path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90127845/cell_8 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.r... | code |
90127845/cell_17 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df... | code |
90127845/cell_14 | [
"image_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.r... | code |
90127845/cell_10 | [
"text_html_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.r... | code |
90127845/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import datetime
import os
import pandas as pd
root = '/kaggle/input/tabular-playground-series-mar-2022'
train_df = pd.read_csv(os.path.join(root, 'train.csv'))
train_df['datetime'] = pd.to_datetime(train_df.time)
train_df['date'] = train_df.datetime.dt.date
train_df['time'] = train_df.datetime.dt.time
test_df = pd.r... | code |
16154359/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold
from sklearn.mixture import GaussianMixture
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
te... | code |
16154359/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import roc_auc_score
y_perfect = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
y_flliped = [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]
roc_auc_score(y_perfect, y_flliped)
y_preds = [0.33, 0.33, 0.33, 0.5, 0.5, 0, 0, 0, 0, 0, 1, 1, 0.5, 0.5, 1, 1, 1, 0.66, 0.66, 0.6... | code |
16154359/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
test['wheezy-copper-turtle-magic'] = test['wheezy-copper-turtle-magic'].astype('category')
magicNum = 131073
default_cols =... | code |
16154359/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.metrics import roc_auc_score
y_perfect = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
y_flliped = [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]
roc_auc_score(y_perfect, y_flliped) | code |
106204398/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y... | code |
106204398/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.describe() | code |
106204398/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pa... | code |
106204398/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any() | code |
106204398/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test)
confusion_matrix(y_test, clf.predict(X_test)) | code |
106204398/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
from sklearn i... | code |
106204398/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x='blue', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106204398/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
106204398/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x='wifi', ... | code |
106204398/cell_15 | [
"text_plain_output_1.png"
] | X_test | code |
106204398/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.head() | code |
106204398/cell_17 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train) | code |
106204398/cell_14 | [
"text_plain_output_1.png"
] | X_train | code |
106204398/cell_22 | [
"image_output_1.png"
] | from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
... | code |
106204398/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_ra... | code |
106204398/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6... | code |
106204398/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.info() | code |
106211686/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_56 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import explained_variance_score, r2_score
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
reg_pred = reg.predict(X_test)
from sklearn.metrics import explained_variance_score, r2_score
explained_variance_score(reg_p... | code |
106211686/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_60 | [
"text_plain_output_1.png"
] | from sklearn.metrics import explained_variance_score, r2_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
tree = DecisionTreeRegressor(splitter='random', max_depth=20, max_features='sqrt')
tree.fit(X_train, y_train)
tree_pred = tree.predict(X_test)
print(explained_va... | code |
106211686/cell_52 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/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 |
106211686/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_59 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.metrics import explained_variance_score, r2_score
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
reg_pred = reg.predict(X_test)
from sklearn import linea... | code |
106211686/cell_58 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.metrics import explained_variance_score, r2_score
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
reg_pred = reg.predict(X_test)
from sklearn import linear_model
ridge = linear_model.Ridge... | code |
106211686/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_3 | [
"text_plain_output_1.png"
] | import random
import random
random.seed(10)
print(random.random()) | code |
106211686/cell_46 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
106211686/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/sample_submission.csv')
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-202... | code |
33106742/cell_25 | [
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_23 | [
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_30 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_29 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_26 | [
"image_output_2.png",
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import math
import matplotlib.pyplot as plt
import numpy as np
from pandas_datareader import data | code |
33106742/cell_28 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_17 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
import math
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
vol = i... | code |
33106742/cell_31 | [
"image_output_1.png"
] | from pandas_datareader import data
import math
import matplotlib.pyplot as plt
import numpy as np
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr ... | code |
33106742/cell_14 | [
"text_plain_output_1.png"
] | from pandas_datareader import data
ibm = data.DataReader('IBM', 'yahoo', start='1/1/2000')
time_elapsed = (ibm.index[-1] - ibm.index[0]).days
price_ratio = ibm['Adj Close'][-1] / ibm['Adj Close'][1]
inverse_number_of_years = 365.0 / time_elapsed
cagr = price_ratio ** inverse_number_of_years - 1
print(cagr) | code |
2033418/cell_2 | [
"text_plain_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import nltk
import numpy as np
import pandas as pd
import re
import tensorflow as tf
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
i... | code |
2033418/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import nltk
import pandas as pd
import re
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import tensorflow as tf
data = pd.read_csv('..... | code |
2033418/cell_3 | [
"text_plain_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
import nltk
import numpy as np
import pandas as pd
import re
import tensorflow as tf
import re
import numpy as np
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
i... | code |
2033418/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
from sklearn import preprocessing
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import nltk
import numpy as np
import pandas as pd
import re
import tensorflow as tf
import re
import numpy as np
import pandas... | code |
16147265/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
16147265/cell_8 | [
"text_plain_output_1.png"
] | print('End') | code |
16147265/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train_data.csv')
test_df = pd.read_csv('../input/test_data.csv') | code |
121149609/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import Counter
from torch.utils.data import DataLoader,Dataset
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import spacy
import torch
import torchvision.transforms as T
import pandas as pd
caption_file = data_location + '... | code |
121149609/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | v = Vocabulary(freq_threshold=1)
v.build_vocab(['This is a good place to find a city'])
print(v.stoi)
print(v.numericalize('This is a good place to find a city here!!')) | code |
121149609/cell_4 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
data_idx = 56
image_path = data_location + '/Images/' + df.iloc[data... | code |
121149609/cell_2 | [
"text_plain_output_1.png"
] | #location of the data
data_location = "../input/flickr8k"
!ls $data_location | code |
121149609/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from collections import Counter
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader,Dataset
from torch.utils.data import DataLoader,Dataset
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import spacy
import t... | code |
121149609/cell_7 | [
"text_plain_output_1.png"
] | import spacy
spacy_eng = spacy.load('en')
text = 'This is a good place to find a city'
[token.text.lower() for token in spacy_eng.tokenizer(text)] | code |
121149609/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
from collections import Counter
from torch.utils.data import DataLoader,Dataset
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import spacy
import torch
import torchvision.transforms as T
import pandas as pd
caption_file = data_location + '... | code |
121149609/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
caption_file = data_location + '/captions.txt'
df = pd.read_csv(caption_file)
print('There are {} image to captions'.format(len(df)))
df.head(7) | code |
105214040/cell_29 | [
"image_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
dt = 0.1
I = np.eye(4)
x = np.matrix([[0.0, 0.0, 0.0, 0.0]]).T
P = np.diag([1000.0, 1000.0, 1000.0, 1000.0])
A = np.matrix([[1.0, 0.0, dt, 0.0], [0.0, 1.0, 0.0,... | code |
105214040/cell_18 | [
"image_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
plt.figure(figsize=(10, 7))
plt.plot(range(m), mx, label='$v_1 (measurements)$')
plt.plot(range(m), my, label='$v_2 (measurements)$')
plt.ylabel('Velocity Measu... | code |
105214040/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
measurements | code |
105214040/cell_31 | [
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
m = 100
vx = 10
vy = 10
mx = np.array(vx + np.random.randn(m))
my = np.array(vy + np.random.randn(m))
measurements = np.vstack((mx, my))
dt = 0.1
I = np.eye(4)
x = np.matrix([[0.0, 0.0, 0.0, 0.0]]).T
P = np.diag([1000.0, 1000.0, 1000.0, 1000.0])
A = np.matrix([[1.0, 0.0, dt, 0.0], [0.0, 1.0, 0.0,... | code |
50223492/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation course': 'pre', 'math score': 'math', 'reading score': ... | code |
50223492/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.head() | code |
50223492/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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation... | code |
50223492/cell_19 | [
"image_output_3.png",
"image_output_2.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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation... | code |
50223492/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50223492/cell_17 | [
"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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation... | code |
50223492/cell_24 | [
"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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation... | code |
50223492/cell_14 | [
"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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation... | code |
50223492/cell_22 | [
"image_output_3.png",
"image_output_2.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
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.rename(columns={'race/ethnicity': 'race', 'parental level of education': 'p_education', 'test preparation... | code |
50223492/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')
df.info() | code |
105194699/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.head() | code |
105194699/cell_20 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Perc... | code |
105194699/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.info() | code |
105194699/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as mno
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().... | code |
105194699/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Perc... | code |
105194699/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
sns.countplot(data=df, x=df.Potability)
df.Potability.value_counts() | code |
105194699/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum() | code |
105194699/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Perc... | code |
105194699/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shap... | code |
105194699/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style | code |
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