{
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"In recent years, there has been a massive rise in the usage of dating apps to find love. Many of these apps use sophisticated data science techniques to recommend possible matches to users and to optimize the user experience. These apps give us access to a wealth of information that we've never had before about how different people experience romance.\n",
"\n",
"\n",
"In this capstone, you will analyze some data from OKCupid, an app that focuses on using multiple choice and short answers to match users.\n",
"\n",
"\n",
"The dataset provided has the following columns of multiple-choice data:\n",
"\n",
"- body_type\n",
"- diet\n",
"- drinks\n",
"- drugs\n",
"- education\n",
"- ethnicity\n",
"- height\n",
"- income\n",
"- job\n",
"- offspring\n",
"- orientation\n",
"- pets\n",
"- religion\n",
"- sex\n",
"- sign\n",
"- smokes\n",
"- speaks\n",
"- status\n",
"\n",
"And a set of open short-answer responses to :\n",
"\n",
"- essay0 - My self summary\n",
"- essay1 - What I’m doing with my life\n",
"- essay2 - I’m really good at\n",
"- essay3 - The first thing people usually notice about me\n",
"- essay4 - Favorite books, movies, show, music, and food\n",
"- essay5 - The six things I could never do without\n",
"- essay6 - I spend a lot of time thinking about\n",
"- essay7 - On a typical Friday night I am\n",
"- essay8 - The most private thing I am willing to admit\n",
"- essay9 - You should message me if…\n",
"\n",
"\n",
"### Introduction\n",
"\n",
"\n",
"In this capstone, you will create a presentation about your findings in this OkCupid dataset.\n",
"\n",
"\n",
"The purpose of this capstone is to practice formulating questions and implementing Machine Learning techniques to answer those questions. We will give you guidance about the kinds of questions we asked, and the kinds of methods we used to answer those questions. But the questions you ask and how you answer them are entirely up to you. We're excited to see what kinds of different things you explore.\n",
"Compared to the other projects you have completed this far, we are requiring few restrictions on how you structure your code. The project is far more open-ended, and you should use your creativity. In addition, much of the code you write for later parts of this project will depend on how you decided to implement earlier parts. **Therefore, we strongly encourage you to read through the entire assignment before writing any code.**\n",
"________________\n",
"\n",
"### Load in the DataFrame\n",
"\n",
"\n",
"The data is stored in **profiles.csv**. We can start to work with it in **dating.py** by using Pandas, which we have imported for you with the line:\n",
"\n",
"\n",
"```\n",
"import pandas as pd\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sbn\n",
"import scipy.stats as stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and then loading the csv into a DataFrame: \n",
"\n",
"\n",
"```\n",
"df = pd.read_csv(\"profiles.csv\")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"profiles.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explore the Data\n",
"\n",
"\n",
"Let's make sure we understand what these columns represent!\n",
"\n",
"Pick some columns and call `.head()` on them to see the first five rows of data. For example, we were curious about `job`, so we called:\n",
"\n",
"\n",
"```\n",
"df.job.head()\n",
"```\n",
"\n",
"\n",
"You can also call `value_counts()` on a column to figure out what possible responses there are, and how many of each response there was."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['age', 'body_type', 'diet', 'drinks', 'drugs', 'education', 'essay0',\n",
" 'essay1', 'essay2', 'essay3', 'essay4', 'essay5', 'essay6', 'essay7',\n",
" 'essay8', 'essay9', 'ethnicity', 'height', 'income', 'job',\n",
" 'last_online', 'location', 'offspring', 'orientation', 'pets',\n",
" 'religion', 'sex', 'sign', 'smokes', 'speaks', 'status'],\n",
" dtype='object')\n",
"average 14652\n",
"fit 12711\n",
"athletic 11819\n",
"thin 4711\n",
"curvy 3924\n",
"a little extra 2629\n",
"skinny 1777\n",
"full figured 1009\n",
"overweight 444\n",
"jacked 421\n",
"used up 355\n",
"rather not say 198\n",
"Name: body_type, dtype: int64\n"
]
},
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age
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body_type
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diet
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drinks
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drugs
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education
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essay0
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essay1
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essay2
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essay3
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...
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location
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offspring
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orientation
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pets
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religion
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sex
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sign
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smokes
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speaks
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status
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0
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22
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a little extra
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strictly anything
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socially
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never
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working on college/university
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about me:<br />\\n<br />\\ni would love to think...
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currently working as an international agent fo...
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making people laugh.<br />\\nranting about a go...
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the way i look. i am a six foot half asian, ha...
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...
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south san francisco, california
\n",
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doesn’t have kids, but might want them
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straight
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likes dogs and likes cats
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agnosticism and very serious about it
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m
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gemini
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sometimes
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english
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single
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working on space camp
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dedicating everyday to being an unbelievable b...
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being silly. having ridiculous amonts of fun w...
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NaN
\n",
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...
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oakland, california
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doesn’t have kids, but might want them
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straight
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likes dogs and likes cats
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agnosticism but not too serious about it
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m
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cancer
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no
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english (fluently), spanish (poorly), french (...
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single
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\n",
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\n",
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2
\n",
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38
\n",
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thin
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anything
\n",
"
socially
\n",
"
NaN
\n",
"
graduated from masters program
\n",
"
i'm not ashamed of much, but writing public te...
\n",
"
i make nerdy software for musicians, artists, ...
\n",
"
improvising in different contexts. alternating...
\n",
"
my large jaw and large glasses are the physica...
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...
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san francisco, california
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NaN
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straight
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has cats
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NaN
\n",
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m
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pisces but it doesn’t matter
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no
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english, french, c++
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available
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3
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23
\n",
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thin
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vegetarian
\n",
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socially
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NaN
\n",
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working on college/university
\n",
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i work in a library and go to school. . .
\n",
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reading things written by old dead people
\n",
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playing synthesizers and organizing books acco...
\n",
"
socially awkward but i do my best
\n",
"
...
\n",
"
berkeley, california
\n",
"
doesn’t want kids
\n",
"
straight
\n",
"
likes cats
\n",
"
NaN
\n",
"
m
\n",
"
pisces
\n",
"
no
\n",
"
english, german (poorly)
\n",
"
single
\n",
"
\n",
"
\n",
"
4
\n",
"
29
\n",
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athletic
\n",
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NaN
\n",
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socially
\n",
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never
\n",
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graduated from college/university
\n",
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hey how's it going? currently vague on the pro...
\n",
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work work work work + play
\n",
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creating imagery to look at:<br />\\nhttp://bag...
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i smile a lot and my inquisitive nature
\n",
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...
\n",
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san francisco, california
\n",
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NaN
\n",
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straight
\n",
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likes dogs and likes cats
\n",
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NaN
\n",
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m
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aquarius
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no
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english
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single
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5 rows × 31 columns
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"
],
"text/plain": [
" age body_type diet drinks drugs \\\n",
"0 22 a little extra strictly anything socially never \n",
"1 35 average mostly other often sometimes \n",
"2 38 thin anything socially NaN \n",
"3 23 thin vegetarian socially NaN \n",
"4 29 athletic NaN socially never \n",
"\n",
" education \\\n",
"0 working on college/university \n",
"1 working on space camp \n",
"2 graduated from masters program \n",
"3 working on college/university \n",
"4 graduated from college/university \n",
"\n",
" essay0 \\\n",
"0 about me: \\n \\ni would love to think... \n",
"1 i am a chef: this is what that means. \\n1... \n",
"2 i'm not ashamed of much, but writing public te... \n",
"3 i work in a library and go to school. . . \n",
"4 hey how's it going? currently vague on the pro... \n",
"\n",
" essay1 \\\n",
"0 currently working as an international agent fo... \n",
"1 dedicating everyday to being an unbelievable b... \n",
"2 i make nerdy software for musicians, artists, ... \n",
"3 reading things written by old dead people \n",
"4 work work work work + play \n",
"\n",
" essay2 \\\n",
"0 making people laugh. \\nranting about a go... \n",
"1 being silly. having ridiculous amonts of fun w... \n",
"2 improvising in different contexts. alternating... \n",
"3 playing synthesizers and organizing books acco... \n",
"4 creating imagery to look at: \\nhttp://bag... \n",
"\n",
" essay3 ... \\\n",
"0 the way i look. i am a six foot half asian, ha... ... \n",
"1 NaN ... \n",
"2 my large jaw and large glasses are the physica... ... \n",
"3 socially awkward but i do my best ... \n",
"4 i smile a lot and my inquisitive nature ... \n",
"\n",
" location \\\n",
"0 south san francisco, california \n",
"1 oakland, california \n",
"2 san francisco, california \n",
"3 berkeley, california \n",
"4 san francisco, california \n",
"\n",
" offspring orientation \\\n",
"0 doesn’t have kids, but might want them straight \n",
"1 doesn’t have kids, but might want them straight \n",
"2 NaN straight \n",
"3 doesn’t want kids straight \n",
"4 NaN straight \n",
"\n",
" pets religion sex \\\n",
"0 likes dogs and likes cats agnosticism and very serious about it m \n",
"1 likes dogs and likes cats agnosticism but not too serious about it m \n",
"2 has cats NaN m \n",
"3 likes cats NaN m \n",
"4 likes dogs and likes cats NaN m \n",
"\n",
" sign smokes \\\n",
"0 gemini sometimes \n",
"1 cancer no \n",
"2 pisces but it doesn’t matter no \n",
"3 pisces no \n",
"4 aquarius no \n",
"\n",
" speaks status \n",
"0 english single \n",
"1 english (fluently), spanish (poorly), french (... single \n",
"2 english, french, c++ available \n",
"3 english, german (poorly) single \n",
"4 english single \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(df.columns)\n",
"\n",
"print(df.body_type.value_counts())\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize some of the Data\n",
"\n",
"\n",
"We can start to build graphs from the data by first importing Matplotlib:\n",
"\n",
"\n",
"```\n",
"from matplotlib import pyplot as plt\n",
"```\n",
"\n",
"\n",
"and then making some plots!\n",
"\n",
"For example, we were curious about the distribution of ages on the site, so we made a histogram of the `age` column:\n",
"\n",
"\n",
"```\n",
"plt.hist(df.age, bins=20)\n",
"plt.xlabel(\"Age\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.xlim(16, 80)\n",
"plt.show()\n",
"```\n",
"\n",
"\n",
"Try this code in your own file and take a look at the histogram it produces!"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig_3 = plt.figure(figsize = (15, 5))\n",
"ax = sbn.kdeplot(data = df.rate_of_i_me, clip = (0,0.125), color = '#E7523E', shade = True)\n",
"plt.xlim(0,0.125)\n",
"plt.xlabel('Rate of \"I\" / \"me\"')\n",
"plt.ylabel('Frequency') \n",
"ax.get_legend().remove()\n",
"plt.tick_params(axis = 'y',\n",
" which = 'both',\n",
" labelleft = False,\n",
" length = 0)\n",
"ax.spines['right'].set_visible(False)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['left'].set_visible(False)\n",
"fig_3.suptitle('Distribution of Rate \"I\" and \"me\" Are Used', fontsize = 24)\n",
"plt.savefig('rate_i_me_dist.png', transparent = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Normalize your Data!\n",
"\n",
"\n",
"In order to get accurate results, we should make sure our numerical data all has the same weight.\n",
"\n",
"\n",
"For our Zodiac features, we used:\n",
"\n",
"\n",
"```\n",
"feature_data = all_data[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length']]\n",
"\n",
"\n",
"x = feature_data.values\n",
"min_max_scaler = preprocessing.MinMaxScaler()\n",
"x_scaled = min_max_scaler.fit_transform(x)\n",
"\n",
"\n",
"feature_data = pd.DataFrame(x_scaled, columns=feature_data.columns)\n",
"```\n",
"\n",
"**Normalization Model**\n",
"\n",
"In our case we are going to use a StandardScaler as there are some datapoints which are extreme outliers so this method reduces their impact"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" smokes_code drinks_code drugs_code essay_len avg_word_length \\\n",
"0 1.55160 0.192311 -0.457207 0.221055 -0.031698 \n",
"1 -0.42115 1.537983 1.956334 -0.370349 -0.248504 \n",
"2 -0.42115 0.192311 -0.457207 -0.775603 0.482377 \n",
"3 -0.42115 0.192311 -0.457207 -0.473252 0.848358 \n",
"4 -0.42115 -2.499033 -0.457207 0.071903 0.018382 \n",
"\n",
" rate_of_i_me \n",
"0 -0.051420 \n",
"1 1.446319 \n",
"2 -0.806581 \n",
"3 -0.930372 \n",
"4 1.140107 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sklearn.preprocessing as preprocessing\n",
"feature_data = df[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me', 'sign_code']]\n",
"feature_data = feature_data.dropna()\n",
"sign_labels = feature_data.sign_code\n",
"feature_data = feature_data[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me']]\n",
"\n",
"\n",
"x = feature_data.values\n",
"standard_scaler = preprocessing.StandardScaler()\n",
"x_scaled = standard_scaler.fit_transform(x)\n",
"\n",
"\n",
"feature_data = pd.DataFrame(x_scaled, columns=feature_data.columns)\n",
"feature_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use Classification Techniques\n",
"\n",
"\n",
"We have learned how to perform classification in a few different ways.\n",
"\n",
"\n",
"- We learned about K-Nearest Neighbors by exploring IMDB ratings of popular movies \n",
"- We learned about Support Vector Machines by exploring baseball statistics\n",
"- We learned about Naive Bayes by exploring Amazon Reviews\n",
"\n",
"\n",
"Some questions we used classification to tackle were:\n",
"\n",
"\n",
"- Can we predict sex with education level and income??\n",
"- Can we predict education level with essay text word counts?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Split the Data into Training and Test sets"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_data, test_data, train_labels, test_labels = train_test_split(feature_data, sign_labels, test_size = 0.2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classification Teqniques\n",
"\n",
"First we will use a K Nearest Neighbors classifier and get the score for the test data for k from one to two hundred"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"kn_classifier_scores = list()\n",
"for k in range(1, 200):\n",
" kn_classifier = KNeighborsClassifier(n_neighbors = k)\n",
" kn_classifier.fit(train_data, train_labels)\n",
" kn_classifier_scores.append(kn_classifier.score(test_data, test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot the results"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"fig.set_size_inches(15, 6)\n",
"plt.plot(range(1, 200), kn_classifier_scores, lw = 2)\n",
"plt.xlim(0, 200)\n",
"ax.set_yticks([0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16])\n",
"ax.set_yticklabels(['10%', '11%', '12%', '13%', '14%', '15%', '16%'])\n",
"plt.xlabel('Number of K Nearest Neigbors')\n",
"plt.ylabel('Accuracy of Classification Model')\n",
"fig.suptitle('Accuracy of classifying Zodiac Sign vs. No. of K Neighbors Used', fontsize = 18)\n",
"plt.savefig('Zodiac_accuracy.png', transparent = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Removing no response data\n",
"\n",
"We think that our accuracy being so much higher than chance is just a correlation with no response to the essays and no response to zodiac sign\n",
"\n",
"The solution is to see the correlation if we remove the "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sully\\miniconda3\\lib\\site-packages\\pandas\\core\\generic.py:5303: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self[name] = value\n"
]
}
],
"source": [
"feature_data2 = df[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me', 'sign_code']]\n",
"feature_data2.sign_code = feature_data2.sign_code.replace(0, np.nan)\n",
"feature_data2 = feature_data2.dropna()\n",
"sign_labels2 = feature_data2.sign_code\n",
"feature_data2 = feature_data2[['smokes_code', 'drinks_code', 'drugs_code', 'essay_len', 'avg_word_length','rate_of_i_me']]\n",
"\n",
"\n",
"x = feature_data2.values\n",
"standard_scaler = preprocessing.StandardScaler()\n",
"x_scaled = standard_scaler.fit_transform(x)\n",
"\n",
"\n",
"feature_data2 = pd.DataFrame(x_scaled, columns=feature_data.columns)\n",
"\n",
"train_data2, test_data2, train_labels2, test_labels2 = train_test_split(feature_data2, sign_labels2, test_size = 0.2)\n",
"kn_classifier_scores2 = list()\n",
"for k in range(1, 200):\n",
" kn_classifier2 = KNeighborsClassifier(n_neighbors = k)\n",
" kn_classifier2.fit(train_data2, train_labels2)\n",
" kn_classifier_scores2.append(kn_classifier2.score(test_data2, test_labels2))\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
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"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"fig.set_size_inches(15, 6)\n",
"plt.plot(range(1, 200), kn_classifier_scores2, lw = 2)\n",
"plt.xlim(0, 200)\n",
"ax.set_yticks([0.082, 0.084, 0.086, 0.088, 0.09])\n",
"ax.set_yticklabels(['8.2%', '8.4%', '8.6%', '8.8%', '9.0%'])\n",
"plt.xlabel('Number of K Nearest Neigbors')\n",
"plt.ylabel('Accuracy of Classification Model')\n",
"fig.suptitle('Accuracy of classifying Zodiac Sign vs. No. of K Neighbors Used', fontsize = 18)\n",
"plt.title('Excluding \"No Answer\" as a Possible Zodiac Sign')\n",
"plt.savefig('Zodiac_accuracy2.png', transparent = True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DescribeResult(nobs=199, minmax=(0.0798425639583919, 0.09263424233904977), mean=0.08620838236727636, variance=7.2677095125672475e-06, skewness=-0.4197467446441147, kurtosis=-0.5196244906968683)\n",
"Ttest_1sampResult(statistic=15.044335813953879, pvalue=1.3020767849862312e-34)\n"
]
}
],
"source": [
"print(stats.describe(kn_classifier_scores2))\n",
"print(stats.ttest_1samp(kn_classifier_scores2, 0.0833333333))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use Regression Techniques\n",
"\n",
"\n",
"We have learned how to perform Multiple Linear Regression by playing with StreetEasy apartment data. Is there a way we can apply the techniques we learned to this dataset?\n",
"\n",
"\n",
"Some questions we used regression to tackle were:\n",
"\n",
"\n",
"- Predict income with length of essays and average word length?\n",
"- Predict age with the frequency of \"I\" or \"me\" in essays?\n",
"\n",
"\n",
"We also learned about K-Nearest Neighbors Regression. Which form of regression works better to answer your question?"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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