path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16136181/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.info() | code |
16136181/cell_29 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_outp... | from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'pro... | code |
16136181/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate'... | code |
16136181/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month',... | code |
16136181/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
16136181/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
print('Descriptive statistics of Data')
data.describe().T | code |
16136181/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month',... | code |
16136181/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month',... | code |
16136181/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape | code |
16136181/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month',... | code |
16136181/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate'... | code |
49126907/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from kaggle_environments import make
from kaggle_environments.envs.rps.agents import agents
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
from kaggle_environments import make
from kaggle_environments.envs.rps.agents import agents
import numpy as np
import pandas as pd
from tqdm.auto import tqdm... | code |
49126907/cell_4 | [
"text_plain_output_1.png"
] | import secrets
Jmin = 0
Jmax = 5
J = Jmin + secrets.randbelow(Jmax - Jmin + 1)
Dmin = 2
Dmax = 5
Hash = []
Map = []
MyMap = []
for D in range(Dmin, Dmax + 1):
Hash.append([0, 0, 0])
Map.append([{}, {}, {}])
MyMap.append([{}, {}, {}])
G = 2
R = 0.4
V = 0.7
VM = 0.7
B = 0
def add(map1, hash1, A):
if hash1... | code |
49126907/cell_6 | [
"text_plain_output_1.png"
] | import random
rng = random.SystemRandom()
def agent(observation, configuration):
S = configuration.signs
return rng.randrange(0, S) | code |
49126907/cell_2 | [
"text_plain_output_1.png"
] | import random
rng = random.SystemRandom()
hash1 = 0
hash2 = 0
hash3 = 0
map1 = {}
map2 = {}
map3 = {}
Jmin = 10
Jmax = 20
J = rng.randrange(Jmin, Jmax + 1)
D = 2
G = 3
R = 0.6
B = 0
def add(map1, hash1, A):
if hash1 not in map1:
map1[hash1] = {'S': 0}
d = map1[hash1]
if A not in d:
d[A] = 1
... | code |
49126907/cell_1 | [
"text_plain_output_1.png"
] | import secrets
import math
Jmax = 2
J = Jmax - int(math.sqrt(secrets.randbelow((Jmax + 1) ** 2)))
Dmin = 2
Dmax = 5
Hash = []
Map = []
MyMap = []
for D in range(Dmin, Dmax + 1):
Hash.append([0, 0, 0])
Map.append([{}, {}, {}])
MyMap.append([{}, {}, {}])
G = 2
R = 0.4
V = 0.8
VM = 0.95
B = 0
DT = 200
def add(... | code |
49126907/cell_7 | [
"text_plain_output_1.png"
] | import secrets
def agent(observation, configuration):
S = configuration.signs
return secrets.randbelow(S) | code |
49126907/cell_3 | [
"text_plain_output_1.png"
] | import secrets
hash1 = 0
hash2 = 0
hash3 = 0
map1 = {}
map2 = {}
map3 = {}
Jmin = 10
Jmax = 30
J = Jmin + secrets.randbelow(Jmax - Jmin + 1)
D = 3
G = 2
R = 0.7
B = 0
def add(map1, hash1, A):
if hash1 not in map1:
map1[hash1] = {'S': 0}
d = map1[hash1]
if A not in d:
d[A] = 1
else:
... | code |
49126907/cell_5 | [
"text_plain_output_1.png"
] | import random
def agent(observation, configuration):
S = configuration.signs
return random.randrange(0, S) | code |
16117706/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].val... | code |
16117706/cell_9 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
... | import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb... | code |
16117706/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb... | code |
16117706/cell_8 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
... | import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb... | code |
16117706/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].val... | code |
17110052/cell_13 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O... | code |
17110052/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_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
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.... | code |
17110052/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.head() | code |
17110052/cell_6 | [
"text_plain_output_1.png",
"image_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
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f, ax = plt.subplots(figsize=(18, 18))
sns... | code |
17110052/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = pl... | code |
17110052/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17110052/cell_7 | [
"image_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
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.... | code |
17110052/cell_18 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O... | code |
17110052/cell_8 | [
"text_plain_output_1.png",
"image_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
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.... | code |
17110052/cell_15 | [
"image_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O... | code |
17110052/cell_16 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O... | code |
17110052/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
print(data2017.info()) | code |
17110052/cell_17 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O... | code |
17110052/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O... | code |
17110052/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
print(' 2015 Correlation of data ')
data2015.corr() | code |
130020662/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130020662/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum() | code |
130020662/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.info() | code |
130020662/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.head(10) | code |
130020662/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130020662/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns | code |
130020662/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from xgboost import XGBClassifier
from xgboost import XGBRegressor
from xgboost import plot_importance
from sklearn.linear_model import LogisticRegression
from sklearn.tree import Deci... | code |
130020662/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130020662/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130020662/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130020662/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130020662/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
dupl... | code |
130020662/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
... | code |
130020662/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.describe() | code |
130020662/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('... | code |
130020662/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns | code |
90135235/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
torch.manual_seed(42)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_tra... | code |
90135235/cell_14 | [
"image_output_1.png"
] | from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets
import torch
import torch.nn as nn
torch.manual_seed(42)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), tra... | code |
90135235/cell_5 | [
"image_output_1.png"
] | from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets
import matplotlib.pyplot as plt
import numpy as np
import torch
torch.manual_seed(42)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_transforms = transforms.Compose([transforms.Resize((224, 224)... | code |
105194300/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
ret... | code |
105194300/cell_9 | [
"image_output_1.png"
] | import numpy as np
import seaborn as sns
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
plotDists(y) | code |
105194300/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
... | code |
105194300/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
... | code |
105194300/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
... | code |
105194300/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
... | code |
105194300/cell_28 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'T... | code |
105194300/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.datasets import make_classification
import pandas as pd
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
x, y = make_classification(NUM_POINTS, NUM_FEATURES, n_informative=NUM_FEATURES // 2, n_classes=NUM_CLASSES)
df = pd.DataFrame(x)
df['y'] = y
df.head() | code |
105194300/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
ret... | code |
105194300/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
... | code |
16115331/cell_4 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktS... | code |
16115331/cell_6 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokeniz... | code |
16115331/cell_7 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokeniz... | code |
16115331/cell_8 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokeniz... | code |
16115331/cell_3 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktS... | code |
16115331/cell_5 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokeniz... | code |
2002501/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] ... | code |
2002501/cell_13 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['S... | code |
2002501/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any() | code |
2002501/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
outcome.shape | code |
2002501/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape | code |
2002501/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
df.shape
d = df[['PassengerId']].copy()
d.shape | code |
2002501/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] ... | code |
2002501/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] ... | code |
2002501/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
df.shape
d = df[['PassengerId']].copy()
d.shape
d.head() | code |
2002501/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
outcome | code |
2002501/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
2002501/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
2002501/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
df.shape | code |
2002501/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['S... | code |
2002501/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.head() | code |
72100024/cell_21 | [
"text_plain_output_2.png",
"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-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
72100024/cell_4 | [
"text_plain_output_2.png",
"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-dataset/train.csv')
train.shape
train.info() | code |
72100024/cell_6 | [
"text_plain_output_2.png",
"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-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
print('No. of columns with missing values:', len(missing... | code |
72100024/cell_11 | [
"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-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
72100024/cell_19 | [
"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-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
72100024/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 |
72100024/cell_18 | [
"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
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
... | code |
72100024/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascendin... | code |
72100024/cell_16 | [
"text_plain_output_2.png",
"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-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
72100024/cell_3 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape | code |
72100024/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
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