path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105206399/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
columns = df.columns
for col in columns:
dtypes = df[col].dtypes
nunique = df[col].nunique()
sum_null = df[col].isnull().sum()
l.appe... | code |
105206399/cell_45 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear... | code |
105206399/cell_49 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear... | code |
105206399/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum()
df_cars.columns
df_cars.drop('car_ID', axis=1, inplace=True)
df_cars.CarName.unique() | code |
105206399/cell_59 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklear... | code |
105206399/cell_58 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklear... | code |
105206399/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.head() | code |
105206399/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum()
df_cars.columns
df_cars.drop('car_ID', axis=1, inplace=True)
df_cars.hist(bins=40, figsize=(20, 15), color='b') | code |
105206399/cell_38 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
105206399/cell_47 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear... | code |
105206399/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
105206399/cell_43 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
columns = df.columns
for co... | code |
105206399/cell_46 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear... | code |
105206399/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum()
df_cars.columns | code |
105206399/cell_22 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum()
df_cars.columns
df_cars.drop('car_ID', axis=1, inplace=True)
df_cars.CarName.unique()
df_cars.CarName.unique()
categorical_cols = df_cars.selec... | code |
105206399/cell_53 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
... | code |
105206399/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum() | code |
105206399/cell_37 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
105206399/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum() | code |
105206399/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
33108902/cell_2 | [
"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 |
33108902/cell_15 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_absolute_error
final_model = RandomForestClassifier(max_leaf_nodes=7, random_state=0)
y_pred = final_model.fit(x_treino, y_treino)
accuracy = final_model.score(x_teste, y_teste)
prin... | code |
33108902/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(train_X, train_y)
preds_val = model.predict(val_X)
mae = me... | code |
33108902/cell_5 | [
"text_plain_output_1.png"
] | 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 pandas as pd
import numpy as np
arquivos_de_treino = pd.read_csv('/kaggle/input/titanic/train.csv')
arquivos_de_teste = pd.read_csv('/kaggle/input/titanic/test.csv... | code |
324947/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for i, n in zip(ids, na... | code |
324947/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
df = pd.read_sql(sql='SELECT {} FROM Match'.format('id, country_id, league_id, season, stage, ' + 'date, home_team_api_id, away_team_api_id, ' + 'home_team_goal, away_team_goal'), con=conn)
df.head() | code |
324947/cell_30 | [
"text_plain_output_1.png"
] | from collections import Counter
import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0]... | code |
324947/cell_33 | [
"image_output_1.png"
] | from collections import Counter
import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0]... | code |
324947/cell_26 | [
"text_html_output_1.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import pandas as pd
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall(... | code |
324947/cell_19 | [
"text_plain_output_1.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n ... | code |
324947/cell_7 | [
"image_output_1.png"
] | import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for i, n in zip(ids, names)}
id_league | code |
324947/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n ... | code |
324947/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from collections import Counter
import datetime as dt
import numpy as np
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n ... | code |
324947/cell_10 | [
"text_plain_output_1.png"
] | import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]
names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]
id_league = {i: n for i, n in zip(ids, names)}
id_league
ids = [i[0] for i in c.execute('SELE... | code |
105198654/cell_6 | [
"text_plain_output_1.png"
] | import os
def convert_bytes(size):
""" Convert bytes to KB, or MB or GB"""
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if size < 1024.0:
return '%3.1f %s' % (size, x)
size /= 1024.0
file_list = ['train_cite_inputs.h5', 'train_cite_targets.h5', 'train_multi_inputs.h5', 'train_multi_... | code |
105198654/cell_11 | [
"text_plain_output_1.png"
] | import h5py
import os
def convert_bytes(size):
""" Convert bytes to KB, or MB or GB"""
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if size < 1024.0:
return '%3.1f %s' % (size, x)
size /= 1024.0
file_list = ['train_cite_inputs.h5', 'train_cite_targets.h5', 'train_multi_inputs.h5', ... | code |
105198654/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import h5py
import hdf5plugin
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import confusi... | code |
73070388/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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 pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', ind... | code |
73070388/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"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 pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
Sample_result = pd.read_csv('../input/... | code |
73070388/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
Sample_result = pd.read_csv('../input/30-days-of-ml/sample_submission.c... | code |
73070388/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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 pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', ind... | code |
73070388/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
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 pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../... | code |
73070388/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 |
73070388/cell_7 | [
"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 pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
Sample_result = pd.read_csv('../input/... | code |
73070388/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
Sample_result = pd.read_csv('../input/30-days-of-ml/sample_submission.c... | code |
73070388/cell_14 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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 pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', ind... | code |
73070388/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
Sample_result = pd.read_csv('../input/30-days-of-ml/sample_submission.c... | code |
73070388/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
Sample_result = pd.read_csv('../input/30-days-of-ml/sample_submission.c... | code |
130013764/cell_13 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_673.png",
"text_plain_output_445.png",
"text_plain_output_640.png",
"text_plain_output_201.png",
"text_plain_output_586.png",
"text_plain_output_261.png",
"text_plain_output_565.png",
"text_plain_output_522.png",
"text_... | from keras.callbacks import EarlyStopping
from keras.layers import Dense
from keras.layers.core import Reshape, Flatten, Dropout
from keras.models import Sequential
from sklearn.preprocessing import normalize
import numpy as np
import pickle
file = open('/kaggle/input/rml2016/RML2016.10b.dat', 'rb')
Xd = pickle... | code |
130013764/cell_12 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers.core import Reshape, Flatten, Dropout
from keras.models import Sequential
from sklearn.preprocessing import normalize
import numpy as np
import pickle
file = open('/kaggle/input/rml2016/RML2016.10b.dat', 'rb')
Xd = pickle.load(file, encoding='bytes')
snrs, mods =... | code |
32067131/cell_21 | [
"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
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
sns.catplot(x='Spectral Class', y='Absolute magnitude(Mv)', data=star_data, hue='Star type Decoded',... | code |
32067131/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
star_data['Star color'] = star_data['Star color'].str.lower()
star_data['Star color'] = star_data['Star color'].str.replace(' ', '')
star_data['Star color']... | code |
32067131/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
star_data['Spectral Class'].value_counts() | code |
32067131/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as ... | code |
32067131/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
star_data['Star color'].value_counts() | code |
32067131/cell_19 | [
"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
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
sns.pairplot(star_data.drop(['Star color', 'Spectral Class'], axis=1), hue='Star type Decoded', diag... | code |
32067131/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from xgboost impo... | code |
32067131/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum() | code |
32067131/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
le_specClass = LabelEncoder()
star_data['SpecClassEnc'] = le_specClass.fit_transform(star_da... | code |
32067131/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.head() | code |
32067131/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
star_data = pd.read_csv('../input/star-dataset/6 class csv.csv')
star_data.isnull().sum()
le_starCol = LabelEncoder()
star_data['StarColEnc'] = le_starCol.fit_transform(star_data['St... | code |
74046055/cell_6 | [
"text_plain_output_1.png"
] | images = Path('/kaggle/input/blood-cells/dataset2-master/dataset2-master/images/')
data = ImageDataLoaders.from_folder(path=images, train='TRAIN', valid='TEST', seed=42, item_tfms=RandomResizedCrop(224, min_scale=0.4), batch_tfms=aug_transforms(mult=2), bs=32)
print(data.vocab) | code |
74046055/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import fastai
from fastai.vision.all import *
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74046055/cell_1 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/fastai/fastai2
!pip install git+https://github.com/fastai/fastcore | code |
74046055/cell_7 | [
"text_html_output_1.png"
] | images = Path('/kaggle/input/blood-cells/dataset2-master/dataset2-master/images/')
data = ImageDataLoaders.from_folder(path=images, train='TRAIN', valid='TEST', seed=42, item_tfms=RandomResizedCrop(224, min_scale=0.4), batch_tfms=aug_transforms(mult=2), bs=32)
learn = cnn_learner(data, resnet34, metrics=error_rate)
l... | code |
74046055/cell_8 | [
"image_output_1.png"
] | images = Path('/kaggle/input/blood-cells/dataset2-master/dataset2-master/images/')
data = ImageDataLoaders.from_folder(path=images, train='TRAIN', valid='TEST', seed=42, item_tfms=RandomResizedCrop(224, min_scale=0.4), batch_tfms=aug_transforms(mult=2), bs=32)
learn = cnn_learner(data, resnet34, metrics=error_rate)
l... | code |
74046055/cell_5 | [
"image_output_1.png"
] | images = Path('/kaggle/input/blood-cells/dataset2-master/dataset2-master/images/')
data = ImageDataLoaders.from_folder(path=images, train='TRAIN', valid='TEST', seed=42, item_tfms=RandomResizedCrop(224, min_scale=0.4), batch_tfms=aug_transforms(mult=2), bs=32)
data.train.show_batch() | code |
74057859/cell_4 | [
"text_plain_output_1.png"
] | import random
s = {('Hello', 'Hi', 'Howdy'), ('Salam', 'Namaste', 'Marhabaan')}
e3 = ('NiHao', 'Konnichiwa', 'Yeoboseyo')
s.add(e3)
import random
el = random.sample(s, 1)[0]
s.remove(el)
print(s) | code |
74057859/cell_2 | [
"text_plain_output_1.png"
] | s = {('Hello', 'Hi', 'Howdy'), ('Salam', 'Namaste', 'Marhabaan')}
e3 = ('NiHao', 'Konnichiwa', 'Yeoboseyo')
s.add(e3)
for item in s:
print(item) | code |
74057859/cell_3 | [
"text_plain_output_1.png"
] | s = {('Hello', 'Hi', 'Howdy'), ('Salam', 'Namaste', 'Marhabaan')}
e3 = ('NiHao', 'Konnichiwa', 'Yeoboseyo')
s.add(e3)
my_list = list(s)
final = [my_list[i] for i in (0, -1)]
print(final) | code |
74057859/cell_5 | [
"text_plain_output_1.png"
] | import random
s = {('Hello', 'Hi', 'Howdy'), ('Salam', 'Namaste', 'Marhabaan')}
e3 = ('NiHao', 'Konnichiwa', 'Yeoboseyo')
s.add(e3)
import random
el = random.sample(s, 1)[0]
s.remove(el)
s.remove(('Salam', 'Namaste', 'Marhabaan'))
print(s) | code |
106204184/cell_13 | [
"text_plain_output_1.png"
] | i = 0
while i < 6:
i += 2
i = 1
while True:
if i % 9 == 0:
break
print(i + 4)
i += 2 | code |
106204184/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
def square(a):
sq = a * a
return sq
square(6) | code |
106204184/cell_4 | [
"text_plain_output_1.png"
] | for i in range(0, 100):
print('Raiyaan') | code |
106204184/cell_6 | [
"text_plain_output_1.png"
] | for i in range(0, 100, 25):
print('RAIYAAN') | code |
106204184/cell_2 | [
"text_plain_output_1.png"
] | num = 50
if num > 50:
if num % 2 == 0:
print('No is even and greater than 50')
else:
print('No is not even and greater than 50')
elif num % 2 != 0:
print('No is odd and less than 50')
else:
print('No is not odd and less than 50') | code |
106204184/cell_11 | [
"text_plain_output_1.png"
] | fact = 1
for i in range(1, 11):
fact = fact * i
import pandas as pd
import numpy as np
def factorial(value):
fact = 1
for i in range(1, value + 1):
fact = fact * i
return fact
n = 5
r = 3
c = factorial(n) / (factorial(r) * factorial(n - r))
def fun(n, l=[]):
for i in range(n):
l.ap... | code |
106204184/cell_8 | [
"text_plain_output_1.png"
] | fact = 1
for i in range(1, 11):
fact = fact * i
print(fact) | code |
106204184/cell_15 | [
"text_plain_output_1.png"
] | def func(x, y):
if x > y:
return x
elif x == y:
return (x, y)
else:
return y
print(func(20, 30)) | code |
106204184/cell_14 | [
"text_plain_output_1.png"
] | string_1 = 'internshala'
for i in range(len(string_1)):
print(string_1)
string_1 = 'z' | code |
106204184/cell_10 | [
"text_plain_output_1.png"
] | fact = 1
for i in range(1, 11):
fact = fact * i
import pandas as pd
import numpy as np
def factorial(value):
fact = 1
for i in range(1, value + 1):
fact = fact * i
return fact
n = 5
r = 3
c = factorial(n) / (factorial(r) * factorial(n - r))
print('No of combination = ' + str(c)) | code |
106204184/cell_12 | [
"text_plain_output_1.png"
] | i = 0
while i < 6:
print(i)
i += 2
else:
print(0) | code |
106204184/cell_5 | [
"text_plain_output_1.png"
] | for i in range(75, 100):
print('Raiyaan') | code |
50239477/cell_42 | [
"text_html_output_1.png"
] | seed = 27912
TX_train.sample(5, random_state=seed) | code |
50239477/cell_56 | [
"text_html_output_1.png"
] | import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
seed = 27912
filepath = '../input/breast-cancer-wisconsin-data/data.csv'
indexC = 'id'
targetC = 'diagnosis'
dataC = utils.load_data(filepath, indexC, targetC)
dataC.sample(5, rando... | code |
50239477/cell_34 | [
"text_plain_output_1.png"
] | seed = 27912
Cy.sample(5, random_state=seed) | code |
50239477/cell_33 | [
"text_html_output_1.png"
] | seed = 27912
TX.sample(5, random_state=seed) | code |
50239477/cell_44 | [
"text_plain_output_1.png"
] | seed = 27912
Cy_train.sample(5, random_state=seed) | code |
50239477/cell_20 | [
"text_plain_output_1.png"
] | import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils
filepath = '../input/breast-cancer-wisconsin-data/data.csv'
indexC = 'id'
targetC = 'diagnosis'
dataC = utils.load_data(filepath, indexC, targetC)
filepathD = '../input/pima-indians-diabetes-database/diabetes.csv'
targetD = 'Outcome'
dataD = utils.pd.rea... | code |
50239477/cell_40 | [
"text_html_output_1.png"
] | seed = 27912
DX_train.sample(5, random_state=seed) | code |
50239477/cell_48 | [
"text_html_output_1.png"
] | seed = 27912
TX_test.sample(5, random_state=seed) | code |
50239477/cell_41 | [
"text_html_output_1.png"
] | seed = 27912
CX_train.sample(5, random_state=seed) | code |
50239477/cell_2 | [
"text_html_output_1.png"
] | from sklearn.dummy import DummyClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.tree import DecisionTreeClassifier
import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils | code |
50239477/cell_54 | [
"text_html_output_1.png"
] | import miner_a_de_datos_an_lisis_exploratorio_utilidad as utils
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
seed = 27912
filepath = '../input/breast-cancer-wisconsin-data/data.csv'
indexC = 'id'
targetC = 'diagnosis'
dataC = utils.load_data(filepath, indexC, targetC)
dataC.sample(5, rando... | code |
50239477/cell_50 | [
"text_plain_output_1.png"
] | seed = 27912
Cy_test.sample(5, random_state=seed) | code |
50239477/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 |
50239477/cell_45 | [
"text_plain_output_1.png"
] | seed = 27912
Ty_train.sample(5, random_state=seed) | code |
50239477/cell_49 | [
"text_plain_output_1.png"
] | seed = 27912
Dy_test.sample(5, random_state=seed) | code |
50239477/cell_32 | [
"text_html_output_1.png"
] | seed = 27912
DX.sample(5, random_state=seed) | code |
50239477/cell_51 | [
"text_plain_output_1.png"
] | seed = 27912
Ty_test.sample(5, random_state=seed) | code |
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