFiM+KgJtm8QiLMzm780KGQqcREREGomNaVmYt3ov/tyXjfziEsREhKNriwSM6dcK3Vom+nvzQoICJxERkUYSNL323RYczClE6+QYxEXFIrewGKt3ZWBXRh4uG9pJwVMDUB8nERGRRtA8x5omBk3dWyYgMSYS4WE2c89lrp//+15TTuqXAicREZEAxz5NbJ5jTZPNVrE/E5e5fmNatikn9UuBk4iISIBjR3D2aYqLct/DJjYqHAXFJaacBHHgdP/995tI2fnWq1evKp/zwQcfmDIxMTE44ogj8MUXXzTY9oqIiPhDfFSE6QjOPk3u5BWWIDoi3JSTIK9x6tu3L3bv3l1++/bbbz2WXbZsGSZMmIDJkydj5cqVGD9+vLmtXr26QbdZRESkITHlAEfP7c7Ih91esR8Tl7m+W8sEU06CPHCKiIhAampq+a158+Yeyz711FM49dRTceutt6J379548MEHMWDAADzzzDMNus0iIiINiXmamHKgaXwUNqRlIyu/CMWlpeaey1x/St9WyucUCoHThg0b0KZNG3Tp0gUTJ07Etm3bPJb9/vvvMWrUqArrxowZY9Z7UlBQgMzMzAo3ERGRxoZ5mphyoF+bZBzKLcKW/Tnm/oi2ycGfiuDVV4GMDAQCvzaGHnfccZg5cyZ69uxpmummTZuGE0880TS9JSZWPgH27NmDVq1aVVjHZa73ZPr06ebvioiINHYMjrqMSAidzOF2O3DPPcC//gW88grw9ddAZGToBk6nnXZa+f/79+9vAqmOHTvi/fffN/2YfOGOO+7ATTfdVL7MGqf27dv75G+LiIg0NAZJ7ZvGhcaOX7bMCprorLP8HjRRQHW/b9KkCXr06IGNGze6fZx9oPbu3VthHZe53pPo6GhzExERkUZm6FBgxgxezIEpUxAI/N7HyVl2djb+/PNPtG7d2u3jgwcPxqJFiyqsW7BggVkvIiIiQaCkpGJ/JrYaBUjQ5PfA6ZZbbsGSJUuwZcsWk2rgrLPOQnh4uEk5QJMmTTJNbQ5Tp07Fl19+iRkzZmDt2rUmD9Ty5ctx3XXX+fFdiIiIiE8UFQETJwIjRwKHDiEQ+bWpbseOHSZIOnDgAFq0aIETTjgBP/zwg/k/cYRdWNjh2G7IkCGYNWsW7r77btx5553o3r07PvnkE/Tr18+P70JERETqrKAAOP984NNPrb5MK1ZYAVSAsdldM2kFOXYOT05ORkZGBpKSkvy9OSIiIpKbC5x9NjBvntWf6eOPgdNPD8jYIKA6h4uIiEiIycoC/vIXK9VAXBzw2WcBWdPkoMBJRERE/OPQIatmiYmsmb+R88+ecEJAHw0FTiIiIuIfBw8CW7YAKSlWM90xxwT8kVDgJCIiIv7RpQuweLHVMfzIIxvFUVDgJCIiIg1nxw5g7VrAMfdsr16Nau8HVAJMERERCWKbNwPDhgFnnAF89RUaIwVOIiIiUv/Wr7eCJgZP7dpZzXSNkAInERERqV+rV1tBE5vp2DS3dCnQsWOj3OsKnERERKT+rFwJjBgB7N0L9O8PLFkCtGnTaPe4OoeLiIhI/diwATjpJGvSXqYa+PJLoGnTRr23FTiJiIhI/ejSxUpwuX078L//AUEw1ZkCJxEREakf4eHA668DhYVAfHxQ7GX1cRIRERHf+fxz4PLLgdJSazkyMmiCJlKNk4iIiPjGBx8AF14IFBcDxx4LXHll0O1Z1TiJiIhI3b35JnDBBVbQxODpb38Lyr2qwElERETq5sUXgUsusZrnJk8G3ngDiAjORi0FTiIiIlJ7Tz0FXHUVYLcD111nBVHsFB6kFDiJiIhI7WzZAtx2m/X/W28F/vMfICy4Q4vgrEcTERGR+tepk9Uh/JdfgHvuAWy2oN/rCpxERETEe3Y7kJYGtGplLf/lL9YtRAR3fZqIiIj4TmkpMGUKMHAgsHlzSO5ZBU4iIiJSvZISa8Tcc88Bu3YB338fkntNTXUiIiJStaIi4OKLgffeOzyNCnM1hSAFTiIiIuJZQYGV2PKTT6zcTO++C5xzTsjuMQVOIiIi4l5eHnD22cCXXwLR0cBHHwFjx4b03lLgJCIiIp5rm/bsAWJjgc8+A0aNCvk9pcBJRERE3GvSBJg/H9i4ERg8WHtJo+pERESkggMHgHfeObzcooWCJieqcRIRERELE1uyOW7VKquZ7tJLtWdcKI+TiIiIADt3AsOHW0FTaipw7LHaK26oxklERCTUbd0KnHwysGkT0L49sGgR0L27v7cqIKnGSUREJJSx4/eJJ1pBU5cuwNKlCpqqoBonERGRUO4IPmwYsHs30LOnVdPUtq2/tyqgqcZJREQkVDVrBlx1FXDEEcCSJQqavGCz2+12hJDMzEwkJycjIyMDSUlJ/t4cERER/2IYkJsLxMeH7JHIrEFsoBonERGRUPLNN8DppwM5OdayzRbSQVNNKXASEREJFQsXAmPGAHPnAg8+6O+taZQUOImIiISCOXOAM86wJu499VTgvvv8vUWNkgInERGRYPfhh8BZZ1nZwMePBz75xJq4V2pMgZOIiEgwe+st4PzzgeJi4IILgPffB6Kj/b1VjZYCJxERkWCVlQXcfDNQWmrNO8cgKjLS31vVqCkBpoiISLBKTATmzQNmzQIefhgIU31JXSlwEhERCTbbt1tzztFRR1k38QmFniIiIsGUzPL++4HevYHvvvP31gQlBU4iIiLBEjTdfjswbZqV3PLHH/29RUFJTXUiIiKNHTt/T50KPPOMtfzkk9ay+JwCJxERkcaspAS4+mrg5Zet6VOefx648kp/b1XQUuAkIiLSWDE3E9MMvP22NWJu5kzg4ov9vVVBTYGTiIhIY+7XlJ0NRERYKQfOPdffWxT0FDiJiIg0Vkxm+d57wE8/ASec4O+tCQkaVSciItKYcMQcO4Gztok4fYqCpgajGicREZHGIjMTGDsW+PZbYPdu4F//8vcWhRwFTiIiIo3BwYPAqadazXLJycCZZ/p7i0KSAicREZFAt28fMHo08OuvQLNmwPz5wIAB/t6qkKTASUREJJDt2gWMGgX88QfQqhWwaBHQt6+/typkKXASEREJVIWFwMiRwNq1QLt2VtDUo4e/tyqkaVSdiIhIoIqKAu65B+jWDVi6VEFTAFCNk4iISAMqLi7Fz9vTcSCnEM3iozCgfQoiIlzqMZhqgNOn0IUXAuecY6UdEL9T4CQiItJAFv2xFzO/24ItB3JQVFKKyPAwdGoWj0uHdsLI3q2sQuwAzrnnPvwQaNvWWqegKWAocBIREWmgoGn63LXIyi8yNU2xUeHIKyzB+rQss55GZm8DxowB0tOBW24B3nlHxybAqI+TiIhIAzTPsaaJQVOHlFgkxkQiIizM3HOZ679//RPY2RGcQdPxxwPPPafjEoBU4yQiIlLP2KeJzXOsaQoLq1hnweWTd67GzS/dAVtRPjB8OPD550Bioo5LAAqYGqeHH34YNpsNN9xwg8cyM2fONGWcbzExMQ26nSIiIjXFjuDs08TmOVdHrVqGaS/+A7FF+dg3ZDjwxRcKmgJYQNQ4/fTTT3jhhRfQv3//assmJSVh3bp15csMnkRERAIZa5rYEZx9mhJjDtdZ2EpLMOHTFxBVXIhveg9BzCuz0CIuzq/bKgFe45SdnY2JEyfipZdeQkpKSrXlGSilpqaW31oxi6qIiEgAY8oBjp5jzVNpaSkKikqQW1iM/BLgoWsfxayh5+DVqf/G0d1SEahKS+3YfjAXa/dkmnsuhyK/1zhNmTIFY8eOxahRo/DPf/7Tq0CrY8eO5sQbMGAAHnroIfStIvV8QUGBuTlkcmZpERGRBsQ8TUw5MO3zNfhjTxY6pe/GjmZtTLqmjfZorPvLFNw7rFvlfE4BYmNaFuat3os/92Ujv7gEMRHh6NoiAWP6tUK3lqHVF8uvR+jdd9/Fzz//jOnTp3tVvmfPnnj11Vfx6aef4q233jLB05AhQ7Bjxw6Pz+HfTk5OLr+1b9/eh+9ARERUE+Gdjs3i0Ll5HC5a8T988szlOOWXRSix2xEdEY5OzePM44G4vxk0vfbdFqzelYEmcZHo0jzB3HOZ6/l4KPFbjdP27dsxdepULFiwwOsO3oMHDzY3BwZNvXv3Nv2jHnzwQbfPueOOO3DTTTdVqHFS8CQi4huqifAOgxvW2Jy58F389X/PmHVDsnZgb8cU9E1NxOaDeZj/+14TlISF2QJmfzu2+2BOIbq3TCjvV8w0CgnREdiQlu3VdgcTvwVOK1asQFpammlucygpKcHSpUvxzDPPmOa18PDKow+cRUZG4uijj8bGjRs9lomOjjY3ERGpn5oIXlRbJ8cgLirW9NthTcSujDxcNrRT0DbjMKDYeSgPOYXFiI+KQNsmsVUGDjvTc9Hl+cdx2gdWbqaXTrgATw6+GOHbDmHzvhz0TE3ExrRs8zfbN42rcn8fyC5AYkwEkmIiUVJailU7D9Xb/ub2MEjj8XUdjGWz2cz66rY72PgtcBo5ciRWrVpVYd1ll12GXr164R//+Ee1QZMj0OLfOP300+txS0VEAveC7C/ONRHdWsQju6AE6bmFiAoPM8sb9+UEbU0EA5i5q/bgpy0HkV1QbGpejunUFKcdkeo+cLHbEXf/PeVB01MjJmHmSRMRGwaUlAL7swuQuaXIPJfHvar9ve1ALgqKivDr9nwUlJQiOjwM7VNikFNQUi/7m9vDmi0Gxe7ERoVjb2a+x+0ORn4LnBITE9GvX78K6+Lj49GsWbPy9ZMmTULbtm3L+0A98MADOP7449GtWzccOnQIjz76KLZu3YrLL7/cL+9BRCRUm70cNRGxkWFYvvWQCZqKS0oRER6GlLgotE6ObpCaiIYONHmMHpzzh6nlyS0oRkmpHeFhNlPLtnxrOu45o3fFY8Xe3zfcgGbP/McsPjTqcswafDYKC4pht9tNrU1UuA15RSXYdjAHMeHuux7zPa7cnm76NTFYLXHq1pSWVYCm8VGIjgzz+f6Oj4ow5yFrEuOjwrE7Ix+5RSWIiww3tU1Mr8A+WiwXKj8m/D6qrirbtm2rkGE1PT0dV1xxBfbs2WNSFwwcOBDLli1Dnz59/LqdIhJc/PFF3diavbhvWFPC4fX5RSWm5oMXbnspL+T5yCybj60+ayLqGmjW9Diz/H+/2ojv/9yPQqfIpajUjvziIrP+2a/+xGPnHlnx70RYl9oHTr8Orx95KkoLS3H42XYUldjBS11uQQn2ZOWjU4uESq+dVcBapkPYn13o9FwLN4Xr+TjL+fI983Hu08Xr9iItIx/puUWmQ3u4zYaUuEi0TI7ByF6tTDlfvm4g/5gIqMDp66+/rnL5iSeeMDcRkfrijy/qxtgBNzYy3FysD+UWglt0qLgIpXY7wjijQ4SV6JGVLSwXiIFmbY7ztgM5WPD73gpBkzOun//7Hmw7udvh4IfH8rHH8N3Rw/HWHxEocfNcrmGTXWFJqQlE3TmYU+A2aHL+G3yc5Xz5nnm+JcVGYN2ebOQVFiOcB9tmBWu7MkqQnleM8Ue1rZcO7YH6YyIwE0aIiPiBv4Zd16QDbqDgVhYUl5iaJV7MIsJtJkjiPZe5no/bGiDQZIDJ5jLec5nrGWh6GqZf2+P8/aYDyC4sqXLb+PiP63YzFw6Qn2+ttNmwv98AU7NUFT7O5k53ft16yGPQ5GAvK+fL98zJiT9ZuRNFJSWICLOZVqAwW5i55zLXf/rLTlPOl69b12NcnxQ4iYj4+Yv6cAfcCI8dcBmEBFIHXHaKZu2JCYxcp77iXKKmFsVuygVSoFmX47xi28Fqty26uBBHTJ0M3HkncPHF5etz84q9CnxYzp1fdrgPiLwpV5f3vHzbQVPjyU7/zRKikBwbaWqgeM9lrl+/N9uU8+XrBvKPCQVOIiJ+/qKOd+qA605DdMCtVeBkt6NlUrTZruISu+ngzPv46Aizvr4Cp7oEmnU5zpm5Vfcfii3Mx8sfPoA+P38DMD/h5Mnlj204kO3Ve/NUjueAN9yVq8t73rA3G4XFpaY2kWUjwm1mzj3eczkmMtw8znK+fN1A/jGhwElExM9f1I4OuByxxJFWzrjM9d1aJlTbAbchJcREmH0COy+A0WifEot2KbHmvnVStFkfFxVuyvlafB0Czboc5zZV7P/4glzM/OA+nLj1FxTGxAJffAGcemr54/ZqmumqK8caGm+4K1eX98zAiDEPg2R3Ss3IQKucL183PoB/TChwEhHx8xc1O9ayoyyHlLNZJCu/CMWlpeaey1x/St9WAdMxnBKjI9GhaZypeeBIKzgunjaYZc65xmHxLBdIgWZ8HY5z/3ZN3D4nKT8bb713D47b8Tsyo+Kw7L9vAyedVKFMlJed5D2VG923+sl/bR7KxdfhPR/TKcUMUGAZ1ya10lI7cgtLTDJOlqvqdXlcMvOKrJxVeUVmuarXDeQfEwqcREQC4Iuao4M4Sqhfm2Qcyi3Clv055v6ItskBl4qAuB+Obp+ClokxaJEYjfyiUpPLifdcbpkQjQEdUuplf9Ul0KzLcU6Oi7RGlVV8Ep6f/RCO3r0O6TGJuPjCh1DiNDWYQ+sm3k0t5qncsZ2aoWuLqvMzdWkRZ8pV9Z45x6tzAMPlqt5zh6bxOLF7c9O0lplfjILiUhMw8Z7LHEV5Qrfmppyn1+Ux+WnzQdO5/sfNB8w9l7ne0+sG8o+JwGkwFxHxI8cXNYc584uZ/S/YlMBfxbywNMQXNYOjLiMSAirZnzf7a39WvsnpwwoJbir7NjVPjKnX/eUINB3D3Jm9mrUXDDT5ulUNr6/tcQ4PCzOdojlisHwQmc2Gx4ZdjMfnPI4p59yJ3R16mHKuerZKQkQYDj/Pjcgwq5w7rMG7dEhnPDhnjdt0CEyiycdZztN7/mNPJub9vres2Y03m8nH1CM10eN75rprT+pm0iSs3plpcnbllSXuZA1jv7ZJ5nFPz+3VOhGzf9lpAh7m9eL+477edCDHTBnDqWY8nSO1Pcb1TYGTiEgAfVHzItJY5vzi/ji5V0u89u1ms7+Yh4ijrLq2TMB5vVrWSxJKXwSajuM855cdmPd7mqk5SYqJwKl9W2HsUW09bneX5vFITY5BODN9FxQhr8hugsVf2/XGWVNeQERMNFonxphyrhigcfsy8j33kWNfIJbztJ84px073pfmFaHYKXaKsMGsZy0ly1X1/u0m4WZpWZBrR5ibQMsV98c9Z/TB3FW78dOW9PJpZo7t1BSneppmpmyb1+7OQuukGLRIiDJNuBl5RYgICzP7iPfr9mThpJ4tqwyeAu3HhAInERGXL+pOw+Lx8/Z08yubv5IHtE9x+0s+1DEHz+yVO02AyU7CxPu9GflmfcdmcdUmOPxy1R6s2pmBnKJixEdGmCC1qouxrwJN5hD6cPl2FJTV3uzMADbtz0FaThH+ddYRbp/TLiUOx3dphl+W/IyH3p6Ge864Ab+37mb6vNjDI0xN03FdmplyrtgXiMFNVn4x3FU68W/wcZZzZ3t6LpZs2IeColKTZTy81FFnxH0As/7r9fswaUguOjaLd5sWICO3CCkx4Vi/r8A0tUVHhKFHizizvroEqzwe1wyPx3FdvP9cOEbVdW+VYAItvndHcM1+UQzAvJmWJ9B+TChwEhGpJsvxT5vTA3K+OH/ixXjWD9vww6YDyCkoNokbGTtxhBVrcNLzivDOj9G4a2wftxdj7ucnF27A+r1ZpmnPYfOBHKzdm4UbRnWvt/191+xVmPXjtkp5lRhEcT25C574PkaHH8LVL92Elpn7cdeXz+LSyTNgRxiKS+2IigjH0G7N3b5fBgkMijy11HE9H/eUvmHT/mzsycg3o9BcW+pKStg8WmIeZznXwMkxz91vOw8hwylPVF5RKf5vWyaSYyOqneeuNp+LHKcJgtm0lxQbGRQTBOsnlIjU+wV2+8FcMzkp7/2R6TfQM4c3RqwBWfDHHlNbUVRsTe7LPi+85zI7ts9fs9eU8xR0cW41Bk1MiMi+RbznMte/8+O2ejlXcnOL8MHy7eVBEy+CjHMcF0OuZ00Uy1Xa7l9/w5ETx5mgaXvrTph2yTTEREWaTuP92iSZGjY2Pbnbbs7lx75RVeHjLOfOvqwCkyfLU1YDrufjLOeK89exM7Zz0OSM6/m4p3nuavu5iA/glAJ10bi2VkQalUCcoDOY5ovz5+TEPLZ7MwvMSDRHrh+KsNkQHhluOhGzNoHlXGtAGEz9sPmgGZHFJh/Hvo6OsCEqPsr83e83HTTlXJ9b1/f8wnd/lneurlCsLHhizMOaJ5a7cXSvw4+vWAH76FMQd+ggdnTqiQf//gQy7bGIKik1CSH5HtiPx1PTE7OOVxcH8nGWO7JD5aH9pSV2r57Pcq72ZeVX2beK+DjLoXWyzz4XbctG1THAYlnnJJiOEYxsmg2k/GTeUOAkIvXCFxN0NmQgUJMsx4HU38JfQerGfdmmeSoyjBmkKz7GZU6tUVRqN+VG9q74+GamWsgrRIuEaLNcYGpS7GaEV1REmKnBOZBdYMpVFTjVpo8UJ6stZ7dqmMq321O5ZcuA005DeGYmNnbpi7uueAT77bGmjxCDRgYBaVkFpsaoWXy026anTftyPL4Pb8ox1YM33JXjxMPeYLnhPVr57HMR5jSCkU2y7NfE84K1iuzv1CwhOuDyk3lDgZOIBGTtTUMHAs79MdxpqP4YtQkW/RGkWlNwWDUHjk7KDmawe1lGaZZzx2YH8ouKcSC71DQxsVM5a6BYPi66+l4kjj5S63Znmr5BDOI46eymfdlV9pHi/Grl79nlMbuHcnj0USAzE/mDh+If4+7Gbnskoz3szig2QQCDgSYxTBLJHQLEuXnPrJXyhqdy7FTtDXflDmZX3URYVTnnzwWPaZZLB+/qPheOkZczv9uC33dlmhF9fI+dmsfjXC9HXgYaBU4iEnC1N74IBGoq3qk/BgM8f/THqE2w6K8gtUerRBPkcDSXo4+TFUjB6igOBkFhppwrDkWPiQrHzkP5psaK2bLDbWGm1imnoAiH8uxoleR+WL+7julFTh3TeVGuqmP6JUM6YtaPh/s4uWMrK1furbeABx9E2pSbcej937HvQA6KKsQnzKBdaPIwxUZFuP3bqclOgVgVPJVrlWjVzlXHXbkeqfGY+3v1z2U5V/Fln4tdh3KxO6PA1Gjx+PJ4p8RFmel2qvpc8NxavDbNjBgc3KWZOR48fhxAwPXVjbwMROocLiI+V5c5quoyo3pjzhxe2w64dZ2cuLavO6hDU/RsZSUv5MsyqzMDKN5z2SQ/TE005Vy1SY5Fk1irIzhrmnjP4Md5mQk1Wa6qjunpOUUmU7njcPHeZDDP8dwxvVNKAmIY4VSBAV/nXVusP0jx8cDDDyM7PAr7swpcgqbDuJ6PM5hzxaDDG57KRVSzzVWVu2poN5Prqcrn2axyrni+81gxf1NaVr5pmkyJjzL3XOZ6Hit3nwvnz3KPVglo3STWBMS853J9fZbrmwInEfG5+DqMpqlrIFBb/pzioS7BonOQyukzdqbnYkNalrnncn0Fqczfw4zRvBAyfxGbbtg/ifdcTk2KwTUjurnN87M7M99cfJvGRaGghCPwCs1r8Z7LXN8kLsqUc4fB3J6MAo8Tz3I9h+a7C/p+3pFu3mNVTv9jKSKOGQhMm1Zh/cEc9mOquqmWj7OcKzOfnxc8lduS5tTnqgruykWZyZarnjOQj7OcW47dVZ5x3NFBzPq/p7DHX5/l+qamOhHxOefRNPFR4cguKCnvF5EQHV7laBp/9jXyV+Zw5wsMcQ4x534kVTVtxpcFqWv3ZJgOzczM7Oh3w+kteqYmICkmqs5Bqrsm1ZG9rY7Er3zzJ9buzTY1TpERYejdKhF/O7FL+eOueOwKi0sRzuY9jgQrtfobhbG5jckdw23mcU/HmNvEjufEf90N0WefJ5Zz7Zi+YW82ikvsiI20mczfriasWYh/zvkPbPZSYNMma+PKplD5dsOBKpv4HNvDcid0b1lhfbyXk/x6Krd6V6ZXz3dX7qctB5FXzeclr6jYlBvcrXmF9Tz2TC3BSXz5uU1jjVtZP6VWSbFITY42j7s7RwKl36CvKXASEZ9znhvry9V7TC2Co/Mv89T0bJ3ksfYm3s99jfwxxYPjApNfFI4/dqdX6kfSqXmcx1ojE3za7fj+z4PlFyP2G2JgwVocrudUIvUZpIbZwkwagpIwm7l3DcIq/d1I9m/Kw96MvPLM3cTgKa/YyjzOC7OnjuXc5urYPZRjExNrEt3NGXfRyi/wz/nPmv//edaF6DpzZnnQRBwJ6A135ZonedfHyVM5pnfwhrtyy7cedDu/nbPCYrsp5xo4Oc4RNtdVGIlot6Zv4f5ksO7uHIkPgH6D9aFxba2INCps5jqYW2Qu+o7Ou/yi5HpPgjX3S1XioyJMDcuKrQdNbRGbTSJjIlBUYje5ddj0w1/zLOeKzWhbmVjUbkekjf2DwlDCkVXMp4RSFNlt2FKWeNQ1+HO+sLmbEqO6C9uiP/Zi+ty1yMorMikEGOgx4GPTJteTu1one6kVHDkHTc64no+znDstvewo7a7cke2S4G6A2uT/m417vnrF/P+1gWdi6H//WyFoIk5I6w135Vone5fCwlO5lFjvAi935Yrt7KxfNXtZOVfxZefmz9vSTU1dYmyECWpZ68RkmwzOPZ2bwfpZVuAkIj7nGPXEnDT8pRodEW2aYPi9zDmyuJ6Zod2NeqrL7PW+UNc0CLVJJ8BJUDk67VBeETqksHxYeULIyLhIbEvPQ6viUlPOFefUY/MJp81gp+jCwsNBaVS4DSnxEUjLLDDlju3czO2FjaPTikqYdbqwvBmmRWIUIsPDMbhrM7cXtuLiUjPEPD2nEDERNhzMKSqvVeQy17++bAuGd29RqZ8Tm9ByPMzJ5sDHWa5Ti4RKj7E/F/dodSPj3A1OWLsnu9Lzpix7D7d+86b5/7PH/xX/HnYJ/rM3Bz1aN6lQ7qz+bTDt8z+q3G5HOVcdvcz95alcv3ZJmLsmrdrns5yrxCjvujO7K+c4N9n3quK5GY7IOFuV56a/P8v1RZ3DRcTnKmSGTohCYiyHxkeaey5zvSMzdFV9jfq1STb9JzjrO+/567Q+UhH4asoVPv7c13/iiQXr8Z9FG8w9l6t7HjtBc64wjk5KL6uhYxDCey4z+GTHa3edpTnhKmvwDuVak8cyWGILF++5zPV8nOVcmZFvrRNNjRX7xuzPtpI48p7LXM/aE3cXNgZi7IReUsq+SCVlHcjt5p7LXM+khyznavm2dK9qQFjOnQiEVcgb5Y6trJyrP3ZnVFp3IM7Klj3jhIkmaGJtnbty76/YUc2rei5X3XQr1ZXr0bpyQORtuax873JAuSvnODd5Dro9N+OiPJ6b/vws1yfVOImIzzlnhnbX6dibzNAN3deorvmQ6pJ7iu+PF58BHVKwZX8uDuYWmiHtHJ3WMikGnZrFeexHwpom/oJnZ2huFUtYCSmt5SL2RLHbTTl37/m7jftRWFxi+kVZKcCZuNJmOmpzPR8/qWfLSu95X3aBmZCWf6O47ObIhMlElLxxHcu5ysz3bmi+p3IMEasLBfi4FUq6/s3K+/Ddo07Fqtbd8XurrlWW+/TXnV5tN8tdOaLi0P5vNuz36rksd2q/yjVWrPn0hrty2dXU7lVVznFuDuzY1Hxe03MLzXGPKDs3mYeJgxmq6gfnj36D9UmBk4jUC46WsnJKu+Nd3hZ+sTbU9CZ1GWFW16ArvqyvETvaDuqUUqmvES9UzE/Ecq5sdlv5yDLX7N2O+/Cycq52sGZw0wHERIShQ9M405fFeeoTdgz/cdMBU66DS4DLYIzl2Q/L5G0qi7tMXxmTl8mOyHBbpZxYxAutNzyVy6kmJUBV5RiEcsTclO/fx6yjTsPBstom56DJUc5Vtpev664ca2i84ancGi9H1bHciS4j+ipkQa+Cu3Lx5edmmBlZl+Xm3GRTHsuFitB5pyLSYJjxmbVKmblFiEkKr9QpNKOs+clTZmh/qMsIs7oO63fuRMvAK8kxgsmLTrSsnSov62bqE3flHDbtzzHHolmi+wurVTNYaMq5Bk7tm8SZJhtHYMbmV/N+rY22ar7sdlPOXVDtDU/lNu6ruumzqnI5ufl47Isncc7qxRiz/nuMn/Q4SsLc1NK4SWKZmhSFrenum6Rcy1XibY5HD+UYsHjDXTlmb+dMLo5O8e7OEXZDc5fl3fnc7NaicvDsTQfv2swpGMgUOImIz7VLicPxXZphwZq9pkmOfZscI3E4AoutOsd1aWbKBYr4Ogydrut8XnXpRMsaIr5meNl0J86NU+zh45hPzlOySFZEscbgYHYh8ooOp41g9uy4aM+XiO2Hck25MJvdek0e1LIe2yYnk6mBsplyR3ZIqbjN1fZQqrpcVoF3tTeVyhUV4YxHbkO31YtRbAvDS8ee7TZoInepEAZ1aYYft1Zf88Nyrro09+5c91SuW6sEczydaxIdbE73LOeqe8tEtEmKNeeXo0XV+bk8Vq2TYk25qlKLzFuz14z6dGCuMAZbVXXwdswpyP5uzs/dfCCnyjkFA5kCJxHxOX6JXnhcBzPaa/2erLJfwVZ9CPvtHNkm0TweSH0c6jJ0Or6O83nVJflmfLRVo8etjYm0WRfGstQP3L0FRVZTGsu56tw8HrERZXPGhdsQHRFmgiH+DXbwPpRXbOY+YzlXtrLmvAjnPk6lFfs4WdOxVD7GrFUD9lZxNJzLVdbWw1QsVZYrKADOOw/dvp6LwrAI/H3cbZjXY4jb53GLGbi66tTU/fZ4Uy6+mszd1ZXr3zrZdNJmcOvYRkcNoyMcYXMay7lqnxKH0X1a4bNfd5pktPwB4xBZlpSW5xjLVanshWymEb6sXdaL0bW/bj9kzhX+IOF5xmZcDljgek+jawOZAicRqRe80PPXJBNgsoqeM9jHRYWjf9smXg/tb0h1qfVxzOe14I+9hy8QZXmYOJ8X+wid0sd9Esq6dqLl5LgM9Ew27hK7CVjYLMM4hsvMws3s7SxXabs5Z1xcpHnP7Oxrd7osmlqq0lKkxEe6DVTY8Z+vy4SLbJiyQjdHt3TrnhdylnPF2sbwxX+6zfh9+H1Z5dw5rnMzhNs2Vvl8zr3GckZuLnDWWcD8+SiNjsF1Z92BBR0HVkpp4NjLrG07umPFWjIa1Llp+chHT5rGRZpyrga0b2r+riPwcScuMsyUc6fQbjfnwsZ9OWbZXc0TH2c5tz9kju+AtOwCrN1tfRa577iP+Zns1ToZEzz8kHH032Nt0Zi+rSrNAsDt8dR/r8Lo2vio8iCaaTai4qOwN7OgfHStp0EigUiBk4jUGwYC1zai0TR1mnKl0nxeNq/m86prh3j2E0tNjjHJCAuKSsyF2aoPsJrbWEvRMjHGbX8yx5xxnG+Ow+DZ2dt5O7jeMWec6zYNaJ9imnbW7M40eZvyizlBr9XXiZ3NucxmHJZzdWzHZibNwdo9WeY5lfaBDSZNAsu5M7BDiglk92V7zuTdNCHalDOuucYETYiLw/63P8DeHUmI3J1pEjqaOXHLDhXfPmsJu7dKREps5YCvY9N4DOzQBF+t328CikpNXmE2DOzYxJRz1SQ+yuyPNbsyzOswaHG8LoMYbgdfl+XciYsMN0E5A7fs/KIKEw3zuUyayhpDlqvyh0xZX6PcomLERUagf7tkjOnnua+Rc/895nBKiq0YgFfVf88Xo2sDkQInEalXDTkyzhdqU+vjPJ8XJ591TifQKjkWqUme5/PyVX+yL1btNgEHL54MnZjpiMvcBk/9yRxDzYd0bWYuXkyUWVRaikhud1I0OjWP95gGgUktLx3aqTxzeNN4Zg63mWAkM6/YBDaXDOnkdpJfrrv5lJ64+5PV2J9lTdbraF7kiL4WidG4aXRPt8+lvdkF6NYyATmFGaZG0DWAYS1K1xbxppzZ3/ffD6xYAbzwApoPHoJTvv7TNFdxImQ2STIxK1+b/dHapsRiTN9Uj7WD7CSfEnvIdB43zZOO9xRmMzVw7d0ETcS/d0qfVLN/dxzIRW7R4Wz6idHhaNc0rsrXddQGsu8VA9WM/OLDcxLGsGmZNYbWCMsqf8icVLNzu67T8th8MLo20ChwEpF6VZtM2o0t2HNcXNhcwQDFtXM4AwMm/quPyUy5rUO7NcfCP9LMxTy8bL44XpSL7XZTY8bHq0uDcEynpjVKg+A8nQoziG85kIOifCvrOGuTGDR5muTX+bmvfrMJ6zlpb1nG8p6tEnDZCZ4nCCbuR9aUndSzBX7bccgk93QEEc3io00tiq3UaW6/zp2BX3/l7MGmg7WjSbZdkxjTnOl4bSYNbZEU67FJ1hEgD+vRArsO5WHXofzy/dW2SQxaN4n1GCA7NwVzpCHTDjhSNvAYNU+MrrKTdV4Rg5cw7EgvNsEiAy7eGKjuzy4yAUybJjGmnC/P7fg6DJro0ghH13pDgZOI1Ju6Tl/SWMS7XFyc0wlQXkFxvU1mysB07e4sM1VHm+Roa9qUslojztXGpqd1e7LcJrGsSxoEBwY4nFaFGcIZwLAvC5vnPNUWuT73xK7NMX/tHlNTl5ocjVN6pSLKTcdsZ/Fl+5v9s8Yf1dZsJ2tw2EzFpiPs3Yvz77sa0bZpwEXnWU8KD/fYJMsghseHtVhVNcnWNUCu7esSa5o4pU5xWYBo6nFM06jVtsn1fNzTxMj+GDTRrhGOrvWGAicRqRd1yaTd2GrJfDmZaU1f29EHpXurBLcT9bLWyFMfFF/NJcYgyXUePG+4y++zZV9utfl9XAO+tk4X3vi03Rj3j0lotWcb7PfeDpw7DoiO9kmTrOukyK6qmxS5tq/rOI/MdCx2O1LiOPDASkXBpk3GShwBmVVQ5DbhaF3Or7qcI2GNcHStNxQ4iYjP1TWTdmOrJfNVAFKb166cQ6qovPaFo56q64NSpw7xdWiOrUt+H0/7O3rbVpx912Q037cTRe3aI3LBArdBk0N2biGeWrQOOw7mo13TGNx7Wh8kuRkFWGlS5M0HUFRcYjqnl0+KnBCFyIhwDO7iflLkutpyINc0zfG10nOLzbF2DEHg54t92xzlOjVP8GkNcF3OkW6NbHStNxQ4iYjPOY/EcUyd4VwLUl0m7cZYS1bXAKS2rx1fVguydk8G1u/JxqG8ovL+Puw/0iM1AUkxUfVSC1Lbi7Ev8vu47m/7unW4fPq1SDm4F4WdOiNqyddAhw4et/uSV3/EkvVO88dtBj5csQvDezTH6387zu1zHJMiv7d8Ow7mFJjaHpuNTWY2c7zZv+rSIZ2q3G91bb5miojiUiYqPTx4k0lII8wou7B6qwGuyznSrZGNrq2OAicR8TlHLUh+UTj+2J1eKSFkp+ZW59j66Cztz1qy2l5c6vLaVu2GHd//edAss+aFE/YWldpNnyOuZ/6d+qgFqe3F2Dm/D/MeMWBiJ/TwsuW0rEKv8vs49nfa98vR/O/XIOJgGuy9eyNq4UKgTeWJcj0GTU64no+7C54qTIrMZFkGgydOO2Mz57SnSZHrsr+oU9M40xGc6SY4go+BpiMPFbeL66Mi7KZcfdUA12WEbFgjG11bFQVOIuJz8VERJifQiq0HTe1HglNCyH1Z+ebXOr9EWS5Q1HW+ubpcIOo6wfDWA7lmqhQ217Cmic/hdZ0X14LiUmw7mGvKVTUtRm1qjWp7MXbk90mMjjBNme6mesnIK/Qqvw//duon7wP70oAjj4SN+ZpaVpzk1llmdoHHoMmBj7Oca7Od86TI7VNiTTJI1v4weSibRdmXx9OkyM77q2vzOOzJLMCezHzTpMrlP/fnVhm8cGocR0oC875NbZdjmp3DsxS6mcvZZ+e2WALnW0tEgiYlQOukGDP/GZuNOqTwtcLKMwZHxkViW3oeWhWXmnKBoq75avz12hzNxgs2h6JzehUORy+0l5oLKwPWZhFsRiow5dx14K5tLUhdL8bFxaXYV1RgLvysaWIXHf6feZXYR4u1Kl6f2/+4DzFhMSi88iq0bt6iigYrYNr/Vnv1d1luxvkD3U6KnBATbgK+nIKS8g7anNImLjrcnPPuJkV27K+ikhJ8+ssuHMh1SqEQF4WerROr3F8Mjpkni6/NHyUM2Mqb6phDKirMPM5ynV36OPnz3A5GCpxEgpw/UgIw0zQzVjump0iIiSgfhpydX2z63rBvi7uM1P4S7zJiynV0mjcjpnzx2jXNlcPmOO7Xlkmx5gLO7XakI3AMkd+ZnmfK+bLWqC4X407N4sw1P7eg2ARI+U7z6zGjNofWMzUCy3n066/Y2Lw9vlx3wOp03GMs4pbtwRFb83BqFZmw/9id7flvelGO+3ZPRrGpyWMtmQMD1uh8K1O7O9wP2w7mmNQRjuSXFjt2FuXjYG4RerdOrDJ4iQoLQ0pSJHLyOYKupDzwYgLN+JgIE8i5E+/HczsYaS+JBDF/pQRwZKQe0CEFW/bnVsik3TIpxlwQPWWk9hfnEVOsDUnPKypvhkmJjTRD7utrxFRd0hkwbxKD0kO5heU1To5mL14goyPZHybMlPNlrVF8HYI9My2LyUvEWhdOc2JNCswgr6DYmiePz2U5tzjn3PjxOHTUMMw5+zYUmXn4rOqXzftyzFQunkblJcZ6l+fIXTmet6YDe4F13jpvXTHsJhgJD3Mf8LF5b8PebFOjRmxKdfRR4j7gaDM+znLuOJJJ8jhbqZtY1VR2zxGC+cWm/6C7ZJL+PLeDUfUZykSkUXKtTeDFzfw6jYk0y1zP2gSW87V4p4zUgzqlmC9lNhPxflDHFLM+0H7hOkZMMUjZdCDHJBZMjo0091zek5FvMmLXRxOnY3g90xawlocjy3hh4z2Xq0pnwGSTTHTJLNbZ+YUmCGESRN5zmetbJkW7nTPucK1RhMdaI0+d+B0XY+4v19xBjmCPiR3dXYwdGc4ZXDOAMFnOS0utZrswlK23mXKVfP457GeeibC8PBSmHwKKikytSdP4aKuGrbTUjMqb9eM2t+f28B4t3L5Xb8pZQY6VeNLUkJkAsywAKltnHnfz95hpnO+HjzE2svoocVqcsmbKsv3Ccu4wSWSv1ERk5BWbnE0RZZM3857LXM/z010ySX+e28FIgZNIkKpJbYKvOV9Uic0uzROiyzNTV3VR9RdHBm72u+Kvdl5zWSvGezORblKMycBdH4Gm8/D6vm2SzDH5bUeGue/XJrnKmkFe7Jg1nBdgTvxqXdjt5p7LXM+RVlVNucJaI3eqrDVyCvaYi2nXoVzszeQ0JLlmuapgjwECt691kxhzXliBUpi55zLX8/FKgdMHHwBnnw1bYSG+PXI47r14GpJSEstrp3jfLCHaBASOTtquYqMr1465464cp5Uxo0PLaos4OS9ri3jP5QgbTLMpy7lavjXdmgSZ5xo7dNvtTrfD61nOE064zOCQU8Own1N2YbG55zLX8/FAPLeDTeD83BMRn/Jnh1BfJYRsSHXJwO1TZfl5TL2FqcWo+mJmAl+bDYO7NsW6Pdnmgpjn6HScEIUerRLM5K/utruuGc8ZzJ3cqyVe+3azqeVx7K+uLRNw7qD2HoM99nlznA/uaqsKi+0mSSLLlXvzTeDSSxkFYO8ZZ+PO465CkyRruwqKDnfSZvDFJq0D2YVuO2kXFnkXHLgrdyCr0BweNn2yZst1cuHICE60a5Vzh2VYo+YIuBysyZgrrvM0Tx6Pxx+7M03zd/kkv3FR6NU6yeM8eQFzbgcJBU4iQSq+Dn1QfMEXGan9FWgygHCdb66+Rx4590drmxJrms947H7fnWk60XuqdXJsd6/UZPROTao0bxuvxZ7mT6trgMttnr1yJ/ZmFZiAhXm6WJSj+Li+Y7M49/2MoiNNn6tVhzJNU6DV9GFHcYnN9NHiedsuJdaUM155BbjiCqstbPJk/HHTgyj+bI0ZuckAiZ2iHf26OLot3s10KA5dWrB2zqrd8YQd1FnOFQNRBnYFJVZznHOTDZeZiyoqwgpYXbHJms1qnFTYlVUDBVNzxHLu8Pjtzy4wNXrsJ8X+SY5+UtmFJdi8PxttmsS5Pc7+PreDjQInkSDly/nTaqsu2YYbWrwfA826jG5z3W7nedsoJ7+oyu2ubYDrmv07JT7a6+zfbDJiExOH5pvgg/19YLOCEdY4lZSYGpHydBWdOwNRUVbw9NRT6JyeZ/pxbT+YZ2p+nM/tvMJiM9ktm/s6u+konRQTaQKU/GLPkRPfB8u56sBO3zZbhdF0DuWJvG02q5yLQe2bmoCuMLeofJ2jc7gDjzXLucNAeGd6rglSWePFGjlHAMgRfswLxT/Gcq7i/fwjKthoL4kEqUBpLmssGYP9GWjWZXSbL7a7NgGuc/Zv1h45Xpe5uqLio0ytk6fs3zwnOTqMAQBH07GmimETmxTZh4jpCDJyCk0509R28snAypVAr14mcGmbHGuSfRaYwMvqXO0IItjcVVxSgpjIMFPOVUJUpBmcUFBs1a64NrcRH2c5V/uzCirVMrmylZXr2qJiwLknKx8JUeFm+iFHpZPdpZaLj7Oca/MisSkyM58pCFhjF1Z+bPg8jsRjbVdmWV6pQPwRFUzUOVwkiDlqE9jBmP0f2GTj6CdRX6kIGqu6jGyrq7qMbvPVdjsC3F6pSea+uvKO7N9N4iLdBnvsZ+TI/u2KfY94oWfSTl7IrdFoVktcQnQk2iRH4+xFs7Dnh58PP6l3b2sIGwPNjDxTyxLNpsGwwwGT6WQdBrOef5/lXOUUFVuBUUy4qXmKCrNGufGey8wAzsdZztX+slxY7ATOgCXM6cZlrncu5/qeuX3tmsYikckqnZ7DZa4vKSvnDhNbcl48ZlbPNwkwrdF9vOcy1zMUY7lAOreDkWqcRIJcY2ou8zd/9cuKr2NTir+22+boxO5W1Z2wOTVIbGQEmsRGmWa78s7d4TZM+PAZnLX4XRSs+BxYvxZo0qTCcxmMsS8UAzzmL2KfH9c+Tswb5m7KFnY4Zx8f1naVRpcit6i0vJM1m7n4uajUMd2xzWW1OXwNBkFslnS8Lpv3+JHiOk8d+vme+X5bJkRXmq6F/aYOZBdUuc+Y1DQlLhw5BaUVMsTz/cZFhXlMgNkY+xwGMgVOIiGgsTSXhWqg6a/mtrpwJGTMzC1CTFJ4pW3m1CTMEO8uISP7HjGAYO1nq6RoRJf1y7GVluKydx/HmKUfm+Wcm25BtEvQ5By0MUt3cmxsxcCLzVasnfMQg7DDeYemcdh+MBfFpXx+GJg/014KFJRNRN3euWO6k07N4hEbFYH8ohIkxUSY4MlqYLSCpsz8YlNryHLVvWfnDtrW/ipEcmyU235Zzvub2dZZU+f6nhkIedrfDvoR5RtqqhMRqWOzVZ2/iP3U3FYXTLR4fJdmpr8Oa0rYlMjaF95zmUHFcV2auU3I2J7P7dzUlOdUMHwOSopx+RsPmaCp1GbD59fdjya33Vxt0EYMvBiwOAKwqoI2BpNHm6ShMSZxKEcdmulTALPM2iBmvHcXpDKwYXoH1i4xSHIELlb/IyspJYf8s5w377l8f+UUmv8P7tLUlPNmf7MzFZsUeV/d/vbnuR2MVOMkIhIAGltTCi+4Fx7XwUwwvH5PlskN5Jj2hMksj2yTaB53d2E2zz2+A9KyC0yyzNycfFz/znSc/MtilISF4c2rp+GEe6Z6vKg7gogFa/aaoCGxrOmNySezyhI7egoinAdN8LlMe8BmOjbX8T0wgaanIJXB1AndWpgRf2kZ+cjILy7PmcXglhnaT+zewm3Q5fqerf1l4fOPbN8EEzzsr7rub/Etm7267GpBJjMzE8nJycjIyEBSUpK/N0dEpNIw/8bUH425nL5cvceaaLeQHdzD0b9tE68mkTbPXbUHnZ7+N8749GUUh0dgwd1PoPu1l3j13CcXbjBBhDWSrCyIsNnQIzXR41x1zs93BKms9WGQymz21QWpjnxbHDnHgMfRx4mBV/PE6GoHXTjes9lfRcWIi4xA/3bJGFPFxMS+2t/im9hAgZOIiPgt2ONzd23bg6bnn4OcG29Bs/PO8vq5dQ0iarvdtQ266vq6vnq+VKbAqQqqcRIRCQBFRUCkUwdsM2tuzS/+/goiFLyEbmygPk4iItKwMjKA0083E/bi5rIO4LUImvw5YlQjVUOXAicREWk4Bw4AY8YAK1YAf/wBTJoEtGhR6z+nmh9paAqcRESkYaSlAaNGAatWAc2bAwsW1Clocu5rxNxNTCLKfFjqKC31SYGTiIjUv507raBp7VogNRVYtAjo06fWf84xuo0TI3Muv7ioWJN5nUlEmWpAUwpJfVECTBERqV9btwLDhllBU/v2wNKldQqa2DzHmiYGTd1axJt+5em5heaey1w///e9plww4vti5vO1ezLNfbC+z0ClGicREamTavsZzZ8PbNoEdOli1TR16uT9c91geTbPcWLb5VsPmaCpuGy6lJS4KLROjsbGtGxTLtimGlLzpP8pcBKRgBWKHX8b23v26kJ+xRV8Y8AZZwBt27pNBplTVIz4yAiTKf3UI6pOBsl9sz+7wJq6pKgECTGRiIyJMBPs7svKR2Z+EZrFR5lywUTNk4FBgZMPFReX4uft6ebDzA/tgPYpiIhQa6hIbVRMbmhNnmouql5mWK5LAFKXz3JdXre2gYS/3nNVF/Li337DmWOPQZfu7a3CV13lPvP33iyTddth84EcrN2bVWXm79jIcOzPLkROQTFaJkaZgCm/qNRkDU+Ji0RaltVsx3LB8n3v3DzZvWVC+aTKiTGRZmJozmnI5skuzROqPOaNLTAPRAETOD388MO44447MHXqVDz55JMey33wwQe45557sGXLFnTv3h2PPPIITmc+ED9b9MdezPxuC7YcyDHzJXHeJM6QfenQThjZu5W/N09CXGP7snRcVNftyUJhUQlKYEc4bNi0Lwdr91R9Ua1r0MXP8mvfbsb6tCwzAz1nnu/BeeRO6FztZ7kur1uXQKKuI8wc75nP5TxsUeFh5rlVveeqLuTdNq/B+LsmI/OlrihdtgRhyUmVnjvrh234dfshRIbZzJQldpsdNrvNBCRc/86P23DX2D5uz1NrjR1FJSXYfjAHmfklZp9xCpSkmHCEhYVx8pWycr4Pfsz++q5sf5WdI2Z/Da3+HKlr8yQDVMe+Lt8fNptZX13zpD9/jATT91dABE4//fQTXnjhBfTv37/KcsuWLcOECRMwffp0nHHGGZg1axbGjx+Pn3/+Gf369YO/8EM0fe5aM5M5P3yxUeHIKywxX7xcTwqexF8aW58Ic1H9cRuWbzloprNwXCaNgiJkbSkyj9/t4aLqaQ6zzV4EXfws3/3JatMMZKbxtJ6KH7ccxJ/7c6r8LNfldZ0DCV6EGXxEhttMTQq/V6oLJOrShMP3PO3zNTiQU2BqbPjnc0pKsHLHIWz7fI3H9+zpQt521XKMu+dKROfmYG+xHbvSc9DOJXDanp6LHzYfRH5RCTKKSpBfXFqeODwmIgzRkeH4ftNBU65js/hKr51bZE2vsmlfIfKLnTpGl9iRW1SKmAgb2jSJNeV8/WPXsb8Olu0vm82O3AI7ftl+yKz3tL/q+nlk0MDyPLbu8LrDiaE9NU/W5fysy3YH4/eX39uRsrOzMXHiRLz00ktISUmpsuxTTz2FU089Fbfeeit69+6NBx98EAMGDMAzzzwDf+EvFn74+OXWISXWfOFFhFlffFzm+teXbTHlRBqa44LKC2iTuEhTjc97LnM9Hw80O9JzsWTdPmQX8AJgM4EEm1x4z2WuX7p+nynnKehioFFSWorEmAg0jY8291zmej7ubhQSP6Mz5q9DWmY+7KV2cyGNiQw391zmej7u7rNcl9d1DiRY88IfX9ERYeb/vOcy/+8IJKqr+eF3D2teeM/lqkaY8b08+9VG0y8oMizMBCOOG5e5/rmvN7p9z4cv5Id/f3f4eRnOuvNyEzRtPep4PH3b08iOqRz4bN6fgz0ZecjML0ZecanZ3qgIm7nnMtfzcZZzh+fDlv05FYMmJ1zPxz011Tl+7PLHLY9RW/PdHVH+Y5ePu+O8vyIY5Jl9FWnuucz1z3rYX3X9PMZHRZiggQGxO/yxzjnzWM7X56e/vkc2Buj3l98DpylTpmDs2LEYxfwe1fj+++8rlRszZoxZ70lBQYGZg8b55kus5uUvFvPlFlZxd3KZ6/nhZzmRhlSXC6o/bdyXjbSsAvPlxAtfRBh/1cPcc5nr92YWmHKuGEz9sOkAwm1As4RocyGxApBws8zalB83HXAbdP209SD+TLP+ZozL63KZ+KuX5Xz5usTviEN5heai4K4ZJjkuEhl5hW4DiZo04bhavu2geYzvMS4qzJwffE542TLXb9ibbcq5ine5kHf+8WuMu+cqRBbkYfMxw/D2Xc8ACYluL+S8WDMALjHNgjbzOtxfvOcy17P/Esu5U1BYjIM5ReXLVkhd8YLGx1nOlz92V2w7aPoSmf0VHVnhHHEsb9ybbcr5+vPI5inWtOzOyLdqQ51wmes50TDL+fL89Nf3SGkAf3/5NXB69913TTMbm968sWfPHrRqVbEKlMtc7wn/Nifuc9zaM4eID7FtnNW8rCZ1h+v5OMuJNKS6XFD96UB2IYpL2W8kvNL0ZVyOjAg3j7Ocq037c5CRW4SkKgKQQ3lFppyrn7YcRJGpabK5f92ypjOW8+XrlpczLYOeLgKeLw7uan5cv4PY5OmuCYfHv6DY+v5yt93Wc0tNuaou5J2XLcKZ909BRFEhNgwdjc/ufQbb8+HxQp5TWIJSu93sV3evy1VsTmI5dz75ZRccYY3j2Y4/41guLSvnyx+76/dafZpiIsPcniNcz/3Fcr7+PLKJls1TTeOjTPDGAI+fA95zmetP6dvKbVNuXc5Pf32P7Azg7y+/BU7bt283HcHffvttxMTE1NvrsMM5Zzt23Pi6vsQPGavyWU3qDtfzcZYTaUh1uaD6U7OEKNNhuLC01O0v66LSUvM4y7lj58XYY7fgajqUVvfjtYrH6/K6XZrHm4tXZm6R2/fMi16TWDZVVG72iq9DEw6fZ4IUD7/aud7qdxRe5YX8l9iWyEtIwpoRY/Hezf/G2kOFVV7ITdNcuHX54Q9Lvjy3gPdcJj7Ocu4czC08XNNks55b3iXNdvjC5ijnqx+7rHk0r8eD7Uap3Qr6HDWUvv48sk8P+6v1a5OMQ7lFpjmS9+zgXV2m9Nqen/76HskJ4O8vv3UOX7FiBdLS0kwfJYeSkhIsXbrU9FliE1t4eMWTLzU1FXv3Vmx75jLXexIdHW1u9YWjMNihkG3j8VHWaA6HUv4qzilEz1aJppxIQ4p3uqCyersmF1R/6tYiAS0So7E3qwB5RdaIJTYxlNhhfu2zaj41MdqUc9W5eTyaxEaZi0mrJNYK2CoFIMmxUaacq0GdUsprlcLD7KYpw4G1I1zPx1nOl69L7VLicHyXZliwZi8OZBcgMZadw8PMBTwrr8gEFMd1aWbKear5Yb8PDkt3fW3WCPHC6q7mZ1DHFPMcNpuxP5Xrc3MLS0w/GJar6kI+LzkWTzz4JvY2aY6oQpjXY9Dk6ULeIiEaSbGR5twsLLGjuPRwZ2VT0xUZhrjoCFPOnZ6pieZSz2dEmGorq7bOERgUszarrFxVP3YTY8Jq9GOXxz4hOtLsL54LzkEhz0trf0W6PUfiffR55D7tMiKhRqPM6nJ++mq7ayo+gL+//FbjNHLkSKxatQq//PJL+W3QoEGmozj/7xo00eDBg7GIWWedLFiwwKz3Fw5d5SgMHtht6XkVqk+5nBQTiUuGdFI+J2lwdekT4U8MDob3bGku6MRgiQEU7/l1z/XDerZ0G0S0ZwDSuakJdExyxGKrSYj3XOb/B3dpasq5OrZjM3RtmWAuwoXF1sWc5XnPZa7v1irBlPPl6xIvehce1wFHtm+C8LAwZOWzD0+Buecy1/NxdxfHujThdGgWjxO6NzeP8aLKZiZru0vNMtef0K2FKVcJB+UsWGAu5NeM6IpLJ47AdaN74cbRPXD18K5V1n7wh2T3lomIDA9H07hIxEWGm4sg75vFRZrm2B5V/OCcMLADEqLDTeBkBV2Hm/y4zDWJ0eGmnKcfu+a4uPShcvzYZQDh7rU7No3Hid2amT5B7MBu9leptb+4zPV8nOXq8/PI48KUA71Sk8x9dUPz63J++ut7pG0Af3/57admYmJipRQC8fHxaNasWfn6SZMmoW3btuV9oNi0N3z4cMyYMcN0KGcfqeXLl+PFF1+EPzmGnjqGtrLTGn+xsKaJQZNSEYg/OC6oHI7OCyj7BDhSZfBLp6oLqj85ggh2EF+3OxMFpinHqgGK5ueqdVKVQcSFx3dAWnaByYfEwMOBzT4MQCZ4eC5/BN18Sk+TjoC1PpzCoywbgbkot0qIwU2je7r9EVSX13VgoMEh4Yfz7FhD7vu3bVLt0Ovymp+yYdscls5ApLqaH27PlJO6mY7Uq3dmmPQAHFrP9Wwi6dc2Gdee1LXydj/yCHD77UBsLLBqFcK6dq3R1CaOH5wmjUteEZonRiEi3IbiEjsy84rNuVnVD86YmAhcOrQznvv6TxMoOYInOGqhwmy4ZGhnU66q1+aPW+cUMgwiqvqxy/1w7cndcCC3EKt3Zpr9ledIoxAZjn5tk3DNSd2qDHD98Xmsy/npr+0OC+DvL5vdNZTzoxEjRuCoo44qT4DJ5U6dOmHmzJkVEmDefffd5Qkw//3vf9coASZH1bGTOPs7JSVVzC1SV8ocLoHIOQ8Kf2HygspfalVdUANBxWR93gcRrhm4c4uKERcZgf7tkjHGywSYr36zCevTssvz+/RsxWSQXbxLgFnL1/VFsr/aPpfbPXfVHtPxnc1QrNU7plNTnOaasZyXi/vvBx54wFq+5x5g2rTDPbNryF0uJdb2ePuDk+kh3ljGEXLF5UFuYmwEJg3uZILg+npts79+242ftqQju7AICVGROLZzCk49orVX56a/Po91OT/9td0bG+h1axIbBFTg1BDqM3ASCVSBlnnXW/4IIvw55Yo/VbvdvFT84x/Ao49ayw89xNE3fv/BmZ9fjPd+3oad6flomxKD8wd0cFvT5OvX9te5WVeNcbtLG+B1FTj5aOeIiIi5cgHXXw/897/W7mCrwNSp2jUSkrFBYA2nERGRwMPuEgya2CT3/PPAlVf6e4tE/EaBk4iIVG3SJGDePGDsWOv/IiFMgZOIiFRWWAgwLQxvERGc6qHWncBFgonf56oTEZEAk58PnHUWcNVVVv8mUtAkYihwEhGRw3JygDPOAL74Apg1C1i7VntHxIma6kRExJKZafVj+vZbICEBmDMH6NNHe0fEiQInEREBDh4ETj0V+OknIDkZ+PJL4PjjtWdEXChwEhEJdWlpwOjRwG+/Ac2aAfPnA04TsIvIYQqcRERC3a+/AmvWAK1aAZxIvW9ff2+RSMBS4CQiEupY2/Thh0Dv3kCPHv7eGpGApsBJRCQUbdxopRjo2tVaHjfO31sk0igoHYGISKj54w9g2DBg5Ehg2zZ/b41Io6LASUQk1PozDR8O7N4NJCYC0dH+3iKRRkWBk4hIqGCqgZNOAvbts0bNffWV1SFcRLymwElEJBQwqSWb5tLTrfxMHD3XvLm/t0ok+DuH/+c//3G73mazISYmBt26dcOwYcMQzokhRUTE/5YtA8aMAXJzrWa6zz+3mulEpP4DpyeeeAL79u1Dbm4uUlJSzLr09HTExcUhISEBaWlp6NKlC7766iu0b9++5lskIiK+1bMn0KUL0KYNMHs2EBenPSzSUE11Dz30EI455hhs2LABBw4cMLf169fjuOOOw1NPPYVt27YhNTUVN954Y223SUREfInZwBcvBj77TEGTSB3Z7Ha7vSZP6Nq1Kz766CMcddRRFdavXLkS55xzDjZt2oRly5aZ/+/mqI0Ak5mZieTkZGRkZCApKcnfmyMiUj/eeceatPeqq7SHRXwYG9S4qY7BUHFxcaX1XLdnzx7z/zZt2iArK6umf1pERHzh1VeByy8H+LuY2cCZs0lE/NNUd9JJJ+Gqq64yNUwO/P8111yDk08+2SyvWrUKnTt39s0WioiI9/77X2DyZCtouvpq4IQTtPdE/Bk4vfLKK2jatCkGDhyI6Ohocxs0aJBZx8eIncRnzJjhy+0UEZHqPPYYcN111v/Zz/TZZ4EwZZ0R8WsfJ4e1a9eaTuHUs2dPc2sM1MdJRIIOv8YffBC47z5r+a67rGXORSciPo0Nah04NVYKnEQk6HDEHJNb0j//aQVOIhIYncNLSkowc+ZMLFq0yORsKi0trfD4Yn6ARUSk4XAalTvuAFq0sJroRKTe1Dhwmjp1qgmcxo4di379+pmM4SIi0sD4ozU/38rLxO/hhx7SIRAJxMDp3Xffxfvvv4/TTz+9frZIRESqxpQwf/sbsHMnMGcOEBurPSbSQGo83CIqKsrMRyciIn5QWAhMmAC8+SawdCnwww86DCKBHDjdfPPNZmqVEOtTLiLif2yaO+cc4MMP+SvWumf/JhEJ3Ka6b7/91kzgO3fuXPTt2xeRkZEVHv/44499uX0iIkI5OcD48cDChUBMDPDJJ8CYMdo3IoEeODVp0gRnnXVW/WyNiIhUximsxo4FvvkGiI+3+jWNGKE9JdIYAqfXXnutfrZERETc27aNc1kBzC/z5ZfA4MHaUyKNJXASEZEG1rcvMG8eEB4ODByo3S8S6IHTgAEDTMLLlJQUHH300VXmbvr55599uX0iIqFp926rpum446zlY4/19xaJiLeB07hx48xkvo7/K+mliEg9YsDEKVT27rWmUxk0SLtbJEBorjoRkUCyaRNw8snA1q1Ap07AokVAly7+3iqRoJZZg7nqapzHqUuXLjhw4ECl9YcOHTKPiYhILa1dC5x4ohU0MdEwE1zqe1UkoNQ4cNqyZYuZ6NdVQUEBduzY4avtEhEJLb/9BgwfDuzaBfTpYwVN7dv7e6tEpLaj6j777LPy/8+bN89UaTkwkGLn8c6dO3v750RExLmmiRnADx4EjjoKmD8faNFC+0ekMQdO45mxlp2ibDZccsklFR5j9vBOnTphxowZvt9CEZFgxx+dxxzDPg/A3LlASoq/t0hE6ho4lZaWmnvWKv30009o3ry5t08VEZGqcNTy7NlAcTGQmKh9JRJMfZw2b96soElEpK6YAfz22wHHhOmxsQqaRII1c3hOTg6WLFmCbdu2obCwsMJj119/va+2TUQkOHGC3vPOA4qKrKzgF1/s7y0SkfoKnFauXInTTz8dubm5JoBq2rQp9u/fj7i4OLRs2VKBk4hIVd57D5g4kaNqgHPPBc4/X/tLJJib6m688UaceeaZSE9PR2xsLH744Qds3boVAwcOxGOPPVY/WykiEgxmzgQuvNAKmljLNGsWEBXl760SkfoMnH755RfcfPPNCAsLQ3h4uMnf1L59e/z73//GnXfeWdM/JyISGp57DrjsMo60Aa680gqiIjTPukjQB05MPcCgidg0x35OxLxO27dv9/0Wiog0dhs2ANddZ/2f/UCffx4o+x4Vkcalxj93jj76aJOOoHv37hg+fDjuvfde08fpzTffRL9+/epnK0VEGrPu3YFXXwXWrQP+9S8mxPP3FolIQ03yu3z5cmRlZeGkk05CWloaJk2ahGXLlplA6pVXXsFRzHobJBP5iYjUGr9aMzKAJk20E0UCXE1igxoHTo2dAicRqXf8Wr3pJuCLL6w551q10k4XCZLYwGeN7D///DPOOOMMX/05EZHGiZ2/r7kGePJJYP16YNEif2+RiPhQjQInTu57yy23mNFzmzZtMuvWrl1r5rE75phjyqdlEREJSZwyhSPnXnjB6sf0yitW+gERCb3O4ey/dMUVV5iEl8zh9PLLL+Pxxx/H3//+d5x//vlYvXo1evfuXb9bKyISqJgF/KKLgPffB8LDgTffBCZM8PdWiYi/apyeeuopPPLII2YE3fvvv2/un332WaxatQrPP/+8giYRCV35+cBf/2oFTZGR1r2CJpHQrnH6888/cS6nBwBw9tlnIyIiAo8++ijatWtXn9snIhL4OHpuzRogOhr4+GPg9NP9vUUi4u/AKS8vz8xHRzabDdHR0WjdunV9bZeISOPBUXPsBM6+nyNG+HtrRCRQEmCyX1NCQoL5f3FxMWbOnInmzZtXKHM9s+KKiAS7Q4eAZcsO1y516GDdRCSoeZ3HqVOnTqamqco/ZrOVj7YLVMrjJCJ1tn8/MGYMJ+8EPviA/Re0U0UasZrEBl7XOG3ZssUX2yYi0rjt2QOMHg2sXg20aAF06+bvLRKRBqSpuUVEvLVjBzBypJXYsk0bq19Tr17afyIhRIGTiIg3Nm+2gibesy/T4sVA167adyIhxmdTrtTGc889h/79+5v2RN4GDx6MuXPneizPzujsR+V8i4mJadBtFpEQlJYGDBtmBU1smvvmGwVNIiHKrzVOzAH18MMPo3v37mAf9ddffx3jxo3DypUr0bdvX7fPYYC1bt268uXqOqyLiNQZ+zKNGwd89RWwcCGgVCwiIcuvgdOZZ55ZYflf//qXqYX64YcfPAZODJRSU1MbaAtFRMwXD/Cf/3DoDdCkiXaJSAgL83aYnre32iopKcG7776LnJwc02TnSXZ2Njp27Ij27dub2qnff/+91q8pIuLRDz8AEycChYXWcliYgiYR8a7GqUmTJl43iTEAqgnOdcdAKT8/3yTXnD17Nvr06eO2bM+ePfHqq6+aflHMtfDYY49hyJAhJnjyNPVLQUGBuTnUJbgTkRCxZAlwxhn8pQb06AHcd5+/t0hEGlMCzCX8EnHK53T77bfj0ksvLa8Z+v77703/pOnTp+OSSy6p0QYUFhZi27ZtJhD68MMPTXZyvp6n4MlZUVGRmVx4woQJePDBB92Wuf/++zFt2rRK671JciUiIWjePOCsszjPlDWK7tNPgfh4f2+ViARIAkyvM4c7jBw5EpdffrkJVpzNmjULL774Ir7++mvUxahRo9C1a1e88MILXpXnxMOccPidd97xusaJzXwKnESkks8+45eK1Tw3dizw4YeARu6KBL3MGgRONU5HwNqlQYMGVVrPdf/3f/+HuiotLa0Q6FTXLMimvqomG+ZkxI50B46biEgl778PnHOOFTTx/uOPFTSJSN0DJ9bWvPTSS5XWs4mNj9XEHXfcgaVLl5rmPwZAXGaN1UR2yAQwadIks87hgQcewPz58818eD///DMuuugibN261dSAiYjUWno6cOWVnL0cuPBC4N13gago7VARqXs6gieeeALnnHOOSVR53HHHmXWsadqwYQM++uijGv2ttLQ0Exzt3r3bVJGx0/e8efMwmvNAAabvUxhHspRJT0/HFVdcgT179iAlJQUDBw7EsmXLvOoPJSLiUUqK1ZeJE/Y+9RQQHq6dJSK+6eNE27dvN/mW1q5da5bZQfvqq6+ucY1ToLdjikiQ27fPSm4pIiEtsz47hzd2CpxExJg+HZgxw8oGfsQR2ikiISyzPjuH0zfffGP6FzGH0s6dO826N998E99++23ttlhEpKHwt+I99wB33gkcOADMn699LyJeq3HgxH5MY8aMQWxsrOmg7RgBxyjtoYcequmfExFp2KDplluAf/7TWn7kEeDmm3UERKT+Aqd//vOfeP75583IusjIyPL1Q4cONYGUiEhAKi0FpkwBHn/cWn76aeC22/y9VSIS7KPq1q1bh2HDhlVaz7bBQ4cO+Wq7RER8h1NBMW3JzJnWhL1MqTJ5svawiNR/jVNqaio2btxYaT37N3Xp0qXmWyAiUt+Y1HLTJivNwFtvKWgSkYYLnJhHaerUqfjxxx/NxL+7du3C22+/jVtuuQXXXHNN7bdERKS+xMYCc+YAc+daCS5FRBqqqY4T/HJaFM5Zl5uba5rtOK0JA6e///3vtd0OERHf4iS9TMp70UXWcmIiUJZcV0Sktmqdx6mwsNA02WVnZ5vM3QkJCWgMlMdJJARkZwN/+YuVo+nf/wZuvdXfWyQioZrH6W9/+xuysrIQFRVlAqZjjz3WBE05OTnmMRERv8rIAMaMsYIm1jIdf7wOiIj4TI0Dp9dffx15rAJ3wXVvvPGGr7ZLRKTmmNBy5Ehg2TKgSRNg4ULgxBO1J0Wk4fs4sRqLrXq8scYpJiam/LGSkhJ88cUXaNmype+2TESkJtLSgFGjgFWrgObNgQULgKOO0j4UEf8ETk2aNDGj6Hjr0aNHpce5ftq0ab7dOhERb+TnAyNGAH/8wZwpwKJFQJ8+2nci4r/A6auvvjK1TSeffLKZdqVp06blj7G/U8eOHdGmTRvfb6GISHVYA86s4OwIzua57t21z0QkMEbVbd26FR06dDA1TI2RRtWJBLGsLKtDuIhIoIyqW7x4MT788MNK6z/44APTcVxEpEGsWQOceipw8ODhdQqaRKSe1Thwmj59Opqz46ULdgx/6KGHfLVdIiKe/fILMHw4MG8ecNNN2lMiEriB07Zt29C5c+dK69nHiY+JiNSrH38ETjoJ2L8fGDgQmDFDO1xEAjdwYs3Sb7/9Vmn9r7/+imbNmvlqu0REKlu61Eo5cOgQMGSINXpO3zsiEsiB04QJE3D99debUXbM38Qb+z1x4t8LLrigfrZSRISj5dinidOpsMaJzXTJydovIhLYk/w++OCD2LJli5nkNyLCejon/Z00aZL6OIlI/SgqAq691pq4l8HTxx8DsbHa2yLSeCb5Xb9+vWmei42NxRFHHGH6ODUGSkcg0kht2AA8+ijw9NNAdLS/t0ZEgkhNYoNaB06NlQInkUZk1y5AiXVFJIBiA6+a6m666SbTRBcfH2/+X5XHH3+8ZlsrIuLOyy8D110HzJ4NnHaa9pGIBASvAqeVK1eiiH0Myv7vSWPNJi4iAYbNcddfb/2fk/UqcBKRAKGmOhEJLJxv7h//sP5/881Wvyb9KBORxjrliohIvWB3y/vvPxw03XOPgiYRaZxNdWeffbbXf/BjDhMWEalp0HT77VZtE02fbi2LiDTGwInVVw4chDd79myzbtCgQWbdihUrcOjQoRoFWCIiTl8sQFqa9f+nnjrcv0lEpLH3cfrHP/6BgwcP4vnnn0d4eLhZx+zh1157rWkXfJT9EQKY0hGIBKiSEmDxYmD0aH9viYiEmMz6zOPUokULfPvtt+jZs2eF9evWrcOQIUNw4MABBDIFTiIBorgYeOEF4OqrgbIfYSIiQdc5vLi4GGvXrq20nus49YqISLUKC4Hzz7fyNDFwEhEJ1rnqLrvsMkyePBl//vknjj32WLPuxx9/xMMPP2weExGpEueb++tfgS++AKKigL/8RTtMRII3cHrssceQmpqKGTNmYPfu3WZd69atceutt+Jm5lwREfEkJ8cKlNiXiZP0fvqp+jSJSOgkwGSbIFXXHhhI1MdJxE8yMoCxY4HvvgMSEoA5c4Dhw3U4RCT4E2Cyn9PChQvxzjvvlE+zsmvXLmRnZ9dui0UkuPH3GWuaGDQ1aWJNo6KgSURCoalu69atOPXUU7Ft2zYUFBRg9OjRSExMxCOPPGKWmaZARKQC/sBiQsuNG62apqOP1g4SkUapxjVOU6dONYkv09PTEcs+CmXOOussLFq0yNfbJyKNmXNPAE7Uy8BJQZOIhFLg9M033+Duu+9GFEfDOOnUqRN27tzpy20TkcZs61Zg2DBgw4bD65x+bImIhETgxFxNzBTuaseOHabJTkTE1CwxaPr2W+CKK7RDRCR0A6dTTjkFTz75ZPkyO4ezU/h9992H008/3dfbJyKNzR9/WEHTtm0AZxh4+21/b5GIiP/SEWzfvt10DufTNmzYYPo78b558+ZYunQpWrZsiUCmdAQi9ejXX628TPv2AUccYY2ea9VKu1xEQneuOkc6gvfeew+//vqrqW0aMGAAJk6cWKGzeKBS4CRST376CRgzBkhPBwYMAObPB5o10+4WkdANnIqKitCrVy/MmTMHvXv3RmOkwEmknrCmaeFCYPBgazoV5msSEQnlBJiRkZHIz8+v6/aJSDB67z3gmmuAefMUNIlI0Kpx5/ApU6aYZJdsrhORELd58+H/N20KPPssoNG1IhLEapw5/KeffjKJLufPn48jjjgC8fHxFR7/+OOPfbl9IhKo+Fm/4ALg8ceB667z99aIiDSIGgdOTZo0wTnnnFM/WyMijcOsWcCkSQBzunH+uSlTrGlVRESCXI0Dp9dee61+tkREGodXXwUuv9yaTuWSS4BXXlHQJCIhI6wmGcPZt2no0KE45phjcPvttyMvL69+t05EAsszzwCTJ1tB09VXW0FUeLi/t0pEJPACp3/961+48847kZCQgLZt2+Kpp54yHcVFJEQ8+ijw979b/7/hBqsjeFiNx5eIiDRqXn/rvfHGG3j22Wcxb948fPLJJ/j888/x9ttvm5ooEQkBjjkq77rL6hCuPk0iEoK8ToAZHR2NjRs3on379uXrYmJizLp27dqhsVACTJE6WLIEGD5cu1BEgkq9JMBk3iYGSq4JMZlNXESCEGuTOaF3ZubhdQqaRCTEeT2qjhVTl156qal5cmAW8auvvrpCLiflcRIJkmY5dv5++WXgk0+AxYvVn0lEpCaB0yUcduzioosu0k4UCTacFeCyy4C33rKCJf5fncBFRGoWOCl/k0gIKCwELrwQ+OgjICICePtt4Lzz/L1VIiKNNwGmiAQpTuB97rnAnDlAVBTw/vvAuHH+3ioRkYCiwElELMwGzqCJg0DYr2nMGO0ZEREXyl4nIpbbbwc6dgTmzlXQJCLigWqcREIZ07g5Eln26wesX28104mIiFuqcRIJVfv2ASecYCW1dFDQJCISuIHTc889h/79+5ssnbwNHjwYc9lMUIUPPvgAvXr1Msk4jzjiCHzxxRcNtr0iQWP3bmDECGDZMmvSXiWyFREJ/MCJU7U8/PDDWLFiBZYvX46TTz4Z48aNw++//+62/LJlyzBhwgRMnjwZK1euxPjx481t9erVDb7tIo3W9u1WBvA1a4C2bYH//Y/TAPh7q0REgmuuuobStGlTPProoyY4cnX++ecjJycHczjyp8zxxx+Po446Cs8//7xXf19z1UlI27QJGDkS2LIF6NQJWLQI6NLF31slIhJ8c9XVt5KSErz77rsmMGKTnTvff/89Ro0aVWHdmDFjzHoRqca6dcCwYVbQ1L07sHSpgiYRkcY2qm7VqlUmUOK8dwkJCZg9ezb69OnjtuyePXvQqlWrCuu4zPWeFBQUmJtzVCkSkjhh786dQN++wMKFQGqqv7dIRKTR8XuNU8+ePfHLL7/gxx9/xDXXXGPmxFvDvhc+Mn36dFP95ri1b9/eZ39bpFF56inglluAr79W0CQi0lgDp6ioKHTr1g0DBw40Qc6RRx6Jp/gF70Zqair27t1bYR2Xud6TO+64w7RZOm7b2TFWJFQwL1Np6eFUA48+CjRv7u+tEhFptPweOLkqLS2t0LTmjE16i9iZ1cmCBQs89omi6Ojo8nQHjptISPjqK2DAAODvf7cSXYqISOPu48TaoNNOOw0dOnRAVlYWZs2aha+//hrz5s0zj0+aNAlt27Y1NVE0depUDB8+HDNmzMDYsWNNZ3KmMXjxxRf9+TZEAs+XXwJnnWVN3LthA1BYyF8R/t4qEZFGz6+BU1pamgmOdu/ebfofMRkmg6bRo0ebx7dt24awsMOVYkOGDDHB1d13340777wT3bt3xyeffIJ+nCpCRCycoPe886yklmeeCbz/voImEZFgzeNU35THSYLae+8BEycyvwdw7rnA228ruaWISDDmcRKROnr9deDCC62gadIkYNYsBU0iIj6mwEkkWCQkADYbcNVVwGuvARF+T9MmIhJ09M0qEizOOQf44Qdg4EArgBIREZ9TjZNIY/b008DWrYeXBw1S0CQiUo8UOIk0RhzTcdddwPXXA5y/MTvb31skIhIS1FQn0hiDpptusuaeo6uvtvo3iYhIvVPgJNKYcPqUa68FXnjBWv7vf61lERFpEAqcRBqL4mJg8mTgjTesfkyvvAJcdpm/t0pEJKQocBJpLO691wqawsOBN98EJkzw9xaJiIQcdQ4XaSymTgX69wc++EBBk4iIn6jGSSTQ+zQ55mts1QpYsUKJLUVE/Eg1TiKBKivLSjUwc+bhdcoGLiLiVwqcRALRoUPAKacAX30F3HgjkJ7u7y0SERE11YkEoP37raBp5UogJQWYN8+6FxERv1MfJ5FAsmeP1Tz3++9AixbAwoVWh3AREQkICpxEAsX27cDIkcCGDUCbNsCiRUCvXv7eKhERcaLASSRQvPeeFTR16AAsXgx07ervLRIRERcKnEQCxc03W9nBL7zQCp5ERCTgKHAS8af164F27YC4OGsaldtv1/EQEQlgSkcg4i8//wwMGQKcfTZQUKDjICLSCChwEvGHH34ATj4ZOHDAytGUl6fjICLSCChwEmloS5YAo0cDGRnACScACxYATZroOIiINAIKnEQa0vz5wGmnAdnZVuqBL78EkpJ0DEREGgkFTiINZc4c4MwzrWa5sWOt5fh47X8RkUZEgZNIQ0lNBWJigHPOAT7+2Pq/iIg0KkpHINJQBg0Cvv8e6NEDiNBHT0SkMVKNk0h9evll4McfDy/36aOgSUSkEVPgJFJfnnwSuOIK4NRTgW3btJ9FRIKAAieR+vDQQ8CNN1r/Z/DUvr32s4hIEFDgJOJLdjtw993AXXdZy/fdBzzyiDWdioiINHrqoSriy6DplluAxx+3lhkw3Xab9q+ISBBR4CTiKy+8cDhoevpp4LrrtG9FRIKMAicRX7nkEis/03nnAZdfrv0qIhKEFDiJ1EVxMRAebvVhio21plAJU9dBEZFgpW94kdoqKADOPRe44w6rf5P5ROkjJSISzFTjJFIbnG/u7LOtGqboaOBvf7MygouISFBT4CRSU9nZwF/+Anz1FRAXB3z2mYImEZEQocBJpCYyMoDTTweWLQMSE4H//Q848UTtQxGREKHAScRbBw4AY8YAK1YATZoA8+YBxx6r/SciEkIUOIl465tvgJ9/Bpo3BxYuBI48UvtORCTEKHAS8db48cDMmcCgQUCfPtpvIiIhSIGTSFW2brVGzaWmWsuTJml/iYiEMCWdEfFkwwar4/eoUcD+/dpPIiKiwEnErTVrgGHDgO3bgZISoLBQO0pERBQ4iVTyyy/A8OHAnj1A//7AkiVAmzbaUSIiosBJpIL/+z/gpJOspjl2AmeSy5YttZNERMRQHycRBya1ZH+mQ4eAoUOtlANNm2r/iIhIOY2qE3Fo184KlI45Bvj0UyAhQftGREQqUOAk4tChA7B0KdCiBRAbq/0iIiKVKHCS0Pbhh4DdDpx77uHgSURExAMFThK63noLuOQSICwM6NTJaqITERGpgjqHS2h66SUrC3hpqXU/YIC/t0hERBoBBU4Sev7zH+DKK60muilTrCAqPNzfWyUiIo2AAicJLY88Akydav3/lluAp5+2mupERES8oCuGhI65c4Hbb7f+f++9wL//Ddhs/t4qERFpRNQ5XELHmDHAFVcAXbocDqBERERqQIGTBDd2/uYkvZGRVpPcCy+olklERGpNTXUSvBgwsRP4BRcAxcXWOjXNiYhIHajGSYITAyXmaJo1y6pp4jx0w4b5e6tERKSRU+AkwaewEJgwAfj4YyAiwgqeFDSJiIgPKHCS4JKfD5xzDvDFF0BUlDWlypln+nurREQkSChwkuCRkwOMGwcsWmRN0vvpp8Do0f7eKhERCSJ+7Rw+ffp0HHPMMUhMTETLli0xfvx4rFu3rsrnzJw5EzabrcItJiamwbZZAtgff1h9mRISgC+/VNAkIiLBVeO0ZMkSTJkyxQRPxcXFuPPOO3HKKadgzZo1iI+P9/i8pKSkCgEWgycRDBoEfPaZFTgdf7x2iIiIBFfg9CVrBVxqk1jztGLFCgyrojMvA6XU1NQG2EIJeGlpwL59QN++1vKoUf7eIhERCWIBlccpIyPD3Ddt2rTKctnZ2ejYsSPat2+PcePG4ffff2+gLZSAsmsXMHw4cNJJwNq1/t4aEREJAQETOJWWluKGG27A0KFD0a9fP4/levbsiVdffRWffvop3nrrLfO8IUOGYMeOHW7LFxQUIDMzs8JNgsDWrVaKAQZM0dGaqFdERBqEzW632xEArrnmGsydOxfffvst2rVr5/XzioqK0Lt3b0yYMAEPPvhgpcfvv/9+TJs2zW3tFvtKSSO0cSMwciSwbRvQubM1io73IiIitcBKleTkZK9ig4CocbruuuswZ84cfPXVVzUKmigyMhJHH300NvJi6sYdd9xhdoTjtn37dh9ttfht5Bxrmhg09egBLF2qoElEREKjczgru/7+979j9uzZ+Prrr9G5FrUGJSUlWLVqFU4//XS3j0dHR5ubBAE2y7FPEzuDszl34UKgVSt/b5WIiIQQvwZOTEUwa9Ys01+JuZz27Nlj1rO6LJYJDAFMmjQJbdu2NTmf6IEHHsDxxx+Pbt264dChQ3j00UexdetWXH755f58K9IQ2rYFunUD2rcH5s0DmjfXfhcRkdAJnJ577jlzP2LEiArrX3vtNVx66aXm/9u2bUMYJ2ktk56ejiuuuMIEWSkpKRg4cCCWLVuGPn36NPDWS4NLTLSmUqEmTXQAREQkdDuHB2IHMAkAixcDy5cDt93m7y0REZEgVZPYQHPVSeBi7dLZZzOnBNClC/DXv/p7i0REJMQFxKg6kUpmzwbGj7eCJk7ce+aZ2kkiIuJ3Cpwk8LzzDnDuuUzSBZx/PvDBB1aSSxERET9T4CSB5dVXgYkTmWcC4ACBt99msi5/b5WIiIihwEkCB+ccZFoJjle45hrglVeA8HB/b5WIiEg5dQ6XwNG3L/DYYwDnHZwxA7DZ/L1FIiIiFShwEv9i7VJeHhAXZy3fdJOOiIiIBCw11Yl/g6Y77rDmnsvI0JEQEZGAp8BJ/KO0FJg6FXjkEWDFCmDuXB0JEREJeGqqk4bHEXNXXw28/LK1zKl3LrhAR0JERAKeAidpWMXFh9MMcA5Cph+45BIdBRERaRQUOEnDKSwELrwQ+OgjICLCCp7OO09HQEREGg0FTtJw9uwBli0DoqKA99+3plIRERFpRBQ4ScPp0AFYtAjYvh045RTteRERaXQ0qk7qV2Ym8M03h5d791bQJCIijZYCJ6k/6enA6NHWjTVNIiIijZwCJ6kf+/YBJ58M/N//AQkJQEqK9rSIiDR66uMkvrd7NzBqFLBmDdCqFbBwIdCvn/a0iIg0egqcxLe2bQNGjgQ2bgTatrWa6Hr21F4WEZGgoMBJfFvTxHnntm4FOnWygqYuXbSHRUQkaChwEt9p2RIYPNjK08SgqX177V0REQkqCpzEd8LDgTfeAA4dAlq00J4VEZGgo1F1UjfLlwN//ztQWmotR0YqaBIRkaClGiepve++A04/3UpyyWa5227T3hQRkaCmGiepncWLrQzgDJrYIfyaa7QnRUQk6ClwkpqbOxcYOxbIzbWygnM5MVF7UkREgp4CJ6mZ2bOBceOA/HzgzDOBzz4D4uK0F0VEJCQocJKaTaNy8cVAURFw7rnAhx8CMTHagyIiEjLUOVy8xxQDs2YBn34KvPACEKHTR0REQouufFI9dgBPSrL+/5e/WDcREZEQpKY6qdrjjwN9+wKbN2tPiYhIyFPgJJ7985/AzTcDO3YAH32kPSUiIiFPTXVSmd0O3H038NBD1vKDDwK33KI9JSIiIU+Bk1QOmm66CXjySWv5scesWicRERFR4CROON/ctddaI+bov/+1lkVERMRQjZMclpMD/PgjYLMBr7wCXHaZ9o6IiIgTBU5yGKdNmT8f+P57pRwQERFxQ6PqQl1BAfD55xWTXCpPk4iIiFsKnEIZJ+l1JLR8+WV/b42IiEjAU1NdqMrKsgKmr78G4uOBLl38vUUiIiIBT4FTKDp0CDjtNOCHH6ypVL74Ahg61N9bJSIiEvAUOIWa/fuBU04BVq4EUlKszuCDBvl7q0RERBoFBU6h1qfppJOA1autTuALFwL9+/t7q0RERBoNBU6hJC4OOOcc4OBBYNEioFcvf2+RiIhIo6JRdaHmvvuAX39V0CQiIlILCpyC3fr1wAUXWFnBiVnBmzf391aJiIg0SmqqC2bsyzRqFLB3r9UR/Lnn/L1FIiIijZpqnILVzz8DI0ZYQdORRwIPPODvLRIREWn0FDgFI+ZnOvlk4MAB4Nhjga++skbRiYiISJ0ocAo2S5YAo0cDGRnACScACxZYzXQiIiJSZwqcgklhITBpEpCdDYwcCXz5pZUZXERERHxCgVMwiYoCPvkEuPBCYM4caw46ERER8RkFTsGACS0djj4aePttICbGn1skIiISlBQ4NXZvvgl07gwsW+bvLREREQl6CpwasxdfBC65BMjMBD74wN9bIyIiEvQUODVWTz4JXHUVYLcDf/87MGOGv7dIREQk6ClwaoymTwduvNH6/223AU89BYTpUIqIiNQ3XW0bE9Yu3XMPcOed1vK0acDDD1vzz4mIiEi901x1jUlJCfDrr9b///1v4NZb/b1FIiIiIUWBU2MSEQG8/76V2HL8eH9vjYiISMhRU11jqGV66y2rmY6Yn0lBk4iIiF8ocApkRUXAxInAxRcD//iHv7dGREQk5KmpLlAVFADnnw98+ikQGQkcd5y/t0hERCTk+bXGafr06TjmmGOQmJiIli1bYvz48Vi3bl21z/vggw/Qq1cvxMTE4IgjjsAXX3yBoJKbC4wbZwVN0dHA7NnAOef4e6tERERCnl8DpyVLlmDKlCn44YcfsGDBAhQVFeGUU05BTk6Ox+csW7YMEyZMwOTJk7Fy5UoTbPG2evVqBIXsbGDsWGDePCAuDvjf/6xlERER8Tub3e7odex/+/btMzVPDKiGDRvmtsz5559vAqs5c+aUrzv++ONx1FFH4fnnn6/2NTIzM5GcnIyMjAwkJSUhoJSWAiedBCxdCiQmWkHTiSf6e6tERESCWmYNYoOA6hzODaamTZt6LPP9999j1KhRFdaNGTPGrHenoKDA7BDnW8Bi9u+rrwaaNQMWLlTQJCIiEmACJnAqLS3FDTfcgKFDh6Jfv34ey+3ZswetWrWqsI7LXO+pHxWjSMetffv2CGgTJgB//gkce6y/t0REREQCNXBiXyf2U3r33Xd9+nfvuOMOU5PluG3fvh0BZedOqw8T7x2Sk/25RSIiIhLI6Qiuu+4602dp6dKlaNeuXZVlU1NTsXfv3grruMz17kRHR5tbQNqyBRg5Eti0Cfjb36wO4SIiIhKw/FrjxH7pDJpmz56NxYsXo3PnztU+Z/DgwVi0aFGFdRyRx/WNyoYNADvAM2jq0gV48UV/b5GIiIgEco0Tm+dmzZqFTz/91ORycvRTYl+k2NhY8/9Jkyahbdu2pq8STZ06FcOHD8eMGTMwduxY07S3fPlyvNiYAo81a6yaJr7fXr2sjuBt2/p7q0RERCSQa5yee+450+9oxIgRaN26dfntvffeKy+zbds27N69u3x5yJAhJthioHTkkUfiww8/xCeffFJlh/KAsnIlMHy4FTT1789kVgqaREREGomAyuPUEPyax4m7+oQTmMUTGDTI6tNUReoFERERqX+NNo9T0LPZOF8McNFFVvOcgiYREZFGRYFTQ3DOMdWmDfDmm0o5ICIi0ggpcKpvn39ujZqbNaveX0pERETqlwKn+sRmubPPBvLygE8+sfo4iYiISKOlwKm+vPUWcMEFQHGxNY3K229bfZxERESk0VLgVB9eeokJqDgBH3DZZVafpsjIenkpERERaTgKnHztP/8BrrzSapabMgV4+WUgPNznLyMiIiINT4GTr23bZt3fcgvw9NNAmHaxiIhIsAiISX6DyqOPAiNGAGPHqk+TiIhIkFHg5GvsAH7GGT7/syIiIuJ/akcSERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvKXASERER8ZICJxEREREvRSDE2O12c5+ZmenvTREREZEA4IgJHDFCVUIucMrKyjL37du39/emiIiISIDFCMnJyVWWsdm9Ca+CSGlpKXbt2oXExETYbDYEWsTLgG779u1ISkry9+YEPO0v7S+dX4FDn0ftr8Z8fjEUYtDUpk0bhIVV3Ysp5GqcuEPatWuHQMaTQoGT9pfOr8Cgz6P2l86v0Pg8JldT0+SgzuEiIiIiXlLgJCIiIuIlBU4BJDo6Gvfdd5+5F+0vnV/+pc+j9pfOr8ARHUDXx5DrHC4iIiJSW6pxEhEREfGSAicRERERLylwEhEREfGSAqcGMn36dBxzzDEm8WbLli0xfvx4rFu3rtrnffDBB+jVqxdiYmJwxBFH4IsvvkAoqM3+mjlzpklq6nzjfgsFzz33HPr371+e42Tw4MGYO3dulc8J1XOrNvsrlM8tdx5++GGzD2644YYqy4XyOVbT/RXq59j9999f6f3z3AnE80uBUwNZsmQJpkyZgh9++AELFixAUVERTjnlFOTk5Hh8zrJlyzBhwgRMnjwZK1euNMEDb6tXr0awq83+Il4Ed+/eXX7bunUrQgGTuvLLecWKFVi+fDlOPvlkjBs3Dr///rvb8qF8btVmf4XyueXqp59+wgsvvGACz6qE+jlW0/1FoX6O9e3bt8L7//bbbwPz/OKoOml4aWlpHM1oX7Jkiccy5513nn3s2LEV1h133HH2q666yh5qvNlfr732mj05OblBtyuQpaSk2F9++WW3j+ncqtn+0rllycrKsnfv3t2+YMEC+/Dhw+1Tp071eP7pHKvZ/gr1c+y+++6zH3nkkV6X9+f5pRonP8nIyDD3TZs29Vjm+++/x6hRoyqsGzNmjFkfarzZX5SdnY2OHTuaOY2qq0EIViUlJXj33XdN7RyboNzRuVWz/UU6t2BqgceOHVvpe0nnWN33l84xYMOGDWauuC5dumDixInYtm1bQJ5fITdXXaBMNMy27qFDh6Jfv34ey+3ZswetWrWqsI7LXB9KvN1fPXv2xKuvvmqqxBloPfbYYxgyZIgJngJ9fkJfWLVqlbnw5+fnIyEhAbNnz0afPn3cltW5VbP9FernFjG4/Pnnn03TkzdC/Ryr6f4K9XPsuOOOM/28uB/YTDdt2jSceOKJpumNfV0D6fxS4OSnXyE8Gapqv5Wa7y9eBJ1rDPil07t3b9O/4MEHHwz6XcovnF9++cV86X744Ye45JJLTF8xT8FAqKvJ/gr1c4sz0k+dOtX0NwylDssNub9C/Rw77bTTyv/P4JGBFFsP3n//fdOPKZAocGpg1113HebMmYOlS5dW+ysiNTUVe/furbCOy1wfKmqyv1xFRkbi6KOPxsaNGxEKoqKi0K1bN/P/gQMHml+6Tz31lPnidaVzq2b7K9TPLXaiT0tLw4ABAyo0cfJz+cwzz6CgoADh4eEVnhPK51ht9leon2OumjRpgh49enh8//48v9THqYFwZhsGAWwOWLx4MTp37lztc/jrY9GiRRXW8RdMVf0wQnl/ueIXFZtjWrdujVDEJk5+QbsTyudWbfZXqJ9bI0eONO+XNXSO26BBg0w/FP7fXRAQyudYbfZXqJ9j7voU/vnnnx7fv1/Pr3rvfi7GNddcY0ZMfP311/bdu3eX33Jzc8v30MUXX2y//fbby5e/++47e0REhP2xxx6z//HHH2bUQWRkpH3VqlVBv1drs7+mTZtmnzdvnv3PP/+0r1ixwn7BBRfYY2Ji7L///rs92HE/cMTh5s2b7b/99ptZttls9vnz55vHdW7VbX+F8rnliesoMZ1jddtfoX6O3Xzzzeb7np9JXvtGjRplb968uRlRHWjnl5rqGjDhHo0YMaLC+tdeew2XXnqp+T9HEISFhVVo4541axbuvvtu3HnnnejevTs++eSTKjtIh/L+Sk9PxxVXXGE6B6akpJjmF+b6CIU+PmwWmDRpkulUmZycbPoIzJs3D6NHjzaP69yq2/4K5XPLWzrH6ra/Qv0c27Fjh8nLdODAAbRo0QInnHCCyePH/wfa+WVj9FTvryIiIiISBNTHSURERMRLCpxEREREvKTASURERMRLCpxEREREvKTASURERMRLCpxEREREvKTASURERMRLCpxEREREvKTASUTEB2bOnGkmJhWR4KbASUTqlc1mq/J2//33N9gR4BQ+jteNiYkxs69Pnz7dTCpdE506dcKTTz5ZYd3555+P9evX+3iLRSTQaK46EalXnA/O4b333sO9996LdevWla9LSEgo/z8DGM4KHxFRf19NnA/sgQceQEFBARYvXowrr7zS1BRdc801dfq7sbGx5iYiwU01TiJSr1JTU8tvnFCXtT2O5bVr1yIxMRFz5841k5pGR0fj22+/NRM5jx8/vsLfueGGGypM+lxaWmpqizp37mwCliOPPBIffvhhtdsTFxdnXrtjx4647LLLzAS/CxYsKH/8zz//xLhx49CqVSsT1B1zzDFYuHBh+ePchq1bt+LGG28sr71y11THmrSjjjoKb775pqmh4nu/4IILkJWVVV6G/584cSLi4+PRunVrPPHEE+bv872KSGBS4CQifnf77bfj4Ycfxh9//GECGW8waHrjjTfw/PPP4/fffzeBzEUXXYQlS5Z49XzWbn3zzTcmeIuKiipfn52djdNPPx2LFi3CypUrceqpp+LMM880s7PTxx9/jHbt2plaK9amOdeouWIQxhnb58yZY27cNr5Ph5tuugnfffcdPvvsMxO8cXt+/vlnr7ZfRPxDTXUi4ncMQkaPHu11eTazPfTQQ6YmaPDgwWZdly5dTG3VCy+8gOHDh3t87rPPPouXX34ZhYWFKCoqMn2drr/++vLHWXPFm8ODDz6I2bNnm+DmuuuuQ9OmTREeHm5qylhzVRXWirEmimXp4osvNgHZv/71L1Pb9Prrr2PWrFkYOXKkefy1115DmzZtvN4PItLwFDiJiN8NGjSoRuU3btyI3NzcSsEWg6Gjjz66yueyaeyuu+5Ceno67rvvPgwZMsTcnGuc2Mz2v//9z9QmFRcXIy8vr7zGqSbYROcImojNcWlpaeb/mzZtMoHbscceW/44m/N69uxZ49cRkYajwElE/I59fJyFhYVVGunGIMM5uCEGN23btq1Qjv2kqsLgpFu3bub/77//vvn/8ccfj1GjRpl1t9xyi2k2e+yxx8xj7D/117/+1QRlNRUZGVlhmf2hWAslIo2XAicRCTgtWrTA6tWrK6z75ZdfygORPn36mACJtUBVNctVh52/p06daoIl9mdiYMM+R+ycftZZZ5UHaVu2bKnwPPaJ4ui/umDTIt/PTz/9hA4dOph1GRkZJqXBsGHD6vS3RaT+qHO4iASck08+GcuXLzedvzds2GCa1JwDKTZ/Mdhhh3D2E2InbHaqfvrpp81yTVx11VUmWPnoo4/Mcvfu3U0HcAZqv/76Ky688MJKtURsglu6dCl27tyJ/fv31+o98j1ccskluPXWW/HVV1+ZDu6TJ082tW2OkXoiEngUOIlIwBkzZgzuuece3HbbbSYdADtST5o0qUIZdtpmGY6u6927txn9xqY7pieoCXb25t9mvyYGSI8//jhSUlJMvyeOpuO2DBgwoFJndtZCde3a1dSO1RZfi53bzzjjDNNUOHToUPNe2GFdRAKTzV7TlLkiIlIvcnJyTJ+tGTNmmNonEQk86uMkIuIn7FfFPFIcWcf+TazJIibgFJHApMBJRMSPOHqPU9CwwzmzpzMJZvPmzXVMRAKUmupEREREvKTO4SIiIiJeUuAkIiIi4iUFTiIiIiJeUuAkIiIi4iUFTiIiIiJeUuAkIiIi4iUFTiIiIiJeUuAkIiIi4iUFTiIiIiLwzv8D7xQwx1ShaVMAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(6, 6))\n",
|
||
"plt.scatter(y_test, y_pred_lasso, alpha=0.5)\n",
|
||
"plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
|
||
"plt.title(\"Part E - Lasso Regression: Actual vs Predicted\")\n",
|
||
"plt.xlabel(\"True Rating\")\n",
|
||
"plt.ylabel(\"Predicted Rating\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"id": "b6d9827d298e4fa6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.087595400Z",
|
||
"start_time": "2026-04-26T14:22:30.048388300Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Model</th>\n",
|
||
" <th>MAE</th>\n",
|
||
" <th>MSE</th>\n",
|
||
" <th>RMSE</th>\n",
|
||
" <th>R2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" <td>0.280613</td>\n",
|
||
" <td>0.149546</td>\n",
|
||
" <td>0.386712</td>\n",
|
||
" <td>0.146514</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" <td>0.295032</td>\n",
|
||
" <td>0.172895</td>\n",
|
||
" <td>0.415806</td>\n",
|
||
" <td>0.013258</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" <td>0.302947</td>\n",
|
||
" <td>0.173539</td>\n",
|
||
" <td>0.416581</td>\n",
|
||
" <td>0.009580</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" <td>0.280977</td>\n",
|
||
" <td>0.149465</td>\n",
|
||
" <td>0.386607</td>\n",
|
||
" <td>0.146976</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 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"
|
||
]
|
||
},
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"results_df = pd.DataFrame(results)\n",
|
||
"results_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"id": "aa66d8d2df993766",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.135775600Z",
|
||
"start_time": "2026-04-26T14:22:30.088593300Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>True</th>\n",
|
||
" <th>Predicted</th>\n",
|
||
" <th>Residual</th>\n",
|
||
" <th>Abs_Error</th>\n",
|
||
" <th>Model</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.306335</td>\n",
|
||
" <td>-0.206335</td>\n",
|
||
" <td>0.206335</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.287329</td>\n",
|
||
" <td>-0.187329</td>\n",
|
||
" <td>0.187329</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.297757</td>\n",
|
||
" <td>0.102243</td>\n",
|
||
" <td>0.102243</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.197993</td>\n",
|
||
" <td>0.402007</td>\n",
|
||
" <td>0.402007</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.371379</td>\n",
|
||
" <td>0.128621</td>\n",
|
||
" <td>0.128621</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>3.2</td>\n",
|
||
" <td>4.304839</td>\n",
|
||
" <td>-1.104839</td>\n",
|
||
" <td>1.104839</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.324286</td>\n",
|
||
" <td>-0.124286</td>\n",
|
||
" <td>0.124286</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>4.005045</td>\n",
|
||
" <td>-0.105045</td>\n",
|
||
" <td>0.105045</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>3.7</td>\n",
|
||
" <td>4.138157</td>\n",
|
||
" <td>-0.438157</td>\n",
|
||
" <td>0.438157</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.312731</td>\n",
|
||
" <td>0.287269</td>\n",
|
||
" <td>0.287269</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.468202</td>\n",
|
||
" <td>0.131798</td>\n",
|
||
" <td>0.131798</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.364965</td>\n",
|
||
" <td>0.135035</td>\n",
|
||
" <td>0.135035</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.443139</td>\n",
|
||
" <td>0.156861</td>\n",
|
||
" <td>0.156861</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.289412</td>\n",
|
||
" <td>0.110588</td>\n",
|
||
" <td>0.110588</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.302132</td>\n",
|
||
" <td>-0.302132</td>\n",
|
||
" <td>0.302132</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>4.373179</td>\n",
|
||
" <td>-0.073179</td>\n",
|
||
" <td>0.073179</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.172033</td>\n",
|
||
" <td>0.227967</td>\n",
|
||
" <td>0.227967</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>4.161517</td>\n",
|
||
" <td>-0.361517</td>\n",
|
||
" <td>0.361517</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.134825</td>\n",
|
||
" <td>0.365175</td>\n",
|
||
" <td>0.365175</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.279803</td>\n",
|
||
" <td>-0.079803</td>\n",
|
||
" <td>0.079803</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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)"
|
||
]
|
||
},
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"id": "7e02905489f7769d",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.173423200Z",
|
||
"start_time": "2026-04-26T14:22:30.136777300Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>True</th>\n",
|
||
" <th>Predicted</th>\n",
|
||
" <th>Residual</th>\n",
|
||
" <th>Abs_Error</th>\n",
|
||
" <th>Model</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.299298</td>\n",
|
||
" <td>-0.199298</td>\n",
|
||
" <td>0.199298</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.277702</td>\n",
|
||
" <td>-0.177702</td>\n",
|
||
" <td>0.177702</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.314717</td>\n",
|
||
" <td>0.085283</td>\n",
|
||
" <td>0.085283</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.189314</td>\n",
|
||
" <td>0.410686</td>\n",
|
||
" <td>0.410686</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.376513</td>\n",
|
||
" <td>0.123487</td>\n",
|
||
" <td>0.123487</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>3.2</td>\n",
|
||
" <td>4.317265</td>\n",
|
||
" <td>-1.117265</td>\n",
|
||
" <td>1.117265</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.345980</td>\n",
|
||
" <td>-0.145980</td>\n",
|
||
" <td>0.145980</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>4.000260</td>\n",
|
||
" <td>-0.100260</td>\n",
|
||
" <td>0.100260</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.279363</td>\n",
|
||
" <td>-0.179363</td>\n",
|
||
" <td>0.179363</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.292264</td>\n",
|
||
" <td>-0.192264</td>\n",
|
||
" <td>0.192264</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.275074</td>\n",
|
||
" <td>0.124926</td>\n",
|
||
" <td>0.124926</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.230751</td>\n",
|
||
" <td>0.369249</td>\n",
|
||
" <td>0.369249</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.287948</td>\n",
|
||
" <td>0.212052</td>\n",
|
||
" <td>0.212052</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>3.2</td>\n",
|
||
" <td>4.233728</td>\n",
|
||
" <td>-1.033728</td>\n",
|
||
" <td>1.033728</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.233425</td>\n",
|
||
" <td>-0.033425</td>\n",
|
||
" <td>0.033425</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>4.081532</td>\n",
|
||
" <td>-0.181532</td>\n",
|
||
" <td>0.181532</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.290152</td>\n",
|
||
" <td>-0.190152</td>\n",
|
||
" <td>0.190152</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.261329</td>\n",
|
||
" <td>-0.161329</td>\n",
|
||
" <td>0.161329</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.250952</td>\n",
|
||
" <td>0.149048</td>\n",
|
||
" <td>0.149048</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.245164</td>\n",
|
||
" <td>0.354836</td>\n",
|
||
" <td>0.354836</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.254136</td>\n",
|
||
" <td>0.245864</td>\n",
|
||
" <td>0.245864</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>3.2</td>\n",
|
||
" <td>4.237515</td>\n",
|
||
" <td>-1.037515</td>\n",
|
||
" <td>1.037515</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.237951</td>\n",
|
||
" <td>-0.037951</td>\n",
|
||
" <td>0.037951</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>4.262971</td>\n",
|
||
" <td>-0.362971</td>\n",
|
||
" <td>0.362971</td>\n",
|
||
" <td>Ridge Regression (alpha=50.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.306335</td>\n",
|
||
" <td>-0.206335</td>\n",
|
||
" <td>0.206335</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.287329</td>\n",
|
||
" <td>-0.187329</td>\n",
|
||
" <td>0.187329</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.297757</td>\n",
|
||
" <td>0.102243</td>\n",
|
||
" <td>0.102243</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.197993</td>\n",
|
||
" <td>0.402007</td>\n",
|
||
" <td>0.402007</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.371379</td>\n",
|
||
" <td>0.128621</td>\n",
|
||
" <td>0.128621</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>3.2</td>\n",
|
||
" <td>4.304839</td>\n",
|
||
" <td>-1.104839</td>\n",
|
||
" <td>1.104839</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.324286</td>\n",
|
||
" <td>-0.124286</td>\n",
|
||
" <td>0.124286</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>31</th>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>4.005045</td>\n",
|
||
" <td>-0.105045</td>\n",
|
||
" <td>0.105045</td>\n",
|
||
" <td>Lasso Regression (alpha=0.0005)</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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)"
|
||
]
|
||
},
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"id": "24cd7fb5c2d8d4b2",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.341040400Z",
|
||
"start_time": "2026-04-26T14:22:30.174424100Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 900x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"results_melted = results_df.melt(\n",
|
||
" id_vars='Model',\n",
|
||
" value_vars=['MAE', 'MSE', 'RMSE', 'R2'],\n",
|
||
" var_name='Metric',\n",
|
||
" value_name='Score'\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(9, 5))\n",
|
||
"sns.barplot(data=results_melted, x='Model', y='Score', hue='Metric')\n",
|
||
"plt.title(\"Part E: Regression Model Comparison (MAE, RMSE, R2)\")\n",
|
||
"plt.xticks(rotation=15)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e12551afcc108484",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Model Performance Analysis\n",
|
||
"\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",
|
||
"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**."
|
||
]
|
||
},
|
||
{
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"id": "ab09192a122e6e8f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.365077800Z",
|
||
"start_time": "2026-04-26T14:22:30.342047500Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"features_to_test = ['Reviews', 'Size in bytes', 'Numeric Installs']\n",
|
||
"feature_results = []"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"id": "3f65e25b63cc8e93",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.416064900Z",
|
||
"start_time": "2026-04-26T14:22:30.366077Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"for feature in features_to_test:\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",
|
||
" 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",
|
||
" })"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"id": "33cc0452de54f874",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.448560800Z",
|
||
"start_time": "2026-04-26T14:22:30.417061900Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Feature</th>\n",
|
||
" <th>MAE</th>\n",
|
||
" <th>MSE</th>\n",
|
||
" <th>RMSE</th>\n",
|
||
" <th>R2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Size in bytes</td>\n",
|
||
" <td>0.304495</td>\n",
|
||
" <td>0.173620</td>\n",
|
||
" <td>0.416677</td>\n",
|
||
" <td>0.009120</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Reviews</td>\n",
|
||
" <td>0.304707</td>\n",
|
||
" <td>0.173865</td>\n",
|
||
" <td>0.416971</td>\n",
|
||
" <td>0.007722</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Numeric Installs</td>\n",
|
||
" <td>0.307489</td>\n",
|
||
" <td>0.174643</td>\n",
|
||
" <td>0.417903</td>\n",
|
||
" <td>0.003281</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 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"
|
||
]
|
||
},
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"feature_results_df = pd.DataFrame(feature_results).sort_values('RMSE')\n",
|
||
"feature_results_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"id": "4f73fdfeefd697e2",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.454588200Z",
|
||
"start_time": "2026-04-26T14:22:30.448560800Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"feature_melted = feature_results_df.melt(\n",
|
||
" id_vars='Feature',\n",
|
||
" value_vars=['MAE', 'MSE', 'RMSE', 'R2'],\n",
|
||
" var_name='Metric',\n",
|
||
" value_name='Score'\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"id": "3b1a720808cf9b77",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.595029300Z",
|
||
"start_time": "2026-04-26T14:22:30.454588200Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
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"text/plain": [
|
||
"<Figure size 900x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(9, 5))\n",
|
||
"sns.barplot(data=feature_melted, x='Feature', y='Score', hue='Metric')\n",
|
||
"plt.title(\"Part F: Single-Feature Comparison (Linear Regression)\")\n",
|
||
"plt.xticks(rotation=15)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"id": "593913702f875fa0",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.631246400Z",
|
||
"start_time": "2026-04-26T14:22:30.595029300Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
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|
||
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|
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|
||
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|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Best Feature</th>\n",
|
||
" <th>MAE</th>\n",
|
||
" <th>MSE</th>\n",
|
||
" <th>RMSE</th>\n",
|
||
" <th>R2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Size in bytes</td>\n",
|
||
" <td>0.304495</td>\n",
|
||
" <td>0.17362</td>\n",
|
||
" <td>0.416677</td>\n",
|
||
" <td>0.00912</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Best Feature MAE MSE RMSE R2\n",
|
||
"0 Size in bytes 0.304495 0.17362 0.416677 0.00912"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"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",
|
||
"}]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"id": "2be649a00dbb9fc7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.685659Z",
|
||
"start_time": "2026-04-26T14:22:30.632246600Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_best = df_encoded[[best_feature]]\n",
|
||
"X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(\n",
|
||
" X_best, y, test_size=0.3, random_state=seed\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
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|
||
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|
||
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|
||
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|
||
"start_time": "2026-04-26T14:22:30.686659800Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"best_model = LinearRegression()\n",
|
||
"best_model.fit(X_train_b, y_train_b)\n",
|
||
"y_pred_b = best_model.predict(X_test_b)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"id": "ba0e16192124782b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.746999400Z",
|
||
"start_time": "2026-04-26T14:22:30.713628100Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
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"<div>\n",
|
||
"<style scoped>\n",
|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>True</th>\n",
|
||
" <th>Predicted</th>\n",
|
||
" <th>Residual</th>\n",
|
||
" <th>Abs_Error</th>\n",
|
||
" <th>Model</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.307502</td>\n",
|
||
" <td>-0.207502</td>\n",
|
||
" <td>0.207502</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>4.267637</td>\n",
|
||
" <td>-0.167637</td>\n",
|
||
" <td>0.167637</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.251691</td>\n",
|
||
" <td>0.148309</td>\n",
|
||
" <td>0.148309</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.243240</td>\n",
|
||
" <td>0.356760</td>\n",
|
||
" <td>0.356760</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.256475</td>\n",
|
||
" <td>0.243525</td>\n",
|
||
" <td>0.243525</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>3.2</td>\n",
|
||
" <td>4.232078</td>\n",
|
||
" <td>-1.032078</td>\n",
|
||
" <td>1.032078</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.232716</td>\n",
|
||
" <td>-0.032716</td>\n",
|
||
" <td>0.032716</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>3.9</td>\n",
|
||
" <td>4.269232</td>\n",
|
||
" <td>-0.369232</td>\n",
|
||
" <td>0.369232</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>3.7</td>\n",
|
||
" <td>4.251691</td>\n",
|
||
" <td>-0.551691</td>\n",
|
||
" <td>0.551691</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.336205</td>\n",
|
||
" <td>0.263795</td>\n",
|
||
" <td>0.263795</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.376070</td>\n",
|
||
" <td>0.223930</td>\n",
|
||
" <td>0.223930</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.231759</td>\n",
|
||
" <td>0.268241</td>\n",
|
||
" <td>0.268241</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>4.289962</td>\n",
|
||
" <td>0.310038</td>\n",
|
||
" <td>0.310038</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.245313</td>\n",
|
||
" <td>0.154687</td>\n",
|
||
" <td>0.154687</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.294746</td>\n",
|
||
" <td>-0.294746</td>\n",
|
||
" <td>0.294746</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>4.261259</td>\n",
|
||
" <td>0.038741</td>\n",
|
||
" <td>0.038741</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>4.275610</td>\n",
|
||
" <td>0.124390</td>\n",
|
||
" <td>0.124390</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>4.241167</td>\n",
|
||
" <td>-0.441167</td>\n",
|
||
" <td>0.441167</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>4.239732</td>\n",
|
||
" <td>0.260268</td>\n",
|
||
" <td>0.260268</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>4.232237</td>\n",
|
||
" <td>-0.032237</td>\n",
|
||
" <td>0.032237</td>\n",
|
||
" <td>Linear Regression (Size in bytes)</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" True Predicted Residual Abs_Error Model\n",
|
||
"0 4.1 4.307502 -0.207502 0.207502 Linear Regression (Size in bytes)\n",
|
||
"1 4.1 4.267637 -0.167637 0.167637 Linear Regression (Size in bytes)\n",
|
||
"2 4.4 4.251691 0.148309 0.148309 Linear Regression (Size in bytes)\n",
|
||
"3 4.6 4.243240 0.356760 0.356760 Linear Regression (Size in bytes)\n",
|
||
"4 4.5 4.256475 0.243525 0.243525 Linear Regression (Size in bytes)\n",
|
||
"5 3.2 4.232078 -1.032078 1.032078 Linear Regression (Size in bytes)\n",
|
||
"6 4.2 4.232716 -0.032716 0.032716 Linear Regression (Size in bytes)\n",
|
||
"7 3.9 4.269232 -0.369232 0.369232 Linear Regression (Size in bytes)\n",
|
||
"8 3.7 4.251691 -0.551691 0.551691 Linear Regression (Size in bytes)\n",
|
||
"9 4.6 4.336205 0.263795 0.263795 Linear Regression (Size in bytes)\n",
|
||
"10 4.6 4.376070 0.223930 0.223930 Linear Regression (Size in bytes)\n",
|
||
"11 4.5 4.231759 0.268241 0.268241 Linear Regression (Size in bytes)\n",
|
||
"12 4.6 4.289962 0.310038 0.310038 Linear Regression (Size in bytes)\n",
|
||
"13 4.4 4.245313 0.154687 0.154687 Linear Regression (Size in bytes)\n",
|
||
"14 4.0 4.294746 -0.294746 0.294746 Linear Regression (Size in bytes)\n",
|
||
"15 4.3 4.261259 0.038741 0.038741 Linear Regression (Size in bytes)\n",
|
||
"16 4.4 4.275610 0.124390 0.124390 Linear Regression (Size in bytes)\n",
|
||
"17 3.8 4.241167 -0.441167 0.441167 Linear Regression (Size in bytes)\n",
|
||
"18 4.5 4.239732 0.260268 0.260268 Linear Regression (Size in bytes)\n",
|
||
"19 4.2 4.232237 -0.032237 0.032237 Linear Regression (Size in bytes)"
|
||
]
|
||
},
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pred_sheet_part_f = regression_prediction_sheet(y_test_b, y_pred_b, f'Linear Regression ({best_feature})')\n",
|
||
"pred_sheet_part_f"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 68,
|
||
"id": "9f485161f95e22d7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.866624300Z",
|
||
"start_time": "2026-04-26T14:22:30.747995400Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(6, 6))\n",
|
||
"plt.scatter(y_test_b, y_pred_b, alpha=0.5)\n",
|
||
"plt.plot([y_test_b.min(), y_test_b.max()], [y_test_b.min(), y_test_b.max()], 'r--')\n",
|
||
"plt.title(f\"Part F - Best Feature ({best_feature}): Actual vs Predicted\")\n",
|
||
"plt.xlabel(\"True Rating\")\n",
|
||
"plt.ylabel(\"Predicted Rating\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "84b748da2e6a61bd",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Part F Analysis\n",
|
||
"\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",
|
||
"- `Reviews` (number of reviews)\n",
|
||
"- `Size in bytes` (app size)\n",
|
||
"- `Numeric Installs` (install count as a number)\n",
|
||
"\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",
|
||
"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"
|
||
}
|
||
},
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 69,
|
||
"id": "137267551f08dae5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.890649300Z",
|
||
"start_time": "2026-04-26T14:22:30.867624800Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import cross_val_score"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"id": "725e85ef4abe3948",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.927351600Z",
|
||
"start_time": "2026-04-26T14:22:30.891653600Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"columns_to_keep_g = ['Category', 'Reviews', 'Content Rating', 'Rating', 'Numeric Installs', 'Size in bytes']\n",
|
||
"df_g = df_new[columns_to_keep_g]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"id": "7a6b3f84b1265599",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:30.974128500Z",
|
||
"start_time": "2026-04-26T14:22:30.927351600Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_g_encoded = pd.get_dummies(df_g, columns=['Category', 'Content Rating'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"id": "679b52eb182f0e1c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.007707400Z",
|
||
"start_time": "2026-04-26T14:22:30.976128500Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"id": "35f87078cd8053fa",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.038020300Z",
|
||
"start_time": "2026-04-26T14:22:31.008708Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"id": "862fb1943bf890b5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.086062700Z",
|
||
"start_time": "2026-04-26T14:22:31.059022100Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Split</th>\n",
|
||
" <th>Rows</th>\n",
|
||
" <th>Pct_of_Total</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Train</td>\n",
|
||
" <td>774</td>\n",
|
||
" <td>69.9</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Test</td>\n",
|
||
" <td>333</td>\n",
|
||
" <td>30.1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Split Rows Pct_of_Total\n",
|
||
"0 Train 774 69.9\n",
|
||
"1 Test 333 30.1"
|
||
]
|
||
},
|
||
"execution_count": 74,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"split_overview(len(X_train_g), len(X_test_g))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"id": "c3c062e2eebf8675",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.167844300Z",
|
||
"start_time": "2026-04-26T14:22:31.087063800Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def evaluate_model_cv(model, X_train, X_test, y_train, y_test, name):\n",
|
||
" \"\"\"Evaluate model with both train/test split AND cross-validation\"\"\"\n",
|
||
" model.fit(X_train, y_train)\n",
|
||
" y_pred = model.predict(X_test)\n",
|
||
"\n",
|
||
" # Cross-validation scores (5-fold)\n",
|
||
" cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')\n",
|
||
"\n",
|
||
" results = {\n",
|
||
" 'Model': name,\n",
|
||
" 'MAE (test)': mean_absolute_error(y_test, y_pred),\n",
|
||
" 'MSE (test)': mean_squared_error(y_test, y_pred),\n",
|
||
" 'RMSE (test)': np.sqrt(mean_squared_error(y_test, y_pred)),\n",
|
||
" 'R2 (test)': r2_score(y_test, y_pred),\n",
|
||
" 'CV R2 (mean)': cv_scores.mean(),\n",
|
||
" 'CV R2 (std)': cv_scores.std()\n",
|
||
" }\n",
|
||
" return results, y_pred, cv_scores"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 76,
|
||
"id": "7bd72610ea9b4047",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.234779200Z",
|
||
"start_time": "2026-04-26T14:22:31.169844400Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results_g = []"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ec07cf5f77d93f3b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Trying Linear Regression for Part G"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"id": "5077d997183c52b7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.261165700Z",
|
||
"start_time": "2026-04-26T14:22:31.238779400Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 78,
|
||
"id": "dcf6fd613a1d2d79",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.277760400Z",
|
||
"start_time": "2026-04-26T14:22:31.261165700Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"residual_lin_g = y_test_g - y_pred_lin_g"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 79,
|
||
"id": "39fd78fe960acb67",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.356530200Z",
|
||
"start_time": "2026-04-26T14:22:31.279268400Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"plt.scatter(y_pred_lin_g, residual_lin_g, alpha=0.5)\n",
|
||
"plt.axhline(y=0, color='r', linestyle='--')\n",
|
||
"plt.title(\"Part G - Linear Regression: Residual Plot\")\n",
|
||
"plt.xlabel(\"Predicted Size in Bytes\")\n",
|
||
"plt.ylabel(\"Residual (y - y_hat)\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5566a6d1839e472f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Trying polynomial regression for Part G"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 80,
|
||
"id": "b5e9cdb3ef42c22f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.380401100Z",
|
||
"start_time": "2026-04-26T14:22:31.372014900Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 81,
|
||
"id": "8d5313ca848e4519",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.543772600Z",
|
||
"start_time": "2026-04-26T14:22:31.380401100Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"id": "8c81b3b6dab60fee",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.640034900Z",
|
||
"start_time": "2026-04-26T14:22:31.544770800Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(6, 6))\n",
|
||
"plt.scatter(y_test_g, y_pred_poly_g, alpha=0.5)\n",
|
||
"plt.plot([y_test_g.min(), y_test_g.max()], [y_test_g.min(), y_test_g.max()], 'r--')\n",
|
||
"plt.title(\"Part G - Polynomial Regression: Actual vs Predicted\")\n",
|
||
"plt.xlabel(\"True Size in Bytes\")\n",
|
||
"plt.ylabel(\"Predicted Size in Bytes\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "636e6b9a053c8f8d",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Trying Ridge Regression for Part G"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 83,
|
||
"id": "a31ad4ed704f4541",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.718195600Z",
|
||
"start_time": "2026-04-26T14:22:31.641035800Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Selection</th>\n",
|
||
" <th>Best alpha</th>\n",
|
||
" <th>CV R2 mean</th>\n",
|
||
" <th>RMSE (test)</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Part G Ridge best alpha by CV R2 mean then RMSE</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.090846</td>\n",
|
||
" <td>2.162718e+07</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 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)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 84,
|
||
"id": "121ba346ffaea0e5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.803996200Z",
|
||
"start_time": "2026-04-26T14:22:31.718195600Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(6, 6))\n",
|
||
"plt.scatter(y_test_g, y_pred_ridge_g, alpha=0.5)\n",
|
||
"plt.plot([y_test_g.min(), y_test_g.max()], [y_test_g.min(), y_test_g.max()], 'r--')\n",
|
||
"plt.title(\"Part G - Ridge Regression: Actual vs Predicted\")\n",
|
||
"plt.xlabel(\"True Size in Bytes\")\n",
|
||
"plt.ylabel(\"Predicted Size in Bytes\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f238f4198de0c527",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Regression Model Results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 85,
|
||
"id": "c7a5e958c9ab89e6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.823840700Z",
|
||
"start_time": "2026-04-26T14:22:31.804997400Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Model</th>\n",
|
||
" <th>MAE (test)</th>\n",
|
||
" <th>MSE (test)</th>\n",
|
||
" <th>RMSE (test)</th>\n",
|
||
" <th>R2 (test)</th>\n",
|
||
" <th>CV R2 (mean)</th>\n",
|
||
" <th>CV R2 (std)</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" <td>1.458116e+07</td>\n",
|
||
" <td>3.634642e+14</td>\n",
|
||
" <td>1.906474e+07</td>\n",
|
||
" <td>0.292880</td>\n",
|
||
" <td>0.245829</td>\n",
|
||
" <td>0.067041</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" <td>1.708302e+07</td>\n",
|
||
" <td>7.093839e+14</td>\n",
|
||
" <td>2.663426e+07</td>\n",
|
||
" <td>-0.380108</td>\n",
|
||
" <td>-0.452607</td>\n",
|
||
" <td>0.900419</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" <td>1.629031e+07</td>\n",
|
||
" <td>4.677350e+14</td>\n",
|
||
" <td>2.162718e+07</td>\n",
|
||
" <td>0.090021</td>\n",
|
||
" <td>0.090846</td>\n",
|
||
" <td>0.056075</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 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",
|
||
" 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 "
|
||
]
|
||
},
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"results_df_g = pd.DataFrame(results_g)\n",
|
||
"results_df_g"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"id": "a8b2b05314122ac3",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:31.875504800Z",
|
||
"start_time": "2026-04-26T14:22:31.849372600Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"results_melted_g = results_df_g.melt(\n",
|
||
" id_vars='Model',\n",
|
||
" value_vars=['MAE (test)', 'MSE (test)', 'RMSE (test)', 'R2 (test)', 'CV R2 (mean)'],\n",
|
||
" var_name='Metric',\n",
|
||
" value_name='Score'\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 87,
|
||
"id": "28a6da6deea25737",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:32.028098900Z",
|
||
"start_time": "2026-04-26T14:22:31.891520200Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Model</th>\n",
|
||
" <th>MAE (test)</th>\n",
|
||
" <th>MSE (test)</th>\n",
|
||
" <th>RMSE (test)</th>\n",
|
||
" <th>R2 (test)</th>\n",
|
||
" <th>CV R2 (mean)</th>\n",
|
||
" <th>CV R2 (std)</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" <td>1.458116e+07</td>\n",
|
||
" <td>3.634642e+14</td>\n",
|
||
" <td>1.906474e+07</td>\n",
|
||
" <td>0.292880</td>\n",
|
||
" <td>0.245829</td>\n",
|
||
" <td>0.067041</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" <td>1.629031e+07</td>\n",
|
||
" <td>4.677350e+14</td>\n",
|
||
" <td>2.162718e+07</td>\n",
|
||
" <td>0.090021</td>\n",
|
||
" <td>0.090846</td>\n",
|
||
" <td>0.056075</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" <td>1.708302e+07</td>\n",
|
||
" <td>7.093839e+14</td>\n",
|
||
" <td>2.663426e+07</td>\n",
|
||
" <td>-0.380108</td>\n",
|
||
" <td>-0.452607</td>\n",
|
||
" <td>0.900419</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Model MAE (test) MSE (test) RMSE (test) \\\n",
|
||
"0 Linear Regression 1.458116e+07 3.634642e+14 1.906474e+07 \n",
|
||
"1 Ridge Regression (alpha=1.0) 1.629031e+07 4.677350e+14 2.162718e+07 \n",
|
||
"2 Polynomial Regression 1.708302e+07 7.093839e+14 2.663426e+07 \n",
|
||
"\n",
|
||
" R2 (test) CV R2 (mean) CV R2 (std) \n",
|
||
"0 0.292880 0.245829 0.067041 \n",
|
||
"1 0.090021 0.090846 0.056075 \n",
|
||
"2 -0.380108 -0.452607 0.900419 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"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()\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']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 88,
|
||
"id": "28a5dae231975e76",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:32.072309500Z",
|
||
"start_time": "2026-04-26T14:22:32.048607700Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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|
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|
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" .dataframe tbody tr th {\n",
|
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|
||
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|
||
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|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Part G Best Model</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Part G Best Model\n",
|
||
"0 Linear Regression"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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|
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"<style scoped>\n",
|
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|
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|
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|
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|
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" }\n",
|
||
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|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>True</th>\n",
|
||
" <th>Predicted</th>\n",
|
||
" <th>Residual</th>\n",
|
||
" <th>Abs_Error</th>\n",
|
||
" <th>Model</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>52428800.0</td>\n",
|
||
" <td>3.171894e+07</td>\n",
|
||
" <td>2.070986e+07</td>\n",
|
||
" <td>2.070986e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>26214400.0</td>\n",
|
||
" <td>1.364562e+07</td>\n",
|
||
" <td>1.256878e+07</td>\n",
|
||
" <td>1.256878e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>15728640.0</td>\n",
|
||
" <td>2.325830e+07</td>\n",
|
||
" <td>-7.529660e+06</td>\n",
|
||
" <td>7.529660e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>10171187.2</td>\n",
|
||
" <td>2.181280e+07</td>\n",
|
||
" <td>-1.164161e+07</td>\n",
|
||
" <td>1.164161e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>18874368.0</td>\n",
|
||
" <td>2.145150e+07</td>\n",
|
||
" <td>-2.577137e+06</td>\n",
|
||
" <td>2.577137e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2831155.2</td>\n",
|
||
" <td>1.371277e+07</td>\n",
|
||
" <td>-1.088162e+07</td>\n",
|
||
" <td>1.088162e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>3250585.6</td>\n",
|
||
" <td>1.375252e+07</td>\n",
|
||
" <td>-1.050193e+07</td>\n",
|
||
" <td>1.050193e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>27262976.0</td>\n",
|
||
" <td>1.933636e+07</td>\n",
|
||
" <td>7.926614e+06</td>\n",
|
||
" <td>7.926614e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>15728640.0</td>\n",
|
||
" <td>1.726232e+07</td>\n",
|
||
" <td>-1.533682e+06</td>\n",
|
||
" <td>1.533682e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>71303168.0</td>\n",
|
||
" <td>3.203716e+07</td>\n",
|
||
" <td>3.926601e+07</td>\n",
|
||
" <td>3.926601e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>97517568.0</td>\n",
|
||
" <td>2.900348e+07</td>\n",
|
||
" <td>6.851409e+07</td>\n",
|
||
" <td>6.851409e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>2621440.0</td>\n",
|
||
" <td>2.145499e+07</td>\n",
|
||
" <td>-1.883355e+07</td>\n",
|
||
" <td>1.883355e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>40894464.0</td>\n",
|
||
" <td>2.853311e+07</td>\n",
|
||
" <td>1.236136e+07</td>\n",
|
||
" <td>1.236136e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>11534336.0</td>\n",
|
||
" <td>3.188308e+07</td>\n",
|
||
" <td>-2.034874e+07</td>\n",
|
||
" <td>2.034874e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>44040192.0</td>\n",
|
||
" <td>3.146167e+07</td>\n",
|
||
" <td>1.257852e+07</td>\n",
|
||
" <td>1.257852e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>22020096.0</td>\n",
|
||
" <td>2.135446e+07</td>\n",
|
||
" <td>6.656323e+05</td>\n",
|
||
" <td>6.656323e+05</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>31457280.0</td>\n",
|
||
" <td>2.574583e+07</td>\n",
|
||
" <td>5.711451e+06</td>\n",
|
||
" <td>5.711451e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>8808038.4</td>\n",
|
||
" <td>2.481990e+07</td>\n",
|
||
" <td>-1.601186e+07</td>\n",
|
||
" <td>1.601186e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>7864320.0</td>\n",
|
||
" <td>1.801774e+07</td>\n",
|
||
" <td>-1.015342e+07</td>\n",
|
||
" <td>1.015342e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>2936012.8</td>\n",
|
||
" <td>1.400022e+07</td>\n",
|
||
" <td>-1.106421e+07</td>\n",
|
||
" <td>1.106421e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" True Predicted Residual Abs_Error Model\n",
|
||
"0 52428800.0 3.171894e+07 2.070986e+07 2.070986e+07 Linear Regression\n",
|
||
"1 26214400.0 1.364562e+07 1.256878e+07 1.256878e+07 Linear Regression\n",
|
||
"2 15728640.0 2.325830e+07 -7.529660e+06 7.529660e+06 Linear Regression\n",
|
||
"3 10171187.2 2.181280e+07 -1.164161e+07 1.164161e+07 Linear Regression\n",
|
||
"4 18874368.0 2.145150e+07 -2.577137e+06 2.577137e+06 Linear Regression\n",
|
||
"5 2831155.2 1.371277e+07 -1.088162e+07 1.088162e+07 Linear Regression\n",
|
||
"6 3250585.6 1.375252e+07 -1.050193e+07 1.050193e+07 Linear Regression\n",
|
||
"7 27262976.0 1.933636e+07 7.926614e+06 7.926614e+06 Linear Regression\n",
|
||
"8 15728640.0 1.726232e+07 -1.533682e+06 1.533682e+06 Linear Regression\n",
|
||
"9 71303168.0 3.203716e+07 3.926601e+07 3.926601e+07 Linear Regression\n",
|
||
"10 97517568.0 2.900348e+07 6.851409e+07 6.851409e+07 Linear Regression\n",
|
||
"11 2621440.0 2.145499e+07 -1.883355e+07 1.883355e+07 Linear Regression\n",
|
||
"12 40894464.0 2.853311e+07 1.236136e+07 1.236136e+07 Linear Regression\n",
|
||
"13 11534336.0 3.188308e+07 -2.034874e+07 2.034874e+07 Linear Regression\n",
|
||
"14 44040192.0 3.146167e+07 1.257852e+07 1.257852e+07 Linear Regression\n",
|
||
"15 22020096.0 2.135446e+07 6.656323e+05 6.656323e+05 Linear Regression\n",
|
||
"16 31457280.0 2.574583e+07 5.711451e+06 5.711451e+06 Linear Regression\n",
|
||
"17 8808038.4 2.481990e+07 -1.601186e+07 1.601186e+07 Linear Regression\n",
|
||
"18 7864320.0 1.801774e+07 -1.015342e+07 1.015342e+07 Linear Regression\n",
|
||
"19 2936012.8 1.400022e+07 -1.106421e+07 1.106421e+07 Linear Regression"
|
||
]
|
||
},
|
||
"execution_count": 88,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 89,
|
||
"id": "fdf6287a5686175e",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-26T14:22:32.087583500Z",
|
||
"start_time": "2026-04-26T14:22:32.073314700Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>True</th>\n",
|
||
" <th>Predicted</th>\n",
|
||
" <th>Residual</th>\n",
|
||
" <th>Abs_Error</th>\n",
|
||
" <th>Model</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>52428800.0</td>\n",
|
||
" <td>3.171894e+07</td>\n",
|
||
" <td>2.070986e+07</td>\n",
|
||
" <td>2.070986e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>26214400.0</td>\n",
|
||
" <td>1.364562e+07</td>\n",
|
||
" <td>1.256878e+07</td>\n",
|
||
" <td>1.256878e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>15728640.0</td>\n",
|
||
" <td>2.325830e+07</td>\n",
|
||
" <td>-7.529660e+06</td>\n",
|
||
" <td>7.529660e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>10171187.2</td>\n",
|
||
" <td>2.181280e+07</td>\n",
|
||
" <td>-1.164161e+07</td>\n",
|
||
" <td>1.164161e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>18874368.0</td>\n",
|
||
" <td>2.145150e+07</td>\n",
|
||
" <td>-2.577137e+06</td>\n",
|
||
" <td>2.577137e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2831155.2</td>\n",
|
||
" <td>1.371277e+07</td>\n",
|
||
" <td>-1.088162e+07</td>\n",
|
||
" <td>1.088162e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>3250585.6</td>\n",
|
||
" <td>1.375252e+07</td>\n",
|
||
" <td>-1.050193e+07</td>\n",
|
||
" <td>1.050193e+07</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>27262976.0</td>\n",
|
||
" <td>1.933636e+07</td>\n",
|
||
" <td>7.926614e+06</td>\n",
|
||
" <td>7.926614e+06</td>\n",
|
||
" <td>Linear Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>52428800.0</td>\n",
|
||
" <td>2.267715e+07</td>\n",
|
||
" <td>2.975165e+07</td>\n",
|
||
" <td>2.975165e+07</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>26214400.0</td>\n",
|
||
" <td>1.925624e+07</td>\n",
|
||
" <td>6.958158e+06</td>\n",
|
||
" <td>6.958158e+06</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>15728640.0</td>\n",
|
||
" <td>2.101692e+07</td>\n",
|
||
" <td>-5.288280e+06</td>\n",
|
||
" <td>5.288280e+06</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>10171187.2</td>\n",
|
||
" <td>2.107864e+07</td>\n",
|
||
" <td>-1.090745e+07</td>\n",
|
||
" <td>1.090745e+07</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>18874368.0</td>\n",
|
||
" <td>2.302922e+07</td>\n",
|
||
" <td>-4.154853e+06</td>\n",
|
||
" <td>4.154853e+06</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>2831155.2</td>\n",
|
||
" <td>2.101016e+07</td>\n",
|
||
" <td>-1.817901e+07</td>\n",
|
||
" <td>1.817901e+07</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>3250585.6</td>\n",
|
||
" <td>2.098037e+07</td>\n",
|
||
" <td>-1.772979e+07</td>\n",
|
||
" <td>1.772979e+07</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>27262976.0</td>\n",
|
||
" <td>2.102464e+07</td>\n",
|
||
" <td>6.238332e+06</td>\n",
|
||
" <td>6.238332e+06</td>\n",
|
||
" <td>Polynomial Regression</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>52428800.0</td>\n",
|
||
" <td>2.375635e+07</td>\n",
|
||
" <td>2.867245e+07</td>\n",
|
||
" <td>2.867245e+07</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>26214400.0</td>\n",
|
||
" <td>2.357213e+07</td>\n",
|
||
" <td>2.642274e+06</td>\n",
|
||
" <td>2.642274e+06</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>15728640.0</td>\n",
|
||
" <td>2.349521e+07</td>\n",
|
||
" <td>-7.766567e+06</td>\n",
|
||
" <td>7.766567e+06</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>10171187.2</td>\n",
|
||
" <td>2.349771e+07</td>\n",
|
||
" <td>-1.332652e+07</td>\n",
|
||
" <td>1.332652e+07</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>18874368.0</td>\n",
|
||
" <td>2.349279e+07</td>\n",
|
||
" <td>-4.618422e+06</td>\n",
|
||
" <td>4.618422e+06</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>2831155.2</td>\n",
|
||
" <td>2.349545e+07</td>\n",
|
||
" <td>-2.066429e+07</td>\n",
|
||
" <td>2.066429e+07</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>3250585.6</td>\n",
|
||
" <td>2.349522e+07</td>\n",
|
||
" <td>-2.024464e+07</td>\n",
|
||
" <td>2.024464e+07</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>27262976.0</td>\n",
|
||
" <td>2.349620e+07</td>\n",
|
||
" <td>3.766777e+06</td>\n",
|
||
" <td>3.766777e+06</td>\n",
|
||
" <td>Ridge Regression (alpha=1.0)</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" True Predicted Residual Abs_Error \\\n",
|
||
"0 52428800.0 3.171894e+07 2.070986e+07 2.070986e+07 \n",
|
||
"1 26214400.0 1.364562e+07 1.256878e+07 1.256878e+07 \n",
|
||
"2 15728640.0 2.325830e+07 -7.529660e+06 7.529660e+06 \n",
|
||
"3 10171187.2 2.181280e+07 -1.164161e+07 1.164161e+07 \n",
|
||
"4 18874368.0 2.145150e+07 -2.577137e+06 2.577137e+06 \n",
|
||
"5 2831155.2 1.371277e+07 -1.088162e+07 1.088162e+07 \n",
|
||
"6 3250585.6 1.375252e+07 -1.050193e+07 1.050193e+07 \n",
|
||
"7 27262976.0 1.933636e+07 7.926614e+06 7.926614e+06 \n",
|
||
"8 52428800.0 2.267715e+07 2.975165e+07 2.975165e+07 \n",
|
||
"9 26214400.0 1.925624e+07 6.958158e+06 6.958158e+06 \n",
|
||
"10 15728640.0 2.101692e+07 -5.288280e+06 5.288280e+06 \n",
|
||
"11 10171187.2 2.107864e+07 -1.090745e+07 1.090745e+07 \n",
|
||
"12 18874368.0 2.302922e+07 -4.154853e+06 4.154853e+06 \n",
|
||
"13 2831155.2 2.101016e+07 -1.817901e+07 1.817901e+07 \n",
|
||
"14 3250585.6 2.098037e+07 -1.772979e+07 1.772979e+07 \n",
|
||
"15 27262976.0 2.102464e+07 6.238332e+06 6.238332e+06 \n",
|
||
"16 52428800.0 2.375635e+07 2.867245e+07 2.867245e+07 \n",
|
||
"17 26214400.0 2.357213e+07 2.642274e+06 2.642274e+06 \n",
|
||
"18 15728640.0 2.349521e+07 -7.766567e+06 7.766567e+06 \n",
|
||
"19 10171187.2 2.349771e+07 -1.332652e+07 1.332652e+07 \n",
|
||
"20 18874368.0 2.349279e+07 -4.618422e+06 4.618422e+06 \n",
|
||
"21 2831155.2 2.349545e+07 -2.066429e+07 2.066429e+07 \n",
|
||
"22 3250585.6 2.349522e+07 -2.024464e+07 2.024464e+07 \n",
|
||
"23 27262976.0 2.349620e+07 3.766777e+06 3.766777e+06 \n",
|
||
"\n",
|
||
" Model \n",
|
||
"0 Linear Regression \n",
|
||
"1 Linear Regression \n",
|
||
"2 Linear Regression \n",
|
||
"3 Linear Regression \n",
|
||
"4 Linear Regression \n",
|
||
"5 Linear Regression \n",
|
||
"6 Linear Regression \n",
|
||
"7 Linear Regression \n",
|
||
"8 Polynomial Regression \n",
|
||
"9 Polynomial Regression \n",
|
||
"10 Polynomial Regression \n",
|
||
"11 Polynomial Regression \n",
|
||
"12 Polynomial Regression \n",
|
||
"13 Polynomial Regression \n",
|
||
"14 Polynomial Regression \n",
|
||
"15 Polynomial Regression \n",
|
||
"16 Ridge Regression (alpha=1.0) \n",
|
||
"17 Ridge Regression (alpha=1.0) \n",
|
||
"18 Ridge Regression (alpha=1.0) \n",
|
||
"19 Ridge Regression (alpha=1.0) \n",
|
||
"20 Ridge Regression (alpha=1.0) \n",
|
||
"21 Ridge Regression (alpha=1.0) \n",
|
||
"22 Ridge Regression (alpha=1.0) \n",
|
||
"23 Ridge Regression (alpha=1.0) "
|
||
]
|
||
},
|
||
"execution_count": 89,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"part_g_prediction_compare = pd.concat([\n",
|
||
" pred_sheet_part_g_linear.head(8),\n",
|
||
" pred_sheet_part_g_poly.head(8),\n",
|
||
" pred_sheet_part_g_ridge.head(8),\n",
|
||
"], ignore_index=True)\n",
|
||
"\n",
|
||
"part_g_prediction_compare"
|
||
]
|
||
},
|
||
{
|
||
"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": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "3.14.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|