Web Scraping for Reddit & Predicting Comments

In this project, we will practice two major skills. Collecting data by scraping a website and then building a binary predictor.

As we discussed in week 2, and earlier today, there are two components to starting a data science problem: the problem statement, and acquiring the data.

For this article, your problem statement will be: What characteristics of a post on Reddit contribute most to the overall interaction (as measured by number of comments)?

Your method for acquiring the data will be scraping the 'hot' threads as listed on the Reddit homepage. You'll acquire AT LEAST FOUR pieces of information about each thread:

  1. The title of the thread
  2. The subreddit that the thread corresponds to
  3. The length of time it has been up on Reddit
  4. The number of comments on the thread

Once you've got the data, you will build a classification model that, using Natural Language Processing and any other relevant features, predicts whether or not a given Reddit post will have above or below the median number of comments.

BONUS PROBLEMS

  1. If creating a logistic regression, GridSearch Ridge and Lasso for this model and report the best hyperparameter values.
  2. Scrape the actual text of the threads using Selenium (you'll learn about this in Webscraping II).
  3. Write the actual article that you're pitching and turn it into a blog post that you host on your personal website.

Scraping Thread Info from Reddit.com

Set up a request (using requests) to the URL below. Use BeautifulSoup to parse the page and extract all results

In [1]:
import requests
from bs4 import BeautifulSoup
import time
import datetime
import pandas as pd
import os
from uuid import uuid4 as uuid  # this is a randomly generated ID
import numpy as np
In [2]:
## YOUR CODE HERE
In [3]:
URL = "http://www.reddit.com"
r = requests.get(URL, headers={"User-agent": str(uuid())})
b = BeautifulSoup(r.text)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.

The code that caused this warning is on line 184 of the file /Users/nmolivo/anaconda/lib/python3.5/runpy.py. To get rid of this warning, change code that looks like this:

 BeautifulSoup([your markup])

to this:

 BeautifulSoup([your markup], "lxml")

  markup_type=markup_type))
In [49]:
print(b.prettify())
<!DOCTYPE html>
<html lang="en" xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">
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  <title>
   reddit: the front page of the internet
  </title>
  <meta content=" reddit, reddit.com, vote, comment, submit " name="keywords"/>
  <meta content="reddit: the front page of the internet" name="description"/>
  <meta content="always" name="referrer"/>
  <meta content="text/html; charset=utf-8" http-equiv="Content-Type"/>
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  <link href="https://www.reddit.com/" rel="canonical"/>
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  <link href="//out.reddit.com" rel="dns-prefetch"/>
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         <a class="title may-blank outbound" data-event-action="title" data-href-url="https://twitter.com/RapSheet/status/926189951280173056" data-outbound-expiration="1509665225000" data-outbound-url="https://out.reddit.com/t3_7aehqx?url=https%3A%2F%2Ftwitter.com%2FRapSheet%2Fstatus%2F926189951280173056&amp;token=AQAAyan7WYfMVvhH7nAEbDCP5op-jEw6oddekNZPpxWUNhekixjS&amp;app_name=reddit.com" href="https://twitter.com/RapSheet/status/926189951280173056" rel="" tabindex="1">
          [Rapoport] Sources: #Texans QB fear Deshaun Watson suffered a torn ACL in practice today. A non-contact injury. MRI coming to confirm. Awful, awful.
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         submitted
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          Not quite what we were expecting - Our foster dog and Golden Mix gave birth yesterday. To baby cows. :) She is one proud Mama.
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          NASA: Ozone hole smallest it's been since 1988
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          Cousin gave birth on Halloween night. This was the doc during the delivery.
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     <div class=" thing id-t3_7abhke linkflair linkflair-discussion odd gilded link self" data-author="JPTIII" data-author-fullname="t2_u4wnz" data-comments-count="3587" data-context="listing" data-domain="self.listentothis" data-fullname="t3_7abhke" data-num-crossposts="0" data-permalink="/r/listentothis/comments/7abhke/the_fcc_is_expected_to_announce_a_vote_to_gut_net/" data-rank="7" data-score="79424" data-subreddit="listentothis" data-subreddit-fullname="t5_2qxzy" data-timestamp="1509629342000" data-type="link" data-url="/r/listentothis/comments/7abhke/the_fcc_is_expected_to_announce_a_vote_to_gut_net/" data-whitelist-status="all_ads" id="thing_t3_7abhke" onclick="click_thing(this)">
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          The FCC is expected to announce a vote to gut net neutrality rules the day before Thanksgiving. Only a big burst of phone calls to Congress can stop them from allowing ISPs to charge us extra fees to access sites, apps, games, and especially the music we all love. [discussion]
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     <div class=" thing id-t3_7abm5p linkflair linkflair-spoiler even link " data-author="JohnnyMrNinja" data-author-fullname="t2_1332z9" data-comments-count="1442" data-context="listing" data-domain="trackrecord.net" data-fullname="t3_7abm5p" data-num-crossposts="0" data-permalink="/r/television/comments/7abm5p/since_30_rock_went_from_netflix_to_hulu_a_chris/" data-rank="8" data-score="18414" data-subreddit="television" data-subreddit-fullname="t5_2qh6e" data-timestamp="1509630611000" data-type="link" data-url="http://trackrecord.net/chris-browns-name-has-apparently-been-edited-out-of-a-3-1819711002" data-whitelist-status="all_ads" id="thing_t3_7abm5p" onclick="click_thing(this)">
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       8
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         <a class="title may-blank outbound" data-event-action="title" data-href-url="http://trackrecord.net/chris-browns-name-has-apparently-been-edited-out-of-a-3-1819711002" data-outbound-expiration="1509665225000" data-outbound-url="https://out.reddit.com/t3_7abm5p?url=http%3A%2F%2Ftrackrecord.net%2Fchris-browns-name-has-apparently-been-edited-out-of-a-3-1819711002&amp;token=AQAAyan7WY5Lgz3u2OQ41alBM5e0zNeicZfRnBVRbQwh3SBOVrvA&amp;app_name=reddit.com" href="http://trackrecord.net/chris-browns-name-has-apparently-been-edited-out-of-a-3-1819711002" rel="" tabindex="1">
          Since 30 Rock went from Netflix to Hulu, a Chris Brown reference has been edited out
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          8 hours ago
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          Quentin Tarantino Has Finished the Script for His Ninth Movie, About the Manson Family Murders
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         submitted
         <time class="live-timestamp" datetime="2017-11-02T15:45:24+00:00" title="Thu Nov 2 15:45:24 2017 UTC">
          6 hours ago
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         by
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          Mechanics of Reddit: What vehicles will you absolutely not buy/drive due to what you've seen at work?
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          6 hours ago
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          Experienced sandwiches only, please.
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         <time class="live-timestamp" datetime="2017-11-02T14:41:50+00:00" title="Thu Nov 2 14:41:50 2017 UTC">
          7 hours ago
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     <div class=" thing id-t3_7abp34 even link " data-author="Johnny_W94" data-author-fullname="t2_b0p25sj" data-comments-count="7421" data-context="listing" data-domain="apnews.com" data-fullname="t3_7abp34" data-num-crossposts="0" data-permalink="/r/news/comments/7abp34/child_sex_offenders_to_be_named_as_such_in_us/" data-rank="12" data-score="60048" data-subreddit="news" data-subreddit-fullname="t5_2qh3l" data-timestamp="1509631385000" data-type="link" data-url="https://www.apnews.com/44f22ca8245441f9993ca76b894b8919/Child-sex-offenders-to-be-named-as-such-in-US-passports" data-whitelist-status="all_ads" id="thing_t3_7abp34" onclick="click_thing(this)">
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          Child sex offenders to be named as such in US passports
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         submitted
         <time class="live-timestamp" datetime="2017-11-02T14:03:05+00:00" title="Thu Nov 2 14:03:05 2017 UTC">
          8 hours ago
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           7421 comments
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          Papa is out of touch
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         submitted
         <time class="live-timestamp" datetime="2017-11-02T15:50:13+00:00" title="Thu Nov 2 15:50:13 2017 UTC">
          6 hours ago
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           1167 comments
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     <div class=" thing id-t3_7ac64p even gilded link self" data-author="yalemartin" data-author-fullname="t2_dp1d2" data-comments-count="1155" data-context="listing" data-domain="self.LifeProTips" data-fullname="t3_7ac64p" data-num-crossposts="0" data-permalink="/r/LifeProTips/comments/7ac64p/lpt_in_a_notatfault_automobile_accident_you_are/" data-rank="14" data-score="29586" data-subreddit="LifeProTips" data-subreddit-fullname="t5_2s5oq" data-timestamp="1509635766000" data-type="link" data-url="/r/LifeProTips/comments/7ac64p/lpt_in_a_notatfault_automobile_accident_you_are/" data-whitelist-status="all_ads" id="thing_t3_7ac64p" onclick="click_thing(this)">
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          LPT: In a not-at-fault automobile accident, you are entitled to a check for the value your vehicle lost after being repaired. This is called "Diminished Value". But you will only get it if you ask.
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         submitted
         <time class="live-timestamp" datetime="2017-11-02T15:16:06+00:00" title="Thu Nov 2 15:16:06 2017 UTC">
          7 hours ago
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           1169 comments
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          The Orville Renewed for Season 2!!
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While this has some more verbose elements removed, we can see that there is some structure to the above:

  • The thread title is within an <a> tag with the attribute data-event-action="title".
  • The time since the thread was created is within a <time> tag with attribute class="live-timestamp".
  • The subreddit is within an <a> tag with the attribute class="subreddit hover may-blank".
  • The number of comments is within an <a> tag with the attribute data-event-action="comments"`.

Write 4 functions to extract these items (one function for each): title, time, subreddit, and number of comments.¶

Example

def extract_title_from_result(result):
    return result.find ...
- Make sure these functions are robust and can handle cases where the data/field may not be available.
  • Remember to check if a field is empty or None for attempting to call methods on it
  • Remember to use try/except if you anticipate errors.
  • Test the functions on the results above and simple examples
In [11]:
## YOUR CODE HERE to extract date

def extract_date(b):
    quote_list =[]
    for quote in b.find_all(name='div'):
        if "datetime=" in str(quote):
            quote_list.append(str(quote))
            
    date_list =[]
    for item in quote_list:
        x = item.find("datetime=")+len("datetime=")
        y = item.find("+", x)
        unit = item[x:y].replace('"','').replace("T"," ")
        if unit in date_list:
            pass
        else:
            date_list.append(unit)
        
    return date_list
In [12]:
# ^^ ok, this was NOT the most efficient way to do this.
In [13]:
extract_date(b)
Out[13]:
['2017-10-30 09:12:12',
 '2017-10-30 09:27:08',
 '2017-10-30 11:54:00',
 '2017-10-30 10:58:40',
 '2017-10-30 09:03:00',
 '2017-10-30 12:27:01',
 '2017-10-30 07:07:11',
 '2017-10-30 05:40:12',
 '2017-10-30 10:35:40',
 '2017-10-30 10:45:51',
 '2017-10-30 06:50:33',
 '2017-10-30 12:01:25',
 '2017-10-30 10:49:36',
 '2017-10-30 01:39:09',
 '2017-10-30 10:33:59',
 '2017-10-30 04:12:13',
 '2017-10-30 01:51:19',
 '2017-10-30 09:17:14',
 '2017-10-30 05:30:40',
 '2017-10-30 01:15:33',
 '2017-10-30 02:16:24',
 '2017-10-30 09:42:57',
 '2017-10-30 03:43:21',
 '2017-10-30 03:21:35',
 '2017-10-30 05:13:46']
In [14]:
## YOUR CODE HERE to extract title

def extract_title(b):
    title_list =[]
    links = b.findAll("a", {"class":"title"})
    for link in links:
        title_list.append(link.text)

    return title_list
In [15]:
extract_title(b)
Out[15]:
["Why don't I ......",
 'The lives of tens of thousands of premature babies could be saved by simply delaying clamping the umbilical cord by one minute, according to Australian doctors.',
 'Paul Manafort, Who Once Ran Trump Campaign, Told to Surrender',
 'Record surge in atmospheric CO2 in 2016',
 'Anteater hit and run',
 'Paul Manafort, Who Once Ran Trump Campaign, Told to Surrender',
 'Would you?',
 'Postgame Thread ⚾ Dodgers 12 @ Astros 13',
 "MRW It's October 30th and my wife still doesn't understand why it's too early for Christmas",
 'Rich millennials are ditching the golf communities of their parents for a new kind of neighborhood - Agrihoods are built around working farms and are replacing the once-popular golf communities favored by Baby Boomers.',
 'My goose attempting to dive but flipping herself upside down.',
 'Paul Manafort and Rick Gates to be indicted',
 'HMB while I do a solo keg stand',
 'Star Trek Discovery Actor Anthony Rapp: Kevin Spacey Made A Sexual Advance Toward Me When I Was 14',
 'First day of paw school!',
 'Kevin Spacey Comes Out as Gay, Apologizes to Anthony Rapp',
 'My cats are named Bill and Ted. Last night they fulfilled their destiny by meeting their other selves.',
 'Google CEO makes fixing hamburger emoji his top priority - The Verge',
 'PsBattle: Squirrel with snow on nose.',
 'What is wrong with people?',
 'TIL In Houston, Texas, a 19-year-old man named Rashaad died attempting to play Russian roulette with a semi-automatic pistol instead of a revolver.',
 'Absolutely wholesome Death.',
 'This half dented tree on a golf course',
 'POTUS picks a twitter fight. Loses.',
 'Throw it back!']
In [16]:
## YOUR CODE HERE to extract subreddit

def extract_subreddit(b):
    taglist =[]
    links = b.findAll("p", {"class":"tagline"})
    for link in links:
        taglist.append(link.text)    
    subred_list=[]
    for item in taglist:
        x = item.find("r/")+len("r/")
        y = len(item)
        unit = item[x:y]
        subred_list.append(unit)

    return subred_list
In [17]:
extract_subreddit(b)
Out[17]:
['PoliticalHumor',
 'worldnews',
 'worldnews',
 'science',
 'AnimalsBeingJerks',
 'news',
 'harrypotter',
 'baseball',
 'reactiongifs',
 'Futurology',
 'AnimalsBeingDerps',
 'RussiaLago',
 'holdmybeer',
 'television',
 'rarepuppers',
 'news',
 'pics',
 'Android',
 'photoshopbattles',
 'BlackPeopleTwitter',
 'todayilearned',
 'wholesomememes',
 'mildlyinteresting',
 'MurderedByWords',
 'sports']
In [18]:
## YOUR CODE HERE to extract comments

def extract_comments(b):
    taglist =[]
    links = b.findAll("li")
    for link in links:
        taglist.append(link.text)
        
    comm_list=[]
    for unit in taglist:
        if unit.find("comments") >0:
            space = unit.find(" ")
            comm_list.append(unit[0:space])
        else:
            pass

    return comm_list
In [19]:
extract_comments(b)
Out[19]:
['445',
 '354',
 '447',
 '196',
 '77',
 '250',
 '229',
 '3251',
 '44',
 '113',
 '68',
 '5',
 '25',
 '7400',
 '14',
 '5167',
 '340',
 '224',
 '138',
 '198',
 '848',
 '42',
 '141',
 '3361',
 '591']
In [20]:
## YOUR CODE HERE to extract author

def extract_user(b):
    user_list =[]
    aa = b.findAll("a", {"class":"author"})
    for a in aa:
        user_list.append(a.text)

    return user_list
In [21]:
extract_user(b)
Out[21]:
['SMRNS2017',
 'lapapinton',
 'charging_bull',
 'ripsgod',
 '-N3ptun3-',
 'Spudgun888',
 'brittany16691',
 'BaseballBot',
 'PapaNixon',
 'mvea',
 'GraeDaBoss',
 'ghqwertt',
 'Ima_Grab_Yo_Snatch',
 'GavinGunhold',
 '-N3ptun3-',
 'piede',
 'MURPHtheSURF',
 'Mearsu',
 'derawin07',
 'L1FE1SP01NTLESS',
 'Rupert_YT',
 'Thou_Art_God',
 'yelnats87',
 'TheTrueForester',
 'FAX_ME_YOUR_BOTTOM']

Now, to scale up our scraping, we need to accumulate more results.

First, look at the source of a Reddit.com page: (https://www.reddit.com/). Try manually changing the page by clicking the 'next' button on the bottom. Look at how the url changes.

After leaving the Reddit homepage, the URLs should look something like this:

https://www.reddit.com/?count=25&after=t3_787ptc

The URL here has two query parameters

  • count is the result number that the page starts with
  • after is the unique id of the last result on the previous page

In order to scrape lots of pages from Reddit, we'll have to change these parameters every time we make a new request so that we're not just scraping the same page over and over again. Incrementing the count by 25 every time will be easy, but the bizarre code after after is a bit trickier.

To start off, let's look at a block of HTML from a Reddit page to see how we might solve this problem:

<div class=" thing id-t3_788tye odd gilded link " data-author="LordSneaux" data-author-fullname="t2_j3pty" data-comments-count="1548" data-context="listing" data-domain="v.redd.it" data-fullname="t3_788tye" data-kind="video" data-num-crossposts="0" data-permalink="/r/funny/comments/788tye/not_all_heroes_wear_capes/" data-rank="25" data-score="51468" data-subreddit="funny" data-subreddit-fullname="t5_2qh33" data-timestamp="1508775581000" data-type="link" data-url="https://v.redd.it/ush0rh2tultz" data-whitelist-status="all_ads" id="thing_t3_788tye" onclick="click_thing(this)">
      <p class="parent">
      </p>
      <span class="rank">
       25
      </span>
      <div class="midcol unvoted">
       <div aria-label="upvote" class="arrow up login-required access-required" data-event-action="upvote" role="button" tabindex="0">
       </div>
       <div class="score dislikes" title="53288">
        53.3k
       </div>
       <div class="score unvoted" title="53289">
        53.3k
       </div>
       <div class="score likes" title="53290">
        53.3k
       </div>
       <div aria-label="downvote" class="arrow down login-required access-required" data-event-action="downvote" role="button" tabindex="0">
       </div>
      </div>

Notice that within the div tag there is an attribute called id and it is set to "thing_t3_788tye". By finding the last ID on your scraped page, you can tell your next request where to start (pass everything after "thing_").

For more info on this, you can take a look at the Reddit API docs

Write one more function that finds the last id on the page, and stores it.

In [22]:
## YOUR CODE HERE
def extract_last_articleid(b):
    id_list =[]
    divs = b.findAll("div", {"class":"thing"})
    for div in divs:
        id_list.append(div["data-fullname"])
    x = id_list[-1]

    return str(x)
In [23]:
extract_last_articleid(b)
Out[23]:
't3_79lpti'

(Optional) Collect more information

While we only require you to collect four features, there may be other info that you can find on the results page that might be useful. Feel free to write more functions so that you have more interesting and useful data.

In [100]:
## YOUR CODE HERE
#I will add this after all the top 100's are scraped
In [ ]:
#meant to be run at a similar time to the previous cell, but not a requirement. 

karmalist =[]
for user in list(df["user"]):
    url = "https://www.reddit.com/user/" + str(user)
    r = requests.get(url, headers={"User-agent": str(uuid())})
    b = BeautifulSoup(r.text, "lxml")
    if len(b) == 0:
        karmalist.append((user, None, None))
        continue
    
    kpoints = b.findAll("span", {"class":"karma"})
    comment_karma=None
    post_karma=None
    for kpoint in kpoints:
        if "comment-karma" in kpoint["class"]:
            comment_karma=kpoint.text.replace(",","")
        else:
            post_karma=kpoint.text.replace(",","")
    karmalist.append((user, post_karma,comment_karma))
In [ ]:
karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
In [ ]:
df = df.merge(karma_df.drop_duplicates(subset="user"), on="user", how = "left")

Now, let's put it all together.

Use the functions you wrote above to parse out the 4 fields - title, time, subreddit, and number of comments. Create a dataframe from the results with those 4 columns.

In [29]:
a_dict = {"title": extract_title(b), "subreddit": extract_subreddit(b),"comments": extract_comments(b), "user": extract_user(b)}
In [30]:
df = pd.DataFrame(a_dict)
In [31]:
df.head()
Out[31]:
comments subreddit title user
0 445 PoliticalHumor Why don't I ...... SMRNS2017
1 354 worldnews The lives of tens of thousands of premature ba... lapapinton
2 447 worldnews Paul Manafort, Who Once Ran Trump Campaign, To... charging_bull
3 196 science Record surge in atmospheric CO2 in 2016 ripsgod
4 77 AnimalsBeingJerks Anteater hit and run -N3ptun3-
In [32]:
#meant to be run at a similar time to the previous cell, but not a requirement. 

karmalist =[]
for user in list(df["user"]):
    url = "https://www.reddit.com/user/" + str(user)
    r = requests.get(url, headers={"User-agent": str(uuid())})
    b = BeautifulSoup(r.text, "lxml")
    if len(b) == 0:
        karmalist.append((user, None, None))
        continue
    
    kpoints = b.findAll("span", {"class":"karma"})
    comment_karma=None
    post_karma=None
    for kpoint in kpoints:
        if "comment-karma" in kpoint["class"]:
            comment_karma=kpoint.text.replace(",","")
        else:
            post_karma=kpoint.text.replace(",","")
    karmalist.append((user, post_karma,comment_karma))
In [33]:
karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
In [34]:
df = df.merge(karma_df.drop_duplicates(subset="user"), on="user", how = "left")
In [35]:
len(df)
Out[35]:
25

Save your results as a CSV

You may do this regularly while scraping data as well, so that if your scraper stops of your computer crashes, you don't lose all your data.

In [37]:
# Export to csv
file_name = time.strftime('top25_%Y%m%d%H%M.csv', time.gmtime())
df.to_csv(os.path.join('reddit', file_name), index=False)

Here is the template of the scraper we are using! It will look messier when implemented because we will be inserting some large blocks of code where there is ##

count = 0
while count < 1000:
    # scrape top 100 reddit posts
    ##
    # save as timestamped csv
    ##
    count += 1
    time.sleep(600) # 10 minutes

files = os.listdir('reddit')
data = pd.DataFrame()
for file in files:
    df = pd.read_csv(os.path.join('reddit', file))
    df['time_collected'] = file.split('_')[1].split('.')[0]
    data = pd.concat(data, df)

unique_users = list(set(data.user)) #set is like .unique() but a vectorized method, so it's faster

# get user info for unique users as karma_df
##
full_data = data.merge(karma_df, on='user', how='left')
In [ ]:
count = 0
max_count = 1000
while count < max_count:
    print(count)
    # scrape top 100 reddit posts every X period of time
    #url_template = "http://www.reddit.com/?count={}&after={}"
    max_results = 100 # Set this to a high-value (5000) to generate more results. 
    # Crawling more results, will also take much longer. First test your code on a small number of results and then expand.

    col1=[]
    col2=[]
    col3=[]
    col4=[]
    col5=[]
    last_id = None
    for start in range(0, max_results, 25):
        if last_id is None:
            url = "http://reddit.com/r/popular/?geo_filter=US_DC"
        else:
            url= "http://www.reddit.com/r/popular/?geo_filter=US_DC/?count=" + str(start) + "&after="+ str(last_id)

        r = requests.get(url, headers={"User-agent": str(uuid())})
        b = BeautifulSoup(r.text, "lxml")

        col1+=extract_title(b)
        col2+=extract_subreddit(b)
        col3+=extract_date(b)
        col4+=extract_comments(b)
        col5+=extract_user(b)

    #variables:
    #col1 = title
    #col2 = subreddit
    #col3 = date
    #col4 = comments
    #col5 = user
        last_id = extract_last_articleid(b)
    df = pd.DataFrame(list(zip(col1, col2, col3, col4, col5)),columns=["title","subreddit", "date", "comments", "user"])
    
    # save as timestamped csv
    file_name = time.strftime('top100_%Y%m%d%H%M.csv', time.gmtime())
    df.to_csv(os.path.join('reddit', file_name), index=False)
    
    count += 1 #add one to the count so this stops
    if count < max_count: # no need to sleep on last iteration
        time.sleep(120) # 2 minutes
print('Done collecting post data')
In [40]:
#read in all files and merge.
print('Reading in data')
files = os.listdir('reddit')
for i, file in enumerate(files):
    df = pd.read_csv(os.path.join('reddit', file))
    df['time_collected'] = file.split('_')[1].split('.')[0]
    df['rank'] = df.index
    if i == 0:
        data = pd.DataFrame(df)
    else:
        data = pd.concat([data, df])
Reading in data
In [75]:
#not every file got all 100. Perhaps the page updated and some exact duplicates were collected and our function removes
#exact duplicates.
data
Out[75]:
Unnamed: 0 comments date rank subreddit time_collected title user
0 NaN 441.0 2017-10-28 17:34:09 0 CrappyDesign 201710282341 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420
1 NaN 1859.0 2017-10-28 23:18:15 1 CFB 201710282341 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee
2 NaN 711.0 2017-10-28 18:12:11 2 mildlyinteresting 201710282341 Paint-chipping making the wheelchair guy look ... Ribwichs
3 NaN 1249.0 2017-10-28 15:51:41 3 videos 201710282341 This guy made a video of various rappers count... floopykid
4 NaN 168.0 2017-10-28 18:52:24 4 pics 201710282341 Lady at Yellowstone took our picture nowthatssomeusername
5 NaN 220.0 2017-10-28 17:02:43 5 AnimalsBeingDerps 201710282341 Dog thinks it's a shark 1Voice1Life
6 NaN 2761.0 2017-10-28 14:11:03 6 LifeProTips 201710282341 LPT: Most people at the gym are too busy with ... lizardman3000000
7 NaN 963.0 2017-10-28 15:17:07 7 funny 201710282341 So much respect for this guy. toiletskidmarks
8 NaN 293.0 2017-10-28 14:38:26 8 thisismylifenow 201710282341 I AM THE Q-tip HOLDER GallowBoob
9 NaN 985.0 2017-10-28 22:56:37 9 CFB 201710282341 [Postgame Thread] Iowa State Defeats TCU 14-7 elefish92
10 NaN 1025.0 2017-10-28 15:03:36 10 television 201710282341 “Stranger Things” star, Gaten Matarazzo, creat... FordMasterTech
11 NaN 799.0 2017-10-28 18:36:13 11 apple 201710282341 Apple fired the engineer whose daughter releas... JBeylovesyou
12 NaN 280.0 2017-10-28 14:23:34 12 standupshots 201710282341 Buzzfeed samantharuddy
13 NaN 289.0 2017-10-28 15:03:55 13 AnimalsBeingBros 201710282341 Baby hippo and baby rhino are best friends 1Voice1Life
14 NaN 152.0 2017-10-28 15:24:07 14 nostalgia 201710282341 My halloween costume this year. mellabarbarella
15 NaN 1124.0 2017-10-28 11:33:22 15 BlackPeopleTwitter 201710282341 Fax jvpyter
16 NaN 105.0 2017-10-28 16:59:18 16 insanepeoplefacebook 201710282341 This doesn't seem like an appropriate way to b... pmyourevilplans
17 NaN 143.0 2017-10-28 14:41:10 17 EarthPorn 201710282341 Fall colors are amazing right now at the Portl... kdsphotography
18 NaN 45.0 2017-10-28 18:14:42 18 KenM 201710282341 Ken M on Jesus pm_your_pc_setup
19 NaN 618.0 2017-10-28 15:30:42 19 woahdude 201710282341 Heavenly pit, world's deepest sinkhole in China catch22milo
20 NaN 13143.0 2017-10-28 21:22:13 20 CFB 201710282341 [Game Thread] Penn State @ Ohio State Second H... CFB_Referee
21 NaN 524.0 2017-10-28 15:22:33 21 Showerthoughts 201710282341 In the tv series Supernatural nothing in that ... dudeARama2
22 NaN 194.0 2017-10-28 15:20:45 22 IASIP 201710282341 The Mcpoyle bloodline has been clean and pure ... bequiet22
23 NaN 361.0 2017-10-28 14:36:00 23 pics 201710282341 "Not a bug, a FEATURE!" GallowBoob
24 NaN 227.0 2017-10-28 15:13:47 24 oldpeoplefacebook 201710282341 You can have my bingo ticket Barack__Obama__
25 NaN 361.0 2017-10-28 18:27:52 25 instant_regret 201710282341 Kid staples his ear sil130
26 NaN 983.0 2017-10-28 15:35:45 26 worldnews 201710282341 India: local government cuts power to 14 pharm... eva01beast
27 NaN 1105.0 2017-10-28 19:52:10 27 baseball 201710282341 Gurriel to serve 5 game suspension in 2018 gullibletrout
28 NaN 362.0 2017-10-28 12:44:30 28 NatureIsFuckingLit 201710282341 Swan babies, called Cygnets (Sig-nits), will r... Nipru
29 NaN 118.0 2017-10-28 14:45:47 29 wholesomebpt 201710282341 Great couples costume idea 🔥 GallowBoob
... ... ... ... ... ... ... ... ...
70 NaN 698.0 2017-10-29 18:01:59 70 aww 201710300953 Whoever brought this child and this dog togeth... dickfromaccounting
71 NaN 85.0 2017-10-30 03:33:21 71 steelers 201710300953 MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! phrausty
72 NaN 186.0 2017-10-29 21:12:57 72 Documentaries 201710300953 BBC One - Blue Planet II, Series 1, One Ocean ... nffDionysos
73 NaN 100.0 2017-10-29 23:42:53 73 OldSchoolCool 201710300953 Some spooky skeletons riding horses for Hallow... biranouk
74 NaN 560.0 2017-10-29 20:46:52 74 OutOfTheLoop 201710300953 What is up with Gordon Ramsay and cocaine? realjohncenawwe
75 NaN 150.0 2017-10-29 18:26:17 75 calvinandhobbes 201710300953 I sure cant wait for these moment with my son Dopplesoldner1
76 NaN 10851.0 2017-10-29 18:21:56 76 videos 201710300953 YouTube user demonstrating how Facebook listen... AnimalCrust
77 NaN 227.0 2017-10-30 02:11:44 77 gifs 201710300953 Girl using her head as table tennis racket 1Voice1Life
78 NaN 236.0 2017-10-29 18:53:49 78 oldpeoplefacebook 201710300953 65 is a great day! assumetehposition
79 NaN 3288.0 2017-10-29 14:27:57 79 PoliticalHumor 201710300953 I'm sure Trump's administration won't add to t... 13704
80 NaN 125.0 2017-10-29 23:20:22 80 StartledCats 201710300953 Oh god, who let this thing in the house H_G_Bells
81 NaN 95.0 2017-10-29 22:40:05 81 StrangerThings 201710300953 I heard you can get 1000 upvotes if you post a... HephaestusXIII
82 NaN 195.0 2017-10-30 04:06:20 82 baseball 201710300953 [Haren] So thankful they juiced the ball after... Jacked1703
83 NaN 363.0 2017-10-30 05:39:43 83 baseball 201710300953 BREGMAN WALKS IT OFF! Meatprint
84 NaN 513.0 2017-10-29 16:10:14 84 technology 201710300953 Company Added the Word ‘Blockchain’ to Its Nam... kashevko
85 NaN 212.0 2017-10-29 18:19:39 85 StoppedWorking 201710300953 Puppy should have listened during geometry class GallowBoob
86 NaN 176.0 2017-10-30 02:50:19 86 Whatcouldgowrong 201710300953 WCGW if I don't know what's lighter fluid? TheNutsCracker
87 NaN 299.0 2017-10-30 01:48:39 87 NintendoSwitch 201710300953 Wall Kicks (and Triple Jumps) Will Work [SMO] MatrixChicken
88 NaN 86.0 2017-10-29 23:01:13 88 EarthPorn 201710300953 Evening Mist Over Echo Lake, British Columbia,... sndon
89 NaN 57.0 2017-10-29 18:41:33 89 BlackPeopleTwitter 201710300953 They made it happen wwf4ever
90 NaN 151.0 2017-10-30 03:52:14 90 baseball 201710300953 Springer ties it with a HR Meatprint
91 NaN 19.0 2017-10-30 02:37:56 91 BlackPeopleTwitter 201710300953 Aftermath of Cavs vs Knicks game 😔 ParadiseProd
92 NaN 145.0 2017-10-29 21:51:43 92 Justfuckmyshitup 201710300953 Landing strip Zaza9000
93 NaN 1164.0 2017-10-29 20:36:47 93 formula1 201710300953 Lewis Hamilton wins the 2017 Formula One World... F1-Bot
94 NaN 227.0 2017-10-29 18:03:30 94 pics 201710300953 Will Poulter dressed as Sid from Toy Story peace_among_worlds
95 NaN 45.0 2017-10-29 19:18:55 95 HistoryMemes 201710300953 kaiser Wilhelm ii (1917) hazembazem
96 NaN 91.0 2017-10-29 19:54:17 96 funny 201710300953 My buddy was the United Airlines Passenger justophicles
97 NaN 620.0 2017-10-29 15:34:36 97 aww 201710300953 This pic alone is worth the $9 I paid for this... deemandaniels
98 NaN 116.0 2017-10-29 18:37:31 98 wholesomememes 201710300953 Every morning. CaptainAmeriHeck
99 NaN 6594.0 2017-10-29 22:07:31 99 AskReddit 201710300953 What, in your opinion, is the single most beau... GiveMeAllYourRupees

99645 rows × 8 columns

In [59]:
len(list(set(data.user)))
Out[59]:
2424
In [58]:
# get user info for unique users as karma_df
unique_users = list(set(data.user)) #set is like .unique() but a vectorized method, so it's faster
print('Getting user data')
karmalist =[]
for user in unique_users:
    url = "https://www.reddit.com/user/" + str(user)
    r = requests.get(url, headers={"User-agent": str(uuid())})
    b = BeautifulSoup(r.text, "lxml")
    if len(b) == 0:
        karmalist.append((user, None, None))
        continue
    
    kpoints = b.findAll("span", {"class":"karma"})
    comment_karma=None
    post_karma=None
    for kpoint in kpoints:
        if "comment-karma" in kpoint["class"]:
            comment_karma=kpoint.text.replace(",","")
        else:
            post_karma=kpoint.text.replace(",","")
    karmalist.append((user, post_karma,comment_karma))
    
karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
Getting user data
In [77]:
full_data = data.merge(karma_df.drop_duplicates(subset="user"), on="user", how = "left")
#there's an unnamed column to remove.
full_data = full_data[["comments", "date", "rank", "subreddit", "time_collected","title", "user", "post-karma", 
                       "comment-karma"]]
In [80]:
full_data.to_csv('full_reddit_data.csv')
In [68]:
# what does this code do?
files = os.listdir('reddit')
data = pd.DataFrame()
current_id = 0
for i, file in enumerate(files):
    df = pd.read_csv(os.path.join('reddit', file))
    df['time_collected'] = file.split('_')[1].split('.')[0]
    df['rank'] = df.index
    #df.set_index(list(range(current_id, current_id + len(df))))
    current_id += len(df)
    data = pd.concat([data, df])
In [82]:
full_data.to_csv('full_reddit_data.csv', index=False)

Predicting comments using Random Forests + Another Classifier

Load in the the data of scraped results

In [ ]:
## YOUR CODE HERE
In [93]:
full_data = pd.read_csv('full_reddit_data.csv')
In [94]:
full_data.head(10)
Out[94]:
comments date rank subreddit time_collected title user post-karma comment-karma
0 441.0 2017-10-28 17:34:09 0 CrappyDesign 201710282341 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560 325.0
1 1859.0 2017-10-28 23:18:15 1 CFB 201710282341 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858 2845.0
2 711.0 2017-10-28 18:12:11 2 mildlyinteresting 201710282341 Paint-chipping making the wheelchair guy look ... Ribwichs 7018 61.0
3 1249.0 2017-10-28 15:51:41 3 videos 201710282341 This guy made a video of various rappers count... floopykid 22034 38536.0
4 168.0 2017-10-28 18:52:24 4 pics 201710282341 Lady at Yellowstone took our picture nowthatssomeusername 7001 64.0
5 220.0 2017-10-28 17:02:43 5 AnimalsBeingDerps 201710282341 Dog thinks it's a shark 1Voice1Life NaN NaN
6 2761.0 2017-10-28 14:11:03 6 LifeProTips 201710282341 LPT: Most people at the gym are too busy with ... lizardman3000000 25710 186.0
7 963.0 2017-10-28 15:17:07 7 funny 201710282341 So much respect for this guy. toiletskidmarks 13816 541.0
8 293.0 2017-10-28 14:38:26 8 thisismylifenow 201710282341 I AM THE Q-tip HOLDER GallowBoob NaN NaN
9 985.0 2017-10-28 22:56:37 9 CFB 201710282341 [Postgame Thread] Iowa State Defeats TCU 14-7 elefish92 24850 17525.0
In [95]:
new_profiles = full_data[pd.isnull(full_data["comment-karma"])]
In [96]:
# get user info for unique users as karma_df
unique_users = list(set(new_profiles["user"])) #set is like .unique() but a vectorized method, so it's faster
karmalist =[]
for user in unique_users:
    url = "https://www.reddit.com/user/" + str(user)
    r = requests.get(url, headers={"User-agent": str(uuid())})
    b = BeautifulSoup(r.text, "lxml")
    if len(b) == 0:
        karmalist.append((user, None, None))
        continue
    
    kpoints = b.findAll("div", {"class":"ProfileSidebar__counterInfo"})
    comment_karma=None
    post_karma=None
    for kpoint in kpoints:
        s = kpoint.text.find(" ")
        
        x = str(kpoint).find("<br/>")+len("<br/>")
        y = str(kpoint).find(" ", x)
        
        post_karma=kpoint.text[0:s].replace(",","")
        comment_karma=str(kpoint)[x:y].replace(",","")
    karmalist.append((user, post_karma,comment_karma))
    
new_karma_df = pd.DataFrame(karmalist,columns=["user","post-karma", 'comment-karma'])
In [97]:
new_karma_df
Out[97]:
user post-karma comment-karma
0 51isnotprime 7214 3014
1 ganggxnggvng 126027 363025
2 Cortimer 1869 293
3 I_AM_STILL_A_IDIOT 357428 351660
4 Redzit69 53899 1727
5 ExpFilm_Student None None
6 Moni3 10402 34623
7 IfaqYurmama 70509 89154
8 BrandonKatrena 54086 19
9 F1NAC 5404 3980
10 TheVineyard00 9369 93223
11 Lorix_In_Oz 30552 5174
12 Dormponk 2023 1
13 MatiasUK 20469 36870
14 GallowBoob 17536350 833693
15 Radical-Momo 9468 11636
16 TakesJonToKnowJuan 21038 36343
17 DarkUnix 375 2144
18 erindizmo 5262 27948
19 sellingspams 275010 3678
20 Dragon029 25889 38804
21 tchikboom 2910 2582
22 A10A10A10 28444 3259
23 pectoid 858 2182
24 kashevko 5703 2214
25 mosen2 None None
26 StratosPhere15 3697 4645
27 Zetice 33182 82283
28 Gamersville101 11369 1256
29 GrandAdmiralMellacus None None
... ... ... ...
191 remain_unaltered 332322 30010
192 MichaelTen 109763 1746
193 manikfox 39565 14092
194 st3dy 3004 380
195 KarlMarxIsntDead 76720 18608
196 unphilievable 11892 1322
197 IceCreamBeavers None None
198 Ibleedcarrots 2790846 21481
199 denvit 14775 11681
200 ihasRyzen None None
201 gntrr 70636 13800
202 solateor 1347934 180541
203 Mega_Buster_Mk-17 101711 31710
204 Snudge 8382 9777
205 Offlinelol 8415 1847
206 MyAngelKami 7278 8268
207 gDisasters 970817 65802
208 MyRSSbot 186056 1502
209 Fourteen_of_Twelve 134251 169262
210 diversity_is_racism 92814 28536
211 OwnTheKnight 528976 98929
212 ByterBit 5615 18500
213 furrutia29 73988 4978
214 liamkr 190514 20382
215 AliChawal 26631 5488
216 THEBIGC01 16956 73137
217 MagicalScarf 113304 341
218 TheGangMakesaPodcast 277682 19102
219 taghere 3574 504
220 Anyun 7901 10995

221 rows × 3 columns

In [98]:
new_profiles = full_data[pd.isnull(full_data["comment-karma"])]
In [99]:
a = full_data.merge(new_karma_df, how='left', left_on='user', right_on='user')
In [100]:
def convert_float(x):
    try:
        return float(x)
    except:
        return np.nan
In [101]:
a['post-karma_x'] = a['post-karma_x'].apply(lambda x: convert_float(x))
a['post-karma_y'] = a['post-karma_y'].apply(lambda x: convert_float(x))
a['post-karma'] = a['post-karma_x']
a['post-karma'].fillna(a['post-karma_y'], inplace=True)
In [102]:
a['comment-karma_x'] = a['comment-karma_x'].apply(lambda x: convert_float(x))
a['comment-karma_y'] = a['comment-karma_y'].apply(lambda x: convert_float(x))
a['comment-karma'] = a['comment-karma_x']
a['comment-karma'].fillna(a['comment-karma_y'], inplace=True)
In [103]:
a
Out[103]:
comments date rank subreddit time_collected title user post-karma_x comment-karma_x post-karma_y comment-karma_y post-karma comment-karma
0 441.0 2017-10-28 17:34:09 0 CrappyDesign 201710282341 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560.0 325.0 NaN NaN 8560.0 325.0
1 1859.0 2017-10-28 23:18:15 1 CFB 201710282341 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858.0 2845.0 NaN NaN 313858.0 2845.0
2 711.0 2017-10-28 18:12:11 2 mildlyinteresting 201710282341 Paint-chipping making the wheelchair guy look ... Ribwichs 7018.0 61.0 NaN NaN 7018.0 61.0
3 1249.0 2017-10-28 15:51:41 3 videos 201710282341 This guy made a video of various rappers count... floopykid 22034.0 38536.0 NaN NaN 22034.0 38536.0
4 168.0 2017-10-28 18:52:24 4 pics 201710282341 Lady at Yellowstone took our picture nowthatssomeusername 7001.0 64.0 NaN NaN 7001.0 64.0
5 220.0 2017-10-28 17:02:43 5 AnimalsBeingDerps 201710282341 Dog thinks it's a shark 1Voice1Life NaN NaN 6476268.0 34958.0 6476268.0 34958.0
6 2761.0 2017-10-28 14:11:03 6 LifeProTips 201710282341 LPT: Most people at the gym are too busy with ... lizardman3000000 25710.0 186.0 NaN NaN 25710.0 186.0
7 963.0 2017-10-28 15:17:07 7 funny 201710282341 So much respect for this guy. toiletskidmarks 13816.0 541.0 NaN NaN 13816.0 541.0
8 293.0 2017-10-28 14:38:26 8 thisismylifenow 201710282341 I AM THE Q-tip HOLDER GallowBoob NaN NaN 17536350.0 833693.0 17536350.0 833693.0
9 985.0 2017-10-28 22:56:37 9 CFB 201710282341 [Postgame Thread] Iowa State Defeats TCU 14-7 elefish92 24850.0 17525.0 NaN NaN 24850.0 17525.0
10 1025.0 2017-10-28 15:03:36 10 television 201710282341 “Stranger Things” star, Gaten Matarazzo, creat... FordMasterTech 6496.0 4484.0 NaN NaN 6496.0 4484.0
11 799.0 2017-10-28 18:36:13 11 apple 201710282341 Apple fired the engineer whose daughter releas... JBeylovesyou 36113.0 4010.0 NaN NaN 36113.0 4010.0
12 280.0 2017-10-28 14:23:34 12 standupshots 201710282341 Buzzfeed samantharuddy 159018.0 9419.0 NaN NaN 159018.0 9419.0
13 289.0 2017-10-28 15:03:55 13 AnimalsBeingBros 201710282341 Baby hippo and baby rhino are best friends 1Voice1Life NaN NaN 6476268.0 34958.0 6476268.0 34958.0
14 152.0 2017-10-28 15:24:07 14 nostalgia 201710282341 My halloween costume this year. mellabarbarella 6506.0 378.0 NaN NaN 6506.0 378.0
15 1124.0 2017-10-28 11:33:22 15 BlackPeopleTwitter 201710282341 Fax jvpyter 7417.0 71.0 NaN NaN 7417.0 71.0
16 105.0 2017-10-28 16:59:18 16 insanepeoplefacebook 201710282341 This doesn't seem like an appropriate way to b... pmyourevilplans 10033.0 553.0 NaN NaN 10033.0 553.0
17 143.0 2017-10-28 14:41:10 17 EarthPorn 201710282341 Fall colors are amazing right now at the Portl... kdsphotography 48784.0 1772.0 NaN NaN 48784.0 1772.0
18 45.0 2017-10-28 18:14:42 18 KenM 201710282341 Ken M on Jesus pm_your_pc_setup 6837.0 401.0 NaN NaN 6837.0 401.0
19 618.0 2017-10-28 15:30:42 19 woahdude 201710282341 Heavenly pit, world's deepest sinkhole in China catch22milo NaN NaN 282245.0 1164236.0 282245.0 1164236.0
20 13143.0 2017-10-28 21:22:13 20 CFB 201710282341 [Game Thread] Penn State @ Ohio State Second H... CFB_Referee 313858.0 2845.0 NaN NaN 313858.0 2845.0
21 524.0 2017-10-28 15:22:33 21 Showerthoughts 201710282341 In the tv series Supernatural nothing in that ... dudeARama2 16637.0 42029.0 NaN NaN 16637.0 42029.0
22 194.0 2017-10-28 15:20:45 22 IASIP 201710282341 The Mcpoyle bloodline has been clean and pure ... bequiet22 5618.0 1071.0 NaN NaN 5618.0 1071.0
23 361.0 2017-10-28 14:36:00 23 pics 201710282341 "Not a bug, a FEATURE!" GallowBoob NaN NaN 17536350.0 833693.0 17536350.0 833693.0
24 227.0 2017-10-28 15:13:47 24 oldpeoplefacebook 201710282341 You can have my bingo ticket Barack__Obama__ 7517.0 31771.0 NaN NaN 7517.0 31771.0
25 361.0 2017-10-28 18:27:52 25 instant_regret 201710282341 Kid staples his ear sil130 42108.0 2896.0 NaN NaN 42108.0 2896.0
26 983.0 2017-10-28 15:35:45 26 worldnews 201710282341 India: local government cuts power to 14 pharm... eva01beast NaN NaN 16120.0 26638.0 16120.0 26638.0
27 1105.0 2017-10-28 19:52:10 27 baseball 201710282341 Gurriel to serve 5 game suspension in 2018 gullibletrout NaN NaN 32546.0 44546.0 32546.0 44546.0
28 362.0 2017-10-28 12:44:30 28 NatureIsFuckingLit 201710282341 Swan babies, called Cygnets (Sig-nits), will r... Nipru 146687.0 48972.0 NaN NaN 146687.0 48972.0
29 118.0 2017-10-28 14:45:47 29 wholesomebpt 201710282341 Great couples costume idea 🔥 GallowBoob NaN NaN 17536350.0 833693.0 17536350.0 833693.0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
99615 698.0 2017-10-29 18:01:59 70 aww 201710300953 Whoever brought this child and this dog togeth... dickfromaccounting 228186.0 217270.0 NaN NaN 228186.0 217270.0
99616 85.0 2017-10-30 03:33:21 71 steelers 201710300953 MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! phrausty 11416.0 5129.0 NaN NaN 11416.0 5129.0
99617 186.0 2017-10-29 21:12:57 72 Documentaries 201710300953 BBC One - Blue Planet II, Series 1, One Ocean ... nffDionysos 26170.0 2147.0 NaN NaN 26170.0 2147.0
99618 100.0 2017-10-29 23:42:53 73 OldSchoolCool 201710300953 Some spooky skeletons riding horses for Hallow... biranouk 18400.0 275.0 NaN NaN 18400.0 275.0
99619 560.0 2017-10-29 20:46:52 74 OutOfTheLoop 201710300953 What is up with Gordon Ramsay and cocaine? realjohncenawwe 35748.0 17563.0 NaN NaN 35748.0 17563.0
99620 150.0 2017-10-29 18:26:17 75 calvinandhobbes 201710300953 I sure cant wait for these moment with my son Dopplesoldner1 5336.0 384.0 NaN NaN 5336.0 384.0
99621 10851.0 2017-10-29 18:21:56 76 videos 201710300953 YouTube user demonstrating how Facebook listen... AnimalCrust 16710.0 38204.0 NaN NaN 16710.0 38204.0
99622 227.0 2017-10-30 02:11:44 77 gifs 201710300953 Girl using her head as table tennis racket 1Voice1Life NaN NaN 6476268.0 34958.0 6476268.0 34958.0
99623 236.0 2017-10-29 18:53:49 78 oldpeoplefacebook 201710300953 65 is a great day! assumetehposition 13906.0 23087.0 NaN NaN 13906.0 23087.0
99624 3288.0 2017-10-29 14:27:57 79 PoliticalHumor 201710300953 I'm sure Trump's administration won't add to t... 13704 15004.0 57097.0 NaN NaN 15004.0 57097.0
99625 125.0 2017-10-29 23:20:22 80 StartledCats 201710300953 Oh god, who let this thing in the house H_G_Bells 66271.0 3814.0 NaN NaN 66271.0 3814.0
99626 95.0 2017-10-29 22:40:05 81 StrangerThings 201710300953 I heard you can get 1000 upvotes if you post a... HephaestusXIII 8247.0 17661.0 NaN NaN 8247.0 17661.0
99627 195.0 2017-10-30 04:06:20 82 baseball 201710300953 [Haren] So thankful they juiced the ball after... Jacked1703 7196.0 18827.0 NaN NaN 7196.0 18827.0
99628 363.0 2017-10-30 05:39:43 83 baseball 201710300953 BREGMAN WALKS IT OFF! Meatprint 90554.0 11564.0 NaN NaN 90554.0 11564.0
99629 513.0 2017-10-29 16:10:14 84 technology 201710300953 Company Added the Word ‘Blockchain’ to Its Nam... kashevko NaN NaN 5703.0 2214.0 5703.0 2214.0
99630 212.0 2017-10-29 18:19:39 85 StoppedWorking 201710300953 Puppy should have listened during geometry class GallowBoob NaN NaN 17536350.0 833693.0 17536350.0 833693.0
99631 176.0 2017-10-30 02:50:19 86 Whatcouldgowrong 201710300953 WCGW if I don't know what's lighter fluid? TheNutsCracker NaN NaN 497072.0 1326.0 497072.0 1326.0
99632 299.0 2017-10-30 01:48:39 87 NintendoSwitch 201710300953 Wall Kicks (and Triple Jumps) Will Work [SMO] MatrixChicken 6217.0 4439.0 NaN NaN 6217.0 4439.0
99633 86.0 2017-10-29 23:01:13 88 EarthPorn 201710300953 Evening Mist Over Echo Lake, British Columbia,... sndon 6864.0 483.0 NaN NaN 6864.0 483.0
99634 57.0 2017-10-29 18:41:33 89 BlackPeopleTwitter 201710300953 They made it happen wwf4ever 308181.0 69855.0 NaN NaN 308181.0 69855.0
99635 151.0 2017-10-30 03:52:14 90 baseball 201710300953 Springer ties it with a HR Meatprint 90554.0 11564.0 NaN NaN 90554.0 11564.0
99636 19.0 2017-10-30 02:37:56 91 BlackPeopleTwitter 201710300953 Aftermath of Cavs vs Knicks game 😔 ParadiseProd 43587.0 29131.0 NaN NaN 43587.0 29131.0
99637 145.0 2017-10-29 21:51:43 92 Justfuckmyshitup 201710300953 Landing strip Zaza9000 17319.0 2892.0 NaN NaN 17319.0 2892.0
99638 1164.0 2017-10-29 20:36:47 93 formula1 201710300953 Lewis Hamilton wins the 2017 Formula One World... F1-Bot 44022.0 2627.0 NaN NaN 44022.0 2627.0
99639 227.0 2017-10-29 18:03:30 94 pics 201710300953 Will Poulter dressed as Sid from Toy Story peace_among_worlds 5886.0 1411.0 NaN NaN 5886.0 1411.0
99640 45.0 2017-10-29 19:18:55 95 HistoryMemes 201710300953 kaiser Wilhelm ii (1917) hazembazem 7299.0 818.0 NaN NaN 7299.0 818.0
99641 91.0 2017-10-29 19:54:17 96 funny 201710300953 My buddy was the United Airlines Passenger justophicles 5776.0 897.0 NaN NaN 5776.0 897.0
99642 620.0 2017-10-29 15:34:36 97 aww 201710300953 This pic alone is worth the $9 I paid for this... deemandaniels 9383.0 344.0 NaN NaN 9383.0 344.0
99643 116.0 2017-10-29 18:37:31 98 wholesomememes 201710300953 Every morning. CaptainAmeriHeck 8892.0 479.0 NaN NaN 8892.0 479.0
99644 6594.0 2017-10-29 22:07:31 99 AskReddit 201710300953 What, in your opinion, is the single most beau... GiveMeAllYourRupees 31910.0 10035.0 NaN NaN 31910.0 10035.0

99645 rows × 13 columns

In [104]:
full_data = a[["comments", "date", "rank", "subreddit", "time_collected", "title", "user", "post-karma", "comment-karma"]]
In [105]:
full_data
Out[105]:
comments date rank subreddit time_collected title user post-karma comment-karma
0 441.0 2017-10-28 17:34:09 0 CrappyDesign 201710282341 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560.0 325.0
1 1859.0 2017-10-28 23:18:15 1 CFB 201710282341 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858.0 2845.0
2 711.0 2017-10-28 18:12:11 2 mildlyinteresting 201710282341 Paint-chipping making the wheelchair guy look ... Ribwichs 7018.0 61.0
3 1249.0 2017-10-28 15:51:41 3 videos 201710282341 This guy made a video of various rappers count... floopykid 22034.0 38536.0
4 168.0 2017-10-28 18:52:24 4 pics 201710282341 Lady at Yellowstone took our picture nowthatssomeusername 7001.0 64.0
5 220.0 2017-10-28 17:02:43 5 AnimalsBeingDerps 201710282341 Dog thinks it's a shark 1Voice1Life 6476268.0 34958.0
6 2761.0 2017-10-28 14:11:03 6 LifeProTips 201710282341 LPT: Most people at the gym are too busy with ... lizardman3000000 25710.0 186.0
7 963.0 2017-10-28 15:17:07 7 funny 201710282341 So much respect for this guy. toiletskidmarks 13816.0 541.0
8 293.0 2017-10-28 14:38:26 8 thisismylifenow 201710282341 I AM THE Q-tip HOLDER GallowBoob 17536350.0 833693.0
9 985.0 2017-10-28 22:56:37 9 CFB 201710282341 [Postgame Thread] Iowa State Defeats TCU 14-7 elefish92 24850.0 17525.0
10 1025.0 2017-10-28 15:03:36 10 television 201710282341 “Stranger Things” star, Gaten Matarazzo, creat... FordMasterTech 6496.0 4484.0
11 799.0 2017-10-28 18:36:13 11 apple 201710282341 Apple fired the engineer whose daughter releas... JBeylovesyou 36113.0 4010.0
12 280.0 2017-10-28 14:23:34 12 standupshots 201710282341 Buzzfeed samantharuddy 159018.0 9419.0
13 289.0 2017-10-28 15:03:55 13 AnimalsBeingBros 201710282341 Baby hippo and baby rhino are best friends 1Voice1Life 6476268.0 34958.0
14 152.0 2017-10-28 15:24:07 14 nostalgia 201710282341 My halloween costume this year. mellabarbarella 6506.0 378.0
15 1124.0 2017-10-28 11:33:22 15 BlackPeopleTwitter 201710282341 Fax jvpyter 7417.0 71.0
16 105.0 2017-10-28 16:59:18 16 insanepeoplefacebook 201710282341 This doesn't seem like an appropriate way to b... pmyourevilplans 10033.0 553.0
17 143.0 2017-10-28 14:41:10 17 EarthPorn 201710282341 Fall colors are amazing right now at the Portl... kdsphotography 48784.0 1772.0
18 45.0 2017-10-28 18:14:42 18 KenM 201710282341 Ken M on Jesus pm_your_pc_setup 6837.0 401.0
19 618.0 2017-10-28 15:30:42 19 woahdude 201710282341 Heavenly pit, world's deepest sinkhole in China catch22milo 282245.0 1164236.0
20 13143.0 2017-10-28 21:22:13 20 CFB 201710282341 [Game Thread] Penn State @ Ohio State Second H... CFB_Referee 313858.0 2845.0
21 524.0 2017-10-28 15:22:33 21 Showerthoughts 201710282341 In the tv series Supernatural nothing in that ... dudeARama2 16637.0 42029.0
22 194.0 2017-10-28 15:20:45 22 IASIP 201710282341 The Mcpoyle bloodline has been clean and pure ... bequiet22 5618.0 1071.0
23 361.0 2017-10-28 14:36:00 23 pics 201710282341 "Not a bug, a FEATURE!" GallowBoob 17536350.0 833693.0
24 227.0 2017-10-28 15:13:47 24 oldpeoplefacebook 201710282341 You can have my bingo ticket Barack__Obama__ 7517.0 31771.0
25 361.0 2017-10-28 18:27:52 25 instant_regret 201710282341 Kid staples his ear sil130 42108.0 2896.0
26 983.0 2017-10-28 15:35:45 26 worldnews 201710282341 India: local government cuts power to 14 pharm... eva01beast 16120.0 26638.0
27 1105.0 2017-10-28 19:52:10 27 baseball 201710282341 Gurriel to serve 5 game suspension in 2018 gullibletrout 32546.0 44546.0
28 362.0 2017-10-28 12:44:30 28 NatureIsFuckingLit 201710282341 Swan babies, called Cygnets (Sig-nits), will r... Nipru 146687.0 48972.0
29 118.0 2017-10-28 14:45:47 29 wholesomebpt 201710282341 Great couples costume idea 🔥 GallowBoob 17536350.0 833693.0
... ... ... ... ... ... ... ... ... ...
99615 698.0 2017-10-29 18:01:59 70 aww 201710300953 Whoever brought this child and this dog togeth... dickfromaccounting 228186.0 217270.0
99616 85.0 2017-10-30 03:33:21 71 steelers 201710300953 MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! phrausty 11416.0 5129.0
99617 186.0 2017-10-29 21:12:57 72 Documentaries 201710300953 BBC One - Blue Planet II, Series 1, One Ocean ... nffDionysos 26170.0 2147.0
99618 100.0 2017-10-29 23:42:53 73 OldSchoolCool 201710300953 Some spooky skeletons riding horses for Hallow... biranouk 18400.0 275.0
99619 560.0 2017-10-29 20:46:52 74 OutOfTheLoop 201710300953 What is up with Gordon Ramsay and cocaine? realjohncenawwe 35748.0 17563.0
99620 150.0 2017-10-29 18:26:17 75 calvinandhobbes 201710300953 I sure cant wait for these moment with my son Dopplesoldner1 5336.0 384.0
99621 10851.0 2017-10-29 18:21:56 76 videos 201710300953 YouTube user demonstrating how Facebook listen... AnimalCrust 16710.0 38204.0
99622 227.0 2017-10-30 02:11:44 77 gifs 201710300953 Girl using her head as table tennis racket 1Voice1Life 6476268.0 34958.0
99623 236.0 2017-10-29 18:53:49 78 oldpeoplefacebook 201710300953 65 is a great day! assumetehposition 13906.0 23087.0
99624 3288.0 2017-10-29 14:27:57 79 PoliticalHumor 201710300953 I'm sure Trump's administration won't add to t... 13704 15004.0 57097.0
99625 125.0 2017-10-29 23:20:22 80 StartledCats 201710300953 Oh god, who let this thing in the house H_G_Bells 66271.0 3814.0
99626 95.0 2017-10-29 22:40:05 81 StrangerThings 201710300953 I heard you can get 1000 upvotes if you post a... HephaestusXIII 8247.0 17661.0
99627 195.0 2017-10-30 04:06:20 82 baseball 201710300953 [Haren] So thankful they juiced the ball after... Jacked1703 7196.0 18827.0
99628 363.0 2017-10-30 05:39:43 83 baseball 201710300953 BREGMAN WALKS IT OFF! Meatprint 90554.0 11564.0
99629 513.0 2017-10-29 16:10:14 84 technology 201710300953 Company Added the Word ‘Blockchain’ to Its Nam... kashevko 5703.0 2214.0
99630 212.0 2017-10-29 18:19:39 85 StoppedWorking 201710300953 Puppy should have listened during geometry class GallowBoob 17536350.0 833693.0
99631 176.0 2017-10-30 02:50:19 86 Whatcouldgowrong 201710300953 WCGW if I don't know what's lighter fluid? TheNutsCracker 497072.0 1326.0
99632 299.0 2017-10-30 01:48:39 87 NintendoSwitch 201710300953 Wall Kicks (and Triple Jumps) Will Work [SMO] MatrixChicken 6217.0 4439.0
99633 86.0 2017-10-29 23:01:13 88 EarthPorn 201710300953 Evening Mist Over Echo Lake, British Columbia,... sndon 6864.0 483.0
99634 57.0 2017-10-29 18:41:33 89 BlackPeopleTwitter 201710300953 They made it happen wwf4ever 308181.0 69855.0
99635 151.0 2017-10-30 03:52:14 90 baseball 201710300953 Springer ties it with a HR Meatprint 90554.0 11564.0
99636 19.0 2017-10-30 02:37:56 91 BlackPeopleTwitter 201710300953 Aftermath of Cavs vs Knicks game 😔 ParadiseProd 43587.0 29131.0
99637 145.0 2017-10-29 21:51:43 92 Justfuckmyshitup 201710300953 Landing strip Zaza9000 17319.0 2892.0
99638 1164.0 2017-10-29 20:36:47 93 formula1 201710300953 Lewis Hamilton wins the 2017 Formula One World... F1-Bot 44022.0 2627.0
99639 227.0 2017-10-29 18:03:30 94 pics 201710300953 Will Poulter dressed as Sid from Toy Story peace_among_worlds 5886.0 1411.0
99640 45.0 2017-10-29 19:18:55 95 HistoryMemes 201710300953 kaiser Wilhelm ii (1917) hazembazem 7299.0 818.0
99641 91.0 2017-10-29 19:54:17 96 funny 201710300953 My buddy was the United Airlines Passenger justophicles 5776.0 897.0
99642 620.0 2017-10-29 15:34:36 97 aww 201710300953 This pic alone is worth the $9 I paid for this... deemandaniels 9383.0 344.0
99643 116.0 2017-10-29 18:37:31 98 wholesomememes 201710300953 Every morning. CaptainAmeriHeck 8892.0 479.0
99644 6594.0 2017-10-29 22:07:31 99 AskReddit 201710300953 What, in your opinion, is the single most beau... GiveMeAllYourRupees 31910.0 10035.0

99645 rows × 9 columns

In [106]:
full_data['time_collected'] = full_data['time_collected'].astype(str)
full_data['time_collected'] = full_data['time_collected'].apply(lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:8] + ' ' + x[8:10] + ':' + x[10:])
full_data['time_collected'] = pd.to_datetime(full_data['time_collected'])
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()
In [107]:
full_data['date'] = pd.to_datetime(full_data['date'])
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
In [118]:
full_data['date'] = full_data['date'].apply(lambda x: x.tz_localize('US/Eastern'))
full_data['time_collected'] = full_data['time_collected'].apply(lambda x: x.tz_localize('UTC').tz_convert('US/Eastern'))
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
In [119]:
full_data.head()
Out[119]:
comments date rank subreddit time_collected title user post-karma comment-karma age-in-hrs
0 441.0 2017-10-28 17:34:09-04:00 0 CrappyDesign 2017-10-28 19:41:00-04:00 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560.0 325.0 17.885833
1 1859.0 2017-10-28 23:18:15-04:00 1 CFB 2017-10-28 19:41:00-04:00 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858.0 2845.0 23.620833
2 711.0 2017-10-28 18:12:11-04:00 2 mildlyinteresting 2017-10-28 19:41:00-04:00 Paint-chipping making the wheelchair guy look ... Ribwichs 7018.0 61.0 18.519722
3 1249.0 2017-10-28 15:51:41-04:00 3 videos 2017-10-28 19:41:00-04:00 This guy made a video of various rappers count... floopykid 22034.0 38536.0 16.178056
4 168.0 2017-10-28 18:52:24-04:00 4 pics 2017-10-28 19:41:00-04:00 Lady at Yellowstone took our picture nowthatssomeusername 7001.0 64.0 19.190000
In [136]:
from datetime import datetime

def hours_between(d1, d2):
    td = d2 - d1
    return abs(td.total_seconds()/3600)
In [137]:
full_data["age-in-hrs"] = full_data.apply(lambda x: hours_between(x["time_collected"], x['date']), axis =1)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
In [138]:
full_data.head()
Out[138]:
comments date rank subreddit time_collected title user post-karma comment-karma age-in-hrs
0 441.0 2017-10-28 17:34:09-04:00 0 CrappyDesign 2017-10-28 19:41:00-04:00 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560.0 325.0 2.114167
1 1859.0 2017-10-28 23:18:15-04:00 1 CFB 2017-10-28 19:41:00-04:00 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858.0 2845.0 3.620833
2 711.0 2017-10-28 18:12:11-04:00 2 mildlyinteresting 2017-10-28 19:41:00-04:00 Paint-chipping making the wheelchair guy look ... Ribwichs 7018.0 61.0 1.480278
3 1249.0 2017-10-28 15:51:41-04:00 3 videos 2017-10-28 19:41:00-04:00 This guy made a video of various rappers count... floopykid 22034.0 38536.0 3.821944
4 168.0 2017-10-28 18:52:24-04:00 4 pics 2017-10-28 19:41:00-04:00 Lady at Yellowstone took our picture nowthatssomeusername 7001.0 64.0 0.810000
In [139]:
full_data.to_csv('full_reddit_data.csv', index=False)

We want to predict a binary variable - whether the number of comments was low or high. Compute the median number of comments and create a new binary variable that is true when the number of comments is high (above the median)

We could also perform Linear Regression (or any regression) to predict the number of comments here. Instead, we are going to convert this into a binary classification problem, by predicting two classes, HIGH vs LOW number of comments.

While performing regression may be better, performing classification may help remove some of the noise of the extremely popular threads. We don't have to choose the median as the splitting point - we could also split on the 75th percentile or any other reasonable breaking point.

In fact, the ideal scenario may be to predict many levels of comment numbers.

In [140]:
## YOUR CODE HERE
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
import sklearn.datasets as datasets
In [141]:
full_data["comments"].quantile(q=0.85)
Out[141]:
890.0
In [142]:
full_data["high-comments"] = [1 if x > full_data["comments"].quantile(q=0.85) else 0 for x in full_data['comments']]
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
In [148]:
full_data["hour-posted"] = full_data.apply(lambda x: x['date'].hour, axis =1)
In [149]:
full_data
Out[149]:
comments date rank subreddit time_collected title user post-karma comment-karma age-in-hrs high-comments hour hour-posted
0 441.0 2017-10-28 17:34:09-04:00 0 CrappyDesign 2017-10-28 19:41:00-04:00 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560.0 325.0 2.114167 0 10 17
1 1859.0 2017-10-28 23:18:15-04:00 1 CFB 2017-10-28 19:41:00-04:00 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858.0 2845.0 3.620833 1 10 23
2 711.0 2017-10-28 18:12:11-04:00 2 mildlyinteresting 2017-10-28 19:41:00-04:00 Paint-chipping making the wheelchair guy look ... Ribwichs 7018.0 61.0 1.480278 0 10 18
3 1249.0 2017-10-28 15:51:41-04:00 3 videos 2017-10-28 19:41:00-04:00 This guy made a video of various rappers count... floopykid 22034.0 38536.0 3.821944 1 10 15
4 168.0 2017-10-28 18:52:24-04:00 4 pics 2017-10-28 19:41:00-04:00 Lady at Yellowstone took our picture nowthatssomeusername 7001.0 64.0 0.810000 0 10 18
5 220.0 2017-10-28 17:02:43-04:00 5 AnimalsBeingDerps 2017-10-28 19:41:00-04:00 Dog thinks it's a shark 1Voice1Life 6476268.0 34958.0 2.638056 0 10 17
6 2761.0 2017-10-28 14:11:03-04:00 6 LifeProTips 2017-10-28 19:41:00-04:00 LPT: Most people at the gym are too busy with ... lizardman3000000 25710.0 186.0 5.499167 1 10 14
7 963.0 2017-10-28 15:17:07-04:00 7 funny 2017-10-28 19:41:00-04:00 So much respect for this guy. toiletskidmarks 13816.0 541.0 4.398056 1 10 15
8 293.0 2017-10-28 14:38:26-04:00 8 thisismylifenow 2017-10-28 19:41:00-04:00 I AM THE Q-tip HOLDER GallowBoob 17536350.0 833693.0 5.042778 0 10 14
9 985.0 2017-10-28 22:56:37-04:00 9 CFB 2017-10-28 19:41:00-04:00 [Postgame Thread] Iowa State Defeats TCU 14-7 elefish92 24850.0 17525.0 3.260278 1 10 22
10 1025.0 2017-10-28 15:03:36-04:00 10 television 2017-10-28 19:41:00-04:00 “Stranger Things” star, Gaten Matarazzo, creat... FordMasterTech 6496.0 4484.0 4.623333 1 10 15
11 799.0 2017-10-28 18:36:13-04:00 11 apple 2017-10-28 19:41:00-04:00 Apple fired the engineer whose daughter releas... JBeylovesyou 36113.0 4010.0 1.079722 0 10 18
12 280.0 2017-10-28 14:23:34-04:00 12 standupshots 2017-10-28 19:41:00-04:00 Buzzfeed samantharuddy 159018.0 9419.0 5.290556 0 10 14
13 289.0 2017-10-28 15:03:55-04:00 13 AnimalsBeingBros 2017-10-28 19:41:00-04:00 Baby hippo and baby rhino are best friends 1Voice1Life 6476268.0 34958.0 4.618056 0 10 15
14 152.0 2017-10-28 15:24:07-04:00 14 nostalgia 2017-10-28 19:41:00-04:00 My halloween costume this year. mellabarbarella 6506.0 378.0 4.281389 0 10 15
15 1124.0 2017-10-28 11:33:22-04:00 15 BlackPeopleTwitter 2017-10-28 19:41:00-04:00 Fax jvpyter 7417.0 71.0 8.127222 1 10 11
16 105.0 2017-10-28 16:59:18-04:00 16 insanepeoplefacebook 2017-10-28 19:41:00-04:00 This doesn't seem like an appropriate way to b... pmyourevilplans 10033.0 553.0 2.695000 0 10 16
17 143.0 2017-10-28 14:41:10-04:00 17 EarthPorn 2017-10-28 19:41:00-04:00 Fall colors are amazing right now at the Portl... kdsphotography 48784.0 1772.0 4.997222 0 10 14
18 45.0 2017-10-28 18:14:42-04:00 18 KenM 2017-10-28 19:41:00-04:00 Ken M on Jesus pm_your_pc_setup 6837.0 401.0 1.438333 0 10 18
19 618.0 2017-10-28 15:30:42-04:00 19 woahdude 2017-10-28 19:41:00-04:00 Heavenly pit, world's deepest sinkhole in China catch22milo 282245.0 1164236.0 4.171667 0 10 15
20 13143.0 2017-10-28 21:22:13-04:00 20 CFB 2017-10-28 19:41:00-04:00 [Game Thread] Penn State @ Ohio State Second H... CFB_Referee 313858.0 2845.0 1.686944 1 10 21
21 524.0 2017-10-28 15:22:33-04:00 21 Showerthoughts 2017-10-28 19:41:00-04:00 In the tv series Supernatural nothing in that ... dudeARama2 16637.0 42029.0 4.307500 0 10 15
22 194.0 2017-10-28 15:20:45-04:00 22 IASIP 2017-10-28 19:41:00-04:00 The Mcpoyle bloodline has been clean and pure ... bequiet22 5618.0 1071.0 4.337500 0 10 15
23 361.0 2017-10-28 14:36:00-04:00 23 pics 2017-10-28 19:41:00-04:00 "Not a bug, a FEATURE!" GallowBoob 17536350.0 833693.0 5.083333 0 10 14
24 227.0 2017-10-28 15:13:47-04:00 24 oldpeoplefacebook 2017-10-28 19:41:00-04:00 You can have my bingo ticket Barack__Obama__ 7517.0 31771.0 4.453611 0 10 15
25 361.0 2017-10-28 18:27:52-04:00 25 instant_regret 2017-10-28 19:41:00-04:00 Kid staples his ear sil130 42108.0 2896.0 1.218889 0 10 18
26 983.0 2017-10-28 15:35:45-04:00 26 worldnews 2017-10-28 19:41:00-04:00 India: local government cuts power to 14 pharm... eva01beast 16120.0 26638.0 4.087500 1 10 15
27 1105.0 2017-10-28 19:52:10-04:00 27 baseball 2017-10-28 19:41:00-04:00 Gurriel to serve 5 game suspension in 2018 gullibletrout 32546.0 44546.0 0.186111 1 10 19
28 362.0 2017-10-28 12:44:30-04:00 28 NatureIsFuckingLit 2017-10-28 19:41:00-04:00 Swan babies, called Cygnets (Sig-nits), will r... Nipru 146687.0 48972.0 6.941667 0 10 12
29 118.0 2017-10-28 14:45:47-04:00 29 wholesomebpt 2017-10-28 19:41:00-04:00 Great couples costume idea 🔥 GallowBoob 17536350.0 833693.0 4.920278 0 10 14
... ... ... ... ... ... ... ... ... ... ... ... ... ...
99615 698.0 2017-10-29 18:01:59-04:00 70 aww 2017-10-30 05:53:00-04:00 Whoever brought this child and this dog togeth... dickfromaccounting 228186.0 217270.0 11.850278 0 10 18
99616 85.0 2017-10-30 03:33:21-04:00 71 steelers 2017-10-30 05:53:00-04:00 MARTAVIS WHO? JUJU FOR MVP UPVOTE PARTY!!! phrausty 11416.0 5129.0 2.327500 0 10 3
99617 186.0 2017-10-29 21:12:57-04:00 72 Documentaries 2017-10-30 05:53:00-04:00 BBC One - Blue Planet II, Series 1, One Ocean ... nffDionysos 26170.0 2147.0 8.667500 0 10 21
99618 100.0 2017-10-29 23:42:53-04:00 73 OldSchoolCool 2017-10-30 05:53:00-04:00 Some spooky skeletons riding horses for Hallow... biranouk 18400.0 275.0 6.168611 0 10 23
99619 560.0 2017-10-29 20:46:52-04:00 74 OutOfTheLoop 2017-10-30 05:53:00-04:00 What is up with Gordon Ramsay and cocaine? realjohncenawwe 35748.0 17563.0 9.102222 0 10 20
99620 150.0 2017-10-29 18:26:17-04:00 75 calvinandhobbes 2017-10-30 05:53:00-04:00 I sure cant wait for these moment with my son Dopplesoldner1 5336.0 384.0 11.445278 0 10 18
99621 10851.0 2017-10-29 18:21:56-04:00 76 videos 2017-10-30 05:53:00-04:00 YouTube user demonstrating how Facebook listen... AnimalCrust 16710.0 38204.0 11.517778 1 10 18
99622 227.0 2017-10-30 02:11:44-04:00 77 gifs 2017-10-30 05:53:00-04:00 Girl using her head as table tennis racket 1Voice1Life 6476268.0 34958.0 3.687778 0 10 2
99623 236.0 2017-10-29 18:53:49-04:00 78 oldpeoplefacebook 2017-10-30 05:53:00-04:00 65 is a great day! assumetehposition 13906.0 23087.0 10.986389 0 10 18
99624 3288.0 2017-10-29 14:27:57-04:00 79 PoliticalHumor 2017-10-30 05:53:00-04:00 I'm sure Trump's administration won't add to t... 13704 15004.0 57097.0 15.417500 1 10 14
99625 125.0 2017-10-29 23:20:22-04:00 80 StartledCats 2017-10-30 05:53:00-04:00 Oh god, who let this thing in the house H_G_Bells 66271.0 3814.0 6.543889 0 10 23
99626 95.0 2017-10-29 22:40:05-04:00 81 StrangerThings 2017-10-30 05:53:00-04:00 I heard you can get 1000 upvotes if you post a... HephaestusXIII 8247.0 17661.0 7.215278 0 10 22
99627 195.0 2017-10-30 04:06:20-04:00 82 baseball 2017-10-30 05:53:00-04:00 [Haren] So thankful they juiced the ball after... Jacked1703 7196.0 18827.0 1.777778 0 10 4
99628 363.0 2017-10-30 05:39:43-04:00 83 baseball 2017-10-30 05:53:00-04:00 BREGMAN WALKS IT OFF! Meatprint 90554.0 11564.0 0.221389 0 10 5
99629 513.0 2017-10-29 16:10:14-04:00 84 technology 2017-10-30 05:53:00-04:00 Company Added the Word ‘Blockchain’ to Its Nam... kashevko 5703.0 2214.0 13.712778 0 10 16
99630 212.0 2017-10-29 18:19:39-04:00 85 StoppedWorking 2017-10-30 05:53:00-04:00 Puppy should have listened during geometry class GallowBoob 17536350.0 833693.0 11.555833 0 10 18
99631 176.0 2017-10-30 02:50:19-04:00 86 Whatcouldgowrong 2017-10-30 05:53:00-04:00 WCGW if I don't know what's lighter fluid? TheNutsCracker 497072.0 1326.0 3.044722 0 10 2
99632 299.0 2017-10-30 01:48:39-04:00 87 NintendoSwitch 2017-10-30 05:53:00-04:00 Wall Kicks (and Triple Jumps) Will Work [SMO] MatrixChicken 6217.0 4439.0 4.072500 0 10 1
99633 86.0 2017-10-29 23:01:13-04:00 88 EarthPorn 2017-10-30 05:53:00-04:00 Evening Mist Over Echo Lake, British Columbia,... sndon 6864.0 483.0 6.863056 0 10 23
99634 57.0 2017-10-29 18:41:33-04:00 89 BlackPeopleTwitter 2017-10-30 05:53:00-04:00 They made it happen wwf4ever 308181.0 69855.0 11.190833 0 10 18
99635 151.0 2017-10-30 03:52:14-04:00 90 baseball 2017-10-30 05:53:00-04:00 Springer ties it with a HR Meatprint 90554.0 11564.0 2.012778 0 10 3
99636 19.0 2017-10-30 02:37:56-04:00 91 BlackPeopleTwitter 2017-10-30 05:53:00-04:00 Aftermath of Cavs vs Knicks game 😔 ParadiseProd 43587.0 29131.0 3.251111 0 10 2
99637 145.0 2017-10-29 21:51:43-04:00 92 Justfuckmyshitup 2017-10-30 05:53:00-04:00 Landing strip Zaza9000 17319.0 2892.0 8.021389 0 10 21
99638 1164.0 2017-10-29 20:36:47-04:00 93 formula1 2017-10-30 05:53:00-04:00 Lewis Hamilton wins the 2017 Formula One World... F1-Bot 44022.0 2627.0 9.270278 1 10 20
99639 227.0 2017-10-29 18:03:30-04:00 94 pics 2017-10-30 05:53:00-04:00 Will Poulter dressed as Sid from Toy Story peace_among_worlds 5886.0 1411.0 11.825000 0 10 18
99640 45.0 2017-10-29 19:18:55-04:00 95 HistoryMemes 2017-10-30 05:53:00-04:00 kaiser Wilhelm ii (1917) hazembazem 7299.0 818.0 10.568056 0 10 19
99641 91.0 2017-10-29 19:54:17-04:00 96 funny 2017-10-30 05:53:00-04:00 My buddy was the United Airlines Passenger justophicles 5776.0 897.0 9.978611 0 10 19
99642 620.0 2017-10-29 15:34:36-04:00 97 aww 2017-10-30 05:53:00-04:00 This pic alone is worth the $9 I paid for this... deemandaniels 9383.0 344.0 14.306667 0 10 15
99643 116.0 2017-10-29 18:37:31-04:00 98 wholesomememes 2017-10-30 05:53:00-04:00 Every morning. CaptainAmeriHeck 8892.0 479.0 11.258056 0 10 18
99644 6594.0 2017-10-29 22:07:31-04:00 99 AskReddit 2017-10-30 05:53:00-04:00 What, in your opinion, is the single most beau... GiveMeAllYourRupees 31910.0 10035.0 7.758056 1 10 22

99645 rows × 13 columns

Thought experiment: What is the baseline accuracy for this model?

Baseline accuracy for this model will be if it can predict a 'viral' reddit post, definition of 'viral' being the top 15% comment count.

EDA

In [154]:
%matplotlib inline
In [150]:
full_data['age-in-hrs-int'] = full_data['age-in-hrs'].astype(int)
In [152]:
by_time = full_data.groupby('age-in-hrs-int')['comments'].mean().reset_index()
by_time.head()
Out[152]:
age-in-hrs-int comments
0 0 338.827723
1 1 377.472455
2 2 422.340780
3 3 474.231038
4 4 515.989068
In [170]:
by_time.plot(x='age-in-hrs-int', y='comments', kind='line', figsize = (10,10), fontsize=24, legend=14)
Out[170]:
<matplotlib.axes._subplots.AxesSubplot at 0x12b0dfe80>
In [209]:
by_subreddit = full_data.groupby('subreddit')['comments'].mean().reset_index()
pop_subreddits = by_subreddit.sort_values("comments", ascending=False)[0:10]
In [210]:
pop_subreddits.plot(x='subreddit', y='comments', kind='bar', figsize = (10,10), fontsize=24, legend=14)
Out[210]:
<matplotlib.axes._subplots.AxesSubplot at 0x122019e10>
In [160]:
subreddit_by_time = full_data.groupby(['subreddit', 'age-in-hrs-int'])['comments'].mean().reset_index()
subreddit_by_time.head()
Out[160]:
subreddit age-in-hrs-int comments
0 13or30 5 35.500000
1 2healthbars 1 11.000000
2 2healthbars 2 17.840000
3 2healthbars 3 37.107143
4 2healthbars 4 66.206897
In [163]:
#https://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html
subreddit_by_time_2 = pd.pivot_table(subreddit_by_time, values='comments', columns='subreddit', index='age-in-hrs-int')
subreddit_by_time_2.head()
Out[163]:
subreddit 13or30 2healthbars 2meirl4meirl 30ROCK 49ers 90DayFiance AFCON ANormalDayInRussia ATBGE AbandonedPorn ... writing wtfstockphotos xboxone xkcd xmen xxfitness youdontsurf youseeingthisshit youtubehaiku zelda
age-in-hrs-int
0 NaN NaN 63.596154 NaN 52.0 2063.0 NaN 28.461538 NaN NaN ... NaN NaN NaN NaN 8.0 NaN 7.000000 634.0 34.829268 NaN
1 NaN 11.000000 132.967742 NaN NaN 9.0 25.0 19.615385 NaN 86.0 ... 373.0 NaN NaN NaN 6.0 NaN 6.666667 NaN 71.333333 NaN
2 NaN 17.840000 179.562500 NaN NaN 212.0 NaN 10.571429 NaN 6.0 ... NaN NaN NaN NaN NaN NaN 4.000000 NaN 62.500000 NaN
3 NaN 37.107143 194.857143 NaN 5.0 NaN NaN 11.666667 142.000000 NaN ... NaN 5.0 NaN NaN 107.0 NaN 14.666667 NaN 223.333333 NaN
4 NaN 66.206897 210.766667 NaN NaN NaN NaN NaN 116.444444 68.0 ... NaN NaN 351.0 NaN NaN NaN 16.000000 NaN 189.500000 NaN

5 rows × 1051 columns

In [197]:
max_values = pd.DataFrame(subreddit_by_time_2.apply(lambda x: max(x.fillna(0)), axis=0), columns=['max_value'])
In [198]:
max_values.head()
Out[198]:
max_value
subreddit
13or30 35.500000
2healthbars 130.000000
2meirl4meirl 266.687500
30ROCK 21.333333
49ers 52.000000
In [199]:
max_values.sort_values(by='max_value', inplace=True, ascending=False)
In [200]:
max_values.head()
Out[200]:
max_value
subreddit
ImaginaryWitches 9919.000000
AskReddit 8850.000000
television 5452.457143
videos 5329.583333
Accounting 5142.000000
In [193]:
max_subreddits = list(max_values[:10].index)
In [201]:
subreddit_by_time_3 = subreddit_by_time_2[max_subreddits]
In [205]:
subreddit_by_time_3.plot(style='.-', figsize=(10,10), fontsize = 24, )
Out[205]:
<matplotlib.axes._subplots.AxesSubplot at 0x120ce2ef0>
In [220]:
unique_posts = full_data.drop_duplicates(subset='title')
pop_auth_unique_posts = unique_posts.groupby('user')['title'].count()
pop_auth_unique_posts.head()
Out[220]:
user
-N3ptun3-               10
-melodymelodymelody-     1
13704                    1
14chickc                 1
19djafoij02              1
Name: title, dtype: int64
In [232]:
pop_auth_1 = pd.DataFrame(pop_auth_unique_posts).sort_values("title", ascending = False)
In [235]:
len(pop_auth_1[pop_auth_1["title"] ==1])
Out[235]:
2273
In [236]:
len(pop_auth_1)
Out[236]:
2397
In [237]:
2273/2397
Out[237]:
0.9482686691697956
In [227]:
pop_auth_2 = pd.DataFrame(pop_auth_unique_posts).sort_values("title", ascending = False)[0:10]
In [230]:
pop_auth_2.plot(kind='bar', fontsize=24, figsize=(10,10))
Out[230]:
<matplotlib.axes._subplots.AxesSubplot at 0x12fd46cc0>
In [213]:
#ok, now to post the most popular author:
pop_auth = full_data.groupby(['user', 'post-karma','comment-karma', 'age-in-hrs-int'])['comments'].mean().reset_index()
In [215]:
full_data.head()
Out[215]:
comments date rank subreddit time_collected title user post-karma comment-karma age-in-hrs high-comments hour hour-posted age-in-hrs-int
0 441.0 2017-10-28 17:34:09-04:00 0 CrappyDesign 2017-10-28 19:41:00-04:00 Requires 3 AAA batteries, in the most annoying... Peef_Rimgar420 8560.0 325.0 2.114167 0 10 17 2
1 1859.0 2017-10-28 23:18:15-04:00 1 CFB 2017-10-28 19:41:00-04:00 [Postgame Thread] Ohio State Defeats Penn Stat... CFB_Referee 313858.0 2845.0 3.620833 1 10 23 3
2 711.0 2017-10-28 18:12:11-04:00 2 mildlyinteresting 2017-10-28 19:41:00-04:00 Paint-chipping making the wheelchair guy look ... Ribwichs 7018.0 61.0 1.480278 0 10 18 1
3 1249.0 2017-10-28 15:51:41-04:00 3 videos 2017-10-28 19:41:00-04:00 This guy made a video of various rappers count... floopykid 22034.0 38536.0 3.821944 1 10 15 3
4 168.0 2017-10-28 18:52:24-04:00 4 pics 2017-10-28 19:41:00-04:00 Lady at Yellowstone took our picture nowthatssomeusername 7001.0 64.0 0.810000 0 10 18 0
In [214]:
pop_auth
Out[214]:
user post-karma comment-karma age-in-hrs-int comments
0 -Mavoko- 12230.0 42473.0 7 20.000000
1 -N3ptun3- 611097.0 4930.0 0 64.796875
2 -N3ptun3- 611097.0 4930.0 1 149.011905
3 -N3ptun3- 611097.0 4930.0 2 289.250000
4 -N3ptun3- 611097.0 4930.0 3 334.954545
5 -N3ptun3- 611097.0 4930.0 4 344.862069
6 -N3ptun3- 611097.0 4930.0 5 336.611650
7 -N3ptun3- 611097.0 4930.0 6 347.864662
8 -N3ptun3- 611097.0 4930.0 7 352.454545
9 -N3ptun3- 611097.0 4930.0 8 381.621622
10 -N3ptun3- 611097.0 4930.0 9 467.505618
11 -N3ptun3- 611097.0 4930.0 10 637.073171
12 -N3ptun3- 611097.0 4930.0 11 442.272727
13 -N3ptun3- 611097.0 4930.0 12 374.333333
14 -N3ptun3- 611097.0 4930.0 13 254.000000
15 -N3ptun3- 611097.0 4930.0 14 493.000000
16 -N3ptun3- 611097.0 4930.0 15 250.000000
17 -N3ptun3- 611097.0 4930.0 17 484.000000
18 -melodymelodymelody- 1121.0 6868.0 6 17.000000
19 13704 15004.0 57097.0 0 524.603175
20 13704 15004.0 57097.0 1 906.613636
21 13704 15004.0 57097.0 2 1746.433333
22 13704 15004.0 57097.0 3 2149.700000
23 13704 15004.0 57097.0 4 2305.593750
24 13704 15004.0 57097.0 5 2536.000000
25 13704 15004.0 57097.0 6 2742.193548
26 13704 15004.0 57097.0 7 2877.733333
27 13704 15004.0 57097.0 8 2984.222222
28 13704 15004.0 57097.0 9 3063.592593
29 13704 15004.0 57097.0 10 3116.520000
... ... ... ... ... ...
7864 zibsha 6141.0 2307.0 9 497.111111
7865 zibsha 6141.0 2307.0 10 561.333333
7866 zibsha 6141.0 2307.0 11 602.500000
7867 zibsha 6141.0 2307.0 12 609.500000
7868 zibsha 6141.0 2307.0 13 611.000000
7869 zydeco 6766.0 5103.0 0 83.466667
7870 zydeco 6766.0 5103.0 1 132.000000
7871 zydeco 6766.0 5103.0 2 215.133333
7872 zydeco 6766.0 5103.0 3 383.468750
7873 zydeco 6766.0 5103.0 4 450.757576
7874 zydeco 6766.0 5103.0 5 410.562500
7875 zydeco 6766.0 5103.0 6 456.032258
7876 zydeco 6766.0 5103.0 7 491.321429
7877 zydeco 6766.0 5103.0 8 503.482759
7878 zydeco 6766.0 5103.0 9 517.360000
7879 zydeco 6766.0 5103.0 10 526.206897
7880 zydeco 6766.0 5103.0 11 537.468750
7881 zydeco 6766.0 5103.0 12 551.263158
7882 zydeco 6766.0 5103.0 13 564.000000
7883 zydeco 6766.0 5103.0 17 652.000000
7884 zyyp 49895.0 345.0 2 25.333333
7885 zyyp 49895.0 345.0 3 32.000000
7886 zyyp 49895.0 345.0 4 38.275862
7887 zyyp 49895.0 345.0 5 51.200000
7888 zyyp 49895.0 345.0 6 64.538462
7889 zyyp 49895.0 345.0 7 73.533333
7890 zyyp 49895.0 345.0 8 81.652174
7891 zyyp 49895.0 345.0 9 89.692308
7892 zyyp 49895.0 345.0 10 105.566667
7893 zyyp 49895.0 345.0 11 14.000000

7894 rows × 5 columns

Create a Random Forest model to predict High/Low number of comments using Sklearn. Start by ONLY using the subreddit as a feature.

In [238]:
## YOUR CODE HERE
from sklearn.preprocessing import LabelEncoder
y = LabelEncoder().fit_transform(full_data['high-comments'])
X = pd.get_dummies(full_data["subreddit"])
In [239]:
from sklearn.cross_validation import cross_val_score, StratifiedKFold
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier

cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=41)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
In [240]:
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3)))
Random Forest Score:	0.83 ± 0.001

it does pretty well just based on subreddit. It'd be interesting to include ...

  • hours since post (age)
  • user
  • and of course subreddit.
In [269]:
model_data = full_data[["high-comments", "post-karma", 'comment-karma', "age-in-hrs-int", "subreddit"]].dropna()
In [270]:
len(model_data)
Out[270]:
97261
In [271]:
## YOUR CODE HERE
from sklearn.preprocessing import LabelEncoder
y = model_data['high-comments']
X = pd.concat([model_data[["post-karma", 'comment-karma', "age-in-hrs-int"]], pd.get_dummies(model_data["subreddit"])]
               , axis=1)
In [272]:
X.head(1)
Out[272]:
post-karma comment-karma age-in-hrs-int 13or30 2healthbars 2meirl4meirl 30ROCK 49ers 90DayFiance AFCON ... writing wtfstockphotos xboxone xkcd xmen xxfitness youdontsurf youseeingthisshit youtubehaiku zelda
0 8560.0 325.0 2 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

1 rows × 1039 columns

In [273]:
cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=41)
In [274]:
rf = RandomForestClassifier(class_weight='balanced')
s = cross_val_score(rf, X, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Random Forest", s.mean().round(3), s.std().round(3)))
Random Forest Score:	0.979 ± 0.0
In [275]:
# once you've cv'd the model, train the whole data and get feature importances
rf.fit(X, y)
imp = pd.DataFrame({'importance': list(rf.feature_importances_)}, index=X.columns)
imp.sort_values(by='importance', ascending=False, inplace=True)
imp_top10 = imp.head(10)
imp_top10.sort_values(by='importance', ascending=True, inplace=True)
/Users/nmolivo/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [276]:
imp_top10.plot(kind='barh', fontsize=24, figsize=(10,10))
Out[276]:
<matplotlib.axes._subplots.AxesSubplot at 0x134c51a58>
In [253]:
full_data.loc[full_data['user']=='2martin4u', 'comments'].max()
Out[253]:
3232.0
In [277]:
from sklearn.linear_model import LogisticRegression
In [279]:
lg = LogisticRegression()
s = cross_val_score(lg, X, y, cv=cv, n_jobs=-1)
print("{} Score:\t{:0.3} ± {:0.3}".format("Logistic Regression"</