path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
88087713/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
sample_trans_data.drop(labels=['t_dat', 'sales_channel_id'], axis=1, inplace=True)
sample_trans_data.reset_index(drop=True, inplace=True) | code |
88087713/cell_8 | [
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True) | code |
88087713/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
articles_data[['prod_name', 'product_type_name', 'product_group_name']].describe() | code |
88087713/cell_17 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
articles_data_new = articles_data[['prod_name', 'product_type_name', 'product_group_name']].copy()
articles_data_new.head(5) | code |
88087713/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
interval_range_age = pd.interval_range(start=0, freq=10, end=100)
customers_data_new['age_group'] = pd.cut(customers_data_new['age'], bins=interval_range_age)
customers_data_new.head(5) | code |
88087713/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
sample_trans_data.drop(labels=['t_dat', 'sales_channel_id'], axis=1, inplace=True)
sample_trans_data.reset_index(drop=True, inplace=True)
sample_trans_data.isna().sum()
sample_trans_data.head(5) | code |
88087713/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
articles_data.head(5) | code |
88087713/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes | code |
88087713/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.head(5) | code |
88087713/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.info() | code |
88087713/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
interval_range_age = pd.interval_range(start=0, freq=10, end=100)
customers_data_new['age_group'] = pd.cut(customers_data_new['age'], bins=interval_range_age)
customers_data_new.isna().sum() | code |
16154469/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pylab as plt
import networkx as nx
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
structures_df = pd.read_csv('../input/structures.csv')
test_df['scalar_coupling_constant'] = np.nan
df = pd.concat([train_df, test_df])
del train_df
del test_df
for atom_index in [0, 1]:
renamed_columns = {col: col + '_' + str(atom_index) for col in ['x', 'y', 'z', 'atom_index', 'atom']}
df = df.merge(structures_df.rename(columns=renamed_columns), on=['molecule_name', 'atom_index_' + str(atom_index)], how='inner')
df['distance_l2'] = ((df['x_0'] - df['x_1']) ** 2 + (df['y_0'] - df['y_1']) ** 2 + (df['z_0'] - df['z_1']) ** 2) ** 0.5
MOLECULE_NAMES = df['molecule_name'].unique()
def get_molecule_graph(df, molecule_name):
molecule_df = df.loc[lambda df: df['molecule_name'] == molecule_name]
labels = molecule_df[['atom_1', 'atom_index_1']].set_index('atom_index_1')['atom_1'].to_dict()
labels.update(molecule_df[['atom_0', 'atom_index_0']].set_index('atom_index_0')['atom_0'].to_dict())
graph = nx.from_pandas_edgelist(molecule_df, source='atom_index_0',
target='atom_index_1', edge_attr='scalar_coupling_constant',
create_using=nx.Graph())
return graph, labels
def draw_graph(graph, labels, weight="distance_l2"):
position = nx.spring_layout(graph, weight=weight)
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
nx.draw_networkx_nodes(graph, position, node_color='red', alpha = 0.8, ax=ax)
nx.draw_networkx_edges(graph, position, edge_color='blue', alpha = 0.6, ax=ax)
nx.draw_networkx_labels(graph, position, labels, font_size=16, ax=ax)
return ax
for molecule_name in MOLECULE_NAMES[:10]:
graph, labels = get_molecule_graph(df, molecule_name)
ax = draw_graph(graph, labels)
ax.set_title(f'Graph for {molecule_name}') | code |
130014083/cell_42 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
text_data = df['Departure City'].str.cat(sep=' ')
wordcloud = WordCloud(width=800, height=400, background_color='black').generate(text_data)
plt.axis('off')
text_data = df['Arrival City'].str.cat(sep=' ')
wordcloud = WordCloud(width=800, height=400, background_color='black').generate(text_data)
plt.axis('off')
text_data = df['Name'].str.cat(sep=' ')
wordcloud = WordCloud(width=800, height=400, background_color='black').generate(text_data)
plt.figure(figsize=(10, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
130014083/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
df.describe() | code |
130014083/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df.head() | code |
130014083/cell_25 | [
"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)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
max_value = np.max(df['Ticket Price'])
max_value
min_value = np.min(df['Ticket Price'])
min_value | code |
130014083/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.pairplot(df, hue='Frequent Flyer Status') | code |
130014083/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
df['Ticket Price'].mean() | code |
130014083/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.countplot(x='Booking Class', data=df) | code |
130014083/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.pairplot(df, hue='Booking Class') | code |
130014083/cell_44 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
X = df[['Flight Duration', 'Ticket Price', 'Competitor Price', 'Demand']]
y = df['Delay Minutes']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
for prediction in predictions:
print(prediction) | code |
130014083/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna() | code |
130014083/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130014083/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
text_data = df['Departure City'].str.cat(sep=' ')
wordcloud = WordCloud(width=800, height=400, background_color='black').generate(text_data)
plt.figure(figsize=(10, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
130014083/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.scatterplot(x='Demand', y='Profitability', data=df) | code |
130014083/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
df.head() | code |
130014083/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
df.head() | code |
130014083/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
text_data = df['Departure City'].str.cat(sep=' ')
wordcloud = WordCloud(width=800, height=400, background_color='black').generate(text_data)
plt.axis('off')
text_data = df['Arrival City'].str.cat(sep=' ')
wordcloud = WordCloud(width=800, height=400, background_color='black').generate(text_data)
plt.figure(figsize=(10, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
130014083/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
Avg_ticket_price.head() | code |
130014083/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 |
130014083/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.info() | code |
130014083/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
Avg_delay_min.head() | code |
130014083/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.pairplot(df) | code |
130014083/cell_28 | [
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.histplot(x='Demand', data=df, bins=10) | code |
130014083/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
sorted_df.head() | code |
130014083/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean() | code |
130014083/cell_35 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.displot(df['Loyalty Points'], kde=True, bins=10) | code |
130014083/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.boxplot(x='Flight Duration', data=df) | code |
130014083/cell_24 | [
"text_html_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)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
max_value = np.max(df['Ticket Price'])
max_value | code |
130014083/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df = df[df['Churned'] == False]
filtered_df.head() | code |
130014083/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
df['Ticket Price'].sum() | code |
130014083/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130014083/cell_27 | [
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm') | code |
130014083/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
msno.matrix(df) | code |
130014083/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)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
selected_columns = ['Departure City', 'Arrival City', 'Flight Duration', 'Delay Minutes', 'Booking Class']
df_selected = df[selected_columns]
df_selected | code |
130014083/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
correlation_matrix = df.corr()
sns.barplot(x='Ticket Price', y='Competitor Price', data=df) | code |
72118922/cell_13 | [
"text_html_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/test')
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
train_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'
trainset = []
trainlabel = []
trainidt = []
for i in tqdm(range(len(labels))):
idt = labels.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(train_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
trainset += [image]
trainlabel += [labels.loc[i, 'MGMT_value']]
trainidt += [idt]
test_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/test'
testset = []
testidt = []
for i in tqdm(range(len(sample))):
idt = sample.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(test_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
testset += [image]
testidt += [idt]
from tensorflow.keras.utils import to_categorical
y0 = np.array(trainlabel)
Y_train = to_categorical(y0)
X_train = np.array(trainset)
X_test = np.array(testset)
model = Sequential()
model.add(Conv2D(64, (4, 4), input_shape=(9, 9, 1), activation='relu'))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
his = model.fit(X_train, Y_train, validation_split=0.2, epochs=100, batch_size=64, verbose=2) | code |
72118922/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample | code |
72118922/cell_6 | [
"text_html_output_1.png"
] | import cv2
import numpy as np
import pydicom
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
print(img1.shape)
print(img2.shape) | code |
72118922/cell_7 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/test')
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
train_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'
trainset = []
trainlabel = []
trainidt = []
for i in tqdm(range(len(labels))):
idt = labels.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(train_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
trainset += [image]
trainlabel += [labels.loc[i, 'MGMT_value']]
trainidt += [idt] | code |
72118922/cell_18 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/test')
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
train_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'
trainset = []
trainlabel = []
trainidt = []
for i in tqdm(range(len(labels))):
idt = labels.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(train_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
trainset += [image]
trainlabel += [labels.loc[i, 'MGMT_value']]
trainidt += [idt]
test_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/test'
testset = []
testidt = []
for i in tqdm(range(len(sample))):
idt = sample.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(test_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
testset += [image]
testidt += [idt]
from tensorflow.keras.utils import to_categorical
y0 = np.array(trainlabel)
Y_train = to_categorical(y0)
X_train = np.array(trainset)
X_test = np.array(testset)
model = Sequential()
model.add(Conv2D(64, (4, 4), input_shape=(9, 9, 1), activation='relu'))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
his = model.fit(X_train, Y_train, validation_split=0.2, epochs=100, batch_size=64, verbose=2)
y_pred = model.predict(X_test)
pred = np.argmax(y_pred, axis=1)
result = pd.DataFrame(testidt)
result[1] = pred
result.columns = ['BraTS21ID', 'MGMT_value']
result2 = result.groupby('BraTS21ID', as_index=False).mean()
result2
result2['BraTS21ID'] = sample['BraTS21ID']
result2['MGMT_value'] = result2['MGMT_value'].apply(lambda x: round(x * 10) / 10)
result2.to_csv('submission.csv', index=False)
result2 | code |
72118922/cell_8 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/test')
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
train_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'
trainset = []
trainlabel = []
trainidt = []
for i in tqdm(range(len(labels))):
idt = labels.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(train_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
trainset += [image]
trainlabel += [labels.loc[i, 'MGMT_value']]
trainidt += [idt]
test_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/test'
testset = []
testidt = []
for i in tqdm(range(len(sample))):
idt = sample.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(test_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
testset += [image]
testidt += [idt] | code |
72118922/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels | code |
72118922/cell_17 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.notebook import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/test')
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
train_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'
trainset = []
trainlabel = []
trainidt = []
for i in tqdm(range(len(labels))):
idt = labels.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(train_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
trainset += [image]
trainlabel += [labels.loc[i, 'MGMT_value']]
trainidt += [idt]
test_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/test'
testset = []
testidt = []
for i in tqdm(range(len(sample))):
idt = sample.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(test_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
testset += [image]
testidt += [idt]
from tensorflow.keras.utils import to_categorical
y0 = np.array(trainlabel)
Y_train = to_categorical(y0)
X_train = np.array(trainset)
X_test = np.array(testset)
model = Sequential()
model.add(Conv2D(64, (4, 4), input_shape=(9, 9, 1), activation='relu'))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
his = model.fit(X_train, Y_train, validation_split=0.2, epochs=100, batch_size=64, verbose=2)
y_pred = model.predict(X_test)
pred = np.argmax(y_pred, axis=1)
result = pd.DataFrame(testidt)
result[1] = pred
result.columns = ['BraTS21ID', 'MGMT_value']
result2 = result.groupby('BraTS21ID', as_index=False).mean()
result2 | code |
72118922/cell_14 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
from tqdm.notebook import tqdm
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pydicom
train_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train')
test_name = os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/test')
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels
sample = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
sample
def load_dicom(path):
dicom = pydicom.read_file(path)
data = dicom.pixel_array
data = data - np.min(data)
if np.max(data) != 0:
data = data / np.max(data)
data = (data * 255).astype(np.uint8)
return data
path0 = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00000/T1wCE/Image-1.dcm'
img1 = load_dicom(path0)
img2 = cv2.resize(img1, (9, 9))
train_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'
trainset = []
trainlabel = []
trainidt = []
for i in tqdm(range(len(labels))):
idt = labels.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(train_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
trainset += [image]
trainlabel += [labels.loc[i, 'MGMT_value']]
trainidt += [idt]
test_dir = '../input/rsna-miccai-brain-tumor-radiogenomic-classification/test'
testset = []
testidt = []
for i in tqdm(range(len(sample))):
idt = sample.loc[i, 'BraTS21ID']
idt2 = ('00000' + str(idt))[-5:]
path = os.path.join(test_dir, idt2, 'T1wCE')
for im in os.listdir(path):
img = load_dicom(os.path.join(path, im))
img = cv2.resize(img, (9, 9))
image = img_to_array(img)
image = image / 255.0
testset += [image]
testidt += [idt]
from tensorflow.keras.utils import to_categorical
y0 = np.array(trainlabel)
Y_train = to_categorical(y0)
X_train = np.array(trainset)
X_test = np.array(testset)
model = Sequential()
model.add(Conv2D(64, (4, 4), input_shape=(9, 9, 1), activation='relu'))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
his = model.fit(X_train, Y_train, validation_split=0.2, epochs=100, batch_size=64, verbose=2)
get_acc = his.history['accuracy']
value_acc = his.history['val_accuracy']
get_loss = his.history['loss']
validation_loss = his.history['val_loss']
epochs = range(len(get_acc))
plt.plot(epochs, get_acc, 'r', label='Accuracy of Training data')
plt.plot(epochs, value_acc, 'b', label='Accuracy of Validation data')
plt.title('Training vs validation accuracy')
plt.legend(loc=0)
plt.figure()
plt.show() | code |
72118922/cell_12 | [
"text_html_output_1.png"
] | from keras.models import Sequential
from tensorflow.keras.layers import Dense,Flatten,Dropout,Conv2D,MaxPooling2D,BatchNormalization
model = Sequential()
model.add(Conv2D(64, (4, 4), input_shape=(9, 9, 1), activation='relu'))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary() | code |
90103606/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
def missing_values(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum() / data.isnull().count() * 100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Number of Missing Values', 'Percentage'])
customers_missing = missing_values(customers)
customers_missing.loc[customers_missing['Percentage'] > 0] | code |
90103606/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
customers.head() | code |
90103606/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
def missing_values(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum() / data.isnull().count() * 100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Number of Missing Values', 'Percentage'])
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',
names='index_group_name',
color_discrete_sequence=px.colors.sequential.RdBu,
hover_data=['index_group_name'],
labels={'index_group_name':'Index Group Name'},
height=450)
fig.show()
fig = px.histogram(articles, x='garment_group_name',color="index_group_name",
title="Index Group Name per Garment Group Name",
color_discrete_sequence=px.colors.sequential.Agsunset,
height=600)
fig.show()
df1 = articles.groupby(["section_name"]).count().reset_index()
fig = px.bar(df1,
x=articles.groupby(["section_name"]).size(),
y="section_name",
color='section_name',
title='Distribution by Section Name',
hover_data=['section_name'],
text_auto='.2s',
labels={'section_name':'Section Name'},
orientation='h',
color_discrete_sequence=px.colors.diverging.Temps,
height=1000)
fig.update_traces(textfont_size=11, textangle=0, textposition="outside", cliponaxis=False)
fig.update_layout(xaxis_title = 'Count')
fig.show()
df4= articles.groupby(["index_name"])["article_id"].nunique()
df4 = pd.DataFrame({'IndexName': df4.index,
'Articles': df4.values
})
labels=df4['IndexName']
values=df4['Articles']
fig = px.pie(labels, values = values, hole = 0.35,
names = labels,
title = 'Distribution by Index Name',
color_discrete_sequence =px.colors.cyclical.mygbm
)
fig.show()
df5 = articles.groupby(["perceived_colour_master_name"]).count().reset_index()
colors = ['#F5F5DC','#000000','#023e8a','#168aad','#7f5539','#90be6d','#b7b7a4','#606c38','#9d4edd','#b7b7a4','#9e2a2b','#f77f00','#ffafcc','#d00000','#34a0a4','#3e1f47','#ffffff','#fcbf49','#dddf00','#9e0059']
fig = px.bar(df5,
y=articles.groupby(["perceived_colour_master_name"]).size(),
x="perceived_colour_master_name",
color='perceived_colour_master_name',
hover_data=['perceived_colour_master_name'],
text_auto='.2s',
color_discrete_sequence =colors,
title='Distribution by Percieved Color Master Name',
labels={'perceived_colour_master_name':'Percieved Color Master Name'})
fig.update_traces(textfont_size=11, textangle=0, textposition="outside", cliponaxis=False)
fig.update_layout(yaxis_title = 'Count')
fig.show()
fig = px.histogram(customers, x='age', range_x=['0', '100'], title='Age Distribution', height=450)
fig.show() | code |
90103606/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
def missing_values(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum() / data.isnull().count() * 100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Number of Missing Values', 'Percentage'])
transactions_missing = missing_values(transactions)
transactions_missing.loc[transactions_missing['Percentage'] > 0] | code |
90103606/cell_30 | [
"text_html_output_2.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',
names='index_group_name',
color_discrete_sequence=px.colors.sequential.RdBu,
hover_data=['index_group_name'],
labels={'index_group_name':'Index Group Name'},
height=450)
fig.show()
fig = px.histogram(articles, x='garment_group_name',color="index_group_name",
title="Index Group Name per Garment Group Name",
color_discrete_sequence=px.colors.sequential.Agsunset,
height=600)
fig.show()
df1 = articles.groupby(['section_name']).count().reset_index()
fig = px.bar(df1, x=articles.groupby(['section_name']).size(), y='section_name', color='section_name', title='Distribution by Section Name', hover_data=['section_name'], text_auto='.2s', labels={'section_name': 'Section Name'}, orientation='h', color_discrete_sequence=px.colors.diverging.Temps, height=1000)
fig.update_traces(textfont_size=11, textangle=0, textposition='outside', cliponaxis=False)
fig.update_layout(xaxis_title='Count')
fig.show() | code |
90103606/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from tabulate import tabulate
import cufflinks as cf
import plotly.express as px
import plotly.graph_objects as go | code |
90103606/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
articles.head() | code |
90103606/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
def missing_values(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum() / data.isnull().count() * 100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Number of Missing Values', 'Percentage'])
articles_missing = missing_values(articles)
articles_missing.loc[articles_missing['Percentage'] > 0] | code |
90103606/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 |
90103606/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
def missing_values(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum() / data.isnull().count() * 100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Number of Missing Values', 'Percentage'])
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',
names='index_group_name',
color_discrete_sequence=px.colors.sequential.RdBu,
hover_data=['index_group_name'],
labels={'index_group_name':'Index Group Name'},
height=450)
fig.show()
fig = px.histogram(articles, x='garment_group_name',color="index_group_name",
title="Index Group Name per Garment Group Name",
color_discrete_sequence=px.colors.sequential.Agsunset,
height=600)
fig.show()
df1 = articles.groupby(["section_name"]).count().reset_index()
fig = px.bar(df1,
x=articles.groupby(["section_name"]).size(),
y="section_name",
color='section_name',
title='Distribution by Section Name',
hover_data=['section_name'],
text_auto='.2s',
labels={'section_name':'Section Name'},
orientation='h',
color_discrete_sequence=px.colors.diverging.Temps,
height=1000)
fig.update_traces(textfont_size=11, textangle=0, textposition="outside", cliponaxis=False)
fig.update_layout(xaxis_title = 'Count')
fig.show()
df4= articles.groupby(["index_name"])["article_id"].nunique()
df4 = pd.DataFrame({'IndexName': df4.index,
'Articles': df4.values
})
labels=df4['IndexName']
values=df4['Articles']
fig = px.pie(labels, values = values, hole = 0.35,
names = labels,
title = 'Distribution by Index Name',
color_discrete_sequence =px.colors.cyclical.mygbm
)
fig.show()
df5 = articles.groupby(['perceived_colour_master_name']).count().reset_index()
colors = ['#F5F5DC', '#000000', '#023e8a', '#168aad', '#7f5539', '#90be6d', '#b7b7a4', '#606c38', '#9d4edd', '#b7b7a4', '#9e2a2b', '#f77f00', '#ffafcc', '#d00000', '#34a0a4', '#3e1f47', '#ffffff', '#fcbf49', '#dddf00', '#9e0059']
fig = px.bar(df5, y=articles.groupby(['perceived_colour_master_name']).size(), x='perceived_colour_master_name', color='perceived_colour_master_name', hover_data=['perceived_colour_master_name'], text_auto='.2s', color_discrete_sequence=colors, title='Distribution by Percieved Color Master Name', labels={'perceived_colour_master_name': 'Percieved Color Master Name'})
fig.update_traces(textfont_size=11, textangle=0, textposition='outside', cliponaxis=False)
fig.update_layout(yaxis_title='Count')
fig.show() | code |
90103606/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',
names='index_group_name',
color_discrete_sequence=px.colors.sequential.RdBu,
hover_data=['index_group_name'],
labels={'index_group_name':'Index Group Name'},
height=450)
fig.show()
fig = px.histogram(articles, x='garment_group_name', color='index_group_name', title='Index Group Name per Garment Group Name', color_discrete_sequence=px.colors.sequential.Agsunset, height=600)
fig.show() | code |
90103606/cell_8 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
l = os.listdir('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
print(f'Folders: {l}') | code |
90103606/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
transactions.head() | code |
90103606/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
def missing_values(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum() / data.isnull().count() * 100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Number of Missing Values', 'Percentage'])
fig = px.pie(articles, values='article_id',
title='Distribution by Index Group Name',
names='index_group_name',
color_discrete_sequence=px.colors.sequential.RdBu,
hover_data=['index_group_name'],
labels={'index_group_name':'Index Group Name'},
height=450)
fig.show()
fig = px.histogram(articles, x='garment_group_name',color="index_group_name",
title="Index Group Name per Garment Group Name",
color_discrete_sequence=px.colors.sequential.Agsunset,
height=600)
fig.show()
df1 = articles.groupby(["section_name"]).count().reset_index()
fig = px.bar(df1,
x=articles.groupby(["section_name"]).size(),
y="section_name",
color='section_name',
title='Distribution by Section Name',
hover_data=['section_name'],
text_auto='.2s',
labels={'section_name':'Section Name'},
orientation='h',
color_discrete_sequence=px.colors.diverging.Temps,
height=1000)
fig.update_traces(textfont_size=11, textangle=0, textposition="outside", cliponaxis=False)
fig.update_layout(xaxis_title = 'Count')
fig.show()
df4 = articles.groupby(['index_name'])['article_id'].nunique()
df4 = pd.DataFrame({'IndexName': df4.index, 'Articles': df4.values})
labels = df4['IndexName']
values = df4['Articles']
fig = px.pie(labels, values=values, hole=0.35, names=labels, title='Distribution by Index Name', color_discrete_sequence=px.colors.cyclical.mygbm)
fig.show() | code |
90103606/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
transactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
transactions.info() | code |
90103606/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
articles.info() | code |
90103606/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
fig = px.pie(articles, values='article_id', title='Distribution by Index Group Name', names='index_group_name', color_discrete_sequence=px.colors.sequential.RdBu, hover_data=['index_group_name'], labels={'index_group_name': 'Index Group Name'}, height=450)
fig.show() | code |
90103606/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)
articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
customers.info() | code |
128009329/cell_9 | [
"text_plain_output_1.png"
] | df0 = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', nrows=reads[0], dtype=dtyping, low_memory=True)
mem_usage = df0.memory_usage().sum() / 1024 ** 2
print(f'Memory Usage : {mem_usage} MB') | code |
128009329/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
labels['session'] = labels['session_id'].str.split('_', expand=True)[0].astype(np.uint64)
labels['q'] = labels['session_id'].str.split('_q', expand=True)[1].astype(int)
labels
labels.dtypes | code |
128009329/cell_6 | [
"text_html_output_1.png"
] | import gc
import gc
gc.enable()
gc.collect() | code |
128009329/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
display(tmp) | code |
128009329/cell_11 | [
"text_plain_output_1.png"
] | df0 | code |
128009329/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import GroupKFold
import gc
import joblib
import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
labels['session'] = labels['session_id'].str.split('_', expand=True)[0].astype(np.uint64)
labels['q'] = labels['session_id'].str.split('_q', expand=True)[1].astype(int)
labels
labels.dtypes
import gc
gc.enable()
gc.collect()
ITER = 10
PIECES = int(np.ceil(len(tmp) / ITER))
reads = []
skips = [0]
for k in range(ITER):
a = k * PIECES
b = (k + 1) * PIECES
if b > len(tmp):
b = len(tmp)
r = tmp.iloc[a:b].sum()
reads.append(r)
skips.append(skips[-1] + r)
dtyping = {'session_id': np.uint64, 'index': np.uint8, 'elapsed_time': np.uint8, 'event_name': 'category', 'name': 'category', 'level': np.uint8, 'page': np.float32, 'room_coor_x': np.float32, 'room_coor_y': np.float32, 'screen_coor_x': np.float32, 'screen_coor_y': np.float32, 'hover_duration': np.float32, 'text': 'category', 'fqid': 'category', 'room_fqid': 'category', 'text_fqid': 'category', 'fullscreen': np.bool8, 'hq': np.bool8, 'music': np.bool8, 'level_group': 'category'}
CATS = ['event_name', 'fqid', 'room_fqid', 'text']
NUMS = ['elapsed_time', 'level', 'page', 'room_coor_x', 'room_coor_y', 'screen_coor_x', 'screen_coor_y', 'hover_duration']
EVENTS = ['navigate_click', 'person_click', 'cutscene_click', 'object_click', 'map_hover', 'notification_click', 'map_click', 'observation_click', 'checkpoint']
def process_level(train):
dfs = []
for c in CATS:
tmp = train.groupby(['session_id', 'level_group'])[c].agg('nunique')
tmp.name = tmp.name + '_nunique'
dfs.append(tmp)
for c in NUMS:
tmp = train.groupby(['session_id', 'level_group'])[c].agg('mean')
tmp.name = tmp.name + '_mean'
dfs.append(tmp)
for c in NUMS:
tmp = train.groupby(['session_id', 'level_group'])[c].agg('std')
tmp.name = tmp.name + '_std'
dfs.append(tmp)
for c in EVENTS:
train[c] = (train.event_name == c).astype('int8')
for c in EVENTS + ['elapsed_time']:
tmp = train.groupby(['session_id', 'level_group'])[c].agg('sum')
tmp.name = tmp.name + '_sum'
dfs.append(tmp)
train = train.drop(EVENTS, axis=1)
df = pd.concat(dfs, axis=1)
df = df.fillna(-1)
df = df.reset_index()
df = df.set_index('session_id')
return df
FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_level_df = train_df.pivot_table(columns='level_group', values=[x for x in train_df.columns if not x == 'level_group'], index='session_id', aggfunc='sum', fill_value=0)
train_level_df.isna().sum()
from sklearn.model_selection import GroupKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
gkf = GroupKFold(n_splits=5)
oof = pd.DataFrame(data=np.zeros((len(ALL_USERS), 18)), index=ALL_USERS)
models = {}
for i, (t, v) in enumerate(gkf.split(X=train_level_df, groups=train_level_df.index)):
print(f'FOLD {i}')
print('')
for l in range(1, 19):
xtrain = train_level_df.iloc[t]
train_users = xtrain.index.values
ytrain = labels.loc[labels.q == l].set_index('session').loc[train_users]
xval = train_level_df.iloc[v]
val_users = xval.index.values
yval = labels.loc[labels.q == l].set_index('session').loc[val_users]
model = LogisticRegression(random_state=0, solver='liblinear', max_iter=1500, n_jobs=-1)
model.fit(xtrain[FEATURES].astype(np.float32), ytrain['correct'])
yhat = model.predict_proba(xval)[:, 1]
score = f1_score(yval['correct'], np.round(yhat).astype(int))
print(f'FOLD {i} LEVEL {l} : {score}')
models.update({f'{i}': model})
joblib.dump(model, f'model-fold{i}-level{l}.pkl')
oof.loc[val_users, l - 1] = yhat
del xtrain, train_users, ytrain, xval, val_users, yval, model, yhat, score
gc.collect()
print() | code |
128009329/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
labels['session'] = labels['session_id'].str.split('_', expand=True)[0].astype(np.uint64)
labels['q'] = labels['session_id'].str.split('_q', expand=True)[1].astype(int)
labels
ITER = 10
PIECES = int(np.ceil(len(tmp) / ITER))
reads = []
skips = [0]
for k in range(ITER):
a = k * PIECES
b = (k + 1) * PIECES
if b > len(tmp):
b = len(tmp)
r = tmp.iloc[a:b].sum()
reads.append(r)
skips.append(skips[-1] + r)
print(f'To avoid memory error, we will read train in {PIECES} pieces of sizes:')
print(reads) | code |
128009329/cell_18 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_level_df = train_df.pivot_table(columns='level_group', values=[x for x in train_df.columns if not x == 'level_group'], index='session_id', aggfunc='sum', fill_value=0)
train_level_df.isna().sum() | code |
128009329/cell_15 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
print('We will train with', len(FEATURES), 'features')
ALL_USERS = train_df.index.unique()
print('We will train with', len(ALL_USERS), 'users info') | code |
128009329/cell_16 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_df | code |
128009329/cell_3 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
labels['session'] = labels['session_id'].str.split('_', expand=True)[0].astype(np.uint64)
labels['q'] = labels['session_id'].str.split('_q', expand=True)[1].astype(int)
labels | code |
128009329/cell_17 | [
"text_plain_output_1.png"
] | FEATURES = [c for c in train_df.columns if not c in ['level_group']]
ALL_USERS = train_df.index.unique()
train_level_df = train_df.pivot_table(columns='level_group', values=[x for x in train_df.columns if not x == 'level_group'], index='session_id', aggfunc='sum', fill_value=0)
display(train_level_df) | code |
128009329/cell_14 | [
"text_plain_output_1.png"
] | all_pieces = []
for k in range(ITER):
print(k, ',', end=' ')
SKIPS = 0
if k > 0:
SKIPS = range(1, skips[k] + 1)
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', nrows=reads[k], skiprows=SKIPS, dtype=dtyping, low_memory=True)
df = process_level(train)
all_pieces.append(df)
del train
del df
gc.collect()
print('\n')
train_df = pd.concat(all_pieces, axis=0)
print(f'Shape of Train DF : {train_df.shape}')
display(train_df.head()) | code |
128009329/cell_12 | [
"text_html_output_1.png"
] | df0.groupby(['session_id', 'level'])['elapsed_time'].sum() | code |
128009329/cell_5 | [
"text_html_output_1.png"
] | import numpy as np # Import library NumPy (Kalkulasi dsb)
import pandas as pd # Import library Pandas (Baca data)
tmp = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv', usecols=[0], low_memory=True)
tmp = tmp.groupby('session_id').session_id.agg('count')
labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
labels['session'] = labels['session_id'].str.split('_', expand=True)[0].astype(np.uint64)
labels['q'] = labels['session_id'].str.split('_q', expand=True)[1].astype(int)
labels
labels.dtypes
labels['q'].value_counts() | code |
1005554/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
df.describe() | code |
1005554/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
col_names = df.columns
bool_cols = []
nonbool_cols = []
for c in col_names:
if len(df[c].unique()) == 2:
bool_cols.append(c)
df[c] = df[c].astype('bool')
else:
nonbool_cols.append(c)
df.pivot_table(index=['bacon'], values=['calories', 'rating'], aggfunc=np.mean) | code |
1005554/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df.head() | code |
1005554/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as pylab
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
col_names = df.columns
bool_cols = []
nonbool_cols = []
for c in col_names:
if len(df[c].unique()) == 2:
bool_cols.append(c)
df[c] = df[c].astype('bool')
else:
nonbool_cols.append(c)
df.pivot_table(index=['bacon'], values=['calories', 'rating'], aggfunc=np.mean)
pylab.rcParams['figure.figsize'] = (12, 12)
corrmat = df[bool_cols[0:20]].corr()
sns.heatmap(corrmat, vmax=0.8, square=True) | code |
1005554/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns)) | code |
1005554/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
sorted(list(df.columns))
col_names = df.columns
bool_cols = []
nonbool_cols = []
for c in col_names:
if len(df[c].unique()) == 2:
bool_cols.append(c)
df[c] = df[c].astype('bool')
else:
nonbool_cols.append(c)
print('{} non-boolean columns'.format(len(col_names) - len(bool_cols)))
print(sorted(nonbool_cols)) | code |
49120031/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv')
[os.mkdir(os.path.join('/kaggle/working', 'label_' + str(x))) for x in df.label.unique()] | code |
49120031/cell_7 | [
"text_plain_output_1.png"
] | from keras_preprocessing.image import ImageDataGenerator
import os
import tensorflow as tf
from keras_preprocessing.image import ImageDataGenerator
from tensorflow import keras
from tensorflow.keras import layers
train_data_dir = '/kaggle/working/'
img_height = 300
img_width = 300
batch_size = 64
num_classes = 5
train_datagen = ImageDataGenerator(rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, validation_split=0.1)
train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training')
validation_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation') | code |
49120031/cell_3 | [
"text_plain_output_1.png"
] | from shutil import copyfile
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv')
[os.mkdir(os.path.join('/kaggle/working', 'label_' + str(x))) for x in df.label.unique()]
df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv')
from shutil import copyfile
for a, b in df.iterrows():
src = os.path.join('/kaggle/input/cassava-leaf-disease-classification/train_images', b.image_id)
dst = os.path.join('/kaggle/working', 'label_' + str(b.label), b.image_id)
print(src)
print(dst)
copyfile(src, dst) | code |
88100444/cell_4 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', '3Pper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
X.describe() | code |
88100444/cell_6 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', '3Pper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
bd_model.fit(train_X, train_y) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.