path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
105194628/cell_3 | [
"text_plain_output_1.png"
] | place = input('1. Hill Station, 2. Beach Enter he place you would like to visit')
if place == '1' or 'Hill Station':
print('Let us! Plan our trip to Hill Station now')
elif place == '2' or 'Beach':
print('Let us pack our bags for the Sun View') | code |
105194628/cell_5 | [
"text_plain_output_1.png"
] | ask = input('We are planning to go to vist an Hill Station or a temple would you like to join us? ')
if ask == 'Yes' or ask == 'yes':
ask2 = input('Do you mind to pick a place for our visit, We are thinking about Beach and Temple ')
if ask2 == 'Beach' or ask2 == 'beach':
print('Let us pack our bags for the best road trip to vitness the beautiful Sun View')
elif ask2 == 'Temple' or ask2 == 'temple':
print('Let us witness the glorious arcitecture of the temple')
else:
print('We will try knowing others opinion before picking it up')
else:
print('We will be looking ahead for you to join us') | code |
74050036/cell_13 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns) | code |
74050036/cell_25 | [
"text_html_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns)
df.head() | code |
74050036/cell_34 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns)
df['complaint_what_happened'].replace('', np.nan, inplace=True)
df.dropna(subset=['complaint_what_happened'], inplace=True)
len(df[df['complaint_what_happened'] == ''])
df.shape
df['complaint_what_happened'].head() | code |
74050036/cell_23 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns) | code |
74050036/cell_30 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns)
df['complaint_what_happened'].replace('', np.nan, inplace=True)
df.dropna(subset=['complaint_what_happened'], inplace=True)
len(df[df['complaint_what_happened'] == ''])
df.shape | code |
74050036/cell_29 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns)
df['complaint_what_happened'].replace('', np.nan, inplace=True)
df.dropna(subset=['complaint_what_happened'], inplace=True)
len(df[df['complaint_what_happened'] == '']) | code |
74050036/cell_11 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape | code |
74050036/cell_19 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0) | code |
74050036/cell_15 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.info() | code |
74050036/cell_17 | [
"text_html_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.describe() | code |
74050036/cell_24 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns) | code |
74050036/cell_10 | [
"text_html_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.head() | code |
74050036/cell_27 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns)
len(df[df['complaint_what_happened'] == '']) | code |
74050036/cell_36 | [
"text_html_output_1.png"
] | import json
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
f = open('../input/automatic-ticket-classification-data/complaints-2021-05-14_08_16.json')
data = json.load(f)
df = pd.json_normalize(data)
df.shape
list(df.columns)
df.isna().sum(0)
df.columns = [re.sub('^_', '', col) for col in df.columns]
list(df.columns)
df.columns = [re.sub('^\\bsource\\b\\.', '', col) for col in df.columns]
list(df.columns)
df['complaint_what_happened'].replace('', np.nan, inplace=True)
df.dropna(subset=['complaint_what_happened'], inplace=True)
len(df[df['complaint_what_happened'] == ''])
df.shape
def clean_text(text):
"""This function
- makes the given text lowercase
- removes text in square brackets
- removes punctuation and
- removes words containing numbers.
:param text: text to be cleaned
:return: cleaned text
"""
text = text.lower()
text = re.sub('\\[.*?\\]', '', text)
text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
text = re.sub('\\w*\\d\\w*', '', text)
return text
df_clean = pd.DataFrame(df['complaint_what_happened'].apply(lambda x: clean_text(x)))
df_clean.head() | code |
34125586/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import requests
import json
import pandas as pd
import time
import plotly
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34125586/cell_18 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url += summoner_name + '?api_key=' + api_key
data = requests.get(url).json()
id = data['accountId']
url = 'https://na1.api.riotgames.com/lol/match/v4/matchlists/by-account/'
url += id + '?api_key=' + api_key
data = requests.get(url).json()
game_IDs = [x['gameId'] for x in data['matches']]
list_of_games = []
for game_id in game_IDs:
url = 'https://na1.api.riotgames.com/lol/match/v4/matches/'
url += str(game_id) + '?api_key=' + api_key
list_of_games.append(requests.get(url).json())
open('Output.txt', 'w').close()
with open('Output.txt', 'w', encoding='utf-8') as f:
for game in list_of_games:
f.write('%s\n' % json.dumps(game))
def read_text_file():
with open('/kaggle/input/leaguedata/Output.txt') as f:
content = f.readlines()
return [json.loads(x) for x in content]
def return_participant_ID(dict, name):
for x in dict['participantIdentities']:
if x['player']['summonerName'] == name:
return x['participantId']
def get_stats(dict, ID):
for x in dict['participants']:
if x['participantId'] == ID:
stat_dict = x['stats']
stat_dict['championId'] = x['championId']
stat_dict['spell1Id'] = x['spell1Id']
stat_dict['spell2Id'] = x['spell2Id']
stat_dict['game mode'] = dict['gameMode']
stat_dict['gameDuration'] = dict['gameDuration']
stat_dict['role'] = x['timeline']['role']
stat_dict['lane'] = x['timeline']['lane']
team_pos = 0
if x['teamId'] == 200:
team_pos = 1
stat_dict['teamfirstTower'] = dict['teams'][team_pos]['firstTower']
stat_dict['teamfirstDragon'] = dict['teams'][team_pos]['firstDragon']
stat_dict['teamfirstBaron'] = dict['teams'][team_pos]['firstBaron']
return stat_dict
def get_champ_dict():
data = open('/kaggle/input/leaguedata/Champion Ids.txt', 'r', encoding='utf-8').read()
data = json.loads(data)
champions = [x for x in data['data']]
dict = {int(data['data'][x]['key']): str(x) for x in champions}
return dict
data = read_text_file()
list_of_df = []
for game in data:
if 'status' not in game:
id = return_participant_ID(game, name)
stats = get_stats(game, id)
list_of_df.append(pd.DataFrame(stats, index=[0]))
final_df = pd.concat(list_of_df).reset_index(drop=True)
final_df['championId'] = final_df['championId'].apply(lambda x: get_champ_dict()[x].strip())
final_df.rename(columns={'championId': 'Champion'}, inplace=True)
champ_list = final_df["Champion"].value_counts().iloc[0:10].index.to_list()
champ_df = final_df[final_df["Champion"].isin(champ_list)]
sns.set(style="darkgrid")
ax = sns.countplot(x="Champion", data=champ_df, order = champ_list)
ax.set_xticklabels(labels = champ_list, rotation=50)
ax.set_title("Tamoor's 10 Most Played Champions")
plt.show()
labels = ["Win", "Loss"]
counts = [len(final_df[final_df["win"]==True]),len(final_df[final_df["win"]==False])]
fig1, ax1 = plt.subplots()
ax1.pie(counts, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Win/Loss Percentage")
plt.show()
jungle_df = final_df[final_df["lane"]=="JUNGLE"]
# jungle_df["teamfirstDragon"].value_counts()
ax = sns.countplot(x="teamfirstDragon", data=jungle_df)
ax.set_title("Tamoor's Team Obtained the First Dragon")
plt.show()
jungle_df.loc[(jungle_df['totalDamageDealtToChampions'] < 8000) & (jungle_df['gameDuration'] > 1920), 'Champion'].to_string(index=False) | code |
34125586/cell_8 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url += summoner_name + '?api_key=' + api_key
data = requests.get(url).json()
id = data['accountId']
url = 'https://na1.api.riotgames.com/lol/match/v4/matchlists/by-account/'
url += id + '?api_key=' + api_key
data = requests.get(url).json()
game_IDs = [x['gameId'] for x in data['matches']]
list_of_games = []
for game_id in game_IDs:
url = 'https://na1.api.riotgames.com/lol/match/v4/matches/'
url += str(game_id) + '?api_key=' + api_key
list_of_games.append(requests.get(url).json())
open('Output.txt', 'w').close()
with open('Output.txt', 'w', encoding='utf-8') as f:
for game in list_of_games:
f.write('%s\n' % json.dumps(game))
def read_text_file():
with open('/kaggle/input/leaguedata/Output.txt') as f:
content = f.readlines()
return [json.loads(x) for x in content]
def return_participant_ID(dict, name):
for x in dict['participantIdentities']:
if x['player']['summonerName'] == name:
return x['participantId']
def get_stats(dict, ID):
for x in dict['participants']:
if x['participantId'] == ID:
stat_dict = x['stats']
stat_dict['championId'] = x['championId']
stat_dict['spell1Id'] = x['spell1Id']
stat_dict['spell2Id'] = x['spell2Id']
stat_dict['game mode'] = dict['gameMode']
stat_dict['gameDuration'] = dict['gameDuration']
stat_dict['role'] = x['timeline']['role']
stat_dict['lane'] = x['timeline']['lane']
team_pos = 0
if x['teamId'] == 200:
team_pos = 1
stat_dict['teamfirstTower'] = dict['teams'][team_pos]['firstTower']
stat_dict['teamfirstDragon'] = dict['teams'][team_pos]['firstDragon']
stat_dict['teamfirstBaron'] = dict['teams'][team_pos]['firstBaron']
return stat_dict
def get_champ_dict():
data = open('/kaggle/input/leaguedata/Champion Ids.txt', 'r', encoding='utf-8').read()
data = json.loads(data)
champions = [x for x in data['data']]
dict = {int(data['data'][x]['key']): str(x) for x in champions}
return dict
data = read_text_file()
list_of_df = []
for game in data:
if 'status' not in game:
id = return_participant_ID(game, name)
stats = get_stats(game, id)
list_of_df.append(pd.DataFrame(stats, index=[0]))
final_df = pd.concat(list_of_df).reset_index(drop=True)
final_df['championId'] = final_df['championId'].apply(lambda x: get_champ_dict()[x].strip())
final_df.rename(columns={'championId': 'Champion'}, inplace=True)
print('Number of Double Kills: {}'.format(final_df['doubleKills'].sum()))
print('Number of Triple Kills: {}'.format(final_df['tripleKills'].sum()))
print('Number of Quadra Kills: {}'.format(final_df['quadraKills'].sum()))
print('Number of Penta Kills: {}'.format(final_df['pentaKills'].sum())) | code |
34125586/cell_16 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url += summoner_name + '?api_key=' + api_key
data = requests.get(url).json()
id = data['accountId']
url = 'https://na1.api.riotgames.com/lol/match/v4/matchlists/by-account/'
url += id + '?api_key=' + api_key
data = requests.get(url).json()
game_IDs = [x['gameId'] for x in data['matches']]
list_of_games = []
for game_id in game_IDs:
url = 'https://na1.api.riotgames.com/lol/match/v4/matches/'
url += str(game_id) + '?api_key=' + api_key
list_of_games.append(requests.get(url).json())
open('Output.txt', 'w').close()
with open('Output.txt', 'w', encoding='utf-8') as f:
for game in list_of_games:
f.write('%s\n' % json.dumps(game))
def read_text_file():
with open('/kaggle/input/leaguedata/Output.txt') as f:
content = f.readlines()
return [json.loads(x) for x in content]
def return_participant_ID(dict, name):
for x in dict['participantIdentities']:
if x['player']['summonerName'] == name:
return x['participantId']
def get_stats(dict, ID):
for x in dict['participants']:
if x['participantId'] == ID:
stat_dict = x['stats']
stat_dict['championId'] = x['championId']
stat_dict['spell1Id'] = x['spell1Id']
stat_dict['spell2Id'] = x['spell2Id']
stat_dict['game mode'] = dict['gameMode']
stat_dict['gameDuration'] = dict['gameDuration']
stat_dict['role'] = x['timeline']['role']
stat_dict['lane'] = x['timeline']['lane']
team_pos = 0
if x['teamId'] == 200:
team_pos = 1
stat_dict['teamfirstTower'] = dict['teams'][team_pos]['firstTower']
stat_dict['teamfirstDragon'] = dict['teams'][team_pos]['firstDragon']
stat_dict['teamfirstBaron'] = dict['teams'][team_pos]['firstBaron']
return stat_dict
def get_champ_dict():
data = open('/kaggle/input/leaguedata/Champion Ids.txt', 'r', encoding='utf-8').read()
data = json.loads(data)
champions = [x for x in data['data']]
dict = {int(data['data'][x]['key']): str(x) for x in champions}
return dict
data = read_text_file()
list_of_df = []
for game in data:
if 'status' not in game:
id = return_participant_ID(game, name)
stats = get_stats(game, id)
list_of_df.append(pd.DataFrame(stats, index=[0]))
final_df = pd.concat(list_of_df).reset_index(drop=True)
final_df['championId'] = final_df['championId'].apply(lambda x: get_champ_dict()[x].strip())
final_df.rename(columns={'championId': 'Champion'}, inplace=True)
champ_list = final_df["Champion"].value_counts().iloc[0:10].index.to_list()
champ_df = final_df[final_df["Champion"].isin(champ_list)]
sns.set(style="darkgrid")
ax = sns.countplot(x="Champion", data=champ_df, order = champ_list)
ax.set_xticklabels(labels = champ_list, rotation=50)
ax.set_title("Tamoor's 10 Most Played Champions")
plt.show()
labels = ["Win", "Loss"]
counts = [len(final_df[final_df["win"]==True]),len(final_df[final_df["win"]==False])]
fig1, ax1 = plt.subplots()
ax1.pie(counts, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Win/Loss Percentage")
plt.show()
jungle_df = final_df[final_df["lane"]=="JUNGLE"]
# jungle_df["teamfirstDragon"].value_counts()
ax = sns.countplot(x="teamfirstDragon", data=jungle_df)
ax.set_title("Tamoor's Team Obtained the First Dragon")
plt.show()
ax = sns.scatterplot(x='gameDuration', y='totalDamageDealtToChampions', data=jungle_df)
ax.set_title('Champion Damage vs. Game Duration')
ax.set(xlabel='Game Duration (in minutes)', ylabel='Total Damage to Champs')
ax.set_xticklabels([round(x / 60, 0) for x in ax.get_xticks()])
plt.show() | code |
34125586/cell_14 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url += summoner_name + '?api_key=' + api_key
data = requests.get(url).json()
id = data['accountId']
url = 'https://na1.api.riotgames.com/lol/match/v4/matchlists/by-account/'
url += id + '?api_key=' + api_key
data = requests.get(url).json()
game_IDs = [x['gameId'] for x in data['matches']]
list_of_games = []
for game_id in game_IDs:
url = 'https://na1.api.riotgames.com/lol/match/v4/matches/'
url += str(game_id) + '?api_key=' + api_key
list_of_games.append(requests.get(url).json())
open('Output.txt', 'w').close()
with open('Output.txt', 'w', encoding='utf-8') as f:
for game in list_of_games:
f.write('%s\n' % json.dumps(game))
def read_text_file():
with open('/kaggle/input/leaguedata/Output.txt') as f:
content = f.readlines()
return [json.loads(x) for x in content]
def return_participant_ID(dict, name):
for x in dict['participantIdentities']:
if x['player']['summonerName'] == name:
return x['participantId']
def get_stats(dict, ID):
for x in dict['participants']:
if x['participantId'] == ID:
stat_dict = x['stats']
stat_dict['championId'] = x['championId']
stat_dict['spell1Id'] = x['spell1Id']
stat_dict['spell2Id'] = x['spell2Id']
stat_dict['game mode'] = dict['gameMode']
stat_dict['gameDuration'] = dict['gameDuration']
stat_dict['role'] = x['timeline']['role']
stat_dict['lane'] = x['timeline']['lane']
team_pos = 0
if x['teamId'] == 200:
team_pos = 1
stat_dict['teamfirstTower'] = dict['teams'][team_pos]['firstTower']
stat_dict['teamfirstDragon'] = dict['teams'][team_pos]['firstDragon']
stat_dict['teamfirstBaron'] = dict['teams'][team_pos]['firstBaron']
return stat_dict
def get_champ_dict():
data = open('/kaggle/input/leaguedata/Champion Ids.txt', 'r', encoding='utf-8').read()
data = json.loads(data)
champions = [x for x in data['data']]
dict = {int(data['data'][x]['key']): str(x) for x in champions}
return dict
data = read_text_file()
list_of_df = []
for game in data:
if 'status' not in game:
id = return_participant_ID(game, name)
stats = get_stats(game, id)
list_of_df.append(pd.DataFrame(stats, index=[0]))
final_df = pd.concat(list_of_df).reset_index(drop=True)
final_df['championId'] = final_df['championId'].apply(lambda x: get_champ_dict()[x].strip())
final_df.rename(columns={'championId': 'Champion'}, inplace=True)
champ_list = final_df["Champion"].value_counts().iloc[0:10].index.to_list()
champ_df = final_df[final_df["Champion"].isin(champ_list)]
sns.set(style="darkgrid")
ax = sns.countplot(x="Champion", data=champ_df, order = champ_list)
ax.set_xticklabels(labels = champ_list, rotation=50)
ax.set_title("Tamoor's 10 Most Played Champions")
plt.show()
labels = ["Win", "Loss"]
counts = [len(final_df[final_df["win"]==True]),len(final_df[final_df["win"]==False])]
fig1, ax1 = plt.subplots()
ax1.pie(counts, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
ax1.set_title("Win/Loss Percentage")
plt.show()
jungle_df = final_df[final_df['lane'] == 'JUNGLE']
ax = sns.countplot(x='teamfirstDragon', data=jungle_df)
ax.set_title("Tamoor's Team Obtained the First Dragon")
plt.show() | code |
34125586/cell_10 | [
"text_plain_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url += summoner_name + '?api_key=' + api_key
data = requests.get(url).json()
id = data['accountId']
url = 'https://na1.api.riotgames.com/lol/match/v4/matchlists/by-account/'
url += id + '?api_key=' + api_key
data = requests.get(url).json()
game_IDs = [x['gameId'] for x in data['matches']]
list_of_games = []
for game_id in game_IDs:
url = 'https://na1.api.riotgames.com/lol/match/v4/matches/'
url += str(game_id) + '?api_key=' + api_key
list_of_games.append(requests.get(url).json())
open('Output.txt', 'w').close()
with open('Output.txt', 'w', encoding='utf-8') as f:
for game in list_of_games:
f.write('%s\n' % json.dumps(game))
def read_text_file():
with open('/kaggle/input/leaguedata/Output.txt') as f:
content = f.readlines()
return [json.loads(x) for x in content]
def return_participant_ID(dict, name):
for x in dict['participantIdentities']:
if x['player']['summonerName'] == name:
return x['participantId']
def get_stats(dict, ID):
for x in dict['participants']:
if x['participantId'] == ID:
stat_dict = x['stats']
stat_dict['championId'] = x['championId']
stat_dict['spell1Id'] = x['spell1Id']
stat_dict['spell2Id'] = x['spell2Id']
stat_dict['game mode'] = dict['gameMode']
stat_dict['gameDuration'] = dict['gameDuration']
stat_dict['role'] = x['timeline']['role']
stat_dict['lane'] = x['timeline']['lane']
team_pos = 0
if x['teamId'] == 200:
team_pos = 1
stat_dict['teamfirstTower'] = dict['teams'][team_pos]['firstTower']
stat_dict['teamfirstDragon'] = dict['teams'][team_pos]['firstDragon']
stat_dict['teamfirstBaron'] = dict['teams'][team_pos]['firstBaron']
return stat_dict
def get_champ_dict():
data = open('/kaggle/input/leaguedata/Champion Ids.txt', 'r', encoding='utf-8').read()
data = json.loads(data)
champions = [x for x in data['data']]
dict = {int(data['data'][x]['key']): str(x) for x in champions}
return dict
data = read_text_file()
list_of_df = []
for game in data:
if 'status' not in game:
id = return_participant_ID(game, name)
stats = get_stats(game, id)
list_of_df.append(pd.DataFrame(stats, index=[0]))
final_df = pd.concat(list_of_df).reset_index(drop=True)
final_df['championId'] = final_df['championId'].apply(lambda x: get_champ_dict()[x].strip())
final_df.rename(columns={'championId': 'Champion'}, inplace=True)
champ_list = final_df['Champion'].value_counts().iloc[0:10].index.to_list()
champ_df = final_df[final_df['Champion'].isin(champ_list)]
sns.set(style='darkgrid')
ax = sns.countplot(x='Champion', data=champ_df, order=champ_list)
ax.set_xticklabels(labels=champ_list, rotation=50)
ax.set_title("Tamoor's 10 Most Played Champions")
plt.show() | code |
34125586/cell_12 | [
"text_plain_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
import seaborn as sns
name = 'sexualcuddler'
key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
def get_match_data(summoner_name, api_key):
url = 'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/'
url += summoner_name + '?api_key=' + api_key
data = requests.get(url).json()
id = data['accountId']
url = 'https://na1.api.riotgames.com/lol/match/v4/matchlists/by-account/'
url += id + '?api_key=' + api_key
data = requests.get(url).json()
game_IDs = [x['gameId'] for x in data['matches']]
list_of_games = []
for game_id in game_IDs:
url = 'https://na1.api.riotgames.com/lol/match/v4/matches/'
url += str(game_id) + '?api_key=' + api_key
list_of_games.append(requests.get(url).json())
open('Output.txt', 'w').close()
with open('Output.txt', 'w', encoding='utf-8') as f:
for game in list_of_games:
f.write('%s\n' % json.dumps(game))
def read_text_file():
with open('/kaggle/input/leaguedata/Output.txt') as f:
content = f.readlines()
return [json.loads(x) for x in content]
def return_participant_ID(dict, name):
for x in dict['participantIdentities']:
if x['player']['summonerName'] == name:
return x['participantId']
def get_stats(dict, ID):
for x in dict['participants']:
if x['participantId'] == ID:
stat_dict = x['stats']
stat_dict['championId'] = x['championId']
stat_dict['spell1Id'] = x['spell1Id']
stat_dict['spell2Id'] = x['spell2Id']
stat_dict['game mode'] = dict['gameMode']
stat_dict['gameDuration'] = dict['gameDuration']
stat_dict['role'] = x['timeline']['role']
stat_dict['lane'] = x['timeline']['lane']
team_pos = 0
if x['teamId'] == 200:
team_pos = 1
stat_dict['teamfirstTower'] = dict['teams'][team_pos]['firstTower']
stat_dict['teamfirstDragon'] = dict['teams'][team_pos]['firstDragon']
stat_dict['teamfirstBaron'] = dict['teams'][team_pos]['firstBaron']
return stat_dict
def get_champ_dict():
data = open('/kaggle/input/leaguedata/Champion Ids.txt', 'r', encoding='utf-8').read()
data = json.loads(data)
champions = [x for x in data['data']]
dict = {int(data['data'][x]['key']): str(x) for x in champions}
return dict
data = read_text_file()
list_of_df = []
for game in data:
if 'status' not in game:
id = return_participant_ID(game, name)
stats = get_stats(game, id)
list_of_df.append(pd.DataFrame(stats, index=[0]))
final_df = pd.concat(list_of_df).reset_index(drop=True)
final_df['championId'] = final_df['championId'].apply(lambda x: get_champ_dict()[x].strip())
final_df.rename(columns={'championId': 'Champion'}, inplace=True)
champ_list = final_df["Champion"].value_counts().iloc[0:10].index.to_list()
champ_df = final_df[final_df["Champion"].isin(champ_list)]
sns.set(style="darkgrid")
ax = sns.countplot(x="Champion", data=champ_df, order = champ_list)
ax.set_xticklabels(labels = champ_list, rotation=50)
ax.set_title("Tamoor's 10 Most Played Champions")
plt.show()
labels = ['Win', 'Loss']
counts = [len(final_df[final_df['win'] == True]), len(final_df[final_df['win'] == False])]
fig1, ax1 = plt.subplots()
ax1.pie(counts, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
ax1.axis('equal')
ax1.set_title('Win/Loss Percentage')
plt.show() | code |
121150743/cell_5 | [
"text_plain_output_1.png"
] | pip install Keras-Preprocessing | code |
128026093/cell_25 | [
"image_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize = (13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i//3, i%3]
sns.histplot(x = col, data = train_df, kde = True, ax = ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1)
plt.xticks(rotation=90)
X = train_df.drop(['yield'], axis=1)
y = train_df['yield']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=14)
def adj_r2_score(predictors, targets, predictions):
r2 = r2_score(targets, predictions)
n = predictors.shape[0]
k = predictors.shape[1]
return 1 - (1 - r2) * (n - 1) / (n - k - 1)
def mape_score(targets, predictions):
return np.mean(np.abs(targets - predictions) / targets) * 100
def model_performance_regression(model, predictors, target):
"""
Function to compute different metrics to check regression model performance
model: regressor
predictors: independent variables
target: dependent variable
"""
pred = model.predict(predictors)
r2 = r2_score(target, pred)
adjr2 = adj_r2_score(predictors, target, pred)
rmse = np.sqrt(mean_squared_error(target, pred))
mae = mean_absolute_error(target, pred)
mape = mape_score(target, pred)
df_perf = pd.DataFrame({'RMSE': rmse, 'MAE': mae, 'R-squared': r2, 'Adj. R-squared': adjr2, 'MAPE': mape}, index=[0])
return df_perf
dt_regressor = DecisionTreeRegressor(random_state=1)
dt_regressor.fit(X_train, y_train)
dt_regressor_perf_test = model_performance_regression(dt_regressor, X_val, y_val)
dt_regressor_perf_test | code |
128026093/cell_30 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize = (13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i//3, i%3]
sns.histplot(x = col, data = train_df, kde = True, ax = ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1)
plt.xticks(rotation=90)
X = train_df.drop(['yield'], axis=1)
y = train_df['yield']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=14)
def adj_r2_score(predictors, targets, predictions):
r2 = r2_score(targets, predictions)
n = predictors.shape[0]
k = predictors.shape[1]
return 1 - (1 - r2) * (n - 1) / (n - k - 1)
def mape_score(targets, predictions):
return np.mean(np.abs(targets - predictions) / targets) * 100
def model_performance_regression(model, predictors, target):
"""
Function to compute different metrics to check regression model performance
model: regressor
predictors: independent variables
target: dependent variable
"""
pred = model.predict(predictors)
r2 = r2_score(target, pred)
adjr2 = adj_r2_score(predictors, target, pred)
rmse = np.sqrt(mean_squared_error(target, pred))
mae = mean_absolute_error(target, pred)
mape = mape_score(target, pred)
df_perf = pd.DataFrame({'RMSE': rmse, 'MAE': mae, 'R-squared': r2, 'Adj. R-squared': adjr2, 'MAPE': mape}, index=[0])
return df_perf
dt_regressor = DecisionTreeRegressor(random_state=1)
dt_regressor.fit(X_train, y_train)
dt_regressor_perf_test = model_performance_regression(dt_regressor, X_val, y_val)
dt_regressor_perf_test
features = list(X_train.columns)
importances = dt_regressor.feature_importances_
indices = np.argsort(importances)
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
regressor = RandomForestRegressor(n_estimators=100, random_state=1)
regressor.fit(X_train, y_train)
regressor_perf_test = model_performance_regression(regressor, X_val, y_val)
regressor_perf_test | code |
128026093/cell_20 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize = (13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i//3, i%3]
sns.histplot(x = col, data = train_df, kde = True, ax = ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1)
plt.xticks(rotation=90)
X = train_df.drop(['yield'], axis=1)
y = train_df['yield']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=14)
print(X_train.shape, X_val.shape, y_train.shape, y_val.shape) | code |
128026093/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
summary(train_df) | code |
128026093/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize = (13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i//3, i%3]
sns.histplot(x = col, data = train_df, kde = True, ax = ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1)
plt.xticks(rotation=90)
X = train_df.drop(['yield'], axis=1)
y = train_df['yield']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=14)
def adj_r2_score(predictors, targets, predictions):
r2 = r2_score(targets, predictions)
n = predictors.shape[0]
k = predictors.shape[1]
return 1 - (1 - r2) * (n - 1) / (n - k - 1)
def mape_score(targets, predictions):
return np.mean(np.abs(targets - predictions) / targets) * 100
def model_performance_regression(model, predictors, target):
"""
Function to compute different metrics to check regression model performance
model: regressor
predictors: independent variables
target: dependent variable
"""
pred = model.predict(predictors)
r2 = r2_score(target, pred)
adjr2 = adj_r2_score(predictors, target, pred)
rmse = np.sqrt(mean_squared_error(target, pred))
mae = mean_absolute_error(target, pred)
mape = mape_score(target, pred)
df_perf = pd.DataFrame({'RMSE': rmse, 'MAE': mae, 'R-squared': r2, 'Adj. R-squared': adjr2, 'MAPE': mape}, index=[0])
return df_perf
dt_regressor = DecisionTreeRegressor(random_state=1)
dt_regressor.fit(X_train, y_train)
dt_regressor_perf_test = model_performance_regression(dt_regressor, X_val, y_val)
dt_regressor_perf_test
features = list(X_train.columns)
importances = dt_regressor.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(5, 5))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show() | code |
128026093/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
128026093/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T | code |
128026093/cell_28 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize = (13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i//3, i%3]
sns.histplot(x = col, data = train_df, kde = True, ax = ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1)
plt.xticks(rotation=90)
X = train_df.drop(['yield'], axis=1)
y = train_df['yield']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=14)
def adj_r2_score(predictors, targets, predictions):
r2 = r2_score(targets, predictions)
n = predictors.shape[0]
k = predictors.shape[1]
return 1 - (1 - r2) * (n - 1) / (n - k - 1)
def mape_score(targets, predictions):
return np.mean(np.abs(targets - predictions) / targets) * 100
def model_performance_regression(model, predictors, target):
"""
Function to compute different metrics to check regression model performance
model: regressor
predictors: independent variables
target: dependent variable
"""
pred = model.predict(predictors)
r2 = r2_score(target, pred)
adjr2 = adj_r2_score(predictors, target, pred)
rmse = np.sqrt(mean_squared_error(target, pred))
mae = mean_absolute_error(target, pred)
mape = mape_score(target, pred)
df_perf = pd.DataFrame({'RMSE': rmse, 'MAE': mae, 'R-squared': r2, 'Adj. R-squared': adjr2, 'MAPE': mape}, index=[0])
return df_perf
dt_regressor = DecisionTreeRegressor(random_state=1)
dt_regressor.fit(X_train, y_train)
dt_regressor_perf_test = model_performance_regression(dt_regressor, X_val, y_val)
dt_regressor_perf_test
features = list(X_train.columns)
importances = dt_regressor.feature_importances_
indices = np.argsort(importances)
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
bagging_estimator = BaggingRegressor(random_state=1)
bagging_estimator.fit(X_train, y_train)
bagging_estimator_perf_test = model_performance_regression(bagging_estimator, X_val, y_val)
bagging_estimator_perf_test | code |
128026093/cell_8 | [
"image_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
summary(test_df) | code |
128026093/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize=(13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i // 3, i % 3]
sns.histplot(x=col, data=train_df, kde=True, ax=ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1) | code |
128026093/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
# explore correlation of features
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize = (13,13))
sns.heatmap(corr, mask = mask, annot = True, fmt=".3f")
fig, axes = plt.subplots(6, 3, figsize = (13, 13))
fig.suptitle('Histogram for all numerical variables in the dataset')
for i, col in enumerate(train_df.columns):
ax = axes[i//3, i%3]
sns.histplot(x = col, data = train_df, kde = True, ax = ax)
ax.axvline(x=train_df[col].mean(), c='r', ls='-', lw=1)
plt.figure(figsize=(15, 8))
sns.boxplot(x='variable', y='value', data=pd.melt(train_df.drop(['yield'], axis=1))).set_title('Boxplot of each feature', size=15)
plt.xticks(rotation=90)
plt.show() | code |
128026093/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
train_df.describe().T
corr = train_df.corr()
mask = np.triu(corr)
ax, fig = plt.subplots(figsize=(13, 13))
sns.heatmap(corr, mask=mask, annot=True, fmt='.3f') | code |
128026093/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train_df.drop(['id'], axis=1, inplace=True)
test_df.drop(['id'], axis=1, inplace=True)
test_df.describe().T | code |
128020724/cell_2 | [
"image_output_1.png"
] | pip install fuzzy-c-means | code |
128020724/cell_11 | [
"text_plain_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.metrics import adjusted_rand_score
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1,101):
ax = plt.subplot(10,10,i)
ax.imshow(images[i],cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
fcm = FCM(n_clusters=K, m=1.1, max_iter=100, error=1e-09, random_state=911)
fcm.fit(X)
centers = fcm.centers
labels = fcm.predict(X)
print('ARI = {ari:.2}'.format(ari=adjusted_rand_score(y, labels))) | code |
128020724/cell_19 | [
"text_html_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1,101):
ax = plt.subplot(10,10,i)
ax.imshow(images[i],cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
fcm = FCM(n_clusters=K, m=1.1, max_iter=100, error=1e-09, random_state=911)
fcm.fit(X)
centers = fcm.centers
labels = fcm.predict(X)
for i in range(K):
ax = plt.subplot(1,10,i+1)
ax.imshow(np.reshape(fcm.centers[i],[npixels,npixels]),cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
i1 = np.where(y==9) # label = 3 -> digit = 9
i2 = np.where(labels==8) # label = 8 -> digit = 3
S = list(set(i2[0]).intersection(set(i1[0])))
for i in range(len(S)):
ax = plt.subplot(1,len(S),i+1)
ax.imshow(np.reshape(X[S[i]],[npixels,npixels]),cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax.set(title=str(i))
for i in range(3,6):
plt.figure()
ax = plt.subplot(2,3,1)
ax.imshow(np.reshape(X[S[i]],[npixels,npixels]),cmap='Greys')
ax.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax = plt.subplot(2,3,3)
ax.plot(np.log(fcm.soft_predict(X[S])[i]));
ax.grid(visible=True)
ax.set_xticks(list(range(10)))
ax.set(xlabel='Label',ylabel='Membership (log-scale)')
fcm.soft_predict(X[[0]]) | code |
128020724/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X, feature_range=(0, 1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1, 101):
ax = plt.subplot(10, 10, i)
ax.imshow(images[i], cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False) | code |
128020724/cell_18 | [
"text_plain_output_1.png"
] | ax = plt.subplot(2,3,1)
ax.imshow(np.reshape(X[0],[npixels,npixels]),cmap='Greys')
ax.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax = plt.subplot(2,3,3)
ax.plot(np.log(fcm.soft_predict(X[[0]])[0]));
ax.grid(visible=True)
ax.set_xticks(list(range(10)))
ax.set(xlabel='Label',ylabel='Membership (log-scale)'); | code |
128020724/cell_16 | [
"image_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1,101):
ax = plt.subplot(10,10,i)
ax.imshow(images[i],cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
fcm = FCM(n_clusters=K, m=1.1, max_iter=100, error=1e-09, random_state=911)
fcm.fit(X)
centers = fcm.centers
labels = fcm.predict(X)
for i in range(K):
ax = plt.subplot(1,10,i+1)
ax.imshow(np.reshape(fcm.centers[i],[npixels,npixels]),cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
i1 = np.where(y == 9)
i2 = np.where(labels == 8)
S = list(set(i2[0]).intersection(set(i1[0])))
for i in range(len(S)):
ax = plt.subplot(1, len(S), i + 1)
ax.imshow(np.reshape(X[S[i]], [npixels, npixels]), cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax.set(title=str(i))
for i in range(3, 6):
plt.figure()
ax = plt.subplot(2, 3, 1)
ax.imshow(np.reshape(X[S[i]], [npixels, npixels]), cmap='Greys')
ax.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
ax = plt.subplot(2, 3, 3)
ax.plot(np.log(fcm.soft_predict(X[S])[i]))
ax.grid(visible=True)
ax.set_xticks(list(range(10)))
ax.set(xlabel='Label', ylabel='Membership (log-scale)') | code |
128020724/cell_3 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
from matplotlib import pyplot as plt
from fcmeans import FCM
from sklearn.metrics import adjusted_rand_score
from pandas import crosstab | code |
128020724/cell_14 | [
"text_plain_output_1.png"
] | from fcmeans import FCM
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1,101):
ax = plt.subplot(10,10,i)
ax.imshow(images[i],cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
fcm = FCM(n_clusters=K, m=1.1, max_iter=100, error=1e-09, random_state=911)
fcm.fit(X)
centers = fcm.centers
labels = fcm.predict(X)
for i in range(K):
ax = plt.subplot(1, 10, i + 1)
ax.imshow(np.reshape(fcm.centers[i], [npixels, npixels]), cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False) | code |
128020724/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fcmeans import FCM
from pandas import crosstab
from sklearn.datasets import load_digits
from sklearn.preprocessing import minmax_scale
import numpy as np # linear algebra
digits = load_digits()
K = 10 # number of clusters
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X,feature_range=(0,1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1,101):
ax = plt.subplot(10,10,i)
ax.imshow(images[i],cmap='Greys')
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
fcm = FCM(n_clusters=K, m=1.1, max_iter=100, error=1e-09, random_state=911)
fcm.fit(X)
centers = fcm.centers
labels = fcm.predict(X)
crosstab(y, labels) | code |
128020724/cell_5 | [
"image_output_1.png"
] | from sklearn.datasets import load_digits
digits = load_digits()
print(digits.DESCR) | code |
128031562/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import nltk
nltk.download('punkt') | code |
128031562/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install newspaper3k | code |
128031562/cell_1 | [
"text_plain_output_1.png"
] | !pip install transformers torch | code |
128031562/cell_10 | [
"text_plain_output_1.png"
] | from newspaper import Article
from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer, pipeline
from urllib.request import urlopen
import feedparser
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-cnndm')
tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-cnndm')
def prophetnet_summarizer(text, config=None):
if config:
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased')
model.config.update(config)
else:
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased')
tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
inputs = tokenizer(text, return_tensors='pt')
summary_ids = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], num_beams=4, length_penalty=2.0, max_length=200, min_length=100, no_repeat_ngram_size=3)
summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True)
return summary
def prophetnet_summarizer(text, config=None):
if config:
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased')
model.config.update(config)
else:
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased')
tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
inputs = tokenizer(text, return_tensors='pt')
summary_ids = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], num_beams=4, length_penalty=2.0, max_length=200, min_length=100, no_repeat_ngram_size=3)
summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True)
return summary
site = 'https://news.google.com/rss/search?q=politics'
op = urlopen(site)
rd = op.read()
op.close()
sp_page = soup(rd, 'xml')
news_list = sp_page.find_all('item')
rss_feed = feedparser.parse('https://www.aljazeera.com/xml/rss/all.xml')
news_list = rss_feed.entries
c = 0
for i, news in enumerate(news_list):
c += 1
print(f'News Article {i + 1}:')
print('Title: ', news.title)
print('News Link: ', news.link)
news_data = Article(news.link)
news_data.download()
news_data.parse()
news_text = news_data.text
summary = prophetnet_summarizer(news_text)
print('News Summary: ', summary[0].capitalize())
print('News Poster Link: ', news_data.top_image)
print('-' * 60)
if c >= 2:
break | code |
128031562/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer, pipeline
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-cnndm')
tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-cnndm') | code |
128041917/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
import seaborn as sns
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
fig, ((ax1, ax2, ax3, ax4), (ax5, ax6, ax7, ax8)) = plt.subplots(nrows= 2, ncols = 4, figsize = (25,20))
sns.boxplot(ax = ax1, y = 'Length', data = a, color = 'green')
sns.boxplot(ax = ax2, y = 'Diameter', data = a, color = 'red')
sns.boxplot(ax = ax3, y = 'Height', data = a, color = 'limegreen')
sns.boxplot(ax = ax4, y = 'Whole weight', data = a, color = 'cyan')
sns.boxplot(ax = ax5, y = 'Shucked weight', data = a, color = 'salmon')
sns.boxplot(ax = ax6, y = 'Viscera weight', data = a, color = 'mediumorchid')
sns.boxplot(ax = ax7, y = 'Shell weight', data = a, color = 'lime')
sns.boxplot(ax = ax8, y = 'age', data = a, color = 'plum')
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['age'], axis=1, inplace=True)
a_f.drop(['age'], axis=1, inplace=True)
a_i.drop(['age'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='cyan'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='violet'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='lightsteelblue'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_f.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_i.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='limegreen'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='olive'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='seagreen'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
sns.pairplot(a, hue='Sex', palette='viridis_r') | code |
128041917/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a | code |
128041917/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes | code |
128041917/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a | code |
128041917/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['age'], axis=1, inplace=True)
a_f.drop(['age'], axis=1, inplace=True)
a_i.drop(['age'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='cyan'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='violet'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='lightsteelblue'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_f.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_i.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='limegreen'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='olive'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='seagreen'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig) | code |
128041917/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.tail() | code |
128041917/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import cufflinks as cf
import plotly.offline as py
color = sns.color_palette()
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import plotly.tools as tls
import warnings
warnings.filterwarnings('ignore') | code |
128041917/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['age'], axis=1, inplace=True)
a_f.drop(['age'], axis=1, inplace=True)
a_i.drop(['age'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='cyan'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='violet'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='lightsteelblue'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig) | code |
128041917/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape | code |
128041917/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
fig, ((ax1, ax2, ax3, ax4), (ax5, ax6, ax7, ax8)) = plt.subplots(nrows=2, ncols=4, figsize=(25, 20))
sns.boxplot(ax=ax1, y='Length', data=a, color='green')
sns.boxplot(ax=ax2, y='Diameter', data=a, color='red')
sns.boxplot(ax=ax3, y='Height', data=a, color='limegreen')
sns.boxplot(ax=ax4, y='Whole weight', data=a, color='cyan')
sns.boxplot(ax=ax5, y='Shucked weight', data=a, color='salmon')
sns.boxplot(ax=ax6, y='Viscera weight', data=a, color='mediumorchid')
sns.boxplot(ax=ax7, y='Shell weight', data=a, color='lime')
sns.boxplot(ax=ax8, y='age', data=a, color='plum') | code |
128041917/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe | code |
128041917/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a_sex = a['Sex'].value_counts()
print(a_sex)
a['Sex'].value_counts().plot(kind='bar') | code |
128041917/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a.info() | code |
128041917/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum()
a['age'] = a['Rings'] + 1.5
a.drop('Rings', axis=1, inplace=True)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['age'], axis=1, inplace=True)
a_f.drop(['age'], axis=1, inplace=True)
a_i.drop(['age'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='cyan'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='violet'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='lightsteelblue'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
a1 = a.copy()
a2 = a.copy()
a3 = a.copy()
a_m = a1[a1['Sex'] == 'M']
a_m.drop('Sex', axis=1, inplace=True)
a_f = a2[a2['Sex'] == 'F']
a_f.drop('Sex', axis=1, inplace=True)
a_i = a3[a3['Sex'] == 'I']
a_i.drop('Sex', axis=1, inplace=True)
a_m.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_f.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_i.drop(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight'], axis=1, inplace=True)
a_m = a_m.mean()
a_f = a_f.mean()
a_i = a_i.mean()
trace1 = go.Bar(x=a_m.index[::-1], y=a_m.values[::-1], name='M', marker=dict(color='limegreen'))
trace2 = go.Bar(x=a_f.index[::-1], y=a_f.values[::-1], name='F', marker=dict(color='olive'))
trace3 = go.Bar(x=a_i.index[::-1], y=a_i.values[::-1], name='I', marker=dict(color='seagreen'))
data = [trace1, trace2, trace3]
layout = go.Layout(title='Feature Distribution', width=800)
fig = go.Figure(data=data, layout=layout)
a.columns | code |
128041917/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.shape
a.describe
a.dtypes
a.isna().sum() | code |
128041917/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
a = pd.read_csv('/kaggle/input/abalone-dataset/abalone.csv')
a
a.head() | code |
128025497/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import chi2
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
plt.xticks(rotation=90)
train_data['symptom_sum'] = train_data.iloc[:, 1:-1].sum(axis=1)
X = train_data.drop(['id', 'prognosis'], axis=1).reset_index(drop=True)
y = train_data['prognosis'].reset_index(drop=True)
le = LabelEncoder()
y = le.fit_transform(y)
y = np.array(y)
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
scores, p_values = chi2(X_scaled, y)
feature_scores = {feature: score for feature, score in zip(X.columns, p_values)}
sorted_scores = sorted(feature_scores.items(), key=lambda x: x[1], reverse=True)
fig, ax = plt.subplots(figsize=(10, 10))
ax.barh(range(len(sorted_scores)), [score[1] for score in sorted_scores])
ax.set_yticks(range(len(sorted_scores)))
ax.set_yticklabels([score[0] for score in sorted_scores])
ax.set_xlabel('p_value')
ax.set_title('Feature p_value by chi-squared test')
plt.show() | code |
128025497/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
print('Number of missing values in each column:')
print(train_data.isnull().sum())
print('Number of duplicate rows:')
print(train_data.duplicated().sum())
print('Distribution of the target variable:')
print(train_data['prognosis'].value_counts())
plt.hist(train_data['prognosis'], bins=30, width=0.5)
plt.title('Histogram of the target variable')
plt.xticks(rotation=90)
plt.show() | code |
128025497/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils.class_weight import compute_sample_weight
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
plt.xticks(rotation=90)
train_data['symptom_sum'] = train_data.iloc[:, 1:-1].sum(axis=1)
X = train_data.drop(['id', 'prognosis'], axis=1).reset_index(drop=True)
y = train_data['prognosis'].reset_index(drop=True)
le = LabelEncoder()
y = le.fit_transform(y)
y = np.array(y)
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
def map3_score(estimator, X_train, y_true):
"""
Computes the mean Average Precision (MAP) at 3 score between the true labels and the predicted labels.
Assumes that there is only one true label per sample.
Args:
y_true (array-like): True labels array, shape (n_samples,)
y_pred (array-like): Predicted probabilities matrix, shape (n_samples, n_classes)
Returns:
float: MAP@3 score
"""
score = 0.0
y_pred_top3 = estimator.predict(X_train)[0]
for i in range(len(y_true)):
true_label = y_true[i]
pred_labels = y_pred_top3[i]
ap = 0.0
for j in range(3):
if pred_labels[j] == true_label:
ap = 1.0 / (j + 1)
break
score += ap
map3 = score / len(y_true)
return map3
map3_scorer = make_scorer(map3_score, greater_is_better=True)
class_weights = compute_sample_weight('balanced', y=y)
svm_classifier = SVCTop3Classifier()
param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'gamma': ['scale', 'auto'], 'max_iter': [-1]}
grid_search = GridSearchCV(svm_classifier, param_grid, scoring=map3_score, cv=5, verbose=3)
grid_search.fit(X_scaled, y, sample_weight=class_weights)
results_df = pd.DataFrame(grid_search.cv_results_)
for i, row in results_df.sort_values(by=['rank_test_score']).head(10).iterrows():
print(row['mean_test_score'], row['std_test_score'], row['params']) | code |
128025497/cell_2 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
print(train_data.sample(5)) | code |
128025497/cell_19 | [
"image_output_1.png"
] | from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils.class_weight import compute_sample_weight
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
plt.xticks(rotation=90)
train_data['symptom_sum'] = train_data.iloc[:, 1:-1].sum(axis=1)
X = train_data.drop(['id', 'prognosis'], axis=1).reset_index(drop=True)
y = train_data['prognosis'].reset_index(drop=True)
le = LabelEncoder()
y = le.fit_transform(y)
y = np.array(y)
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
def map3_score(estimator, X_train, y_true):
"""
Computes the mean Average Precision (MAP) at 3 score between the true labels and the predicted labels.
Assumes that there is only one true label per sample.
Args:
y_true (array-like): True labels array, shape (n_samples,)
y_pred (array-like): Predicted probabilities matrix, shape (n_samples, n_classes)
Returns:
float: MAP@3 score
"""
score = 0.0
y_pred_top3 = estimator.predict(X_train)[0]
for i in range(len(y_true)):
true_label = y_true[i]
pred_labels = y_pred_top3[i]
ap = 0.0
for j in range(3):
if pred_labels[j] == true_label:
ap = 1.0 / (j + 1)
break
score += ap
map3 = score / len(y_true)
return map3
map3_scorer = make_scorer(map3_score, greater_is_better=True)
class_weights = compute_sample_weight('balanced', y=y)
svm_classifier = SVCTop3Classifier()
param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'gamma': ['scale', 'auto'], 'max_iter': [-1]}
grid_search = GridSearchCV(svm_classifier, param_grid, scoring=map3_score, cv=5, verbose=3)
grid_search.fit(X_scaled, y, sample_weight=class_weights)
print('Best Parameters: ', grid_search.best_params_)
print('Best Score: ', grid_search.best_score_) | code |
128025497/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_data = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
plt.xticks(rotation=90)
train_data['symptom_sum'] = train_data.iloc[:, 1:-1].sum(axis=1)
plt.hist(train_data['symptom_sum'], bins=len(np.unique(train_data['symptom_sum'])))
plt.title('Histogram of symptom count')
plt.xlabel('Symptom count')
plt.ylabel('Number of entries')
plt.show() | code |
128025497/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """X = train_data.drop(['id', 'prognosis'], axis=1).reset_index(drop = True)
print(len(X.columns))
significant_features = [score[0] for score in sorted_scores if score[1] < 0.05]
X_significant = X[significant_features]
print(len(X_significant.columns))
X_scaled = scaler.fit_transform(X_significant)""" | code |
104129266/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols | code |
104129266/cell_6 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_cols + ['failure']]
training
woe = pd.crosstab(training['product_code'], training['failure'])
woe['sum'] = woe[0] + woe[1]
woe['%0'] = woe[0] / woe[0].sum()
woe['%1'] = woe[1] / woe[1].sum()
woe['woe'] = np.log(woe['%1'] / woe['%0'])
woe | code |
104129266/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.info() | code |
104129266/cell_7 | [
"text_html_output_1.png"
] | import category_encoders as ce
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_cols + ['failure']]
training
import category_encoders as ce
woe_encoder = ce.WOEEncoder(cols=cat_cols).fit(X=training[cat_cols], y=training['failure'])
temp = woe_encoder.transform(training[cat_cols])
temp | code |
104129266/cell_8 | [
"text_html_output_1.png"
] | import category_encoders as ce
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_cols + ['failure']]
training
import category_encoders as ce
woe_encoder = ce.WOEEncoder(cols=cat_cols).fit(X=training[cat_cols], y=training['failure'])
temp = woe_encoder.transform(training[cat_cols])
temp
temp['failure'] = training['failure']
temp.corr() | code |
104129266/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0) | code |
104129266/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
training.fillna(0)
cat_cols = [col for col in training.columns if training[col].dtype == 'object']
cat_cols
training = training[cat_cols + ['failure']]
training | code |
33099008/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape | code |
33099008/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['software_os_version'].value_counts() | code |
33099008/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df.head() | code |
33099008/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['device_brand'].value_counts() | code |
33099008/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.head() | code |
33099008/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33099008/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['software_os_vendor'].value_counts() | code |
33099008/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['software_os_name'].value_counts() | code |
33099008/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
vodafone_subset_6.head(10) | code |
33099008/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['device_type_rus'].value_counts() | code |
33099008/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts() | code |
33099008/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df.info() | code |
34128312/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/youtube-new/USvideos.csv')
print(df.columns) | code |
34128312/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(['category_id']).count().reset_index()
cnt_video_per_category = cnt_video_per_category.loc[:, ['category_id', 'video_id']]
df_1 = pd.merge(cnt_video_per_category, category_df, left_on='category_id', right_on='category_id', how='left')
df_1 = df_1.sort_values(by='video_id', ascending=False)
df_1['Proportion'] = round(df_1['video_id'] / sum(df_1['video_id']) * 100, 2)
print(df_1)
sns.set(style='whitegrid')
plt.figure(figsize=(11, 10))
ax = sns.barplot(x='category_name', y='video_id', data=df_1)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right')
plt.tight_layout()
plt.show() | code |
34128312/cell_7 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/youtube-new/USvideos.csv')
cnt_video_per_category = df.groupby(["category_id"]).count().reset_index()
cnt_video_per_category = cnt_video_per_category.loc[:,['category_id','video_id']]
df_1 = pd.merge(cnt_video_per_category,category_df,left_on='category_id',right_on='category_id',how='left')
df_1 = df_1.sort_values(by='video_id', ascending = False)
df_1["Proportion"] = round((df_1["video_id"]/sum(df_1["video_id"]) * 100),2)
print(df_1)
sns.set(style="whitegrid")
plt.figure(figsize=(11, 10))
ax = sns.barplot(x="category_name",y="video_id", data=df_1)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
plt.show()
df['likes_rate'] = df['likes'] / df['views'] * 100
df['dislikes_rate'] = df['dislikes'] / df['views'] * 100
df['comment_rate'] = df['comment_count'] / df['views'] * 100
cnt_likes_per_video_per_category = df.groupby('category_id').mean().reset_index()
cnt_likes_per_video_per_category = cnt_likes_per_video_per_category.loc[:, ['category_id', 'likes_rate', 'dislikes_rate', 'comment_rate']]
df_2 = pd.merge(cnt_likes_per_video_per_category, category_df, left_on='category_id', right_on='category_id', how='left')
print(df_2)
df_2 = df_2.sort_values(by='likes_rate', ascending=False)
sns.set(style='whitegrid')
plt.figure(figsize=(11, 10))
plt.title('likes rate')
ax = sns.barplot(x='category_name', y='likes_rate', data=df_2)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right')
plt.tight_layout()
plt.show()
df_2 = df_2.sort_values(by='dislikes_rate', ascending=False)
sns.set(style='whitegrid')
plt.figure(figsize=(11, 10))
plt.title('dislikes rate')
ax = sns.barplot(x='category_name', y='dislikes_rate', data=df_2)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right')
plt.tight_layout()
plt.show()
df_2 = df_2.sort_values(by='comment_rate', ascending=False)
sns.set(style='whitegrid')
plt.figure(figsize=(11, 10))
plt.title('comments rate')
ax = sns.barplot(x='category_name', y='comment_rate', data=df_2)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha='right')
plt.tight_layout()
plt.show() | code |
34128312/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/youtube-new/USvideos.csv')
df.head() | code |
122260425/cell_21 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
display(df['Item_Fat_Content'].value_counts(normalize=True).round(2))
print('\n')
df['Item_Fat_Content'].value_counts() | code |
122260425/cell_9 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum() | code |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.