{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# *Finding the Best Markets to Advertise in an E-Learning Product*\n", "\n", "In this project, we'll aim to find the two best markets to advertise our product in — we're working for an e-learning company that offers courses on programming. Most of our courses are on web and mobile development, but we also cover many other domains, like data science, game development, etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding the Data\n", "\n", "To avoid spending money on organizing a survey, we'll first try to make use of existing data to determine whether we can reach any reliable result.\n", "\n", "One good candidate for our purpose is [freeCodeCamp's 2017 New Coder Survey](https://medium.freecodecamp.org/we-asked-20-000-people-who-they-are-and-how-theyre-learning-to-code-fff5d668969). [freeCodeCamp](https://www.freecodecamp.org/) is a free e-learning platform that offers courses on web development. Because they run a popular [Medium](https://medium.freecodecamp.org/) publication (over 400,000 followers), their survey attracted new coders with varying interests (not only web development), which is ideal for the purpose of our analysis.\n", "\n", "The survey data is publicly available in this GitHub repository. Below, we'll do a quick exploration of the 2017-fCC-New-Coders-Survey-Data.csv file stored in the clean-data folder of the repository we just mentioned. We'll read in the file using the direct link [here](https://github.com/freeCodeCamp/2017-new-coder-survey/blob/master/clean-data/2017-fCC-New-Coders-Survey-Data.csv)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(18175, 136)\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "pd.set_option('display.max_columns', 150)\n", "pd.set_option('display.width', 1000)\n", "\n", "data = pd.read_csv('2017-fCC-New-Coders-Survey-Data.csv', low_memory=False)\n", "print(data.shape)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking for Sample Representativity\n", "As we mentioned in the introduction, most of our courses are on web and mobile development, but we also cover many other domains, like data science, game development, etc. For the purpose of our analysis, we want to answer questions about a population of new coders that are interested in the subjects we teach. We'd like to know:\n", "\n", "- Where are these new coders located.\n", "- What locations have the greatest densities of new coders.\n", "- How much money they're willing to spend on learning.\n", "\n", "So we first need to clarify whether the data set has the right categories of people for our purpose. The JobRoleInterest column describes for every participant the role(s) they'd be interested in working in. If a participant is interested in working in a certain domain, it means that they're also interested in learning about that domain. So let's take a look at the frequency distribution table of this column and determine whether the data we have is relevant." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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JobRoleInterest
Full-Stack Web Developer11.770595
Front-End Web Developer6.435927
Data Scientist2.173913
Back-End Web Developer2.030892
Mobile Developer1.673341
Game Developer1.630435
Information Security1.315789
Full-Stack Web Developer, Front-End Web Developer0.915332
Front-End Web Developer, Full-Stack Web Developer0.800915
Product Manager0.786613
Data Engineer0.758009
User Experience Designer0.743707
User Experience Designer, Front-End Web Developer0.614989
Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer0.557780
DevOps / SysAdmin0.514874
Back-End Web Developer, Front-End Web Developer, Full-Stack Web Developer0.514874
Back-End Web Developer, Full-Stack Web Developer, Front-End Web Developer0.514874
Full-Stack Web Developer, Front-End Web Developer, Back-End Web Developer0.443364
Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer0.429062
Front-End Web Developer, User Experience Designer0.414760
Full-Stack Web Developer, Mobile Developer0.414760
Back-End Web Developer, Full-Stack Web Developer0.386156
Full-Stack Web Developer, Back-End Web Developer0.371854
Back-End Web Developer, Front-End Web Developer0.286041
Data Engineer, Data Scientist0.271739
Full-Stack Web Developer, Back-End Web Developer, Front-End Web Developer0.271739
Front-End Web Developer, Mobile Developer0.257437
Full-Stack Web Developer, Data Scientist0.243135
Data Scientist, Data Engineer0.228833
Mobile Developer, Game Developer0.228833
......
Back-End Web Developer, Full-Stack Web Developer, Game Developer, DevOps / SysAdmin, Mobile Developer, Front-End Web Developer, User Experience Designer0.014302
Full-Stack Web Developer, Back-End Web Developer, User Experience Designer, Front-End Web Developer, Product Manager0.014302
Back-End Web Developer, Front-End Web Developer, Information Security, Game Developer, User Experience Designer, Full-Stack Web Developer, Mobile Developer0.014302
Mobile Developer, Front-End Web Developer, Data Scientist, Data Engineer, Back-End Web Developer, Full-Stack Web Developer, Game Developer, Quality Assurance Engineer0.014302
Full-Stack Web Developer, Data Scientist, Mobile Developer0.014302
Front-End Web Developer, User Experience Designer, DevOps / SysAdmin, Mobile Developer, Full-Stack Web Developer0.014302
Front-End Web Developer, Back-End Web Developer, Game Developer, Full-Stack Web Developer, Data Scientist0.014302
Product Manager, Mobile Developer, Game Developer, Back-End Web Developer0.014302
Back-End Web Developer, Game Developer, Full-Stack Web Developer, Mobile Developer, DevOps / SysAdmin0.014302
Data Engineer, Front-End Web Developer, Full-Stack Web Developer0.014302
Full-Stack Web Developer, Back-End Web Developer, User Experience Designer, Product Manager, Mobile Developer, Information Security, Front-End Web Developer0.014302
Information Security, Product Manager, Full-Stack Web Developer0.014302
Quality Assurance Engineer, Front-End Web Developer, User Experience Designer, Full-Stack Web Developer, Information Security0.014302
Game Developer, Front-End Web Developer, Product Manager, Mobile Developer, Full-Stack Web Developer, Data Scientist, Data Engineer, Quality Assurance Engineer, User Experience Designer, Back-End Web Developer0.014302
Data Engineer, Data Scientist, Machine Learning0.014302
Information Security, DevOps / SysAdmin, Back-End Web Developer, Mobile Developer, User Experience Designer, Game Developer, Front-End Web Developer0.014302
Data Scientist, Back-End Web Developer, Information Security, Data Engineer0.014302
User Experience Designer, Front-End Web Developer, Product Manager, Quality Assurance Engineer0.014302
Data Engineer, Full-Stack Web Developer, Information Security, DevOps / SysAdmin, Back-End Web Developer0.014302
Information Security, DevOps / SysAdmin, Game Developer0.014302
Product Manager, User Experience Designer, Game Developer, Mobile Developer, DevOps / SysAdmin, Quality Assurance Engineer0.014302
Quality Assurance Engineer, Mobile Developer, Back-End Web Developer, User Experience Designer, Front-End Web Developer, Game Developer0.014302
Data Engineer, Full-Stack Web Developer, Data Scientist, Information Security, Back-End Web Developer0.014302
Data Scientist, Quality Assurance Engineer, Product Manager, Front-End Web Developer, Back-End Web Developer, User Experience Designer, DevOps / SysAdmin, Information Security, Game Developer, Full-Stack Web Developer, Data Engineer, Mobile Developer0.014302
User Experience Designer, Full-Stack Web Developer, Data Scientist0.014302
Back-End Web Developer, Game Developer, DevOps / SysAdmin, Data Scientist, Data Engineer, Front-End Web Developer, Mobile Developer, Full-Stack Web Developer0.014302
Mobile Developer, Full-Stack Web Developer, Information Security, Back-End Web Developer, Front-End Web Developer, Data Scientist0.014302
Mobile Developer, Game Developer, Front-End Web Developer, DevOps / SysAdmin0.014302
Mobile Developer, Back-End Web Developer, Information Security, Front-End Web Developer0.014302
Front-End Web Developer, Game Developer, User Experience Designer, Data Scientist, Full-Stack Web Developer0.014302
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" ], "text/plain": [ " JobRoleInterest\n", "Full-Stack Web Developer 11.770595\n", " Front-End Web Developer 6.435927\n", " Data Scientist 2.173913\n", "Back-End Web Developer 2.030892\n", " Mobile Developer 1.673341\n", "Game Developer 1.630435\n", "Information Security 1.315789\n", "Full-Stack Web Developer, Front-End Web Devel... 0.915332\n", " Front-End Web Developer, Full-Stack Web Devel... 0.800915\n", " Product Manager 0.786613\n", "Data Engineer 0.758009\n", " User Experience Designer 0.743707\n", " User Experience Designer, Front-End Web Dev... 0.614989\n", " Front-End Web Developer, Back-End Web Develop... 0.557780\n", " DevOps / SysAdmin 0.514874\n", "Back-End Web Developer, Front-End Web Develop... 0.514874\n", "Back-End Web Developer, Full-Stack Web Develope... 0.514874\n", "Full-Stack Web Developer, Front-End Web Devel... 0.443364\n", " Front-End Web Developer, Full-Stack Web Devel... 0.429062\n", " Front-End Web Developer, User Experience De... 0.414760\n", "Full-Stack Web Developer, Mobile Developer 0.414760\n", "Back-End Web Developer, Full-Stack Web Developer 0.386156\n", "Full-Stack Web Developer, Back-End Web Developer 0.371854\n", "Back-End Web Developer, Front-End Web Developer 0.286041\n", "Data Engineer, Data Scientist 0.271739\n", "Full-Stack Web Developer, Back-End Web Develope... 0.271739\n", " Front-End Web Developer, Mobile Developer 0.257437\n", "Full-Stack Web Developer, Data Scientist 0.243135\n", " Data Scientist, Data Engineer 0.228833\n", " Mobile Developer, Game Developer 0.228833\n", "... ...\n", "Back-End Web Developer, Full-Stack Web Develope... 0.014302\n", "Full-Stack Web Developer, Back-End Web Develope... 0.014302\n", "Back-End Web Developer, Front-End Web Develop... 0.014302\n", " Mobile Developer, Front-End Web Developer, ... 0.014302\n", "Full-Stack Web Developer, Data Scientist, M... 0.014302\n", " Front-End Web Developer, User Experience De... 0.014302\n", " Front-End Web Developer, Back-End Web Develop... 0.014302\n", " Product Manager, Mobile Developer, Game Dev... 0.014302\n", "Back-End Web Developer, Game Developer, Full-St... 0.014302\n", "Data Engineer, Front-End Web Developer, Full-... 0.014302\n", "Full-Stack Web Developer, Back-End Web Develope... 0.014302\n", "Information Security, Product Manager, Full-S... 0.014302\n", " Quality Assurance Engineer, Front-End Web D... 0.014302\n", "Game Developer, Front-End Web Developer, Pr... 0.014302\n", "Data Engineer, Data Scientist, Machine Learning 0.014302\n", "Information Security, DevOps / SysAdmin, Back... 0.014302\n", " Data Scientist, Back-End Web Developer, Infor... 0.014302\n", " User Experience Designer, Front-End Web Dev... 0.014302\n", "Data Engineer, Full-Stack Web Developer, Inform... 0.014302\n", "Information Security, DevOps / SysAdmin, Game... 0.014302\n", " Product Manager, User Experience Designer, ... 0.014302\n", " Quality Assurance Engineer, Mobile Develope... 0.014302\n", "Data Engineer, Full-Stack Web Developer, Data... 0.014302\n", " Data Scientist, Quality Assurance Engineer,... 0.014302\n", " User Experience Designer, Full-Stack Web Deve... 0.014302\n", "Back-End Web Developer, Game Developer, DevOp... 0.014302\n", " Mobile Developer, Full-Stack Web Developer, I... 0.014302\n", " Mobile Developer, Game Developer, Front-End... 0.014302\n", " Mobile Developer, Back-End Web Developer, Inf... 0.014302\n", " Front-End Web Developer, Game Developer, Us... 0.014302\n", "\n", "[3213 rows x 1 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jobs_interested = data['JobRoleInterest'].value_counts(normalize=True) * 100\n", "pd.DataFrame(jobs_interested)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The information we got here about job role interests is quite granular. But from top-level view it looks like:\n", "- A lot of people are interested in web development (full-stack web development, front-end web development and back-end web development).\n", "- A few people are interested in mobile development.\n", "- A few people are interested in domains other than web and mobile development.\n", "\n", "It's also interesting to note that many respondents are interested in more than one subject. It'd be useful to get a better picture of how many people are interested in a single subject and how many have mixed interests. Consequently, in the next code block, we'll:\n", "\n", "- Split each string in the JobRoleInterest column to find the number of options for each participant.\n", "- We'll first drop the null values because we can't split Nan values.\n", "- Generate a frequency table for the variable describing the number of options." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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JobRoleInterest
1[Full-Stack Web Developer]
2[ Front-End Web Developer, Back-End Web Deve...
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4[Full-Stack Web Developer, Information Securi...
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" ], "text/plain": [ " JobRoleInterest\n", "1 [Full-Stack Web Developer]\n", "2 [ Front-End Web Developer, Back-End Web Deve...\n", "3 [ Front-End Web Developer, Full-Stack Web De...\n", "4 [Full-Stack Web Developer, Information Securi...\n", "6 [Full-Stack Web Developer]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Split each string in the 'JobRoleInterest' Column\n", "jobs_interested_nonulls = data['JobRoleInterest'].dropna()\n", "pd.DataFrame(jobs_interested_nonulls.str.split(',')).head()\n", "# print(interests_split.head(), '\\n')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 [Full-Stack Web Developer]\n", "2 [ Front-End Web Developer, Back-End Web Deve...\n", "3 [ Front-End Web Developer, Full-Stack Web De...\n", "4 [Full-Stack Web Developer, Information Securi...\n", "6 [Full-Stack Web Developer]\n", "Name: JobRoleInterest, dtype: object \n", "\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " JobRoleInterest\n", "1 31.650458\n", "3 15.889588\n", "4 15.217391\n", "5 12.042334\n", "2 10.883867\n", "6 6.721968\n", "7 3.861556\n", "8 1.759153\n", "9 0.986842\n", "10 0.471968\n", "12 0.300343\n", "11 0.185927\n", "13 0.028604" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Split each string in the 'JobRoleInterest' Column\n", "jobs_interested_nonulls = data['JobRoleInterest'].dropna()\n", "interests_split = jobs_interested_nonulls.str.split(',')\n", "print(interests_split.head(), '\\n')\n", "\n", "# Frequency table for the var describing the number of options\n", "no_of_interests = interests_split.apply(lambda x: len(x))\n", "pd.DataFrame(no_of_interests.value_counts(normalize=True) * 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It turns out that only 31.7% of the participants have a clear idea about what programming niche they'd like to work in, while the vast majority of students have mixed interests. But given that we offer courses on various subjects, the fact that new coders have mixed interest might be actually good for us.\n", "\n", "The focus of our courses is on web and mobile development, so let's find out how many respondents chose at least one of these two options." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True 86.241419\n", "False 13.758581\n", "Name: JobRoleInterest, dtype: float64 \n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Extracting Frequency Table for Jobs/Roles in Web/Mobile\n", "\n", "web_or_mobile_interests = jobs_interested_nonulls.str.contains('Web Developer|Mobile Developer')\n", "web_or_mobile = web_or_mobile_interests.value_counts(normalize=True)* 100\n", "print(web_or_mobile, '\\n')\n", "\n", "import matplotlib.pyplot as plt\n", "plt.style.use('fivethirtyeight')\n", "\n", "web_or_mobile.plot.bar()\n", "plt.title('Most Participants are Interested in \\nWeb or Mobile Development',\n", " y = 1.08) # y pads the title upward\n", "plt.ylabel('Percentage')\n", "plt.xticks([0,1],['Web or mobile\\ndevelopment', 'Other subject'],\n", " rotation = 0) # the initial xtick labels were True and False\n", "plt.ylim([0,100])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It turns out that most people in this survey (roughly 86%) are interested in either web or mobile development. These figures offer us a strong reason to consider this sample representative for our population of interest. We want to advertise our courses to people interested in all sorts of programming niches but mostly web and mobile development.\n", "\n", "Now we need to figure out what are the best markets to invest money in for advertising our courses. We'd like to know:\n", "\n", "- Where are these new coders located.\n", "- What are the locations with the greatest number of new coders.\n", "- How much money new coders are willing to spend on learning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### New Coders - Locations and Densities\n", "\n", "Let's begin with finding out where these new coders are located, and what are the densities (how many new coders there are) for each location. This should be a good start for finding out the best two markets to run our ads campaign in.\n", "\n", "The data set provides information about the location of each participant at a country level. We can think of each country as an individual market, so we can frame our goal as finding the two best countries to advertise in.\n", "\n", "We can start by examining the frequency distribution table of the CountryLive variable, which describes what country each participant lives in (not their origin country). We'll only consider those participants who answered what role(s) they're interested in, to make sure we work with a representative sample." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Absolute FrequencyPercentages
United States of America312545.700497
India5287.721556
United Kingdom3154.606610
Canada2603.802281
Poland1311.915765
\n", "
" ], "text/plain": [ " Absolute Frequency Percentages\n", "United States of America 3125 45.700497\n", "India 528 7.721556\n", "United Kingdom 315 4.606610\n", "Canada 260 3.802281\n", "Poland 131 1.915765" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dropping rows where JobRoleInterest is null\n", "good_data = data[data['JobRoleInterest'].notnull()].copy()\n", "\n", "# Creating Frequency Tables\n", "country_abs_freq_table = good_data['CountryLive'].value_counts()\n", "country_rel_freq_table = good_data['CountryLive'].value_counts(normalize=True) * 100\n", "\n", "# Displaying Frequency Tables as a Dataframe\n", "pd.DataFrame(data ={'Absolute Frequency': country_abs_freq_table,\n", " 'Percentages': country_rel_freq_table}).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "45.7% of our potential customers are located in the US, and this definitely seems like the most interesting market. India has the second customer density, but it's just 7.7%, which is not too far from the United Kingdom (4.6%) or Canada (3.8%).\n", "\n", "This is useful information, but we need to go more in depth than this and figure out how much money people are actually willing to spend on learning. Advertising in high-density markets where most people are only willing to learn for free is extremely unlikely to be profitable for us." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spending Money for Learning\n", "\n", "The MoneyForLearning column describes in American dollars the amount of money spent by participants from the moment they started coding until the moment they completed the survey. Our company sells subscriptions at a price of \\$59 per month, and for this reason we're interested in finding out how much money each student spends per month.\n", "\n", "We'll narrow down our analysis to only four countries: the US, India, the United Kingdom, and Canada. We do this for two reasons:\n", "\n", "- These are the countries having the highest frequency in the frequency table above, which means we have a decent amount of data for each.\n", "- Our courses are written in English, and English is an official language in all these four countries. The more people know English, the better our chances to target the right people with our ads.\n", "\n", "Let's start with creating a new column that describes the amount of money a student has spent per month so far. To do that, we'll need to divide the MoneyForLearning column to the MonthsProgramming column. The problem is that some students answered that they have been learning to code for 0 months (it might be that they have just started). To avoid dividing by 0, we'll replace 0 with 1 in the MonthsProgramming column." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 13.333333\n", "2 200.000000\n", "3 0.000000\n", "4 0.000000\n", "6 0.000000\n", "Name: money_per_month, dtype: float64\n" ] } ], "source": [ "# Replacing Value of 0 with 1 in Months Programming column\n", "good_data['MonthsProgramming'].replace(0.0,1, inplace=True)\n", "\n", "# Calculating Money per month \n", "good_data['money_per_month'] = good_data['MoneyForLearning']/good_data['MonthsProgramming']\n", "print(good_data['money_per_month'].head())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's keep only the rows that don't have null values for the money_per_month column." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "675\n" ] } ], "source": [ "# Dropping Rows with null values for money per month\n", "print(good_data['money_per_month'].isnull().sum())\n", "good_data = good_data[good_data['money_per_month'].notnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to group the data by country, and then measure the average amount of money that students spend per month in each country. First, let's remove the rows having null values for the CountryLive column, and check out if we still have enough data for the four countries that interest us." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "United States of America 2933\n", "India 463\n", "United Kingdom 279\n", "Canada 240\n", "Poland 122\n", "Name: CountryLive, dtype: int64 \n", "\n", "CountryLive\n", "United States of America 227.997996\n", "India 135.100982\n", "Canada 113.510961\n", "United Kingdom 45.534443\n", "Name: money_per_month, dtype: float64\n" ] } ], "source": [ "# Dropping rows with null values in CountryLive\n", "good_data = good_data[good_data['CountryLive'].notnull()]\n", "print(good_data['CountryLive'].value_counts().head(), '\\n')\n", "\n", "# Mean sum of money spent by students each month\n", "country_mean = good_data.groupby(['CountryLive']).mean()\n", "print(country_mean['money_per_month'][['United States of America', 'India', 'Canada', 'United Kingdom']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results for the United Kingdom and Canada are a bit surprising relative to the values we see for India. If we considered a few socio-economical metrics (like [GDP per capita](https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita#Lists_of_countries_and_dependencies)), we'd intuitively expect people in the UK and Canada to spend more on learning than people in India.\n", "\n", "It might be that we don't have have enough representative data for the United Kingdom and Canada, or we have some outliers (maybe coming from wrong survey answers) making the mean too large for India, or too low for the UK and Canada. Or it might be that the results are correct." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dealing with Extreme Outliers" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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G9OjRg88//5ynT58ye/ZsDA0NGTVqFJBZm5o+fTo+Pj7cvXuX9u3bU61aNWJjYzl+/DjvvfeeMnhJLTs7O1avXk1QUBB169alatWqL/1HF/J/X19Fy5YtlUFFOenQoQMtW7Zk3LhxJCQk0KBBAw4fPsyGDRsYN26c3uAeNUxNTalRowZBQUE0atSIKlWqYG1t/Y/UUAvj56+zYsUKunfvTtu2bfH29lYmJbh27RqbNm3i2bNneHh40KBBA3r27MmcOXNIS0vDycmJ8PBw5s2bR8+ePZUvQ+bm5rRu3ZqFCxdSs2ZNTExM2LZtm97jRfn1sp9VAwMD+vfvz9KlS2nYsCFOTk4vHUNxJMlSZDFo0CAMDAz47rvv6N+/P1WqVKFDhw5Mnz79pZogO3bsyM8//8z8+fMZPXo0jx8/xtTUlObNm9OjR48s5bt3786+ffvw8vJSdfw+ffqQlpbG1q1bWbBgAQ8fPsTc3JwPPviACRMmKH1FOn379qVy5cpMmDCBu3fv4uDgwOrVq/WajLy8vHjzzTf57rvv2LFjB8+ePcPCwoJWrVrRuHHjfN+DMWPGEBkZyejRo0lJSaF169av/AB6fu9rYSpTpgxbt25lxowZLF68mMTERKysrJg1a5beyNH8HO+7775jxowZdO/enbS0NL2RtoWtoH/+Oubm5vzyyy+sWLGCXbt2ERAQQFpamvLM4vOPhSxbtow6deqwadMmZST0mDFj8PX11TvmypUrGTduHL6+vlSqVIkBAwYwfvx4Ro8e/VIxvspntXv37ixdulT17+7rRJOcnJy1/UqIIjR06FBOnDjBuXPnsh2E9CqMjIzw8fEpFlPgCVHSzJgxg+XLl3P58mWqVatW1OEUKKlZimIjIiKCCxcuEBQUxKxZswo8UQohCse5c+eIjIxk+fLlDBw4sMQlSpBkKYqRDh06ULVqVfr168eQIUOKOhwhhEoDBgzgzp07uLq66s0lXJJIM6wQQgiRB2nnEkIIIfIgyVIIIYTIgyRLIQrI+PHjlbk8Y2JilCWOjIyMMDU15a233qJr164sXbo02wW3GzdunK8VJWJiYpg9ezbR0dH5es+Ly12NGDFCmSO0IOQWV36vsaD9/fffWFhYcPr06SKLQbyeZICPEAXgzz//ZN26dRw+fFhv+7hx43BzcyMtLY07d+7w+++/M3v2bJYvX05QUJDeHKGbNm3K1yjCGzdu4OfnR8uWLXNdleV55ubmHDlyRG8KtYKWW1z5vcaCZm5uzieffMLUqVP5+eefiywO8fqRZClEAVi6dClvv/12lvlGX1xiqmvXrgwdOpQOHTowcOBAjh07psy1qVvwuDBotVqePXtGxYoV8z31XEEqzGtUy8vLC2dnZ06fPo2jo2NRhyNeE9IMK8QrevLkCdu2bcuyPFVObGxsGD9+PBcvXuS3335Ttr/YRBkXF8fw4cOxt7fH1NQUOzs7+vTpw507dwgNDeXDDz8EMmdN0TX3hoaGKsfy9vZm48aNNG/eHBMTEw4dOpRtM6zOyZMnadu2LWZmZjRu3JgVK1bo7Z89e3aWFTcgsxlXN6uNmrhebIY9ffo03bp1480336RWrVp07do1SzOprqn43LlzuLm5YWFhgYODg96aq3ndMx17e3saNmzIhg0bsvvxCJEtSZZCvKKIiAju3bunTG6tRseOHQE4ceJEjmWGDRtGREQE06dP56effsLPz49atWrx8OFD3nnnHfz9/QHw8/NTVrV/vuYWGhrK0qVL8fX1ZefOnblOrv7gwQMGDx5Mv3792Lx5M++99x6+vr7ZJtXcqInreX/88QceHh4kJyezdOlSli1bxoMHD/Dw8ODChQtZYhw6dCi9e/dmy5YtODg4MG7cOL0vHLnds+e1bt2a//u//8vXtYnSTZphhXhFp06dQqPR0KhRI9Xv0U0Qn91SaDoRERFMnTpVbyHu7t27K//XLZdlZ2eXbdNqcnIyv/76q96qKzExMdme68GDByxatIh//etfALRv357Y2Fhmz55N//79VS9DVa1atTzjet7cuXOpUKECu3fvVmqtbdu2pUmTJvj5+bFp0ya9GDdv3sz7778PZK68ERwczM6dO5Vted0zncaNG/PDDz8QGxurLM0mRG6kZinEK4qNjcXQ0FBvHce86JYUyy0JNW3alO+//55ly5Zx8eLFbJchy02zZs2yXcQ7O2XLlqVr165623r06MGtW7e4fft2vs6bH7///judO3fWa97VraF5/PhxvbKVK1dWkiJkLiVlY2PDrVu3lG1q75mxsTGQOTpWCDUkWQrxip48eaKsv6jWX3/9BWRda/N5a9euxc3Nje+++47WrVvToEED/Pz8yMjIUHWO7Fa0z4mRkVGWBaNNTU2BzC8DhSUpKSnbe2BmZkZycnKWGF9UoUIFHj9+rLxWe88MDAyAolkvU7yeJFkK8Ypq1KiR5Q97Xg4dOgSAs7NzjmVMTEzw9/fn8uXLRERE0L9/f2bPns3atWtVnUNt0ylkNtk+e/ZMb1t8fDyA0kxZqVIlAJ4+fapXLjExUfV5XlS9evVsm6Lj4uL0lkxTS+09S0pKAsj3Opui9JJkKcQrsrW15dmzZ0ptMS/Xr19n/vz5NGnShDZt2qg+x9dff42RkRGXL18GUGqzBVE7Sk9PZ8+ePXrbgoKCsLS0pFatWgDUrl0bgEuXLillkpOTCQ8P13tffuJq3bo1hw8f1puk4cGDBxw8eJDWrVu/3MX8f9ndM52YmBgqVKiAtbX1K51DlB6qB/icPXuWsLAwrl69yt27d9FoNNSsWZO33noLJyenLM+XCVFa6EbBnj59OstC09HR0URERJCenk5CQgLHjx9n06ZN1KhRg3Xr1uVY+7t37x7du3enV69evPXWW5QvX579+/eTnJxM27ZtAahfvz7lypVj06ZNVK9enYoVK1K/fn0MDQ3zfQ2GhoZ888033L17FxsbG3bs2MGvv/7K0qVLlRjbt29PtWrV+OKLL5g0aRJPnjzhu+++o0qVKnrHyk9c48eP59ChQ3Tr1o0vvvgCjUbD4sWLefToERMmTMjXNai5ZzqnTp3CwcFBqS0LkZdck+WdO3f44Ycf+PHHH/nrr7/QarWUL1+e6tWro9VqlaYbjUZDrVq16NevH0OHDlX6OoQoDaytrXF0dOTgwYNZBsksWLCABQsWUL58eYyMjLC3t2fSpEl8/PHHuSa1SpUq8c4777BhwwZu3rxJmTJlqF+/Pj/88AMeHh5AZvPvvHnzWLRoER4eHqSnp7N3717VtdXnGRoasnr1aiZOnMilS5cwMTFhzpw59O/fXyljZGTE1q1b+eqrr/Dy8qJWrVpMmDCBX3/9lWPHjinl8hPX22+/zb59+5gxYwYjR45Eq9XSrFkz9u/frzy7qZaaewaZNd7ffvuNqVOn5vs+idIrxyW6/v3vf7Ny5UqqVKlCt27dcHFxwcHBQWmS0fnrr784c+YMISEh7Nu3j9TUVLy9vfnmm2/+kQsQojjYvHkzkyZN4r///S+VK1cu6nBELoKCghg9ejR//PFHtoOGhMhOjsnS1dWVL774gg8//FD1ivUZGRns3buXxYsXExwcXKCBClGcpaen06pVKwYMGMDnn39e1OGIXLz//vt4eHjg6+tb1KGI14gs/ixEAYmIiODcuXMMGTKkqEMROYiLi2Pt2rWMHj1aWgBEvkiyFEIIIfKg+tGRy5cvZxla/ttvv9GjRw9cXV0JCAgo8OCEEEKI4kB1zVK3qO3WrVsBuHXrFs7OzlSsWBETExOuXr3KkiVL9EbPCSGEECWB6prl+fPn9VZV2LZtGxkZGYSGhnLixAk6derEqlWrCiVIIYQQoiipTpaJiYl6U0MdOXKENm3aKI+SdOrUicjIyIKPUAghhChiqpOliYkJN27cADKnuDp16pTerBhPnjwp+OiEEEKIYkD1dHdt27Zl5cqVVKtWTZmtw93dXdn/3//+N8tUX0IIIURJoDpZfv3110RGRjJ16lTKly/PtGnTsLKyAuDx48fs2rVLb8FVIYQQoqTI93OW9+/fp1KlSnoL3T569IjIyEgsLS1falkdIYQQojhT1Wf56NEjPvzwQzZt2kS1atWyrAhvYGBA48aN85Uo09PTmTlzJk2aNMHMzIwmTZowc+ZM0tLSlDJarZbZs2djb2+Pubk5Hh4eWZbaSU5OxtvbGysrK6ysrPD29s6ytuDFixdxd3fH3NxcWQz2xRXUd+/ejZOTE6ampjg5ObF3717V1/K6SExM5KuvvlLW8hNCCKGOqmRpYGDAuXPnSE9PL7ATL1q0iFWrVuHn50d4eDhz5szhhx9+YMGCBUqZxYsXExAQgJ+fH8HBwZiYmODp6am39t2QIUM4f/4827dvZ8eOHZw/f55hw4Yp++/fv4+npyempqYEBwczZ84cvv/+e5YsWaKUCQ8PZ/DgwfTq1YvQ0FB69erFoEGDOHXqVIFdb3Gwbds2Ll++rDwrK4QQQh3VzbD9+/fH0NCQFStWFMiJ+/TpQ/Xq1Vm+fLmybfjw4SQlJbF161a0Wi329vYMHToUHx8fILOGa2try4wZM/Dy8uLKlSs4OTlx8OBBZcX5sLAw3NzciIiIwNbWltWrVzNt2jSuXr2KgYEBAPPmzWPNmjVcunQJjUaDl5cXSUlJ7Nq1S4mlW7duGBsbs3r16gK53qKWmJjI8OHDefr0KRUqVGDFihXSZC6EECqpfnTEz8+PM2fOMHXqVKKjo8nIyHilEzs7O3Ps2DGuXr0KZI6mDQ0NpUOHDkDmSuZxcXG4uroq7zEwMKBVq1acPHkSyKwRVq1aFScnJ73jVqlSRa9My5YtlUQJ0K5dO2JjY4mJiQEyJ8B+/jy6MrpjlAS6SSQgc3UYqV0KIYR6qkfDNm/eHK1WS0BAAAEBAZQpU4by5cvrldFoNNy+fVvV8caMGUNKSgpOTk6ULVuWtLQ0fHx8lBUb4uLigMznO59nYmJCbGwsAPHx8dSsWVNvtXmNRoOxsTHx8fFKmRfX4NQdMz4+njp16hAXF5fteXTHyMm1a9dUXWtxEBISovQHp6WlERISQrt27Yo4KiGEKB5sbW1z3a86WXp6euolpVcVFBREYGAgq1atwt7engsXLjBx4kSsrKz45JNPlHIvnlOr1WZJji/Kq4xucE9eZfK63rxubnHStm1bfvnlF9LS0ihXrhxt27Z9reIXQoiipDpZLlu2rEBP/PXXX/PZZ5/xr3/9C4BGjRpx8+ZNFi5cyCeffIKZmRmQWfuztLRU3peQkKDUAk1NTUlISNBLbFqtlrt37+qVebGGmJCQAPyvhmlmZpZtmRdrm6+z3r17KwtylylTRpkYXwghRN5U91kWtIcPH1K2bFm9bWXLllX61aytrTEzMyMkJETZ//jxY8LCwpQ+yhYtWpCSkkJ4eLhSJjw8nNTUVL0yYWFhPH78WCkTEhKChYUF1tbWQGYT8/Pn0ZV5vi/0dVejRg1cXV3RaDS4urrK4B4hhMgH1TVLndjYWM6dO8e9e/eyHeTTr18/Vcfp3LkzixYtwtraGnt7e86fP09AQAB9+/YFMptFR4wYwfz587G1taV+/fr4+/tTpUoVevbsCYCdnR3t27dn7NixLF68GK1Wy9ixY+nUqZPSxNizZ0/8/PwYOXIkPj4+REZGsmjRIiZMmKDURocPH467uzsLFiygS5cu7Nu3j9DQUA4ePJjf21Os9e7dmxs3bkitUggh8kn1oyNPnz7ls88+Y+fOnWRkZKDRaLLt+0tMTFR14gcPHjBr1iz27dtHQkICZmZm/Otf/2LChAlUqlQJyGxSnTNnDuvWrSM5ORlHR0f8/f1p2LChcpykpCR8fX05cOAAAG5ubsydOxcjIyOlzMWLF/Hx8eHMmTMYGRnh5eWFr6+vXty7d+9m5syZREdHU7duXaZMmULXrl1VXYsQQoiSTXWynDZtGt9//z2TJk2iZcuWdOnShWXLlmFubs6SJUu4c+cOy5cvp0GDBoUdsxBCCPGPUt1nuXPnTvr06YOPj4+SEC0sLPjggw/Yvn07lStXZs2aNYUWqBBCCFFUVCfL+Ph4ZcBLuXKZXZ26QTMajYZu3bqxZ8+eQggkTGDqAAAgAElEQVRRCCGEKFqqk2XNmjWVCcoNDQ0xMDAgOjpa2f/s2TNSU1MLPEAhhBCiqKkeDdu4cWMiIiKAzJpk69atWbp0KU2aNCEjI4OVK1fSuHHjQgtUCCGEKCqqa5aDBg1Cq9UqTa8zZswgNTUVDw8PunTpwsOHD5k1a1ahBSqEEEIUlXwv/vy8+/fvExoaStmyZXF2dtZ7XEMIIYQoKV4pWQohhBClQZFNdyeEEEK8LnIc4FO9evV8rzKi0Wi4e/fuKwclhBBCFCc5Jsvn504VQgghSjPpsxRCCCHyIH2WQgghRB5ybIY9fvz4Sx2wdevWLx2MEEIIURzl2Ayb3wE+Wq0WjUajeokuIYQQ4nWRY81y7969/2QcQgghRLElA3yEEEKIPKieSP159+7d49atWwBYWlryxhtvFGhQQgghRHGSr9GwZ86cwc3NjXr16tGmTRvatGlDvXr1cHd358yZM4UVoxBCCFGkVDfDnj59Gg8PD8qXL0/Pnj2xs7NDq9Vy9epVduzYQVpaGvv378fBwaGwYxZCCCH+UaqTpaenJ5GRkRw+fBgLCwu9fbGxsXTs2BFbW1uCgoIKJVAhhBCiqKhuhj116hSDBw/OkigBLCwsGDx4sLI4tBBCCFGSqE6WWq2WsmXL5nygMmXQamVgrRBCiJJHdbJs2rQp69atIykpKcu+pKQk1q9fL/2VQgghSiTVfZZhYWF0796dqlWr0r9/f2xtbQG4evUqgYGBPHjwgN27d+Ps7FyoAQshhBD/tHxNSnD8+HEmT57MuXPn9La/++67zJo1i1atWhV4gEIIIURRe6kZfOLj47lx4wYAVlZWmJqaFnhgQgghRHEh090JIYQQechxursff/zxpQ7Yr1+/lw5GCCGEKI5yXaIrS+H/v2TXi4+IPL+UlyzRJYQQoqTJsWb54iCeBw8eMHz4cAwNDRk2bBj169dHq9USGRnJihUrSE1NZfny5YUesBBCCPFPU91nOXr0aKKjo9m9e3eWRaEzMjLo1q0b9erVY/HixYUSqBBCCFFUVE9KsHfvXj788MMsiRIyZ+/p2rWrLBgthBCiRFKdLNPS0pTHRbITHR3Ns2fPCiQoIYQQojhRnSxdXV1ZuXIl27dv1xvgo9Vq2bZtG6tWraJdu3aFEqQQQghRlFT3WcbFxdG1a1euXbuGiYkJdevWRaPREBUVRXx8PLa2tuzZswdzc/PCjlkIIYT4R+VrUoInT56wbt06Dh8+zM2bN9FqtVhZWdGxY0cGDhxIpUqVCjNWIYQQokjIDD5CCCFEHlT3WQohhBCllSRLIYQQIg+SLIUQQog8SLIUQggh8iDJUgghhMhDkSbLv//+m+HDh2NjY4OZmRlOTk4cO3ZM2a/Vapk9ezb29vaYm5vj4eHB5cuX9Y6RnJyMt7c3VlZWWFlZ4e3tTXJysl6Zixcv4u7ujrm5OQ0aNMDPzy/Lyim7d+/GyckJU1NTnJycZOo+IYQQilyTZVxcHMePH+fBgwd629PS0pgzZw5NmzbFwsICFxcXDh48mK8TJycn06lTJ2UGoJMnTzJ37lxMTEyUMosXLyYgIAA/Pz+Cg4MxMTHB09NTL54hQ4Zw/vx5tm/fzo4dOzh//jzDhg1T9t+/fx9PT09MTU0JDg5mzpw5fP/99yxZskQpEx4ezuDBg+nVqxehoaH06tWLQYMGcerUqXxdkxBCiJIp1+csJ06cyJYtW7hy5QoGBgbK9q+++orly5dTrVo16taty5UrV3j69Cn79u3D2dlZ1YmnT5/O8ePHOXToULb7tVot9vb2DB06FB8fHwAePXqEra0tM2bMwMvLiytXruDk5MTBgweV84aFheHm5kZERAS2trasXr2aadOmcfXqVeUa5s2bx5o1a7h06RIajQYvLy+SkpLYtWuXcv5u3bphbGzM6tWrVV2PEEKIkivXmmVYWBju7u56iTIxMZGVK1dSv359zp49S0hICCdOnMDY2FivtpaX/fv34+joiJeXF/Xr1+e9995j5cqVSvNoTEwMcXFxuLq6Ku8xMDCgVatWnDx5EsisEVatWhUnJyeljLOzM1WqVNEr07JlS71raNeuHbGxscTExAAQERGhdx5dGd0xhBBClG45Lv4McOvWLXr27Km37fDhw6Snp/PZZ59hZGQEgJWVFf3792fz5s2qTxwdHc3q1asZOXIkY8aM4cKFC/j6+gLg7e1NXFwcgF6zrO51bGwsAPHx8dSsWVNv2TCNRoOxsTHx8fFKmVq1amU5hm5fnTp1iIuLy/Y8umPk5Nq1a6qvVwghRPFla2ub6/5ck+WjR49444039LadOHECjUaDi4uL3va6deuSlJSkOrCMjAyaNm3KN998A8A777xDVFQUq1atwtvbWyn34vqZWq02S3J8UV5ldLXXvMpkd+zn5XVzhRBClAy5NsNaWlpy6dIlvW3Hjh3DzMwMa2trve3ZJdbcmJmZYWdnp7ftrbfe4tatW8p+IEvtLiEhQakFmpqakpCQkGXJsLt37+qVye4Y8L8appmZWa7nEUIIUbrlmizbtWvHli1bOHbsGBkZGWzcuJHr16/j4eGRpezZs2epXbu26hM7OzsTGRmpty0yMlI5hrW1NWZmZoSEhCj7Hz9+TFhYmNJH2aJFC1JSUggPD1fKhIeHk5qaqlcmLCyMx48fK2VCQkKwsLBQEn7z5s31zqMr83xfqBBCiNIr12Tp4+NDtWrV6Nq1KyYmJowePRpjY2PGjh2rVy4lJYV9+/ZlaZrNzciRI4mIiMDf35+oqCh27drFypUrGTJkCJDZLDpixAgWLVrEnj17uHTpEiNHjqRKlSpKP6qdnR3t27dn7NixREREEB4eztixY+nUqZPSRNqzZ08MDAwYOXIkly5dYs+ePSxatIiRI0cqzazDhw/nt99+Y8GCBVy9epUFCxYQGhrKiBEj1N9JIYQQJVaeS3QlJyezYcMGoqOjsbKy4uOPP6ZmzZp6ZU6dOsXWrVsZPHgwDRo0UH3yQ4cOMX36dCIjI7G0tGTo0KEMGzZMSWJarZY5c+awbt06kpOTcXR0xN/fn4YNGyrHSEpKwtfXlwMHDgDg5ubG3LlzlcFHkDkpgY+PD2fOnMHIyAgvLy98fX31+iR3797NzJkziY6Opm7dukyZMoWuXbuqvhYhhBAll6xnKYQQQuRB5oYVQggh8pDroyO5DXDRaDRUrFgRKysrOnbsSL9+/ShXLtfDCSGEEK+lXJthPTw8cn3W8NGjR0RFRZGcnEzz5s3ZtWsXlStXLpRAhRBCiKLyyn2WGRkZBAYG8tlnnzFu3DimTJlSULEJIYQQxUKBDfAZMWIEp06dIiIioiAOJ4QQQhQbBTbAp1mzZty8ebOgDieEEEIUGwWWLFNSUqhQoUJBHU4IIYQoNgokWWZkZLB//34aNWpUEIcTQgghipVcn/W4c+dOrm9+9OgRkZGRrF69mlOnTrFq1aoCDU4IIYQoDnJNlm+99Vaey1QBlC9fnsmTJ9OjR48CC0wIIYQoLnJNlhMmTMg1WVaqVInatWvzwQcfZJkvVgghhCgpZG5YIYQQIg8yN6wQQgiRhxyT5YYNG0hLS8v3AZ89e8aGDRteKSghhBCiOMmxGbZhw4aUKVOGjz/+mB49eiiLKefkypUr7Ny5k82bNwOZa0gKIYQQJUGOyfLx48esWLGCZcuWER8fj4WFBe+++y7W1tYYGRmh1WpJTk4mJiaGs2fP8vfff2NiYsLIkSMZNmwYlSpV+qevReQhMTERf39/xo8fT/Xq1Ys6HCGEeG3kOcAnPT2dw4cPs3//fk6cOEFUVBRabeZbNBoNNjY2ODk54e7uTqdOnShbtuw/ErjIv+XLl3Po0CE6derE8OHDizocIYR4beR7NGx6ejpJSUkA1KhRgzJlZIzQ6yAxMZHhw4fz9OlTKlSowIoVK6R2KYQQKuU705UtWxZjY2OMjY0lUb5Gtm3bRkZGBpA5PeHWrVuLOCIhhHh9SLYrJY4ePaqMbk5LS+Po0aNFHJEQQrw+JFmWEi4uLpQrlzlhU7ly5XBxcSniiIQQ4vUhybKU6N27t9JsXqZMGfr06VPEEQkhxOtDkmUpUaNGDVxdXdFoNLi6usrgHiGEyIdcJ1IXJUvv3r25ceOG1CqFECKfZCJ1IYQQIg/5qlkmJyezc+dOoqOjSUpKUiYn0NFoNCxZsqRAAxRCCCGKmupkefToUT7++GMePHiAoaEhRkZGWcqoWShaCCGEeN2oboZt3bo1KSkpbNq0icaNGxd2XEIIIUSxoXo0bGRkJCNGjJBEKYQQotRRnSytra15/PhxYcYihBBCFEuqk+W4ceNYu3atMom6EEIIUVrkOMBn8eLFWbbVqFEDBwcHPD09sbS0zLIcl0ajYfTo0QUfpRBCCFGEchzg8zIzvGg0GhITE185KCGEEKI4ybFmee7cuX8yDiGEEKLYkhl8hBBCiDyoHuBTo0YNtm/fnuP+oKAgatSoUSBBCSGEEMWJ6mT54tR2L8rIyJAZfIQQQpRI+VqiK7dkeOrUqWynwBNCCCFed7n2WS5btozly5cDcOPGDYyNjalcuXKWcvfu3eP+/fv06dOHZcuWFV60QgghRBHIdSL1mjVrYmtrC2QmSzMzM8zNzfXKaDQaKleuTNOmTfH29i68SIUQQogiono0bJcuXRg/fjwuLi6FHZMQQghRrMijI0IIIUQe8jXAB+DKlSscOnSIwMBAfvzxxyz/Xtb8+fMxMjJi/PjxyjatVsvs2bOxt7fH3NwcDw8PLl++rPe+5ORkvL29sbKywsrKCm9vb5KTk/XKXLx4EXd3d8zNzWnQoAF+fn5ZRvfu3r0bJycnTE1NcXJyYu/evS99LUIIIUoW1Ys/x8TEMGzYMMLDw3N8jESj0dCvX798BxEREcH69etp1KiR3vbFixcTEBBAQEAAtra2zJ07F09PTyIiIjA0NARgyJAh3Lp1i+3btytz0w4bNoytW7cCcP/+fTw9PWnVqhXBwcFcu3aNUaNGUblyZT7//HMAwsPDGTx4MJMmTeLDDz9k7969DBo0iEOHDtGsWbN8X48QQoiSRXUzbI8ePThx4gRTp06ldevWOT4mYmVlla8A7t27h4uLC4sXL2bu3Lk0bNiQefPmodVqsbe3Z+jQofj4+ADw6NEjbG1tmTFjBl5eXly5cgUnJycOHjyIs7MzAGFhYbi5uREREYGtrS2rV69m2rRpXL16FQMDAwDmzZvHmjVruHTpEhqNBi8vL5KSkti1a5cSV7du3TA2Nmb16tX5uh4hhBAlj+pm2LCwMD7//HNGjBhBkyZNlGbPF//l15gxY+jWrVuWgUMxMTHExcXh6uqqbDMwMKBVq1acPHkSyKwRVq1aFScnJ6WMs7MzVapU0SvTsmVLJVECtGvXjtjYWGJiYoDMmu3z59GV0R1DCCFE6aa6GfaNN96gZs2aBXry9evXExUVxYoVK7Lsi4uLA8DExERvu4mJCbGxsQDEx8dTs2ZNvckSNBoNxsbGxMfHK2Vq1aqV5Ri6fXXq1CEuLi7b8+iOkZNr166puUwhhBDFnO4xyZyoTpb9+/dn165dBfYs5bVr15g+fToHDhygQoUKOZZ7cdYgrVabJTm+KK8yuj7XvMrkNX1fXjdXCCFEyaA6WXbo0IGQkBA+/PBDvLy8sl38GcDR0VHV8cLDw7l79y4tW7ZUtqWnp/P777+zZs0aTpw4AWTW/iwtLZUyCQkJSi3Q1NSUhIQEvcSm1Wq5e/euXpkXa4gJCQnA/2qYZmZm2ZZ5sbYphBCidFKdLN3d3ZX/Hz9+PMt+XcJSu/izh4cHTZs21ds2atQobGxsGDduHPXr18fMzIyQkBAcHBwAePz4MWFhYUyfPh2AFi1akJKSQnh4uNJvGR4eTmpqqvK6RYsWTJs2jcePH1OpUiUAQkJCsLCwwNraGoDmzZsTEhLC6NGjlVhCQkL0+kKFEEKUXqqTZUBAQIGe2MjIKMuI2sqVK1O9enUaNmwIwIgRI5g/fz62trbUr18ff39/qlSpQs+ePQGws7Ojffv2jB07lsWLF6PVahk7diydOnVSmkh79uyJn58fI0eOxMfHh8jISBYtWsSECROU2ujw4cNxd3dnwYIFdOnShX379hEaGsrBgwcL9JqFEEK8norVDD4eHh7KoyOQWVudM2cO69atIzk5GUdHR/z9/ZVkCpCUlISvry8HDhwAwM3Njblz5+ol4osXL+Lj48OZM2cwMjLCy8sLX19fvT7J3bt3M3PmTKKjo6lbty5Tpkyha9eu/9CVCyGEKM5eKlneu3ePW7duAWBpackbb7xR4IEJIYQQxUW+prs7c+YMbm5u1KtXjzZt2tCmTRvq1auHu7s7Z86cKawYhRBCiCKlumZ5+vRpPDw8KF++PD179sTOzg6tVsvVq1fZsWMHaWlp7N+/XxmMI4QQQpQUqpOlp6cnkZGRHD58GAsLC719sbGxdOzYEVtbW4KCggolUCGEEKKoqG6GPXXqFIMHD86SKAEsLCwYPHgwERERBRqcEEIIURyoTpZarTbbSQiUA5Upk+NqJEIIIcTrTHWybNq0KevWrSMpKSnLvqSkJNavXy/9lUIIIUok1X2WYWFhdO/enapVq9K/f3/lof+rV68SGBjIgwcP2L17t7JUlhBCCFFS5Os5y+PHjzN58mTOnTunt/3dd99l1qxZtGrVqsADFEIIIYraS01KEB8fz40bN4DMxZ5NTU0LPDAhhBCiuChW090JIYQQxZHqidR1zp07R3R0NMnJydmOfh00aFBBxCWEEEIUG6prlteuXePTTz/ljz/+yPERkfws0SWEEEK8LlTXLEeNGsX169eZNm0ajo6OVKtWrTDjEkIIIYoN1TVLc3Nzxo8fz5dfflnYMQkhhBDFiupJCaysrKhUqVJhxiKEEEIUS6qT5bhx45RFmIUQQojSRHWfZd++fUlLS8PBwQF3d3dq1aqVZa5YjUbDhAkTCjxIIYQQoiip7rM8d+4cffr0IS4uLueDyWhYIYQQJZDqmuW4ceN4+vQpy5Yto1mzZjIaVgghRKmhOlleunSJr776ir59+xZmPEIIIUSxo3qAT926dUlPTy/MWIQQQohiSXWynDx5MqtWrSImJqYw4xFCCCGKHdXNsMHBwRgZGdGiRQvef/993nzzzWxHw/r7+xd4kEIIIURRUj0atnr16nkfTEbDCiGEKIFkiS4hhBAiD6r7LIUQQojSSpKlEEIIkQdJlkIIIUQeJFkKIYQQeZBkKYQQQuRBkqUQQgiRB1XJ8tGjR9SoUUMmHBBCCFEqqUqWBgYGmJiYyEojQgghSiXVzbCenp789NNPZGRkFGY8QgghRLGjegaf0NBQfH19qVq1Kp988gl16tTBwMAgSzlHR8cCD1IIIYQoSi89N6xGo9F7rdVqZW5YIYQQJZLqVUcCAgIKMw4hhBCi2JKJ1IUQQog8vNRzlrdu3eLs2bOkpKQUdDxCCCFEsZOvZLlv3z4cHBxo0qQJrq6unD59GoC7d+/SqlUr9u7dWyhBCiGEEEVJdbI8dOgQn3zyCcbGxvj6+qLV/q/1tmbNmlhaWrJly5ZCCVIIIYQoSqqT5dy5c3FycuLw4cMMHTo0y/7mzZtz4cKFAg1OCCGEKA5UJ8tLly7Ro0ePHPebmZmRkJCg+sQLFiygbdu21K5dGxsbG/r06cOlS5f0ymi1WmbPno29vT3m5uZ4eHhw+fJlvTLJycl4e3tjZWWFlZUV3t7eJCcn65W5ePEi7u7umJub06BBA/z8/PRqxgC7d+/GyckJU1NTnJycpElZCCGEQnWyrFChAk+ePMlx/82bN/M1Hd6xY8f49NNPOXToEHv27KFcuXJ0796dpKQkpczixYsJCAjAz8+P4OBgTExM8PT05MGDB0qZIUOGcP78ebZv386OHTs4f/48w4YNU/bfv38fT09PTE1NCQ4OZs6cOXz//fcsWbJEKRMeHs7gwYPp1asXoaGh9OrVi0GDBnHq1CnV1yOEEKLkUv3oSJ8+fUhISOD//u//SExMxMbGhl27duHi4sL9+/dxdnamZcuWrF69+qUCSUlJwcrKis2bN+Pm5oZWq8Xe3p6hQ4fi4+MDZE7obmtry4wZM/Dy8uLKlSs4OTlx8OBBnJ2dAQgLC8PNzY2IiAhsbW1ZvXo106ZN4+rVq8qMQ/PmzWPNmjVcunQJjUaDl5cXSUlJ7Nq1S4mnW7duGBsbv/T1CCGEKDlU1ywnTpzIxYsX6d69OwcOHADg/PnzrFmzRkmYEyZMeOlAUlJSyMjIwMjICICYmBji4uJwdXVVyhgYGNCqVStOnjwJZNYIq1atipOTk1LG2dmZKlWq6JVp2bKl3tR87dq1IzY2lpiYGAAiIiL0zqMrozuGEEKI0k31DD5NmzZlx44djB07ls8++wyAr7/+GgAbGxt27NiBnZ3dSwcyceJEGjduTIsWLQCIi4sDwMTERK+ciYkJsbGxAMTHx1OzZk29qfc0Gg3GxsbEx8crZWrVqpXlGLp9derUIS4uLtvz6I6Rk2vXruX3MoUQQhRDtra2ue5XnSwB3nvvPSIiIjh//jxRUVFkZGRQt25d3n333SxzxebHV199xYkTJzh48CBly5bV25fTHLQ57VdTRje4J68yeV1TXjdXCCFEyZCvZKnTpEkTmjRpUiABTJo0iaCgIPbu3UudOnWU7WZmZkBm7c/S0lLZnpCQoNQCTU1NSUhI0EtsWq2Wu3fv6pV5sYaoG7WrK2NmZpZtmRdrm0IIIUqnfM3gk5aWxoYNGxgwYAAuLi64uLgwYMAANmzYwLNnz/J9cl9fX3bs2MGePXt466239PZZW1tjZmZGSEiIsu3x48eEhYUpfZQtWrQgJSWF8PBwpUx4eDipqal6ZcLCwnj8+LFSJiQkBAsLC6ytrYHMZ0SfP4+uzPN9oUIIIUqvshMnTpympmBcXBxubm5s3LiR+Ph4jIyM0Gq1XLhwgV27drFv3z66dOlC1apVVZ3Yx8eHwMBA1q1bh6WlJampqaSmpgKZj6loNBrS09NZuHAh9evXJz09ncmTJxMXF8eiRYuoWLEixsbGnDp1ih07dtCkSRP++usvxo4di4ODg/L4iI2NDWvXruXChQvY2toSFhbG119/zZgxY5RkaGFhwbfffkv58uWpWbMm69evZ/PmzSxevDhLf6cQQojSR/WjIwMHDuTAgQMsWLCAfv36KX2L6enpbNmyhS+//BI3NzfWr1+v6sS6Ua8v8vX1ZdKkSUBmk+qcOXNYt24dycnJODo64u/vT8OGDZXySUlJ+Pr6KiN03dzcmDt3rt7xL168iI+PD2fOnMHIyAgvLy98fX31+iR3797NzJkziY6Opm7dukyZMoWuXbuqupbXRWJiIv7+/owfPz7L+qRCCCFypjpZWlpa4uXlxYwZM7LdP3nyZDZs2MDNmzcLNEBRcJYvX86hQ4fo1KkTw4cPL+pwhBDitaG6z7JixYrUrl07x/3W1tZUrFixQIISBS8xMZHg4GC0Wi3BwcF6MyUJIYTInepk2aNHD3bu3JntQJ6nT5+yc+dOPD09CzQ4UXC2bdtGRkYGABkZGWzdurWIIxJCiNeH6mbY0NBQJk6ciEajYfDgwdSrVw+NRkNkZCRr164FYM6cOXoz5QA4OjoWfNQi3/r168ejR4+U1wYGBvz4449FGJEQQrw+VCfLFweEPP9c44vbdNs1Gg2JiYkFEad4RcuXL+fgwYPK686dO0u/pRBCqKR6UoKAgIDCjEMUMmdnZ71k2apVqyKMRgghXi+qa5bi9fb555/rjVSuXbs233//fRFGJIQQr498zeAjXl8vPtIjj/gIIYR6kixLCQsLC73XMjOREEKoJ8mylKhbt26ur4UQQuRMkmUp8Z///Efv9ZkzZ4ooEiGEeP1IsiwlXFxccn0thBAiZ6qTZXJycmHGIQpZx44d9V537ty5iCIRQojXj+pkaWdnxyeffML+/ftfau1KUbQOHz6sTBqh0Wj0nrkUQgiRO9XJ0tvbm9OnTzNgwADs7Ozw8fEhIiKiMGMTBejo0aPKbEtarZajR48WcURCCPH6UJ0sZ8yYwR9//MGuXbvo1KkT27Zto1OnTjg4ODB37lyio6MLMUzxqlxcXPRqltJnKYQQ6uVrgI/uj+yyZcu4evUqP/zwA7a2tsybNw8HBwfc3NyUhZpF8dKxY0e9mqX0WYqSLDExka+++kqWohMFpuzEiROnvcwby5UrR8OGDalduzbJyclcuXKFW7ducejQIVasWEFcXBzOzs6yxmUxsXXrVq5fvw78b8L7Zs2aFWVIQhSa9evXc+LECR4/fiyfc1EgXurRkevXr/Ptt9/StGlT3N3dCQ8P57PPPuP48eNERETw6aefsnHjRlnVohiRPktRWshC56IwqF515O7du+zcuZNt27Zx5swZKlSogLu7O3PnzqVdu3aUKfO/vDtz5kzMzMyYPXt2oQQt8s/FxYUjR46Qnp5O2bJlpc9SlFjZLXQuX9zFq1KdLO3t7UlLS6NFixYsWLAAT09P3njjjRzL29raYmxsXCBBilfXu3dvDh8+DGTWLPv06VPEEQlROI4ePUpaWhoAaWlpHD16VJKleGWqk+UXX3xBv379sLGxUVW+c+fOMoikmHn+27YQJZWLiwu//PILaWlplCtXTlpRRIFQ3Wc5ZcoU1YlSFD/btm3Te71169YiikSIwtW7d2+lW6hMmTLSiiIKRL4G+Ny/fx9/f3+6du1Kq1atOHXqFJDZob548WJltKUofkJCQnJ9LURJUaNGDVxdXdFoNLi6ulK9evWiDkmUAKqbYW/fvo27uzt//fUXNjY2XL16ldTUVCDzw7lhwwZu376Nn59foQUrXl65cuV48uSJ3mshSqrevXtz48YNqVWKAqP6L+a0aYOVarcAABYISURBVNO4f/8+R48exczMjPr16+vt9/DwUAaQiOJH98Ump9dClCQ1atTg22+/LeowRAmiuhn2l19+YdiwYTRs2FB5qP15derU4fbt2wUanCg4tWvXzvW1EEKInKlOlg8fPsTMzCzX/TLKsvjq3bu33uu+ffsWUSRCCPH6UZ0sbWxsOH36dI77f/nlFxo2bFggQYmC9+Jo2MDAwCKKRAghXj+qk+XAgQMJDAwkMDBQqUFqNBpSU1OZOnUqv/32G59++mmhBSpezc2bN3N9LURJcvbsWXr06MH58+eLOpRSpSRPYJ+v9SwHDBjAiBEjcHBwAGDw4MFYW1uzZMkSvL29ZeRZMWZhYaH3ulatWkUUiRCFb968eWRkZMjo/H/Ytm3buHz5col8jjtfzw8sXLiQvn378tNPPxEVFUVGRgZ169alR48etGzZsrBiFAWgbNmyeq/l0RFRUp09e1YZ7Z2amsr58+dp0qRJEUdV8r04gX2fPn1K1DOumuTkZG1RByEKX/fu3bNs27VrVxFEIkTh+uijj/QejapSpQqbN28uwohKh+XLl+tNM9i+ffsSNSfvSy3RJYQQxZU8U1w0spvAviTJV7LcsGED7dq1w8bGhho1amT5V7NmzcKKUwghVDEwMMj1tSgcLi4uSvdOSZzAXnXH1fTp01m0aBGNGjWiV69eGBkZFWZcQgjxUho1aqTMWw3w9ttvF2E0pUfv3r0JDg4GSuYE9qqT5aZNm3B3d2fTpk2FGY8QQrySP/74Q+/1hQsXiiiS0kU3gf2hQ4dK5AT2qpthU1NTad++fWHGIoQQr8zExCTX16LwODs7o9FoaNWqVVGHUuBUJ0tnZ2cuXrxYmLEIIcQru3PnTq6vReFZsWIFGRkZLF++vKhDKXCqk+W8efM4dOgQmzZtQquVp02EUCsqKor+/fsTHR1d1KGUCk2bNtV7rZtERRSuqKgoYmNjgcwlHUva5111suzXrx9Pnz5l9OjRvPnmmzRr1gwnJye9f87OzoUZqxCvpW+//ZaHDx8ya9asog6lVIiKisr1tSgc8+bN03s9d+7cIoqkcKge4GNsbIyJiUmWdSyFEDmLiooiISEByGwOjI6Opk6dOkUbVAkXFxen9/rvv/8uokhKF12tUqekLdmoOlnu37+/MOMQokR6cQHiWbNm8cMPPxRRNEKIlyUz+Dxn1apVNGnSBDMzM1xcXPj999+LOiTxmtPVKnVksIkQr6d8zaadnp7Oli1bOHz4MDdu3ADAysqKTp060a9fvyyTdb9OgoKCmDhxIvPnz8fZ2ZlVq1bRq1cvTpw4Qe3atYs0tuzmdS3q48q8skKI0kT1ROr379+nR48enDlzhqpVq1KnTh20Wi0xMTGkpKTg6OhIUFAQhoaGhR1zoWjXrh2NGjXiu+++U7Y5ODjQrVs3vvnmm3wfr7ASXElUkIlX7rt6ct+Lhtz3ovGq9111M+zMmTP5z3/+w7fffktkZCS//fYboaGhXL9+ndmzZ3PmzBlmzpz5SsEUladPn3L27FlcXV3/X3v3HhRV/f9x/AmKRC24WsqlREZuLWGhmIhYX4xEC8xEEFG8RV7QrJEUyxsIIjqp2YyiaCmWzEiiQt7NEchbhiZh6gje0CzNTC7iBRR+fzjur23VXQh2V3w/ZpzhnPPZs6/94Oybz7l9NNa/8cYbHDx40EiphBBCmAq9D8Nu3ryZkSNHak25YmFhwZgxYygqKmLTpk2P5WSrV69e5e7duw988seff/750NcVFxc3drQngvSjcUi/G4f0u3Ho6ndXV9dHbte7WF69ehWVSvXQ7R4eHo/9c2PNzMw0lmtra7XW/ZOuzhX6kX40Dul345B+N47/2u96F8t27dqRk5NDVFTUA7fn5OQY/UKY+nr22Wdp1qyZ1ijyr7/+qvdzJU3xAph/nt8wxXwNwdQ+18yZMyksLFQvd+7cmZkzZxoxUeMwpX4fOnQoFRUV6uWWLVuyevVqIyZqPKbU7wUFBcTHx6uXExISePnll40XqIHpfc4yMjKSLVu2EB0dzYkTJ6iurqa6uprjx48zfvx4tm7dyrBhwxoza6Np0aIFXl5e5OTkaKzPycnBx8fHSKlEU5CQkKCx3BQLpan55ptvNJabaqE0NV5eXhrLTalQQh1Glh999BElJSWkpaWRkZGhPjxZW1tLbW0tI0eO5MMPP2y0oI1t/PjxjBkzBm9vb3x8fFi5ciWXLl1i5MiRxo7WYEzpr9Anycsvv0xhYaE8o9SArK2tqaiooGXLlsaO8kSJj48nISFBY4TZVOh968h9J06cYMeOHRr3WQYGBuLh4dEoAQ3pyy+/5IsvvuDy5cuoVCrmzJmDn5+fsWMJIYQwsjoXSyGEEOJJI4+7E0IIIXR45DnLul7cYmZmxo8//vifAgkhhBCm5pHFsqioCCsrK7y8vDA3l0GoEEKIJ9Mji2WnTp04cuQIZ86cISQkhIEDB2pdHiyEEEI0dTov8Dlz5gwZGRlkZmZy9uxZnJ2dGThwIGFhYTKJrRBCiCdCna6GPXToEBkZGWRnZ/PXX3/h7e3NwIEDGTBgAK1bt27MnEIIIYTR1OvWkbt375KTk8OiRYvYv38/U6ZMYcqUKY2RTwghhDC6Ol+1U1ZWRnp6Ol988QX79+/HxsZGHgwshDBZ4eHhREdHq5eDgoKYPHmyEROJh/H19SU5OdnYMR5Ir2JZXV3Npk2bGDZsGO7u7sTGxtKqVStWr15NUVERISEhjZ1T6OFhXwLp6ek8//zzANy4cYOEhAQ6deqEra0tHTp0oHfv3mRmZho6bpOhT78nJyfj6+ursf3w4cN06NCB6Oho7ty5Y5Csj5vo6GjCw8MbdJ9r1qx5op/R++effzJlyhS8vLxo27YtKpWK0NBQdu7caexoJu2RV8Pu3buXdevWkZ2dTUVFBX5+fnz22Wf069cPGxsbQ2UUDWjixIkcPHiQuXPnolKpuHbtGocPH+batWvGjvZEyc3NJTIykuHDhzN79uxHTgUnGlarVq2MHcFoSkpK6NOnDwqFgri4ODw9PampqSEvL4+YmBh+/fVXY0c0WY8cWfbt25d169bh7+9PWloacXFxeHh4UFxczOHDhx/4T5i2bdu2ERMTQ58+fWjfvj1eXl5ERUUxatQoY0d7YmRlZREeHk5MTAxJSUlSKPV0f5S5dOlSVCoV7du3Z9y4cdy4cUPd5saNG0RHR/P888/j6urKggULtPbz7yMBGRkZ9OzZkxdeeAEXFxeGDx/O77//bpDPZGiTJk2itraWnJwc+vfvj6urK+7u7owePZq9e/cCsHjxYrp3746DgwMqlYoJEyZQWlqq3sf9IyZ5eXn4+vri4OBAcHAw586dU7c5e/YsERERuLm54eDgwOuvv8727ds1sly5coWIiAjs7Ozw9PTUmi1GnyyGpHPWkZs3b5Kdnc133333yHb3J0r++++/GyycaHi2trbs2rWLfv36yYwMRrBq1SpiY2NZsGDBYzulnTEdOHAAW1tbsrKyuHjxIiNGjMDFxYWYmBgAZsyYQW5uLl9//TX29vbMmzeP/fv3Exwc/NB9VlVV8emnn+Lm5sbVq1eJi4sjKiqKbdu2GepjGcS1a9fYtWsX06dPR6FQaG1XKpUAmJubk5ycjJOTExcuXCA2NpbY2FiWL1+ubnv79m0WLlzI4sWLsbS0JDo6mpiYGDZs2ADA9evX6dWrF9OnT8fKyooNGzYwdOhQ9u3bh5ubGwDjxo3jwoULZGVlYWVlxdSpU9UTdNynTxZDeWSxXLJkiaFyCANZtGgRo0ePxtnZGQ8PD3x8fHj77bfp2bOnsaM1eadPn2bixIl89tlnUijrydramoULF9K8eXPc3d1599131YcQr1+/zjfffMPixYsJCAgA7n2H6ZoRaejQoeqfnZycWLhwIV27duXixYvqc85NwZkzZ6itrVUXq4cZN26c+uf27duTkJDA4MGDWbZsmfpJbnfu3GH+/PnqizsnTJjA+PHjqampwdzcnI4dO9KxY0f1fiZNmsT27dvJzs5m8uTJnDp1iu+//57t27fTrVs3AJYuXar10Bt9shjKI4vl4MGDDZVDGIifnx8FBQXk5+dz8OBBfvjhB/r378+IESNYtGiRseM1aba2trRp04aUlBT69OlDu3btjB3psePu7k7z5v//tWVnZ8ehQ4eAe4f+qqqq6Nq1q3q7QqHgpZdeeuQ+CwoKmDdvHkePHqW0tJTa2nt30/32229Nqlje/1y65OXl8fnnn1NUVER5eTl3796lqqqKy5cvY29vD4ClpaXGXRB2dnZUV1dTVlZGq1atqKysZN68eezYsYNLly5x584dbt26pf5dnDx5EnNzc7y9vdX7cHR0VO+/LlkMRR742oRYW1tTVlamtb6srEzjgiwLCwu6d+/OxIkT2bhxI9OmTSMtLY2SkhJDxm0y9O13hUJBVlYWbdq0ITg4WOuQk9DNwsJCY9nMzExdBPQtBv9UWVnJgAEDePrpp0lNTWX37t3qK8Orqqr+e2AT4uzsjJmZGUVFRQ9tc/78ecLDw3FzcyMtLY3c3FwWL14MaPbHP/9gAdTn3WtqaoB7h8OzsrKYOnUqW7ZsYc+ePXh7e6v3oc/vSt8shiLFsglxdXWlsLBQ6z/iL7/8gouLy0Nf5+7uDtz74hB1V5d+t7GxYf369Tg4OBAUFKRxUYT4bzp06ICFhQX5+fnqdZWVlRw/fvyhrykuLubq1avMmDEDPz8/3NzcuHLliiHiGlyrVq0ICAhgxYoVXL9+XWt7aWkpR44coaqqiuTkZLp27YqLiwt//PFHnd/rxx9/ZNCgQfTr1w9PT08cHBw4e/aseru7uzs1NTX8/PPP6nUXLlzQeK+GytJQpFg2IVFRUZw7d47Y2FiOHj1KcXExS5YsYf369UyYMAG4dyXgqlWrKCgooKSkhJ07d5KYmKi+Kk7UnT79/k/W1tZkZmbi6OhIcHCwxpeIqD+FQsHQoUOJj48nJyeHEydO8MEHH6hHOw/ywgsvYGlpyYoVKzh37hw7duxgzpw5BkxtWPPnz6e2tpaePXuSlZVFcXExRUVFfPXVV/To0QNnZ2dqampISUnh3LlzZGZmsmzZsjq/j7OzM5s3b6agoIBjx44xevRobt++rd7u6urKm2++ycSJE/npp58oLCxk3LhxWFlZaeyjIbI0FCmWTYiTkxNbt27l9OnThISEEBAQwIYNG0hLSyMwMBCAgIAAMjIyCAkJoWvXrnz88cf4+vqyceNGmjVrZuRP8HjSp9//7ZlnnmHdunU4OzsTFBTE6dOnDZy6aUpMTKRHjx5ERkbSt29fVCoV3bt3f2j75557jqVLl7JlyxZ8fHyYN28eSUlJBkxsWE5OTuTl5eHv709cXBx+fn688847bNu2jc8//xxPT0/mzp1LSkoK3bp14+uvvyYxMbHO75OUlESbNm14++23CQsL49VXX9V6KEdKSgqOjo688847REREEBYWhqOjo3p7Q2VpKPV6NqwQQgjxJJGRpRBCCKGDFEshhBBCBymWQgghhA5SLIUQQggdpFgKIYQQOkixFEIIIXSQYimEEELoIMVSiMfAlStXiI+Pp1u3bjg4OGBvb0/37t2Jj4/n0qVLxo4HwMWLF0lOTqawsNDYUYRocPJQAiFM3JEjRwgLC6OiooIBAwbg7e2Nubk5x44dY/369bRu3dokJl7Pz8+nV69eLFmyhCFDhhg7jhANSufkz0II4yktLWXIkCGYmZmRm5uLSqXS2D5jxozHdmq1Gzdu8PTTTxs7hhB6kcOwQpiwtLQ0fv/9d2bPnq1VKAFatmxJXFycxrqtW7cSEBCAvb097du3Z8iQIVrTMkVHR2tMzntfeno6SqVSY7q2jh07MmDAAA4fPkyfPn2ws7PjpZdeIiUlRd1mz5499OrVC4Dx48ejVCpRKpUkJyer38/W1pbz588zePBgHB0dCQsLY/Xq1SiVSn755RetLCtWrECpVD5y1hAhDEWKpRAmbNu2bTz11FP0799fr/aZmZkMGTKEqqoqpk2bxpgxY9i3bx+BgYH/aTqwkpISBg0ahI+PD0lJSTg5OTF16lR2794N3Jty6ZNPPgFgxIgRpKamkpqaSt++fdX7qKmpISQkBIVCwaxZswgPD6d///5YWVmRkZGh9Z7ffvstHTt2xMPDo965hWgochhWCBN28uRJXFxcaNGihc621dXVTJs2DRcXF7Zv384zzzwD3JuWrWfPnsyZM4fly5fXK8epU6fIysrC398fgMjISDw9PVm9ejVvvPEGbdu2JSAggLlz5/Lqq68SHh7+wHyBgYFaU2AFBQWxfv16EhMT1TPfnD17lvz8fGbPnl2vvEI0NBlZCmHCKioqsLa21qttQUEBly9fJioqSl0oAV555RX8/f3ZuXOnXjPUP4izs7O6UAJYWlrSpUuXOo9W33//fa11ERERXL58mdzcXPW6tWvX0qxZM0JDQ+uVV4iGJsVSCBNmbW1NRUWFXm3Pnz8PgJubm9Y2d3d3SktLKS8vr1eOdu3aaa1TKpVcu3ZN732Ym5trzFd4n7+/P/b29hqHYtetW4e/vz92dnb1yitEQ5NiKYQJc3d359SpU1RVVf2n/fx7RGlmZvbAdnfv3n3g+odNDF6XkaqFhQXNm2uf+WnWrBkDBw5ky5YtVFZWkp+fz5kzZx54KFcIY5FiKYQJe+utt7h16xZZWVk6294ftf37yleA4uJilEolNjY2wL1RYVlZmVa7+6PT+nhYAdZHREQElZWVbN68mYyMDBQKBcHBwfXenxANTYqlECZsxIgRODg4MH36dE6ePKm1vby8nISEBAC8vLywtbVl5cqV3Lx5U93m6NGj5OTkEBgYqC5oHTp0oLy8XOOWjevXr7N27dp6Z71/z2RpaWmdX/viiy/SqVMn0tPT2bhxI3379pV7MIVJkathhTBhSqWS9PR0wsLC+N///kdoaCidO3fWeoLPzJkzsbCwICkpiVGjRtG7d2/Cw8MpLy9n+fLl2NjYMHXqVPV+Q0NDmTVrFpGRkYwdO5Y7d+6wZs0annvuOX777bd6ZXV2dsbGxoaVK1eiUChQKBSoVCq9b/2IiIggNjYWgEGDBtUrgxCNRUaWQpi4Tp06ceDAAcaMGUN+fj7Tpk3jk08+Yd++fYwcOZKtW7eq24aGhrJmzRqaN29OYmIiS5cuxdfXl507d+Lk5KRup1QqWbNmDS1btiQ+Pp6vvvqK9957j1GjRtU7p6WlJampqVhaWjJp0iSioqLIzs7W+/WhoaG0aNECBwcHXnvttXrnEKIxyLNhhRAmoaysDDc3N8aOHcusWbOMHUcIDTKyFEKYhIyMDG7fvk1ERISxowihRc5ZCiGMKi8vj6KiIubOncubb77Jiy++aOxIQmiRw7BCCKMKCgrip59+okuXLqSmpj7wwQVCGJsUSyGEEEIHOWcphBBC6CDFUgghhNBBiqUQQgihgxRLIYQQQgcplkIIIYQOUiyFEEIIHf4PJ/+5tBsi/joAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "only4 = good_data[good_data['CountryLive'].str.contains('United States of America|India|Canada|United Kingdom')]\n", "\n", "import seaborn as sns\n", "sns.boxplot(x='CountryLive', y='money_per_month', data=only4)\n", "plt.title('Money Spent Per Month Per Country\\n(Distributions)',\n", " fontsize = 16)\n", "plt.ylabel('Money per month (US dollars)')\n", "plt.xlabel('Country')\n", "plt.xticks(range(4), ['US', 'UK', 'India', 'Canada'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's hard to see on the plot above if there's anything wrong with the data for the United Kingdom, India, or Canada, but we can see immediately that there's something really off for the US: two persons spend each month USD 50000 or more for learning. This is not impossible, but it seems extremely unlikely, so we'll remove every value that goes over USD 20,000 per month." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CountryLive\n", "United States of America 183.800110\n", "India 135.100982\n", "Canada 113.510961\n", "United Kingdom 45.534443\n", "Name: money_per_month, dtype: float64\n" ] } ], "source": [ "# Removing rows with money per month more then 20000\n", "good_data = good_data[good_data['money_per_month'] < 20000]\n", "\n", "# Recomputing mean for cleaned data\n", "country_mean = good_data.groupby(['CountryLive']).mean()\n", "print(country_mean['money_per_month'][['United States of America', 'India', 'Canada', 'United Kingdom']])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualizing Countries again using Boxplots\n", "only4 = good_data[good_data['CountryLive'].str.contains('United States of America|India|Canada|United Kingdom')]\n", "\n", "sns.boxplot(x='CountryLive', y='money_per_month', data=only4)\n", "plt.title('Money Spent Per Month Per Country\\n(Distributions)',\n", " fontsize = 16)\n", "plt.ylabel('Money per month (US dollars)')\n", "plt.xlabel('Country')\n", "plt.xticks(range(4), ['US', 'UK', 'India', 'Canada'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "We can see a few extreme outliers for India (values over \\$2500 per month), but it's unclear whether this is good data or not. Maybe these persons attended several bootcamps, which tend to be very expensive. Let's examine these two data points to see if we can find anything relevant." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Age AttendedBootcamp BootcampFinish BootcampLoanYesNo BootcampName BootcampRecommend ChildrenNumber CityPopulation CodeEventConferences CodeEventDjangoGirls CodeEventFCC CodeEventGameJam CodeEventGirlDev CodeEventHackathons CodeEventMeetup CodeEventNodeSchool CodeEventNone CodeEventOther CodeEventRailsBridge CodeEventRailsGirls CodeEventStartUpWknd CodeEventWkdBootcamps CodeEventWomenCode CodeEventWorkshops CommuteTime CountryCitizen CountryLive EmploymentField EmploymentFieldOther EmploymentStatus EmploymentStatusOther ExpectedEarning FinanciallySupporting FirstDevJob Gender GenderOther HasChildren HasDebt HasFinancialDependents HasHighSpdInternet HasHomeMortgage HasServedInMilitary HasStudentDebt HomeMortgageOwe HoursLearning ID.x ID.y Income IsEthnicMinority IsReceiveDisabilitiesBenefits IsSoftwareDev IsUnderEmployed \\\n", "1728 24.0 0.0 NaN NaN NaN NaN NaN between 100,000 and 1 million NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN India India NaN NaN A stay-at-home parent or homemaker NaN 70000.0 NaN NaN male NaN NaN 0.0 0.0 1.0 NaN 0.0 NaN NaN 30.0 d964ec629fd6d85a5bf27f7339f4fa6d 950a8cf9cef1ae6a15da470e572b1b7a NaN 0.0 0.0 0.0 NaN \n", "1755 20.0 0.0 NaN NaN NaN NaN NaN more than 1 million NaN NaN 1.0 NaN NaN 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN India India NaN NaN Not working and not looking for work NaN 100000.0 NaN NaN male NaN NaN 0.0 0.0 1.0 NaN 0.0 NaN NaN 10.0 811bf953ef546460f5436fcf2baa532d 81e2a4cab0543e14746c4a20ffdae17c NaN 0.0 0.0 0.0 NaN \n", "7989 28.0 0.0 NaN NaN NaN NaN NaN between 100,000 and 1 million 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 15 to 29 minutes India India software development and IT NaN Employed for wages NaN 500000.0 1.0 NaN male NaN 0.0 1.0 1.0 1.0 0.0 0.0 1.0 NaN 20.0 a6a5597bbbc2c282386d6675641b744a da7bbb54a8b26a379707be56b6c51e65 300000.0 0.0 0.0 0.0 0.0 \n", "8126 22.0 0.0 NaN NaN NaN NaN NaN more than 1 million NaN NaN NaN 1.0 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN India India NaN NaN Not working but looking for work NaN 80000.0 NaN NaN male NaN NaN 1.0 0.0 1.0 0.0 0.0 1.0 NaN 80.0 69e8ab9126baee49f66e3577aea7fd3c 9f08092e82f709e63847ba88841247c0 NaN 0.0 0.0 0.0 NaN \n", "13398 19.0 0.0 NaN NaN NaN NaN NaN more than 1 million NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN India India NaN NaN Unable to work NaN 100000.0 NaN NaN male NaN NaN 0.0 0.0 0.0 NaN 0.0 NaN NaN 30.0 b7fe7bc4edefc3a60eb48f977e4426e3 80ff09859ac475b70ac19b7b7369e953 NaN 0.0 0.0 0.0 NaN \n", "15587 27.0 0.0 NaN NaN NaN NaN NaN more than 1 million NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15 to 29 minutes India India software development and IT NaN Employed for wages NaN 65000.0 0.0 NaN male NaN 0.0 1.0 1.0 1.0 0.0 0.0 1.0 NaN 36.0 5a7394f24292cb82b72adb702886543a 8bc7997217d4a57b22242471cc8d89ef 60000.0 0.0 0.0 0.0 1.0 \n", "\n", " JobApplyWhen JobInterestBackEnd JobInterestDataEngr JobInterestDataSci JobInterestDevOps JobInterestFrontEnd JobInterestFullStack JobInterestGameDev JobInterestInfoSec JobInterestMobile JobInterestOther JobInterestProjMngr JobInterestQAEngr JobInterestUX JobPref JobRelocateYesNo JobRoleInterest JobWherePref LanguageAtHome MaritalStatus MoneyForLearning MonthsProgramming NetworkID Part1EndTime Part1StartTime Part2EndTime Part2StartTime PodcastChangeLog PodcastCodeNewbie PodcastCodePen PodcastDevTea PodcastDotNET PodcastGiantRobots PodcastJSAir PodcastJSJabber PodcastNone PodcastOther PodcastProgThrowdown PodcastRubyRogues PodcastSEDaily PodcastSERadio PodcastShopTalk PodcastTalkPython PodcastTheWebAhead ResourceCodecademy ResourceCodeWars ResourceCoursera ResourceCSS \\\n", "1728 Within the next 6 months 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN 1.0 NaN 1.0 work for a startup 1.0 User Experience Designer, Mobile Developer... in an office with other developers Bengali single, never married 20000.0 4.0 38d312a990 2017-03-10 10:22:34 2017-03-10 10:17:42 2017-03-10 10:24:38 2017-03-10 10:22:40 NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN \n", "1755 I haven't decided NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN NaN NaN NaN NaN work for a multinational corporation 1.0 Information Security, Full-Stack Web Developer... no preference Hindi single, never married 50000.0 15.0 4611a76b60 2017-03-10 10:48:31 2017-03-10 10:42:29 2017-03-10 10:51:37 2017-03-10 10:48:38 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN 1.0 1.0 1.0 NaN \n", "7989 more than 12 months from now 1.0 NaN NaN NaN 1.0 1.0 1.0 NaN NaN NaN NaN NaN 1.0 work for a multinational corporation 1.0 User Experience Designer, Back-End Web Devel... in an office with other developers Marathi married or domestic partnership 5000.0 1.0 c47a447b5d 2017-03-26 14:06:48 2017-03-26 14:02:41 2017-03-26 14:13:13 2017-03-26 14:07:17 NaN NaN NaN NaN NaN NaN NaN NaN NaN Not listened to anything yet. NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "8126 I'm already applying 1.0 NaN NaN NaN 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN work for a startup 1.0 Back-End Web Developer, Full-Stack Web Develop... in an office with other developers Malayalam single, never married 5000.0 1.0 0d3d1762a4 2017-03-27 07:10:17 2017-03-27 07:05:23 2017-03-27 07:12:21 2017-03-27 07:10:22 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 \n", "13398 I haven't decided NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN work for a multinational corporation 1.0 Mobile Developer no preference Hindi single, never married 20000.0 2.0 51a6f9a1a7 2017-04-01 00:31:25 2017-04-01 00:28:17 2017-04-01 00:33:44 2017-04-01 00:31:32 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "15587 I haven't decided NaN NaN 1.0 NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN work for a startup NaN Full-Stack Web Developer, Data Scientist from home Hindi single, never married 100000.0 24.0 8af0c2b6da 2017-04-03 09:43:53 2017-04-03 09:39:38 2017-04-03 09:54:39 2017-04-03 09:43:57 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " ResourceEdX ResourceEgghead ResourceFCC ResourceHackerRank ResourceKA ResourceLynda ResourceMDN ResourceOdinProj ResourceOther ResourcePluralSight ResourceSkillcrush ResourceSO ResourceTreehouse ResourceUdacity ResourceUdemy ResourceW3S SchoolDegree SchoolMajor StudentDebtOwe YouTubeCodeCourse YouTubeCodingTrain YouTubeCodingTut360 YouTubeComputerphile YouTubeDerekBanas YouTubeDevTips YouTubeEngineeredTruth YouTubeFCC YouTubeFunFunFunction YouTubeGoogleDev YouTubeLearnCode YouTubeLevelUpTuts YouTubeMIT YouTubeMozillaHacks YouTubeOther YouTubeSimplilearn YouTubeTheNewBoston money_per_month \n", "1728 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 bachelor's degree Computer Programming NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 5000.000000 \n", "1755 1.0 NaN 1.0 NaN 1.0 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 1.0 1.0 bachelor's degree Computer Science NaN NaN NaN 1.0 NaN NaN NaN NaN 1.0 NaN NaN 1.0 NaN 1.0 NaN NaN NaN NaN 3333.333333 \n", "7989 NaN NaN 1.0 NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN bachelor's degree Aerospace and Aeronautical Engineering 2500.0 NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 5000.000000 \n", "8126 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN bachelor's degree Electrical and Electronics Engineering 10000.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN 1.0 NaN NaN NaN 1.0 5000.000000 \n", "13398 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN 1.0 bachelor's degree Computer Science NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10000.000000 \n", "15587 1.0 NaN 1.0 NaN NaN 1.0 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN 1.0 bachelor's degree Communications 25000.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 NaN 1.0 NaN NaN NaN NaN 4166.666667 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Inspect the extreme outliers for India\n", "india_outliers = only4[\n", " (only4['CountryLive'] == 'India') & \n", " (only4['money_per_month'] >= 2500)]\n", "india_outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "It seems that neither participant attended a bootcamp. Overall, it's really hard to figure out from the data whether these persons really spent that much money with learning. The actual question of the survey was \"Aside from university tuition, about how much money have you spent on learning to code so far (in US dollars)?\", so they might have misunderstood and thought university tuition is included. It seems safer to remove these two rows." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Remove the outliers for India\n", "only4 = only4.drop(india_outliers.index) # using the row labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Looking back at the box plot above, we can also see more extreme outliers for the US (values over \\$6000 per month). Let's examine these participants in more detail." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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JobApplyWhen JobInterestBackEnd JobInterestDataEngr JobInterestDataSci JobInterestDevOps JobInterestFrontEnd JobInterestFullStack JobInterestGameDev JobInterestInfoSec JobInterestMobile JobInterestOther JobInterestProjMngr JobInterestQAEngr JobInterestUX JobPref JobRelocateYesNo JobRoleInterest JobWherePref LanguageAtHome MaritalStatus MoneyForLearning MonthsProgramming NetworkID Part1EndTime Part1StartTime Part2EndTime Part2StartTime PodcastChangeLog PodcastCodeNewbie PodcastCodePen PodcastDevTea PodcastDotNET PodcastGiantRobots PodcastJSAir PodcastJSJabber PodcastNone PodcastOther PodcastProgThrowdown PodcastRubyRogues PodcastSEDaily PodcastSERadio PodcastShopTalk PodcastTalkPython PodcastTheWebAhead ResourceCodecademy \\\n", "718 0.0 0.0 1.0 Within the next 6 months 1.0 NaN NaN NaN 1.0 1.0 NaN NaN 1.0 NaN NaN NaN 1.0 work for a startup 1.0 User Experience Designer, Full-Stack Web Dev... in an office with other developers English single, never married 8000.0 1.0 50dab3f716 2017-03-09 21:26:35 2017-03-09 21:21:58 2017-03-09 21:29:10 2017-03-09 21:26:39 NaN 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN \n", "1222 0.0 0.0 NaN Within the next 6 months NaN NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 work for a nonprofit 1.0 Front-End Web Developer, Mobile Developer,... in an office with other developers English single, never married 13000.0 2.0 e512c4bdd0 2017-03-10 02:14:11 2017-03-10 02:10:07 2017-03-10 02:15:32 2017-03-10 02:14:16 NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 \n", "3184 0.0 0.0 0.0 I haven't decided NaN 1.0 1.0 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 1.0 work for a medium-sized company 0.0 Quality Assurance Engineer, DevOps / SysAd... in an office with other developers English single, never married 9000.0 1.0 e7bebaabd4 2017-03-11 23:34:16 2017-03-11 23:31:17 2017-03-11 23:36:02 2017-03-11 23:34:21 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN \n", "3930 0.0 0.0 NaN Within the 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never married 70000.0 5.0 439a4adaf6 2017-03-23 01:37:46 2017-03-23 01:35:01 2017-03-23 01:39:37 2017-03-23 01:37:49 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 \n", "7505 0.0 0.0 NaN Within the next 6 months 1.0 NaN NaN NaN 1.0 1.0 NaN 1.0 1.0 NaN NaN NaN NaN work for a government 1.0 Mobile Developer, Full-Stack Web Developer, ... in an office with other developers English single, never married 20000.0 3.0 96e254de36 2017-03-24 03:26:09 2017-03-24 03:23:02 2017-03-24 03:27:47 2017-03-24 03:26:14 NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 \n", "9778 0.0 0.0 1.0 Within the next 6 months 1.0 1.0 1.0 NaN NaN 1.0 NaN NaN 1.0 NaN NaN 1.0 NaN work for a medium-sized company NaN Full-Stack Web Developer, Data Engineer, Qua... from home English single, never married 8000.0 1.0 ea80a3b15e 2017-04-05 19:48:12 2017-04-05 19:40:19 2017-04-05 19:49:44 2017-04-05 19:49:03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "16650 1.0 0.0 NaN I'm already applying 1.0 1.0 NaN NaN 1.0 1.0 1.0 NaN NaN NaN 1.0 NaN NaN work for a multinational corporation 1.0 Product Manager, Data Engineer, Full-Stack W... in an office with other developers English married or domestic partnership 200000.0 12.0 1a45f4a3ef 2017-03-14 02:42:57 2017-03-14 02:40:10 2017-03-14 02:45:55 2017-03-14 02:43:05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "16997 0.0 0.0 0.0 Within 7 to 12 months NaN NaN NaN NaN 1.0 1.0 1.0 NaN 1.0 NaN 1.0 NaN 1.0 work for a medium-sized company 1.0 Mobile Developer, Game Developer, User Exp... in an office with other developers English single, never married 12500.0 1.0 ad1a21217c 2017-03-20 05:43:28 2017-03-20 05:40:08 2017-03-20 05:45:28 2017-03-20 05:43:32 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 \n", "17231 0.0 0.0 NaN Within the next 6 months NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN work for a nonprofit 0.0 Front-End Web Developer in an office with other developers English married or domestic partnership 30000.0 2.0 38c1b478d0 2017-03-24 18:48:23 2017-03-24 18:46:01 2017-03-24 18:51:20 2017-03-24 18:48:27 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN \n", "\n", " ResourceCodeWars ResourceCoursera ResourceCSS ResourceEdX ResourceEgghead ResourceFCC ResourceHackerRank ResourceKA ResourceLynda ResourceMDN ResourceOdinProj ResourceOther ResourcePluralSight ResourceSkillcrush ResourceSO ResourceTreehouse ResourceUdacity ResourceUdemy ResourceW3S SchoolDegree SchoolMajor StudentDebtOwe YouTubeCodeCourse YouTubeCodingTrain YouTubeCodingTut360 YouTubeComputerphile YouTubeDerekBanas YouTubeDevTips YouTubeEngineeredTruth YouTubeFCC YouTubeFunFunFunction YouTubeGoogleDev YouTubeLearnCode YouTubeLevelUpTuts YouTubeMIT YouTubeMozillaHacks YouTubeOther YouTubeSimplilearn YouTubeTheNewBoston money_per_month \n", "718 1.0 NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN bachelor's degree Architecture NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 8000.000000 \n", "1222 NaN NaN 1.0 NaN 1.0 1.0 NaN NaN NaN 1.0 NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0 NaN bachelor's degree Anthropology NaN NaN 1.0 NaN NaN NaN 1.0 NaN 1.0 NaN NaN 1.0 NaN NaN NaN NaN NaN NaN 6500.000000 \n", "3184 1.0 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 some college credit, no degree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9000.000000 \n", "3930 NaN NaN NaN NaN 1.0 1.0 NaN NaN NaN 1.0 NaN reactivex.io/learnrx/ & jafar husain NaN NaN 1.0 NaN NaN NaN NaN bachelor's degree Biology 40000.0 NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0 1.0 NaN various conf presentations NaN NaN 10833.333333 \n", "6805 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 bachelor's degree Business Administration and Management 45000.0 NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 15000.000000 \n", "7198 NaN 1.0 1.0 NaN NaN 1.0 NaN 1.0 NaN 1.0 NaN NaN NaN NaN 1.0 NaN 1.0 NaN 1.0 professional degree (MBA, MD, JD, etc.) Computer Science NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 1.0 1.0 NaN NaN NaN NaN NaN 14000.000000 \n", "7505 NaN NaN NaN NaN NaN 1.0 NaN NaN 1.0 NaN NaN NaN 1.0 NaN 1.0 NaN NaN 1.0 1.0 bachelor's degree Economics 20000.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN 6666.666667 \n", "9778 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 NaN NaN master's degree (non-professional) Chemical Engineering 45000.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 8000.000000 \n", "16650 NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN 1.0 associate's degree Computer Programming 30000.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 16666.666667 \n", "16997 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN 1.0 1.0 some college credit, no degree NaN 12500.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 12500.000000 \n", "17231 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN bachelor's degree Computer Programming NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 15000.000000 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Examine the extreme outliers for the US\n", "us_outliers = only4[\n", " (only4['CountryLive'] == 'United States of America') & \n", " (only4['money_per_month'] >= 6000)]\n", "\n", "us_outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Out of these 11 extreme outliers, six people attended bootcamps, which justify the large sums of money spent on learning. For the other five, it's hard to figure out from the data where they could have spent that much money on learning. Consequently, we'll remove those rows where participants reported thay they spend \\$6000 each month, but they have never attended a bootcamp.\n", "\n", "Also, the data shows that eight respondents had been programming for no more than three months when they completed the survey. They most likely paid a large sum of money for a bootcamp that was going to last for several months, so the amount of money spent per month is unrealistic and should be significantly lower (because they probably didn't spend anything for the next couple of months after the survey). As a consequence, we'll remove every these eight outliers.\n", "\n", "In the next code block, we'll remove respondents that:\n", "\n", "- Didn't attend bootcamps.\n", "- Had been programming for three months or less when at the time they completed the survey." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Remove the respondents who didn't attendent a bootcamp\n", "no_bootcamp = only4[\n", " (only4['CountryLive'] == 'United States of America') & \n", " (only4['money_per_month'] >= 6000) &\n", " (only4['AttendedBootcamp'] == 0)\n", "]\n", "\n", "only4 = only4.drop(no_bootcamp.index)\n", "\n", "\n", "# Remove the respondents that had been programming for less than 3 months\n", "less_than_3_months = only4[\n", " (only4['CountryLive'] == 'United States of America') & \n", " (only4['money_per_month'] >= 6000) &\n", " (only4['MonthsProgramming'] <= 3)\n", "]\n", "\n", "only4 = only4.drop(less_than_3_months.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking again at the last box plot above, we can also see an extreme outlier for Canada — a person who spends roughly \\$5000 per month. Let's examine this person in more depth." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeAttendedBootcampBootcampFinishBootcampLoanYesNoBootcampNameBootcampRecommendChildrenNumberCityPopulationCodeEventConferencesCodeEventDjangoGirlsCodeEventFCCCodeEventGameJamCodeEventGirlDevCodeEventHackathonsCodeEventMeetupCodeEventNodeSchoolCodeEventNoneCodeEventOtherCodeEventRailsBridgeCodeEventRailsGirlsCodeEventStartUpWkndCodeEventWkdBootcampsCodeEventWomenCodeCodeEventWorkshopsCommuteTimeCountryCitizenCountryLiveEmploymentFieldEmploymentFieldOtherEmploymentStatusEmploymentStatusOtherExpectedEarningFinanciallySupportingFirstDevJobGenderGenderOtherHasChildrenHasDebtHasFinancialDependentsHasHighSpdInternetHasHomeMortgageHasServedInMilitaryHasStudentDebtHomeMortgageOweHoursLearningID.xID.yIncomeIsEthnicMinorityIsReceiveDisabilitiesBenefitsIsSoftwareDevIsUnderEmployedJobApplyWhenJobInterestBackEndJobInterestDataEngrJobInterestDataSciJobInterestDevOpsJobInterestFrontEndJobInterestFullStackJobInterestGameDevJobInterestInfoSecJobInterestMobileJobInterestOtherJobInterestProjMngrJobInterestQAEngrJobInterestUXJobPrefJobRelocateYesNoJobRoleInterestJobWherePrefLanguageAtHomeMaritalStatusMoneyForLearningMonthsProgrammingNetworkIDPart1EndTimePart1StartTimePart2EndTimePart2StartTimePodcastChangeLogPodcastCodeNewbiePodcastCodePenPodcastDevTeaPodcastDotNETPodcastGiantRobotsPodcastJSAirPodcastJSJabberPodcastNonePodcastOtherPodcastProgThrowdownPodcastRubyRoguesPodcastSEDailyPodcastSERadioPodcastShopTalkPodcastTalkPythonPodcastTheWebAheadResourceCodecademyResourceCodeWarsResourceCourseraResourceCSSResourceEdXResourceEggheadResourceFCCResourceHackerRankResourceKAResourceLyndaResourceMDNResourceOdinProjResourceOtherResourcePluralSightResourceSkillcrushResourceSOResourceTreehouseResourceUdacityResourceUdemyResourceW3SSchoolDegreeSchoolMajorStudentDebtOweYouTubeCodeCourseYouTubeCodingTrainYouTubeCodingTut360YouTubeComputerphileYouTubeDerekBanasYouTubeDevTipsYouTubeEngineeredTruthYouTubeFCCYouTubeFunFunFunctionYouTubeGoogleDevYouTubeLearnCodeYouTubeLevelUpTutsYouTubeMITYouTubeMozillaHacksYouTubeOtherYouTubeSimplilearnYouTubeTheNewBostonmoney_per_month
1365924.01.00.00.0Bloc.io1.0NaNmore than 1 million1.0NaN1.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaN1.030 to 44 minutesCanadaCanadafinanceNaNEmployed for wagesNaN60000.0NaNNaNmaleNaNNaN1.00.01.01.00.00.0250000.010.0739b584aef0541450c1f713b8202518128381a455ab25cc2a118d78af44d8749140000.01.01.00.00.0I haven't decided1.0NaN1.0NaN1.01.01.0NaN1.0NaN1.0NaN1.0work for a multinational corporationNaNMobile Developer, Full-Stack Web Developer, ...from homeYue (Cantonese) Chinesesingle, never married10000.02.041c26f29322017-03-25 23:23:032017-03-25 23:20:332017-03-25 23:24:342017-03-25 23:23:06NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaN1.01.01.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN1.0bachelor's degreeFinanceNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaN1.0NaNNaNNaNNaN5000.0
\n", "
" ], "text/plain": [ " Age AttendedBootcamp BootcampFinish BootcampLoanYesNo BootcampName BootcampRecommend ChildrenNumber CityPopulation CodeEventConferences CodeEventDjangoGirls CodeEventFCC CodeEventGameJam CodeEventGirlDev CodeEventHackathons CodeEventMeetup CodeEventNodeSchool CodeEventNone CodeEventOther CodeEventRailsBridge CodeEventRailsGirls CodeEventStartUpWknd CodeEventWkdBootcamps CodeEventWomenCode CodeEventWorkshops CommuteTime CountryCitizen CountryLive EmploymentField EmploymentFieldOther EmploymentStatus EmploymentStatusOther ExpectedEarning FinanciallySupporting FirstDevJob Gender GenderOther HasChildren HasDebt HasFinancialDependents HasHighSpdInternet HasHomeMortgage HasServedInMilitary HasStudentDebt HomeMortgageOwe HoursLearning ID.x ID.y Income IsEthnicMinority IsReceiveDisabilitiesBenefits IsSoftwareDev IsUnderEmployed JobApplyWhen JobInterestBackEnd \\\n", "13659 24.0 1.0 0.0 0.0 Bloc.io 1.0 NaN more than 1 million 1.0 NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 30 to 44 minutes Canada Canada finance NaN Employed for wages NaN 60000.0 NaN NaN male NaN NaN 1.0 0.0 1.0 1.0 0.0 0.0 250000.0 10.0 739b584aef0541450c1f713b82025181 28381a455ab25cc2a118d78af44d8749 140000.0 1.0 1.0 0.0 0.0 I haven't decided 1.0 \n", "\n", " JobInterestDataEngr JobInterestDataSci JobInterestDevOps JobInterestFrontEnd JobInterestFullStack JobInterestGameDev JobInterestInfoSec JobInterestMobile JobInterestOther JobInterestProjMngr JobInterestQAEngr JobInterestUX JobPref JobRelocateYesNo JobRoleInterest JobWherePref LanguageAtHome MaritalStatus MoneyForLearning MonthsProgramming NetworkID Part1EndTime Part1StartTime Part2EndTime Part2StartTime PodcastChangeLog PodcastCodeNewbie PodcastCodePen PodcastDevTea PodcastDotNET PodcastGiantRobots PodcastJSAir PodcastJSJabber PodcastNone PodcastOther PodcastProgThrowdown PodcastRubyRogues PodcastSEDaily PodcastSERadio PodcastShopTalk PodcastTalkPython PodcastTheWebAhead ResourceCodecademy ResourceCodeWars ResourceCoursera ResourceCSS ResourceEdX ResourceEgghead ResourceFCC ResourceHackerRank ResourceKA ResourceLynda \\\n", "13659 NaN 1.0 NaN 1.0 1.0 1.0 NaN 1.0 NaN 1.0 NaN 1.0 work for a multinational corporation NaN Mobile Developer, Full-Stack Web Developer, ... from home Yue (Cantonese) Chinese single, never married 10000.0 2.0 41c26f2932 2017-03-25 23:23:03 2017-03-25 23:20:33 2017-03-25 23:24:34 2017-03-25 23:23:06 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 NaN NaN NaN 1.0 NaN NaN NaN \n", "\n", " ResourceMDN ResourceOdinProj ResourceOther ResourcePluralSight ResourceSkillcrush ResourceSO ResourceTreehouse ResourceUdacity ResourceUdemy ResourceW3S SchoolDegree SchoolMajor StudentDebtOwe YouTubeCodeCourse YouTubeCodingTrain YouTubeCodingTut360 YouTubeComputerphile YouTubeDerekBanas YouTubeDevTips YouTubeEngineeredTruth YouTubeFCC YouTubeFunFunFunction YouTubeGoogleDev YouTubeLearnCode YouTubeLevelUpTuts YouTubeMIT YouTubeMozillaHacks YouTubeOther YouTubeSimplilearn YouTubeTheNewBoston money_per_month \n", "13659 NaN NaN NaN NaN NaN 1.0 NaN NaN NaN 1.0 bachelor's degree Finance NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN 1.0 NaN NaN NaN NaN 5000.0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Examine the extreme outliers for Canada\n", "canada_outliers = only4[\n", " (only4['CountryLive'] == 'Canada') & \n", " (only4['money_per_month'] > 4500)]\n", "\n", "canada_outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the situation is similar to some of the US respondents — this participant had been programming for no more than two months when he completed the survey. He seems to have paid a large sum of money in the beginning to enroll in a bootcamp, and then he probably didn't spend anything for the next couple of months after the survey. We'll take the same approach here as for the US and remove this outlier." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Remove the extreme outliers for Canada\n", "only4 = only4.drop(canada_outliers.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's recompute the mean values and generate the final box plots." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CountryLive\n", "Canada 93.065400\n", "India 65.758763\n", "United Kingdom 45.534443\n", "United States of America 142.654608\n", "Name: money_per_month, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Recompute mean sum of money spent by students each month\n", "only4.groupby('CountryLive').mean()['money_per_month']" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize the distributions again\n", "plt.figure(figsize=(7, 4))\n", "sns.boxplot(y = 'money_per_month', x = 'CountryLive',\n", " data = only4)\n", "plt.title('Money Spent / Month / Country \\n(Distributions)',\n", " fontsize = 16)\n", "plt.ylabel('Money per month (US dollars)')\n", "plt.xlabel('Country')\n", "plt.xticks(range(4), ['US', 'UK', 'India', 'Canada']) # avoids tick labels overlap\n", "plt.tight_layout()\n", "plt.savefig('distributions.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choosing the Two Best Markets\n", "Obviously, one country we should advertise in is the US. Lots of new coders live there and they are willing to pay a good amount of money each month (roughly \\\\$143).\n", "\n", "We sell subscriptions at a price of \\\\$59 per month, and Canada seems to be the best second choice because people there are willing to pay roughly \\\\$93 per month, compared to India (\\\\$66) and the United Kingdom (\\\\$45).\n", "\n", "The data suggests strongly that we shouldn't advertise in the UK, but let's take a second look at India before deciding to choose Canada as our second best choice:\n", "\n", "- \\\\$59 doesn't seem like an expensive sum for people in India since they spend on average \\$66 each month.\n", "- We have almost twice as more potential customers in India than we have in Canada:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "United States of America 74.967908\n", "India 11.732991\n", "United Kingdom 7.163030\n", "Canada 6.136072\n", "Name: CountryLive, dtype: float64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Frequency table for the 'CountryLive' column\n", "only4['CountryLive'].value_counts(normalize = True) * 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So it's not crystal clear what to choose between Canada and India. Although it seems more tempting to choose Canada, there are good chances that India might actually be a better choice because of the large number of potential customers.\n", "\n", "At this point, it seems that we have several options:\n", "\n", "1. Advertise in the US, India, and Canada by splitting the advertisement budget in various combinations:\n", " - 60% for the US, 25% for India, 15% for Canada.\n", " - 50% for the US, 30% for India, 20% for Canada; etc.\n", "\n", "\n", "2. Advertise only in the US and India, or the US and Canada. Again, it makes sense to split the advertisement budget unequally. For instance:\n", " - 70% for the US, and 30% for India.\n", " - 65% for the US, and 35% for Canada; etc.\n", "\n", "\n", "3. Advertise only in the US.\n", "\n", "At this point, it's probably best to send our analysis to the marketing team and let them use their domain knowledge to decide. They might want to do some extra surveys in India and Canada and then get back to us for analyzing the new survey data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion\n", "In this project, we analyzed survey data from new coders to find the best two markets to advertise in. The only solid conclusion we reached is that the US would be a good market to advertise in.\n", "\n", "For the second best market, it wasn't clear-cut what to choose between India and Canada. We decided to send the results to the marketing team so they can use their domain knowledge to take the best decision." ] } ], "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }