path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88102789/cell_11 | [
"text_plain_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
df.head() | code |
88102789/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alone', hue='alive', palette='deep') | code |
88102789/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.distplot(df['fare'], kde=False, bins=30, color='y') | code |
88102789/cell_18 | [
"text_html_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='alone', palette='deep') | code |
88102789/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.kdeplot(df['fare'], shade=True, color='y') | code |
88102789/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
df['who'].unique() | code |
88102789/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='who', palette='deep') | code |
88102789/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
df.head() | code |
88102789/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='who', hue='alive', palette='deep') | code |
88102789/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alone', hue='sex') | code |
88102789/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='alive', hue='sex', palette='deep') | code |
88102789/cell_22 | [
"text_plain_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='embark_town', hue='class', palette='deep') | code |
88102789/cell_10 | [
"text_html_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.jointplot(data=df[df['fare'] <= 100], x='age', y='fare', kind='scatter', color='c') | code |
88102789/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.countplot(data=df, x='survived', palette='dark') | code |
88102789/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
df = sns.load_dataset('titanic')
sns.distplot(df['age'], kde=False, bins=30, color='m') | code |
104117096/cell_13 | [
"text_html_output_1.png"
] | import glob
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*')
images_path = glob.glob('../input/skin-cancer-mnist-ham10000/HAM10000_images_part_1/*')
img_id = []
for i in images_path:... | code |
104117096/cell_4 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*')
images_path = glob.glob('../input/skin-cancer-mnist-ham10000/HAM10000_images_part_1/*')
img_id = []
for i in images_path:
img_id.append(i.split('/')[4].split(... | code |
104117096/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/skin-cancer-mnist-ham10000/HAM10000_metadata.csv')
data.info() | code |
104117096/cell_2 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*') | code |
104117096/cell_7 | [
"image_output_1.png"
] | import glob
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*')
images_path = glob.glob('../input/skin-cancer-mnist-ham10000/HAM10000_images_part_1/*')
img_id = []
for i in images_path:
img_id.append(i... | code |
104117096/cell_15 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*')
images_path = glob.glob('../input/skin-cancer-mnist-ham10000/HAM10000_images_part_1/*')
img_id = []
for i in images_path:... | code |
104117096/cell_16 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*')
images_path = glob.glob('../input/skin-cancer-mnist-ham10000/HAM10000_images_part_1/*')
... | code |
104117096/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/skin-cancer-mnist-ham10000/HAM10000_metadata.csv')
img_with_class = data.loc[:, ['image_id', 'dx']].to_dict('list')
img_with_class.keys() | code |
104117096/cell_12 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
glob.glob('../input/skin-cancer-mnist-ham10000/*')
images_path = glob.glob('../input/skin-cancer-mnist-ham10000/HAM10000_images_part_1/*')
img_id = []
for i in images_path:... | code |
104117096/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/skin-cancer-mnist-ham10000/HAM10000_metadata.csv')
data.head() | code |
17118100/cell_21 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from google.cloud import bigquery
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '.... | code |
17118100/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from google.cloud import bigquery
import os
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id... | code |
17118100/cell_9 | [
"text_html_output_1.png"
] | from google.cloud import bigquery
import os
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id)
job_config ... | code |
17118100/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '.... | code |
17118100/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from google.cloud import bigquery
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '.... | code |
17118100/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17118100/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
import os
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id)
job_config ... | code |
17118100/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
import os
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id)
job_config ... | code |
17118100/cell_15 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from google.cloud import bigquery
import os
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id... | code |
17118100/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
import os
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id... | code |
17118100/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US') | code |
17118100/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
import os
import time
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id... | code |
17118100/cell_10 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
import os
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id)
job_config ... | code |
17118100/cell_12 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from google.cloud import bigquery
import os
import numpy as np
import pandas as pd
import os
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id)
job_config ... | code |
17118100/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.cloud import bigquery
PROJECT_ID = 'YOUR_OWN_PROJECT_ID'
from google.cloud import bigquery
client = bigquery.Client(project=PROJECT_ID, location='US')
path = '../input/'
dataset_id = 'titanic'
dataset_ref = client.dataset(dataset_id)
job_config = bigquery.LoadJobConfig()
job_config.source_format = bigquer... | code |
129037699/cell_13 | [
"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/videogamesales/vgsales.csv')
df
dfsorted = df.sort_values('Global_Sales', ascending=False)
top20 = dfsorted.head(20)
top20
NAsorted = df.sort_values('NA_Sales', ascending=False)
NA_median = NAsorted['NA_Sales'].med... | code |
129037699/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
genre = df['Genre'].value_counts().idxmax()
genre | code |
129037699/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
dfsorted = df.sort_values('Global_Sales', ascending=False)
top20 = dfsorted.head(20)
top20 | code |
129037699/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 |
129037699/cell_7 | [
"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/videogamesales/vgsales.csv')
df
platform = df['Platform'].value_counts().idxmax()
platform | code |
129037699/cell_15 | [
"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/videogamesales/vgsales.csv')
df
dfsorted = df.sort_values('Global_Sales', ascending=False)
top20 = dfsorted.head(20)
top20
NAsorted= df.sort_values('NA_Sales', ascending=False)
NA_median = NAsorted['NA_Sales'].medi... | code |
129037699/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df | code |
129037699/cell_17 | [
"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/videogamesales/vgsales.csv')
df
dfsorted = df.sort_values('Global_Sales', ascending=False)
top20 = dfsorted.head(20)
top20
NAsorted= df.sort_values('NA_Sales', ascending=False)
NA_median = NAsorted['NA_Sales'].medi... | code |
129037699/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)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
publisher = df['Publisher'].value_counts().idxmax()
publisher | code |
16153263/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import metrics
import pandas as pd
train = pd.read_csv('../input/emnist-balanced-train.csv', header=None)
test = pd.read_csv('../input/emnist-balanced-test.csv', header=None)
train_data = train.values[:, 1:]
train_labels = train.values[:, 0]
test_data = test.values[:, 1:]
test_labels = test.values[:, 0... | code |
16153263/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16153263/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('../input/emnist-balanced-train.csv', header=None)
test = pd.read_csv('../input/emnist-balanced-test.csv', header=None)
train_data = train.values[:, 1:]
train_labels = train.values[:, 0]
test_data = test.values[:, 1:]
test_l... | code |
16153263/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/emnist-balanced-train.csv', header=None)
train.head() | code |
48165764/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
intercept = 0.9668835542286371
coef = [-0.964522806, 1.17172755, 0.179847765, 0.715775158, 0.943909901, 0.771809223, 0.166641048, 3.36556037, -3.04227717, -1.27799877, 3.24426534, 0.0839780267, -2.01609571, -0.204835338, -0.915522223, -2.855... | code |
128047328/cell_42 | [
"image_output_1.png"
] | best = create_model('et')
bagged_ensemble = ensemble_model(best) | code |
128047328/cell_63 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train[... | code |
128047328/cell_21 | [
"text_html_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train['SMILES'][:10]
mol_lis... | code |
128047328/cell_25 | [
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train['SMILES'][:10]
mol_lis... | code |
128047328/cell_4 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | pip install deepchem | code |
128047328/cell_57 | [
"application_vnd.jupyter.stderr_output_16.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"application_vnd.jupyter.stderr_output_18.png",
"application_vnd.ju... | from rdkit import Chem
from rdkit.Chem import AllChem
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train[... | code |
128047328/cell_23 | [
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train['SMILES'][:10]
mol_lis... | code |
128047328/cell_79 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import deepchem as dc
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Oc... | code |
128047328/cell_30 | [
"text_html_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv... | code |
128047328/cell_44 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | best = create_model('et')
boosted_ensemble = ensemble_model(best, method='Boosting') | code |
128047328/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Ocurrences', 'Group', 'co... | code |
128047328/cell_76 | [
"text_html_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
128047328/cell_40 | [
"text_plain_output_1.png"
] | best = create_model('et')
plot_model(best, 'feature') | code |
128047328/cell_29 | [
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv... | code |
128047328/cell_39 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | best = create_model('et')
plot_model(best, 'error') | code |
128047328/cell_26 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
128047328/cell_48 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv... | code |
128047328/cell_73 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_24.png",
"application_vnd.jupyter.stderr_output_16.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_32.png",
"application_vnd.... | from rdkit import Chem
from rdkit.Chem import AllChem
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train[... | code |
128047328/cell_41 | [
"image_output_1.png"
] | best = create_model('et')
prediction_holdout = predict_model(best) | code |
128047328/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | pip install rdkit | code |
128047328/cell_72 | [
"text_plain_output_1.png"
] | import deepchem as dc
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Oc... | code |
128047328/cell_67 | [
"text_html_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
128047328/cell_60 | [
"text_html_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
128047328/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Ocurrences', 'Group', 'co... | code |
128047328/cell_69 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | !pip install mordred | code |
128047328/cell_50 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"application_vnd.jupyt... | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv... | code |
128047328/cell_64 | [
"text_plain_output_1.png"
] | import deepchem as dc
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Oc... | code |
128047328/cell_45 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | best = create_model('et')
boosted_ensemble_50estm = ensemble_model(best, method='Boosting', n_estimators=50) | code |
128047328/cell_32 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupy... | best_top3_models = compare_models(n_select=3) | code |
128047328/cell_59 | [
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubili... | code |
128047328/cell_16 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Ocurrences', 'Group', 'co... | code |
128047328/cell_38 | [
"text_html_output_1.png"
] | best = create_model('et')
print(best) | code |
128047328/cell_75 | [
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubili... | code |
128047328/cell_3 | [
"text_plain_output_1.png"
] | pip install pycaret | code |
128047328/cell_66 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_24.png",
"application_vnd.jupyter.stderr_output_16.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_32.png",
"application_vnd.... | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubili... | code |
128047328/cell_17 | [
"text_plain_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
128047328/cell_35 | [
"text_html_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv... | code |
128047328/cell_77 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | et = create_model('et') | code |
128047328/cell_43 | [
"text_html_output_1.png"
] | best = create_model('et')
bagged_ensemble_50estm = ensemble_model(best, n_estimators=50) | code |
128047328/cell_31 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolFromSmiles, Descriptors, MolFromSmarts
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv... | code |
128047328/cell_46 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | best_top3_models = compare_models(n_select=3)
best_top3_models = compare_models(n_select=3)
best_top3_models = compare_models(n_select=3)
best_top3_models = compare_models(n_select=3)
stacker = stack_models(best_top3_models) | code |
128047328/cell_14 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.columns | code |
128047328/cell_22 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train['SMILES'][:10]
mol_lis... | code |
128047328/cell_10 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.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.pn... | import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
df_train.info() | code |
128047328/cell_37 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | best = create_model('et') | code |
128047328/cell_12 | [
"text_plain_output_100.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_98.png",
"text_plain_output_43.png",
"text_plain_output_78.png",
"text_plain_output_106.png",
"text_plain_output_37.png",
"text_plain_output_90.png",
"text_plain_ou... | from rdkit import Chem
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train['SMILES'][:10]
mol_list = []
for smile in smiles_list:
mol = Chem.MolF... | code |
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