path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
129035264/cell_16 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
men = train.loc[train.Sex == 'male']['Survived']
men_sur_rate = sum(men) / len(men)
import missingno as msno
msno.matrix(train)
train.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1, inplace=True)
msno.matrix(train)
train.Age.fillna(train.Age.mean(), inplace=True)
msno.matrix(train)
train.dropna(inplace=True)
msno.matrix(train) | code |
129035264/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.head() | code |
129035264/cell_14 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
men = train.loc[train.Sex == 'male']['Survived']
men_sur_rate = sum(men) / len(men)
import missingno as msno
msno.matrix(train)
train.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1, inplace=True)
msno.matrix(train) | code |
129035264/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum() | code |
129035264/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
print('% of women who survived:', women_sur_rate)
men = train.loc[train.Sex == 'male']['Survived']
men_sur_rate = sum(men) / len(men)
print('% of men who survived:', men_sur_rate) | code |
104116914/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
plt.figure(figsize=(20, 3))
sns.countplot(y='Survived', data=df) | code |
104116914/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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.head(10) | code |
104116914/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
fig = plt.figure(figsize=(10, 10))
sns.distplot(df.loc[df['Survived'] == 1]['Sex'])
sns.distplot(df.loc[df['Survived'] == 0]['Sex']) | code |
104116914/cell_4 | [
"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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df.head(10) | code |
104116914/cell_23 | [
"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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
l2 = df['Name']
l2_num = []
l5 = []
for i in l2:
l3 = i.split(',')
s = l3[-1]
l4 = s.split('.')
s1 = l4[0]
l5.append(s1)
if s1 == ' Mr':
l2_num.append(1)
elif s1 == ' Miss':
l2_num.append(2)
elif s1 == ' Mrs':
l2_num.append(3)
elif s1 == ' Master':
l2_num.append(4)
else:
l2_num.append(5)
df['Title'] = l5
df['Title_num'] = l2_num
print(df['Title_num'].value_counts())
print(df['Title'].value_counts()) | code |
104116914/cell_20 | [
"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)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df.head() | code |
104116914/cell_6 | [
"text_plain_output_1.png"
] | import missingno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
missingno.matrix(df, figsize=(20, 5)) | code |
104116914/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
fig=plt.figure(figsize=(10,10))
sns.distplot(df.loc[df['Survived']==1]['Sex'])
sns.distplot(df.loc[df['Survived']==0]['Sex'])
fig = plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
sns.countplot(y='Fare', data=df)
plt.subplot(1, 2, 2)
sns.distplot(df1.loc[df['Survived'] == 1]['Fare'])
sns.distplot(df1.loc[df['Survived'] == 0]['Fare'])
sns.distplot() | code |
104116914/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
fig=plt.figure(figsize=(10,10))
sns.distplot(df.loc[df['Survived']==1]['Sex'])
sns.distplot(df.loc[df['Survived']==0]['Sex'])
df['Ticket'].value_counts(sort=True, ascending=False) | code |
104116914/cell_2 | [
"text_plain_output_1.png",
"image_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 |
104116914/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)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.head(10) | code |
104116914/cell_19 | [
"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)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Embarked'].value_counts() | code |
104116914/cell_7 | [
"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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.head(10) | code |
104116914/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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
l = df['Embarked']
l_num = []
for i in l:
if i == 'S':
l_num.append(1)
elif i == 'C':
l_num.append(2)
else:
l_num.append(3)
df['Embarked'] = l_num
df['Embarked'].head() | code |
104116914/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
fig=plt.figure(figsize=(10,10))
sns.distplot(df.loc[df['Survived']==1]['Sex'])
sns.distplot(df.loc[df['Survived']==0]['Sex'])
df.head() | code |
104116914/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import missingno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
missingno.matrix(df, figsize=(20, 5))
df['Age'] = df['Age'].fillna(df['Age'].median())
missingno.matrix(df, figsize=(20, 5)) | code |
104116914/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)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes | code |
104116914/cell_16 | [
"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)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Age'].value_counts() | code |
104116914/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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Embarked'].value_counts() | code |
104116914/cell_24 | [
"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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
df.tail(20) | code |
104116914/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
plt.figure(figsize=(20, 3))
sns.countplot(y='Sex', data=df) | code |
104116914/cell_22 | [
"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('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df['Age'] = df['Age'].fillna(df['Age'].median())
df.dtypes
df['Sex'].value_counts()
df.head(10) | code |
104116914/cell_12 | [
"text_html_output_1.png"
] | import missingno
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
missingno.matrix(df, figsize=(20, 5))
df['Age'] = df['Age'].fillna(df['Age'].median())
missingno.matrix(df, figsize=(20, 5))
missingno.matrix(df, figsize=(20, 2)) | code |
104116914/cell_5 | [
"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)
df = pd.read_csv('../input/titanic/train.csv')
df1 = pd.read_csv('../input/titanic/train.csv')
df.describe() | code |
48167508/cell_13 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
import seaborn as sns
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
def add_date_features(df: pd.DataFrame):
df['START_DATE'] = pd.to_datetime(df['START_DATE'], format='%Y%m%d')
df['START_YEAR'] = df['START_DATE'].dt.year
df['START_MONTH'] = df['START_DATE'].dt.month
df['START_YM'] = df['START_YEAR'] * 100 + df['START_MONTH']
df['END_DATE'] = pd.to_datetime(df['END_DATE'], format='%Y%m%d')
df['END_YEAR'] = df['END_DATE'].dt.year
df['END_MONTH'] = df['END_DATE'].dt.month
df['END_YM'] = df['END_YEAR'] * 100 + df['END_MONTH']
df['N_DAYS'] = (df['END_DATE'] - df['START_DATE']) / timedelta(days=1)
return df
train = add_date_features(train)
test = add_date_features(test)
years = list(range(2006, 2014))
plt.figure(figsize=(5, 4.5))
ax1 = sns.lineplot(x='START_YEAR', y='MEMBERSHIP_NUMBER', data=train.groupby('START_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(), label='START_YEAR')
plt.ylabel('Join')
ax2 = ax1.twinx()
ax2 = sns.lineplot(x='END_YEAR', y='MEMBERSHIP_NUMBER', data=train.groupby('END_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(), ax=ax2, label='END_YEAR', color='orange', legend=False)
plt.ylabel('Churn')
plt.xlabel('START/END_YEAR')
handler1, label1 = ax1.get_legend_handles_labels()
handler2, label2 = ax2.get_legend_handles_labels()
ax1.legend(handler1 + handler2, label1 + label2, loc=2, borderaxespad=0.0, bbox_to_anchor=(1.0, 1.05), frameon=False)
plt.suptitle('Number of rows by year fields')
for year_field in ('START_YEAR', 'END_YEAR'):
display(train.groupby(year_field)['MEMBERSHIP_NUMBER'].count().sort_index().reset_index().rename(columns={'MEMBERSHIP_NUMBER': 'N_ROWS'})) | code |
48167508/cell_20 | [
"image_output_1.png"
] | from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
import seaborn as sns
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
def add_date_features(df: pd.DataFrame):
df['START_DATE'] = pd.to_datetime(df['START_DATE'], format='%Y%m%d')
df['START_YEAR'] = df['START_DATE'].dt.year
df['START_MONTH'] = df['START_DATE'].dt.month
df['START_YM'] = df['START_YEAR'] * 100 + df['START_MONTH']
df['END_DATE'] = pd.to_datetime(df['END_DATE'], format='%Y%m%d')
df['END_YEAR'] = df['END_DATE'].dt.year
df['END_MONTH'] = df['END_DATE'].dt.month
df['END_YM'] = df['END_YEAR'] * 100 + df['END_MONTH']
df['N_DAYS'] = (df['END_DATE'] - df['START_DATE']) / timedelta(days=1)
return df
train = add_date_features(train)
test = add_date_features(test)
years = list(range(2006, 2014))
plt.figure(figsize=(5, 4.5))
ax1 = sns.lineplot(x='START_YEAR',
y='MEMBERSHIP_NUMBER',
data=train.groupby('START_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(),
label='START_YEAR')
plt.ylabel('Join')
ax2 = ax1.twinx()
ax2 = sns.lineplot(x='END_YEAR',
y='MEMBERSHIP_NUMBER',
data=train.groupby('END_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(),
ax=ax2,
label='END_YEAR',
color='orange',
legend=False)
plt.ylabel('Churn')
plt.xlabel('START/END_YEAR')
handler1, label1 = ax1.get_legend_handles_labels()
handler2, label2 = ax2.get_legend_handles_labels()
ax1.legend(handler1 + handler2, label1 + label2, loc=2, borderaxespad=0., bbox_to_anchor=(1.0, 1.05), frameon=False)
plt.suptitle('Number of rows by year fields')
for year_field in ('START_YEAR', 'END_YEAR'):
display(train.groupby(year_field)['MEMBERSHIP_NUMBER'] \
.count() \
.sort_index() \
.reset_index() \
.rename(columns={'MEMBERSHIP_NUMBER': 'N_ROWS'}))
df_cancelled = train.query(f'{TARGET} == 1')
# 辞めた会員は何年に入会したのか？
plt.figure(figsize=(16.5, 8))
# years = np.sort(train.query(f'{TARGET} == 1').END_YEAR.unique())
for i, year in enumerate(years):
plt.subplot(2, 4, i + 1)
ax = sns.countplot(x='START_YEAR', data=train.query(f'END_YEAR == {year}'),
order=[y for y in years if y <= year])
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)
plt.xlabel('')
plt.ylabel('')
plt.title(f'END_YEAR = {year}')
plt.ylim((0, 550))
plt.suptitle('When cancelled members join the club? ')
display(pd.pivot_table(index='START_YEAR', columns='END_YEAR', values=TARGET, data=train, aggfunc=np.sum, fill_value=0, margins=True).astype(int))
sns.countplot(x='START_YEAR', hue='PAYMENT_MODE', data=train)
plt.legend(bbox_to_anchor=(1, 1), loc='upper left', frameon=False)
plt.title('Number of records by PAYMENT_MODE', fontsize=14) | code |
48167508/cell_18 | [
"text_html_output_2.png",
"text_html_output_1.png",
"image_output_1.png"
] | from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
import seaborn as sns
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
def add_date_features(df: pd.DataFrame):
df['START_DATE'] = pd.to_datetime(df['START_DATE'], format='%Y%m%d')
df['START_YEAR'] = df['START_DATE'].dt.year
df['START_MONTH'] = df['START_DATE'].dt.month
df['START_YM'] = df['START_YEAR'] * 100 + df['START_MONTH']
df['END_DATE'] = pd.to_datetime(df['END_DATE'], format='%Y%m%d')
df['END_YEAR'] = df['END_DATE'].dt.year
df['END_MONTH'] = df['END_DATE'].dt.month
df['END_YM'] = df['END_YEAR'] * 100 + df['END_MONTH']
df['N_DAYS'] = (df['END_DATE'] - df['START_DATE']) / timedelta(days=1)
return df
train = add_date_features(train)
test = add_date_features(test)
years = list(range(2006, 2014))
plt.figure(figsize=(5, 4.5))
ax1 = sns.lineplot(x='START_YEAR',
y='MEMBERSHIP_NUMBER',
data=train.groupby('START_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(),
label='START_YEAR')
plt.ylabel('Join')
ax2 = ax1.twinx()
ax2 = sns.lineplot(x='END_YEAR',
y='MEMBERSHIP_NUMBER',
data=train.groupby('END_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(),
ax=ax2,
label='END_YEAR',
color='orange',
legend=False)
plt.ylabel('Churn')
plt.xlabel('START/END_YEAR')
handler1, label1 = ax1.get_legend_handles_labels()
handler2, label2 = ax2.get_legend_handles_labels()
ax1.legend(handler1 + handler2, label1 + label2, loc=2, borderaxespad=0., bbox_to_anchor=(1.0, 1.05), frameon=False)
plt.suptitle('Number of rows by year fields')
for year_field in ('START_YEAR', 'END_YEAR'):
display(train.groupby(year_field)['MEMBERSHIP_NUMBER'] \
.count() \
.sort_index() \
.reset_index() \
.rename(columns={'MEMBERSHIP_NUMBER': 'N_ROWS'}))
df_cancelled = train.query(f'{TARGET} == 1')
plt.figure(figsize=(16.5, 8))
for i, year in enumerate(years):
plt.subplot(2, 4, i + 1)
ax = sns.countplot(x='START_YEAR', data=train.query(f'END_YEAR == {year}'), order=[y for y in years if y <= year])
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)
plt.xlabel('')
plt.ylabel('')
plt.title(f'END_YEAR = {year}')
plt.ylim((0, 550))
plt.suptitle('When cancelled members join the club? ')
display(pd.pivot_table(index='START_YEAR', columns='END_YEAR', values=TARGET, data=train, aggfunc=np.sum, fill_value=0, margins=True).astype(int)) | code |
48167508/cell_8 | [
"text_html_output_1.png",
"image_output_1.png"
] | from datetime import timedelta
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
def add_date_features(df: pd.DataFrame):
df['START_DATE'] = pd.to_datetime(df['START_DATE'], format='%Y%m%d')
df['START_YEAR'] = df['START_DATE'].dt.year
df['START_MONTH'] = df['START_DATE'].dt.month
df['START_YM'] = df['START_YEAR'] * 100 + df['START_MONTH']
df['END_DATE'] = pd.to_datetime(df['END_DATE'], format='%Y%m%d')
df['END_YEAR'] = df['END_DATE'].dt.year
df['END_MONTH'] = df['END_DATE'].dt.month
df['END_YM'] = df['END_YEAR'] * 100 + df['END_MONTH']
df['N_DAYS'] = (df['END_DATE'] - df['START_DATE']) / timedelta(days=1)
return df
train = add_date_features(train)
test = add_date_features(test)
display(train[['START_DATE', 'START_YEAR', 'START_MONTH', 'START_YM', 'END_DATE', 'END_YEAR', 'END_MONTH', 'END_YM', 'N_DAYS', TARGET]])
display(test[['START_DATE', 'START_YEAR', 'START_MONTH', 'START_YM', 'END_DATE', 'END_YEAR', 'END_MONTH', 'END_YM']]) | code |
48167508/cell_16 | [
"text_plain_output_1.png"
] | from datetime import timedelta
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
import seaborn as sns
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
def add_date_features(df: pd.DataFrame):
df['START_DATE'] = pd.to_datetime(df['START_DATE'], format='%Y%m%d')
df['START_YEAR'] = df['START_DATE'].dt.year
df['START_MONTH'] = df['START_DATE'].dt.month
df['START_YM'] = df['START_YEAR'] * 100 + df['START_MONTH']
df['END_DATE'] = pd.to_datetime(df['END_DATE'], format='%Y%m%d')
df['END_YEAR'] = df['END_DATE'].dt.year
df['END_MONTH'] = df['END_DATE'].dt.month
df['END_YM'] = df['END_YEAR'] * 100 + df['END_MONTH']
df['N_DAYS'] = (df['END_DATE'] - df['START_DATE']) / timedelta(days=1)
return df
train = add_date_features(train)
test = add_date_features(test)
years = list(range(2006, 2014))
plt.figure(figsize=(5, 4.5))
ax1 = sns.lineplot(x='START_YEAR',
y='MEMBERSHIP_NUMBER',
data=train.groupby('START_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(),
label='START_YEAR')
plt.ylabel('Join')
ax2 = ax1.twinx()
ax2 = sns.lineplot(x='END_YEAR',
y='MEMBERSHIP_NUMBER',
data=train.groupby('END_YEAR')['MEMBERSHIP_NUMBER'].count().reset_index(),
ax=ax2,
label='END_YEAR',
color='orange',
legend=False)
plt.ylabel('Churn')
plt.xlabel('START/END_YEAR')
handler1, label1 = ax1.get_legend_handles_labels()
handler2, label2 = ax2.get_legend_handles_labels()
ax1.legend(handler1 + handler2, label1 + label2, loc=2, borderaxespad=0., bbox_to_anchor=(1.0, 1.05), frameon=False)
plt.suptitle('Number of rows by year fields')
for year_field in ('START_YEAR', 'END_YEAR'):
display(train.groupby(year_field)['MEMBERSHIP_NUMBER'] \
.count() \
.sort_index() \
.reset_index() \
.rename(columns={'MEMBERSHIP_NUMBER': 'N_ROWS'}))
df_cancelled = train.query(f'{TARGET} == 1')
plt.figure(figsize=(12, 5))
sns.boxplot(x='START_YEAR', y='N_DAYS', data=df_cancelled)
plt.title('N_DAYS by START_YEAR') | code |
48167508/cell_10 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from datetime import timedelta
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
def add_date_features(df: pd.DataFrame):
df['START_DATE'] = pd.to_datetime(df['START_DATE'], format='%Y%m%d')
df['START_YEAR'] = df['START_DATE'].dt.year
df['START_MONTH'] = df['START_DATE'].dt.month
df['START_YM'] = df['START_YEAR'] * 100 + df['START_MONTH']
df['END_DATE'] = pd.to_datetime(df['END_DATE'], format='%Y%m%d')
df['END_YEAR'] = df['END_DATE'].dt.year
df['END_MONTH'] = df['END_DATE'].dt.month
df['END_YM'] = df['END_YEAR'] * 100 + df['END_MONTH']
df['N_DAYS'] = (df['END_DATE'] - df['START_DATE']) / timedelta(days=1)
return df
train = add_date_features(train)
test = add_date_features(test)
print(f'データの採取期間...\ntrain.csv:\n START_DATE:\n From. {train.START_DATE.min()} To. {train.START_DATE.max()}\n END_DATE:\n From. {train.END_DATE.min()} To. {train.END_DATE.max()}\ntest.csv\n START_DATE:\n From. {test.START_DATE.min()} To. {test.START_DATE.max()}') | code |
48167508/cell_5 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs
TARGET = 'Is_CANCELLED'
NA_CATEGORY = 'NA_'
train = pd.read_csv('/kaggle/input/techcom-ai-competition/train.csv')
test = pd.read_csv('/kaggle/input/techcom-ai-competition/test.csv')
sample_sub = pd.read_csv('/kaggle/input/techcom-ai-competition/sample_submission.csv')
train.rename(columns={'MEMBERSHIP_STATUS': 'Is_CANCELLED'}, inplace=True)
train[TARGET] = train[TARGET].map({'INFORCE': 0, 'CANCELLED': 1})
test['END_DATE'] = np.nan
display(train)
display(test)
display(sample_sub) | code |
2041206/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
best_sorted = best.sort_values(by='price', ascending=True) # sort by points
num_best = best.shape[0] # number of wines
cheapestngood = best_sorted.head(int(0.25*num_of_wines))
cheapngoodest = cheapestngood.sort_values(by = 'points', ascending = False)
topareas = cheapestngood['region_1'].value_counts().head(10)
topareas | code |
2041206/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
import operator
sorted_good = sorted(goodlist.items(), key=operator.itemgetter(0))
sorted_bad = sorted(not_so_good_list.items(), key=operator.itemgetter(1), reverse=True)
sorted_good | code |
2041206/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
plt.scatter(data['points'], data['price']) | code |
2041206/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(x_train, y_train)
pred = clf.predict(x_test)
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
pred = reg.predict(x_test)
r2_score(y_test, pred) | code |
2041206/cell_44 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import euclidean_distances
pd.set_option('display.max_colwidth', 1500)
vectorizer = TfidfVectorizer(stop_words='english', binary=False, max_df=0.95, min_df=0.15, ngram_range=(1, 2), use_idf=False, norm=None)
doc_vectors = vectorizer.fit_transform(data['description'])
print(doc_vectors.shape)
print(vectorizer.get_feature_names()) | code |
2041206/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
import operator
sorted_good = sorted(goodlist.items(), key=operator.itemgetter(0))
sorted_bad = sorted(not_so_good_list.items(), key=operator.itemgetter(1), reverse=True)
sorted_bad | code |
2041206/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
best_sorted = best.sort_values(by='price', ascending=True) # sort by points
num_best = best.shape[0] # number of wines
cheapestngood = best_sorted.head(int(0.25*num_of_wines))
cheapngoodest = cheapestngood.sort_values(by = 'points', ascending = False)
cheapestngood['region_1'].value_counts() | code |
2041206/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(x_train, y_train)
pred = clf.predict(x_test)
accuracy_score(y_test, pred) | code |
2041206/cell_48 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import euclidean_distances
pd.set_option('display.max_colwidth', 1500)
vectorizer = TfidfVectorizer(stop_words='english', binary=False, max_df=0.95, min_df=0.15, ngram_range=(1, 2), use_idf=False, norm=None)
doc_vectors = vectorizer.fit_transform(data['description'])
def comp_description(query, results_number=20):
results = []
q_vector = vectorizer.transform([query])
results.append(cosine_similarity(q_vector, doc_vectors.toarray()))
f = 0
elem_list = []
for i in results[:10]:
for elem in i[0]:
elem_list.append(elem)
f += 1
comp_description('This wine highlights how the power of Lake County’s Red Hills seamlessly compliments the elegance and aromatic freshness of the High Valley. Aromas of plum, allspice and clove develop into flavors of fresh dark cherry and cedar on the palate. The Red Hills’ fine tannins provide a smoothly textured palate sensation from start to finish. Fresh acidity from the High Valley culminates in a bright finish of cherry with a gentle note of French oak.') | code |
2041206/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2041206/cell_50 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import euclidean_distances
pd.set_option('display.max_colwidth', 1500)
vectorizer = TfidfVectorizer(stop_words='english', binary=False, max_df=0.95, min_df=0.15, ngram_range=(1, 2), use_idf=False, norm=None)
doc_vectors = vectorizer.fit_transform(data['description'])
def comp_description(query, results_number=20):
results = []
q_vector = vectorizer.transform([query])
results.append(cosine_similarity(q_vector, doc_vectors.toarray()))
f = 0
elem_list = []
for i in results[:10]:
for elem in i[0]:
elem_list.append(elem)
f += 1
comp_description('This wine is very bad, do not drink.') | code |
2041206/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88
sigma = 3
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--') | code |
2041206/cell_49 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import euclidean_distances
pd.set_option('display.max_colwidth', 1500)
vectorizer = TfidfVectorizer(stop_words='english', binary=False, max_df=0.95, min_df=0.15, ngram_range=(1, 2), use_idf=False, norm=None)
doc_vectors = vectorizer.fit_transform(data['description'])
def comp_description(query, results_number=20):
results = []
q_vector = vectorizer.transform([query])
results.append(cosine_similarity(q_vector, doc_vectors.toarray()))
f = 0
elem_list = []
for i in results[:10]:
for elem in i[0]:
elem_list.append(elem)
f += 1
comp_description('On the nose are those awful love-heart candies, but the palate is nothing but Nesquik strawberry powder. This alcoholic Powerade is what gives box wine a bad name. Pair with BBQ chicken') | code |
2041206/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
goodlist | code |
2041206/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
best_sorted = best.sort_values(by='price', ascending=True) # sort by points
num_best = best.shape[0] # number of wines
cheapestngood = best_sorted.head(int(0.25*num_of_wines))
cheapngoodest = cheapestngood.sort_values(by = 'points', ascending = False)
cheapngoodest.head(10) | code |
2041206/cell_47 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import euclidean_distances
pd.set_option('display.max_colwidth', 1500)
vectorizer = TfidfVectorizer(stop_words='english', binary=False, max_df=0.95, min_df=0.15, ngram_range=(1, 2), use_idf=False, norm=None)
doc_vectors = vectorizer.fit_transform(data['description'])
def comp_description(query, results_number=20):
results = []
q_vector = vectorizer.transform([query])
results.append(cosine_similarity(q_vector, doc_vectors.toarray()))
f = 0
elem_list = []
for i in results[:10]:
for elem in i[0]:
elem_list.append(elem)
f += 1
comp_description('Delicate pink hue with strawberry flavors; easy to drink and very refreshing. Perfect with lighter foods. Serve chilled.') | code |
2041206/cell_17 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
not_so_good_list | code |
2041206/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train) | code |
2041206/cell_46 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import LabelEncoder
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
import matplotlib.mlab as mlab
num_bins = 20
n, bins, patches = plt.hist(data['points'], num_bins, normed=1, facecolor='blue', alpha=0.5)
plt.title('Distribution of Wine Scores')
plt.xlabel('Score out of 100')
plt.ylabel('Frequency')
mu = 88 # mean of distribution
sigma = 3 # standard deviation of distribution
y = mlab.normpdf(bins, mu, sigma) # create the y line
plt.plot(bins, y, 'r--')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words='english', analyzer='word')
X1 = vectorizer.fit_transform(best['description'])
idf = vectorizer.idf_
goodlist = vectorizer.vocabulary_
X = vectorizer.fit_transform(worst['description'])
idf = vectorizer.idf_
not_so_good_list = vectorizer.vocabulary_
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(best['variety'])
reg = linear_model.Ridge(alpha=0.5, solver='sag')
y = data['points']
x = vectorizer.fit_transform(data['description'])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=32)
reg.fit(x_train, y_train)
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import nltk
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import euclidean_distances
pd.set_option('display.max_colwidth', 1500)
vectorizer = TfidfVectorizer(stop_words='english', binary=False, max_df=0.95, min_df=0.15, ngram_range=(1, 2), use_idf=False, norm=None)
doc_vectors = vectorizer.fit_transform(data['description'])
def comp_description(query, results_number=20):
results = []
q_vector = vectorizer.transform([query])
results.append(cosine_similarity(q_vector, doc_vectors.toarray()))
f = 0
elem_list = []
for i in results[:10]:
for elem in i[0]:
elem_list.append(elem)
f += 1
comp_description('Bright, fresh fruit aromas of cherry, raspberry, and blueberry.Youthfully with lots of sweet fruit on the palate with hints of spice and vanilla.') | code |
2041206/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
df = data.dropna(subset=['description']) # drop all NaNs
df_sorted = df.sort_values(by='points', ascending=True) # sort by points
num_of_wines = df_sorted.shape[0] # number of wines
worst = df_sorted.head(int(0.25*num_of_wines)) # 25 % of worst wines listed
best = df_sorted.tail(int(0.25*num_of_wines)) # 25 % of best wines listed
best_sorted = best.sort_values(by='price', ascending=True) # sort by points
num_best = best.shape[0] # number of wines
cheapestngood = best_sorted.head(int(0.25*num_of_wines))
cheapngoodest = cheapestngood.sort_values(by = 'points', ascending = False)
cheapestngood.head(10) | code |
2041206/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/winemag-data_first150k.csv')
data.head(5) | code |
72077953/cell_25 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from xgboost import plot_tree
from xgboost import plot_tree
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
plot_tree(model, rankdir='LR', num_trees=1) | code |
72077953/cell_4 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
72077953/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
import warnings
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X.isna().sum()
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
trees = model.get_booster().get_dump()
importance_df = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_}).sort_values('importance', ascending=False)
from sklearn.model_selection import KFold
def rmse(a, b):
return mean_squared_error(a, b, squared=False)
def train_and_evaluate(X_train, y_train, X_valid, y_valid, **params):
model = XGBRegressor(random_state=42, n_jobs=-1, **params)
model.fit(X_train, y_train)
train_rmse = rmse(model.predict(X_train), y_train)
val_rmse = rmse(model.predict(X_valid), y_valid)
return (model, train_rmse, val_rmse)
kfold = KFold(n_splits=5)
models = []
for train_idxs, val_idxs in kfold.split(X):
X_train, y_train = (X.iloc[train_idxs], y.iloc[train_idxs])
X_valid, y_valid = (X.iloc[val_idxs], y.iloc[val_idxs])
model, train_rmse, val_rmse = train_and_evaluate(X_train, y_train, X_valid, y_valid, max_depth=4, n_estimators=20)
models.append(model)
print('Train RMSE: {}, Validation RMSE: {}'.format(train_rmse, val_rmse))
import warnings
warnings.filterwarnings('ignore') | code |
72077953/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
plt.hist(train.target.sample(2000)) | code |
72077953/cell_30 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import OrdinalEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X.isna().sum()
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
trees = model.get_booster().get_dump()
importance_df = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_}).sort_values('importance', ascending=False)
import seaborn as sns
plt.figure(figsize=(10, 6))
plt.title('Feature Importance')
sns.barplot(data=importance_df.head(10), x='importance', y='feature') | code |
72077953/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
pp.ProfileReport(train) | code |
72077953/cell_29 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X.isna().sum()
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
trees = model.get_booster().get_dump()
importance_df = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_}).sort_values('importance', ascending=False)
importance_df.head() | code |
72077953/cell_39 | [
"image_output_1.png"
] | model.fit(X, y) | code |
72077953/cell_26 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from xgboost import plot_tree
from xgboost import plot_tree
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
plot_tree(model, rankdir='LR', num_trees=39) | code |
72077953/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X_test.head() | code |
72077953/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X.isna().sum() | code |
72077953/cell_19 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
model = XGBRegressor(random_state=42, n_jobs=-1, n_estimators=40, max_depth=4, learning_rate=0.5)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
print(mean_squared_error(y_valid, preds, squared=False)) | code |
72077953/cell_18 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
print(mean_squared_error(y_valid, preds_valid, squared=False)) | code |
72077953/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
72077953/cell_24 | [
"text_plain_output_1.png"
] | from matplotlib.pylab import rcParams
from matplotlib.pylab import rcParams
from sklearn.ensemble import RandomForestRegressor
from xgboost import plot_tree
from xgboost import plot_tree
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
rcParams['figure.figsize'] = (20, 20)
plot_tree(model, rankdir='LR') | code |
72077953/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X.head() | code |
72077953/cell_27 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
trees = model.get_booster().get_dump()
print(len(trees))
print(trees[0]) | code |
72077953/cell_36 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import numpy as np
import pandas as pd
import warnings
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(X[object_cols])
X_test[object_cols] = ordinal_encoder.transform(X_test[object_cols])
X.isna().sum()
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
trees = model.get_booster().get_dump()
importance_df = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_}).sort_values('importance', ascending=False)
from sklearn.model_selection import KFold
def rmse(a, b):
return mean_squared_error(a, b, squared=False)
def train_and_evaluate(X_train, y_train, X_valid, y_valid, **params):
model = XGBRegressor(random_state=42, n_jobs=-1, **params)
model.fit(X_train, y_train)
train_rmse = rmse(model.predict(X_train), y_train)
val_rmse = rmse(model.predict(X_valid), y_valid)
return (model, train_rmse, val_rmse)
kfold = KFold(n_splits=5)
models = []
for train_idxs, val_idxs in kfold.split(X):
X_train, y_train = (X.iloc[train_idxs], y.iloc[train_idxs])
X_valid, y_valid = (X.iloc[val_idxs], y.iloc[val_idxs])
model, train_rmse, val_rmse = train_and_evaluate(X_train, y_train, X_valid, y_valid, max_depth=4, n_estimators=20)
models.append(model)
import warnings
warnings.filterwarnings('ignore')
def predict_avg(models, inputs):
return np.mean([model.predict(inputs) for model in models], axis=0)
preds = predict_avg(models, X)
preds | code |
1009871/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1) | code |
1009871/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values | code |
1009871/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train.info() | code |
1009871/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model = model.fit(train_data[:, 2:], train_data[:, 0])
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True)
test_data = test.values
output = model.predict(test_data[:, 1:]) | code |
1009871/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
train.info() | code |
1009871/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True) | code |
1009871/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0 | code |
1009871/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
train.info() | code |
1009871/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model = model.fit(train_data[:, 2:], train_data[:, 0])
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True)
test_data = test.values
output = model.predict(test_data[:, 1:])
result = np.c_[test_data[:, 0].astype(int), output.astype(int)]
result_df = pd.DataFrame(result[:, 0:2], columns=['Passenger_id', 'Survived'])
result_df.to_csv('result1.csv')
result_df.shape | code |
1009871/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model = model.fit(train_data[:, 2:], train_data[:, 0])
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True)
test_data = test.values
output = model.predict(test_data[:, 1:])
result = np.c_[test_data[:, 0].astype(int), output.astype(int)] | code |
1009871/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True) | code |
1009871/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
test.info() | code |
1009871/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
a = []
for i in range(1, len(train['Fare'])):
a.append(train['Embarked'][i]) | code |
1009871/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age) | code |
1009871/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model = model.fit(train_data[:, 2:], train_data[:, 0])
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True)
test_data = test.values
output = model.predict(test_data[:, 1:])
result = np.c_[test_data[:, 0].astype(int), output.astype(int)]
result_df = pd.DataFrame(result[:, 0:2], columns=['Passenger_id', 'Survived'])
result_df.to_csv('result1.csv') | code |
1009871/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0] | code |
1009871/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model = model.fit(train_data[:, 2:], train_data[:, 0]) | code |
1009871/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv') | code |
1009871/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv') | code |
1009871/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True) | code |
1009871/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True)
test_data = test.values | code |
1009871/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.info() | code |
1009871/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.info() | code |
1009871/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols]
train_data = train.values
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model = model.fit(train_data[:, 2:], train_data[:, 0])
test = pd.read_csv('test.csv')
test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
test.Age = test.Age.fillna(med_age)
mean_fare0 = test.pivot_table(index='Pclass', values='Fare')
mean_fare0
test.Fare = test[['Fare', 'Pclass']].apply(lambda row: mean_fare[row['Pclass']] if pd.isnull(row['Fare']) else row['Fare'], axis=1)
test['Gender'] = test.Sex.map({'male': 1, 'female': 0})
test['Port'] = test.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
test.drop(['Sex', 'Embarked'], axis=1, inplace=True)
test_data = test.values
output = model.predict(test_data[:, 1:])
result = np.c_[test_data[:, 0].astype(int), output.astype(int)]
result_df = pd.DataFrame(result[:, 0:2], columns=['Passenger_id', 'Survived']) | code |
1009871/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import mode
import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
med_age = train.Age.median()
train.Age = train.Age.fillna(med_age)
train.Embarked = train.Embarked.fillna(train.Embarked.mode())
mode(train.Embarked.tolist())[0][0]
train.Embarked.value_counts()
train.Embarked = train.Embarked.fillna('S')
train['Gender'] = train.Sex.map({'male': 1, 'female': 0})
train['Port'] = train.Embarked.map({'S': 1, 'C': 2, 'Q': 3})
train.drop(['Sex', 'Embarked'], axis=1, inplace=True)
cols = train.columns.tolist()
cols = cols[1:2] + cols[0:1] + cols[2:]
train = train[cols] | code |
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