The idea behind this project is to help developers understand which type of apps are likely to attract more users.
There is a high chance that the most liked apps are also the ones that could lead to an increase in revenue.
To figure that out, I am exploring apps from Google Apps Store and the Apple Store.
The data used to create this evaluation can be found on Kaggle -> Google Store data set and Apple Store data set.
Without further ado, let's get started!
I'll begin by creating a function that helps print the rows and columns of a given data set. So that I can explore the data set I will be eventually importing.
def explore_data(dataset, start, end, rows_and_columns=False):
dataset_slice = dataset[start:end]
for row in dataset_slice:
print(row)
# Adds new line after a row
print('\n')
if rows_and_columns:
print("Number of rows:", len(dataset))
print("Number of columns:", len(dataset[0]))Now let's import the data we got from Kaggle, read it and store it as a list of lists. I'll store the header values separately.
from csv import reader
# The Google Play data set #
opened_file = open('googleplaystore.csv')
read_file = reader(opened_file)
android = list(read_file)
android_header = android[0]
android = android[1:]
# The App Store data set #
opened_file = open('AppleStore.csv')
read_file = reader(opened_file)
ios = list(read_file)
ios_header = ios[0]
ios = ios[1:]I want to explore our data by printing the number of rows, and the count of rows/column of each data set, to see what's in it.
I'll also print the headers separately to ensure we have stored the right information in the header.
explore_data(android, 0, 3)
explore_data(ios, 0, 3)
# Printing number of rows/columns for android apps in Google Store
row_an = len(android)
column_an = len(android[1])
print("Number of colums: ", column_an, " and rows ", row_an, " for Google store")
# Printing number of rows/columns for iOS apps in Apple Store
row_ios = len(ios)
column_ios = len(ios[1])
print("Number of colums: ", column_ios, " and rows ", row_ios, " for Apple store")
print('\n')
# Printing column names for android and ios apps
print(android_header)
print('\n')
print(ios_header)
['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['Coloring book moana', 'ART_AND_DESIGN', '3.9', '967', '14M', '500,000+', 'Free', '0', 'Everyone', 'Art & Design;Pretend Play', 'January 15, 2018', '2.0.0', '4.0.3 and up']
['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']
['284882215', 'Facebook', '389879808', 'USD', '0.0', '2974676', '212', '3.5', '3.5', '95.0', '4+', 'Social Networking', '37', '1', '29', '1']
['389801252', 'Instagram', '113954816', 'USD', '0.0', '2161558', '1289', '4.5', '4.0', '10.23', '12+', 'Photo & Video', '37', '0', '29', '1']
['529479190', 'Clash of Clans', '116476928', 'USD', '0.0', '2130805', '579', '4.5', '4.5', '9.24.12', '9+', 'Games', '38', '5', '18', '1']
Number of colums: 13 and rows 10841 for Google store
Number of colums: 16 and rows 7197 for Apple store
['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']
['id', 'track_name', 'size_bytes', 'currency', 'price', 'rating_count_tot', 'rating_count_ver', 'user_rating', 'user_rating_ver', 'ver', 'cont_rating', 'prime_genre', 'sup_devices.num', 'ipadSc_urls.num', 'lang.num', 'vpp_lic']
Some rows are incomplete, for example below we see the column of the rating comes as "19" which is wrong. So we need to clean our data by deleting that row to fix it.
# Rating comes 19 which is wrong
print(android[10472]) ['Life Made WI-Fi Touchscreen Photo Frame', '1.9', '19', '3.0M', '1,000+', 'Free', '0', 'Everyone', '', 'February 11, 2018', '1.0.19', '4.0 and up']
#We delete the wrong row and check previous and current count
print(len(android))
del android[10472]
print(len(android))10841
10840
The data has a number of duplicate entries so we would have to delete them. Look below for an example of duplicate rows. In the dataset for android apps we see facebook existing twice. The only difference appears in column with index 3 which shows the numbers of ratings. The lesser number indicates the data was collected earlier than than that with a larger rating count.
for app in android:
name = app[0]
if name == "Facebook":
print(app)['Facebook', 'SOCIAL', '4.1', '78158306', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'August 3, 2018', 'Varies with device', 'Varies with device']
['Facebook', 'SOCIAL', '4.1', '78128208', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'August 3, 2018', 'Varies with device', 'Varies with device']
I won't be removing the duplicates randomly. I will be removing only the ones with the lowest rating count number. That because, as said, indicates the data being older than that with a higher count.
reviews_max = {}
for app in android:
name = app[0]
n_reviews = float(app[3])
if name in reviews_max and (reviews_max[name] < n_reviews):
reviews_max[name] = n_reviews
if name not in reviews_max:
reviews_max[name]=n_reviews
print(len(reviews_max))9659
Now we'll begin cleaning the data from duplicates.
# Will store our clean data set here
android_clean = []
already_added=[]
for app in android:
name = app[0]
n_reviews = float(app[3])
if name in reviews_max and name not in already_added:
android_clean.append(app)
already_added.append(name)
print(len(android_clean))9659
Some apps are not English, we want to eventually remove them. Below I am creating a function which tells me if the given input is a valid English word or not.
# Function that checks if string is English
def belongs(word):
for char in word:
if ord(char) > 127:
return False
return TrueLet's check to see if our function can tell if a given input is or isn't an Eglish word.
belongs("Instagram")True
belongs("爱奇艺PPS -《欢乐颂2》电视剧热播")False
belongs("Docs To Go™ Free Office Suite")False
belongs("Instachat 😜")False
Our previous code has some issues. It thinks that things like emojis automatically make a word non English. To minimize such occurences, we will allow up to 3 non English characters to exist in a word before we qualify it as non-English.
That still doesn't make things perfect but it reduces the errors.
# Function that checks if string is English
def belongs(word):
count = 0
for char in word:
if ord(char) > 127:
count+=1
if count > 3:
return False
return Truebelongs("爱奇艺PPS -《欢乐颂2》电视剧热播")False
belongs("Docs To Go™ Free Office Suite")True
belongs("Instachat 😜")True
android_english = []
ios_english = []
for app in android_clean:
name = app[0]
if belongs(name):
android_english.append(app)
for app in ios:
name = app[1]
if belongs(name):
ios_english.append(app)
explore_data(android_english, 0, 3, True)
print('\n')
explore_data(ios_english, 0, 3, True)
['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['Coloring book moana', 'ART_AND_DESIGN', '3.9', '967', '14M', '500,000+', 'Free', '0', 'Everyone', 'Art & Design;Pretend Play', 'January 15, 2018', '2.0.0', '4.0.3 and up']
['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']
Number of rows: 9614
Number of columns: 13
['284882215', 'Facebook', '389879808', 'USD', '0.0', '2974676', '212', '3.5', '3.5', '95.0', '4+', 'Social Networking', '37', '1', '29', '1']
['389801252', 'Instagram', '113954816', 'USD', '0.0', '2161558', '1289', '4.5', '4.0', '10.23', '12+', 'Photo & Video', '37', '0', '29', '1']
['529479190', 'Clash of Clans', '116476928', 'USD', '0.0', '2130805', '579', '4.5', '4.5', '9.24.12', '9+', 'Games', '38', '5', '18', '1']
Number of rows: 6183
Number of columns: 16
Currently we have both paid and none paid apps in our data. We need to only get the free apps. So let's go ahead and fix that.
# Here I'll store the free android/ios apps
free_android = []
free_ios = []
# Checking if the price of the app equals to zero
for app in android_english:
price = app[7]
if price == '0':
free_android.append(app)
for app in ios_english:
price = app[4]
if price == '0.0':
free_ios.append(app)
explore_data(free_android, 0, 3, True)
print('\n')
explore_data(free_ios, 0, 3, True)['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']
['Coloring book moana', 'ART_AND_DESIGN', '3.9', '967', '14M', '500,000+', 'Free', '0', 'Everyone', 'Art & Design;Pretend Play', 'January 15, 2018', '2.0.0', '4.0.3 and up']
['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']
Number of rows: 8862
Number of columns: 13
['284882215', 'Facebook', '389879808', 'USD', '0.0', '2974676', '212', '3.5', '3.5', '95.0', '4+', 'Social Networking', '37', '1', '29', '1']
['389801252', 'Instagram', '113954816', 'USD', '0.0', '2161558', '1289', '4.5', '4.0', '10.23', '12+', 'Photo & Video', '37', '0', '29', '1']
['529479190', 'Clash of Clans', '116476928', 'USD', '0.0', '2130805', '579', '4.5', '4.5', '9.24.12', '9+', 'Games', '38', '5', '18', '1']
Number of rows: 3222
Number of columns: 16
Our aim is to determine the kinds of apps that are likely to attract more users because our revenue is highly influenced by the number of people using our apps. Because our end goal is to add the app on both Google Play and the App Store, we need to find app profiles that are successful on both markets.
def freq_table(dataset, index):
table = {}
total = 0
# If a value already exists in our table, we increase
# its count or else we create that value and assign
# it a count of 1
for row in dataset:
total += 1
value = row[index]
if value in table:
table[value] += 1
else:
table[value] = 1
table_percentages = {}
for key in table:
percentage = (table[key] / total) * 100
table_percentages[key] = percentage
return table_percentagesWe want to view our data as tuples so we'd convert our table into a list of tuples through the display_table function below.
def display_table(dataset,index):
table = freq_table(dataset, index)
table_display = []
for key in table:
key_val_as_tuple = (table[key], key)
table_display.append(key_val_as_tuple)
table_sorted = sorted(table_display, reverse = True)
for entry in table_sorted:
print(entry[1], ':', entry[0])display_table(free_android, 1)FAMILY : 18.449559918754233
GAME : 9.873617693522906
TOOLS : 8.440532611148726
BUSINESS : 4.5926427443015125
LIFESTYLE : 3.9043105393816293
PRODUCTIVITY : 3.8930264048747465
FINANCE : 3.7011961182577298
MEDICAL : 3.5206499661475967
SPORTS : 3.39652448657188
PERSONALIZATION : 3.3175355450236967
COMMUNICATION : 3.238546603475513
HEALTH_AND_FITNESS : 3.080568720379147
PHOTOGRAPHY : 2.945159106296547
NEWS_AND_MAGAZINES : 2.798465357707064
SOCIAL : 2.663055743624464
TRAVEL_AND_LOCAL : 2.335815842924848
SHOPPING : 2.2455427668697814
BOOKS_AND_REFERENCE : 2.143985556307831
DATING : 1.8618821936357481
VIDEO_PLAYERS : 1.782893252087565
MAPS_AND_NAVIGATION : 1.399232678853532
EDUCATION : 1.2863913337846988
FOOD_AND_DRINK : 1.2412547957571656
ENTERTAINMENT : 1.128413450688332
LIBRARIES_AND_DEMO : 0.9365831640713158
AUTO_AND_VEHICLES : 0.9252990295644324
HOUSE_AND_HOME : 0.8350259535093659
WEATHER : 0.8011735499887158
EVENTS : 0.7109004739336493
ART_AND_DESIGN : 0.6770480704129994
PARENTING : 0.6544798013992327
COMICS : 0.6206273978785828
BEAUTY : 0.598059128864816
# Frequency table of genre
freq_table(free_android, 9) {'Art & Design': 0.598059128864816,
'Art & Design;Pretend Play': 0.011284134506883321,
'Art & Design;Creativity': 0.06770480704129993,
'Art & Design;Action & Adventure': 0.011284134506883321,
'Auto & Vehicles': 0.9252990295644324,
'Beauty': 0.598059128864816,
'Books & Reference': 2.143985556307831,
'Business': 4.5926427443015125,
'Comics': 0.6093432633716994,
'Comics;Creativity': 0.011284134506883321,
'Communication': 3.238546603475513,
'Dating': 1.8618821936357481,
'Education;Education': 0.3385240352064997,
'Education': 5.348679756262695,
'Education;Creativity': 0.045136538027533285,
'Education;Music & Video': 0.033852403520649964,
'Education;Action & Adventure': 0.033852403520649964,
'Education;Pretend Play': 0.056420672534416606,
'Education;Brain Games': 0.033852403520649964,
'Entertainment': 6.070864364703228,
'Entertainment;Music & Video': 0.16926201760324985,
'Entertainment;Brain Games': 0.07898894154818326,
'Entertainment;Creativity': 0.033852403520649964,
'Events': 0.7109004739336493,
'Finance': 3.7011961182577298,
'Food & Drink': 1.2412547957571656,
'Health & Fitness': 3.080568720379147,
'House & Home': 0.8350259535093659,
'Libraries & Demo': 0.9365831640713158,
'Lifestyle': 3.8930264048747465,
'Lifestyle;Pretend Play': 0.011284134506883321,
'Adventure;Action & Adventure': 0.033852403520649964,
'Arcade': 1.8505980591288649,
'Casual': 1.7603249830737984,
'Card': 0.45136538027533285,
'Casual;Pretend Play': 0.23696682464454977,
'Action': 3.1031369893929135,
'Strategy': 0.9140148950575491,
'Puzzle': 1.128413450688332,
'Sports': 3.4642292936131795,
'Music': 0.2031144211238998,
'Word': 0.2595350936583164,
'Racing': 0.9930038366057323,
'Casual;Creativity': 0.06770480704129993,
'Casual;Action & Adventure': 0.13540961408259986,
'Simulation': 2.0424283457458814,
'Adventure': 0.6770480704129994,
'Board': 0.3723764387271496,
'Trivia': 0.41751297675468296,
'Role Playing': 0.9365831640713158,
'Action;Action & Adventure': 0.1015572105619499,
'Casual;Brain Games': 0.13540961408259986,
'Simulation;Action & Adventure': 0.07898894154818326,
'Educational;Creativity': 0.033852403520649964,
'Puzzle;Brain Games': 0.16926201760324985,
'Educational;Education': 0.3949447077409162,
'Educational;Brain Games': 0.06770480704129993,
'Educational;Pretend Play': 0.09027307605506657,
'Entertainment;Education': 0.011284134506883321,
'Casual;Education': 0.022568269013766643,
'Music;Music & Video': 0.022568269013766643,
'Racing;Action & Adventure': 0.16926201760324985,
'Arcade;Pretend Play': 0.011284134506883321,
'Role Playing;Action & Adventure': 0.033852403520649964,
'Simulation;Pretend Play': 0.022568269013766643,
'Puzzle;Creativity': 0.022568269013766643,
'Sports;Action & Adventure': 0.022568269013766643,
'Educational;Action & Adventure': 0.033852403520649964,
'Arcade;Action & Adventure': 0.12412547957571654,
'Entertainment;Action & Adventure': 0.033852403520649964,
'Puzzle;Action & Adventure': 0.033852403520649964,
'Strategy;Action & Adventure': 0.011284134506883321,
'Music & Audio;Music & Video': 0.011284134506883321,
'Health & Fitness;Education': 0.011284134506883321,
'Adventure;Education': 0.011284134506883321,
'Board;Brain Games': 0.09027307605506657,
'Board;Action & Adventure': 0.022568269013766643,
'Casual;Music & Video': 0.011284134506883321,
'Role Playing;Pretend Play': 0.045136538027533285,
'Entertainment;Pretend Play': 0.022568269013766643,
'Video Players & Editors;Creativity': 0.011284134506883321,
'Medical': 3.5206499661475967,
'Social': 2.663055743624464,
'Shopping': 2.2455427668697814,
'Photography': 2.945159106296547,
'Travel & Local': 2.324531708417964,
'Travel & Local;Action & Adventure': 0.011284134506883321,
'Tools': 8.429248476641842,
'Tools;Education': 0.011284134506883321,
'Personalization': 3.3175355450236967,
'Productivity': 3.8930264048747465,
'Parenting': 0.49650191830286616,
'Parenting;Music & Video': 0.06770480704129993,
'Parenting;Education': 0.07898894154818326,
'Parenting;Brain Games': 0.011284134506883321,
'Weather': 0.8011735499887158,
'Video Players & Editors': 1.7716091175806816,
'Video Players & Editors;Music & Video': 0.022568269013766643,
'News & Magazines': 2.798465357707064,
'Maps & Navigation': 1.399232678853532,
'Health & Fitness;Action & Adventure': 0.011284134506883321,
'Educational': 0.3723764387271496,
'Casino': 0.4287971112615662,
'Trivia;Education': 0.011284134506883321,
'Lifestyle;Education': 0.011284134506883321,
'Card;Action & Adventure': 0.011284134506883321,
'Books & Reference;Education': 0.011284134506883321,
'Simulation;Education': 0.011284134506883321,
'Puzzle;Education': 0.011284134506883321,
'Role Playing;Brain Games': 0.011284134506883321,
'Strategy;Education': 0.011284134506883321,
'Racing;Pretend Play': 0.011284134506883321,
'Communication;Creativity': 0.011284134506883321,
'Strategy;Creativity': 0.011284134506883321}
# Frequency table in category
display_table(free_android,1) FAMILY : 18.449559918754233
GAME : 9.873617693522906
TOOLS : 8.440532611148726
BUSINESS : 4.5926427443015125
LIFESTYLE : 3.9043105393816293
PRODUCTIVITY : 3.8930264048747465
FINANCE : 3.7011961182577298
MEDICAL : 3.5206499661475967
SPORTS : 3.39652448657188
PERSONALIZATION : 3.3175355450236967
COMMUNICATION : 3.238546603475513
HEALTH_AND_FITNESS : 3.080568720379147
PHOTOGRAPHY : 2.945159106296547
NEWS_AND_MAGAZINES : 2.798465357707064
SOCIAL : 2.663055743624464
TRAVEL_AND_LOCAL : 2.335815842924848
SHOPPING : 2.2455427668697814
BOOKS_AND_REFERENCE : 2.143985556307831
DATING : 1.8618821936357481
VIDEO_PLAYERS : 1.782893252087565
MAPS_AND_NAVIGATION : 1.399232678853532
EDUCATION : 1.2863913337846988
FOOD_AND_DRINK : 1.2412547957571656
ENTERTAINMENT : 1.128413450688332
LIBRARIES_AND_DEMO : 0.9365831640713158
AUTO_AND_VEHICLES : 0.9252990295644324
HOUSE_AND_HOME : 0.8350259535093659
WEATHER : 0.8011735499887158
EVENTS : 0.7109004739336493
ART_AND_DESIGN : 0.6770480704129994
PARENTING : 0.6544798013992327
COMICS : 0.6206273978785828
BEAUTY : 0.598059128864816
# Frequency table for prime genre
display_table(free_ios,11) Games : 58.16263190564867
Entertainment : 7.883302296710118
Photo & Video : 4.9658597144630665
Education : 3.662321539416512
Social Networking : 3.2898820608317814
Shopping : 2.60707635009311
Utilities : 2.5139664804469275
Sports : 2.1415270018621975
Music : 2.0484171322160147
Health & Fitness : 2.0173805090006205
Productivity : 1.7380509000620732
Lifestyle : 1.5828677839851024
News : 1.3345747982619491
Travel : 1.2414649286157666
Finance : 1.1173184357541899
Weather : 0.8690254500310366
Food & Drink : 0.8069522036002483
Reference : 0.5586592178770949
Business : 0.5276225946617008
Book : 0.4345127250155183
Navigation : 0.186219739292365
Medical : 0.186219739292365
Catalogs : 0.12414649286157665
Now, I'm looking to find what genres are the most popular (have the most users) by calculating the average number of installs for each app genre. Let's start with the ios apps and procees to the android ones.
# Below we are using the genre table
ios_prime_genre = freq_table(free_ios, 11)
for genre in ios_prime_genre:
total = 0
len_genre = 0
for app in free_ios:
genre_app = app[11]
if genre_app == genre:
total += float(app[5])
len_genre += 1
avg_total = total/len_genre
print(genre, ':', avg_total)Social Networking : 71548.34905660378
Photo & Video : 28441.54375
Games : 22788.6696905016
Music : 57326.530303030304
Reference : 74942.11111111111
Health & Fitness : 23298.015384615384
Weather : 52279.892857142855
Utilities : 18684.456790123455
Travel : 28243.8
Shopping : 26919.690476190477
News : 21248.023255813954
Navigation : 86090.33333333333
Lifestyle : 16485.764705882353
Entertainment : 14029.830708661417
Food & Drink : 33333.92307692308
Sports : 23008.898550724636
Book : 39758.5
Finance : 31467.944444444445
Education : 7003.983050847458
Productivity : 21028.410714285714
Business : 7491.117647058823
Catalogs : 4004.0
Medical : 612.0
The recommended ios app profile for profitability is the Navigation section.
Now let's calculate the average number of installs per app genre for the Google Play data set.
display_table(free_android,1)FAMILY : 18.449559918754233
GAME : 9.873617693522906
TOOLS : 8.440532611148726
BUSINESS : 4.5926427443015125
LIFESTYLE : 3.9043105393816293
PRODUCTIVITY : 3.8930264048747465
FINANCE : 3.7011961182577298
MEDICAL : 3.5206499661475967
SPORTS : 3.39652448657188
PERSONALIZATION : 3.3175355450236967
COMMUNICATION : 3.238546603475513
HEALTH_AND_FITNESS : 3.080568720379147
PHOTOGRAPHY : 2.945159106296547
NEWS_AND_MAGAZINES : 2.798465357707064
SOCIAL : 2.663055743624464
TRAVEL_AND_LOCAL : 2.335815842924848
SHOPPING : 2.2455427668697814
BOOKS_AND_REFERENCE : 2.143985556307831
DATING : 1.8618821936357481
VIDEO_PLAYERS : 1.782893252087565
MAPS_AND_NAVIGATION : 1.399232678853532
EDUCATION : 1.2863913337846988
FOOD_AND_DRINK : 1.2412547957571656
ENTERTAINMENT : 1.128413450688332
LIBRARIES_AND_DEMO : 0.9365831640713158
AUTO_AND_VEHICLES : 0.9252990295644324
HOUSE_AND_HOME : 0.8350259535093659
WEATHER : 0.8011735499887158
EVENTS : 0.7109004739336493
ART_AND_DESIGN : 0.6770480704129994
PARENTING : 0.6544798013992327
COMICS : 0.6206273978785828
BEAUTY : 0.598059128864816
# Below we are using the category column
android_categ = freq_table(free_android,1)
for category in android_categ:
total = 0
len_category = 0
for app in free_android:
category_app = app[1]
if category_app == category:
num_installs = app[5]
num_installs = num_installs.replace("+","")
num_installs = num_installs.replace(",","")
total += float(num_installs)
len_category += 1
avg_total_and = total/len_category
print(category, ':', avg_total_and)
ART_AND_DESIGN : 1905351.6666666667
AUTO_AND_VEHICLES : 647317.8170731707
BEAUTY : 513151.88679245283
BOOKS_AND_REFERENCE : 8767811.894736841
BUSINESS : 1712290.1474201474
COMICS : 817657.2727272727
COMMUNICATION : 38456119.167247385
DATING : 854028.8303030303
EDUCATION : 3082017.543859649
ENTERTAINMENT : 21134600.0
EVENTS : 253542.22222222222
FINANCE : 1387692.475609756
FOOD_AND_DRINK : 1924897.7363636363
HEALTH_AND_FITNESS : 4188821.9853479853
HOUSE_AND_HOME : 1313681.9054054054
LIBRARIES_AND_DEMO : 638503.734939759
LIFESTYLE : 1437816.2687861272
GAME : 15837565.085714286
FAMILY : 2691618.159021407
MEDICAL : 120616.48717948717
SOCIAL : 23253652.127118643
SHOPPING : 7036877.311557789
PHOTOGRAPHY : 17805627.643678162
SPORTS : 3638640.1428571427
TRAVEL_AND_LOCAL : 13984077.710144928
TOOLS : 10695245.286096256
PERSONALIZATION : 5201482.6122448975
PRODUCTIVITY : 16787331.344927534
PARENTING : 542603.6206896552
WEATHER : 5074486.197183099
VIDEO_PLAYERS : 24852732.40506329
NEWS_AND_MAGAZINES : 9549178.467741935
MAPS_AND_NAVIGATION : 4056941.7741935486
The recommended Google app store profile for profitability is the COMMUNICATION section.
In this project, I analyzed data from the App Store and Google Play mobile apps with the goal of recommending an app profile that can be profitable for both markets.
In this occassion "Navigation" apps seemed to be more profitable in the Apple store while "Communication" apps in the Android stores.
ipynb code license: MPL 2.0