path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
122248046/cell_16 | [
"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/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
df.head() | code |
122248046/cell_3 | [
"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/traveler-trip-data/Travel details dataset.csv')
df.head() | code |
122248046/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/traveler-trip-data/Travel details dataset.csv')
df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.shape[0]
df = df.drop('Destination', axis=1)
popular_cities = df['City'].value_counts()[:10]
popular_cities.plot.bar()
plt.title('10 most popular travel cities')
plt.xlabel('Cities')
plt.ylabel('Trips') | code |
16125131/cell_21 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
print(f"total foreign population: {df['IBGE_RES_POP_ESTR'].sum():10.0f}")
print(f"% of foreign population {df['IBGE_RES_POP_ESTR'].sum() / df['IBGE_RES_POP'].sum() * 100:10.2f}") | code |
16125131/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
df['STATE'].value_counts().shape | code |
16125131/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.head() | code |
16125131/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
scale_factor = 20000
avg_growth = (df['ESTIMATED_POP'].sum() - df['IBGE_RES_POP'].sum()) / df['IBGE_RES_POP'].sum() * 100
pop_by_state = df[['STATE', 'ESTIMATED_POP']].groupby(by='STATE').sum().sort_values(by='ESTIMATED_POP', ascending=False)
fastest_growing_states = df[['STATE', 'IBGE_RES_POP', 'ESTIMATED_POP']].groupby(by='STATE').sum()
fastest_growing_states['%GROWTH'] = (fastest_growing_states['ESTIMATED_POP'] - fastest_growing_states['IBGE_RES_POP']) / fastest_growing_states['IBGE_RES_POP'] * 100
fgs = fastest_growing_states.sort_values(by='%GROWTH', ascending=False)
fastest_growing_capitals = df[df['CAPITAL'] == 1][['CITY', 'STATE', 'IBGE_RES_POP', 'ESTIMATED_POP']]
fastest_growing_capitals['%GROWTH'] = (fastest_growing_capitals['ESTIMATED_POP'] - fastest_growing_capitals['IBGE_RES_POP']) / fastest_growing_capitals['IBGE_RES_POP'] * 100
fgc = fastest_growing_capitals.sort_values(by='%GROWTH', ascending=False)
plt.figure(figsize=(40, 20))
plt.bar(fgc['CITY'], fgc['%GROWTH'], label='% growth')
plt.plot(fgc['CITY'], [avg_growth] * fgc.shape[0], color='red', label='% avg growth')
plt.legend()
plt.title('Fastest growing capital cities')
plt.show() | code |
16125131/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16125131/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
scale_factor = 20000
avg_growth = (df['ESTIMATED_POP'].sum() - df['IBGE_RES_POP'].sum()) / df['IBGE_RES_POP'].sum() * 100
pop_by_state = df[['STATE', 'ESTIMATED_POP']].groupby(by='STATE').sum().sort_values(by='ESTIMATED_POP', ascending=False)
fastest_growing_states = df[['STATE', 'IBGE_RES_POP', 'ESTIMATED_POP']].groupby(by='STATE').sum()
fastest_growing_states['%GROWTH'] = (fastest_growing_states['ESTIMATED_POP'] - fastest_growing_states['IBGE_RES_POP']) / fastest_growing_states['IBGE_RES_POP'] * 100
fgs = fastest_growing_states.sort_values(by='%GROWTH', ascending=False)
plt.figure(figsize=(15, 10))
plt.bar(fgs.index, fgs['%GROWTH'], label='% growth')
plt.plot(fgs.index, [avg_growth] * fgs.index.shape[0], color='red', label='% avg growth')
plt.legend()
plt.title('Fastest growing states')
plt.show() | code |
16125131/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
print(f"total population (2018): {df['ESTIMATED_POP'].sum():10.0f}")
avg_growth = (df['ESTIMATED_POP'].sum() - df['IBGE_RES_POP'].sum()) / df['IBGE_RES_POP'].sum() * 100
print(f'% population growth between 2010 and 2018: {avg_growth:2.2f}') | code |
16125131/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
scale_factor = 20000
pop_by_state = df[['STATE', 'ESTIMATED_POP']].groupby(by='STATE').sum().sort_values(by='ESTIMATED_POP', ascending=False)
plt.figure(figsize=(15, 10))
plt.bar(pop_by_state.index, pop_by_state['ESTIMATED_POP'])
plt.title('Population by state (2018)')
plt.show() | code |
16125131/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
plt.figure(figsize=(10, 10))
plt.title('Population per Latitude and Longitude')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
scale_factor = 20000
plt.scatter(df[mask1 & mask2 & mask3]['LONG'], df[mask1 & mask2 & mask3]['LAT'], s=df[mask1 & mask2 & mask3]['ESTIMATED_POP'] / scale_factor, alpha=1, label='Capital city')
plt.scatter(df[mask1 & mask2 & ~mask3]['LONG'], df[mask1 & mask2 & ~mask3]['LAT'], s=df[mask1 & mask2 & ~mask3]['ESTIMATED_POP'] / scale_factor, alpha=1, label='Other')
plt.legend()
plt.show() | code |
16125131/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
df['STATE'].value_counts() | code |
16125131/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.fillna(0.0, inplace=True)
mask1 = df['LONG'] != 0
mask2 = df['LAT'] != 0
mask3 = df['CAPITAL'] == 1
plt.figure(figsize=(10, 10))
plt.title('Cities Latitude and Longitude')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.scatter(df[mask1 & mask2 & mask3]['LONG'], df[mask1 & mask2 & mask3]['LAT'], s=20, alpha=1, label='Capital city')
plt.scatter(df[mask1 & mask2 & ~mask3]['LONG'], df[mask1 & mask2 & ~mask3]['LAT'], s=1, alpha=1, label='Other')
plt.legend()
plt.show() | code |
16125131/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BRAZIL_CITIES.csv', sep=';', decimal=',')
df.info() | code |
121151296/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.boxplot('x', data=train) | code |
121151296/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count() | code |
121151296/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample | code |
121151296/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.bar('x', 'y', data=train) | code |
121151296/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train) | code |
121151296/cell_44 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_
y_pred = slr.predict(x_test)
mean_squared_error(y_test, y_pred) | code |
121151296/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.plot('x', 'y', data=train) | code |
121151296/cell_40 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_
y_pred = slr.predict(x_test)
accuracy = slr.score(x_test, y_test)
print(accuracy) | code |
121151296/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape | code |
121151296/cell_41 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_
y_pred = slr.predict(x_test)
plt.plot(x_test, y_pred, color='red')
plt.scatter('x', 'y', data=test)
plt.show() | code |
121151296/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.info() | code |
121151296/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 |
121151296/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
test.head() | code |
121151296/cell_45 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_
y_pred = slr.predict(x_test)
r2_score(y_test, y_pred) | code |
121151296/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['y'].describe() | code |
121151296/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
plt.scatter('x', 'y', data=test) | code |
121151296/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.tail() | code |
121151296/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['x'].value_counts() | code |
121151296/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['y'].value_counts() | code |
121151296/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_
y_pred = slr.predict(x_test)
y_pred | code |
121151296/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121151296/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['x'].describe() | code |
121151296/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_ | code |
121151296/cell_43 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_
y_pred = slr.predict(x_test)
mean_absolute_error(y_test, y_pred) | code |
121151296/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.boxplot('x', data=train) | code |
121151296/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train['x'].isnull().count() | code |
121151296/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
plt.scatter('x', 'y', data=train) | code |
121151296/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape | code |
121151296/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
plt.scatter('x', 'y', data=train) | code |
121151296/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.head() | code |
121151296/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train.sample
train.shape
train.isnull().count()
train = train.dropna()
train.shape
slr = linear_model.LinearRegression()
x_train = np.array(train.iloc[:, :-1].values)
y_train = np.array(train.iloc[:, :1].values)
x_test = np.array(test.iloc[:, :-1].values)
y_test = np.array(test.iloc[:, :1].values)
slr.fit(x_train, y_train)
slr.coef_
slr.intercept_ | code |
322308/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import xgboost as xgb
from scipy import sparse
from sklearn.feature_extraction import FeatureHasher
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, scale
from sklearn.decomposition import TruncatedSVD, SparsePCA
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.feature_selection import SelectPercentile, f_classif, chi2
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.metrics import log_loss | code |
322308/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
print('# Read App Events')
app_ev = pd.read_csv('../input/app_events.csv', dtype={'device_id': np.str})
app_ev = app_ev.groupby('event_id')['app_id'].apply(lambda x: ' '.join(set(('app_id:' + str(s) for s in x)))) | code |
322308/cell_5 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
app_ev = pd.read_csv('../input/app_events.csv', dtype={'device_id': np.str})
app_ev = app_ev.groupby('event_id')['app_id'].apply(lambda x: ' '.join(set(('app_id:' + str(s) for s in x))))
print('# Read Events')
events = pd.read_csv('../input/events.csv', dtype={'device_id': np.str})
events['app_id'] = events['event_id'].map(app_ev)
events = events.dropna()
del app_ev
events = events[['device_id', 'app_id']]
events = events.groupby('device_id')['app_id'].apply(lambda x: ' '.join(set(str(' '.join((str(s) for s in x))).split(' '))))
events = events.reset_index(name='app_id')
events = pd.concat([pd.Series(row['device_id'], row['app_id'].split(' ')) for _, row in events.iterrows()]).reset_index()
events.columns = ['app_id', 'device_id'] | code |
130010335/cell_9 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, ax = plt.subplots(figsize=(4, 4))
res = stats.probplot(df_clinic['updrs_1'], dist='norm', plot=ax) | code |
130010335/cell_4 | [
"text_plain_output_2.png",
"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_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.info() | code |
130010335/cell_23 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
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_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_2'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_3'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_4'],dist='norm', plot=ax)
plt.yticks(rotation=0)
df_eda = df_clinic.groupby('patient_id')['visit_month'].max()
ax = sns.histplot(df_eda, kde=True, color="blue", line_kws={'linewidth': 2, 'linestyle': '--'})
ax.lines[0].set_color('orange')
plt.title(f"Max month", fontweight="bold", pad=15)
plt.show()
df_eda = df_clinic.groupby('visit_month', as_index=False).mean().dropna()
df_eda
for i in range(4):
sns.regplot(x=df_eda['visit_month'].values, y=df_eda[f'updrs_{i + 1}'].values, color='green', ci=None, line_kws={'color': 'orange', 'linestyle': '--'})
plt.title(f'Mean of updrs_{i + 1} by timeseries', fontweight='bold', pad=15)
plt.text(0, df_eda[f'updrs_{i + 1}'].values.max() * 0.99, f"CORR: {round(df_eda.corr().loc['visit_month', f'updrs_{i + 1}'], 3)}")
plt.show() | code |
130010335/cell_20 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
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_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_2'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_3'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_4'],dist='norm', plot=ax)
plt.yticks(rotation=0)
df_eda = df_clinic.groupby('patient_id')['visit_month'].max()
ax = sns.histplot(df_eda, kde=True, color='blue', line_kws={'linewidth': 2, 'linestyle': '--'})
ax.lines[0].set_color('orange')
plt.title(f'Max month', fontweight='bold', pad=15)
plt.show() | code |
130010335/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique() | code |
130010335/cell_11 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_2'],dist='norm', plot=ax)
from scipy import stats
fig, ax = plt.subplots(figsize=(4, 4))
res = stats.probplot(df_clinic['updrs_3'], dist='norm', plot=ax) | code |
130010335/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn import model_selection
import warnings, gc
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130010335/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe() | code |
130010335/cell_8 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16, 30))
features = ['updrs_1', 'updrs_2', 'updrs_3', 'updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i + 1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show() | code |
130010335/cell_16 | [
"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_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import pandas as pd
import numpy as np
dataframe = pd.DataFrame(df_clinic, columns=['updrs_1', 'updrs_2', 'updrs_3', 'updrs_4'])
print(dataframe.head())
dataframe.boxplot(grid='false', color='blue', fontsize=10, rot=30) | code |
130010335/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic | code |
130010335/cell_17 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
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_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_2'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_3'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_4'],dist='norm', plot=ax)
sns.heatmap(df_clinic.filter(regex='updrs_*').dropna().corr(), cmap='crest', annot=True, annot_kws={'fontweight': 'bold'})
plt.yticks(rotation=0)
plt.show() | code |
130010335/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
skewness_1 = df_clinic['updrs_1'].skew()
print(f'Skewness for updrs_1 = {skewness_1}')
skewness_2 = df_clinic['updrs_2'].skew()
print(f'Skewness for updrs_2 = {skewness_2}')
skewness_3 = df_clinic['updrs_3'].skew()
print(f'Skewness for updrs_3 = {skewness_3}')
skewness_4 = df_clinic['updrs_4'].skew()
print(f'Skewness for updrs_4 = {skewness_4}') | code |
130010335/cell_22 | [
"image_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
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_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_2'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_3'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_4'],dist='norm', plot=ax)
plt.yticks(rotation=0)
df_eda = df_clinic.groupby('patient_id')['visit_month'].max()
df_eda = df_clinic.groupby('visit_month', as_index=False).mean().dropna()
df_eda | code |
130010335/cell_10 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, ax = plt.subplots(figsize=(4, 4))
res = stats.probplot(df_clinic['updrs_2'], dist='norm', plot=ax) | code |
130010335/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy import stats
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum()
df_clinic.nunique()
df_clinic.groupby('patient_id').size().describe()
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(16,30))
features= ['updrs_1','updrs_2','updrs_3','updrs_4']
for i in range(len(features)):
fig.add_subplot(9, 5, i+1)
sns.distplot(df_clinic[features[i]])
plt.tight_layout()
plt.show()
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_1'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_2'],dist='norm', plot=ax)
from scipy import stats
fig, (ax) = plt.subplots(figsize = (4,4))
res = stats.probplot(df_clinic['updrs_3'],dist='norm', plot=ax)
from scipy import stats
fig, ax = plt.subplots(figsize=(4, 4))
res = stats.probplot(df_clinic['updrs_4'], dist='norm', plot=ax) | code |
130010335/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_clinic = []
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
train_clinical = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
tmp['CSF'] = 1
df_clinic.append(tmp)
tmp = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/supplemental_clinical_data.csv')
tmp['CSF'] = 0
df_clinic.append(tmp)
df_clinic = pd.concat(df_clinic, axis=0).reset_index(drop=True)
df_clinic = df_clinic.rename(columns={'upd23b_clinical_state_on_medication': 'medication'})
df_clinic.isna().sum() | code |
32063980/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['customer_type'].unique() | code |
32063980/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data.info() | code |
32063980/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['customer_type'] = label_encoder.fit_transform(data['customer_type'])
data['assigned_room_type'] = label_encoder.fit_transform(data['assigned_room_type'])
data['deposit_type'] = label_encoder.fit_transform(data['deposit_type'])
data['reservation_status'] = label_encoder.fit_transform(data['reservation_status'])
data['meal'] = label_encoder.fit_transform(data['meal'])
data['country'] = label_encoder.fit_transform(data['country'])
data['distribution_channel'] = label_encoder.fit_transform(data['distribution_channel'])
data['market_segment'] = label_encoder.fit_transform(data['market_segment'])
data['reserved_room_type'] = label_encoder.fit_transform(data['reserved_room_type'])
data['reservation_status_date'] = label_encoder.fit_transform(data['reservation_status_date'])
print('customer_type:', data['customer_type'].unique())
print('reservation_status', data['reservation_status'].unique())
print('deposit_type', data['deposit_type'].unique())
print('assigned_room_type', data['assigned_room_type'].unique())
print('meal', data['meal'].unique())
print('Country:', data['country'].unique())
print('Dist_Channel:', data['distribution_channel'].unique())
print('Market_seg:', data['market_segment'].unique())
print('reserved_room_type:', data['reserved_room_type'].unique()) | code |
32063980/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data.head() | code |
32063980/cell_34 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
X = data.drop(['previous_cancellations'], axis=1)
y = data['previous_cancellations']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
y_pred = ridge.predict(X_test)
clf = Lasso(alpha=0.1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
logreg = LogisticRegression(solver='lbfgs')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
print('Mean Absolute Error_logreg:', metrics.mean_absolute_error(y_test, y_pred).round(3))
print('Mean Squared Error_logreg:', metrics.mean_squared_error(y_test, y_pred).round(3))
print('Root Mean Squared Error_logreg:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(3))
print('r2_score_logreg:', r2_score(y_test, y_pred).round(3)) | code |
32063980/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)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['reservation_status'].unique() | code |
32063980/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
X = data.drop(['previous_cancellations'], axis=1)
y = data['previous_cancellations']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
y_pred = ridge.predict(X_test)
clf = Lasso(alpha=0.1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Mean Absolute Error_lasso:', metrics.mean_absolute_error(y_test, y_pred).round(3))
print('Mean Squared Error_lasso:', metrics.mean_squared_error(y_test, y_pred).round(3))
print('Root Mean Squared Error_lasso:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(3))
print('r2_score_lasso:', r2_score(y_test, y_pred).round(3)) | code |
32063980/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['arrival_date_month'] = data['arrival_date_month'].map({'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5, 'June': 6, 'July': 7, 'August': 8, 'September': 9, 'October': 10, 'November': 11, 'December': 12})
data['arrival_date_month'].unique() | code |
32063980/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data.info() | code |
32063980/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['arrival_date_month'].unique() | code |
32063980/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 |
32063980/cell_32 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
X = data.drop(['previous_cancellations'], axis=1)
y = data['previous_cancellations']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
y_pred = ridge.predict(X_test)
print('Mean Absolute Error_ridge:', metrics.mean_absolute_error(y_test, y_pred).round(3))
print('Mean Squared Error_ridge:', metrics.mean_squared_error(y_test, y_pred).round(3))
print('Root Mean Squared Error_ridge:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(3))
print('r2_score_ridge:', r2_score(y_test, y_pred).round(3)) | code |
32063980/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
print('Nan in each columns', data.isna().sum(), sep='\n') | code |
32063980/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['hotel'].unique() | code |
32063980/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['hotel'] = data['hotel'].map({'Resort Hotel': 0, 'City Hotel': 1})
data['hotel'].unique() | code |
32063980/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
X = data.drop(['previous_cancellations'], axis=1)
y = data['previous_cancellations']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
y_pred = ridge.predict(X_test)
from sklearn.model_selection import GridSearchCV
parameters = {'alpha': [50, 75, 100, 200, 230, 250], 'random_state': [5, 10, 20, 50], 'max_iter': [0.1, 0.5, 1, 2, 3, 5]}
grid = GridSearchCV(ridge, parameters, cv=5)
grid.fit(X_train, y_train)
print('Best_Score_Ridge : ', grid.best_score_)
print('best_para_Ridge:', grid.best_params_) | code |
32063980/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
X = data.drop(['previous_cancellations'], axis=1)
y = data['previous_cancellations']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
print('Mean Absolute Error_lng:', metrics.mean_absolute_error(y_test, y_pred).round(3))
print('Mean Squared Error_lng:', metrics.mean_squared_error(y_test, y_pred).round(3))
print('Root Mean Squared Error_lng:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(3))
print('r2_score_lng:', r2_score(y_test, y_pred).round(3)) | code |
32063980/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)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['assigned_room_type'].unique() | code |
32063980/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)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
data['deposit_type'].unique() | code |
32063980/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data.describe() | code |
32063980/cell_36 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
data = data.drop(['company'], axis=1)
data = data.dropna(axis=0)
data1 = data.copy()
X = data.drop(['previous_cancellations'], axis=1)
y = data['previous_cancellations']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
y_pred = ridge.predict(X_test)
clf = Lasso(alpha=0.1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
from sklearn.model_selection import GridSearchCV
parameters = {'alpha': [50, 75, 100, 200, 230, 250], 'random_state': [5, 10, 20, 50], 'max_iter': [0.1, 0.5, 1, 2, 3, 5]}
grid = GridSearchCV(ridge, parameters, cv=5)
grid.fit(X_train, y_train)
from sklearn.model_selection import GridSearchCV
parameters = {'alpha': [200, 230, 250, 265, 270, 275, 290, 300], 'random_state': [2, 5, 10, 20, 50], 'max_iter': [5, 10, 15, 20, 30, 50, 100]}
grid = GridSearchCV(clf, parameters, cv=5)
grid.fit(X_train, y_train)
print('Best_Score_Lasso : ', grid.best_score_)
print('best_para_Lasso:', grid.best_params_) | code |
90151799/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
dataset.head() | code |
90151799/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=25, criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
from sklearn.metrics import confusion_matrix, accuracy_score
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred) | code |
90151799/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
print(objList) | code |
90151799/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for feat in objList:
dataset[feat] = le.fit_transform(dataset[feat].astype(str))
print(dataset.info()) | code |
90151799/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=25, criterion='entropy', random_state=0)
classifier.fit(X_train, y_train) | code |
90151799/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for feat in objList:
dataset[feat] = le.fit_transform(dataset[feat].astype(str))
dataset.head() | code |
90151799/cell_22 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=25, criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
from sklearn.metrics import confusion_matrix, accuracy_score
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
accuracy_score(y_test, y_pred)
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=10)
print('Accuracy: {:.2f} %'.format(accuracies.mean() * 100))
print('Standard Deviation: {:.2f} %'.format(accuracies.std() * 100)) | code |
90151799/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
objList = dataset.select_dtypes(include='object').columns
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for feat in objList:
dataset[feat] = le.fit_transform(dataset[feat].astype(str))
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
print(X) | code |
18139612/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
returns[tick + 'return'] = Banks_Stock[tick]['Close'].pct_change()
returns.idxmax()
returns.idxmin()
returns.std()
returns.ix['2015-01-01':'2015-12-31'].std() | code |
18139612/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
returns[tick + 'return'] = Banks_Stock[tick]['Close'].pct_change()
returns.head() | code |
18139612/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.head() | code |
18139612/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
returns[tick + 'return'] = Banks_Stock[tick]['Close'].pct_change()
returns.idxmax()
returns.idxmin() | code |
18139612/cell_1 | [
"text_plain_output_1.png"
] | !pip3 list |grep pandas
from pandas_datareader import data, wb
import numpy as np
import pandas as pd
import datetime
start = datetime.datetime(2006,1,1)
end = datetime.datetime(2016,1,1)
BAC = data.DataReader('BAC',"yahoo",start,end,)
C = data.DataReader('c',"yahoo",start,end)
GS = data.DataReader('GS',"yahoo",start,end)
JPM = data.DataReader('JPM',"yahoo",start,end)
MS = data.DataReader('MS',"yahoo",start,end)
WFC = data.DataReader('WFC',"yahoo",start,end) | code |
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