path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105178234/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum() | code |
105178234/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum() | code |
105178234/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.head() | code |
105178234/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplica... | code |
105178234/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.head() | code |
105178234/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.head(3) | code |
105178234/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isn... | code |
50227013/cell_9 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import MaxPooling2D,Flatten,Dense,LSTM,Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processin... | code |
50227013/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset_train = pd.read_csv('../input/gooogle-stock... | code |
50227013/cell_6 | [
"text_plain_output_1.png"
] | print('done') | code |
50227013/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import numpy as np
import pandas as pd
import os
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D, Flatten, Dense, LSTM, Dropout
try:
tpu = tf.distribute.cluster_resolver.TPUClu... | code |
50227013/cell_1 | [
"text_plain_output_1.png"
] | # !pip install tensorflow==2.2-rc1
!pip install tensorflow | code |
50227013/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import MaxPooling2D,Flatten,Dense,LSTM,Dropout
from tensorflow.keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv... | code |
50227013/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import MaxPooling2D,Flatten,Dense,LSTM,Dropout
from tensorflow.keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv... | code |
33096624/cell_13 | [
"image_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_25 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_4 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls() | code |
33096624/cell_23 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_20 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_1 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"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 |
33096624/cell_7 | [
"text_html_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
data.show_batch(3, figsize=(7, 6)) | code |
33096624/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_8 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
data.show_batch(4, figsize=(7, 6)) | code |
33096624/cell_15 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_16 | [
"text_plain_output_1.png"
] | torch.cuda.is_available() | code |
33096624/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | torch.cuda.is_available()
torch.backends.cudnn.enabled | code |
33096624/cell_24 | [
"text_html_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_14 | [
"image_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_22 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
33096624/cell_10 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
path = Path('/kaggle/input/pretrained-pytorch-models/resnet50-19c8e357.pth')
path.cwd() | code |
33096624/cell_27 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='... | code |
2030951/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
data.head() | code |
2030951/cell_20 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.... | code |
2030951/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False) | code |
2030951/cell_26 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train.ravel())
y_pred = model.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_tes... | code |
2030951/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.... | code |
2030951/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train.ravel())
y_pred = model.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_tes... | code |
2030951/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.iloc[:, 1].values, data.iloc[:, 5].values)
ax.set_title('Highly Co... | code |
2030951/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.iloc[:,1].values, data.iloc[:,5].values)
ax.se... | code |
2017076/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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_9 | [
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')... | code |
2017076/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_16 | [
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
2017076/cell_5 | [
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('... | code |
32071161/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 |
72062686/cell_2 | [
"text_plain_output_1.png"
] | !pip install dtreeviz
!pip install graphviz | code |
72062686/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
model = DecisionTreeClassifier(random_state=42)
model.fit(iris.data, iris.target)
model.predict(iris.data) | code |
72062686/cell_12 | [
"text_plain_output_1.png"
] | from dtreeviz.trees import dtreeviz
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
model = DecisionTreeClassifier(random_state=42)
model.fit(iris.data, iris.target)
model.predict(iris.data)
viz = dtreeviz(model, iris.data, iris.target, target_name='target'... | code |
72104768/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from functools import partial
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import Sequential
from te... | code |
72104768/cell_1 | [
"text_plain_output_1.png"
] | ##### DEEP LEARNING FOR TABULAR DATA ##########################
# The functions used in this Kernel are based on:
# https://github.com/lmassaron/deep_learning_for_tabular_data
# You can watch the full tutorial presented at the DEVFEST 2019
# explaining how to process tabular data with TensorFlow:
# https://www.youtube... | code |
72104768/cell_15 | [
"text_plain_output_1.png"
] | from functools import partial
from keras.losses import MeanSquaredError
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from tabular import TabularTransformer, DataGenerator
from tabular import gelu, Mish, mish
from tensorflow.keras.callbacks import EarlyStopping... | code |
72104768/cell_14 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from functools import partial
from keras.losses import MeanSquaredError
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from tabular import TabularTransformer, DataGenerator
from tabular import gelu, Mish, mish
from tensorflow.keras.callbacks import EarlyStopping... | code |
88086649/cell_21 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape)
from sklearn.compose import C... | code |
88086649/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
df['Species'].value_counts() | code |
88086649/cell_25 | [
"image_output_1.png"
] | (X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
88086649/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
y_pred[:10] | code |
88086649/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.metrics ... | code |
88086649/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.describe() | code |
88086649/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_log_error, r2_score
from sklearn.metrics import r2_score
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200)
reg.fit(X_t... | code |
88086649/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr | code |
88086649/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.info() | code |
88086649/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape)
(type(X), type(y)) | code |
88086649/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum() | code |
88086649/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
sns.boxplot(x='Species', y='Weight', data=df) | code |
88086649/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
np.array(y_test)
np.array(y_test) | code |
88086649/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape) | code |
88086649/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200)
reg.fit(X_train, y_train) | code |
88086649/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
np.array(y_test) | code |
88086649/cell_22 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape)
from sklearn.compose import C... | code |
88086649/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
df['Species'].value_counts().plot.bar() | code |
88086649/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train) | code |
88086649/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200)
reg.fit(X_train, y_train)
y_pred_reg = reg.predict(X_test)
y_pred_reg | code |
88086649/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
plt.figure(figsize=(11, 8))
sns.heatmap(corr, cmap='Greens', annot=True)
plt.show() | code |
88086649/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.head() | code |
130011577/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130011577/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
print(thai... | code |
130011577/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_acci... | code |
130011577/cell_2 | [
"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)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df.tail() | code |
130011577/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130011577/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import geopandas as gpd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130011577/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_acci... | code |
130011577/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
print(f'Chech Datatype\n{df.dtypes}')
print('\nShape check')
print(df.shape)
print()
print(df.isnull().sum())
df['official_death_date'] = pd.... | code |
130011577/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130011577/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130011577/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('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_acci... | code |
16127029/cell_13 | [
"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)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_30 | [
"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)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = da... | code |
16127029/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
print(f"The mean of loan amount: {data['loa... | code |
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