path
stringlengths 13
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sequencelengths 1
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|---|---|---|---|
129001471/cell_9 | [
"image_output_1.png"
] | from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
import os
import pandas as pd
import plotly.graph_objects as go
import talib
import vectorbt as vbt
import os
import json
import pytz
import talib
import pickle
import numpy as np
import pandas as pd
import datetime as dt
import vectorbt as vbt
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from histDataHandler import loadSuchData
from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
current_directory = os.getcwd()
parent_directory = os.path.dirname(current_directory)
os.chdir(parent_directory)
vbt.settings.set_theme('dark')
vbt.settings['plotting']['layout']['width'] = 700
vbt.settings['plotting']['layout']['height'] = 350
subplots = ['trades', 'trade_pnl', 'cum_returns', 'underwater', 'net_exposure']
data = pd.read_csv('backtesting_ohlcv_data.csv', index_col=0, parse_dates=True)
data
shiftWindow = 48
modelName = f'articleModelSMA{shiftWindow}'
model, modelParamsDict, targetScaler, scalers = loadModel(modelName, data)
preds = pd.concat([genPreds(data, model, modelParamsDict, targetScaler, scalers), talib.SMA(data['Close'], timeperiod=shiftWindow).rename('Current_SMA')], axis=1).dropna()
exposure = genExposure(pd.concat([preds, data['Close'].shift(1).rename('Shifted_Close')], axis=1), 2.5, 10, 'Prediction')
preds_SMA = pd.concat([genShiftedSMA(data, shiftWindow, shiftWindow + 1).rename('Prediction'), talib.SMA(data['Close'], timeperiod=shiftWindow).rename('Current_SMA')], axis=1).dropna()
exposure_SMA = genExposure(pd.concat([preds_SMA, data['Close'].shift(1).rename('Shifted_Close')], axis=1), 1.25, 10, 'Prediction')
pf_model = vbt.Portfolio.from_orders(data['Open'][exposure.index], exposure, size_type='targetpercent', freq=modelParamsDict['frequency'])
pf_shiftedSMA = vbt.Portfolio.from_orders(data['Open'][exposure_SMA.index], exposure_SMA, size_type='targetpercent', freq=modelParamsDict['frequency'])
pf_model.stats()
figSpot_model = go.Figure(pf_model.plot(subplots=subplots))
figSpot_model.show() | code |
129001471/cell_2 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import vectorbt as vbt
import os
import json
import pytz
import talib
import pickle
import numpy as np
import pandas as pd
import datetime as dt
import vectorbt as vbt
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from histDataHandler import loadSuchData
from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
current_directory = os.getcwd()
parent_directory = os.path.dirname(current_directory)
os.chdir(parent_directory)
vbt.settings.set_theme('dark')
vbt.settings['plotting']['layout']['width'] = 700
vbt.settings['plotting']['layout']['height'] = 350
subplots = ['trades', 'trade_pnl', 'cum_returns', 'underwater', 'net_exposure']
data = pd.read_csv('backtesting_ohlcv_data.csv', index_col=0, parse_dates=True)
data | code |
129001471/cell_8 | [
"text_plain_output_1.png"
] | from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
import os
import pandas as pd
import talib
import vectorbt as vbt
import os
import json
import pytz
import talib
import pickle
import numpy as np
import pandas as pd
import datetime as dt
import vectorbt as vbt
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from histDataHandler import loadSuchData
from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
current_directory = os.getcwd()
parent_directory = os.path.dirname(current_directory)
os.chdir(parent_directory)
vbt.settings.set_theme('dark')
vbt.settings['plotting']['layout']['width'] = 700
vbt.settings['plotting']['layout']['height'] = 350
subplots = ['trades', 'trade_pnl', 'cum_returns', 'underwater', 'net_exposure']
data = pd.read_csv('backtesting_ohlcv_data.csv', index_col=0, parse_dates=True)
data
shiftWindow = 48
modelName = f'articleModelSMA{shiftWindow}'
model, modelParamsDict, targetScaler, scalers = loadModel(modelName, data)
preds = pd.concat([genPreds(data, model, modelParamsDict, targetScaler, scalers), talib.SMA(data['Close'], timeperiod=shiftWindow).rename('Current_SMA')], axis=1).dropna()
exposure = genExposure(pd.concat([preds, data['Close'].shift(1).rename('Shifted_Close')], axis=1), 2.5, 10, 'Prediction')
preds_SMA = pd.concat([genShiftedSMA(data, shiftWindow, shiftWindow + 1).rename('Prediction'), talib.SMA(data['Close'], timeperiod=shiftWindow).rename('Current_SMA')], axis=1).dropna()
exposure_SMA = genExposure(pd.concat([preds_SMA, data['Close'].shift(1).rename('Shifted_Close')], axis=1), 1.25, 10, 'Prediction')
pf_model = vbt.Portfolio.from_orders(data['Open'][exposure.index], exposure, size_type='targetpercent', freq=modelParamsDict['frequency'])
pf_shiftedSMA = vbt.Portfolio.from_orders(data['Open'][exposure_SMA.index], exposure_SMA, size_type='targetpercent', freq=modelParamsDict['frequency'])
pf_model.stats() | code |
32071289/cell_9 | [
"text_plain_output_1.png"
] | """mae = mean_absolute_error(y_valid_cc, preds_cc['preds'])
msle = mean_squared_log_error(y_valid_cc, preds_cc['preds'])
print("CC MAE: %f MSLE %f" % (mae, msle))
mae = mean_absolute_error(y_valid_ft, preds_ft['preds'])
msle = mean_squared_log_error(y_valid_ft, preds_ft['preds'])
print("FT MAE: %f MSLE %f" % (mae, msle))"""
'\nCC MAE: 53.621829 MSLE 0.032919\nFT MAE: 3.685815 MSLE 0.008674\n' | code |
32071289/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', index_col='Id', parse_dates=['Date'])
covid_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', index_col='ForecastId', parse_dates=['Date'])
last_register = pd.to_datetime(covid_train['Date'].iloc[covid_train.shape[0] - 1])
def adjustState(row):
if pd.isna(row['Province_State']):
row['Province_State'] = row['Country_Region']
return row
covid_train = covid_train.apply(adjustState, axis=1)
covid_test = covid_test.apply(adjustState, axis=1)
covid_train.fillna('NA', inplace=True)
covid_test.fillna('NA', inplace=True)
n_cases_cc = 50
n_cases_ft = 50
data_mark_date = pd.DataFrame(columns=['Country_Region', 'Province_State', 'Date_cc', 'Date_ft'])
data_mark_date.set_index(['Country_Region', 'Province_State'])
for country in covid_train['Country_Region'].unique():
for state in covid_train[covid_train['Country_Region'] == country]['Province_State'].unique():
data_df = covid_train[(covid_train['Country_Region'] == country) & (covid_train['Province_State'] == state)]
if data_df[data_df['ConfirmedCases'] >= n_cases_cc].shape[0] > 0:
date_cc = data_df[data_df['ConfirmedCases'] >= n_cases_cc].iloc[0]['Date']
else:
date_cc = last_register
if data_df[data_df['Fatalities'] >= n_cases_ft].shape[0] > 0:
date_ft = data_df[data_df['Fatalities'] >= n_cases_ft].iloc[0]['Date']
else:
date_ft = last_register
data_state = pd.DataFrame({'Country_Region': [country], 'Province_State': [state], 'Date_cc': [date_cc], 'Date_ft': [date_ft]})
data_state.set_index(['Country_Region', 'Province_State'])
data_mark_date = data_mark_date.append(data_state.iloc[0])
def mark_date(row):
data_df = data_mark_date[(data_mark_date['Country_Region'] == row['Country_Region']) & (data_mark_date['Province_State'] == row['Province_State'])].iloc[0]
if not pd.isna(data_df['Date_cc']):
row['Date_cc'] = (row['Date'] - data_df['Date_cc']).days
if not pd.isna(data_df['Date_ft']):
row['Date_ft'] = (row['Date'] - data_df['Date_ft']).days
return row
covid_train = covid_train[(covid_train['ConfirmedCases'] > 0) | (covid_train['Fatalities'] > 0)]
covid_train['Date_cc'] = [0 for i in range(covid_train.shape[0])]
covid_train['Date_ft'] = [0 for i in range(covid_train.shape[0])]
covid_train = covid_train.apply(mark_date, axis=1)
covid_test['Date_cc'] = [0 for i in range(covid_test.shape[0])]
covid_test['Date_ft'] = [0 for i in range(covid_test.shape[0])]
covid_test = covid_test.apply(mark_date, axis=1)
covid_train['Date_st'] = covid_train['Date'].map(lambda x: x.timestamp())
covid_test['Date_st'] = covid_test['Date'].map(lambda x: x.timestamp())
enc = LabelEncoder()
covid_train['Province_State_enc'] = enc.fit_transform(covid_train['Province_State'])
covid_test['Province_State_enc'] = enc.transform(covid_test['Province_State'])
enc = LabelEncoder()
covid_train['Country_Region_enc'] = enc.fit_transform(covid_train['Country_Region'])
covid_test['Country_Region_enc'] = enc.transform(covid_test['Country_Region'])
X_features = ['Province_State', 'Country_Region', 'Date_st', 'Date_cc', 'Date_ft']
X = covid_train[X_features]
y_cc = covid_train['ConfirmedCases']
y_ft = covid_train['Fatalities']
print('Adjust data complete') | code |
32071289/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', index_col='Id', parse_dates=['Date'])
covid_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', index_col='ForecastId', parse_dates=['Date'])
last_register = pd.to_datetime(covid_train['Date'].iloc[covid_train.shape[0] - 1])
print('Len train %d, Len test %d' % (covid_train.shape[0], covid_test.shape[0]))
print('Last train "Date": ', last_register) | code |
32071289/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_log_error
print('Setup complete') | code |
32071289/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', index_col='Id', parse_dates=['Date'])
covid_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', index_col='ForecastId', parse_dates=['Date'])
last_register = pd.to_datetime(covid_train['Date'].iloc[covid_train.shape[0] - 1])
covid_train.info() | code |
32071289/cell_10 | [
"text_plain_output_1.png"
] | """X_train_2, X_valid_2, y_train_cc_2, y_valid_cc_2 = train_test_split(covid_train[X_features_cc], y_cc, random_state=42)
model_cc_2 = RandomForestRegressor(n_estimators=100, random_state=42)
model_cc_2.fit(X_train_2, y_train_cc_2)
predic = model_cc_2.predict(X_valid_2)
mae = mean_absolute_error(y_valid_cc_2, predic)
msle = mean_squared_log_error(y_valid_cc_2, predic)
print("CC MAE: %f MSLE %f" % (mae, msle))
X_train_2, X_valid_2, y_train_ft_2, y_valid_ft_2 = train_test_split(covid_train[X_features_ft], y_ft, random_state=42)
model_ft_2 = RandomForestRegressor(n_estimators=50, random_state=42)
model_ft_2.fit(X_train_2, y_train_ft_2)
predic = model_ft_2.predict(X_valid_2)
mae = mean_absolute_error(y_valid_ft_2, predic)
msle = mean_squared_log_error(y_valid_ft_2, predic)
print("CC MAE: %f MSLE %f" % (mae, msle))"""
'\nCC MAE: 217.231728 MSLE 0.881407\nCC MAE: 14.093100 MSLE 0.503043\n' | code |
49116605/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
order = pd.read_csv('/kaggle/input/market-basket-id-ndsc-2020/association_order.csv')
order | code |
49116605/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 |
2036189/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import itertools
import numpy as np # linear algebra
import random
"""
create sample data
user and product are segmented
"""
random.seed(0)
NN_word = 2000
NN_sentence = 10000
NN_SEG = 7
class Seq(object):
def __init__(self, neg_sample=5, batch_size=32, stop=None, common_prods=[50, 100, 200, 500, 1000]):
self.common_prods = common_prods
self.batch_size = batch_size
self.product_list = [ee + 1 for ee in range(NN_word)]
self.product_set = set(self.product_list)
self.neg_sample = neg_sample
nn_each_group, amari = divmod(len(self.product_list), NN_SEG)
if amari != 0:
nn_each_group += 1
del_none = lambda l: filter(lambda x: x is not None, l)
self.product_group = [[e1 for e1 in del_none(ee)] for ee in itertools.zip_longest(*[iter(self.product_list)] * nn_each_group)]
self.user_list = [ee + 1 for ee in range(NN_sentence)]
self.user_list_org = [ee for ee in self.user_list]
if stop is not None:
self.user_list = self.user_list[:stop]
self.user_set = set(self.user_list)
self.user_id_next = self.user_list[0]
self.create_seed()
'estimate self length'
self.initialize_it()
self.len = 1
for _ in self.it:
self.len += 1
self.initialize_it()
def initialize_it(self):
self.it = iter(range(0, len(self.user_list), self.batch_size))
self.idx_next = self.it.__next__()
def create_seed(self):
self.seeds = {}
for ii, user_id in enumerate(self.user_list):
self.seeds[user_id] = ii
def __len__(self):
return self.len
def __iter__(self):
return self
def __next__(self):
idx = self.idx_next
self.user_ids_part = self.user_list[idx:idx + self.batch_size if idx + self.batch_size < len(self.user_list) else len(self.user_list)]
res = self.getpart(self.user_ids_part)
try:
self.idx_next = self.it.__next__()
except StopIteration:
self.initialize_it()
return res
def __getitem__(self, user_id):
ret_users, ret_prods, ret_y = self.get_data(user_id)
return ({'input_user': np.array(ret_users), 'input_prod': np.array(ret_prods)}, ret_y)
def get_data(self, user_id):
random.seed(self.seeds[user_id])
nword = random.randint(5, 20)
a, _ = divmod(len(self.user_list_org), NN_SEG)
ii = int(user_id / (a + 1))
prods = random.sample(self.product_group[ii], nword)
prods.extend(self.common_prods)
prods = list(set(prods))
neg = self.get_neg(prods)
ret_users = [user_id] * (len(prods) * (1 + self.neg_sample))
ret_prods = prods + neg
ret_y = [1] * len(prods) + [0] * len(neg)
return (ret_users, ret_prods, ret_y)
def get_neg(self, prods):
o = self.product_set.difference(prods)
random.seed()
neg = random.sample(o, len(prods) * self.neg_sample)
return neg
def getpart(self, user_ids_part):
x_input_user = []
x_input_prod = []
y = []
for user_id in user_ids_part:
x_train, y_train = self[user_id]
x_input_prod.extend(x_train['input_prod'].tolist())
x_input_user.extend(x_train['input_user'].tolist())
y.append(y_train)
return ({'input_prod': np.array(x_input_prod), 'input_user': np.array(x_input_user)}, np.concatenate(y))
seq = Seq(neg_sample=1, common_prods=[])
print(len(seq))
X_list = [np.zeros((1, NN_word + 1))]
for iuser in seq.user_list:
x_train, y_train = seq[iuser]
prods = x_train['input_prod'][np.array(y_train) == 1]
irow = np.zeros((1, NN_word + 1))
irow[0, prods] = 1
X_list.append(irow)
X = np.concatenate(X_list)
print(X.shape)
X | code |
2036189/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import itertools
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import random
"""
create sample data
user and product are segmented
"""
random.seed(0)
NN_word = 2000
NN_sentence = 10000
NN_SEG = 7
class Seq(object):
def __init__(self, neg_sample=5, batch_size=32, stop=None, common_prods=[50, 100, 200, 500, 1000]):
self.common_prods = common_prods
self.batch_size = batch_size
self.product_list = [ee + 1 for ee in range(NN_word)]
self.product_set = set(self.product_list)
self.neg_sample = neg_sample
nn_each_group, amari = divmod(len(self.product_list), NN_SEG)
if amari != 0:
nn_each_group += 1
del_none = lambda l: filter(lambda x: x is not None, l)
self.product_group = [[e1 for e1 in del_none(ee)] for ee in itertools.zip_longest(*[iter(self.product_list)] * nn_each_group)]
self.user_list = [ee + 1 for ee in range(NN_sentence)]
self.user_list_org = [ee for ee in self.user_list]
if stop is not None:
self.user_list = self.user_list[:stop]
self.user_set = set(self.user_list)
self.user_id_next = self.user_list[0]
self.create_seed()
'estimate self length'
self.initialize_it()
self.len = 1
for _ in self.it:
self.len += 1
self.initialize_it()
def initialize_it(self):
self.it = iter(range(0, len(self.user_list), self.batch_size))
self.idx_next = self.it.__next__()
def create_seed(self):
self.seeds = {}
for ii, user_id in enumerate(self.user_list):
self.seeds[user_id] = ii
def __len__(self):
return self.len
def __iter__(self):
return self
def __next__(self):
idx = self.idx_next
self.user_ids_part = self.user_list[idx:idx + self.batch_size if idx + self.batch_size < len(self.user_list) else len(self.user_list)]
res = self.getpart(self.user_ids_part)
try:
self.idx_next = self.it.__next__()
except StopIteration:
self.initialize_it()
return res
def __getitem__(self, user_id):
ret_users, ret_prods, ret_y = self.get_data(user_id)
return ({'input_user': np.array(ret_users), 'input_prod': np.array(ret_prods)}, ret_y)
def get_data(self, user_id):
random.seed(self.seeds[user_id])
nword = random.randint(5, 20)
a, _ = divmod(len(self.user_list_org), NN_SEG)
ii = int(user_id / (a + 1))
prods = random.sample(self.product_group[ii], nword)
prods.extend(self.common_prods)
prods = list(set(prods))
neg = self.get_neg(prods)
ret_users = [user_id] * (len(prods) * (1 + self.neg_sample))
ret_prods = prods + neg
ret_y = [1] * len(prods) + [0] * len(neg)
return (ret_users, ret_prods, ret_y)
def get_neg(self, prods):
o = self.product_set.difference(prods)
random.seed()
neg = random.sample(o, len(prods) * self.neg_sample)
return neg
def getpart(self, user_ids_part):
x_input_user = []
x_input_prod = []
y = []
for user_id in user_ids_part:
x_train, y_train = self[user_id]
x_input_prod.extend(x_train['input_prod'].tolist())
x_input_user.extend(x_train['input_user'].tolist())
y.append(y_train)
return ({'input_prod': np.array(x_input_prod), 'input_user': np.array(x_input_user)}, np.concatenate(y))
seq = Seq(neg_sample=1, common_prods=[])
X_list = [np.zeros((1, NN_word + 1))]
for iuser in seq.user_list:
x_train, y_train = seq[iuser]
prods = x_train['input_prod'][np.array(y_train) == 1]
irow = np.zeros((1, NN_word + 1))
irow[0, prods] = 1
X_list.append(irow)
X = np.concatenate(X_list)
X
plt.figure(figsize=(10, 10))
plt.imshow(X) | code |
2036189/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot | code |
90124843/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
print('The population mean is', population_mean, 'and nobody knows this value.') | code |
90124843/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
sample = np.random.choice(population, size=10)
print('\nThe sample mean is', np.round(np.mean(sample), 2), '\n\nWe collected the data from the sample and we know this value.') | code |
90124843/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import scipy.stats as st
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
sample = np.random.choice(population, size=10)
def confidence_interval(sample, prob):
return st.t.interval(prob, len(sample) - 1, loc=np.mean(sample), scale=st.sem(sample))
print('The 0.95 confidence interval generated from this sample is', confidence_interval(sample, prob=0.95)) | code |
90124843/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
plt.figure(figsize=(15, 5))
plt.hist(population, bins=100)
plt.grid()
plt.title('Population distribution')
plt.show() | code |
90124843/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import scipy.stats as st
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
sample = np.random.choice(population, size=10)
def confidence_interval(sample, prob):
return st.t.interval(prob, len(sample) - 1, loc=np.mean(sample), scale=st.sem(sample))
number_of_correct_guesses = 0
for i in range(100):
sample = np.random.choice(population, size=10)
conf_interval = confidence_interval(sample, prob=0.95)
if population_mean >= conf_interval[0] and population_mean <= conf_interval[1]:
number_of_correct_guesses += 1
print(f'Out of 100 samples, the confidence intervals for {number_of_correct_guesses} of them contained the population mean.') | code |
73067313/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.title('Analog Signal', fontsize=20)
plt.plot(t, x, linewidth=3, label='x(t) = (0.85)^t')
plt.xlabel('t', fontsize=15)
plt.ylabel('amplitude', fontsize=15)
plt.legend(loc='upper right')
plt.subplot(2, 2, 2)
plt.title('Sampling', fontsize=20)
plt.plot(t, x, linewidth=3, label='x(t) = (0.85)^t')
n = t
markerline, stemlines, baseline = plt.stem(n, x, label='x(n) = (0.85)^n')
plt.setp(stemlines, 'linewidth', 3)
plt.xlabel('n', fontsize=15)
plt.ylabel('amplitude', fontsize=15)
plt.legend(loc='upper right')
plt.subplot(2, 2, 3)
plt.title('Quantization', fontsize=20)
plt.plot(t, x, linewidth=3)
markerline, stemlines, baseline = plt.stem(n, x)
plt.setp(stemlines, 'linewidth', 3)
plt.xlabel('n', fontsize=15)
plt.ylabel('Range of Quantizer', fontsize=15)
plt.axhline(y=0.1, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.2, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.3, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.4, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.5, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.6, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.7, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.8, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.9, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=1.0, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.subplot(2, 2, 4)
plt.title('Quantized Signal', fontsize=20)
xq = np.around(x, 1)
markerline, stemlines, baseline = plt.stem(n, xq)
plt.setp(stemlines, 'linewidth', 3)
plt.xlabel('n', fontsize=15)
plt.ylabel('Range of Quantizer', fontsize=15)
plt.axhline(y=0.1, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.2, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.3, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.4, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.5, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.6, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.7, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.8, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=0.9, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.axhline(y=1.0, xmin=0, xmax=10, color='r', linewidth=3.0)
plt.tight_layout() | code |
73067313/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
n = t
markerline, stemlines, baseline = plt.stem(n, x, label='x(n) = (0.85)^n')
plt.setp(stemlines, 'linewidth', 3)
markerline, stemlines, baseline = plt.stem(n, x)
plt.setp(stemlines, 'linewidth', 3)
xq = np.around(x, 1)
markerline, stemlines, baseline = plt.stem(n, xq)
plt.setp(stemlines, 'linewidth', 3)
plt.tight_layout()
impulse = signal.unit_impulse(10, 'mid')
shifted_impulse = signal.unit_impulse(7, 2)
t = np.linspace(0, 10, 100)
amp = 5
f = 50
x = amp * np.sin(2 * np.pi * f * t)
x_ = amp * np.exp(-t)
plt.figure(figsize=(10, 6))
plt.subplot(2, 2, 1)
plt.plot(np.arange(-5, 5), impulse, linewidth=3, label='Unit impulse function')
plt.ylim(-0.01, 1)
plt.xlabel('time.', fontsize=15)
plt.ylabel('Amplitude', fontsize=15)
plt.legend(fontsize=10, loc='upper right')
plt.subplot(2, 2, 2)
plt.plot(shifted_impulse, linewidth=3, label='Shifted Unit impulse function')
plt.xlabel('time.', fontsize=15)
plt.ylabel('Amplitude', fontsize=15)
plt.legend(fontsize=10, loc='upper right')
plt.subplot(2, 2, 3)
plt.plot(t, x, linewidth=3, label='Sine wave')
plt.xlabel('time.', fontsize=15)
plt.ylabel('Amplitude', fontsize=15)
plt.legend(fontsize=10, loc='upper right')
plt.subplot(2, 2, 4)
plt.plot(t, x_, linewidth=3, label='Exponential Signal')
plt.xlabel('time.', fontsize=15)
plt.ylabel('Amplitude', fontsize=15)
plt.legend(fontsize=10, loc='upper right')
plt.tight_layout() | code |
73067313/cell_10 | [
"image_output_1.png"
] | from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
n = t
markerline, stemlines, baseline = plt.stem(n, x, label='x(n) = (0.85)^n')
plt.setp(stemlines, 'linewidth', 3)
markerline, stemlines, baseline = plt.stem(n, x)
plt.setp(stemlines, 'linewidth', 3)
xq = np.around(x, 1)
markerline, stemlines, baseline = plt.stem(n, xq)
plt.setp(stemlines, 'linewidth', 3)
plt.tight_layout()
impulse = signal.unit_impulse(10, 'mid')
shifted_impulse = signal.unit_impulse(7, 2)
t = np.linspace(0, 10, 100)
amp = 5
f = 50
x = amp * np.sin(2 * np.pi * f * t)
x_ = amp * np.exp(-t)
plt.ylim(-0.01, 1)
plt.tight_layout()
n = np.linspace(0, 10, 100)
amp = 5
f = 50
x = amp * np.sin(2 * np.pi * f * n)
x_ = amp * np.exp(-n)
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.stem(n, x, 'yo', label='Sine wave')
plt.xlabel('time.', fontsize=15)
plt.ylabel('Amplitude', fontsize=15)
plt.legend(fontsize=10, loc='upper right')
plt.subplot(2, 2, 2)
plt.stem(n, x_, 'yo', label='Exponential Signal')
plt.xlabel('time.', fontsize=15)
plt.ylabel('Amplitude', fontsize=15)
plt.legend(fontsize=10, loc='upper right') | code |
128010001/cell_9 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
stationsdf.head(10) | code |
128010001/cell_25 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
stationsdf.isna().sum()
stationsdf.count()
stationsdf['rental_methods'].value_counts() | code |
128010001/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
print(DATA_PATH) | code |
128010001/cell_23 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
tripsdf.info() | code |
128010001/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.countplot(y=tripsdf['gender']) | code |
128010001/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.set(rc={'figure.figsize': (15, 5)})
sns.countplot(data=tripsdf, x=tripsdf['cust_hour'], hue='gender') | code |
128010001/cell_2 | [
"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 |
128010001/cell_19 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count() | code |
128010001/cell_18 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
stationsdf.isna().sum()
stationsdf.count() | code |
128010001/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
tripsdf['tripduration'].value_counts() | code |
128010001/cell_59 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
tripsdf.sort_values(by='manhattan_distance', ascending=True) | code |
128010001/cell_58 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
print(tripsdf['manhattan_distance'].min()) | code |
128010001/cell_28 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
tripsdf.describe() | code |
128010001/cell_15 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum() | code |
128010001/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.countplot(y=tripsdf['usertype']) | code |
128010001/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
plt.figure(figsize=(15, 20))
sns.countplot(y=tripsdf['birth_year']) | code |
128010001/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
tripsdf['cust_hour'].value_counts().sort_index() | code |
128010001/cell_14 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
stationsdf.isna().sum() | code |
128010001/cell_22 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
stationsdf.isna().sum()
stationsdf.count()
stationsdf.info() | code |
128010001/cell_53 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.set(rc={'figure.figsize': (10, 5)})
sns.set(rc={'figure.figsize': (15, 5)})
sns.set_style('whitegrid')
sns.displot(data=tripsdf, x=np.log10(tripsdf['tripduration']), kind='hist', aspect=5, hue='gender', palette='Blues').set_axis_labels('log trip_duration')
sns.displot(data=tripsdf, x=np.log10(tripsdf['tripduration']), kind='ecdf', aspect=5, hue='gender').set_axis_labels('log trip_duration') | code |
128010001/cell_27 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
stationsdf.isna().sum()
stationsdf.count()
stationsdf.describe() | code |
128010001/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233]
tripsdf.isna().sum()
tripsdf.count()
sns.set(rc={'figure.figsize': (15, 30)})
sns.countplot(data=tripsdf, y=tripsdf[tripsdf.cust_age <= 99]['cust_age'], hue='gender', dodge=True) | code |
128010001/cell_12 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
stationsdf = pd.read_csv(DATA_PATH + 'citibike-stations.csv')
tripsdf = pd.read_csv(DATA_PATH + 'citibike-trips.csv')
tripsdf[tripsdf.start_station_id == 3233] | code |
129024960/cell_42 | [
"image_output_1.png"
] | grid_result_lasso.best_params_ | code |
129024960/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
df.describe() | code |
129024960/cell_56 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x="Room_Occupancy_Count", y=feat, data=df, ax=ax)
ax.set_xlabel("")
ax.set_ylabel(feat)
fig.tight_layout();
X = df.drop(['Room_Occupancy_Count'], axis=1)
y = df[['Room_Occupancy_Count']]
grid_result_lasso.best_params_
coef = grid_result_lasso.best_estimator_.coef_
coef
X.columns[coef == 0]
# Creating a correlation matrix
corr_matrix = df.corr()
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(corr_matrix,
cmap=sns.diverging_palette(220, 10, as_cmap=True),
annot=True,
annot_kws={"fontsize":7}
)
plt.xticks(rotation=45, ha='right', fontsize=7)
plt.yticks(fontsize=7)
plt.show()
def get_correlated_variables(dataset, threshold):
corr_columns = set()
corr_matrix = dataset.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i][j]) > threshold:
column_name = corr_matrix.columns[i]
corr_columns.add(column_name)
return corr_columns
corr_features = get_correlated_variables(X, 0.8)
corr_features
X_final = X[corr_features]
X_final.head() | code |
129024960/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x='Room_Occupancy_Count', y=feat, data=df, ax=ax)
ax.set_xlabel('')
ax.set_ylabel(feat)
fig.tight_layout() | code |
129024960/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
plt.subplots_adjust(wspace=0.5)
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color='b')
ax[4].set_xlabel('S5_CO2')
for subplot in ax:
subplot.set_xticklabels([])
plt.show() | code |
129024960/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
sns.pairplot(data=df, hue='Room_Occupancy_Count') | code |
129024960/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x="Room_Occupancy_Count", y=feat, data=df, ax=ax)
ax.set_xlabel("")
ax.set_ylabel(feat)
fig.tight_layout();
X = df.drop(['Room_Occupancy_Count'], axis=1)
y = df[['Room_Occupancy_Count']]
grid_result_lasso.best_params_
coef = grid_result_lasso.best_estimator_.coef_
coef
X.columns[coef == 0] | code |
129024960/cell_55 | [
"image_output_1.png"
] | from mlxtend.feature_selection import SequentialFeatureSelector as SFS
sffs = SFS(RandomForestClassifier(), k_features=(1, len(X.columns)), forward=True, floating=True, scoring='accuracy', cv=5)
sffs.fit(X, y)
corr_features = list(sffs.k_feature_names_)
corr_features | code |
129024960/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.head() | code |
129024960/cell_40 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x="Room_Occupancy_Count", y=feat, data=df, ax=ax)
ax.set_xlabel("")
ax.set_ylabel(feat)
fig.tight_layout();
X = df.drop(['Room_Occupancy_Count'], axis=1)
y = df[['Room_Occupancy_Count']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
lasso_reg = Lasso()
lasso_reg.fit(X_scaled, y) | code |
129024960/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x="Room_Occupancy_Count", y=feat, data=df, ax=ax)
ax.set_xlabel("")
ax.set_ylabel(feat)
fig.tight_layout();
X = df.drop(['Room_Occupancy_Count'], axis=1)
y = df[['Room_Occupancy_Count']]
corr_matrix = df.corr()
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(corr_matrix, cmap=sns.diverging_palette(220, 10, as_cmap=True), annot=True, annot_kws={'fontsize': 7})
plt.xticks(rotation=45, ha='right', fontsize=7)
plt.yticks(fontsize=7)
plt.show() | code |
129024960/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | grid = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1]}
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=42)
grid_search_lasso = GridSearchCV(estimator=lasso_reg, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy', error_score=0)
grid_result_lasso = grid_search_lasso.fit(X_scaled, y) | code |
129024960/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape | code |
129024960/cell_49 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x="Room_Occupancy_Count", y=feat, data=df, ax=ax)
ax.set_xlabel("")
ax.set_ylabel(feat)
fig.tight_layout();
X = df.drop(['Room_Occupancy_Count'], axis=1)
y = df[['Room_Occupancy_Count']]
# Creating a correlation matrix
corr_matrix = df.corr()
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(corr_matrix,
cmap=sns.diverging_palette(220, 10, as_cmap=True),
annot=True,
annot_kws={"fontsize":7}
)
plt.xticks(rotation=45, ha='right', fontsize=7)
plt.yticks(fontsize=7)
plt.show()
df.shape | code |
129024960/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
df.info() | code |
129024960/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
# To tune the spacing between the subplots
plt.subplots_adjust(wspace=0.5)
# Drawing boxplot for S1_Light in the 1st subplot
sns.violinplot(data=df['S1_Light'], ax=ax[0], color='brown')
ax[0].set_xlabel('S1_Light')
# Drawing boxplot for S2_Light in the 1st subplot
sns.violinplot(data=df['S2_Light'], ax=ax[1], color='g')
ax[1].set_xlabel('S2_Light')
# Drawing boxplot for S3_Light in the 1st subplot
sns.violinplot(data=df['S3_Light'], ax=ax[2])
ax[2].set_xlabel('S3_Light')
# Drawing boxplot for S4_Light in the 1st subplot
sns.violinplot(data=df['S4_Light'], ax=ax[3], color='y')
ax[3].set_xlabel('S4_Light')
# Drawing boxplot for S5_CO2 in the 1st subplot
sns.violinplot(data=df['S5_CO2'], ax=ax[4], color = 'b')
ax[4].set_xlabel('S5_CO2')
# by default, you'll see x-tick label set to 0 in each subplot
# remove it by setting it to empty list
for subplot in ax:
subplot.set_xticklabels([])
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(20, 15))
for idx, feat in enumerate(df.columns.to_list(), start=0):
ax = axes[int(idx / 4), idx % 4]
sns.boxplot(x="Room_Occupancy_Count", y=feat, data=df, ax=ax)
ax.set_xlabel("")
ax.set_ylabel(feat)
fig.tight_layout();
X = df.drop(['Room_Occupancy_Count'], axis=1)
y = df[['Room_Occupancy_Count']]
grid_result_lasso.best_params_
coef = grid_result_lasso.best_estimator_.coef_
coef
X.columns[coef == 0]
# Creating a correlation matrix
corr_matrix = df.corr()
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(corr_matrix,
cmap=sns.diverging_palette(220, 10, as_cmap=True),
annot=True,
annot_kws={"fontsize":7}
)
plt.xticks(rotation=45, ha='right', fontsize=7)
plt.yticks(fontsize=7)
plt.show()
def get_correlated_variables(dataset, threshold):
corr_columns = set()
corr_matrix = dataset.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i][j]) > threshold:
column_name = corr_matrix.columns[i]
corr_columns.add(column_name)
return corr_columns
corr_features = get_correlated_variables(X, 0.8)
corr_features | code |
129024960/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df['Room_Occupancy_Count'].value_counts().plot(kind='pie') | code |
129024960/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()] | code |
129024960/cell_43 | [
"text_html_output_1.png"
] | grid_result_lasso.best_params_
coef = grid_result_lasso.best_estimator_.coef_
coef | code |
129024960/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
df.head() | code |
129024960/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()] | code |
2022682/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show() | code |
2022682/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape | code |
2022682/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
iris_main.info() | code |
2022682/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
X = iris_main.drop(['Id', 'Species'], axis=1)
Y = iris_main['Species']
X.head() | code |
2022682/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_palette('husl')
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2022682/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
iris_main.describe() | code |
2022682/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
iris_main['Species'].value_counts() | code |
2022682/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
K_range = list(range(1, 26))
scores = []
for k in K_range:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
scores.append(metrics.accuracy_score(Y_test, Y_pred))
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
print('the accuracy sore for logistic regressiion is : ', metrics.accuracy_score(Y_test, Y_pred)) | code |
2022682/cell_16 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
K_range = list(range(1, 26))
scores = []
for k in K_range:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
scores.append(metrics.accuracy_score(Y_test, Y_pred))
knn = KNeighborsClassifier(n_neighbors=12)
knn.fit(X_train, Y_train)
knn.predict([[2, 5, 1, 1.5]]) | code |
2022682/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main | code |
2022682/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
K_range = list(range(1, 26))
scores = []
for k in K_range:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
scores.append(metrics.accuracy_score(Y_test, Y_pred))
plt.plot(K_range, scores)
plt.xlabel('Accuracy scores')
plt.ylabel('value of for knn')
plt.title('accuracy scores with respect to each value of K for knn')
plt.show() | code |
2022682/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
X = iris_main.drop(['Id', 'Species'], axis=1)
Y = iris_main['Species']
Y.head() | code |
2022682/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe | code |
34150026/cell_13 | [
"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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.head() | code |
34150026/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
id = test['qid']
id.shape | code |
34150026/cell_57 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.score(X_train, y_train)
y = logit.predict(X_test)
from sklearn.metrics import classification_report
print('classification_report of X_test data is : ', classification_report(y_test, y)) | code |
34150026/cell_44 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from string import punctuation
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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
test.rename(columns={'question_text': 'text'}, inplace=True)
test.isnull().sum()
test.drop('qid', inplace=True, axis=1)
stop = set(stopwords.words('english'))
punctuation = list(string.punctuation)
stop.update(punctuation)
def strip_html(text):
soup = BeautifulSoup(text, 'html.parser')
return soup.get_text()
def remove_between_square_brackets(text):
return re.sub('\\[[^]]*\\]', '', text)
def remove_urls(text):
return re.sub('http\\S+', '', text)
def remove_hash(text):
text = ' '.join((word.strip() for word in re.split('#|_', text)))
return text
def remove_stopwords(text):
final_text = []
for i in text.split():
if i.strip().lower() not in stop:
final_text.append(i.strip())
return ' '.join(final_text)
def denoise_text(text):
text = strip_html(text)
text = remove_between_square_brackets(text)
text = remove_urls(text)
text = remove_hash(text)
text = remove_stopwords(text)
return text
max_features = 20000
maxlen = 100
tokenizer = text.Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(train.text)
tokenized_train = tokenizer.texts_to_sequences(train.text)
X = sequence.pad_sequences(tokenized_train, maxlen=maxlen)
tokenized_test = tokenizer.texts_to_sequences(test.text)
sub_test = sequence.pad_sequences(tokenized_test, maxlen=maxlen)
sub_test.shape | code |
34150026/cell_20 | [
"text_plain_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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.head() | code |
34150026/cell_55 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.score(X_train, y_train)
y = logit.predict(X_test)
from sklearn.metrics import accuracy_score
print('accuracy of X_test data is : ', accuracy_score(y_test, y)) | code |
34150026/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
test.rename(columns={'question_text': 'text'}, inplace=True)
test.isnull().sum() | code |
34150026/cell_61 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
submission.head() | code |
34150026/cell_11 | [
"text_plain_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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
sns.barplot(x.index, x)
plt.gca().set_ylabel('samples') | code |
34150026/cell_60 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from sklearn.linear_model import LogisticRegression
from string import punctuation
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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
test.rename(columns={'question_text': 'text'}, inplace=True)
test.isnull().sum()
test.drop('qid', inplace=True, axis=1)
stop = set(stopwords.words('english'))
punctuation = list(string.punctuation)
stop.update(punctuation)
def strip_html(text):
soup = BeautifulSoup(text, 'html.parser')
return soup.get_text()
def remove_between_square_brackets(text):
return re.sub('\\[[^]]*\\]', '', text)
def remove_urls(text):
return re.sub('http\\S+', '', text)
def remove_hash(text):
text = ' '.join((word.strip() for word in re.split('#|_', text)))
return text
def remove_stopwords(text):
final_text = []
for i in text.split():
if i.strip().lower() not in stop:
final_text.append(i.strip())
return ' '.join(final_text)
def denoise_text(text):
text = strip_html(text)
text = remove_between_square_brackets(text)
text = remove_urls(text)
text = remove_hash(text)
text = remove_stopwords(text)
return text
max_features = 20000
maxlen = 100
tokenizer = text.Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(train.text)
tokenized_train = tokenizer.texts_to_sequences(train.text)
X = sequence.pad_sequences(tokenized_train, maxlen=maxlen)
tokenized_test = tokenizer.texts_to_sequences(test.text)
sub_test = sequence.pad_sequences(tokenized_test, maxlen=maxlen)
sub_test.shape
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.score(X_train, y_train)
y = logit.predict(X_test)
final = logit.predict(sub_test)
final.shape | code |
34150026/cell_52 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.score(X_train, y_train)
score | code |
34150026/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 |
34150026/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train) | code |
34150026/cell_18 | [
"text_plain_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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum() | code |
34150026/cell_62 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from string import punctuation
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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
test.rename(columns={'question_text': 'text'}, inplace=True)
test.isnull().sum()
test.drop('qid', inplace=True, axis=1)
stop = set(stopwords.words('english'))
punctuation = list(string.punctuation)
stop.update(punctuation)
def strip_html(text):
soup = BeautifulSoup(text, 'html.parser')
return soup.get_text()
def remove_between_square_brackets(text):
return re.sub('\\[[^]]*\\]', '', text)
def remove_urls(text):
return re.sub('http\\S+', '', text)
def remove_hash(text):
text = ' '.join((word.strip() for word in re.split('#|_', text)))
return text
def remove_stopwords(text):
final_text = []
for i in text.split():
if i.strip().lower() not in stop:
final_text.append(i.strip())
return ' '.join(final_text)
def denoise_text(text):
text = strip_html(text)
text = remove_between_square_brackets(text)
text = remove_urls(text)
text = remove_hash(text)
text = remove_stopwords(text)
return text
max_features = 20000
maxlen = 100
tokenizer = text.Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(train.text)
tokenized_train = tokenizer.texts_to_sequences(train.text)
X = sequence.pad_sequences(tokenized_train, maxlen=maxlen)
tokenized_test = tokenizer.texts_to_sequences(test.text)
sub_test = sequence.pad_sequences(tokenized_test, maxlen=maxlen)
test.head() | code |
34150026/cell_15 | [
"text_html_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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train['target'].value_counts() | code |
34150026/cell_66 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from sklearn.linear_model import LogisticRegression
from string import punctuation
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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
id = test['qid']
id.shape
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
test.rename(columns={'question_text': 'text'}, inplace=True)
test.isnull().sum()
test.drop('qid', inplace=True, axis=1)
stop = set(stopwords.words('english'))
punctuation = list(string.punctuation)
stop.update(punctuation)
def strip_html(text):
soup = BeautifulSoup(text, 'html.parser')
return soup.get_text()
def remove_between_square_brackets(text):
return re.sub('\\[[^]]*\\]', '', text)
def remove_urls(text):
return re.sub('http\\S+', '', text)
def remove_hash(text):
text = ' '.join((word.strip() for word in re.split('#|_', text)))
return text
def remove_stopwords(text):
final_text = []
for i in text.split():
if i.strip().lower() not in stop:
final_text.append(i.strip())
return ' '.join(final_text)
def denoise_text(text):
text = strip_html(text)
text = remove_between_square_brackets(text)
text = remove_urls(text)
text = remove_hash(text)
text = remove_stopwords(text)
return text
max_features = 20000
maxlen = 100
tokenizer = text.Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(train.text)
tokenized_train = tokenizer.texts_to_sequences(train.text)
X = sequence.pad_sequences(tokenized_train, maxlen=maxlen)
tokenized_test = tokenizer.texts_to_sequences(test.text)
sub_test = sequence.pad_sequences(tokenized_test, maxlen=maxlen)
sub_test.shape
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.score(X_train, y_train)
y = logit.predict(X_test)
final = logit.predict(sub_test)
final.shape
submission = pd.DataFrame({'qid': id, 'prediction': final})
sub = pd.read_csv('samplesubmission.csv')
sub.head() | code |
34150026/cell_43 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from string import punctuation
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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
stop = set(stopwords.words('english'))
punctuation = list(string.punctuation)
stop.update(punctuation)
def strip_html(text):
soup = BeautifulSoup(text, 'html.parser')
return soup.get_text()
def remove_between_square_brackets(text):
return re.sub('\\[[^]]*\\]', '', text)
def remove_urls(text):
return re.sub('http\\S+', '', text)
def remove_hash(text):
text = ' '.join((word.strip() for word in re.split('#|_', text)))
return text
def remove_stopwords(text):
final_text = []
for i in text.split():
if i.strip().lower() not in stop:
final_text.append(i.strip())
return ' '.join(final_text)
def denoise_text(text):
text = strip_html(text)
text = remove_between_square_brackets(text)
text = remove_urls(text)
text = remove_hash(text)
text = remove_stopwords(text)
return text
max_features = 20000
maxlen = 100
tokenizer = text.Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(train.text)
tokenized_train = tokenizer.texts_to_sequences(train.text)
X = sequence.pad_sequences(tokenized_train, maxlen=maxlen)
X.shape | code |
34150026/cell_31 | [
"text_plain_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
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
x = train.target.value_counts()
train.rename(columns={'question_text': 'text'}, inplace=True)
train.isnull().sum()
train.drop('qid', inplace=True, axis=1)
train.head() | code |
34150026/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
test.head() | code |
34150026/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-classification/sample_submission.csv')
print('There are {} rows and {} columns in train'.format(train.shape[0], train.shape[1]))
print('There are {} rows and {} columns in test'.format(test.shape[0], test.shape[1])) | code |
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