path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106212685/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
abnb['name'].head(5) | code |
106212685/cell_16 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_3 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.core.interactiveshell import InteractiveShell
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import os, time, sys, gc
for... | code |
106212685/cell_35 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_43 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_31 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_46 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.head(3) | code |
106212685/cell_27 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_37 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.info() | code |
106212685/cell_5 | [
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv') | code |
130014262/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
... | code |
130014262/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
y_train_processed | code |
130014262/cell_9 | [
"image_output_1.png"
] | obj_cols = x_train.select_dtypes(include='object').columns
obj_cols | code |
130014262/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
... | code |
130014262/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5) | code |
130014262/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
obj_cols = ... | code |
130014262/cell_26 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
... | code |
130014262/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train) | code |
130014262/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130014262/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x... | code |
130014262/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
... | code |
130014262/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('C... | code |
130014262/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x... | code |
130014262/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape | code |
130014262/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
... | code |
130014262/cell_22 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
le.inverse_transform([0, 1]) | code |
130014262/cell_10 | [
"text_plain_output_1.png"
] | obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols | code |
130014262/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
... | code |
128005328/cell_42 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Survived', data=data_train) | code |
128005328/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
emb = pd.get_dummies(data_train['Embarked'], drop_first=True)
emb | code |
128005328/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_v... | code |
128005328/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.tail() | code |
128005328/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Embarked'].value_counts() | code |
128005328/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Sex', data=data_train) | code |
128005328/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Age'].value_counts() | code |
128005328/cell_33 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().so... | code |
128005328/cell_44 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.info()
data_test.info() | code |
128005328/cell_40 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_29 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Age', 'Survived']].groupby(['Age'], as_index=False).mean().sort_values(... | code |
128005328/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
emb = pd.get_dummies(data_train['Embarked'], drop_first=True)
emb
data_train.isnull().sum()
data_train.replac... | code |
128005328/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Pclass'].value_counts() | code |
128005328/cell_48 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'ma... | code |
128005328/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Survived'].value_counts() | code |
128005328/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'ma... | code |
128005328/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum() | code |
128005328/cell_45 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_49 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'ma... | code |
128005328/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(... | code |
128005328/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Age', hue='Survived', data=data_train) | code |
128005328/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'ma... | code |
128005328/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Pclass', hue='Survived', data=data_train) | code |
128005328/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
sns.heatmap(data_train.isnull()) | code |
128005328/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum() | code |
128005328/cell_38 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.head() | code |
128005328/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.describe() | code |
128005328/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Embarked', data=data_train) | code |
128005328/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q... | code |
128005328/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Age', data=data_train) | code |
128005328/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Sex', hue='Survived', data=data_train) | code |
128005328/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
print(data_train['Embarked'].mode()[0]) | code |
128005328/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Sex'].value_counts() | code |
128005328/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.head() | code |
128005328/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Pclass', data=data_train) | code |
128005328/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
print(data_train['Embarked'].mode()) | code |
128005328/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape | code |
128005328/cell_36 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Embarked', hue='Survived', data=data_train) | code |
2012676/cell_21 | [
"text_html_output_1.png"
] | from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from sklearn import cluster, mixture, metrics # For clustering
import matplotlib.pyplot as plt # For graphics
import pandas as pd # Dataframe manipulation
import plotly.graph_objs as go
... | code |
2012676/cell_23 | [
"text_html_output_1.png"
] | from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from sklearn import cluster, mixture, metrics # For clustering
import matplotlib.pyplot as plt # For graphics
import pandas as pd # Dataframe manipulation
import plotly.graph_objs as go
... | code |
2012676/cell_6 | [
"image_output_1.png"
] | import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data.head() | code |
2012676/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import cluster, mixture, metrics
from sklearn.preprocessing import StandardScaler
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(conn... | code |
2012676/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
whr_data_for_clus.head(3) | code |
2012676/cell_18 | [
"text_html_output_1.png"
] | from sklearn import cluster, mixture, metrics # For clustering
import matplotlib.pyplot as plt # For graphics
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
n_clusters = ... | code |
2012676/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
ss = StandardScaler()
ss.fit_transform(whr_data_for_clus) | code |
2012676/cell_15 | [
"text_html_output_1.png"
] | from sklearn import cluster, mixture, metrics # For clustering
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
n_clusters = 2
bandwidth = 0.1
eps = 0.3
damping = 0.9
preference = -200
metric... | code |
16120163/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
boston = datasets.load_boston()
print(boston.keys())
print(boston.data.shape)
print(boston.feature_names)
print(boston.DESCR) | code |
16120163/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input, Dense
from keras.models import Model
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston =... | code |
16120163/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import datasets
import pandas as pd
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
print(bos.head())
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV'] | code |
16120163/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
import numpy as np
import pandas as pd
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
import seaborn as sns
from keras.layers import Input, Dense
from keras.models import Model
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2... | code |
16120163/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input, Dense
from keras.models import Model
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston =... | code |
16120163/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().r... | code |
16120163/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston... | code |
16120163/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input, Dense
from keras.models import Model
from math import sqrt
from sklearn import datasets
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
impor... | code |
16120163/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
cor... | code |
16120163/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.targe... | code |
2002376/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data = np.array(data)
train, test = (data[0:8000,], data[8000:,])
Xtrain, ytrain = (train[:, 0:-1], train[:, -1])
Xtest, ytest = (test[:, 0:... | code |
2002376/cell_1 | [
"text_plain_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 |
2002376/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
print(data.shape)
data = np.array(data)
train, test = (data[0:8000,], data[8000:,])
Xtrain, ytrain = (train[:, 0:-1], train[:, -1])
Xtest, ytest = (test[:, 0:-1], te... | code |
128003343/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', ... | code |
128003343/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px # Интерактивная библиотека для графиков.
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab =... | code |
128003343/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df.describe(include='object') | code |
128003343/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy.stats import ttest_1samp, mannwhitneyu, shapiro, norm, t, kstest, shapiro
from statsmodels.stats.power import TTestIndPower
from statsmodels.stats import proportion
import plotly.express as px
import math
import stat... | code |
128003343/cell_34 | [
"text_plain_output_1.png"
] | if abs(z_pvalue) < 0.05:
print('We may reject the null hypothesis!')
else:
print('We have failed to reject the null hypothesis') | code |
128003343/cell_33 | [
"text_html_output_1.png"
] | from statsmodels.stats import proportion
import numpy as np
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.grou... | code |
128003343/cell_44 | [
"text_plain_output_1.png"
] | if abs(z_pvalue) < 0.05:
print('We may reject the null hypothesis!')
else:
print('We have failed to reject the null hypothesis') | code |
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