path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
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 == 'mal... | 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 == 'mal... | 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 = tra... | 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... | 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'].valu... | 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... | 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'].valu... | 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'].valu... | 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_n... | 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'].valu... | 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, fig... | 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... | 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, fig... | 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.... | 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.... | 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.... | 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'... | 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.... | 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'... | 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('/kag... | 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
wo... | 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=Tr... | 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='blu... | 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
i... | 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... | 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=Tr... | 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
wo... | 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 matp... | 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 matp... | 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... | 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 matp... | 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 po... | 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
wo... | 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 matp... | 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 po... | 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-dat... | 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 matp... | 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
wo... | 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",
"app... | 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',... | 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... | 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... | 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... | 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... | 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['figur... | 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... | 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-da... | 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'], a... | 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... | 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... | 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.Embarke... | 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'], a... | 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'], a... | 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.... | 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.... | 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... | 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'], a... | 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['Embark... | 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'], a... | 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.... | 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.Embarke... | 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'], a... | 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'], a... | 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... | 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.... | 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... | code |
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