path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2010673/cell_9 | [
"image_output_1.png"
] | from collections import Counter
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words = nltk.word_tokenize(text)
wo... | code |
2010673/cell_6 | [
"image_output_1.png"
] | import os
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
dirs | code |
2010673/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words ... | code |
2010673/cell_8 | [
"text_plain_output_1.png"
] | from collections import Counter
import nltk
import os
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words = nltk.word_tokenize(text)
word_counter = Counter(... | code |
2010673/cell_15 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words ... | code |
2010673/cell_16 | [
"text_html_output_1.png"
] | from collections import Counter
import nltk
import os
import string
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words = nltk.word_tokenize(text)
word_cou... | code |
2010673/cell_17 | [
"image_output_1.png"
] | from collections import Counter
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
import string
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename,... | code |
2010673/cell_14 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words ... | code |
2010673/cell_10 | [
"text_plain_output_1.png"
] | from collections import Counter
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words = nltk.word_tokenize(text)
wo... | code |
2010673/cell_12 | [
"text_plain_output_1.png"
] | from collections import Counter
import nltk
import os
import pandas as pd
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
def count_words(word, filename):
file = open(path + '/' + filename, encoding='utf8')
text = file.read().lower()
words = nltk.word_tokenize(text)
wo... | code |
2010673/cell_5 | [
"text_html_output_1.png"
] | import os
path = '../input/state-of-the-union-corpus-1989-2017'
dirs = os.listdir(path)
path1 = '../input'
dirs1 = os.listdir(path1)
dirs1 | code |
128033920/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data['ca'].value_counts() | code |
128033920/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique() | code |
128033920/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.head() | code |
128033920/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
data.duplicated().sum() | code |
128033920/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
data.duplicated().sum()... | code |
128033920/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data['thal'].value_counts() | code |
128033920/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns | code |
128033920/cell_39 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
... | code |
128033920/cell_26 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum() | code |
128033920/cell_41 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
data.duplicated().sum()... | code |
128033920/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data['thal'].nunique() | code |
128033920/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.info() | code |
128033920/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
data.duplicated().sum()... | code |
128033920/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum() | code |
128033920/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data[data['ca'] == 4] | code |
128033920/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
... | code |
128033920/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt | code |
128033920/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data['ca'].value_counts() | code |
128033920/cell_31 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
data.duplicated().sum()... | code |
128033920/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data['thal'].value_counts() | code |
128033920/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data[data['thal'] == 0] | code |
128033920/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes | code |
128033920/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data.loc[data['ca'] == 4, 'ca'] = np.NaN
data.loc[data['thal'] == 0, 'thal'] = np.NaN
data.isnull().sum()
data.isnull().sum()
data.duplicated().sum()... | code |
128033920/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape
data.columns
data.nunique()
data.dtypes
data['ca'].nunique() | code |
128033920/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import os
data = pd.read_csv('/kaggle/input/heart-disease/heart.csv')
data.shape | code |
34149133/cell_21 | [
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary[... | code |
34149133/cell_13 | [
"text_plain_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary[... | code |
34149133/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary[... | code |
34149133/cell_25 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary['shot_type'] == '3PT']
df_2p = df_summary[df_summary['shot_type'] == '2PT']
df_ft = df_summary[df_summary['shot_type'] == 'FT']... | code |
34149133/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
df.head(10) | code |
34149133/cell_20 | [
"text_html_output_1.png"
] | a | code |
34149133/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
dict_player = {}
for team in df['team'].unique():
dict_player[team] = df[df['team'] == team]['player_name'].unique()
display(dict_player['Golden State Warriors']) | code |
34149133/cell_11 | [
"text_html_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary[... | code |
34149133/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary['shot_type'] == '3PT']
df_2p = df_summary[df_summary['shot_type'] == '2PT']
df_ft = df_summary[df_summary['shot_type'] == 'FT']... | code |
34149133/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = [10, 5]
plt.style.use('fivethirtyeight')
sns.set_sty... | code |
34149133/cell_15 | [
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary[... | code |
34149133/cell_17 | [
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
def shooting_summary(df, by):
df_summary = df.copy()
df_3p = df_summary[df_summary[... | code |
34149133/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playoff_shots.csv')
dict_player = {}
for team in df['team'].unique():
dict_player[team] = df[df['team'] == team]['player_name'].unique()
display(dict_player['Cleveland Cavaliers']) | code |
106201341/cell_8 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import requests
import seaborn as sns
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests.get(url)
if response.status_c... | code |
106201341/cell_3 | [
"image_output_1.png"
] | import json
import time
import requests
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette()
from plotly import tools
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
pd.options.mode.chained_assignment = Non... | code |
106201341/cell_17 | [
"image_output_1.png"
] | import datetime
import json
import matplotlib.pyplot as plt
import pandas as pd
import requests
import seaborn as sns
import time
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests... | code |
106201341/cell_14 | [
"text_plain_output_1.png"
] | import datetime
import json
import matplotlib.pyplot as plt
import pandas as pd
import requests
import seaborn as sns
import time
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests... | code |
106201341/cell_5 | [
"image_output_1.png"
] | import json
import requests
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests.get(url)
if response.status_code == 200:
token_info_response = json.loads(response.text)
token_info_re... | code |
18100844/cell_21 | [
"text_plain_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/charts1/chart2.png") | code |
18100844/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from warnings import simplefilter
import numpy as np # linear algebra
import pandas a... | code |
18100844/cell_9 | [
"image_output_1.png"
] | 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)
data = pd.read_csv('../input/classification/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
M = data[data['diagnosis'] == 'M']
B = data[data['di... | code |
18100844/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_spl... | code |
18100844/cell_30 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassi... | code |
18100844/cell_20 | [
"text_plain_output_1.png"
] | clf = DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Accuracy:', metrics.accuracy_score(y_test, y_pred)) | code |
18100844/cell_6 | [
"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)
data = pd.read_csv('../input/classification/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
M = data[data['diagnosis'] == 'M']
B = data[data['diagnosis'] == 'B']
plt.scatter(M.radius_mean, M.textu... | code |
18100844/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18100844/cell_1 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/charts1/MachL.png") | code |
18100844/cell_7 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/classification/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=T... | code |
18100844/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100, random_state=1)
rf.fit(x_train, y_train)
print('Random Forest Classification score: ', rf.score(x_test, y_test))
y_p... | code |
18100844/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | 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)
data = pd.read_csv('../input/classification/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
M = data[data['diagnosis'] == 'M']
B = data[data['di... | code |
18100844/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.m... | code |
18100844/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/classification/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
pima = pd.read_csv('../input/pima-indians-diabetes-database/diabetes.csv')
pima['Pregnancies'] = pima['Pregnancies']... | code |
18100844/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_spl... | code |
18100844/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train... | code |
18100844/cell_14 | [
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/charts1/chart.png") | code |
18100844/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from warnings import simplefilter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. p... | code |
18100844/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/classification/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
data.head() | code |
18100844/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.m... | code |
33101395/cell_13 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_9 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
type(testscores) | code |
33101395/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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.figure(figsize=(15, 2))
plt.... | code |
33101395/cell_11 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib as mlp
import matplotlib.pyplot as plt
import seaborn as sns
import glob
from sklearn import tree
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import ... | code |
33101395/cell_7 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.fig... | code |
33101395/cell_8 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_15 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
testscores_lunch_free = testscores.loc[testscores['lunch'] == 'free/reduced']
testscores... | code |
33101395/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info | code |
33101395/cell_14 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_10 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_12 | [
"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
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
plt.xticks(rotation=90)
plt.xti... | code |
33101395/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tests = '../input/students-performance-in-exams/StudentsPerformance.csv'
test1 = pd.read_csv(tests, sep=',')
testscores = pd.DataFrame(test1)
testscores.info
testscores | code |
18111545/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
... | code |
18111545/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True}) | code |
18111545/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
... | code |
18111545/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
corr = cr.corr()
sns.heatmap(corr, annot=True) | code |
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