path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
129016727/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 |
129016727/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Page_Rank'].value_counts() | code |
129016727/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.tree import DecisionTreeClassifier
DT = DecisionTreeClassifier()
DT.fit(X_train, y_train)
y_pred = DT.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.svm import SVC
SVM = SVC()
SVM.fit(X_train, y_train)
y_pred = SVM.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.ensemble import RandomForestClassifier
RF = RandomForestClassifier()
RF.fit(X_train, y_train)
y_pred = RF.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
129016727/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
129016727/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['double_slash_redirecting'].value_counts() | code |
129016727/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Prefix_Suffix'].value_counts() | code |
129016727/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.tree import DecisionTreeClassifier
DT = DecisionTreeClassifier()
DT.fit(X_train, y_train)
y_pred = DT.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
from sklearn.svm import SVC
SVM = SVC()
SVM.fit(X_train, y_train)
y_pred = SVM.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy) | code |
129016727/cell_27 | [
"text_html_output_1.png"
] | print(X_train.shape)
print(X_test.shape) | code |
105200156/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
common_ids = set(df_fb2021_agg.index) & set(df_fb3.text_id)
len(common_ids)
len(common_ids) / len(df_fb3) | code |
105200156/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item() | code |
105200156/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg | code |
105200156/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb2021_agg.text.str.len().plot.hist(bins=100) | code |
105200156/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
common_ids = set(df_fb2021_agg.index) & set(df_fb3.text_id)
len(common_ids)
df_fb3.query('text_id == "009F4E9310CB"').full_text.item()
df_fb2021_agg.loc['009F4E9310CB'].item()
import numpy as np
all_fb3 = ' '.join(df_fb3.full_text)
all_fb2021 = ' '.join(df_fb2021_agg.text)
print(np.mean([r.full_text in all_fb2021 for i, r in df_fb3.sample(1000).iterrows()]), ' fb3 strings are in fb2021 dataset') | code |
105200156/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3 | code |
105200156/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
common_ids = set(df_fb2021_agg.index) & set(df_fb3.text_id)
len(common_ids)
df_fb2021_agg.loc['009F4E9310CB'].item() | code |
105200156/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
common_ids = set(df_fb2021_agg.index) & set(df_fb3.text_id)
len(common_ids)
df_fb3.query('text_id == "009F4E9310CB"').full_text.item()
import numpy as np
all_fb3 = ' '.join(df_fb3.full_text)
print(np.mean([r.discourse_text in all_fb3 for i, r in df_fb2021.sample(1000).iterrows()]), ' fb2021 strings are in fb3 dataset') | code |
105200156/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021 | code |
105200156/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
common_ids = set(df_fb2021_agg.index) & set(df_fb3.text_id)
len(common_ids)
df_fb3.query('text_id == "009F4E9310CB"').full_text.item() | code |
105200156/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
df_fb3.full_text.str.len().plot.hist(bins=100) | code |
105200156/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item()
df_fb3 = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
df_fb3.sample().full_text.item()
common_ids = set(df_fb2021_agg.index) & set(df_fb3.text_id)
len(common_ids) | code |
105200156/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
df_fb2021 = pd.read_csv('../input/feedback-prize-2021/train.csv', dtype={'discourse_id': int})
df_fb2021['textlen'] = df_fb2021.discourse_text.str.len()
df_fb2021
import re
def join_texts(v):
return re.sub(' +', ' ', ' '.join(v.discourse_text).replace('\n', ' ').replace('\x07', ' '))
df_fb2021_agg = df_fb2021.groupby('id').apply(lambda v: join_texts(v)).to_frame('text')
df_fb2021_agg
df_fb2021_agg.sample().text.item() | code |
90147420/cell_6 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df['Adj Close'],name='Adj Close')
)
fig.update_layout(xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
def bolinger(data):
data['MA20'] = data.Close.rolling(window=20).mean()
data['std'] = data.Close.rolling(window=20).std()
data['upper_20'] = data.MA20 + 2 * data['std']
data['lower_20'] = data.MA20 - 2 * data['std']
data.drop('std', axis=1, inplace=True)
data.dropna(inplace=True)
return data
df = bolinger(df)
df.ffill(inplace=True)
df.reset_index(drop=True, inplace=True)
df | code |
90147420/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df | code |
90147420/cell_11 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import math
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df['Adj Close'],name='Adj Close')
)
fig.update_layout(xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
def bolinger(data):
data['MA20'] = data.Close.rolling(window=20).mean()
data['std'] = data.Close.rolling(window=20).std()
data['upper_20'] = data.MA20 + 2 * data['std']
data['lower_20'] = data.MA20 - 2 * data['std']
data.drop('std', axis=1, inplace=True)
data.dropna(inplace=True)
return data
df = bolinger(df)
df.ffill(inplace=True)
df.reset_index(drop=True, inplace=True)
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df.upper_20,name='Upper 20')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df.lower_20,name='Lower 20')
)
fig.update_layout(title='ploting bolinger band',
xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
total_balance = 1000000
def backtesting(df, total_balance):
buy_price = []
buy_date = []
sell_price = []
sell_date = []
long_earn = []
num_bought = 0
df_balance = []
df_date = []
balance_date = []
trade_num = 0
buy_num = -1
sell_num = -1
trade_num = -1
position = 'none'
for i in range(len(df.Close)):
if total_balance <= 0:
break
elif position == 'none':
if df.Close[i] <= df['lower_20'][i] and abs(df.Close[i] - df['lower_20'][i]) <= 1000:
position = 'long'
buy_price.append(df.Close[i])
buy_date.append(df.Date[i])
if trade_num >= 0:
total_balance = df_balance[trade_num]
num_bought = 0
num_count = math.floor(total_balance / df.Close[i])
num_bought += num_count
remain = total_balance - df.Close[i] * num_count
df_balance.append(total_balance)
df_date.append(df.Date[i])
trade_num += 1
buy_num += 1
elif position == 'long':
if df.Close[i] >= df['upper_20'][i] and abs(df.Close[i] - df['upper_20'][i]) <= 1000:
position = 'none'
sell_price.append(df.Close[i])
sell_date.append(df.Date[i])
total_balance = remain
earn = df.Close[i] - buy_price[buy_num]
total_earn = earn * num_bought + buy_price[buy_num] * num_bought
total_balance += total_earn
df_balance.append(total_balance)
df_date.append(df.Date[i])
sell_num += 1
trade_num += 1
if i % (len(df) - 1) == 0:
if position == 'long':
sell_price.append(df.Close[i])
sell_date.append(df.Date[i])
total_balance = remain
earn = df.Close[i] - buy_price[buy_num]
total_earn = earn * num_bought + buy_price[buy_num] * num_bought
total_balance += total_earn
df_balance.append(total_balance)
df_date.append(df.Date[i])
balance = pd.DataFrame({'Date': df_date, 'balance': df_balance})
final_return_ratio = (balance.iloc[-1][1] / 1000000 - 1) * 100
return balance
df_balance = backtesting(df, total_balance) | code |
90147420/cell_7 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df['Adj Close'],name='Adj Close')
)
fig.update_layout(xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
def bolinger(data):
data['MA20'] = data.Close.rolling(window=20).mean()
data['std'] = data.Close.rolling(window=20).std()
data['upper_20'] = data.MA20 + 2 * data['std']
data['lower_20'] = data.MA20 - 2 * data['std']
data.drop('std', axis=1, inplace=True)
data.dropna(inplace=True)
return data
df = bolinger(df)
df.ffill(inplace=True)
df.reset_index(drop=True, inplace=True)
df
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(go.Scatter(x=df.Date, y=df.Close, name='Close'))
fig.add_trace(go.Scatter(x=df.Date, y=df.upper_20, name='Upper 20'))
fig.add_trace(go.Scatter(x=df.Date, y=df.lower_20, name='Lower 20'))
fig.update_layout(title='ploting bolinger band', xaxis_title='Date', yaxis_title='Price')
fig.show() | code |
90147420/cell_3 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(go.Scatter(x=df.Date, y=df.Close, name='Close'))
fig.add_trace(go.Scatter(x=df.Date, y=df['Adj Close'], name='Adj Close'))
fig.update_layout(xaxis_title='Date', yaxis_title='Price')
fig.show() | code |
90147420/cell_12 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import math
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df['Adj Close'],name='Adj Close')
)
fig.update_layout(xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
def bolinger(data):
data['MA20'] = data.Close.rolling(window=20).mean()
data['std'] = data.Close.rolling(window=20).std()
data['upper_20'] = data.MA20 + 2 * data['std']
data['lower_20'] = data.MA20 - 2 * data['std']
data.drop('std', axis=1, inplace=True)
data.dropna(inplace=True)
return data
df = bolinger(df)
df.ffill(inplace=True)
df.reset_index(drop=True, inplace=True)
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df.upper_20,name='Upper 20')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df.lower_20,name='Lower 20')
)
fig.update_layout(title='ploting bolinger band',
xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
total_balance = 1000000
def backtesting(df, total_balance):
buy_price = []
buy_date = []
sell_price = []
sell_date = []
long_earn = []
num_bought = 0
df_balance = []
df_date = []
balance_date = []
trade_num = 0
buy_num = -1
sell_num = -1
trade_num = -1
position = 'none'
for i in range(len(df.Close)):
if total_balance <= 0:
break
elif position == 'none':
if df.Close[i] <= df['lower_20'][i] and abs(df.Close[i] - df['lower_20'][i]) <= 1000:
position = 'long'
buy_price.append(df.Close[i])
buy_date.append(df.Date[i])
if trade_num >= 0:
total_balance = df_balance[trade_num]
num_bought = 0
num_count = math.floor(total_balance / df.Close[i])
num_bought += num_count
remain = total_balance - df.Close[i] * num_count
df_balance.append(total_balance)
df_date.append(df.Date[i])
trade_num += 1
buy_num += 1
elif position == 'long':
if df.Close[i] >= df['upper_20'][i] and abs(df.Close[i] - df['upper_20'][i]) <= 1000:
position = 'none'
sell_price.append(df.Close[i])
sell_date.append(df.Date[i])
total_balance = remain
earn = df.Close[i] - buy_price[buy_num]
total_earn = earn * num_bought + buy_price[buy_num] * num_bought
total_balance += total_earn
df_balance.append(total_balance)
df_date.append(df.Date[i])
sell_num += 1
trade_num += 1
if i % (len(df) - 1) == 0:
if position == 'long':
sell_price.append(df.Close[i])
sell_date.append(df.Date[i])
total_balance = remain
earn = df.Close[i] - buy_price[buy_num]
total_earn = earn * num_bought + buy_price[buy_num] * num_bought
total_balance += total_earn
df_balance.append(total_balance)
df_date.append(df.Date[i])
balance = pd.DataFrame({'Date': df_date, 'balance': df_balance})
final_return_ratio = (balance.iloc[-1][1] / 1000000 - 1) * 100
return balance
df_balance = backtesting(df, total_balance)
fig = make_subplots()
fig.add_trace(go.Scatter(x=df_balance.Date, y=df_balance.balance, name='your Balance'))
fig.update_layout(xaxis_title='Date', yaxis_title='your Balance')
fig.show() | code |
90147420/cell_5 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv('../input/samsung-electronics-stock-historical-price/005930.KS.csv')
df
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Scatter(x=df.Date,y=df.Close,name='Close')
)
fig.add_trace(
go.Scatter(x=df.Date,y=df['Adj Close'],name='Adj Close')
)
fig.update_layout(xaxis_title='Date',
yaxis_title='Price'
)
fig.show()
665 % (len(df) - 1) | code |
2014493/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0)
X_val = all_data[train.shape[0]:]
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(random_state=0)
tree.fit(X_train, y_train)
print('Train score: {:.3f}'.format(tree.score(X_train, y_train)))
print('Test score: {:.3f}'.format(tree.score(X_test, y_test)))
decision_tree_predicts = tree.predict(X_val) | code |
2014493/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.info() | code |
2014493/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data.info() | code |
2014493/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
all_data.head() | code |
2014493/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
train_cleared = all_data[:train.shape[0]]
train_cleared.info()
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0)
X_train.info()
X_val = all_data[train.shape[0]:]
X_val.info() | code |
2014493/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0)
X_val = all_data[train.shape[0]:]
from sklearn.tree import DecisionTreeClassifier
tree = DecisionTreeClassifier(random_state=0)
tree.fit(X_train, y_train)
decision_tree_predicts = tree.predict(X_val)
result = pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': decision_tree_predicts})
result.to_csv('DecisionTree.csv', index=False)
result.info() | code |
2014493/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data.info() | code |
34120753/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
f"The 3 sentiments are '{', '.join(train_df.sentiment.unique().tolist())}'" | code |
34120753/cell_9 | [
"image_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes | code |
34120753/cell_25 | [
"text_plain_output_1.png"
] | from nltk import tokenize
import os
import pandas as pd
import string
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
f"The 3 sentiments are '{', '.join(train_df.sentiment.unique().tolist())}'"
train_df.dropna(inplace=True)
train_df.columns
full_df = pd.concat([train_df, test_df])
full_df.loc[:, 'text_lower'] = full_df.text.str.lower()
def count_punctuations(text):
text_punctuations = []
punctuation_list = list(string.punctuation)
for token in tokenize.word_tokenize(text):
if not token.isalpha():
if len(token) > 1:
sub_tokens = list(token)
for sub_token in sub_tokens:
if sub_token in punctuation_list:
text_punctuations.append(sub_token)
else:
text_punctuations.append(token)
return pd.Series(text_punctuations).value_counts()
count_punctuation_df = full_df.text.apply(count_punctuations) | code |
34120753/cell_11 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
test_df.dtypes | code |
34120753/cell_19 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
f"The 3 sentiments are '{', '.join(train_df.sentiment.unique().tolist())}'"
train_df.dropna(inplace=True)
train_df.columns | code |
34120753/cell_1 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install --upgrade pandas-profiling
!pip install --upgrade hypertools
!pip install --upgrade pandas | code |
34120753/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.head() | code |
34120753/cell_8 | [
"image_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text'] | code |
34120753/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
f"The 3 sentiments are '{', '.join(train_df.sentiment.unique().tolist())}'"
print(f"Train NaN's:\n{train_df.isna().sum()}") | code |
34120753/cell_16 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
print(f"Test NaN's:\n{test_df.isna().sum()}") | code |
34120753/cell_22 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
f"The 3 sentiments are '{', '.join(train_df.sentiment.unique().tolist())}'"
train_df.dropna(inplace=True)
train_df.columns
full_df = pd.concat([train_df, test_df])
full_df.head() | code |
34120753/cell_10 | [
"text_html_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
test_df.head() | code |
34120753/cell_27 | [
"text_plain_output_1.png"
] | from nltk import tokenize
import matplotlib.pyplot as plt
import os
import pandas as pd
import string
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
f"The 3 sentiments are '{', '.join(train_df.sentiment.unique().tolist())}'"
train_df.dropna(inplace=True)
train_df.columns
full_df = pd.concat([train_df, test_df])
full_df.loc[:, 'text_lower'] = full_df.text.str.lower()
def count_punctuations(text):
text_punctuations = []
punctuation_list = list(string.punctuation)
for token in tokenize.word_tokenize(text):
if not token.isalpha():
if len(token) > 1:
sub_tokens = list(token)
for sub_token in sub_tokens:
if sub_token in punctuation_list:
text_punctuations.append(sub_token)
else:
text_punctuations.append(token)
return pd.Series(text_punctuations).value_counts()
count_punctuation_df = full_df.text.apply(count_punctuations)
count_punctuation_df.fillna(0, inplace=True)
count_punctuation_df.loc[:, 'sentiment'] = full_df.sentiment
grouped_df = count_punctuation_df.groupby('sentiment').sum()
groups = ['negative', 'neutral', 'positive']
colors = ['red', 'silver', 'green']
fig = plt.figure(figsize=(15, 8))
for i, (group, color) in enumerate(zip(groups, colors), start=1):
ax = fig.add_subplot(1, 3, i)
grouped_df.loc[group].sort_values(ascending=False).head(10).plot(kind='bar', color=color, title=f'Top 10 punctuation for {group} sentiment', ax=ax, rot=0)
fig.tight_layout() | code |
34120753/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'tweet-sentiment-extraction')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions')
train_file = os.path.join(input_path, 'train.csv')
test_file = os.path.join(input_path, 'test.csv')
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)
train_df.sample(n=1)['text']
train_df.dtypes
test_df.dtypes
print(f'Train size {train_df.shape[0]}')
print(f'Text size {test_df.shape[0]}') | code |
50228570/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.metrics import accuracy_score
import numpy as np
digits = load_digits()
X = digits.data
y = digits.target
p = 0.75
idx = int(p * X.shape[0]) + 1
X_train, X_test = np.split(X, [idx])
y_train, y_test = np.split(y, [idx])
def euclidian_metric(x, y):
return np.sqrt(np.sum((x - y) ** 2))
y_pred_knn = []
for test_value in X_test:
ind_min_metric = 0
min_metric = euclidian_metric(test_value, X_train[0])
for index, train_value in enumerate(X_train):
metric = euclidian_metric(test_value, train_value)
if metric < min_metric:
min_metric = metric
ind_min_metric = index
y_pred_knn.append(y_train[ind_min_metric])
knn_err_rate = 1 - accuracy_score(y_test, y_pred_knn)
print('1nn classifier error: ' + str(knn_err_rate)) | code |
50228570/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
digits = load_digits()
X = digits.data
y = digits.target
print(digits.DESCR) | code |
50228570/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
rf_clf = RandomForestClassifier(n_estimators=1000)
rf_clf.fit(X_train, y_train) | code |
50228570/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
rf_clf = RandomForestClassifier(n_estimators=1000)
rf_clf.fit(X_train, y_train)
y_pred_rf = rf_clf.predict(X_test)
rf_err_rate = 1 - accuracy_score(y_test, y_pred_rf)
print('Random forest classifier error: ' + str(rf_err_rate)) | code |
32072376/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0] | code |
32072376/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
youtube['sentiment'] = round(youtube['dislikes'] / (youtube['likes'] + youtube['dislikes']) * 100)
avg_senti = youtube['sentiment'].mean()
avg_senti | code |
32072376/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
# for the sake of being concise, we've directly modified the matplotlib object returned by the .plot() method
# you could plot these two scatter plots separately and still see the relationship!
ax1 = youtube.plot(kind='scatter', x='views', y='likes', color='g') # likes are in green
ax2 = youtube.plot(kind='scatter', x='views', y='dislikes', color='r', ax=ax1) # dislikes are in red
ax2.set_ylabel('viewer interactions (likes/dislikes)')
youtube.groupby('title')['views', 'sentiment'].mean().sort_values('views', ascending=False).head()
total_views = youtube.groupby('category_id')['views'].sum().sort_values(ascending=False).head(10)
total_views.plot.barh() | code |
32072376/cell_2 | [
"text_plain_output_1.png"
] | import os
import json
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 |
32072376/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns | code |
32072376/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100) | code |
32072376/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views | code |
32072376/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
# for the sake of being concise, we've directly modified the matplotlib object returned by the .plot() method
# you could plot these two scatter plots separately and still see the relationship!
ax1 = youtube.plot(kind='scatter', x='views', y='likes', color='g') # likes are in green
ax2 = youtube.plot(kind='scatter', x='views', y='dislikes', color='r', ax=ax1) # dislikes are in red
ax2.set_ylabel('viewer interactions (likes/dislikes)')
youtube.groupby('title')['views', 'sentiment'].mean().sort_values('views', ascending=False).head()
total_views = youtube.groupby('category_id')['views'].sum().sort_values(ascending=False).head(10)
total_views.plot.barh()
average_views = youtube.groupby('category_id')['views'].mean().sort_values(ascending=False).head(10)
average_views.plot.barh()
youtube.groupby('channel_title')['title'].size().sort_values(ascending=False).head(10) | code |
32072376/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
# for the sake of being concise, we've directly modified the matplotlib object returned by the .plot() method
# you could plot these two scatter plots separately and still see the relationship!
ax1 = youtube.plot(kind='scatter', x='views', y='likes', color='g') # likes are in green
ax2 = youtube.plot(kind='scatter', x='views', y='dislikes', color='r', ax=ax1) # dislikes are in red
ax2.set_ylabel('viewer interactions (likes/dislikes)')
youtube.groupby('title')['views', 'sentiment'].mean().sort_values('views', ascending=False).head()
total_views = youtube.groupby('category_id')['views'].sum().sort_values(ascending=False).head(10)
total_views.plot.barh()
average_views = youtube.groupby('category_id')['views'].mean().sort_values(ascending=False).head(10)
average_views.plot.barh() | code |
32072376/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
# for the sake of being concise, we've directly modified the matplotlib object returned by the .plot() method
# you could plot these two scatter plots separately and still see the relationship!
ax1 = youtube.plot(kind='scatter', x='views', y='likes', color='g') # likes are in green
ax2 = youtube.plot(kind='scatter', x='views', y='dislikes', color='r', ax=ax1) # dislikes are in red
ax2.set_ylabel('viewer interactions (likes/dislikes)')
youtube.groupby('title')['views', 'sentiment'].mean().sort_values('views', ascending=False).head() | code |
32072376/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
ax1 = youtube.plot(kind='scatter', x='views', y='likes', color='g')
ax2 = youtube.plot(kind='scatter', x='views', y='dislikes', color='r', ax=ax1)
ax2.set_ylabel('viewer interactions (likes/dislikes)') | code |
32072376/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | # return the top 10 categories by trending video frequency using .value_counts()
youtube['category_id']\
.value_counts()\ | code |
32072376/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
avg_views = round(youtube['views'].mean())
avg_views
condition = youtube[youtube.views >= avg_views].shape[0]
round(condition / youtube.shape[0] * 100)
# for the sake of being concise, we've directly modified the matplotlib object returned by the .plot() method
# you could plot these two scatter plots separately and still see the relationship!
ax1 = youtube.plot(kind='scatter', x='views', y='likes', color='g') # likes are in green
ax2 = youtube.plot(kind='scatter', x='views', y='dislikes', color='r', ax=ax1) # dislikes are in red
ax2.set_ylabel('viewer interactions (likes/dislikes)')
youtube.groupby('title')['views', 'sentiment'].mean().sort_values('views', ascending=False).head()
total_views = youtube.groupby('category_id')['views'].sum().sort_values(ascending=False).head(10)
total_views.plot.barh()
average_views = youtube.groupby('category_id')['views'].mean().sort_values(ascending=False).head(10)
average_views.plot.barh()
youtube.groupby('channel_title')['title'].size().sort_values(ascending=False).head(10)
youtube.groupby('channel_title')['views'].mean().sort_values(ascending=False).head(10) | code |
32072376/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.columns
youtube_df.shape[0]
youtube = youtube_df[['video_id', 'title', 'channel_title', 'category_id', 'views', 'likes', 'dislikes']]
youtube.tail(3) | code |
32072376/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube_df = pd.read_csv('/kaggle/input/youtube-new/USvideos.csv')
youtube_df.head() | code |
122247715/cell_42 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['embarked'].value_counts() | code |
122247715/cell_63 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.get_dummies(titanic['who'], drop_first=True)
pd.get_dummies(titanic['class'], drop_first=True)
pd.get_dummies(titanic['embarked'], drop_first=True)
titanic = pd.concat([titanic, pd.get_dummies(titanic['who'], drop_first=True)], axis=1)
titanic | code |
122247715/cell_9 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.info() | code |
122247715/cell_57 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who']) | code |
122247715/cell_56 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
titanic['who'].value_counts() | code |
122247715/cell_34 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['who'].value_counts() | code |
122247715/cell_23 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embarked'].value_counts() | code |
122247715/cell_30 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum() | code |
122247715/cell_39 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['pclass'].value_counts() | code |
122247715/cell_26 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embark_town'].mode()[0] | code |
122247715/cell_65 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.get_dummies(titanic['who'], drop_first=True)
pd.get_dummies(titanic['class'], drop_first=True)
pd.get_dummies(titanic['embarked'], drop_first=True)
titanic = pd.concat([titanic, pd.get_dummies(titanic['who'], drop_first=True)], axis=1)
titanic = pd.concat([titanic, pd.get_dummies(titanic['class'], drop_first=True)], axis=1)
titanic['man'].dtype | code |
122247715/cell_41 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['sibsp'].value_counts() | code |
122247715/cell_54 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum() | code |
122247715/cell_11 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum() | code |
122247715/cell_60 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.get_dummies(titanic['who'], drop_first=True)
pd.get_dummies(titanic['class'], drop_first=True)
pd.get_dummies(titanic['embarked'], drop_first=True) | code |
122247715/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['age'].median() | code |
122247715/cell_45 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['survived'].value_counts() | code |
122247715/cell_18 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['age'].mean() | code |
122247715/cell_32 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.head() | code |
122247715/cell_51 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
x = ['class', 'age', 'sibsp', 'parch', 'fare', 'embarked', 'who', 'alone']
y = ['survived']
titanic[x].info() | code |
122247715/cell_59 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.get_dummies(titanic['who'], drop_first=True)
pd.get_dummies(titanic['class'], drop_first=True) | code |
122247715/cell_58 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic.isnull().sum()
pd.get_dummies(titanic['who'])
pd.get_dummies(titanic['who'], drop_first=True) | code |
122247715/cell_28 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum() | code |
122247715/cell_8 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.head() | code |
122247715/cell_15 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0] | code |
122247715/cell_38 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['class'].value_counts() | code |
122247715/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
plt.hist(titanic['age']) | code |
122247715/cell_35 | [
"text_html_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['sex'].value_counts() | code |
122247715/cell_43 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['embark_town'].value_counts() | code |
122247715/cell_46 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['alive'].value_counts() | code |
122247715/cell_24 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embarked'].mode()[0] | code |
122247715/cell_14 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0] | code |