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Created June 15, 2020 07:55
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Covid19_Data_Analysis_and_ML.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Covid19 Data Analysis and Predictive Modeling (Machine Learning)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"The world has never seen anything quite like the things are going on right now. Covid19 has been a changing player in the this game called life, and its effect has been devastating both in life-related and economical fields. The amount of people who has been infected is uncertained, the spread rate is not controlled so far and the deaths caused in a short period of time has caused to tackle the spreading infectious diseases as one of the main threats in the world from now on. \n",
"\n",
"With all that happening right now, there has been a lot of data that has been generated, and with that a lot of studies and researches have appeared to the public to try to understand, study and analyze the way this virus is affecting people and how it can be stopped as much as we (humans) can.\n",
"\n",
"So, in this work, I will go through some statistical analysis and predictive studies to try to get some better insights about covid19 outbreak."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1. Exploratory Data Analysis (EDA):](#part1)\n",
"\n",
" - [1.1. Analysis of features](#part1_1)\n",
" - [1.2. Null values](#part1_2)\n",
" - [1.3. Most influential variables on new_deaths](#part1_3)\n",
" - [1.4. Correlation between Features](#part1_4)\n",
"\n",
"[2. Feature Engineering and Data Cleaning:](#part2)\n",
" - [2.1. Dummy variables](#part2_1)\n",
" - [2.2. Variable selection with low variances](#part2_2)\n",
" - [2.3. Univariant Variable Selection](#part2_3)\n",
" - [2.4. Variable selection depending on their percentile punctuation](#part2_4)\n",
"\n",
"[3. Predictive Modelling (Scikit-Learn)](#part3)\n",
" - [3.1 Linear Regression](#part3_1)\n",
" - [3.2. Ridge regression (regularization)](#part3_2)\n",
" - [3.3. Lasso regression (regularization)](#part3_3)\n",
" - [3.4. Elastic Net (regularization)](#part3_4)\n",
" - [3.5. Model evaluation through a polynomial grade function](#part3_5)\n",
" - [3.6. Validation curves for new_deaths](#part3_6)\n",
" - [3.7. Conclusions](#part3_7)\n",
"\n",
"[4. Predictive Modelling (TensorFlow)](#part4)\n",
" - [4.1. TensorFlow for Polynomial Linear Regressions to new_deaths](#part4_1)\n",
" \n",
"[5. Conclusions](#part5)\n",
"[6. References](#part6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Exploratory Data Analysis (EDA):<a id='part1'></a>\n",
"\n",
"Let´s dig into the data structure, how it is distributed for all columns and get some statistics out of it."
]
},
{
"cell_type": "code",
"execution_count": 390,
"metadata": {},
"outputs": [],
"source": [
"# Importing libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"plt.style.use(\"fivethirtyeight\")\n",
"import warnings\n",
"import pymongo\n",
"import pylab \n",
"import scipy.stats as stats\n",
"warnings.filterwarnings(\"ignore\")\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 391,
"metadata": {},
"outputs": [],
"source": [
"# The following commands allow us to see all columns in the dataset.\n",
"pd.set_option('display.max_rows', 500)\n",
"pd.set_option('display.max_columns', 50)"
]
},
{
"cell_type": "code",
"execution_count": 392,
"metadata": {},
"outputs": [],
"source": [
"# Setting up the connection to MongoDB as we will be connected to the local mongoDB DB.\n",
"from pymongo import MongoClient\n",
"client = MongoClient('mongodb://localhost:27017/')"
]
},
{
"cell_type": "code",
"execution_count": 393,
"metadata": {},
"outputs": [],
"source": [
"# Connecting to Covid19 DB inside the MongoDB connection\n",
"db = client[\"covid19\"]"
]
},
{
"cell_type": "code",
"execution_count": 394,
"metadata": {},
"outputs": [],
"source": [
"# Getting the collection of cases from the collections inside covid19 DB \n",
"cases = db.cases"
]
},
{
"cell_type": "code",
"execution_count": 395,
"metadata": {
"scrolled": true
},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>_id</th>\n",
" <th>iso_code</th>\n",
" <th>location</th>\n",
" <th>date</th>\n",
" <th>total_cases</th>\n",
" <th>new_cases</th>\n",
" <th>total_deaths</th>\n",
" <th>new_deaths</th>\n",
" <th>total_cases_per_million</th>\n",
" <th>new_cases_per_million</th>\n",
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" <th>new_tests</th>\n",
" <th>total_tests_per_thousand</th>\n",
" <th>new_tests_per_thousand</th>\n",
" <th>new_tests_smoothed</th>\n",
" <th>new_tests_smoothed_per_thousand</th>\n",
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" <th>stringency_index</th>\n",
" <th>population</th>\n",
" <th>population_density</th>\n",
" <th>median_age</th>\n",
" <th>aged_65_older</th>\n",
" <th>aged_70_older</th>\n",
" <th>gdp_per_capita</th>\n",
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" <th>cvd_death_rate</th>\n",
" <th>diabetes_prevalence</th>\n",
" <th>female_smokers</th>\n",
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" <td>5ee30fbc82af1b3050d28c63</td>\n",
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" <td>5ee30fbc82af1b3050d28c64</td>\n",
" <td>AFG</td>\n",
" <td>Afghanistan</td>\n",
" <td>2020-01-03</td>\n",
" <td>0</td>\n",
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" <td>5ee30fbc82af1b3050d28c65</td>\n",
" <td>AFG</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" _id iso_code location date total_cases \\\n",
"0 5ee30fbc82af1b3050d28c61 AFG Afghanistan 2019-12-31 0 \n",
"1 5ee30fbc82af1b3050d28c62 AFG Afghanistan 2020-01-01 0 \n",
"2 5ee30fbc82af1b3050d28c63 AFG Afghanistan 2020-01-02 0 \n",
"3 5ee30fbc82af1b3050d28c64 AFG Afghanistan 2020-01-03 0 \n",
"4 5ee30fbc82af1b3050d28c65 AFG Afghanistan 2020-01-04 0 \n",
"\n",
" new_cases total_deaths new_deaths total_cases_per_million \\\n",
"0 0 0 0 0.0 \n",
"1 0 0 0 0.0 \n",
"2 0 0 0 0.0 \n",
"3 0 0 0 0.0 \n",
"4 0 0 0 0.0 \n",
"\n",
" new_cases_per_million total_deaths_per_million new_deaths_per_million \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
" total_tests new_tests total_tests_per_thousand new_tests_per_thousand \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN \n",
"\n",
" new_tests_smoothed new_tests_smoothed_per_thousand tests_units \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" stringency_index population population_density median_age \\\n",
"0 NaN 38928341.0 54.422 18.6 \n",
"1 0.0 38928341.0 54.422 18.6 \n",
"2 0.0 38928341.0 54.422 18.6 \n",
"3 0.0 38928341.0 54.422 18.6 \n",
"4 0.0 38928341.0 54.422 18.6 \n",
"\n",
" aged_65_older aged_70_older gdp_per_capita extreme_poverty \\\n",
"0 2.581 1.337 1803.987 NaN \n",
"1 2.581 1.337 1803.987 NaN \n",
"2 2.581 1.337 1803.987 NaN \n",
"3 2.581 1.337 1803.987 NaN \n",
"4 2.581 1.337 1803.987 NaN \n",
"\n",
" cvd_death_rate diabetes_prevalence female_smokers male_smokers \\\n",
"0 597.029 9.59 NaN NaN \n",
"1 597.029 9.59 NaN NaN \n",
"2 597.029 9.59 NaN NaN \n",
"3 597.029 9.59 NaN NaN \n",
"4 597.029 9.59 NaN NaN \n",
"\n",
" handwashing_facilities __v \n",
"0 37.746 0 \n",
"1 37.746 0 \n",
"2 37.746 0 \n",
"3 37.746 0 \n",
"4 37.746 0 "
]
},
"execution_count": 395,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Conveterting previous Collection into Dataframe\n",
"df = pd.DataFrame(list(cases.find()))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 396,
"metadata": {},
"outputs": [],
"source": [
"# Setting date column as index in the dataframe\n",
"df.date = pd.to_datetime(df.date)\n",
"df.set_index('date', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 397,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Total</th>\n",
" <th>Percent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>new_tests</th>\n",
" <td>17495</td>\n",
" <td>0.754648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests_per_thousand</th>\n",
" <td>17495</td>\n",
" <td>0.754648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_tests</th>\n",
" <td>16841</td>\n",
" <td>0.726437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_tests_per_thousand</th>\n",
" <td>16841</td>\n",
" <td>0.726437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests_smoothed</th>\n",
" <td>16316</td>\n",
" <td>0.703792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests_smoothed_per_thousand</th>\n",
" <td>16316</td>\n",
" <td>0.703792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>handwashing_facilities</th>\n",
" <td>13852</td>\n",
" <td>0.597507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>extreme_poverty</th>\n",
" <td>9297</td>\n",
" <td>0.401027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male_smokers</th>\n",
" <td>6389</td>\n",
" <td>0.275590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>female_smokers</th>\n",
" <td>6199</td>\n",
" <td>0.267394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stringency_index</th>\n",
" <td>4633</td>\n",
" <td>0.199845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aged_65_older</th>\n",
" <td>2466</td>\n",
" <td>0.106371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdp_per_capita</th>\n",
" <td>2398</td>\n",
" <td>0.103438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aged_70_older</th>\n",
" <td>2282</td>\n",
" <td>0.098434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>median_age</th>\n",
" <td>2175</td>\n",
" <td>0.093819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cvd_death_rate</th>\n",
" <td>2153</td>\n",
" <td>0.092870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diabetes_prevalence</th>\n",
" <td>1480</td>\n",
" <td>0.063840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>population_density</th>\n",
" <td>978</td>\n",
" <td>0.042186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_deaths_per_million</th>\n",
" <td>282</td>\n",
" <td>0.012164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_deaths_per_million</th>\n",
" <td>282</td>\n",
" <td>0.012164</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Percent\n",
"new_tests 17495 0.754648\n",
"new_tests_per_thousand 17495 0.754648\n",
"total_tests 16841 0.726437\n",
"total_tests_per_thousand 16841 0.726437\n",
"new_tests_smoothed 16316 0.703792\n",
"new_tests_smoothed_per_thousand 16316 0.703792\n",
"handwashing_facilities 13852 0.597507\n",
"extreme_poverty 9297 0.401027\n",
"male_smokers 6389 0.275590\n",
"female_smokers 6199 0.267394\n",
"stringency_index 4633 0.199845\n",
"aged_65_older 2466 0.106371\n",
"gdp_per_capita 2398 0.103438\n",
"aged_70_older 2282 0.098434\n",
"median_age 2175 0.093819\n",
"cvd_death_rate 2153 0.092870\n",
"diabetes_prevalence 1480 0.063840\n",
"population_density 978 0.042186\n",
"new_deaths_per_million 282 0.012164\n",
"total_deaths_per_million 282 0.012164"
]
},
"execution_count": 397,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking for null values by column\n",
"# Percentage of missing values.\n",
"total = df.isnull().sum().sort_values(ascending=False)\n",
"percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
"missing_data.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 398,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x537d6a2988>"
]
},
"execution_count": 398,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# chart of missing values\n",
"total_missing = total[total > 0]\n",
"total_missing.sort_values(inplace=True)\n",
"total_missing.plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the previous results we can see that there are multiple columns with a lot of null values. Let´s try to have a solution around this in the following steps."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How many people died after getting being tested positive??"
]
},
{
"cell_type": "code",
"execution_count": 399,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The number of people who died after being tested positive was: 416430 / 7343562 ( 5.67 %)\n"
]
}
],
"source": [
"# We need to filter the data to the values corresponding only to the countries data, not including the world category data\n",
"new_deaths = np.sum(df[\"new_deaths\"][df[\"location\"] != \"World\"])\n",
"new_cases = np.sum(df[\"new_cases\"][df[\"location\"] != \"World\"])\n",
"deaths_percentage = (new_deaths/new_cases)*100\n",
"print(\"The number of people who died after being tested positive was: \", new_deaths, \"/\", new_cases, \"( \", np.round(deaths_percentage, 2), \"%)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After seeing this number, we need to dig in some more to get better insights to guess what types of people died, their age, country, economical situation, etc..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.1 Analysis of features <a id='part1_1'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1.1. Ordinal Features\n",
"\n",
"One ordinal feature is one that can be divided into a limited number of values and sorted by some proceedings. For example, sizes is a categorical varible whose values can be huge, big, medium and small. These values can be sorted by their meanings.\n",
"\n",
"In this dataset we don´t find any feature related to this category."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1.2. Continuous Features\n",
"\n",
"One continuous feature is one that can have a number of values between a min a max number, and they can be decimal, integer, flaot formats with/without ordering and giving measurable information.\n",
"\n",
"Continuos features in this dataset are: **'_id',\n",
" 'date',\n",
" 'total_cases',\n",
" 'new_cases',\n",
" 'total_deaths',\n",
" 'new_deaths',\n",
" 'total_cases_per_million',\n",
" 'new_cases_per_million',\n",
" 'total_deaths_per_million',\n",
" 'new_deaths_per_million',\n",
" 'total_tests',\n",
" 'new_tests',\n",
" 'total_tests_per_thousand',\n",
" 'new_tests_per_thousand',\n",
" 'new_tests_smoothed',\n",
" 'new_tests_smoothed_per_thousand',\n",
" 'stringency_index',\n",
" 'population',\n",
" 'population_density',\n",
" 'aged_65_older',\n",
" 'aged_70_older',\n",
" 'gdp_per_capita',\n",
" 'extreme_poverty',\n",
" 'cvd_death_rate',\n",
" 'diabetes_prevalence',\n",
" 'female_smokers',\n",
" 'male_smokers',\n",
" 'handwashing_facilities'**"
]
},
{
"cell_type": "code",
"execution_count": 400,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([dtype('O'), dtype('int64'), dtype('float64')], dtype=object)"
]
},
"execution_count": 400,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# data types in this dataset\n",
"df.dtypes.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1.3. Categorical Features\n",
"\n",
"One categorical feature is one that can be divided into a limited number of values. For example, vehicles is a categorical varible whose values can be car, truck, motorbike, and cycle. These values can not be sorted or have any correspondence to each other.\n",
"\n",
"One way to get the categorical features from a dataset can be by getting their types and taking the ones corresponding the ones whose type is \"string\" or homologous to it.\n",
"\n",
"Categorical Features in this dataset are: **iso_code, location, tests_units,'median_age',**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analyzing categorical features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Location -> Categorical Feature"
]
},
{
"cell_type": "code",
"execution_count": 401,
"metadata": {},
"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>new_deaths</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>United States</th>\n",
" <td>112924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Kingdom</th>\n",
" <td>41128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brazil</th>\n",
" <td>39680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italy</th>\n",
" <td>34114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>29319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spain</th>\n",
" <td>27136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mexico</th>\n",
" <td>15357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\n",
" <td>9629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Germany</th>\n",
" <td>8755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran</th>\n",
" <td>8506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>8102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Canada</th>\n",
" <td>7960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Russia</th>\n",
" <td>6358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Netherlands</th>\n",
" <td>6042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Peru</th>\n",
" <td>5903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sweden</th>\n",
" <td>4795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>4746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>4638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ecuador</th>\n",
" <td>3720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chile</th>\n",
" <td>2475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>2356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>1959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ireland</th>\n",
" <td>1695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Switzerland</th>\n",
" <td>1674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Portugal</th>\n",
" <td>1495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Colombia</th>\n",
" <td>1433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romania</th>\n",
" <td>1360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Egypt</th>\n",
" <td>1342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Africa</th>\n",
" <td>1210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>1206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>1027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>1012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ukraine</th>\n",
" <td>833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Argentina</th>\n",
" <td>735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hungary</th>\n",
" <td>551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dominican Republic</th>\n",
" <td>550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bolivia</th>\n",
" <td>512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Panama</th>\n",
" <td>413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sudan</th>\n",
" <td>389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nigeria</th>\n",
" <td>382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Moldova</th>\n",
" <td>371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Czech Republic</th>\n",
" <td>330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Finland</th>\n",
" <td>324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guatemala</th>\n",
" <td>316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Honduras</th>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belarus</th>\n",
" <td>288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Korea</th>\n",
" <td>276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Serbia</th>\n",
" <td>251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Morocco</th>\n",
" <td>210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cameroon</th>\n",
" <td>208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greece</th>\n",
" <td>183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bulgaria</th>\n",
" <td>167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Macedonia</th>\n",
" <td>164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bosnia and Herzegovina</th>\n",
" <td>160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Puerto Rico</th>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Luxembourg</th>\n",
" <td>110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovenia</th>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Croatia</th>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mali</th>\n",
" <td>96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Democratic Republic of Congo</th>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kenya</th>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Somalia</th>\n",
" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cuba</th>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lithuania</th>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chad</th>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Estonia</th>\n",
" <td>69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Niger</th>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>El Salvador</th>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mauritania</th>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Haiti</th>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nicaragua</th>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Senegal</th>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burkina Faso</th>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Andorra</th>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sierra Leone</th>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tunisia</th>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ghana</th>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>San Marino</th>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cote d'Ivoire</th>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ethiopia</th>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Djibouti</th>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kosovo</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Liberia</th>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jersey</th>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovakia</th>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Latvia</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Congo</th>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Isle of Man</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uruguay</th>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Venezuela</th>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guinea</th>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Zealand</th>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gabon</th>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tanzania</th>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Sudan</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sint Maarten (Dutch part)</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guernsey</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Togo</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Equatorial Guinea</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guyana</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guinea-Bissau</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sao Tome and Principe</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Costa Rica</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Paraguay</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahamas</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zambia</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jamaica</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Madagascar</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mauritius</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceland</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bermuda</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malta</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Montenegro</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trinidad and Tobago</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maldives</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Barbados</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Taiwan</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>International</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Central African Republic</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United States Virgin Islands</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syria</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Myanmar</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cape Verde</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Monaco</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Palestine</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Libya</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guam</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Angola</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Benin</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zimbabwe</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malawi</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Antigua and Barbuda</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Swaziland</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aruba</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belize</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mozambique</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brunei</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Suriname</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rwanda</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comoros</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Northern Mariana Islands</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>British Virgin Islands</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Western Sahara</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cayman Islands</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Curacao</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Liechtenstein</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Montserrat</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gambia</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burundi</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turks and Caicos Islands</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Botswana</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Grenada</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uganda</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anguilla</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hong Kong</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vietnam</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Laos</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lesotho</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vatican</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greenland</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saint Lucia</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gibraltar</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Namibia</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saint Kitts and Nevis</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Papua New Guinea</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Seychelles</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dominica</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Caledonia</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eritrea</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Faeroe Islands</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Timor</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Falkland Islands</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fiji</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saint Vincent and the Grenadines</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>French Polynesia</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bhutan</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bonaire Sint Eustatius and Saba</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" new_deaths\n",
"location \n",
"United States 112924\n",
"United Kingdom 41128\n",
"Brazil 39680\n",
"Italy 34114\n",
"France 29319\n",
"Spain 27136\n",
"Mexico 15357\n",
"Belgium 9629\n",
"Germany 8755\n",
"Iran 8506\n",
"India 8102\n",
"Canada 7960\n",
"Russia 6358\n",
"Netherlands 6042\n",
"Peru 5903\n",
"Sweden 4795\n",
"Turkey 4746\n",
"China 4638\n",
"Ecuador 3720\n",
"Chile 2475\n",
"Pakistan 2356\n",
"Indonesia 1959\n",
"Ireland 1695\n",
"Switzerland 1674\n",
"Portugal 1495\n",
"Colombia 1433\n",
"Romania 1360\n",
"Egypt 1342\n",
"South Africa 1210\n",
"Poland 1206\n",
"Philippines 1027\n",
"Bangladesh 1012\n",
"Japan 920\n",
"Ukraine 833\n",
"Saudi Arabia 819\n",
"Argentina 735\n",
"Algeria 732\n",
"Austria 673\n",
"Denmark 593\n",
"Hungary 551\n",
"Dominican Republic 550\n",
"Bolivia 512\n",
"Iraq 426\n",
"Panama 413\n",
"Afghanistan 405\n",
"Sudan 389\n",
"Nigeria 382\n",
"Moldova 371\n",
"Czech Republic 330\n",
"Finland 324\n",
"Guatemala 316\n",
"Israel 299\n",
"Honduras 290\n",
"Belarus 288\n",
"United Arab Emirates 284\n",
"South Korea 276\n",
"Kuwait 275\n",
"Serbia 251\n",
"Norway 239\n",
"Armenia 227\n",
"Morocco 210\n",
"Cameroon 208\n",
"Greece 183\n",
"Bulgaria 167\n",
"Macedonia 164\n",
"Bosnia and Herzegovina 160\n",
"Puerto Rico 143\n",
"Yemen 129\n",
"Malaysia 118\n",
"Luxembourg 110\n",
"Slovenia 109\n",
"Croatia 106\n",
"Australia 102\n",
"Azerbaijan 102\n",
"Mali 96\n",
"Democratic Republic of Congo 95\n",
"Kenya 89\n",
"Somalia 85\n",
"Oman 84\n",
"Cuba 83\n",
"Lithuania 74\n",
"Chad 72\n",
"Estonia 69\n",
"Kazakhstan 67\n",
"Qatar 66\n",
"Niger 65\n",
"El Salvador 64\n",
"Mauritania 61\n",
"Haiti 58\n",
"Thailand 58\n",
"Nicaragua 55\n",
"Senegal 54\n",
"Burkina Faso 53\n",
"Andorra 51\n",
"Sierra Leone 50\n",
"Tunisia 49\n",
"Tajikistan 48\n",
"Ghana 48\n",
"San Marino 42\n",
"Cote d'Ivoire 41\n",
"Ethiopia 35\n",
"Djibouti 34\n",
"Albania 34\n",
"Bahrain 31\n",
"Kosovo 31\n",
"Liberia 31\n",
"Jersey 30\n",
"Lebanon 30\n",
"Slovakia 28\n",
"Latvia 26\n",
"Kyrgyzstan 26\n",
"Singapore 25\n",
"Congo 25\n",
"Isle of Man 24\n",
"Uruguay 23\n",
"Venezuela 23\n",
"Guinea 23\n",
"New Zealand 22\n",
"Gabon 22\n",
"Tanzania 21\n",
"Uzbekistan 19\n",
"South Sudan 19\n",
"Cyprus 18\n",
"Nepal 15\n",
"Sint Maarten (Dutch part) 15\n",
"Georgia 13\n",
"Guernsey 13\n",
"Togo 13\n",
"Equatorial Guinea 12\n",
"Guyana 12\n",
"Guinea-Bissau 12\n",
"Sao Tome and Principe 12\n",
"Costa Rica 12\n",
"Sri Lanka 11\n",
"Paraguay 11\n",
"Bahamas 11\n",
"Zambia 10\n",
"Jamaica 10\n",
"Madagascar 10\n",
"Mauritius 10\n",
"Iceland 10\n",
"Bermuda 9\n",
"Jordan 9\n",
"Malta 9\n",
"Montenegro 9\n",
"Trinidad and Tobago 8\n",
"Maldives 8\n",
"Barbados 7\n",
"Taiwan 7\n",
"International 7\n",
"Central African Republic 6\n",
"United States Virgin Islands 6\n",
"Syria 6\n",
"Myanmar 6\n",
"Cape Verde 5\n",
"Monaco 5\n",
"Palestine 5\n",
"Libya 5\n",
"Guam 5\n",
"Angola 4\n",
"Benin 4\n",
"Zimbabwe 4\n",
"Malawi 4\n",
"Antigua and Barbuda 3\n",
"Swaziland 3\n",
"Aruba 3\n",
"Belize 2\n",
"Mozambique 2\n",
"Brunei 2\n",
"Suriname 2\n",
"Rwanda 2\n",
"Comoros 2\n",
"Northern Mariana Islands 2\n",
"British Virgin Islands 1\n",
"Western Sahara 1\n",
"Cayman Islands 1\n",
"Curacao 1\n",
"Liechtenstein 1\n",
"Montserrat 1\n",
"Gambia 1\n",
"Burundi 1\n",
"Turks and Caicos Islands 1\n",
"Botswana 1\n",
"Grenada 0\n",
"Uganda 0\n",
"Anguilla 0\n",
"Hong Kong 0\n",
"Vietnam 0\n",
"Laos 0\n",
"Mongolia 0\n",
"Lesotho 0\n",
"Vatican 0\n",
"Greenland 0\n",
"Saint Lucia 0\n",
"Gibraltar 0\n",
"Namibia 0\n",
"Saint Kitts and Nevis 0\n",
"Papua New Guinea 0\n",
"Seychelles 0\n",
"Dominica 0\n",
"New Caledonia 0\n",
"Eritrea 0\n",
"Faeroe Islands 0\n",
"Timor 0\n",
"Falkland Islands 0\n",
"Cambodia 0\n",
"Fiji 0\n",
"Saint Vincent and the Grenadines 0\n",
"French Polynesia 0\n",
"Bhutan 0\n",
"Bonaire Sint Eustatius and Saba 0"
]
},
"execution_count": 401,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Getting the ranking of countries by number of deaths \n",
"pd.DataFrame(df[df[\"location\"] != \"World\"].groupby([\"location\"])[\"new_deaths\"].sum().sort_values(ascending=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to this data, people died most when their median ages were between 35 and 45 years old."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Median_age -> Categorical Feature"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let´s map the median_age column to an age status so that we can see better if the categories map accordingly to the data we think we have."
]
},
{
"cell_type": "code",
"execution_count": 402,
"metadata": {},
"outputs": [],
"source": [
"df[\"age_status\"] = df[\"median_age\"].apply(lambda x: \"Very old\" if x > 80 else \"Old\" if x > 60 else \"Adult\" if x > 40 else \"Millenial\" if x > 30 else \"Teenager\")"
]
},
{
"cell_type": "code",
"execution_count": 403,
"metadata": {},
"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>new_deaths</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_status</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Millenial</th>\n",
" <td>186868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adult</th>\n",
" <td>180766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Teenager</th>\n",
" <td>48796</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" new_deaths\n",
"age_status \n",
"Millenial 186868\n",
"Adult 180766\n",
"Teenager 48796"
]
},
"execution_count": 403,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Getting the ranking of people by their median age when they died\n",
"pd.DataFrame(df[df[\"location\"] != \"World\"].groupby([\"age_status\"])[\"new_deaths\"].sum().sort_values(ascending=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to this analysis, people have died most where countries median age were 30 or below. Here, Millenial (30-40) and Teenager(<20) make up most of the deaths worlwide "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.2. Null values <a id='part1_2'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, if we check if the values corresponding to the test-related columns have something to do with the data not being populated, then we will need to do so with zero´s. It will not have any effect into the total counts of these columns"
]
},
{
"cell_type": "code",
"execution_count": 404,
"metadata": {},
"outputs": [],
"source": [
"values = {'total_tests':-1, 'new_tests':-1, 'total_tests_per_thousand':-1, 'new_tests_per_thousand':-1, 'new_tests_smoothed':-1, 'new_tests_smoothed_per_thousand':-1}\n",
"df = df.fillna(value=values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Later, all columns related to cases, and deaths per million will be filled also with zeros so that we don´t have any null values in those. It makes sense to substitute this values by zero as they don´t affect other analysis."
]
},
{
"cell_type": "code",
"execution_count": 405,
"metadata": {},
"outputs": [],
"source": [
"values = {'total_deaths_per_million':-1, 'new_deaths_per_million':-1, 'new_cases_per_million':-1, 'total_cases_per_million':-1, }\n",
"df = df.fillna(value=values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Columns related to facts that can not change over time fast can be filled with their mode values. In this dataset, we have: \n",
"- extreme_poverty\n",
"- male_smokers\n",
"- female_smokers\n",
"- stringency_index\n",
"- aged_65_older\n",
"- gdp_per_capita\n",
"- aged_70_older\n",
"- cvd_death_rate\n",
"- median_age\n",
"- diabetes_prevalence\n",
"- population_density\n",
"- population"
]
},
{
"cell_type": "code",
"execution_count": 406,
"metadata": {},
"outputs": [],
"source": [
"cols = [\"extreme_poverty\", \"male_smokers\", \"female_smokers\", \"stringency_index\", \"aged_65_older\", \"gdp_per_capita\", \"aged_70_older\", \"cvd_death_rate\", \"median_age\", \"diabetes_prevalence\", \"population_density\", \"population\"]\n",
"df[cols]=df[cols].fillna(df.mode().iloc[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For handwashing_facilities, we can see the trends of this column, because we may suspect that data is being filled over time, and the values are increasing as people is installing these devices according to government laws."
]
},
{
"cell_type": "code",
"execution_count": 407,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x537db02e48>"
]
},
"execution_count": 407,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.groupby([\"date\"])[\"handwashing_facilities\"].sum().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we were suspecting, this assumption is true. Now we will apply the mean value by country to the null ones, so that each country has a corresponding value according to the rest."
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {},
"outputs": [],
"source": [
"#df.groupby(\"location\")[\"handwashing_facilities\"].apply(lambda x: x.fillna(x.mean()))\n",
"df[\"handwashing_facilities\"] = df[\"handwashing_facilities\"].fillna(df[\"handwashing_facilities\"].mean())"
]
},
{
"cell_type": "code",
"execution_count": 409,
"metadata": {},
"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>Total</th>\n",
" <th>Percent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>age_status</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests_smoothed</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iso_code</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>location</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_cases</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_cases</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_deaths</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_deaths</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_cases_per_million</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_cases_per_million</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_deaths_per_million</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_deaths_per_million</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_tests</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_tests_per_thousand</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests_per_thousand</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new_tests_smoothed_per_thousand</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>__v</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tests_units</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>stringency_index</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Percent\n",
"age_status 0 0.0\n",
"new_tests_smoothed 0 0.0\n",
"iso_code 0 0.0\n",
"location 0 0.0\n",
"total_cases 0 0.0\n",
"new_cases 0 0.0\n",
"total_deaths 0 0.0\n",
"new_deaths 0 0.0\n",
"total_cases_per_million 0 0.0\n",
"new_cases_per_million 0 0.0\n",
"total_deaths_per_million 0 0.0\n",
"new_deaths_per_million 0 0.0\n",
"total_tests 0 0.0\n",
"new_tests 0 0.0\n",
"total_tests_per_thousand 0 0.0\n",
"new_tests_per_thousand 0 0.0\n",
"new_tests_smoothed_per_thousand 0 0.0\n",
"__v 0 0.0\n",
"tests_units 0 0.0\n",
"stringency_index 0 0.0"
]
},
"execution_count": 409,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking for null values by column\n",
"# Percentage of missing values.\n",
"total = df.isnull().sum().sort_values(ascending=False)\n",
"percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
"missing_data.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x537cc72a48>"
]
},
"execution_count": 410,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.groupby([\"date\"])[\"handwashing_facilities\"].sum().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.3. Most influential variables on new_deaths <a id='part1_3'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3.1. Target variable: new_deaths"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To know which variables affect most on the new_deaths column, it is necessary to address an analysis on new_deaths ranges, average death age, visualize outliers on this variable..."
]
},
{
"cell_type": "code",
"execution_count": 411,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23183.000000\n",
"mean 35.925463\n",
"std 332.225249\n",
"min -1918.000000\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 1.000000\n",
"max 10520.000000\n",
"Name: new_deaths, dtype: float64"
]
},
"execution_count": 411,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"new_deaths\"].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As this statistical information shows, this variable has large differences between min and max values, and the mean value is far from the minimum and maximum figures on deaths. It may mean that there are different values that may correspond to outliers. To check this fact, let´s show the boxplot below:"
]
},
{
"cell_type": "code",
"execution_count": 412,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x537cedda48>"
]
},
"execution_count": 412,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(df[\"new_deaths\"], color=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to the values shown previously, we will delete the register where the number of deaths is lower than zero, it does not make sense to have negative values for new deaths."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Outlier analysis will be based over the most important variables. A dataframe will be created to select the observations to be deleted from the original dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3.2. Outliers of \"new_deaths\" variable"
]
},
{
"cell_type": "code",
"execution_count": 413,
"metadata": {},
"outputs": [],
"source": [
"# outliers removal in original data\n",
"df = df.drop(df[df[\"new_deaths\"]<0].index)"
]
},
{
"cell_type": "code",
"execution_count": 414,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x537d03abc8>"
]
},
"execution_count": 414,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import norm\n",
"sns.distplot(df['new_deaths'], fit=norm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The distribution chart for new_deaths doesn´t seem to follow a normal distribution, so we will try to study its properties through probabilistic methods as Shapiro"
]
},
{
"cell_type": "code",
"execution_count": 415,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.08056902885437012, 0.0)"
]
},
"execution_count": 415,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import scipy\n",
"from scipy import stats\n",
"\n",
"# let´s apply Shapiro-Wilks test supposing a null hypothesis where the distribution of this variable is normal.\n",
"x = df[\"new_deaths\"]\n",
"scipy.stats.shapiro(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly, Shapiro shows this data doesn´t follow a normal distribution as its values are very close to 0."
]
},
{
"cell_type": "code",
"execution_count": 416,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# q-norm normality test\n",
"\n",
"stats.probplot(x, dist=\"norm\", plot=pylab)\n",
"pylab.show()"
]
},
{
"cell_type": "code",
"execution_count": 417,
"metadata": {},
"outputs": [],
"source": [
"# Creating an auxiliar column which is a copy of new_deaths to compare the real values against the logarithm of those.\n",
"train_outliers = pd.DataFrame()\n",
"train_outliers[\"new_deaths\"] = df[\"new_deaths\"]"
]
},
{
"cell_type": "code",
"execution_count": 418,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Count of outliers over Ls: 0\n"
]
}
],
"source": [
"# observations to be deleted\n",
"print(\" Count of outliers over Ls: \" + str(train_outliers[\"new_deaths\"][train_outliers[\"new_deaths\"]<0].count()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By looking at the chart above, there are not enough evidences to accept the normality of this data as the p-value is very small, declining the null hypothesis. In this chart, the edges are far from the theoretical quantiles of the normal distribution.\n",
"\n",
"so, we will proceed with previous process of transforming the data into its logarithm form."
]
},
{
"cell_type": "code",
"execution_count": 419,
"metadata": {},
"outputs": [],
"source": [
"# data logarithm\n",
"train_outliers['new_deaths'] = np.log(df['new_deaths'])"
]
},
{
"cell_type": "code",
"execution_count": 420,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# q-norm normality test\n",
"\n",
"y = train_outliers[\"new_deaths\"]\n",
"stats.probplot(y, dist=\"norm\", fit=True, plot=pylab)\n",
"pylab.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, we can see a better trend in the data points corresponding to the new_deaths column, but can not be sure to classify it as a normal distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.4. Correlation between Features <a id='part1_4'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"let´s divide first the variables between categorical and numerical columns as:"
]
},
{
"cell_type": "code",
"execution_count": 421,
"metadata": {},
"outputs": [],
"source": [
"# categorical and numerical variables\n",
"categorical = df.select_dtypes(include = ['object']).copy()\n",
"numerical = df.select_dtypes(include = ['int64','float64']).copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.1. Numerical variables analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A study will be done to measure the correlation between all these variables together."
]
},
{
"cell_type": "code",
"execution_count": 422,
"metadata": {},
"outputs": [],
"source": [
"num_corr = numerical.corr()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There will be a first exploration of data between \"new_deaths\" and the rest"
]
},
{
"cell_type": "code",
"execution_count": 423,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"new_deaths 1.000000\n",
"new_cases 0.923135\n",
"total_deaths 0.794581\n",
"total_cases 0.783721\n",
"population 0.625588\n",
"new_deaths_per_million 0.099934\n",
"new_tests_smoothed 0.087396\n",
"total_deaths_per_million 0.078711\n",
"total_tests 0.069418\n",
"new_tests 0.058344\n",
"aged_70_older 0.045385\n",
"aged_65_older 0.044243\n",
"total_cases_per_million 0.040277\n",
"new_cases_per_million 0.039541\n",
"female_smokers 0.036656\n",
"median_age 0.034299\n",
"gdp_per_capita 0.028032\n",
"handwashing_facilities 0.019772\n",
"diabetes_prevalence 0.014399\n",
"male_smokers 0.004023\n",
"extreme_poverty -0.003281\n",
"total_tests_per_thousand -0.003870\n",
"new_tests_smoothed_per_thousand -0.007553\n",
"new_tests_per_thousand -0.009656\n",
"population_density -0.017467\n",
"stringency_index -0.026154\n",
"cvd_death_rate -0.032237\n",
"__v NaN\n",
"Name: new_deaths, dtype: float64"
]
},
"execution_count": 423,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# correlation with new_deaths\n",
"num_corr[\"new_deaths\"].sort_values(ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make variable selection let´s pick the most three significative variables that have a correlation higher than $±0.5$ to make a correlation matrix limited to the variables which meet this criteria"
]
},
{
"cell_type": "code",
"execution_count": 424,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cm = numerical[[\"new_deaths\",\"new_cases\",\"total_deaths\",\"total_cases\", \"population\"]].corr()\n",
"sns.set(font_scale=1)\n",
"f, ax = plt.subplots(figsize=(9, 5))\n",
"hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2g')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Furthermore, let´s do an analysis more in detail about each of them: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.4.1.1 new_cases"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of new cases seems to be the one more obvious to have relation with the number of deaths. The graphical representation of this variables looks like the following:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By applying the ANOVA statistical proceeding, it indicates how the line fits correctly the relation between two variables, calculating $R^2$ which is a value between 0 and 1, where 0 indicates that there is no correlation at all, and 1 there is perfect fitting, or saying how good the model would fit through regression line over the data:"
]
},
{
"cell_type": "code",
"execution_count": 425,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>new_deaths</td> <th> R-squared: </th> <td> 0.852</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.852</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>1.312e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 15 Jun 2020</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:55:55</td> <th> Log-Likelihood: </th> <td>-1.4277e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 22762</td> <th> AIC: </th> <td>2.855e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 22760</td> <th> BIC: </th> <td>2.856e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 1.2759</td> <td> 0.855</td> <td> 1.492</td> <td> 0.136</td> <td> -0.400</td> <td> 2.952</td>\n",
"</tr>\n",
"<tr>\n",
" <th>new_cases</th> <td> 0.0556</td> <td> 0.000</td> <td> 362.229</td> <td> 0.000</td> <td> 0.055</td> <td> 0.056</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>36755.892</td> <th> Durbin-Watson: </th> <td> 0.357</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>282581302.648</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 9.644</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>548.508</td> <th> Cond. No. </th> <td>5.61e+03</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.61e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: new_deaths R-squared: 0.852\n",
"Model: OLS Adj. R-squared: 0.852\n",
"Method: Least Squares F-statistic: 1.312e+05\n",
"Date: Mon, 15 Jun 2020 Prob (F-statistic): 0.00\n",
"Time: 10:55:55 Log-Likelihood: -1.4277e+05\n",
"No. Observations: 22762 AIC: 2.855e+05\n",
"Df Residuals: 22760 BIC: 2.856e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 1.2759 0.855 1.492 0.136 -0.400 2.952\n",
"new_cases 0.0556 0.000 362.229 0.000 0.055 0.056\n",
"==============================================================================\n",
"Omnibus: 36755.892 Durbin-Watson: 0.357\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 282581302.648\n",
"Skew: 9.644 Prob(JB): 0.00\n",
"Kurtosis: 548.508 Cond. No. 5.61e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 5.61e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 425,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.formula.api as smf\n",
"est = smf.ols(formula='new_deaths ~ new_cases', data=numerical).fit()\n",
"est.summary()\n",
"# Take a look at R"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$R^2$ shows a significant value to make new_cases as a strong variable to determine the number of deaths"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.4.1.2 total_deaths"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of total_deaths seems to be other obvious variable to have relation with the number of deaths. The statistics of this variable looks like the following:"
]
},
{
"cell_type": "code",
"execution_count": 426,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>new_deaths</td> <th> R-squared: </th> <td> 0.631</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.631</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>3.898e+04</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 15 Jun 2020</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:55:55</td> <th> Log-Likelihood: </th> <td>-1.5317e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 22762</td> <th> AIC: </th> <td>3.063e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 22760</td> <th> BIC: </th> <td>3.064e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 10.1511</td> <td> 1.348</td> <td> 7.530</td> <td> 0.000</td> <td> 7.509</td> <td> 12.793</td>\n",
"</tr>\n",
"<tr>\n",
" <th>total_deaths</th> <td> 0.0169</td> <td> 8.58e-05</td> <td> 197.434</td> <td> 0.000</td> <td> 0.017</td> <td> 0.017</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>45511.193</td> <th> Durbin-Watson: </th> <td> 0.177</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>220606812.373</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>16.141</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>484.210</td> <th> Cond. No. </th> <td>1.58e+04</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.58e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: new_deaths R-squared: 0.631\n",
"Model: OLS Adj. R-squared: 0.631\n",
"Method: Least Squares F-statistic: 3.898e+04\n",
"Date: Mon, 15 Jun 2020 Prob (F-statistic): 0.00\n",
"Time: 10:55:55 Log-Likelihood: -1.5317e+05\n",
"No. Observations: 22762 AIC: 3.063e+05\n",
"Df Residuals: 22760 BIC: 3.064e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"--------------------------------------------------------------------------------\n",
"Intercept 10.1511 1.348 7.530 0.000 7.509 12.793\n",
"total_deaths 0.0169 8.58e-05 197.434 0.000 0.017 0.017\n",
"==============================================================================\n",
"Omnibus: 45511.193 Durbin-Watson: 0.177\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 220606812.373\n",
"Skew: 16.141 Prob(JB): 0.00\n",
"Kurtosis: 484.210 Cond. No. 1.58e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.58e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 426,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"est = smf.ols(formula='new_deaths ~ total_deaths', data=numerical).fit()\n",
"est.summary()\n",
"# Take a look at R"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Still, $R^2$ shows a medium-high value so we will consider this variable as a good candidate to get in the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.4.1.3 total_cases"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of total_cases follows the relation with the number of new_cases. The statistics of this variable looks like the following:"
]
},
{
"cell_type": "code",
"execution_count": 427,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>new_deaths</td> <th> R-squared: </th> <td> 0.614</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.614</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>3.624e+04</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 15 Jun 2020</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:55:55</td> <th> Log-Likelihood: </th> <td>-1.5369e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 22762</td> <th> AIC: </th> <td>3.074e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 22760</td> <th> BIC: </th> <td>3.074e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 10.4463</td> <td> 1.379</td> <td> 7.575</td> <td> 0.000</td> <td> 7.743</td> <td> 13.149</td>\n",
"</tr>\n",
"<tr>\n",
" <th>total_cases</th> <td> 0.0011</td> <td> 5.62e-06</td> <td> 190.360</td> <td> 0.000</td> <td> 0.001</td> <td> 0.001</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>44157.847</td> <th> Durbin-Watson: </th> <td> 0.175</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>211754889.465</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>15.018</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>474.561</td> <th> Cond. No. </th> <td>2.47e+05</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.47e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: new_deaths R-squared: 0.614\n",
"Model: OLS Adj. R-squared: 0.614\n",
"Method: Least Squares F-statistic: 3.624e+04\n",
"Date: Mon, 15 Jun 2020 Prob (F-statistic): 0.00\n",
"Time: 10:55:55 Log-Likelihood: -1.5369e+05\n",
"No. Observations: 22762 AIC: 3.074e+05\n",
"Df Residuals: 22760 BIC: 3.074e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"===============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 10.4463 1.379 7.575 0.000 7.743 13.149\n",
"total_cases 0.0011 5.62e-06 190.360 0.000 0.001 0.001\n",
"==============================================================================\n",
"Omnibus: 44157.847 Durbin-Watson: 0.175\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 211754889.465\n",
"Skew: 15.018 Prob(JB): 0.00\n",
"Kurtosis: 474.561 Cond. No. 2.47e+05\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 2.47e+05. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 427,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"est = smf.ols(formula='new_deaths ~ total_cases', data=numerical).fit()\n",
"est.summary()\n",
"# Take a look at R"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Still, $R^2$ shows a medium-high value so we will consider this variable as a good candidate to get in the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.4.1.4 population"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The population of the country may have an effect on new_cases. The statistics of this variable looks like the following:"
]
},
{
"cell_type": "code",
"execution_count": 428,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>new_deaths</td> <th> R-squared: </th> <td> 0.391</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.391</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>1.463e+04</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 15 Jun 2020</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:55:55</td> <th> Log-Likelihood: </th> <td>-1.5888e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 22762</td> <th> AIC: </th> <td>3.178e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 22760</td> <th> BIC: </th> <td>3.178e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 3.8596</td> <td> 1.744</td> <td> 2.213</td> <td> 0.027</td> <td> 0.440</td> <td> 7.279</td>\n",
"</tr>\n",
"<tr>\n",
" <th>population</th> <td> 3.097e-07</td> <td> 2.56e-09</td> <td> 120.975</td> <td> 0.000</td> <td> 3.05e-07</td> <td> 3.15e-07</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>29764.648</td> <th> Durbin-Watson: </th> <td> 0.117</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>32479463.590</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 6.684</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>187.573</td> <th> Cond. No. </th> <td>6.89e+08</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.89e+08. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: new_deaths R-squared: 0.391\n",
"Model: OLS Adj. R-squared: 0.391\n",
"Method: Least Squares F-statistic: 1.463e+04\n",
"Date: Mon, 15 Jun 2020 Prob (F-statistic): 0.00\n",
"Time: 10:55:55 Log-Likelihood: -1.5888e+05\n",
"No. Observations: 22762 AIC: 3.178e+05\n",
"Df Residuals: 22760 BIC: 3.178e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 3.8596 1.744 2.213 0.027 0.440 7.279\n",
"population 3.097e-07 2.56e-09 120.975 0.000 3.05e-07 3.15e-07\n",
"==============================================================================\n",
"Omnibus: 29764.648 Durbin-Watson: 0.117\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 32479463.590\n",
"Skew: 6.684 Prob(JB): 0.00\n",
"Kurtosis: 187.573 Cond. No. 6.89e+08\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 6.89e+08. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 428,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"est = smf.ols(formula='new_deaths ~ population', data=numerical).fit()\n",
"est.summary()\n",
"# Take a look at R"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The value obtained for $R^2$ is low compared to the other variables, so we will avoid including this variable into the models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.2 Categorical variables analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The proceeding consists of transforming the categorical value into a numerical one, study the correlations and then isolate the variables more correlated with each other.\n",
"\n",
"**note**: the data to work with needs to have no-null values, as explained before. For this reason, we can apply the proceeding to numerical categories with no problem at all."
]
},
{
"cell_type": "code",
"execution_count": 429,
"metadata": {},
"outputs": [],
"source": [
"# transformation to numerical values:\n",
"for i in categorical.columns:\n",
" categorical[i] = categorical[i].astype('category')\n",
"\n",
"columns = []\n",
"for i in categorical.columns:\n",
" columns.append(i)\n",
"\n",
"for i in columns:\n",
" categorical[i] = categorical[i].cat.codes"
]
},
{
"cell_type": "code",
"execution_count": 430,
"metadata": {},
"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>_id</th>\n",
" <th>iso_code</th>\n",
" <th>location</th>\n",
" <th>tests_units</th>\n",
" <th>age_status</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-12-31</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-01</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-02</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-03</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-04</th>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" _id iso_code location tests_units age_status\n",
"date \n",
"2019-12-31 0 2 0 0 2\n",
"2020-01-01 1 2 0 0 2\n",
"2020-01-02 2 2 0 0 2\n",
"2020-01-03 3 2 0 0 2\n",
"2020-01-04 4 2 0 0 2"
]
},
"execution_count": 430,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categorical.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 431,
"metadata": {},
"outputs": [],
"source": [
"# adding the new_deaths column\n",
"new_deaths = numerical[['new_deaths']]\n",
"categorical['new_deaths'] = new_deaths"
]
},
{
"cell_type": "code",
"execution_count": 432,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_id 0.118170\n",
"iso_code 0.049627\n",
"location 0.116461\n",
"tests_units -0.010107\n",
"age_status -0.053804\n",
"new_deaths 1.000000\n",
"Name: new_deaths, dtype: float64\n"
]
}
],
"source": [
"# significative correlations with the new_deaths column\n",
"cat_corr = categorical.corr()\n",
"corr_sp = cat_corr['new_deaths']\n",
"print(corr_sp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The only variable which shows some correlation values is location, which is the country itself. Let´s get the statistics from this variable:"
]
},
{
"cell_type": "code",
"execution_count": 433,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>new_deaths</td> <th> R-squared: </th> <td> 0.014</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.014</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 312.9</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 15 Jun 2020</td> <th> Prob (F-statistic):</th> <td>1.46e-69</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:55:56</td> <th> Log-Likelihood: </th> <td>-1.6437e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 22762</td> <th> AIC: </th> <td>3.287e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 22760</td> <th> BIC: </th> <td>3.288e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -30.9271</td> <td> 4.380</td> <td> -7.062</td> <td> 0.000</td> <td> -39.511</td> <td> -22.343</td>\n",
"</tr>\n",
"<tr>\n",
" <th>location</th> <td> 0.6372</td> <td> 0.036</td> <td> 17.690</td> <td> 0.000</td> <td> 0.567</td> <td> 0.708</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>44721.657</td> <th> Durbin-Watson: </th> <td> 0.077</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>87127003.641</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>15.985</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>304.403</td> <th> Cond. No. </th> <td> 243.</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: new_deaths R-squared: 0.014\n",
"Model: OLS Adj. R-squared: 0.014\n",
"Method: Least Squares F-statistic: 312.9\n",
"Date: Mon, 15 Jun 2020 Prob (F-statistic): 1.46e-69\n",
"Time: 10:55:56 Log-Likelihood: -1.6437e+05\n",
"No. Observations: 22762 AIC: 3.287e+05\n",
"Df Residuals: 22760 BIC: 3.288e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -30.9271 4.380 -7.062 0.000 -39.511 -22.343\n",
"location 0.6372 0.036 17.690 0.000 0.567 0.708\n",
"==============================================================================\n",
"Omnibus: 44721.657 Durbin-Watson: 0.077\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 87127003.641\n",
"Skew: 15.985 Prob(JB): 0.00\n",
"Kurtosis: 304.403 Cond. No. 243.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 433,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"est = smf.ols(formula='new_deaths ~ location', data=categorical).fit()\n",
"est.summary()\n",
"# Take a look at R"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The low value of $R^2$ corresponding to this variable shows no correlation at all. So the country is not a definitive variable to look at in terms of prediction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting to this point, we will select for the moment the following variables to include into the prediction models: \n",
"\n",
"- \"new_deaths\"\n",
"- \"new_cases\"\n",
"- \"total_deaths\"\n",
"- \"total_cases\"\n",
"\n",
"Maybe later we can include \"population\" or other variables to see if we can improve our models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.3 Plotting each of these variables to see visually any correlation\n",
"\n",
"Now we will use scatter plots to try to detect any potential correlations between variables"
]
},
{
"cell_type": "code",
"execution_count": 434,
"metadata": {},
"outputs": [],
"source": [
"features = [\"new_deaths\",\"new_cases\",\"total_deaths\",\"total_cases\"]"
]
},
{
"cell_type": "code",
"execution_count": 435,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x1008 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.plotting import scatter_matrix\n",
"\n",
"scatter_matrix(df[features], figsize = (14,14), diagonal = 'kde');\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.4. WoE (Weight of Evidence)\n",
"\n",
"This factor shows the weight that each category of a variable has in terms of relation with the target variable to predict, and therefore can be used to reduce the number of categories and make simpler the prediction model with less categories to take into account"
]
},
{
"cell_type": "code",
"execution_count": 436,
"metadata": {},
"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>new_cases</th>\n",
" </tr>\n",
" <tr>\n",
" <th>new_cases</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>-2461</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-1480</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-713</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-525</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-209</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126684</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127662</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127796</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132786</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133510</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2060 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" new_cases\n",
"new_cases \n",
"-2461 1\n",
"-1480 1\n",
"-713 1\n",
"-525 1\n",
"-209 1\n",
"... ...\n",
" 126684 1\n",
" 127662 1\n",
" 127796 1\n",
" 132786 1\n",
" 133510 1\n",
"\n",
"[2060 rows x 1 columns]"
]
},
"execution_count": 436,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(df[\"new_cases\"].groupby(df[\"new_cases\"]).count())"
]
},
{
"cell_type": "code",
"execution_count": 437,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"new_cases\"].hist(bins=200)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The distribution for new_deaths is clearly unbalanced because there are so many values close to 0 cases that we may need to categorize as follows:"
]
},
{
"cell_type": "code",
"execution_count": 438,
"metadata": {},
"outputs": [],
"source": [
"def get_WoE_one(data, var, target):\n",
" crosstab = pd.crosstab(data[target], data[var])\n",
" \n",
" print(\"Obtaining WoE for variable \", var, \":\")\n",
" \n",
" for col in crosstab.columns:\n",
" if crosstab[col][1] == 0:\n",
" print(\" WoE for \", col, \"[\", sum(crosstab[col]), \"] is infinity\")\n",
" else:\n",
" print(\" WoE for \", col, \"[\", sum(crosstab[col]), \"] is\", np.log(float(crosstab[col][0]) / float(crosstab[col][1])))"
]
},
{
"cell_type": "code",
"execution_count": 439,
"metadata": {},
"outputs": [],
"source": [
"df.loc[:, \"new_cases_grp\"] = df[\"new_cases\"].map(lambda x: \"<5\" if x <5 else \"<50\" if x < 50 else \"<100\" if x <100 else \"<250\" if x <250 else \"<500\" if x <500 else \"<5000\" if x <5000 else \">5000\")\n",
"df.loc[:, \"total_cases_grp\"] = df[\"total_cases\"].map(lambda x: \"<5\" if x <5 else \"<50\" if x < 50 else \"<100\" if x <100 else \"<250\" if x <250 else \"<500\" if x <500 else \"<5000\" if x <5000 else \">5000\")\n",
"df.loc[:, \"total_deaths_grp\"] = df[\"total_deaths\"].map(lambda x: \"<5\" if x <5 else \"<50\" if x < 50 else \"<100\" if x <100 else \"<250\" if x <250 else \"<500\" if x <500 else \"<5000\" if x <5000 else \">5000\")"
]
},
{
"cell_type": "code",
"execution_count": 440,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obtaining WoE for variable new_cases_grp :\n",
" WoE for <100 [ 1280 ] is 0.6931471805599453\n",
" WoE for <250 [ 1509 ] is 0.4126079956205445\n",
" WoE for <5 [ 12172 ] is 3.5658776284485927\n",
" WoE for <50 [ 4701 ] is 1.3644684116915666\n",
" WoE for <500 [ 990 ] is 0.36772478012531734\n",
" WoE for <5000 [ 1754 ] is 0.9098182173685376\n",
" WoE for >5000 [ 356 ] is infinity\n"
]
}
],
"source": [
"get_WoE_one(df, 'new_cases_grp','new_deaths')"
]
},
{
"cell_type": "code",
"execution_count": 441,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obtaining WoE for variable total_cases_grp :\n",
" WoE for <100 [ 1445 ] is 2.4810766948957013\n",
" WoE for <250 [ 1914 ] is 2.1613435619435557\n",
" WoE for <5 [ 4711 ] is 5.889729928565458\n",
" WoE for <50 [ 4450 ] is 3.553720721830038\n",
" WoE for <500 [ 1562 ] is 1.7038900913277886\n",
" WoE for <5000 [ 4738 ] is 1.0360514565081735\n",
" WoE for >5000 [ 3942 ] is 0.8353840932613548\n"
]
}
],
"source": [
"get_WoE_one(df, 'total_cases_grp','new_deaths')"
]
},
{
"cell_type": "code",
"execution_count": 442,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obtaining WoE for variable total_deaths_grp :\n",
" WoE for <100 [ 1114 ] is 0.5635543515143511\n",
" WoE for <250 [ 1261 ] is 0.6654226325450905\n",
" WoE for <5 [ 12380 ] is 3.3786403471830004\n",
" WoE for <50 [ 5079 ] is 1.3120954592093432\n",
" WoE for <500 [ 727 ] is 0.29334780998745824\n",
" WoE for <5000 [ 1450 ] is 1.5668782980153044\n",
" WoE for >5000 [ 751 ] is 0.3364722366212129\n"
]
}
],
"source": [
"get_WoE_one(df, 'total_deaths_grp','new_deaths')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By looking at the previous values, we can conclude all levels of these columns can have sufficient weight to have positive impact when including it into the future model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.5 Conclusions"
]
},
{
"cell_type": "code",
"execution_count": 443,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x360 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# correlation matrix with most relevant variables to include in the model\n",
"cm = df[[\"new_deaths\",\"new_cases\",\"total_deaths\",\"total_cases\"]].corr()\n",
"sns.set(font_scale=1)\n",
"f, ax = plt.subplots(figsize=(9,5))\n",
"hm = sns.heatmap(cm, cbar=True, annot= True, square= True, fmt ='.2g')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly all variables show strong correlation against the new_deaths target variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Feature Engineering and Data Cleaning: <a id='part2'></a>\n",
"\n",
"With this transformed variable, we are going to obtain the dummy variables, delete colineal variables and separate the datasets into training and validation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1. Dummy variables <a id='part2_1'></a>\n",
"\n",
"Creating dummy variables means that every category group will be transformed into a unique number or index so that the future model and feature selection can work with."
]
},
{
"cell_type": "code",
"execution_count": 444,
"metadata": {},
"outputs": [],
"source": [
"data_model = pd.concat(((pd.get_dummies(df['new_cases_grp'], prefix= 'new_cases_grp')),\n",
" (pd.get_dummies(df['total_cases_grp'], prefix= 'total_cases_grp')),\n",
" (pd.get_dummies(df['total_deaths_grp'], prefix= 'total_deaths_grp'))), axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 445,
"metadata": {},
"outputs": [
{
"data": {
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" <th>new_cases_grp_&lt;100</th>\n",
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" <th>2019-12-31</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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"text/plain": [
" new_cases_grp_<100 new_cases_grp_<250 new_cases_grp_<5 \\\n",
"date \n",
"2019-12-31 0 0 1 \n",
"2020-01-01 0 0 1 \n",
"2020-01-02 0 0 1 \n",
"2020-01-03 0 0 1 \n",
"2020-01-04 0 0 1 \n",
"\n",
" new_cases_grp_<50 new_cases_grp_<500 new_cases_grp_<5000 \\\n",
"date \n",
"2019-12-31 0 0 0 \n",
"2020-01-01 0 0 0 \n",
"2020-01-02 0 0 0 \n",
"2020-01-03 0 0 0 \n",
"2020-01-04 0 0 0 \n",
"\n",
" new_cases_grp_>5000 total_cases_grp_<100 total_cases_grp_<250 \\\n",
"date \n",
"2019-12-31 0 0 0 \n",
"2020-01-01 0 0 0 \n",
"2020-01-02 0 0 0 \n",
"2020-01-03 0 0 0 \n",
"2020-01-04 0 0 0 \n",
"\n",
" total_cases_grp_<5 total_cases_grp_<50 total_cases_grp_<500 \\\n",
"date \n",
"2019-12-31 1 0 0 \n",
"2020-01-01 1 0 0 \n",
"2020-01-02 1 0 0 \n",
"2020-01-03 1 0 0 \n",
"2020-01-04 1 0 0 \n",
"\n",
" total_cases_grp_<5000 total_cases_grp_>5000 \\\n",
"date \n",
"2019-12-31 0 0 \n",
"2020-01-01 0 0 \n",
"2020-01-02 0 0 \n",
"2020-01-03 0 0 \n",
"2020-01-04 0 0 \n",
"\n",
" total_deaths_grp_<100 total_deaths_grp_<250 total_deaths_grp_<5 \\\n",
"date \n",
"2019-12-31 0 0 1 \n",
"2020-01-01 0 0 1 \n",
"2020-01-02 0 0 1 \n",
"2020-01-03 0 0 1 \n",
"2020-01-04 0 0 1 \n",
"\n",
" total_deaths_grp_<50 total_deaths_grp_<500 \\\n",
"date \n",
"2019-12-31 0 0 \n",
"2020-01-01 0 0 \n",
"2020-01-02 0 0 \n",
"2020-01-03 0 0 \n",
"2020-01-04 0 0 \n",
"\n",
" total_deaths_grp_<5000 total_deaths_grp_>5000 \n",
"date \n",
"2019-12-31 0 0 \n",
"2020-01-01 0 0 \n",
"2020-01-02 0 0 \n",
"2020-01-03 0 0 \n",
"2020-01-04 0 0 "
]
},
"execution_count": 445,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_model.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will try to apply deletion of colineal variables through Linear Regression methods. For that we will define a function called VIF (Variance Inflation Factor) which quantifies the multicolineality between variables or how much the variance increases because of colineality. \n",
"\n",
"$VIF = \\dfrac{1}{1 - Ri^2}$"
]
},
{
"cell_type": "code",
"execution_count": 446,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"def calculateVIF(data):\n",
" features = list(data.columns)\n",
" num_features = len(features)\n",
" \n",
" model = LinearRegression()\n",
" \n",
" result = pd.DataFrame(index = ['VIF'], columns = features)\n",
" result = result.fillna(0)\n",
" \n",
" for ite in range(num_features):\n",
" x_features = features[:]\n",
" y_featue = features[ite]\n",
" x_features.remove(y_featue)\n",
" \n",
" x = data[x_features]\n",
" y = data[y_featue]\n",
" \n",
" model.fit(data[x_features], data[y_featue])\n",
" try:\n",
" result[y_featue] = 1/(1 - model.score(data[x_features], data[y_featue]))\n",
" except ZeroDivisionError:\n",
" result[y_featue] = 5 \n",
" return result\n",
"\n",
"def selectDataUsingVIF(data, max_VIF = 5):\n",
" result = data.copy(deep = True)\n",
" \n",
" VIF = calculateVIF(result)\n",
" \n",
" while VIF.to_numpy().max() > max_VIF:\n",
" col_max = np.where(VIF == VIF.to_numpy().max())[1][0]\n",
" features = list(result.columns)\n",
" features.remove(features[col_max])\n",
" result = result[features]\n",
" \n",
" VIF = calculateVIF(result)\n",
" \n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 447,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>new_cases_grp_&lt;250</th>\n",
" <th>new_cases_grp_&lt;50</th>\n",
" <th>new_cases_grp_&lt;500</th>\n",
" <th>new_cases_grp_&lt;5000</th>\n",
" <th>new_cases_grp_&gt;5000</th>\n",
" <th>total_cases_grp_&lt;250</th>\n",
" <th>total_cases_grp_&lt;5</th>\n",
" <th>total_cases_grp_&lt;50</th>\n",
" <th>total_cases_grp_&lt;500</th>\n",
" <th>total_cases_grp_&lt;5000</th>\n",
" <th>total_deaths_grp_&lt;250</th>\n",
" <th>total_deaths_grp_&lt;50</th>\n",
" <th>total_deaths_grp_&lt;500</th>\n",
" <th>total_deaths_grp_&lt;5000</th>\n",
" <th>total_deaths_grp_&gt;5000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>VIF</th>\n",
" <td>1.361721</td>\n",
" <td>1.384048</td>\n",
" <td>1.339346</td>\n",
" <td>2.03413</td>\n",
" <td>1.573668</td>\n",
" <td>1.770469</td>\n",
" <td>3.06613</td>\n",
" <td>2.848104</td>\n",
" <td>1.686894</td>\n",
" <td>2.524725</td>\n",
" <td>1.40448</td>\n",
" <td>1.81879</td>\n",
" <td>1.392213</td>\n",
" <td>1.995398</td>\n",
" <td>1.978372</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" new_cases_grp_<250 new_cases_grp_<50 new_cases_grp_<500 \\\n",
"VIF 1.361721 1.384048 1.339346 \n",
"\n",
" new_cases_grp_<5000 new_cases_grp_>5000 total_cases_grp_<250 \\\n",
"VIF 2.03413 1.573668 1.770469 \n",
"\n",
" total_cases_grp_<5 total_cases_grp_<50 total_cases_grp_<500 \\\n",
"VIF 3.06613 2.848104 1.686894 \n",
"\n",
" total_cases_grp_<5000 total_deaths_grp_<250 total_deaths_grp_<50 \\\n",
"VIF 2.524725 1.40448 1.81879 \n",
"\n",
" total_deaths_grp_<500 total_deaths_grp_<5000 total_deaths_grp_>5000 \n",
"VIF 1.392213 1.995398 1.978372 "
]
},
"execution_count": 447,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We will proceed to delete those variables where VIF is infinity through SelectDataUsingVIF, to obtain VIF for each of them.\n",
"# In the function there is a try except for divisions by zero to avoid them and assign them a 5 in case their denominator is infinite.\n",
"\n",
"model_vars = selectDataUsingVIF(data_model)\n",
"calculateVIF(model_vars)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All these variables show good VIF score, and therefore will be considered for further analysis and predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2. Variable selection with low variances <a id='part2_2'></a>\n",
"\n",
"This allows us to delete the variables whose variance is not big enough"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original variables 3\n",
"Final variables 3\n",
"Variable List ['new_cases' 'total_deaths' 'total_cases']\n"
]
}
],
"source": [
"from sklearn.feature_selection import VarianceThreshold\n",
"features.remove(\"new_deaths\")\n",
"x = df[features]\n",
"y = df[\"new_deaths\"]\n",
"var_th = VarianceThreshold(threshold = 0.2)\n",
"x_var = var_th.fit_transform(x)\n",
"\n",
"print(\"Original variables \", x.shape[1])\n",
"print(\"Final variables \", x_var.shape[1])\n",
"\n",
"print(\"Variable List \", np.asarray(list(x))[var_th.get_support()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3. Univariant Variable Selection <a id='part2_3'></a>\n",
"\n",
"In this step, variables are analyzed if they can explain by themselfes part of the variance of the objective variable."
]
},
{
"cell_type": "code",
"execution_count": 449,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(22762, 3)\n",
"Variable list ['new_cases' 'total_deaths' 'total_cases']\n"
]
}
],
"source": [
"from sklearn.feature_selection import SelectKBest\n",
"from sklearn.feature_selection import f_regression \n",
"from sklearn.feature_selection import chi2 \n",
"\n",
"# For Lineal regression classification use f_regression\n",
"\n",
"S_f3 = SelectKBest(f_regression, k = 3)\n",
"X_f3 = S_f3.fit_transform(x, y)\n",
"\n",
"print(X_f3.shape)\n",
"print(\"Variable list \", np.asarray(list(x))[S_f3.get_support()])\n",
"\n",
"# For classification models use chi2\n",
"#S_chi3 = SelectKBest(chi2, k = 3)\n",
"#X_chi3 = S_chi3.fit_transform(x, y)\n",
"\n",
"#print(X_chi3.shape)\n",
"#print(\"Variable list \", np.asarray(list(x))[S_chi3.get_support()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4. Variable selection depending on their percentile punctuation <a id='part2_4'></a>"
]
},
{
"cell_type": "code",
"execution_count": 450,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(22762, 1)\n",
"Listado de variables ['new_cases']\n",
"(22762, 2)\n",
"Listado de variables ['new_cases' 'total_deaths']\n"
]
}
],
"source": [
"from sklearn.feature_selection import SelectPercentile\n",
"from sklearn.feature_selection import f_regression \n",
"\n",
"S_per5 = SelectPercentile(f_regression, percentile = 50)\n",
"X_per5 = S_per5.fit_transform(x, y)\n",
"\n",
"print(X_per5.shape)\n",
"print(\"Listado de variables \", np.asarray(list(x))[S_per5.get_support()])\n",
"\n",
"S_per7 = SelectPercentile(f_regression, percentile = 70)\n",
"X_per7 = S_per7.fit_transform(x, y)\n",
"\n",
"print(X_per7.shape)\n",
"print(\"Listado de variables \", np.asarray(list(x))[S_per7.get_support()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Predictive Modelling (Scikit-Learn):<a id='part3'></a>\n",
"\n",
"Now that we have analyzed the data some more with the EDA methods, we will need to predict and see if the variables considered to predict the model for the new_deaths values make sense or not, and how big the error is for these predictions. As we want to predict a numeric value we will use some of the algorithms related to regression as:\n",
"\n",
"- Linear Regression\n",
"- Ridge\n",
"- Lasso\n",
"\n",
"\n",
"(All these algorithms are used for classication ML Models, so here will not be used\n",
"- Logistic Regression\n",
"- Support Vector Machines (Linear and radial)\n",
"- Random Forest\n",
"- K-Nearest Neighbours\n",
"- Naive Bayes\n",
"- Decision Tree\n",
"- Logistic Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1. Linear Regression<a id='part3_1'></a>"
]
},
{
"cell_type": "code",
"execution_count": 451,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R2 in training data is: 0.9148664220466903\n",
"R2 in validation is: 0.8805702099526586\n"
]
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# We will obtain the dataset for training and validation\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y)\n",
"\n",
"# Creación de un modelo\n",
"model = LinearRegression()\n",
"model.fit(x_train, y_train)\n",
"\n",
"predit_train = model.predict(x_train)\n",
"predit_test = model.predict(x_test)\n",
"\n",
"# R2 evaluation\n",
"print('R2 in training data is: ', model.score(x_train, y_train))\n",
"print('R2 in validation is: ', model.score(x_test, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we get strong values of correlation for R2!!! close to 0.9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we will try to apply factors to the different coefficients of the regression lineal model obtained from the Ridge and Lasso models\n",
"\n",
"### 3.2. Ridge regression (regularization)<a id='part3_2'></a>\n",
"\n",
"#### Regularized Linear Models\n",
"\n",
"a good way to reduce overfitting is to regularize the model (i.e., to constrain it): the fewer degrees of freedom it has, the harder it will be for it to overfit the data. For example, a simple way to regularize a polynomial model is to reduce the number of polynomial degrees.\n",
"\n",
"For a linear model, regularization is typically achieved by constraining the weights of the model. We will now look at Ridge Regression, Lasso Regression, and Elastic Net, which implement three different ways to constrain the weights.\n",
"\n",
"\n",
"###### Ridge Regression\n",
"Ridge Regression (also called Tikhonov regularization) is a regularized version of Linear Regression: a regularization term equal to alpha sigma-summation Underscript i equals 1 Overscript n Endscripts theta Subscript i Baseline Superscript 2 is added to the cost function. This forces the learning algorithm to not only fit the data but also keep the model weights as small as possible. Note that the regularization term should only be added to the cost function during training. Once the model is trained, you want to evaluate the model’s performance using the unregularized performance measure.\n",
"\n",
"NOTE\n",
"It is quite common for the cost function used during training to be different from the performance measure used for testing. Apart from regularization, another reason why they might be different is that a good training cost function should have optimization-friendly derivatives, while the performance measure used for testing should be as close as possible to the final objective. A good example of this is a classifier trained using a cost function such as the log loss but evaluated using precision/recall.\n",
"\n",
"The hyperparameter α controls how much you want to regularize the model. If α = 0 then Ridge Regression is just Linear Regression. If α is very large, then all weights end up very close to zero and the result is a flat line going through the data’s mean. Equation 4-8 presents the Ridge Regression cost function.11\n",
"\n",
"Equation 4-8. Ridge Regression cost function\n",
"<img src=\"./images_notebook/ridge_eq.png\"></img>\n",
"\n",
"Note that the bias term θ0 is not regularized (the sum starts at i = 1, not 0). If we define w as the vector of feature weights (θ1 to θn), then the regularization term is simply equal to ½(∥ w ∥2)2, where ∥ w ∥2 represents the ℓ2 norm of the weight vector.12 For Gradient Descent, just add αw to the MSE gradient vector (Equation 4-6).\n",
"\n",
"WARNING\n",
"\n",
"It is important to scale the data (e.g., using a StandardScaler) before performing Ridge Regression, as it is sensitive to the scale of the input features. This is true of most regularized models.\n",
"\n",
"Figure 4-17 shows several Ridge models trained on some linear data using different α value. On the left, plain Ridge models are used, leading to linear predictions. On the right, the data is first expanded using PolynomialFeatures(degree=10), then it is scaled using a StandardScaler, and finally the Ridge models are applied to the resulting features: this is Polynomial Regression with Ridge regularization. Note how increasing α leads to flatter (i.e., less extreme, more reasonable) predictions; this reduces the model’s variance but increases its bias.\n",
"\n",
"\n",
"<img src=\"./images_notebook/ridge_pic.png\"></img>\n",
"\n",
"As with Linear Regression, we can perform Ridge Regression either by computing a closed-form equation or by performing Gradient Descent. The pros and cons are the same. Equation 4-9 shows the closed-form solution (where A is the (n + 1) × (n + 1) identity matrix13 except with a 0 in the top-left cell, corresponding to the bias term).\n",
"\n",
"\n",
"<img src=\"./images_notebook/ridge_closed_eq.png\"></img>"
]
},
{
"cell_type": "code",
"execution_count": 452,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R2 in training is: 0.9148664220466903\n",
"R2 in validation is: 0.8805702099526569\n",
"b_0 is: -0.6429560725485146 and b_1 is: 0.08104705049056199\n",
"[ 0.08104705 0.02450849 -0.00214294]\n",
"R2 scores mean is: 0.106540017150107\n",
"R2 scores are: [ 0.59743214 0.27258325 0.76480953 -1.96319703 0.86107221]\n"
]
}
],
"source": [
"from sklearn.linear_model import Ridge\n",
"\n",
"model_ridge = Ridge(alpha = 0.01)\n",
"model_ridge.fit(x_train, y_train)\n",
"\n",
"predit_train = model_ridge.predict(x_train)\n",
"predit_test = model_ridge.predict(x_test)\n",
"\n",
"# Evaluación de R2\n",
"print('R2 in training is: ', model_ridge.score(x_train, y_train))\n",
"print('R2 in validation is: ', model_ridge.score(x_test, y_test))\n",
"print(\"b_0 is:\", model_ridge.intercept_, \"and b_1 is:\", model_ridge.coef_[0])\n",
"print(model_ridge.coef_)\n",
"\n",
"scores = cross_val_score(model_ridge, x, y, cv = 5)\n",
"\n",
"print('R2 scores mean is: ', scores.mean())\n",
"print('R2 scores are: ', scores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3. Lasso regression (regularization)<a id='part3_3'></a>\n",
"\n",
"Least Absolute Shrinkage and Selection Operator Regression (simply called Lasso Regression) is another regularized version of Linear Regression: just like Ridge Regression, it adds a regularization term to the cost function, but it uses the ℓ1 norm of the weight vector instead of half the square of the ℓ2 norm (see Equation 4-10).\n",
"\n",
"\n",
"<img src=\"./images_notebook/lasso_eq.png\"></img>\n",
"\n",
"<img src=\"./images_notebook/lasso_pics.png\"></img>\n",
"\n",
"An important characteristic of Lasso Regression is that it tends to completely eliminate the weights of the least important features (i.e., set them to zero). For example, the dashed line in the right plot on Figure 4-18 (with α = 10-7) looks quadratic, almost linear: all the weights for the high-degree polynomial features are equal to zero. In other words, Lasso Regression automatically performs feature selection and outputs a sparse model (i.e., with few nonzero feature weights)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4. Elastic Net (regularization)<a id='part3_4'></a>\n",
"\n",
"Elastic Net is a middle ground between Ridge Regression and Lasso Regression. The regularization term is a simple mix of both Ridge and Lasso’s regularization terms, and you can control the mix ratio r. When r = 0, Elastic Net is equivalent to Ridge Regression, and when r = 1, it is equivalent to Lasso Regression (see Equation 4-12).\n",
"\n",
"\n",
"<img src=\"./images_notebook/elastic_net_eq.png\"></img>\n",
"\n",
"So when should you use plain Linear Regression (i.e., without any regularization), Ridge, Lasso, or Elastic Net? It is almost always preferable to have at least a little bit of regularization, so generally you should avoid plain Linear Regression. Ridge is a good default, but if you suspect that only a few features are actually useful, you should prefer Lasso or Elastic Net since they tend to reduce the useless features’ weights down to zero as we have discussed. In general, Elastic Net is preferred over Lasso since Lasso may behave erratically when the number of features is greater than the number of training instances or when several features are strongly correlated.\n"
]
},
{
"cell_type": "code",
"execution_count": 453,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R2 in training is: 0.9148664220466509\n",
"R2 in validation is: 0.8805702056805743\n",
"b_0 is: -0.6429528277543568 and b_1 is: 0.08104702808946578\n",
"[ 0.08104703 0.02450847 -0.00214293]\n",
"R2 scores mean is: 0.10654021668708305\n",
"R2 scores are: [ 0.59743234 0.27258365 0.76480959 -1.96319595 0.86107145]\n"
]
}
],
"source": [
"from sklearn.linear_model import Lasso\n",
"\n",
"model_lasso = Lasso(alpha = 0.1)\n",
"model_lasso.fit(x_train, y_train)\n",
"\n",
"predit_train = model_lasso.predict(x_train)\n",
"predit_test = model_lasso.predict(x_test)\n",
"\n",
"# Evaluación de R2\n",
"print('R2 in training is: ', model_lasso.score(x_train, y_train))\n",
"print('R2 in validation is: ', model_lasso.score(x_test, y_test))\n",
"print(\"b_0 is:\", model_lasso.intercept_, \"and b_1 is:\", model_lasso.coef_[0])\n",
"print(model_lasso.coef_)\n",
"\n",
"scores = cross_val_score(model_lasso, x, y, cv = 5)\n",
"print('R2 scores mean is: ', scores.mean())\n",
"print('R2 scores are: ', scores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For these values of alpha we get very good values of R2 close to 0.9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.5. Model evaluation through a polynomial grade function <a id='part3_5'></a>\n",
"\n",
"We will create a function that can make the fitting of a polynomial function and through cross validation allows to obtain the best settings and variations"
]
},
{
"cell_type": "code",
"execution_count": 454,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#X = np.arange(1,n_samples, 1)\n",
"#y = list(y)\n",
"y_result = df.groupby([\"date\"])[\"new_deaths\"].sum().values\n",
"n_samples = len(y_result)\n",
"X = np.arange(1,n_samples+1, 1)\n",
"#y = df[\"new_deaths\"].values"
]
},
{
"cell_type": "code",
"execution_count": 455,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"\n",
"def evaluateFit(degrees, X, y):\n",
" polynomial_features = PolynomialFeatures(degree = degrees, include_bias = False)\n",
" \n",
" linear_regression = LinearRegression()\n",
" pipeline = Pipeline([(\"polynomial_features\", polynomial_features), (\"linear_regression\", linear_regression)])\n",
" pipeline.fit(X[:, np.newaxis], y)\n",
" \n",
" scores = cross_val_score(pipeline, X[:, np.newaxis], y, scoring = \"neg_mean_squared_error\", cv = 10)\n",
" \n",
" X_test = np.arange(1,n_samples +1, 1)\n",
" plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label=\"Model\")\n",
" plt.plot(X_test, y, label = \"True function\")\n",
" plt.scatter(X, y, label = \"Samples\")\n",
" plt.xlabel(\"x\")\n",
" plt.ylabel(\"y\")\n",
" plt.legend(loc=\"best\")\n",
" plt.title(\"Degree {}\\nMSE = {:.2e}(+/- {:.2e})\".format(degrees, -scores.mean(), scores.std()))"
]
},
{
"cell_type": "code",
"execution_count": 456,
"metadata": {},
"outputs": [],
"source": [
"df.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 457,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.close()\n",
"evaluateFit(1, X, y_result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using a simple Linear Regression shows how the fitted line adapts to the trend of the data as previous pic shows."
]
},
{
"cell_type": "code",
"execution_count": 458,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.close()\n",
"evaluateFit(5, X, y_result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using a 5-degree Linear Regression shows how the fitted line adapts much better to the trend of the data than the simple Linear Regression as previous pic shows."
]
},
{
"cell_type": "code",
"execution_count": 459,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.close()\n",
"evaluateFit(15, X, y_result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using a 15-degree Linear Regression shows how the fitted line adapts even much better to the trend of the data than the 5-degree Linear Regression as previous pic shows."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.6. Validation curves for new_deaths <a id='part3_6'></a>\n",
"\n",
"As data may show imperfections and unbalances we should proceed with computing the validation curve for a class of models. Here we will use again the polynomial regression model (by changing the hyperparameter of degree).\n",
"\n",
"The question here is to answer which degree gives a suitable relation between bias (under-fitting) and variance (over-fitting).\n",
"Let´s use validation_curve from sklearn. \n",
"\n",
"The more erroneous the assumptions with respect to the true relationship, the higher the bias, and vice-versa.A low-biased method fits training data very well.\n",
"\n",
"<img src=\"./images_notebook/low_bias_high_variance.png\">\n",
"\n",
"You can see that a low-biased method captures most of the differences (even the minor ones) between the different training sets. varies a lot as we change training sets, and this indicates high variance.\n",
"\n",
"\n",
"The reverse also holds: the greater the bias, the lower the variance. A high-bias method builds simplistic models that generally don’t fit well training data. As we change training sets, the models we get from a high-bias algorithm are, generally, not very different from one another.\n",
"\n",
"\n",
"<img src=\"./images_notebook/high_bias_low_variance.png\">\n",
"\n",
"In practice, however, we need to accept a trade-off. We can’t have both low bias and low variance, so we want to aim for something in the middle.\n",
"\n",
"\n",
"<img src=\"./images_notebook/acceptable_bias_variance.png\">\n",
"\n",
"Having a model, data, parameter name and a range to look at to, this function will automatically compute both training score and validation scores across the range of hyperparameter values:"
]
},
{
"cell_type": "code",
"execution_count": 460,
"metadata": {},
"outputs": [],
"source": [
"x_features = df[['new_cases', 'total_deaths', 'total_cases']]\n",
"y_feature = df[\"new_deaths\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sciki-Learn has a polynomial preprocessor to be used along with the LinearRegression method."
]
},
{
"cell_type": "code",
"execution_count": 461,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import make_pipeline\n",
"def PolynomialRegression(degree=2, **kwargs):\n",
" return make_pipeline(PolynomialFeatures(degree),\n",
" LinearRegression(**kwargs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By using learning_curve(), it will give us the correspondent MSE for a regression model. As we specified 7 training set sizes, seven rows with six error scores. This is due because learning_curve() runs a k-fold cross-validation under the hood, where the value of k is given by what we specify for the cv parameter. In our case, cv= 5, so there will be five splits. For each split, an estimator is trained for every training sset size specified. Each column in the two arrays above designates a split, and each row corresponds to a test size."
]
},
{
"cell_type": "code",
"execution_count": 462,
"metadata": {},
"outputs": [],
"source": [
"train_sizes = [1, 10, 40, 60, 80, 100, 120, 140]\n",
"from sklearn.model_selection import learning_curve\n",
"\n",
"train_sizes, train_scores, val_scores = learning_curve(estimator=PolynomialRegression(2), X=x_features, y=y_feature, train_sizes=train_sizes, cv=5, scoring=\"neg_mean_squared_error\")\n",
"#train_sizes, train_scores, val_scores = learning_curve(estimator=LinearRegression(), X=x_features, y=y_feature, train_sizes=train_sizes, cv=5, scoring=\"neg_mean_squared_error\")\n",
"\n",
"train_scores_mean = -train_scores.mean(axis = 1)\n",
"validation_scores_mean = -val_scores.mean(axis = 1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 463,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x537d5d2448>"
]
},
"execution_count": 463,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use('seaborn')\n",
"plt.plot(train_sizes, train_scores_mean, label = 'Training error')\n",
"plt.plot(train_sizes, validation_scores_mean, label = 'Validation error')\n",
"plt.ylabel('MSE', fontsize = 14)\n",
"plt.xlabel('Training set size', fontsize = 14)\n",
"plt.title('Learning curves for a linear regression model', fontsize = 18, y = 1.03)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As of now, the MSE can be seen not very high so we can tune the hyperparameter degree inside PolynomialFeatures() to obtain the lower error in this graph. As the training set size increases, we can see that there is a peak around 100 features size but then the error lowers to ideal values. To be able to see more trends, we would need to have a higher number of data points, because now we have less than 200"
]
},
{
"cell_type": "code",
"execution_count": 464,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def plot_learning_curves(model, X, y):\n",
" X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)\n",
" train_errors, val_errors = [], []\n",
" for m in range(1, len(X_train)):\n",
" model.fit(X_train[:m], y_train[:m])\n",
" y_train_predict = model.predict(X_train[:m])\n",
" y_val_predict = model.predict(X_val)\n",
" train_errors.append(mean_squared_error(y_train[:m], y_train_predict))\n",
" val_errors.append(mean_squared_error(y_val, y_val_predict))\n",
" plt.plot(np.sqrt(train_errors), \"r-+\", linewidth=2, label=\"train\")\n",
" plt.plot(np.sqrt(val_errors), \"b-\", linewidth=3, label=\"val\")"
]
},
{
"cell_type": "code",
"execution_count": 465,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y_result = df.groupby([\"date\"])[\"new_deaths\"].sum().values\n",
"n_samples = len(y_result)\n",
"X = np.arange(1,n_samples+1, 1).reshape(-1, 1)\n",
"lin_reg = LinearRegression()\n",
"plot_learning_curves(lin_reg, X, y_result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"when there are just one or two instances in the training set, the model can fit them perfectly, which is why the curve starts at zero. But as new instances are added to the training set, it becomes impossible for the model to fit the training data perfectly, both because the data is noisy and because it is not linear at all. So the error on the training data goes up until it reaches a plateau, at which point adding new instances to the training set doesn’t make the average error much better or worse. Now let’s look at the performance of the model on the validation data. When the model is trained on very few training instances, it is incapable of generalizing properly, which is why the validation error is initially quite big. Then as the model is shown more training examples, it learns and thus the validation error slowly goes down. However, once again a straight line cannot do a good job modeling the data, so the error ends up at a plateau, crossing by some values the other curve.\n",
"\n",
"These learning curves are typical of an underfitting model. Both curves have reached a plateau; they are close and fairly high."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let’s look at the learning curves of a 10th-degree polynomial model on the same data:"
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.pipeline import Pipeline\n",
"\n",
"polynomial_regression = Pipeline([\n",
" (\"poly_features\", PolynomialFeatures(degree=10, include_bias=False)),\n",
" (\"lin_reg\", LinearRegression()),\n",
" ])\n",
"\n",
"plot_learning_curves(polynomial_regression, X, y_result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These learning curves look a bit like the previous ones, but there are two very important differences:\n",
"\n",
"The error on the training data is much lower than with the Linear Regression model.\n",
"\n",
"There is a gap between the curves. This means that the model performs significantly better on the training data than on the validation data, which is the hallmark of an overfitting model. However, if you used a much larger training set, the two curves would continue to get closer.\n",
"\n",
"One way to improve an overfitting model is to feed it more training data until the validation error reaches the training error."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.6.1. The Bias/Variance Tradeoff\n",
"\n",
"An important theoretical result of statistics and Machine Learning is the fact that a model’s generalization error can be expressed as the sum of three very different errors:\n",
"\n",
"**Bias**\n",
"This part of the generalization error is due to wrong assumptions, such as assuming that the data is linear when it is actually quadratic. A high-bias model is most likely to underfit the training data.10\n",
"\n",
"**Variance**\n",
"This part is due to the model’s excessive sensitivity to small variations in the training data. A model with many degrees of freedom (such as a high-degree polynomial model) is likely to have high variance, and thus to overfit the training data.\n",
"\n",
"**Irreducible error**\n",
"This part is due to the noisiness of the data itself. The only way to reduce this part of the error is to clean up the data (e.g., fix the data sources, such as broken sensors, or detect and remove outliers).\n",
"\n",
"Increasing a model’s complexity will typically increase its variance and reduce its bias. Conversely, reducing a model’s complexity increases its bias and reduces its variance. This is why it is called a tradeoff."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is how to perform Ridge Regression with Scikit-Learn using a closed-form solution (a variant of Equation 4-9 using a matrix factorization technique by André-Louis Cholesky):\n"
]
},
{
"cell_type": "code",
"execution_count": 467,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import Ridge, SGDRegressor\n",
"ridge_reg = Ridge(alpha=1, solver=\"cholesky\")\n",
"ridge_reg.fit(X, y_result)\n",
"y_ridge_pred = ridge_reg.predict(X)\n",
"plot_learning_curves(polynomial_regression, X, y_ridge_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And using Stochastic Gradient Descent:"
]
},
{
"cell_type": "code",
"execution_count": 468,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sgd_reg = SGDRegressor(penalty=\"l2\")\n",
"sgd_reg.fit(X, y_result)\n",
"y_sdg_pred = sgd_reg.predict(X)\n",
"plot_learning_curves(polynomial_regression, X, y_sdg_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The penalty hyperparameter sets the type of regularization term to use. Specifying \"l2\" indicates that you want SGD to add a regularization term to the cost function equal to half the square of the ℓ2 norm of the weight vector: this is simply Ridge Regression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following will show how to perform the model with a Lasso regression model"
]
},
{
"cell_type": "code",
"execution_count": 469,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import Lasso\n",
"lasso_reg = Lasso(alpha=0.1)\n",
"lasso_reg.fit(X, y_result)\n",
"y_lasso_pred = lasso_reg.predict(X)\n",
"plot_learning_curves(polynomial_regression, X, y_lasso_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following picture will show how to perform the model using ElasticNet estimator"
]
},
{
"cell_type": "code",
"execution_count": 470,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import ElasticNet\n",
"elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)\n",
"elastic_net.fit(X, y_result)\n",
"y_elastNet_pred = elastic_net.predict(X)\n",
"plot_learning_curves(polynomial_regression, X, y_elastNet_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.7. Conclusions <a id='part3_7'></a>\n",
"\n",
"as we can see, the models have become better by changing from pure LinearRegression() (where the error of the training and validation sets converged at around 3000) to PolynominalFeatures (where the error of the training and validation sets converged at around 500) and better still with the regularization methods in PolynominalFeatures (Ridge, Lasso, Stochastic Gradient Descent and Elastic Net in values close to 100 of error)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Predictive Modelling (TensorFlow)<a id='part4'></a>\n",
"\n",
"In this section we will use the TensorFlow library to create models of prediction for the new_deaths variable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1. TensorFlow for Polynomial Linear Regressions to new_deaths <a id='part4_1'></a>"
]
},
{
"cell_type": "code",
"execution_count": 471,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import, division, print_function"
]
},
{
"cell_type": "code",
"execution_count": 472,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow.compat.v1 as tf\n",
"import numpy as np\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"from sklearn.metrics import r2_score as r2\n",
"tf.disable_v2_behavior() \n",
"\n",
"rng = np.random"
]
},
{
"cell_type": "code",
"execution_count": 473,
"metadata": {},
"outputs": [],
"source": [
"alpha = 0.01\n",
"epochs = 200\n",
"m = len(x)\n",
"errors = []"
]
},
{
"cell_type": "code",
"execution_count": 474,
"metadata": {},
"outputs": [],
"source": [
"Y = df.groupby([\"date\"])[\"new_deaths\"].sum().values\n",
"Y = Y[np.where(Y>50)]\n",
"n_samples = len(Y)\n",
"X = np.arange(1,n_samples+1, 1)"
]
},
{
"cell_type": "code",
"execution_count": 475,
"metadata": {},
"outputs": [],
"source": [
"predDegree = 5\n",
"\n",
"x_reshaped = X.reshape((len(X), 1))\n",
"y_reshaped = Y.reshape((len(Y), 1))\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(x_reshaped,y_reshaped, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 476,
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler().fit(x_train)\n",
"x_train = scaler.transform(x_train)\n",
"x_test = scaler.transform(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 477,
"metadata": {},
"outputs": [],
"source": [
"X = tf.placeholder(tf.float32, shape=[None, 1], name='x-input')\n",
"Y = tf.placeholder(tf.float32, shape=[None, 1], name='y-input')\n",
"\n",
"theta_1 = tf.Variable(tf.zeros([1, 1]))\n",
"theta_2 = tf.Variable(tf.zeros([1, 1]))\n",
"theta_3 = tf.Variable(tf.zeros([1, 1]))\n",
"theta_4 = tf.Variable(tf.zeros([1, 1]))\n",
"theta_5 = tf.Variable(tf.zeros([1, 1]))\n",
"theta_6 = tf.Variable(tf.zeros([1, 1]))"
]
},
{
"cell_type": "code",
"execution_count": 478,
"metadata": {},
"outputs": [],
"source": [
"model = tf.matmul(tf.pow(X, 5), theta_1) + tf.matmul(tf.pow(X, 4), theta_2) + tf.matmul(tf.pow(X, 3), theta_3) + tf.matmul(tf.pow(X, 2), theta_3) +tf.matmul(X, theta_5) + theta_6\n",
"\n",
"cost = tf.reduce_sum(tf.square(Y-model))/(2*m)\n",
"\n",
"optimizer = tf.train.GradientDescentOptimizer(alpha).minimize(cost)\n",
"\n",
"init = tf.global_variables_initializer()"
]
},
{
"cell_type": "code",
"execution_count": 479,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ww0/g+PFCPP/8MzCZTDAajXjyyWec6+6Eyu+/QmJiIgCgqqoK999/P6ZNm4YXX3wRyu8N9BMTE1FZWYmqqiqkpaV5PK6yshKapuneNlBgkp5uhslkDO1VRjF/3e9aG54LgefBJVrOxZkznj87HInIyjp7zx8t5yEaNNa5MJsBQ4SrLs3mWGRlxQbcLjk5/vdtxWtLSzNDVe1YsmQZAODtt19HVlYy4uLiYDaL/e3atQmlpaewdOkS1NXV4cYbb8TVV1+OlJQU535vueVmvPzyy7jllpuwYsUKXHbZpcjNzcLOnTtxww3X48orr8TJkycxadIk/OlPtyM21oT0dDMSE+OQlBSPrKxknDljRmysCW3aJOGvf52NDz/8EJmZmfjb3/6GH374BjabDX379sajjz6KX375BSaTI+x/o4DhYVFREe677z5MnDgRo0aNwpw5c5y/s1gsSElJQVJSEixyAPb3+5OTk50LEOnZNpCysmrdL6q5aAlthSOF50LgeXCJpnNx+LABQCISEjTU1Cg4cKAGxcX2s/Lc0XQemlpjnouHHxb/RVpxceBtKitrUV1tdb628vJq5OR0dP7scKgoLq5EXJwVFovIwGzevB3btm3H+PE3AQBqa63YsWMfunc/x7nftm3zUFpajl27fsPHHy/Bffc9gOLiShgMCfjqq//Fl1+ugNmciLo68dxWqx1lZdWwWOoQH1+L4uJKlJZaYLXasW/fEZw8eQr33jsFAFBXV4eBAwfjllvuwLFj7+LWW29HYmIS7r77Pl3/RiG3pC8pKcEdd9yB6dOnY+xYkebp0aMH1q9fDwBYvXo1BgwYgP79+2PNmjVQVRXHjx+HqqrIyMgIalsiomglh3I6dhSrsnLKMDU2g8H1HouNjcXp0yXQNA379/8KQKw43K/fAMyf/zr+8Y/XcNllV6BDhw4N9jNy5HVYuvQT1NXVIj+/KwDgo4/eR0FBb8yc+Swuu+wK1F8yLzY2DqdPnwYA/PrrHgBAamoa2rZtixdemIv581/Hrbfegf79B2DNmu/Rp08//P3vC3HppZdj8eJ3w37tfjMmr732Gs6cOYMFCxZgwYIFAIAnnngCs2bNwty5c5Gfn48RI0bAaDRiwIABGD9+PFRVxcyZMwEAjzzyCGbMmKFrWyKiaCWTvHl5Gn79lYEJnV0TJ96C6dMfQLt2Oc4RhosuGobNmzfi3nsno6amGsOGXQqzObHBY4cPvwp//ONIPPDAQ877LrpoGF5+eTa+/vp/kZqaCqPRCKvVtTDl5ZcPx8yZj2HLlk0499zzAQAGgwEPPPAQpk9/AJqmwWxOxIwZz6C6uhp/+csMGI1GGAwGTJ36P2G/Xq4u3ISYonXhuRB4Hlyi6VwsX27C5MkJuP12K95+Oxa33GLFyy8HX9QYimg6D02N50JoCeeBqwsTEYVBLuCXl8ehHKLGxsCEiCgAWWOSm6tBUTQGJkSNiIEJEVEAsh19SoqGtDRmTIgaEwMTIqIAZPFrYqKGjAwNJSUMTIgaCwMTIqIAZMYkKQnIyNBQVqZAVZv4oIhaKAYmREQByBqTxEQNmZkqHA6lQTdYIooMBiZERAHIWTkyYwKwzoSosTAwISIKwD1jIgOT06cZmBA1BgYmREQBVFUpMJk0xMUBmZnMmBA1JgYmREQBWCxAYiKgKAxMiBobAxMiogAsFgVJSSIg4VAOUeNiYEJEFIDImHgGJsyYEDUOBiZERAGIjIn4fwYmRI2LgQkRkR92O1BbqzgzJqwxIWpcDEyIiPxwb0cPACkpgNGo4fRpfnwSNQb+ZRER+SHb0Scmip8VRQznsPiVqHEwMCEi8sO9uZqUmalxKIeokTAwISLyw70dvZSRoaG8XIHd3jTHRNSSMTAhIvLDW8ZEzswpK2PWhCjSGJgQEfkha0xkgzWAM3OIGhMDEyIiP1yzclz3MTAhajwMTIiI/PCWMWFbeqLGw8CEiMiP+n1MAHZ/JWpMDEyIiPxwZUxc9zEwIWo8DEyIiPzw1ccE4FAOUWNgYEJE5IccyvFWY8KMCVHkMTAhIvLDlTFx3ScDk02bjCgvb4qjImq5GJgQEfkhAxP3jEliInDLLVb89psB48aZGZwQRRADEyIiP2RLerPZ8/6XXqrDxIlWbN1qxNixZpSVnf1jI2qJGJgQEflhsSgwmzUYjZ73GwzA3Ll1uPlmK7ZtM2LcODOs1qY5RqKWhIEJEZEfVVWA2ax5/Z3BAPz1r3W4+mobtm0zYvNmo9ftiEg/BiZERH5YLIpHD5P6DAZgwAAVAFhrQhQBDEyIiPyoqlI8eph4k54ufl9ezunDROFiYEJE5IOmiT4mgQKT1FQGJkSRwsCEiMiHmhpAVf0P5QDMmBBFEgMTIiIfvLWj94YZE6LIYWBCROSD7GHCjAnR2cPAhIjIB70Zk7Q0BiZEkcLAhIjIh6qqhu3ovUlMBEwmjYEJUQQwMCEi8qG6Wty6L+DnjaKIrAkDE6LwMTAhIvJBb8YEEIFJRUVjHxFRy8fAhIjIB4tF3AaqMQGAtDSgrEyBFnhTIvKDgQkRkQ+u4tfA26alaXA4FGcwQ0ShYWBCROSDHMrRlzER25SVsc6EKBwMTIiIfJDZD701JgCnDBOFi4EJEZEProxJ4G0ZmBBFBgMTIiIfZI0JMyZEZw8DEyIiH2RLemZMiM4eBiZERD6EkjFh8StReBiYEBH5UFWlwGjUEBcXeFsZmLDJGlF4GJgQEflgsYiVhRUdSZC0NHHLoRyi8DAwIaJmyW4H3nknxlkH0hiqqhRdPUwA1pgQRQoDEyJqlr791oiHH47H4sUxjfYc1dX6mqsBDEyIIkVXYLJ161ZMmjQJALBz505cfPHFmDRpEiZNmoQVK1YAAObPn4+xY8diwoQJ2LZtGwDg8OHDuOmmmzBx4kQ89dRTUFXV57ZERMEoKREfX4cPN973K5Ex0bdtbCxgNnOFYaJwmQJtsGjRIixfvhwJCQkAgF27duH222/HHXfc4dxm586d2LBhA5YsWYKioiJMnToVn332GWbPno1p06Zh0KBBmDlzJlauXImcnByv2xIRBePMGXFbWNg4gUBdHVBbqyAlRf+qfOnpDEyIwhXwq0ZeXh7mzZvn/HnHjh1YtWoVbr75Zjz++OOoqqrCxo0bMXToUCiKgpycHDgcDpSWlmLnzp0YOHAgAGDYsGH46aeffG5LRBSMM2dEAFBY2DgZE7n/1FT9gUlqKgMTonAFzJiMGDECx44dc/7cu3dvjBs3DgUFBVi4cCFeffVVJCcnI02WpANITExEZWUlNE2Doige91VVVXndNiMjw+9xpKebYTIZg36B0S4rK7mpDyFq8FwIPA8u/s6FzSZui4qMDbbTNKC4GGjbNvTnLisTt+3axSArS18dS9u2wK5dQHp6MkwBP139s9tdr5HvCReeC6Eln4eg/3SGDx+OlJQU5/8/++yzuPzyy2FxW+vbYrEgOTkZBoPB476UlBQkJSV53TaQsrLqYA816mVlJaO4uLKpDyMq8FwIPA8ugc7FyZPxAGJQXAwcOVKJ30ebAQDffWfE+PFmvPhiLW6/3RbS8x84YACQiNhYK4qL63Q9xmwWx7R/fxUyM/VnWryZOTMOS5eacOSIAVVVfE8A/PuQWsJ58BdYBZ0DvfPOO50Fq2vXrkXPnj3Rv39/rFmzBqqq4vjx41BVFRkZGejRowfWr18PAFi9ejUGDBjgc1siomDIGhMAOH7cc/hk0yaRXf3LX+JCrkGpqAh+KCc9Xc7MCekpPWzcaERJiQEnToS/L6LmJOiMydNPP41nn30WMTExaNOmDZ599lkkJSVhwIABGD9+PFRVxcyZMwEAjzzyCGbMmIG5c+ciPz8fI0aMgNFo9LotEVEwKitdAUdhoQFduzqcPx88KL5zWSwKHn00Hu+9V6OrSZo7WWMSTPFraqq4FXUm4WVMTpwQz19aKpq8EbUWugKT3NxcfPrppwCAnj174uOPP26wzdSpUzF16lSP+7p06YIPPvhA17ZERMGQgQPQcGbOwYMGGI0aBg1y4P/9PxO++sqEUaPsQe1fFrHK/iR6uDIm+qKg2lqxbbt2ns+hqp6BSV6e7kMgavbYYI2ImiX3wOTYMc+PsoMHFXTsqGHu3FrExWl47LG4oNewCXVWDqA/MHnppVgMHpyI06c9tz99WoHN5gpMiFoTBiZE1CydOaMgLk4EAu41JpWVovlafr6K/HwNDz5oxalTBjz3nI6V+NzIQCbYPiaA/sDk11+NqK5WsH+/50exzJYADEyo9WFgQkTNjqaJ4tdu3UQ3afeMyaFD4v+7dBG/u+8+K9q1U/HVV8GV1MngQtaN6BFsxqS0VGx37Jjn9kVFDEyo9WJgQkTNTnU14HAoyM7WkJmpemRMZOGrDExiYoDu3VWUlBhQU6P/OUIZygk2Y1JW5r1J3PHjrp8ZmFBrw8CEiJodOSMnJUVDTo6GwkIDtN/jh/qBCQDk5jYc8gkklOnCwWZM5LTio0c9t+dQDrVmDEyIqNmR2YzkZA0dOqiorlacnVpFYzTPwKRDh4ZDPnqeIy5OQ3y8/uMKJmOiqq6MSf3jKipixoRarzCbJhMRnX2yuVpqqobYWPH/hYUGZGSoOHhQgcGgoWNHV6ajY0f19230Z0zKy5WgsiUAkJwMKIrmDJL8OXMGUFX/NSaKojnrUIhaC2ZMiKjZcTU/c2VDZNBx8KABubka4twm4XToIAKMo0eDyZgEN4wDAAYDkJbmGgbyR2ZLAJEx0dye6sQJERSlp2s4fTqoQyBq9hiYEFGz4z6UI+tHCgsNsFiAkycN6NxZ9dg+N1d1bqOHpong4vdlwYKSmqp5BB2+uG9jsSgebeyLigxo315FejqHcqj1YWBCRM2Oe7v4nBxXxqT+VGEpJ0cEL/WHTHyprgZstuCHcgBRZ1JRoXhkQLypH7zIOpPqahEUtWunIS1NQ2kpAu6LqCVhYEJEzY6sMUlJ8cyYeJuRAwDx8UDbtqru4lcZ+ATTjl5KS9NQV6cEnJosAxN5rHKYSc7Iad9eDOXYbIDbguxELR4DEyJqdlzThYHsbA1Go4bCQsUtMGkYUOTmajh+XIGqNvhVA7JGJJiur5IMZgLVmcjApHdvsfigrJGRPUzat1ed+9I7/ZioJWBgQkTNjvtQjtEosguFhQYcOuSZhXDXoYMKq1VBcXHgi3woPUwkGUwEqjORs2169fLMmMgZOe3aac7px3pqVohaCgYmRNTs1M9odOigoqhIwb594iOtU6eGgYkc8tFTZyLXyQknMAmUMZFZkF69HB7HJXuYMGNCrRUDEyJqdtw7vwJiOrCqKti82YicHBUJCQ0fI2fm6KkzcWVMgj82vRkT+ftzzlERF6c5j6t+jQkQucDkzBnoyhgRNSUGJkTU5A4eVOBw6N9eFr8mJYlb2cukrk7xOowDBJcxCWWdHMmVMfG/nRzKycjQ0KGD5pYxcQUmeoMcPY4eVTBsWCKuvtoc9r6IGhMDEyJqUvv2GTB4cCL+8Y9Y3Y85c0ZBUpKoLwFcDdQA7/UlQHAZE5mhCK34VdwGCibKyxUkJGhISBCBlVxk8MQJA2JiNGRmRi5jUlysYNw4M44fN+DIEQMqK8PaHVGjYmBCRE1q/34DNE3B8uX6V8iorPTsMSIzJgDQubP3YKJ+h1h/5FBOqNOF3ffhS2mp4tzWvWV+UZHoYSK6yIafMTlzBpgwIQEHDoiW/UBwHXCJzja+O4moSZWUiIvuzp1G3av/iq6s7oFJ4IxJejpgNmu6MibufVKCFUyNicyIyGGmw4cNOHlSBCbimGXGJOjDAABYrcD//b8J2L7diEmTrLj3XhuAhqsZE0UTBiZE1KRkYAIA334bOGuiaUBlpWhHL7lnTHwFJooihnOCGcoJp/jV3/CLzSayPhkZMjARx7x5sxEOh4L27VWPfYWaMVm92oh160y4+mobXnqpzjlbiRkTimZ8dxJRk/IMTIwBt7dYxKq87uvYpKWJbAiABuvkuMvN1VBerqCqyv9zhFP8KoON06d9BxMyaJGBh8yY/PKLeP3t22u/P7/n9sE6cUJ8xF97rR1Go2vI6MgRfvRT9OK7k4ialLyAp6drWBHEf40AACAASURBVL3ahNpa/9u7N1eTFAUoKHDgnHMczpk63sjMSqCsSUWFgsREDSb9ZS9OcXHitZw86TuYkBkQ11COOC4ZmLRrJ342GkXQFWrGRE4NbtNG1rLIVZYbZyhHVYH9+zlMROFhYEJETUpePMeMsaG6WsFPP/nPmrivLOzuvfdq8K9/+V+gRl6YAxXAVlSEtoCflJ2t4uRJ3x+vMtCQ2ZWcHA2Kojlfm8yYiG1CD0xkNiorS+yvTRsNCQlaow3lvP56DIYMScK6dYEzX0S+MDAhoiZVUiIKWa+91g4gcJ2JLEytHzhkZLguwL7IjEmgC3O4gUnbtmKFYV8L+ZWViVs5lBMbC2fBK+BaDRkQryvUoZz6gYmiiOGcxgpMliyJASBqW4hCxcCEiJrU6dMKMjM1DBrkQFKShm++MUHzExO4hnKCfy7XSsS+L/SqKoKf8DIm4rGnTnl/nvoZE/djA1xDOQCQmQnU1gZerdgbmY3KzHTtu2NHDWVlSsR7mRw6pGD7dhGQyCEpolAwMCGiJqOqIjBp00ZDbCxwySV2HD5swP79vj+afA3l6KGnyVpVFaBpSkgzcqTsbPE8vupM6teYuB8b4Jk9ycgQt+5Zk48/NmH69Di/ARwgMibp6RpiYlz3yQLYSGdN/v1vV6Zr0yajrlWcibxhYEJETaa8HHA4FLRpI65iw4fL4Rzf37i9Fb/qJRqXaX7b0ofT9VWSGRNfdSYyMJFdYgFXYJKRoSI+3nW/DEzc60xeey0W774bGzDrUVLiOrdSYxXAfvVVDIxGDVdcYceZM64FFYmCxXcOETWZkhLxESRnjVx2mVgwx1+dSTiBSUyMKCwtLPT90edawK9phnLcsyViG3ErAyabDc6LvjwX3tjtrmyUu7y8yGdMjh9XsHGjEUOGOJzBJYdzKFQMTIioycipwvLimZ2toVcvB9atM8Ju9/4YmSVITg7tOTt0UFFUpPjcfzg9TCRXxkT/UI4cYnGfkQM0zJj89psBNpv4f39t70tLFWia0qAguDF6mchhnGuvtWPAABFc/vILLy8UGr5ziKjJyFkj7t/qu3RRYbMpKC72/phwA4fcXA0Oh4ITJ7xf1COTMZE1JoGGclzPkZ+vetxK9TMme/a49ukvY+Lt3AKuzEwkh3K++soERREzq84/X4XZrDFjQiFjYEJETaZ+AzDAlW0oKvL+GBk4hFoDEqgAtqJC3IY7XRjwnzFJTvYsSs3P17BsWTX+53+sHtu6Mibidvdu98DE9zF4O7eAmDocHx+5XianTilYt86IgQMdyM4WTen69XNg716j81wSBYOBCRE1GTmU4z6dNVBgUlkZXmAiiz8PHQqUMQlp9wCApCQgMdF391f3BfzcDR3q8DgXQMOMiXtg4m8op34PEynSvUxWrDBB0xSMHOkaG5PDOZs2MWtCwWNgQkRNxttwQ9u2IqNx4oT3x5w5AyiKhsTE0J7z/PPFRXPnTu8XzUgM5QAiaxJsYOJN/RqT3btdxx3KUA4Q2V4mX33lqi+RXHUmDEwoeAxMiKjJeA9M/GdMzpxRkJwMGEL89OrRQ4WiaNi509dQTvjThQFRZ3L6dMMi25oaoKYm+MBELj54+LABMTGax7F642soB4hsL5Offzbi/PMdHg3iLrjAc+0fomAwMCGiJlNSokBRNI9ps3qGcsLJZiQlAV26aNixw+i1QVmkMibZ2Ro0TXEGCJIcknF/zf6kp7set3ev+Mju21dc+PVkTLKyGnY6i1Qvk+pqEWTVn+Lcpo2GLl1UbNzIRmsUPAYmRNRkTp8WmQP3VXwDBSYiYxJe0FBQ4EB5ueK1Nb282LvPmAmFrynDpaXB7T8mBkhKEkMvchhn0CD778fq+3GyR4y39YO89TJ54ok4PPFEnK5jkrxNe5YuuMCBM2cUv118ibzhO4aImozoTFq/2FODyaR5rTFRVdHHJNxhloICcWH2NpxTUSFqWJKSwnoKnzNzZMZE71CO3LasTHFOFR40yPH7sfofyomL8/466vcy2b7dgEWLYvH++zFBZTj8BSaR6mdSVeVZ8EstH/+1iahJ2O1AaamhQWBiMIhv+d4yJnIdm1AW8HNXUCAumjt2NKyBqKgQ+w+1hkXy1ctEZkz0DuUAIrsiMiZiX3/4gzj+QEM5WVkaFC+b1B/KmT8/FoBYLNBXt1pv/AUm8hjDrTP561/jcNllZhw4ENkW+s3dRx+ZsGVLy7yEt8xXRURRT16g60+PBcQwSFERGtSAhLOAnzuZMdmxw1vGJLwaFslXW3p/F3Nf0tI0VFeL1Xvz8lRkZADx8ZrPwETTRMbEW+Er4NnL5NAhBV984RpLO3RI/2XBW2t96fzzVSQlafjhB/+rRQdy5IgCh0PBmjW+lylobcrLgQceSMBjj8UH3rgZYmBCRE3C33TW7GwNdXUNayjCWSen/v4zM1WfGZNIBiaRGsqRj+3RQ2QiUlI0n0M5FovIfvgKTNx7mSxcGAtVVTBkiKhbOXIk+IyJt3oZk0nfatGByPO1di1n+EinT4vbLVsMEZnyHW0YmBBRk/AXmMheJvWHQSKxjg0gLsw9e6o4fNjgEfzYbIDFEqnARL4G78WvoQQmAHDeeWK/qamaz4uSnAnkrfBVkr1MPvwwBnl5KqZMER1nDx+OTMYEAK68UgQ733wTelAh/83Xr2dgIskuwA6H6Lrb0jAwIaIm4T8w8Z5tCHcBP3dyOGfXroYNy8LNyABimm9srIZTpzw/ZkMZynHf9vzz1d+PUWR3vA2TuHqY+K5klQWwdXUK7rnH6lyjJ5jAJFCQJVeL/uab0IdhZMbk2DEDDh8OeTctigxMALTIIS4GJkTUJOqvLOzO1zBIpJqfAe4FsO4t3sVtuFOFAZGV8db9VV5Ugq0xkVyBiQabTUFNTcPt5VRhX0M5gKsANiNDxU032ZCbq8Fg0HD4sP6hHBk0+Dpfbdtq6N9frBYd6ro57nU0q1eHto+Wxj0w+fFHZkyIiCIiUI0J0LBwNJIZDW9Thl2BT9i7ByBex6lTnlmNsjIFBoMW1HPIICYmRkPXrq6hHMC1dpA7X+vkuDvvPBGY3XWXDWYzEBsLdOigOacQ6xFoKAcAhg+3w+FQ8N13wX+zV1URLMpiZwYmQnm56/+3bzd4BCotAQMTImoSodSYhLuAn7tu3VTExWkeBbCR6voqtW2rwmZTnEMegGudnGCmI6eluY5Zrkgsz4G3Alh/7eil4cMd+Pzzakyb5lrNOC9PRVGRAbW1+o6rtFSB0eg/yHLVmQQfmFgsgKoqGDjQ8fsMn6B3ETYxRf3sP68/MhDp188BTVOwdm3LGs5hYEJETUIGJpmZDesgfA3lyELVcKcLA2LWyPnnq9izxwCbTe4/soGJt9dRVqY4Aw29ZMZEDuMA7oFJw+39BX2SogBDhjhgdBsJ6NQpuDV0ysvFMI63XilSQYGKdu1UrFxphMOha7du+3fVsAwc6MDevQ2zaI1p0yYDunVLwpdfRteFXwYmcuHEljacw8CEiJpESYkBRqPm9SIti1/rrzPjChwicwwFBQ7U1bnapkc6Y1I/MNG04FYWlnr3dmDECDtuvtnmvE+eg1CHcrzp1Elsr7fORM9rURQxnFNaasDGjcFdcuS/R1qahgsvFFGN++yc06cV/Pxz413GvvnGBFVVsGpVdF34ZWBy2WV2xMdrWLMmuo4vXAxMiKhJlJQoyMz0PqQRGwtkZnqblRO5oRxATBkGRAHshg0GvPKK6IDqvlJuOOoHJlVVgN2uBNX1FQDMZuD992tw8cWulIOeoRxvzev8kRkTPTNzZJClJ/szfHhowznuxc6DB4vXLvuZVFYCo0Yl4NprE7FiReNkNDZuFM/lrd9NU5I1JllZIpO0e7fRGYy2BAxMiKhJeFsnx127dg1rTOSFKhJDOYArMJk3LxbXX2/G8eMKHn64znkRDJfsZSKnDMtv+zk54S+56y8wKSlRkJGheiyOqIdc3E9PYFJZKfpo6AmyLr7Ygbg4LeTAJC1NQ9++DsTHA+vWiVWhp02Lx/794nz+z//E4cSJyF6YVRXYtEnsf/du13BfNJAZk9RUDUOHivfqTz9FV/AUDgYmRHTW1dWJ7Ie/b/Tt24sLk/t02DNnRLFlYmJkjqNnT/GhvmePEVlZGpYtq8FDD1n91kwEo37G5O9/FxmZW28N/yonh5u8taWX6+QEK5ihnGAaxSUmAkOHOrBrlxHHjuk/ubJ+JjVVQ1wcMGiQmEX14oux+PLLGAwebMdzz9WitNSA++6LD2oBwkB+/dXgPLfuw33RoKxMLEkQHw8MHSqyUT/8ENnARNMQ0fMZjOg500TUasgeJv4unu3bi1s5LKGqwL59BnTo4L/YMhjJycANN9gwapQN//1vNYYMiUymRHIPTNatM2L9ehOuvNLuzNSEQ2ZM6rftt9m8L46oR5s2GsxmTVfGJFAPk/rkBXTzZv0XUFfNj/h52DCxiOPcuXFo21bFokW1mDzZhiuvtOOHH0xYuDBG976l4mIFV15pbnBhl4sPymnV3tZVaiplZa7AtE8fFYmJWsQLYB98MA4DB0boG0CQoudME1GroWfWSLt24lZmG/bsMaC8XHEWQUbKa6/V4s03a0O6kAfSpo1oWnbypOLMltx/f11E9i2n6NYfypGZjFBej6KIOpMjRwwBp8gGu0qynFEkV0jWQwY/8iI8bJi432jU8MYbtcjOFkHqK6/UIitLxfPPx2H79uAuaxs3GrBlixGvvhrb4H7Ald3avj30C/9nn5kiWgcjZ0MBQEwMMHiwA/v3G1FUFJmI/cABsVRBJBoNhoKBCRGdda6pwoEzJrLORK4JEqn6j7PBaBQBwvbtRqxcacKFF9oxcGBk8uO+hnL0rJPjT6dOKqqqPHuveBNsa30ZmOzZo/+yU3/69tChopB27txaj/dBVpaGV16phc2m4K23gsuayOBn9WqjR6OyX34xwmzWMHasDYqieTTiC4amAQ89FI8HHoiPSJ2KponAxL13zBVXiGxUpKY1L1ggFnaU6yedbbrO9NatWzFp0iQAwOHDh3HTTTdh4sSJeOqpp6D+Pgg1f/58jB07FhMmTMC2bduC3paIWg89GRMZmMi+FTIwufBCe+MeXIRlZ2uorhav4YEHIvdB7xrK8R6YhJoB0ltnEuwqye3aaUhN1YIKTOpnTOLjgcWLa3DTTQ3fA5dc4oDRqOHXX4PLbMgAzG5X8J//iAt7RQWwd68R/fo5kJoKdOkiGvGF0mitvFwsDFlRoWDDhvCHW6qqAIfDcwht1Cg7jEYN//pX8ENZ9Z08qeCTT2LQqZOKkSOb5m8t4Dtk0aJFePLJJ1FXJ9KPs2fPxrRp0/Dhhx9C0zSsXLkSO3fuxIYNG7BkyRLMnTsXzzzzTNDbElHrEWxgomlimmhWloouXaKsDWcAss6kVy8HLr00ctkesxkwmbQGQzl6zq0/eqcMB7tKsqKIeo0DB/R3lpVBl54hhdhYoHNnDfv2BR6GcieDHwBYvlxc2OU04QEDxL9XQYEDZWUKCguDHyopLHSdx6+/Dj+jUT9YA0Tfn4svdmDjRiMOHgxvOOfNN2NQV6fg3nutQc/qipSAgUleXh7mzZvn/Hnnzp0YOHAgAGDYsGH46aefsHHjRgwdOhSKoiAnJwcOhwOlpaVBbUtErYdrAT/fwxruNSaHDik4edKACy90RKzw9WyRU4MfeCBys30AcaFPSdEaFL+G2lxNkoFJoDVzQlkl+bzzVKiqgn379GVNKioAg0H/LKzu3R0oL1eC6ukhA6zUVA3ffy+GcxoGJq5+N8FyD2YiEZi4T6F2d8MNYpwonKxJVRXw9tuxaNNGxYQJTTc/OuBZHjFiBExuYZOmaVB+/+tKTExEZWUlqqqqkJSU5NxG3h/MtkTUesjVb/XWmDTH+hJp6lQr5s6tbZS0eEqKv6Gc0GpZ9A7lhBqYAPrrTCoqFKSmQve6Qt27i/0HM7VXZiAmTLA5h3PkjJwLLhD7kytRh1IAKzMm8fEafvvNgN9+Cy869bXC9rXX2hEXp+Gzz0whr+3z3nsxqKhQcNddNiQkhHWYYQk6fDO4vUMsFgtSUlKQlJQEi8XicX9ycnJQ2waSnm6GydRyGshIWVmBX3trwXMhtNTzUFYGLFsGfPwx8N//im/8PXokwdefv6aJ4YrSUhM2bxYfVddcE4+srPizeNThy8oCBgwAgNCP29d7IjMT2L3b8/dVVeL23HMTkZUV/HPJ743Hj8ciKyvW53byY/ycc5J0ZzQuvFDcHjmSoOvYKiuB9HTP1+fv76N/f3F74oRZ92uX5+uBB2Lxz38C//u/Cdi0CejWDTj/fHEyLrlEbLNvXxyysuL07fh3skvrTTcpePtt4KefkjB4cFC78CCDjtxcz2PJygJGjQKWLjXi+PFk9O0b3H6tVmDRIvHvP316HNLTg3udkRR0YNKjRw+sX78egwYNwurVqzF48GDk5eVhzpw5uPPOO3HixAmoqoqMjIygtg2krKw6pBcYzbKyklFczGwRwHMhtdTzcOCAghEjEp3f9i64wIHbbrOittbus94gKysZbduqKCwESkuBlBQF2dlVKC4+iwceBfy9J8zmBFRXm3D8eKVz1eFjxxIAmGAwVIZ8rrKzE7F/P1BcbPG5zcmTZsTFGWCxVKFa58dzdrYCIAmbNtlRXFwTcPuysiR0766iuFg8QaC/j+xsA4BEbNpkRXGxvmnZp06ZkZhoQJs2VejVy4z//Ed8AR4+3IbiYvHmFLOrErFpk/9z4s2vv8YDiMH48Ra8/XYili2zY9KkwK/dlyNHTAASYDLVoLjYMwt3zTUmLF2agDffrMPMmcEVWm/ebEBhYSImTbLCbq9r9L8zfwFm0IHJI488ghkzZmDu3LnIz8/HiBEjYDQaMWDAAIwfPx6qqmLmzJlBb0tELdecOXGoqBAFdbffbnUOFwTStq2KDRvEx9Tw4XaPlXDJc2aOHBYrLFSQmBhed9xOnVT88osRNhucAU99Yp2c4JrdZWZqaNtW1TWUY7UC1dVKUOsiyaEcvTUsgHgdshfLddfZncM1sr4EENm9ggIVq1aZfu8honv3OH5cgaJo6NVLRf/+DqxbZ0RFRegLUdZvOufuiivsSE4Ws3OefNKqewgMcNUm6f3bbEy6ApPc3Fx8+umnAIAuXbrggw8+aLDN1KlTMXXqVI/7gtmWiFqmvXsNWLbMhJ49HZg5sy6oD0s5owUABg1qfvUljU3OzKioEMM6qgocOmRAt25qWIW2nTpp2LBBzELp3Nn7haqsTAlpzZ/zzlOxerUJlZXwOYwH+C7y9Cc1VQSzwdSYlJUp6NpVvI5Ro2x47jkxhPGHP3i+33r1cmDVKhN27jTioov0vxePHzcgO1tDTAxw5ZV2bNoUh//+14QxY+xQVeCrr0zo1ElFnz76zqW/FbDj44GRI+346KMYbNhgDKomy1U03UR96N2wwRoRNao5c2KhaQoeeSS4oATwDEyaW/+Ss0Fe2GUBbFGRgpoa14U2VIEW83M4xAUylM6gstHa3r3+3wxytpG3C7A/3burOHpU0TW8VFcnsjKygDc/X8MFFzjQpo3qPE4plJk5DofImHToIPYvV1n++msTiosV3HxzAiZPTsD99+uvP/I2XdidnJ3z+efBDYjoKUg/WxiYEFGj2bHDgOXLY9C3rwMjRgSf8ZCBSUKCpvsbZWviypiIi9Vvv4mP9Pz88M5VoF4mwTZXc+eameN/XM51AQ5u/926qdA0BQcO6F/vx/11fPBBDb7+urpBDw8ZmAQzM6e4WIHdrqBDBzm7R0VOjoqvvzbhkkvMWLnSBINBw969Bo/FKvUcs6+g8MILHTAYgu9UG27/m0hiYEJEjeall8SsjkceqQtpaKFtW/GBfsEFDsT6niDSatVvSy8vxuFmTOTwja8pw7J1u951ctzJRfEC1Zn4G7Lw55xz9NeZeGsSl5mpITe34XPm56swm7Wg1uKRPUxycsT+FEVkTSorFZSXK3jmmVrceqsNqqoENYUa8H1eYmOBjh01XYGZO9lbiBkTImqxtm414D//icGAAQ5cdllo9SGyy+uwYawv8SY52TMwiXTGxFeTNdnDJJShnHPP1beYX6iBSbdu+gOTYDI/RiPQo4eKffv0ZzdkD5PcXNe/x333WXHjjTasWFGNe+6xoXfv4DIxFRUKTCb4LW7u2lVFcbGhQfM9fxiYEFGL9/rr4WVLAFHwunRpNe65p2kWE4t2cpijokLcym/J4QYm2dka4uI0n0M5ruZqwe87KUnUsDRWxiSYJmvBttXv29cBu13Brl36Lp31MyaAyEbNn1/rHJrs1UsE3XprVyoqxKwgf39TMmMWTNakpERBQkJ4s7kihYEJETWKTZuMSE3Vwsp2KIrIlsQ1Xa+nqFZ/KOe33wzIzFRDChjcGQxAx46qz8BEXtBDGcoBRJ1JcbHBb+v4UGblACIIMJu1oDImep+jd2/xXt6yRV924/hxcQyyxsSbc85RYTKJRQL1KC9XAv77ysBUZtD0OH1aTDmPhiUfGJgQUcRVVYkPxV69mt/aNs2Jex8Tmw04ckRBfn5kUvGdOmkoK1O8DgcEe0GvT9aZ+JuZI7NAwfQxAURQ1a2bit9+M8ARICYONsDq21dc8Ldu1RdEHDsm9i9n5XgTHy+yPLt2BT5eTRMBW6DARGZM9AYmmiYCk2gofAUYmBBRI5Df/uRMBmoc8qJdUaHg6FExAyTcwlfJ38wcOZQTTsYE8F8A66+RWCDduqmorVWcgYEvsl283gCre3dRALt1q75L5/HjBsTGagEv+AUFKqqrlYArA9fWAlar/oyJ3qEciwWoqVGior4EYGBCRI1AzlyQqW9qHHIop7IycoWvkr/AJNjajPpkYOKvADbUGhPANTMnUJ1JsAGW0ShqQvbuNejqk1JYqKB9ey1g/x5XnYn/TIxreMv//jp0EDVCegOTaCp8BRiYEFEjkDMMevVixqQxJScDiqKhokJxBiaRypjk5fmeMhxOHxNAZB4URcOvv/q+BAVqJBZo/wD87h9wn12kf999+6pQVSVgsWpdHXDqlMFjRo4vepu3uc67//0ZjUCXLmI4S89Kw9HUwwRgYEJEjWDbNgMSEjTn1E1qHAaDmOVSUaFEbEaO1JgZk7g48a3+0CHfl6AzZ8QskVAKn+X7Tm/GJJhamT59RHYjUJ1JUVHDGTm+9OypL2OiNzABxPugslJBcXHgIi9mTIioRautFd9Ue/RQuejeWZCaqqGy0pUx6dIlsoGJt14m5eViocBwmt516aLixAnfPUHKy4NbwM9dfr4KgyHwzJyyMvEc9bu8+tO3r76ZOXpm5Ejp6aLXSaDmbbIQWU9gEsyUYRmYRMM6OQADEyKKsD17DLDbFdaXnCUpKZozY5KTo8Jsjsx+k5OBzEzvU4bLypSQsyVS587+296fORP6rJ+4ODGrSE/GJNjXkZ+vISkpcAGsnhk57goKHCguNuDkSd8ZDtdsKH3HCeibmVNcHD3r5AAMTIgowlhfcnalpGg4c0ZBYaEhYvUlUqdOGo4eVRpMYy0tDT8w6dRJPP7gwYaXITktNiUl9P1366aipMTgnHnjjegJEvx05D59HNi3z4CqKt/bBZMxAVx1Jv7WuJHFr8FlTDiUQ0St3LZtnJFzNrlPp41UfYmUl6fCalVw4oTr4iZX5A01myHJIadDhxpeOC0WwOEI7zlkQCADhPqqq4Ha2tACrD59xEKB/trIe+v66o+rANb3PoOtMQH0ZUxY/EpELdqOHUaYTJpzTRRqXO51GJHPmDQcbpEXx1B7mEhyKMdbAax8jlBrTABXQCCLUH09RyiBiavOxPclVK6Toz9jIvbpr85EdvjVE5hkZWlITtY3ZZgZEyJqsex2kYo+7zyVbeTPksYNTMS+jxxxXdzDnZEjyYyJt6GcUNvRu2vf3n/GJJzXoWdmTmGhgqQkTfdwVMeOGlJT/bemD6bGRFHE++HgwcAdZU+fVmA2R8c6OQADEyKKoP37DaitVVhfcha59/mI9FCOzJi4ZzXC7WEiJSUBbdqoXjMmMjMQSg8TSWZMjh/3nzEJJfjp3FkEEf4Ck+PHDejQQdW9JIOiiKzJgQOKz9oV2aZf71pI+fkq6uoU57CSLyUl0dP1FWBgQkQRxPqSs09mTIxGzdkULVLy8hoO5UQqYwKIC/yxY2KdH3fhNFeTcnLEsfsaygmnrb6iiPf4b78ZvK4lVFUlsj5660ukggJRu+KrI255uQJF0XS36dfTml6uk8PAhIhaJNeMHAYmZ4u8SHXqpCEmJrL77tBBg9GoNWJgosJub7imjcwMhBOYtG8vMybeL3OhNFdzJ+tMtm1rmDWR9SV6ur66k0NxvhrPyZlKgVrc19+fvwJYi0UUAUdL4SvAwISIImj7dgMURUOPHhzKOVtkxiTS9SUAYDIBubmaR43Jv/8tupGdf374z+eameN5KQpnAT/JbBbBU2NkTADXSsMbN3oLTIKbkSPJDNXRo74Dk2CCNT1N1uSMHGZMiKjFUVWRMenWTUVSUlMfTeshL1SRri+ROnVSceqUWLRu924D/vtfEy680I7evcN/Pl8zc8JZwM9d+/Zqo2VMBg4UGZMNGxoGJrKgV++MHKljR3EsR4/6rosJ5pzomTIsZ+QwY0JELc6RIwoqK1n4erZdeKED999fh8mTrY2yf/fW9AsWiB70994bmeeSgUn9mTmRCkxyckS7/srKhr+TgUmoQ1LZ2Ro6d1axYYMRar23/Lp1Ilj5wx+CG9KUQz/elgGw2YLvH5OSIgqM/QUmroxJ9PzdMjAhooiQ0xx79oyeD7jWICYGePJJq3Nqb6TJ/a5fb8SyZSZ06+bA8OGRqSHq3Fnsu36TtUhmTADvdSZlZeI2nFqZQYMcqKhQsHevSouyOAAAIABJREFUa/+aBvz4oxHt2qnOtvB6JSSI9Wq8DeWEWhDctauKo0cVWH3EksyYEFGLJZdsl42iqGWQGZOXXoqFzabgnntsuosvA2nTRqw7U3+9nEj0MQH8TxkuKwtuhos3gwaJ9/r69a7hnD17DCgpMWDoUIfuqcLu8vI0FBYqDbIwcvZPsOfk3HNVqKqCn37yPrW5pEScewYmRNTi7NolPk6YMWlZZGBSXGxAmzYqxo2zBXiEfooihnMOHTJAc7suVlQAiqKFXavkb8pwebmCtDT9M1y88RaYrFkj/v/ii+0h7bNjRxU2m9JgMT9XN9zg9jdxovj3eu0170tBs/iViFqsHTuMaNtWRdu20fMBR+GTgQkATJ5sQ3x8ZPffubOKmhrPC7GYfRJe0AD4nzIciYUIu3VTkZGhehTAysBk6NDQMocdO3qvMwk1i9S/v4pBg+z4739N2LOn4XngUA4RtUjl5cCxYwZmS1qgtDRR15CQoOG22yJfYOttyrDo1xH+hdLXejmaFtrKwvUpipidc/SoAYWFYhXmn34yIS9Pdc6wCVZurveZOeE0nbvnHpE1ef31ho1umDEhohZp507xLZH1JS2PogD/+EctFi2qQUZG5PcvC2APHnRdiMUwSyQCE+/FrxYLYLOFH5gAwODBrmnDO3YYUFGhhDyMA/juZRJO3c2IEXZ07qxiyZIYnDrlGfDIdXLM5hAPuBEwMCGisO3cyfqSluzqq+248srGCTrr9zKR02LDnZEDiPV4kpO1BsWv4fYwcSfrTNatM+KHH8IbxgF89zIJZ6aS0QjcfbcVdXUK3nnHM2ty+nR0dX0FGJgQUQTIqcIFBQxMKDj1h3K++kp0ls3KiszFMidHRVGR56Uu3K6v7nr1UpGQoGH9eiN+/FEceziBia9eJnI4KtQhlwkTbEhL0/DOOzGoqRH3aVr0LeAHMDAhoiBYraIlef1voDt3GhAfrzVa91Fqudq31xAbq+HgQQN+/NGIqVPjkZSkYerUyNSztG+vobxcgcXiui+SGZPYWKB/fwd27zZg7Vojund3IDs79P2azaIpWv2hnC1bjIiJ0XDuuaH9jSUmArfcYkVJiQFLloisicUC1NUxY0JEzZDVCrz7bgwGD07E7bcnYMoU19QMmw3Yu9eA889XYTI14UFSs2Q0irqKX3814NZbE6BpwDvv1ERsWFDWmZw44Qqmw+36Wt+gQQ5omoLqaiWsbIlUv5dJba3oE1RQoIY1K2ryZBtiYzX84x+xsFqB4uLoK3wFGJgQUQBbtxoweHAipk+PR0mJgnbtVKxZY8KBA+JDbd8+A6xWBT17svCVQtOli4bqagVnzij4+99rMWxY5N5L3qYMRzowkevmAOEN40gdO6qwWhVnoerOnQbYbAr69Qtv3+3aabj1VhuOHDFg8eIYt6nC0ZXpZGBCRH7NmxeLY8cMmDzZip9/tmDGjDoAwEcfiXSw7PjKwlcK1XnniQvujBl1GDs29Bkt3njr/hrpwOQPf3DAYBD7GjIkMoEJAOeqzps2iRqu/v3D3/f991thNmt45ZVYFBaKv11mTIioWdm82Yg2bVQ891wdsrM1jBxpR0qKho8/joHdzsJXCt+f/2zFV19ZMGVK5PukeJsyHOnAJDkZuO46O0aNskXkIu+amSOOOZKBSXa2hjvvtOLECQPmzhXdYBmYEFGzceqUgqNHDejXT3Wu+5GQAIwda8PJkwasXGl0myrMoRwKTVISMHCgGtLaMoGcjYwJALz+ei3efLM2IvuSGRP3wCQ1VQt6UUBfpkyxIjlZw+7dIuCJ1AyoSGFgQkQ+bdkiPiLqj23ffLPoJPnBB7HYudOAzp3VsNc1IWoMrvVyGi9jEmnuvUzKyoCDBw3o188RscUT09OBe+5xZaeYMSGiZsNXCrlXLxV9+jjw9ddGlJYamC2hqJWSApjNriZrJSUKfvzRiA4dVCQnN/HB+eDey2Tz5sgN47i7+24rMjLE8zAwIaJmQ34o9u3b8EPx5ptt0DTxYc/6EopWiiKbrIn36muvxaC6WsGUKdZGGTqKhMREMVPm2DFDROtL3CUnA3/7Wy3uvNPqXJ8nWjAwISKvNE0EJp07q17XSLnhBhsSEsQHGjMmFM3at9dw+rQBRUUK3nwzFtnZqnM4Mlp17Kjh2DEFGzeKwKRfv8gH/1dd5cDs2XVRF6AxMCEirw4eVFBervj8ppaSAtx4ow3x8VqjfGgSRYosgH3mmThYLCJbEk6jsrOhY0cVdXUK1qwxIi9PjboC1cbEwISIvJLDOP6aOj33XB3Wr7eE1YKbqLHJAthly2LQpo2KSZOiO1sCuApg6+rCb6zW3DAwISKv9AQmsbGuzppE0cr9PXrffVaYzU14MDrJKcNA5OtLoh0DEyLyatMmI4xGjYWt1OzJjElmpopbb43+bAlQPzBpXX+DDEyIqAGbDdi+XSzM1xy+XRL506ePiowMFY8+am02/XbkUI7RqKFXr9aVMeFaoETUwO7dhlY5tk0tU3a2ht27LVE3+8Sf3FwViqKhR4/W9+WAgQkRNeDqndC6UsjUcjWnoAQQbfoXLKhFXl7r+xtkYEJEDegpfCWixvXHP0Z2peXmgjUmRNTA5s0GmM0azj239X1bI6KmxcCEiDxs2mTA3r0G9OnjgNHY1EdDRK0NAxMicjp5UsFttyVAUYBp06yBH0BEFGEMTIgIAFBXB9xxRwJOnDDgiSesuPRS1pcQ0dnHwISIAACPPx6Hn3824oYbbJgyhdkSImoaIc/KGT16NJKTkwEAubm5GD9+PJ577jkYjUYMHToUU6ZMgaqqePrpp7F3717ExsZi1qxZ6NSpE7Zs2dJgWyJqOkuWmPD++7EoKHBg7tzaZje1kohajpACk7q6OgDA+++/77zv+uuvx7x589CxY0f86U9/ws6dO1FYWAir1YpPPvkEW7ZswQsvvICFCxfiqaeearBtz549I/OKiCgoDgfw8stxiI3V8PbbNa2umRMRRZeQApM9e/agpqYGd9xxB+x2O6ZOnQqr1Yq8vDwAwNChQ7F27VoUFxfj4osvBgD07dsXO3bsQFVVlddtGZgQNY0VK0w4eNCASZOs6NSJC/IRUdMKKTCJj4/HnXfeiXHjxuHQoUO46667kJKS4vx9YmIijh49iqqqKiS5LUxgNBob3Ce3DSQ93QyTqeXNXczKSm7qQ4gaPBfC2TwPmgb885+iK+YTT8QiKyv2rD23HnxPCDwPLjwXQks+DyEFJl26dEGnTp2gKAq6dOmC5ORklJeXO39vsViQkpKC2tpaWCwW5/2qqiIpKcnjPrltIGVl1aEcalTLykpGcXFlUx9GVOC5EM72eVi71ogNG8y4+mobMjJqUVx81p46IL4nBJ4HF54LoSWcB3+BVUizcpYuXYoXXngBAHDy5EnU1NTAbDbjyJEj0DQNa9aswYABA9C/f3+sXr0aALBlyxacc845SEpKQkxMTINtiejsmz9fZEg4C4eIokVIGZOxY8fisccew0033QRFUfD888/DYDDgoYcegsPhwNChQ9GnTx/06tULP/74IyZMmABN0/D8888DAJ555pkG2xLR2bV7twHffGPCwIF2/OEPbD1PRNFB0TStWVS7Nfe0lTctIR0XKTwXwtk8D1OnxuOTT2Lw3nvVuOqq6GumxveEwPPgwnMhtITzEPGhHCJq3s6cAZYtM6FbNweuvDL6ghIiar0YmBC1QqtWmWCzKRgzxg4DPwWIKIrwI4moFfrmG1FeNny4vYmPhIjIEwMTolZGVYGVK43IylLRuzeLXokoujAwIWpltmwxoKTEgOHDOYxDRNGHH0tErYwcxrniCha9ElH0YWBC1Mp8+60JMTEaLrmE9SVEFH0YmBC1IidPKti61YjBgx1wW7KKiChqMDAhakW+/ZazcYgoujEwIWpFvvlGrNDNwISIohUDE6JWoq4O+P57E/LzVXTt2ixWoiCiVoiBCVErsXatERaLwmwJEUU1BiZErcCRIwoeeigeAHD11QxMiCh6MTAhakFKS4F582Kxa5frT/vgQQWjR5tx5IgBDz9chyFD2L+EiKIXAxOiFmT27Dg8+2wcLrkkEdddl4D33ovB6NFmHDtmwBNP1OGhh6xNfYhERH6ZmvoAiCgyysuBJUti0L69inPPVbFqlQnr1ok/8aefrsW999qa+AiJiAJjYELUQnzwQQyqqxVMn16H++6z4cABBR9+GIPu3VWMH8+6EiJqHhiYELUAdjvw1luxMJs13HyzyIzk52t48kkO3RBR88IaE6IW4D//MeHYMQPGjbMhLa2pj4aIKHQMTIhagEWLYgAAd93FOhIiat4YmBA1c9u3G7B2rQmXXGLHOeeoTX04RERhYWBC1My98UYsAOCuu1hPQvT/t3f/wVHUef7Hn909PzLJJIH84IdAkN8gASUsLugCq4j89HBRyI818VT0uPqyVbre6VpXZ1mLBVtb55anV1LglZR6tX6PVU8Pq76nLPDll4qA8vuH/A4/BcKPzEwmM9PTn/ujk0liEuRXpifJ+1E11T3dPTPv+UxPz2v6p2j/JJgI0Y5duKDx8cf29W8mTZITpwkh2j8JJkK0Y++/7yYS0Zg3L4ou32YhRAcgizIh2qlYDJYvd+P3K0pKZKdXIUTHIMFEiHZq5UoXZ8/qlJXF8PudrkYIIW4NCSZCtFNvv+1B0xRPPSU7vQohOg4JJkK0Q99+q7Ntm8GDD8bp1085XY4QogNxfbOZ7OJfkf03Ux15fQkmQrRDy5bZhwjPmydrS4QQt4Zry2ay5z5M15mT8axdjdWtuzN1OPKqQogbdvq0xn//t4uhQ+NMmCCHCAshboJSeNaswvfm63i+3AhAdPwvCf3jS5hjxzlSkgQTIdqRDRsMFixIwzQ1nnkmhqY5XZEQol0yTbyffET6v/0rrr27AYjeN4maZ/+B2Lh7HS1NgokQ7UA0CosXe3nrLTeGAS+9FKGsTA4RFkJcp1CItA/eJ33Jv2GcqETpOrWzH6Xm/zxLfMRIp6sDJJgIkfKqq6GkJJ2tWw369bNYsiRMUZFcE0cIce30Y0fxvfsOaX9+D/3SJZTPR/jJp6n5+99g9b3d6fKakGAiRAqrroa5c9P59luDX/0qxmuv1co5S4QQ1yYex7NmFWnL/x3P6lVoSmHl5hL67QuE581H5eU5XWGLJJgIkaIah5Li4hivv16LYThdlRAi1WlVVaT9+X18776DUXkMgNjP7ib85NNEHnoYvF5nC/wJEkyESEGBgIQSIcR1iMXwrF5F2n/+Gc8X/w8tFkOlpxMu/1tq//YpzBF3Ol3hNZNgIoSDzp7V6N5dNTu65sUX0ySUCCGuTilcu3bg/c8/k/bxX9CrqgAwh91B7a8rqC0uQ2V3cbjI6yfBRAiHrF9v8Oij6ZSXR/mXf4kkwsl//ZeLDz90U1QU509/klAihPiRo0fxLf8P0j78v7j27QXAysuj5pm/J1Jchlk4kvZ8LgEJJkI45L333AC8/74Hnw8WLoxw4gS88EIa6emKt94K43Y7XKQQIiXoRw7j/exTvCs/hR3f4QeUx0Nk5ixq55YSnTSZjrLAkGAihAMuX4bPP3fRr5+F16tYtsxDWppi1y64ckXjtddq6d9froEjRKelFMahg3g/+xTPyk9x795pD3a5YMoUAlMfIjJtBion1+FCbz0JJkI44NNP3UQiGr/+dZTi4hizZqXzxhv2nvJTp8Z47DE5eZoQnU5tLe4vN+D56xd4V32OcfwYAMrtJvLAg0T+5ldEp0wjb3Bfas8HnK21DUkwEcIBK1a40TTFo4/G6N5d8dFHNcyalU48rvOnP0Xa8+ZhIcR10E+ewLN6FZ6/fo5nwzq0mhoALH8mkZmziEyZRnTq9Ha5E+uNkmAiRJIdOaKxZYvBhAkmt91mb67p1UuxaVOI7OxMamtlE44QHZV27hyeTetxb1yPZ8M6jGNHE+PMQYOJPjCF6OQpxO4eCx6Pg5U6R4KJEEm2YoW9g1pxcdPNNV4vZGZCba0TVQkh2oL2ww+4t2y2N9FsXI9r/77EOCszi8jU6UQn/JLoA1Owbu/nYKWpQ4KJEG3o4EGdpUvd3H9/nGnTTJSCDz90k5GhmD7ddLo8IcStZJoY+/baQWTLZtxbvkmceRVApacTvW8S0XsnEBs/wT7pmUt+hn9MWkSINhCJwJtvenj9dQ/RqMZ778HYsSYzZphUVuqUlMTIyHC6SiHEDbMsjKOHce3Ybt92bse1/Tv0ULBhkq5diTw4ldiYn2P+fByxop912s0z10OCiRC3SCAA33+vc+CAzltvefj+e4MePSxeeCHCF18Y/M//uPn6a/srN3euHHUjRLsRDuM69D3G3j24du+yQ8iunejBhiNjlKYRHzSYyN1j7SAy5ufEBwxs1yc6c4oEEyFuwuHDGh984OaTT9xUVuqJ4ZqmeOKJKP/0TxGysuCxx2J89VWMRYs8uN1wzz1xB6sWQrQoEsE4dhTj+/249u3FtX8fxr49GEePoFlWYjKl68QHDSY64k7MO+/CvHMUZuEIlD/TweI7DgkmHcyVK+D3I6cxb0PRKHzyiYv33nPzzTf2VygrS/HLX5oMGWIxaJDFmDFxhg2zmjxu3Lg4K1eGnShZCFHPNNFPn7IDyKGDGEcO4Tp0EOPwIfQTlU0CCICV3YXY3WOJDx2GOWw45h2FmIUjkG2xbUeCSQeycaNBWZmPwYMt3nknTEGBHHZ6Mw4c0Fm92qBXL0W/fhZ5eYqPPnLz9ttuzp7V0TTFxIkmpaUxpk0z8fmcrlgIgWminzmNcaISvfI4xolKu/9EJUblcfTTp9DizddYWnn5dgAZMJD4wMGYw+4gPuwOrB49ZXNMkkkw6SB279Z5/HEfkQjs3GkweXIGS5aEuf9+2WRwIzZsMKio8BEKNV8gZWQo/u7vosybF6VvXwl/QiSFUlBVhbHvEPrZM+g/nE3cjLN1/WfPoJ85jWY2P+JNaRpWj56Yo8cQ71NAvG9f4gMG2UFkwMBOdQKzVCfBpAOorNQoKfERCGgsWxYmENB46SUvpaU+5s2L0bevhWHY13caMsRi1Kh4qzuGKwX79un88IPGwIEWvXopdL3laVtTVaXxyScuvv7aYNo0k4cfNq/7OZz02Wcu5s9PA2Dhwlo0DY4e1Tl1SmPMGIuKiijZ2Q4XKUR7Z5poly6hX6xCv1iFVlXVrF+ru69fuID+w1mIRslp5emUYWB16445arQdPAr6YvUpaOjv1ds+WZBIeRJMUkhtLcRiEI1qmKZ9lMflyxpXrmhcvqw16ff5FF272rfXX/dy7pzOq6/W8vDD9j+FwsI4Tz7p4+23myeQ9HTFmDFxioriZGcrsrLA7VZ8843B6tUuTp/Wm0zbv7+Fx2PXFovZazUzMxV+f33X7vf7FXv26Kxe7SIWs9c0fPqpmyVL4vzzP0eYMMHZtTdVVRoHD+ocOaIRCtltbJoamqbIyLDXhJw5o7N4sX2133ffDTtesxApxbKgpgYtHEYL13VrQmjBIFp1NVqgGr36it1fXY0WDKBV1w0LVCeG64HqxKnXf4ryerFy8zALR+Du05tw1zys7j2wevTE6t6dePeeWD16onJzaVf/gESrHAsmlmXxyiuvcODAATweD6+++ip9+/Z1qpyrsiz7vBSNN0taFlRXNwSGhtAAgYBGPG5PY1ka0SjU1EA4rBEO28Msyz6vzvnz6Zw/r3H+vEZNzY1vx1ywIMIzzzQcgjpqlMW6dSG+/togFrPrCYdh+3aDTZsM1q1zsW5d84+/SxfF7Nkx+vWzOHLEPvT18GE9Ua/HY7dDMGi/j5YUFsaZMyfG2LFxli718PHHbh59NJ2hQ+P07q3o2dOiZ0/FkCEWw4fHuf12e3PI5ctw7JjOyZM6wSCEQhrBoIam2QHJ57MDVDCoEQhoXLliX4nX7teortaorm4YppRdr9eriMXsz+ha5ORYfPBBmFGjrJ+eWIhbRSn7yxWP22sTLLuLCqOfu2L3J8ZZiftavG54JIoWi6JFI4l+IhG0aBSidcOjUbRIBGIxtEikblisrhtBq40kAocdQBqFj3AY7SZOS6w8HlRWFsqfidm9h93fNQcrJxcrNweVk2v35+Sichv6SU9P7OORn59JsANfvE7YNKWUIxvJv/jiC9asWcMf/vAHtm/fztKlS1myZEmr059vg5mxttY+CdaRIzpVVRoXLtg/btEoRCIaEft7jGm23Y5PhqHIy1N066bIyVF4vfaPr9tt/4PPzrbDQv0tO9u+RaMaVVUaFy9qdOmimDHj+jaXnDtnrz0IBOwgFQpp3HFHnNGjrWs6okcpO+gEAhrBIImwkJ9vB47GduzQWbzYy+bNRqv7bLjddqi7GW633TaZmfZRMrpO4jPUNOjfXzFwoMXAgRbZ2QqXS+Fy2QEyFLLboLYWHnzQbHnHYcuyVxmZJpoZA9NOn5pVl0LjDV1NWWCp5sPijadVzR7bJdPLlUtBiFsNw5Vl76zX+DUsC00p+4Oov1lW0/tKAXZXazKO5tMpe3yz5/zxc9PwHNrVXrfxtC3Vmqih+WO1usekeV3UhqPNaqx/rNbi6zYd1nwamr9mi/X9eJof1dfiND967frHoRrmAdMEK27PO41DRX3YSBFK01DpGeDzodIzUOk+VH2/zwe+dFR6uj3Ml47y+1FZWVhZ2Sh/ph066m5WZjYqKwvS0m66rvz8zDb5LWhvOkI75Oe3fmi1Y8Fk8eLFjBw5khkzZgAwfvx4NmzY0Or0bfEhHDqkcc89/sR9v1+RlWWHA69X4fE0/OP2eJqeOVjT7B+/+rDQODxkZdmBQ9ft6Twe+x9/ejqkpdk/hpoG3bplEgwGOtXax0AAzpzROXlSY88eg717dfbu1QGDPn1M+vYx6dMtTHZahExXmAytBmIxwkGLmqAiVmvhd4fp4gqS7QqRbQTpoleTrQfwxYPo9f/8ItG6bm1Dv73txv63aJpQFy40M9ZomNkQPGJmo+lS64dD3FpK0+wvpaaR+OI2vo/WwjQ0mwat0XSNn0fXwTBQhmEvSHQD5XKBoYPhsse57O6Pp/NmpFFrWvZ0Lpc9zrAfm3iMbqC8XvB4UHULLuVpdN/rRbk94LWHN+5vMo3HYwcSrzclj0TpCD/It0JHaIerBRPHNuUEg0H8/oZQYBgGpmniauW6AV27puNy3dqTc+TnQ2Wl/QcnPx98Pg17aZM8GRnt/IQ8tbX2yVPsbSgN3ZaGBYPkh0L0r6mBUMi+1ffX1MDBkB0MksHlsvcGdrsb+uu76b7mw1qaLvGjoCd+eJLavdqP6bWOu9nxbfHYloZfy/u9kWnqbtfyrXfyZ/rm1zV0HFf7QetMOnI7OBZM/H4/oVAocd+yrFZDCcClS9e2o9T1ql+7aG+OaJOXaFVKpd5g0N77/dJFtIsX7e6li+gXG7qJYZcv2zuyBQL29usboNxuexVw3epgV7duxNzeutXG6faq4/rVxmk++59cWhp4vA3/7upuiWFpaY3++dWNq+vH47b/JdaHiRT8NwgpNk847Jraom5LCQDN9lPuGGu4ZJ5oIG1h6wjtkJJrTIqKili7di3Tp09n+/btDB482KlSOi7TRD/3Q6PbuVb7tZrQTz8foNLSsLp0xcrNQ93eD5WZhcrMwsrKquvPbL6tOTMTlZWNyqgPHRn2GodG8vMzudzOv2hCCCFunmPBZPLkyWzatImSkhKUUixatMipUtonpdAuXcQ4dRL91Cn0UycwTp5EP33S7p46iX72zFX3i1C6jpXfDXPAQFR+ft1e8HV7x3fNQeXkYHXNadJPenoS36QQQojOxrFgous6v//97516+fYhHrev6XDksH07egTjaF335IlWzwOgDAPrtl7E7h6L1bMnVrceWN26Y3XrVte1byonRy6qI4QQIqXICdZSgFZVhevAPozvD9QFkLrwcexoi/twWFnZmP0HYvXujdWrN/FefbB69yZ+W297WPceEjiEEEK0SxJMkkgLBjD27sW1fy/GgX1w+Htyd+1GP3+u2bRWVjbmHcOJ9x9A/Pb+drdff+L9B9prOlJ0500hhBDiZkgwaSNaVRWuXTtw7dqJa9d2XLt2Yhw5bJ90qRFV0JfI5CnEhwzDHDzEvqCUhA8hhBCdlASTWyEcxrVzB+5tW3Bv24Lr260Yp042mcTKyiZ273jM4SPsNSFDhtL13jFcDMvVaYUQQoh6EkxugH7qJO6vNtkhZNsWXLt3NbnMtpWXT+SBBzFHjMQccRfmiJFYBX2brwHx+yEsh8gKIYQQ9SSYXAP9RCXuTRtwf7UJz6aNGJXHEuOUx4N55yhiPxuDOXoMsdFjsHr3kc0wQgghxA2QYNIC7cIFPOvW4Pn/a3B/uRHjRGVinJXdhcjU6cTG/YLY3T/HLBxpX1dCCCGEEDdNggmAaeLathXP2lV41vwV147tiZ1Ura5diUx/iNg99xId9wvidwyXQ3GFEEKINtKpg4kWDOD/x+fwrPocvfoKYF/DJXbPL4je9wDR+yYRH15Ip7r8rxBCCOGgzh1MLlzAu/ITrB63EZ79KNH7JxP7xXiUv+NetVEIIYRIZZ06mFi39+PCsbMpfbVZIYQQojPp1MEEAJc0gRBCCJEqZOcJIYQQQqQMCSZCCCGESBkSTIQQQgiRMiSYCCGEECJlSDARQgghRMqQYCKEEEKIlCHBRAghhBApQ4KJEEIIIVKGBBMhhBBCpAwJJkIIIYRIGRJMhBBCCJEyNKWUcroIIYQQQgiQNSZCCCGESCESTIQQQgiRMiSYCCGEECJlSDARQgghRMqQYCKEEEKIlCHBRAghhBApQ4KJA1atWsXzzz/f4rhXX32V2bNnU15eTnl5OYFAIMnVJc/V2mHFihXMnj2buXPnsnbt2iRXljy1tbX85je/oaxjC+cjAAAEwklEQVSsjKeffpqLFy82m2b+/PmUlJRQXl7OvHnzHKiy7ViWxcsvv0xxcTHl5eUcP368yfjOMh/AT7dFZ1o2AOzYsYPy8vJmw9esWcMjjzxCcXExK1ascKCy5GqtHZYvX86MGTMS88ORI0ccqK6NKJFUCxcuVFOmTFHPPvtsi+NLSkpUVVVVkqtKvqu1w7lz59TMmTNVJBJR1dXVif6O6J133lFvvPGGUkqpzz77TC1cuLDZNNOmTVOWZSW7tKT4/PPP1YsvvqiUUuq7775T8+fPT4zrTPOBUldvC6U6z7JBKaWWLVumZs6cqebMmdNkeDQaVQ888IC6fPmyikQiavbs2ercuXMOVdn2WmsHpZR6/vnn1a5duxyoqu3JGpMkKyoq4pVXXmlxnGVZHD9+nJdffpmSkhI+/PDD5BaXRFdrh507dzJq1Cg8Hg+ZmZkUFBSwf//+5BaYJNu2bWP8+PEATJgwga+++qrJ+AsXLlBdXc38+fMpLS3tcGsNGr//u+66i927dyfGdab5AK7eFp1p2QBQUFDAm2++2Wz44cOHKSgoIDs7G4/Hw+jRo9m6dasDFSZHa+0AsGfPHpYtW0ZpaSlLly5NcmVty+V0AR3VX/7yF959990mwxYtWsT06dPZvHlzi4+pqanhscce44knniAej1NRUUFhYSFDhw5NRslt4kbaIRgMkpmZmbifkZFBMBhs0zqToaW2yM3NTbzXjIyMZqvnY7EYTz75JBUVFVy5coXS0lJGjhxJbm5u0upuS8FgEL/fn7hvGAamaeJyuTrsfNCaq7VFR1w2XM2UKVM4efJks+GdbZ5orR0AZsyYQVlZGX6/nwULFrB27Vruu+++JFfYNiSYtJE5c+YwZ86c63qMz+ejoqICn88HwNixY9m/f3+7XvjcSDv4/X5CoVDifigUarIwaq9aaosFCxYk3msoFCIrK6vJ+Ly8PEpKSnC5XOTm5jJs2DCOHj3aYYLJjz9ry7JwuVwtjuso80FrrtYWHXHZcCM62zzRGqUUjz/+eOK9T5w4kb1793aYYCKbclLIsWPHKCsrIx6PE4vF+Pbbbxk+fLjTZSXdyJEj2bZtG5FIhEAgwOHDhxk8eLDTZbWJoqIi1q1bB8D69esZPXp0k/Fffvklzz77LGAvhA8ePEj//v2TXmdbKSoqYv369QBs3769yefcmeYDuHpbyLLBNmDAAI4fP87ly5eJRqNs3bqVUaNGOV1W0gWDQWbOnEkoFEIpxebNmyksLHS6rFtG1pikgOXLl1NQUMCkSZN46KGHmDt3Lm63m1mzZjFo0CCny0uaxu1QXl5OWVkZSimee+45vF6v0+W1idLSUl588UVKS0txu9289tprAPzxj39k6tSpTJw4kY0bNzJ37lx0Xee3v/0tOTk5Dld960yePJlNmzZRUlKCUopFixZ1yvkAfrotOvOyYeXKldTU1FBcXMzvfvc7nnrqKZRSPPLII3Tv3t3p8pKmcTs899xzVFRU4PF4GDduHBMnTnS6vFtGri4shBBCiJQhm3KEEEIIkTIkmAghhBAiZUgwEUIIIUTKkGAihBBCiJQhwUQIIYQQKUOCiRBCCCFShgQTIYQQQqQMCSZCCCGESBn/CyNN5CO658GhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"R2 Correlation: -1.2114084755099954\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" sess.run(init)\n",
" for i in range(epochs):\n",
" sess.run(optimizer, feed_dict={X:x_train, Y:y_train})\n",
" loss = sess.run(cost, feed_dict={X:x_train, Y:y_train})\n",
" errors.append(loss)\n",
" \n",
" theta1, theta2, theta3, theta4, theta5, theta6 = sess.run([theta_1, theta_2, theta_3, theta_4, theta_5, theta_6])\n",
"\n",
"plt.plot(list(range(epochs)), errors)\n",
"plt.title(\"Cost vs Iteration\")\n",
"plt.show()\n",
"\n",
"x = scaler.transform(x_reshaped)\n",
"pred = theta1 * x**5 + theta2 * x**4 + theta3 * x**3 + theta4 * x**2 + theta5 * x + theta6\n",
"\n",
"plt.plot(x, pred, 'red', label=\"Prediction\")\n",
"plt.plot(x, y_reshaped, 'blue', label=\"True Values\")\n",
"plt.legend()\n",
"plt.title(\"Salary vs Position\")\n",
"plt.show()\n",
"\n",
"print(\"R2 Correlation: \", r2(y_reshaped, pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"this model doesn´t seem to adapt very well to the data shown, so tuning the parameters and factor inside the model should be the right approach to get it better at prediction and fitting to the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Conclusions<a id='part5'></a>\n",
"\n",
"At this point, we can deduce that diving into machine learning algorithms is not very confortable, but it is interesting how we can guess the trends of a specific variable or parameter to try to understand its behaviour and maybe future values that can come up afterwards. So, here we used scikit learn and tensorFlow to predict and create models to fit that data, but still, there are mistakes applied and poor data brought in. Still I hope this is a initial guide into diving to this fantastic and still place-to-explore called machine learning and deep learning."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. References<a id='part6'></a>\n",
"\n",
" - https://www.kaggle.com/ash316/eda-to-prediction-dietanic\n",
" - https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html\n",
" - https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/ch04.html\n",
" - https://github.com/aymericdamien/TensorFlow-Examples/\n",
" - https://towardsdatascience.com/linear-regression-from-scratch-with-tensorflow-2-part-1-3e2443804df0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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