path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
121150522/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.pivot_table(index='CryoSleep', columns='Transported', aggfunc={'Transported': 'count'})
sns.countplot(x='Dest... | code |
121150522/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.pivot_table(index='CryoSleep', columns='Transported', aggfunc={'Transported': 'count'})
df_count = df[['Age']... | code |
121150522/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
sns.catplot(x='HomePlanet', kind='count', hue='Transported', data=df)
plt.show() | code |
121150522/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 |
121150522/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
sns.countplot(x='HomePlanet', data=df) | code |
121150522/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.pivot_table(index='CryoSleep', columns='Transported', aggfunc={'Transported': 'count'})
df.head(3) | code |
121150522/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
sns.catplot(x='CryoSleep', kind='count', hue='Transported', data=df) | code |
121150522/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.head(100) | code |
121150522/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.pivot_table(index='CryoSleep', columns='Transported', aggfunc={'Transported': 'count'}) | code |
121150522/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.CryoSleep.value_counts().plot(kind='pie', figsize=(12, 5), autopct='%0.1f%%')
plt.xlabel('Percentage of Passen... | code |
121150522/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.describe() | code |
16122912/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X) | code |
16122912/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.head() | code |
16122912/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_ | code |
16122912/cell_20 | [
"text_plain_output_1.png"
] | df_final.Value.plot(kind='box') | code |
16122912/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df['Year'] | code |
16122912/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
test_predict = model.predict(test_X) | code |
16122912/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16122912/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Dat... | code |
16122912/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16122912/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns | code |
16122912/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Dat... | code |
16122912/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
print(mean_squared_error(train_Y, ... | code |
16122912/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
cols_to_Encode = ['Gender', 'Race/ Ethnicit... | code |
16122912/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Dat... | code |
16122912/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Dat... | code |
16122912/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_ | code |
16122912/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y) | code |
16122912/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
print(mean_absolute_error(train_Y,... | code |
16122912/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
cols_to_Encode = ['Gender', 'Race/ Ethnicity', 'Indicator Category']
continuous_cols = ['Value']
... | code |
16122912/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df['Notes'].value_counts() | code |
303338/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import seaborn as sns
sns.set_style('whitegrid')
zika = pd.read_csv('../input/cdc_zika.csv')
zika.groupby('location').size().reset_index().rename(columns={0: 'count'}) | code |
104114996/cell_13 | [
"text_html_output_1.png"
] | df_embds.head() | code |
104114996/cell_9 | [
"text_html_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import GlobalMaxPooling2D
import tensorflow as tf
import tensorflow as tf
import keras
from keras import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.vgg16 import VGG16
fro... | code |
104114996/cell_4 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
DATASET_PATH = '../input/fashion-product-images-small/myntradataset'
DATASET_PATH = '../input/fashion-product-images-small/myntradataset'
df = pd.read_csv... | code |
104114996/cell_23 | [
"text_html_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applica... | code |
104114996/cell_33 | [
"text_html_output_1.png"
] | df_sample = df.copy()
map_embeddings = df_sample['image'].apply(lambda img: get_embedding(model_3, img))
df_embds_vgg19 = map_embeddings.apply(pd.Series) | code |
104114996/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg19 import preprocess_input, decode_predictions... | code |
104114996/cell_29 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applica... | code |
104114996/cell_26 | [
"text_html_output_1.png"
] | def get_recommendations(indices, cosine_sim, index, df, top_n=5):
sim_index = indices[index]
sim_scores = list(enumerate(cosine_sim[sim_index]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:top_n + 1]
index_rec = [i[0] for i in sim_scores]
index_sim... | code |
104114996/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg19 import preprocess_input, decode_predictions... | code |
104114996/cell_19 | [
"text_html_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg19 import preprocess_input, decode_predictions... | code |
104114996/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 |
104114996/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg19 import preprocess_input, decode_predictions... | code |
104114996/cell_32 | [
"text_html_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applica... | code |
104114996/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applica... | code |
104114996/cell_8 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import keras
from keras import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.preprocessing import image
from tensorflow.k... | code |
104114996/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
cosine_sim = 1 - pairwise_distances(df_embds, metric='cosine')
cosine_sim[:4, :4] | code |
104114996/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | def get_recommendations(indices, cosine_sim, index, df, top_n=5):
sim_index = indices[index]
sim_scores = list(enumerate(cosine_sim[sim_index]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:top_n + 1]
index_rec = [i[0] for i in sim_scores]
index_sim... | code |
104114996/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
DATASET_PATH = '../input/fashion-product-images-small/myntradataset'
print(os.listdir(DATASET_PATH)) | code |
104114996/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg19 import preprocess_input, decode_predictions... | code |
104114996/cell_31 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.layers import GlobalMaxPooling2D
import tensorflow as tf
import tensorflow as tf
import keras
from keras import Model
from te... | code |
104114996/cell_24 | [
"text_html_output_1.png"
] | df_sample = df.copy()
map_embeddings = df_sample['image'].apply(lambda img: get_embedding(model_2, img))
df_embds_vgg16 = map_embeddings.apply(pd.Series) | code |
104114996/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import GlobalMaxPooling2D
import tensorflow as tf
import tensorflow as tf
import keras
from keras import Model
from tensorflow.keras.applications.resnet50 import ResNet50
fr... | code |
104114996/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applica... | code |
104114996/cell_12 | [
"text_html_output_1.png"
] | df_sample = df.copy()
map_embeddings = df_sample['image'].apply(lambda img: get_embedding(model_1, img))
df_embds = map_embeddings.apply(pd.Series) | code |
104114996/cell_5 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
DATASET_PATH = '../input/fashion-product-images-small/myntradataset'
DATASET_PATH = '../input/fashion-product-images-small/myntradataset'
df = pd.read_csv... | code |
49120120/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import keras
import tensorflow as tf
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
img_height, img_width = (256, 256)
checkpoint_filepath = './weights.b... | code |
49120120/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os # accessing directory structure
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
49120120/cell_6 | [
"text_plain_output_1.png"
] | import keras
import tensorflow as tf
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
img_height, img_width = (256, 256)
checkpoint_filepath = './weights.b... | code |
49120120/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import keras
import tensorflow as tf
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
img_height, img_width = (256, 256)
checkpoint_filepath = './weights.b... | code |
129032747/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
129032747/cell_23 | [
"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 matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn... | code |
129032747/cell_30 | [
"image_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 matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn... | code |
129032747/cell_33 | [
"image_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 matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn... | code |
129032747/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
129032747/cell_19 | [
"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 matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn... | code |
129032747/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
129032747/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
129032747/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
129032747/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
129032747/cell_27 | [
"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 matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn... | code |
129032747/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectF... | code |
128006019/cell_6 | [
"image_output_1.png"
] | import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv')
unintended_b... | code |
128006019/cell_11 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv')
unintended_b... | code |
128006019/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-clas... | code |
128006019/cell_7 | [
"image_output_1.png"
] | import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv')
unintended_b... | code |
128006019/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-clas... | code |
128006019/cell_32 | [
"image_output_1.png"
] | from nltk import FreqDist
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-mult... | code |
128006019/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv')
unintended_b... | code |
128006019/cell_3 | [
"image_output_1.png"
] | import nltk
nltk.download('vader_lexicon')
from nltk.sentiment import SentimentIntensityAnalyzer
nltk.download('stopwords')
nltk.download('wordnet') | code |
128006019/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-clas... | code |
128006019/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic... | code |
128006019/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-clas... | code |
128006019/cell_27 | [
"image_output_1.png"
] | import pandas as pd
import random
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv... | code |
128006019/cell_37 | [
"image_output_1.png"
] | from nltk import FreqDist
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-mult... | code |
128006019/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv')
unintended_b... | code |
128006019/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
toxic_comment_processed_seqlen = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train-processed-seqlen128.csv')
toxic_comment = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv')
unintended_b... | code |
73101177/cell_21 | [
"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
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train... | code |
73101177/cell_13 | [
"text_html_output_1.png",
"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/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sns.pairplot(train) | code |
73101177/cell_9 | [
"image_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/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(train.isnull().sum())
sns.heatmap(train.isn... | code |
73101177/cell_34 | [
"image_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.preprocessing import StandardScaler, RobustScaler,OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-... | code |
73101177/cell_23 | [
"text_plain_output_2.png",
"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
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train... | code |
73101177/cell_6 | [
"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/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(test.shape)
test.head() | code |
73101177/cell_39 | [
"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/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train.drop('id', axis=1)
def get_uniq... | code |
73101177/cell_26 | [
"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
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train... | code |
73101177/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 |
73101177/cell_7 | [
"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/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.info() | code |
73101177/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/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train... | code |
73101177/cell_28 | [
"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
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train... | code |
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