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sequencelengths 1
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|---|---|---|---|
32068206/cell_41 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
sns.set(rc={'figure.figsize': (13, 13)})
ax = sns.barplot(x=sub_sales_df['index_shop'], y=sub_sales_df['item_cnt_day'], order=sub_sales_df['index_shop'], color='salmon') | code |
32068206/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068206/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize': (11.7, 12)})
ax = sns.countplot(y='item_category_id', data=items, order=items['item_category_id'].value_counts(ascending=True).index) | code |
32068206/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
item_categories.head(5) | code |
32068206/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
print('Count of time blocks: ', len(sales_train['date_block_num'].unique())) | code |
32068206/cell_49 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sub_sales_df['index_shop'], y=sub_sales_df['item_cnt_day'], order=sub_sales_df['index_shop'],color="salmon")
sns.set(rc={'figure.figsize':(10,10)})
ax = sns.kdeplot(sales_train['item_price'], color="black", shade=True)
sns.set(rc={'figure.figsize':(10,10)})
ax = sns.kdeplot(sales_train['item_cnt_day'], color="green", bw=1.5, shade=True)
sns.set(rc={'figure.figsize': (12, 10)})
ax = sns.pointplot(sales_train['date'], sales_train['date_block_num'], color='red')
ax.set_xlabel('') | code |
32068206/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
shops.describe() | code |
32068206/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items.head(5) | code |
32068206/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize': (13, 13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color='salmon') | code |
32068206/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
print('Count of items overall:', len(sales_train))
print('Count of items < 0:', len(sales_train[sales_train['item_cnt_day'] < 0]))
print('Count of items < 10:', len(sales_train[sales_train['item_cnt_day'] < 10]))
print('Count of items 10 <= x <= 100:', len(sales_train) - len(sales_train[sales_train['item_cnt_day'] > 100]) - len(sales_train[sales_train['item_cnt_day'] < 10]))
print('Count of items > 100:', len(sales_train[sales_train['item_cnt_day'] > 100])) | code |
32068206/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sales_train.describe() | code |
32068206/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sub_sales_df['index_shop'], y=sub_sales_df['item_cnt_day'], order=sub_sales_df['index_shop'],color="salmon")
sns.set(rc={'figure.figsize': (10, 10)})
ax = sns.kdeplot(sales_train['item_price'], color='black', shade=True) | code |
32068206/cell_46 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sns.set(rc={'figure.figsize':(11.7,12)})
ax = sns.countplot(y = 'item_category_id',
data = items,
order = items['item_category_id'].value_counts(ascending=True).index)
sales_train.groupby('shop_id').mean()
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sales_train.groupby('shop_id').mean().index, y=sales_train.groupby('shop_id').mean()['item_cnt_day'], color="salmon")
sales_train.groupby('shop_id').sum()
sub_sales_df = sales_train.groupby('shop_id').sum()
sub_sales_df['index_shop'] = sub_sales_df.index
sub_sales_df = sub_sales_df.sort_values(['item_cnt_day']).reset_index(drop=True)
sns.set(rc={'figure.figsize':(13,13)})
ax = sns.barplot(x=sub_sales_df['index_shop'], y=sub_sales_df['item_cnt_day'], order=sub_sales_df['index_shop'],color="salmon")
sns.set(rc={'figure.figsize':(10,10)})
ax = sns.kdeplot(sales_train['item_price'], color="black", shade=True)
sns.set(rc={'figure.figsize': (10, 10)})
ax = sns.kdeplot(sales_train['item_cnt_day'], color='green', bw=1.5, shade=True) | code |
32068206/cell_14 | [
"image_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
print('Count of categories with count of items < 100 is', len(list(filter(lambda x: x < 100, items['item_category_id'].value_counts(ascending=True))))) | code |
32068206/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items.describe() | code |
32068206/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
shops.info() | code |
32068206/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
sales_train.groupby('shop_id').mean() | code |
32068206/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
sample_submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
print('Count of categories with count of items > 1000 is', len(list(filter(lambda x: x > 1000, items['item_category_id'].value_counts(ascending=True))))) | code |
122259364/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
sample = pd.read_csv('sample_solution.csv')
test_data = pd.read_csv('test_data.csv', index_col=0)
train_data = pd.read_csv('train_data.csv', index_col=0)
sample.head() | code |
329837/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, axis=1):
summary = group[otherfeat].count()
if chisquare(summary)[1] < 0.05:
flags.append(feat)
flags.append(otherfeat)
flags = set(flags)
from sklearn.decomposition import PCA
dums = df[chars].select_dtypes(include=['bool']).astype(float)
dums = dums.join(pd.get_dummies(df[[i for i in chars if i not in dums.columns.values]]))
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
components = pca.fit_transform(dums)
import numpy as np
components = {}
index = 0
for feature in dums.columns.values:
components[feature] = [pca.components_[0][index]]
index += 1
sortedcomps = pca.components_[0]
sortedcomps.sort()
maxcap = sortedcomps[-3]
mincap = sortedcomps[2]
components = {i: x for i, x in components.items() if x >= maxcap or x <= mincap}
components = pd.DataFrame(components)
components.plot(kind='bar', figsize=(12, 4)) | code |
329837/cell_4 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, axis=1):
summary = group[otherfeat].count()
if chisquare(summary)[1] < 0.05:
flags.append(feat)
flags.append(otherfeat)
flags = set(flags)
print('It looks like {}% of the characteristics might be related to one another.'.format(len(flags) / len(chars) * 100)) | code |
329837/cell_6 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, axis=1):
summary = group[otherfeat].count()
if chisquare(summary)[1] < 0.05:
flags.append(feat)
flags.append(otherfeat)
flags = set(flags)
from sklearn.decomposition import PCA
dums = df[chars].select_dtypes(include=['bool']).astype(float)
dums = dums.join(pd.get_dummies(df[[i for i in chars if i not in dums.columns.values]]))
print('Before PCA the full size of the characteristics is {} features'.format(len(dums.columns.values))) | code |
329837/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
print(df.head()) | code |
329837/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, axis=1):
summary = group[otherfeat].count()
if chisquare(summary)[1] < 0.05:
flags.append(feat)
flags.append(otherfeat)
flags = set(flags)
from sklearn.decomposition import PCA
dums = df[chars].select_dtypes(include=['bool']).astype(float)
dums = dums.join(pd.get_dummies(df[[i for i in chars if i not in dums.columns.values]]))
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
components = pca.fit_transform(dums)
print(pca.explained_variance_ratio_) | code |
318221/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment['gid'].value_counts() | code |
318221/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment[(comment.gid == '117291968282998') & (comment.rid == '')]['name'].value_counts().head(10) | code |
318221/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
comment[(comment.gid == '117291968282998') & (comment.rid != '')]['rname'].value_counts().head(10) | code |
318221/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
post = pd.read_sql_query('SELECT * FROM post', con)
comment = pd.read_sql_query('SELECT * FROM comment', con)
like = pd.read_sql_query('SELECT * FROM like', con)
rmember = pd.read_sql_query('SELECT distinct id as rid, name rname FROM member', con)
comment = pd.merge(comment, rmember, left_on='rid', right_on='rid', how='left')
rmember.head(3) | code |
106205992/cell_9 | [
"text_html_output_2.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.shape[1])])
y_names = target.columns.tolist()
train = pd.concat([train, target], axis=1)
splits = RandomSplitter(valid_pct=0.2)(range_of(train))
to = TabularPandas(train, procs=[Categorify, Normalize], cat_names=cat_cols, cont_names=num_cols, y_names=y_names, splits=splits)
dls = to.dataloaders(bs=16).to('cuda')
xb_cat, xb_cont, yb = dls.one_batch()
(xb_cat.shape, xb_cont.shape, yb.shape) | code |
106205992/cell_4 | [
"text_plain_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.shape[1])])
y_names = target.columns.tolist()
target.head(2) | code |
106205992/cell_11 | [
"text_html_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.shape[1])])
y_names = target.columns.tolist()
train = pd.concat([train, target], axis=1)
splits = RandomSplitter(valid_pct=0.2)(range_of(train))
to = TabularPandas(train, procs=[Categorify, Normalize], cat_names=cat_cols, cont_names=num_cols, y_names=y_names, splits=splits)
dls = to.dataloaders(bs=16).to('cuda')
xb_cat, xb_cont, yb = dls.one_batch()
(xb_cat.shape, xb_cont.shape, yb.shape)
learn = tabular_learner(dls, metrics=[accuracy_multi], loss_func=BCEWithLogitsLossFlat())
learn.lr_find()
learn.fit_one_cycle(5, 0.001) | code |
106205992/cell_3 | [
"text_html_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
train.head(2) | code |
106205992/cell_10 | [
"text_html_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.shape[1])])
y_names = target.columns.tolist()
train = pd.concat([train, target], axis=1)
splits = RandomSplitter(valid_pct=0.2)(range_of(train))
to = TabularPandas(train, procs=[Categorify, Normalize], cat_names=cat_cols, cont_names=num_cols, y_names=y_names, splits=splits)
dls = to.dataloaders(bs=16).to('cuda')
xb_cat, xb_cont, yb = dls.one_batch()
(xb_cat.shape, xb_cont.shape, yb.shape)
learn = tabular_learner(dls, metrics=[accuracy_multi], loss_func=BCEWithLogitsLossFlat())
learn.lr_find() | code |
106205992/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train = pd.DataFrame(X, columns=[f'feat_{idx + 1}' for idx in range(X.shape[1])])
num_cols = train.columns.tolist()
categories = ['a'] * 50 + ['b'] * 25 + ['c'] * 25
random.shuffle(categories)
train['feat_5'] = categories
cat_cols = ['feat_5']
target = pd.DataFrame(y, columns=[f'target_{idx + 1}' for idx in range(y.shape[1])])
y_names = target.columns.tolist()
train = pd.concat([train, target], axis=1)
train.head(2) | code |
32065262/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape | code |
32065262/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size()
sales.groupby('Product Type').size().plot()
sales.groupby('Product Type')['Total Net Sales'].sum().sort_values().plot.barh()
sales.groupby('Product Type')['Net Quantity'].sum().sort_values(ascending=False).head(3) | code |
32065262/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales['Total Net Sales'].sort_values(ascending=False).head(10) | code |
32065262/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum() | code |
32065262/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales['Returns'].loc['Basket'] | code |
32065262/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.head() | code |
32065262/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum() | code |
32065262/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales['Total Net Sales'].max() | code |
32065262/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales['Total Net Sales'].idxmax() | code |
32065262/cell_61 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size()
sales.groupby('Product Type').size().plot()
sales.groupby('Product Type')['Total Net Sales'].sum().sort_values().plot.barh()
sales.groupby('Product Type')['Net Quantity'].sum().sort_values(ascending=False).head(3)
a = sales.groupby('Product Type').sum()
(a['Discounts'] / a['Net Quantity']).round(2).sort_values()
a = sales.groupby('Product Type').sum()
a | code |
32065262/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns | code |
32065262/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size()
sales.groupby('Product Type').size().plot() | code |
32065262/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size()
sales.groupby('Product Type').size().plot()
sales.groupby('Product Type').size().plot.pie() | code |
32065262/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 |
32065262/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values() | code |
32065262/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket'] | code |
32065262/cell_59 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size()
sales.groupby('Product Type').size().plot()
sales.groupby('Product Type')['Total Net Sales'].sum().sort_values().plot.barh()
sales.groupby('Product Type')['Net Quantity'].sum().sort_values(ascending=False).head(3)
a = sales.groupby('Product Type').sum()
(a['Discounts'] / a['Net Quantity']).round(2).sort_values() | code |
32065262/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.tail(10) | code |
32065262/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales['Returns'] | code |
32065262/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size() | code |
32065262/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean() | code |
32065262/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales[['Gross Sales', 'Returns']].loc['Basket'] | code |
32065262/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns'] | code |
32065262/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales.groupby('Product Type').sum()
sales.groupby('Product Type')['Returns'].sum().sort_values()
sales.groupby('Product Type').size()
sales.groupby('Product Type').size().plot()
sales.groupby('Product Type')['Total Net Sales'].sum().sort_values().plot.barh() | code |
32065262/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum() | code |
32065262/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sales = pd.read_csv('/kaggle/input/retail-business-sales-20172019/business.retailsales.csv', index_col=0)
sales.columns
sales.shape
sales.loc['Basket']
sales.loc['Basket']['Returns']
sales.sum()
sales.loc['Basket'].sum()
sales.loc['Basket'].mean()
sales['Total Net Sales'].sort_values(ascending=False).head(10) | code |
33119952/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 |
50239348/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.shape | code |
50239348/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_addons as tfa
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.shape
Y_train = train['label']
X_train = train.drop('label', axis=1).values
test = test.values
RANDOM_STATE = 75
def random_seed(seed):
random.seed(RANDOM_STATE)
os.environ['PYTHONHASHSEED'] = str(RANDOM_STATE)
np.random.seed(RANDOM_STATE)
tf.random.set_seed(RANDOM_STATE)
def show_image(image):
plt.rcParams['axes.grid'] = False
plt.axis('off')
AUTOTUNE = tf.data.experimental.AUTOTUNE
EPOCHS = 25
SHUFFLE_BUFFER_SIZE = 5000
BATCH_SIZE = 64
@tf.function
def reshape(image, label):
return (tf.reshape(image, (28, 28, 1)), label)
@tf.function
def normalize(image, label):
image = tf.cast(image, tf.float32)
return (image / 255.0, label)
@tf.function
def rotate_tf(image):
random_angles = tf.random.uniform(shape=(), minval=-np.pi / 15, maxval=np.pi / 15)
return tfa.image.rotate(image, random_angles)
@tf.function
def augment(image, label):
image = rotate_tf(image)
return (image, label)
def get_data_set(images, labels, is_training=False):
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
dataset = dataset.map(reshape, num_parallel_calls=AUTOTUNE)
dataset = dataset.map(normalize, num_parallel_calls=AUTOTUNE)
if is_training:
dataset = dataset.map(augment, num_parallel_calls=AUTOTUNE)
dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(AUTOTUNE)
return dataset
train_dataset = get_data_set(X_train, y_train, is_training=True)
test_dataset = get_data_set(X_val, y_val)
sample_x, sample_y = next(iter(train_dataset))
show_image(sample_x[0][:, :, 0]) | code |
50239348/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_addons as tfa
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.shape
Y_train = train['label']
X_train = train.drop('label', axis=1).values
test = test.values
RANDOM_STATE = 75
def random_seed(seed):
random.seed(RANDOM_STATE)
os.environ['PYTHONHASHSEED'] = str(RANDOM_STATE)
np.random.seed(RANDOM_STATE)
tf.random.set_seed(RANDOM_STATE)
AUTOTUNE = tf.data.experimental.AUTOTUNE
EPOCHS = 25
SHUFFLE_BUFFER_SIZE = 5000
BATCH_SIZE = 64
@tf.function
def reshape(image, label):
return (tf.reshape(image, (28, 28, 1)), label)
@tf.function
def normalize(image, label):
image = tf.cast(image, tf.float32)
return (image / 255.0, label)
@tf.function
def rotate_tf(image):
random_angles = tf.random.uniform(shape=(), minval=-np.pi / 15, maxval=np.pi / 15)
return tfa.image.rotate(image, random_angles)
@tf.function
def augment(image, label):
image = rotate_tf(image)
return (image, label)
def get_data_set(images, labels, is_training=False):
dataset = tf.data.Dataset.from_tensor_slices((images, labels))
dataset = dataset.map(reshape, num_parallel_calls=AUTOTUNE)
dataset = dataset.map(normalize, num_parallel_calls=AUTOTUNE)
if is_training:
dataset = dataset.map(augment, num_parallel_calls=AUTOTUNE)
dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(AUTOTUNE)
return dataset
train_dataset = get_data_set(X_train, y_train, is_training=True)
test_dataset = get_data_set(X_val, y_val)
sample_x, sample_y = next(iter(train_dataset))
print(sample_x.shape)
print(sample_y.shape) | code |
50239348/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
print(tf.__version__) | code |
50239348/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
train.head() | code |
73072014/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
# generate random numbers from a normal distribution
sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])
# plot a histogram of the output
ax = sns.distplot(data_normal,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');
# import bernoulli
from scipy.stats import bernoulli
# generate bernoulli data
sample_size = 100000
param_p = 0.3 # probability of sucess
data_bern = bernoulli.rvs(size=sample_size,p=param_p)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_bern[0:5])
# Create the Plot
ax= sns.distplot(data_bern,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Bernoulli Distribution: Sample Size = {sample_size}, p={param_p}');
ax.legend();
from scipy.stats import binom
# Generate Binomial Data
sample_size = 10000
param_n = 10 # number of trials
param_p = 0.7 # probability of success in one trial
data_binom = binom.rvs(size=sample_size, n=param_n,p=param_p,)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_binom[0:5])
# Create the Plot
ax = sns.distplot(data_binom,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Binomial Distribution: n={param_n} ,p={param_p}')
ax.legend();
from scipy.stats import poisson
sample_size = 10000
param_mu = 3
data_poisson = poisson.rvs(size=sample_size, mu=param_mu)
print('The first 5 values from this distribution:')
print(data_poisson[0:5])
ax = sns.distplot(data_poisson, kde=False, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Poisson Distribution: Sample Size = {sample_size}, mu={param_mu}')
ax.legend() | code |
73072014/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
from scipy.stats import uniform
sample_size = 10000
param_loc = 5
param_scale = 10
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
ax = sns.distplot(data_uniform, bins=100, kde_kws={'label': 'KDE'}, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}') | code |
73072014/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
# generate random numbers from a normal distribution
sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])
# plot a histogram of the output
ax = sns.distplot(data_normal,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
data_normal = norm.rvs(size=100, loc=3, scale=2)
print(data_normal) | code |
73072014/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import beta
from scipy.stats import beta
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
# generate random numbers from a normal distribution
sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])
# plot a histogram of the output
ax = sns.distplot(data_normal,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');
# import bernoulli
from scipy.stats import bernoulli
# generate bernoulli data
sample_size = 100000
param_p = 0.3 # probability of sucess
data_bern = bernoulli.rvs(size=sample_size,p=param_p)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_bern[0:5])
# Create the Plot
ax= sns.distplot(data_bern,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Bernoulli Distribution: Sample Size = {sample_size}, p={param_p}');
ax.legend();
from scipy.stats import binom
# Generate Binomial Data
sample_size = 10000
param_n = 10 # number of trials
param_p = 0.7 # probability of success in one trial
data_binom = binom.rvs(size=sample_size, n=param_n,p=param_p,)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_binom[0:5])
# Create the Plot
ax = sns.distplot(data_binom,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Binomial Distribution: n={param_n} ,p={param_p}')
ax.legend();
from scipy.stats import poisson
# Generate Poisson Data
sample_size = 10000
param_mu = 3 #(rate of events per time, often denoted lambda)
data_poisson = poisson.rvs(size=sample_size, mu=param_mu)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_poisson[0:5])
# Create the Plot
ax = sns.distplot(data_poisson,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Poisson Distribution: Sample Size = {sample_size}, mu={param_mu}');
ax.legend();
from scipy.stats import beta
# Generate beta Data
sample_size = 100000
param_a = 1
param_b = 5
data_beta = beta.rvs(param_a, param_b, size=sample_size)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_beta[0:5])
# Create the Plot
ax = sns.distplot(data_beta,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Beta({param_a},{param_b}) Distribution: Sample Size = {sample_size}');
ax.legend();
from scipy.stats import beta
sample_size = 100000
param_a = 0.5
param_b = 0.5
data_beta = beta.rvs(param_a, param_b, size=sample_size)
print('The first 5 values from this distribution:')
print(data_beta[0:5])
ax = sns.distplot(data_beta, kde_kws={'label': 'KDE'}, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Beta({param_a},{param_b}) Distribution: Sample Size = {sample_size}')
ax.legend() | code |
73072014/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
# generate random numbers from a normal distribution
sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])
# plot a histogram of the output
ax = sns.distplot(data_normal,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');
from scipy.stats import bernoulli
sample_size = 100000
param_p = 0.3
data_bern = bernoulli.rvs(size=sample_size, p=param_p)
print('The first 5 values from this distribution:')
print(data_bern[0:5])
ax = sns.distplot(data_bern, kde=False, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Bernoulli Distribution: Sample Size = {sample_size}, p={param_p}')
ax.legend() | code |
73072014/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
# generate random numbers from a normal distribution
sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])
# plot a histogram of the output
ax = sns.distplot(data_normal,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');
# import bernoulli
from scipy.stats import bernoulli
# generate bernoulli data
sample_size = 100000
param_p = 0.3 # probability of sucess
data_bern = bernoulli.rvs(size=sample_size,p=param_p)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_bern[0:5])
# Create the Plot
ax= sns.distplot(data_bern,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Bernoulli Distribution: Sample Size = {sample_size}, p={param_p}');
ax.legend();
from scipy.stats import binom
sample_size = 10000
param_n = 10
param_p = 0.7
data_binom = binom.rvs(size=sample_size, n=param_n, p=param_p)
print('The first 5 values from this distribution:')
print(data_binom[0:5])
ax = sns.distplot(data_binom, kde=False, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Binomial Distribution: n={param_n} ,p={param_p}')
ax.legend() | code |
73072014/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import beta
from scipy.stats import binom
from scipy.stats import norm
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
# generate random numbers from a normal distribution
sample_size = 100
param_loc = 3 # mean
param_scale = 2 # standard deviation
data_normal = norm.rvs(size=sample_size,loc=param_loc,scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_normal[0:5])
# plot a histogram of the output
ax = sns.distplot(data_normal,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}');
# import bernoulli
from scipy.stats import bernoulli
# generate bernoulli data
sample_size = 100000
param_p = 0.3 # probability of sucess
data_bern = bernoulli.rvs(size=sample_size,p=param_p)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_bern[0:5])
# Create the Plot
ax= sns.distplot(data_bern,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Bernoulli Distribution: Sample Size = {sample_size}, p={param_p}');
ax.legend();
from scipy.stats import binom
# Generate Binomial Data
sample_size = 10000
param_n = 10 # number of trials
param_p = 0.7 # probability of success in one trial
data_binom = binom.rvs(size=sample_size, n=param_n,p=param_p,)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_binom[0:5])
# Create the Plot
ax = sns.distplot(data_binom,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Binomial Distribution: n={param_n} ,p={param_p}')
ax.legend();
from scipy.stats import poisson
# Generate Poisson Data
sample_size = 10000
param_mu = 3 #(rate of events per time, often denoted lambda)
data_poisson = poisson.rvs(size=sample_size, mu=param_mu)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_poisson[0:5])
# Create the Plot
ax = sns.distplot(data_poisson,
kde=False,
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Poisson Distribution: Sample Size = {sample_size}, mu={param_mu}');
ax.legend();
from scipy.stats import beta
sample_size = 100000
param_a = 1
param_b = 5
data_beta = beta.rvs(param_a, param_b, size=sample_size)
print('The first 5 values from this distribution:')
print(data_beta[0:5])
ax = sns.distplot(data_beta, kde_kws={'label': 'KDE'}, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Beta({param_a},{param_b}) Distribution: Sample Size = {sample_size}')
ax.legend() | code |
73072014/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (9.5, 5)})
# import uniform distribution
from scipy.stats import uniform
# generate random numbers from a uniform distribution
sample_size = 10000
param_loc = 5 # left-hand endpoint of the domain interval
param_scale = 10 # width of the domain interval
data_uniform = uniform.rvs(size=sample_size, loc=param_loc, scale=param_scale)
# print a few values from the distribution:
print('The first 5 values from this distribution:')
print(data_uniform[0:5])
# plot a historgram of the output
ax = sns.distplot(data_uniform,
bins=100,
kde_kws={"label": "KDE"},
hist_kws={"label": "Histogram"})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Uniform Distribution: Sample Size = {sample_size}. loc={param_loc}, scale={param_scale}');
from scipy.stats import norm
sample_size = 100
param_loc = 3
param_scale = 2
data_normal = norm.rvs(size=sample_size, loc=param_loc, scale=param_scale)
print('The first 5 values from this distribution:')
print(data_normal[0:5])
ax = sns.distplot(data_normal, bins=100, kde_kws={'label': 'KDE'}, hist_kws={'label': 'Histogram'})
ax.set(xlabel='x ', ylabel='Frequency', title=f'Normal Distribution: Sample Size = {sample_size}, loc={param_loc}, scale={param_scale}') | code |
106194764/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import gc
import numpy as np # linear algebra
import os
import pandas as pd
import shutil
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import gc
from tqdm import tqdm
cols = {'countryCode': np.float32, 'c2': np.float32, 'c4': np.float32, 'size': np.float32, 'mediationProviderVersion': np.int32, 'bidFloorPrice': np.float32, 'sentPrice': np.float32, 'winBid': np.float32, 'c1std_bidWin': np.float32, 'c1max_bidWin': np.float32, 'c1min_bidWin': np.float32, 'c1mean_bidWin': np.float32, 'c1median_bidWin': np.float32, 'c1sem_bidWin': np.float32, 'c1var_bidWin': np.float32, 'deviceIdstd_bidWin': np.float32, 'deviceIdmax_bidWin': np.float32, 'deviceIdmin_bidWin': np.float32, 'deviceIdmean_bidWin': np.float32, 'deviceIdmedian_bidWin': np.float32, 'deviceIdsem_bidWin': np.float32, 'deviceIdvar_bidWin': np.float32, 'unitDisplayTypestd_bidWin': np.float32, 'unitDisplayTypemax_bidWin': np.float32, 'unitDisplayTypemean_bidWin': np.float32, 'unitDisplayTypemedian_bidWin': np.float32, 'unitDisplayTypesem_bidWin': np.float32, 'unitDisplayTypevar_bidWin': np.float32, 'bundleIdstd_bidWin': np.float32, 'bundleIdmax_bidWin': np.float32, 'bundleIdmin_bidWin': np.float32, 'bundleIdmean_bidWin': np.float32, 'bundleIdmedian_bidWin': np.float32, 'bundleIdsem_bidWin': np.float32, 'bundleIdvar_bidWin': np.float32, 'ver': np.float32, 'unitDisplayType_0': np.uint8, 'unitDisplayType_1': np.uint8, 'unitDisplayType_2': np.uint8, 'connectionType_0': np.uint8, 'connectionType_1': np.uint8, 'connectionType_2': np.uint8, 'connectionType_3': np.uint8, 'bundleId_0': np.uint8, 'bundleId_1': np.uint8, 'bundleId_2': np.uint8, 'bundleId_3': np.uint8, 'bundleId_4': np.uint8, 'bundleId_5': np.uint8, 'bundleId_6': np.uint8, 'bundleId_7': np.uint8, 'bundleId_8': np.uint8, 'bundleId_9': np.uint8, 'bundleId_10': np.uint8, 'bundleId_11': np.uint8, 'bundleId_12': np.uint8, 'bundleId_13': np.uint8, 'bundleId_14': np.uint8, 'bundleId_15': np.uint8, 'bundleId_16': np.uint8, 'bundleId_17': np.uint8}
for x in range(50):
cols[f'c1_{x}'] = np.uint8
df = pd.read_csv('../input/fork-of-eda-item-price/prepared_train.csv', float_precision='round_trip', dtype=cols)
s = df.deviceIdstd_bidWin.mean()
df['deviceIdstd_bidWin'].fillna(s, inplace=True)
s = df.deviceIdvar_bidWin.mean()
df['deviceIdvar_bidWin'].fillna(s, inplace=True)
s = df.deviceIdsem_bidWin.mean()
df['deviceIdsem_bidWin'].fillna(s, inplace=True)
train, val = train_test_split(df, test_size=0.33, random_state=42)
len(train.columns)
import tensorflow as tf
import glob, math
from IPython.display import clear_output
class Auction_Sequence(tf.keras.utils.Sequence):
def __init__(self, data, batch_size):
self.data = data
self.batch_size = batch_size
def __len__(self):
return math.ceil(len(self.data) / self.batch_size)
def __getitem__(self, idx):
dc = ['winBid']
batch = self.data[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_x = batch.drop(dc, axis=1)
batch_y = batch['winBid']
return (batch_x.values, batch_y.values)
def on_epoch_end(self):
self.data = self.data.sample(frac=1, ignore_index=True, random_state=11)
train_seq = Auction_Sequence(train, 4096)
test_seq = Auction_Sequence(val, 4096)
def get_model(size):
tf.random.set_seed(11)
np.random.seed(11)
activation = tf.keras.layers.LeakyReLU(alpha=0.3)
feature = tf.keras.Input(shape=(size,))
hidden_state = tf.keras.layers.Dense(128, activation=activation, name='ind')(feature)
hidden_state = tf.keras.layers.Dense(64, activation=activation)(hidden_state)
hidden_state = tf.keras.layers.Dense(32, activation=activation)(hidden_state)
hidden_state = tf.keras.layers.Dense(16, activation=activation)(hidden_state)
hidden_state = tf.keras.layers.Dense(8, activation=activation)(hidden_state)
hidden_state = tf.keras.layers.Dense(4, activation=activation)(hidden_state)
outputs = tf.keras.layers.Dense(1, activation=None)(hidden_state)
model = tf.keras.models.Model(inputs=feature, outputs=outputs)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mse')
return model
from tqdm import tqdm
from sklearn.metrics import f1_score
import shutil
import os
path = f'./models/train_model/'
os.makedirs(path, exist_ok=True)
model = get_model(len(train.columns) - 1)
checkpoint = tf.keras.callbacks.ModelCheckpoint(path + 'model-{val_loss:03f}-{epoch:03d}-.h5', verbose=1, monitor='val_loss', save_weights_only=True, save_best_only=True, mode='min')
model.fit(train_seq, epochs=100, validation_data=test_seq, workers=-1, max_queue_size=10, use_multiprocessing=True, callbacks=[checkpoint], verbose=2)
files = glob.glob(os.path.expanduser(f'{path}*'))
best_weight = sorted(files, key=os.path.getmtime)[-1]
print(best_weight)
model.load_weights(best_weight)
shutil.rmtree(path)
score = float(best_weight.split('-')[1])
model.save(f'train_{score}.h5')
del model, best_weight
gc.collect()
tf.keras.backend.clear_session() | code |
106194764/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np
import pandas as pd
import gc
from tqdm import tqdm
cols = {'countryCode': np.float32, 'c2': np.float32, 'c4': np.float32, 'size': np.float32, 'mediationProviderVersion': np.int32, 'bidFloorPrice': np.float32, 'sentPrice': np.float32, 'winBid': np.float32, 'c1std_bidWin': np.float32, 'c1max_bidWin': np.float32, 'c1min_bidWin': np.float32, 'c1mean_bidWin': np.float32, 'c1median_bidWin': np.float32, 'c1sem_bidWin': np.float32, 'c1var_bidWin': np.float32, 'deviceIdstd_bidWin': np.float32, 'deviceIdmax_bidWin': np.float32, 'deviceIdmin_bidWin': np.float32, 'deviceIdmean_bidWin': np.float32, 'deviceIdmedian_bidWin': np.float32, 'deviceIdsem_bidWin': np.float32, 'deviceIdvar_bidWin': np.float32, 'unitDisplayTypestd_bidWin': np.float32, 'unitDisplayTypemax_bidWin': np.float32, 'unitDisplayTypemean_bidWin': np.float32, 'unitDisplayTypemedian_bidWin': np.float32, 'unitDisplayTypesem_bidWin': np.float32, 'unitDisplayTypevar_bidWin': np.float32, 'bundleIdstd_bidWin': np.float32, 'bundleIdmax_bidWin': np.float32, 'bundleIdmin_bidWin': np.float32, 'bundleIdmean_bidWin': np.float32, 'bundleIdmedian_bidWin': np.float32, 'bundleIdsem_bidWin': np.float32, 'bundleIdvar_bidWin': np.float32, 'ver': np.float32, 'unitDisplayType_0': np.uint8, 'unitDisplayType_1': np.uint8, 'unitDisplayType_2': np.uint8, 'connectionType_0': np.uint8, 'connectionType_1': np.uint8, 'connectionType_2': np.uint8, 'connectionType_3': np.uint8, 'bundleId_0': np.uint8, 'bundleId_1': np.uint8, 'bundleId_2': np.uint8, 'bundleId_3': np.uint8, 'bundleId_4': np.uint8, 'bundleId_5': np.uint8, 'bundleId_6': np.uint8, 'bundleId_7': np.uint8, 'bundleId_8': np.uint8, 'bundleId_9': np.uint8, 'bundleId_10': np.uint8, 'bundleId_11': np.uint8, 'bundleId_12': np.uint8, 'bundleId_13': np.uint8, 'bundleId_14': np.uint8, 'bundleId_15': np.uint8, 'bundleId_16': np.uint8, 'bundleId_17': np.uint8}
for x in range(50):
cols[f'c1_{x}'] = np.uint8
df = pd.read_csv('../input/fork-of-eda-item-price/prepared_train.csv', float_precision='round_trip', dtype=cols)
s = df.deviceIdstd_bidWin.mean()
df['deviceIdstd_bidWin'].fillna(s, inplace=True)
s = df.deviceIdvar_bidWin.mean()
df['deviceIdvar_bidWin'].fillna(s, inplace=True)
s = df.deviceIdsem_bidWin.mean()
df['deviceIdsem_bidWin'].fillna(s, inplace=True)
train, val = train_test_split(df, test_size=0.33, random_state=42)
df.isnull().sum().sum() | code |
106194764/cell_5 | [
"text_plain_output_1.png"
] | len(train.columns) | code |
16131921/cell_9 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/insurance.csv')
data.describe().T
num_data = data.select_dtypes(include=np.number)
cat_data = data.select_dtypes(exclude=np.number)
encode_cat_data = pd.get_dummies(cat_data)
fin_df = [num_data, encode_cat_data]
fin_data = pd.concat(fin_df, axis=1)
fin_data.head() | code |
16131921/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/insurance.csv')
data.info() | code |
16131921/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.pairplot(data)
graphs.set() | code |
16131921/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !pwd
!ls /kaggle/input
import seaborn as sns
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os | code |
16131921/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.pairplot(data)
graphs.set()
num_data = data.select_dtypes(include=np.number)
cat_data = data.select_dtypes(exclude=np.number)
encode_cat_data = pd.get_dummies(cat_data)
fin_df = [num_data, encode_cat_data]
fin_data = pd.concat(fin_df, axis=1)
graphs = sns.pairplot(fin_data)
graphs.set()
boxP = sns.boxplot(data=fin_data.age, orient='h', color='red') | code |
16131921/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16131921/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.pairplot(data)
graphs.set()
num_data = data.select_dtypes(include=np.number)
cat_data = data.select_dtypes(exclude=np.number)
encode_cat_data = pd.get_dummies(cat_data)
fin_df = [num_data, encode_cat_data]
fin_data = pd.concat(fin_df, axis=1)
graphs = sns.pairplot(fin_data)
graphs.set() | code |
16131921/cell_12 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/insurance.csv')
data.describe().T
graphs = sns.pairplot(data)
graphs.set()
num_data = data.select_dtypes(include=np.number)
cat_data = data.select_dtypes(exclude=np.number)
encode_cat_data = pd.get_dummies(cat_data)
fin_df = [num_data, encode_cat_data]
fin_data = pd.concat(fin_df, axis=1)
graphs = sns.pairplot(fin_data)
graphs.set()
boxP = sns.boxplot(data = fin_data.age ,orient = 'h' ,color = 'red')
fin_data.corr() | code |
16131921/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/insurance.csv')
data.describe().T | code |
16136283/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000] | code |
16136283/cell_13 | [
"text_html_output_1.png"
] | (y_train[0], X_train[0]) | code |
16136283/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_train_vectorized
X_train_vectorized.shape | code |
16136283/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.info() | code |
16136283/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.dropna(inplace=True)
df = df[df['Rating'] != 3]
df['Positively Rated'] = np.where(df['Rating'] > 3, 1, 0)
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_train_vectorized
X_train_vectorized.shape
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train_vectorized, y_train)
pred = model.predict(vect.transform(X_test))
feature_names = np.array(vect.get_feature_names())
sorted_coef_index = model.coef_[0].argsort()
print('Smallest Coefs:\n{}\n'.format(feature_names[sorted_coef_index[:10]]))
print('Largest Coefs: \n{}'.format(feature_names[sorted_coef_index[:-11:-1]])) | code |
16136283/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10] | code |
16136283/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df['Brand Name'].value_counts().head() | code |
16136283/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_train_vectorized
X_train_vectorized.shape
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train_vectorized, y_train) | code |
16136283/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.dropna(inplace=True)
df = df[df['Rating'] != 3]
df['Positively Rated'].mean() | code |
16136283/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names()) | code |
16136283/cell_18 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
vect | code |
16136283/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_train_vectorized
X_train_vectorized.shape
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train_vectorized, y_train)
pred = model.predict(vect.transform(X_test))
from sklearn.metrics import roc_auc_score
roc_auc_score(y_test, pred) | code |
16136283/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Amazon_Unlocked_Mobile.csv')
df.head() | code |
16136283/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
X_train.shape
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer().fit(X_train)
len(vect.get_feature_names())
vect.get_feature_names()[0:10]
vect.get_feature_names()[::3000]
X_train_vectorized = vect.transform(X_train)
X_train_vectorized | code |
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