path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105197097/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.c... | code |
105197097/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.info() | code |
105197097/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.c... | code |
105197097/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 |
105197097/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.c... | code |
105197097/cell_8 | [
"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_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/... | code |
105197097/cell_15 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.c... | code |
105197097/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna()... | code |
105197097/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape | code |
2015893/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign... | code |
2015893/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
2015893/cell_23 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
2015893/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
2015893/cell_11 | [
"text_plain_output_1.png"
] | import keras.backend as K
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocess... | code |
2015893/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign... | code |
2015893/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
2015893/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import h5py
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2015893/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
2015893/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
2015893/cell_10 | [
"text_plain_output_1.png"
] | import h5py
import matplotlib.pyplot as plt
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_datase... | code |
2015893/cell_27 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
... | code |
106196793/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pokemon = pd.read_csv('../input/pokemon/Pokemon.csv')
pokemon['Type'] = np.where(pokemon['Type 2'].notnull(), pokemon['Type 1'] + '/' + pokemon['Type 2'], pokemon['Type 1'])
pokemon_new = pokemon.drop(['Type 1', 'Type 2'], axis=1)
print(pokemon['Type'].unique())
print(pokemon_n... | code |
106196793/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
pokemon = pd.read_csv('../input/pokemon/Pokemon.csv')
print(pokemon.info())
print(pokemon.describe()) | code |
129007439/cell_21 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
df2 = df['Gender']... | code |
129007439/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1 | code |
129007439/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum() | code |
129007439/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.clust... | code |
129007439/cell_20 | [
"text_html_output_2.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
fig = px.scatter(df, x="Annual Income (k$)", ... | code |
129007439/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
plt.figure(figsize=(12, 6))
sns.scatterplot(data=df, x=df['Annual Income (k$)'], y=df['Spending Score (1-100)'], hue=df['Gender'... | code |
129007439/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4... | code |
129007439/cell_26 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
model = KMeans(n_clusters=4, max_iter=1000)
model.fit(df1) | code |
129007439/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df | code |
129007439/cell_19 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
x = df1['Annual In... | code |
129007439/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129007439/cell_18 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.clust... | code |
129007439/cell_28 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
model = KMeans(n_clusters=4,... | code |
129007439/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df_copy | code |
129007439/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
x = df1['Annual Income (k$)']
y = df1['Spending Score ... | code |
129007439/cell_16 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1) | code |
129007439/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.info() | code |
129007439/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.clust... | code |
129007439/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
df2 = df['Gender']... | code |
129007439/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
fig = px.scatter(df, x='Annual Income (k$)', y='Spending Score (1-100)', color='G... | code |
129007439/cell_22 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.clust... | code |
129007439/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
df2 = df['Gender']
df2 | code |
129007439/cell_27 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
model = KMeans(n_clusters=4, max_iter=1000)
model.fit(df1)
model.predict(... | code |
129007439/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df.describe() | code |
33099181/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regres... | code |
33099181/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
print(train_data.head())
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(test_data.head()) | code |
33099181/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 |
33099181/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regres... | code |
33099181/cell_5 | [
"text_plain_output_1.png"
] | code | |
89127515/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 |
89127515/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from urllib.request import urlopen
from PIL import Image
from math import sin,cos,pi
import catboost as cb
from sklearn.metrics import mean_squared_error
from skl... | code |
333462/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
333462/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
prin... | code |
333462/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_24 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_28.png",
"text_plain_output_20.png",
"image_output_23.png",
"text_plain_output_4.pn... | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
print(clf.feature_importances_) | code |
333462/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import auc
from sklearn.cross_validation import train_test_split, cross_val_score | code |
333462/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
333462/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
128030251/cell_13 | [
"text_html_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_9 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_sp... | code |
128030251/cell_34 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
import seaborn as sns
clf = tree.Decisio... | code |
128030251/cell_23 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(clf.score(X_test, y_test)) | code |
128030251/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_tr... | code |
128030251/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_sp... | code |
128030251/cell_29 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
import seaborn as sns
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
s... | code |
128030251/cell_2 | [
"text_plain_output_1.png"
] | pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html | code |
128030251/cell_11 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import os
import networkx as nx
import numpy as np
import pandas as pd
import networkx as nx
import gensim
import numpy as np
import pandas as... | code |
128030251/cell_7 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_18 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (... | code |
128030251/cell_32 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_tr... | code |
128030251/cell_28 | [
"image_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, ... | code |
128030251/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_15 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib... | code |
128030251/cell_38 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassif... | code |
128030251/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns | code |
128030251/cell_17 | [
"text_html_output_1.png"
] | comp = []
for i in range(1, 171):
comp.append('comp' + str(i))
comp | code |
128030251/cell_24 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Confusion Matrix :')
sns.set(rc={'figure.fig... | code |
128030251/cell_14 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (... | code |
128030251/cell_22 | [
"text_html_output_1.png"
] | from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train) | code |
128030251/cell_10 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, ... | code |
128030251/cell_12 | [
"text_html_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.co... | code |
128030251/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_sp... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.