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|---|---|---|---|
105186076/cell_17 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_)
y_pred = (78.35 * X + 0).reshape(4)
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
y_pred1 = (78.35 * X + b1).reshape(4)
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2
y_pred2 = (78.35 * X + b2).reshape(4)
loss_slope = -2 * np.sum(y - m * X.ravel() - b2)
step_size = loss_slope * lr
b3 = b2 - step_size
b3
y_pred3 = (78.35 * X + b3).reshape(4)
b = -100
m = 78.35
lr = 0.01
epochs = 100
plt.figure(figsize=(18, 6))
for i in range(epochs):
loss_slope = -2 * np.sum(y - m * X.ravel() - b)
b = b - lr * loss_slope
y_pred = m * X + b
plt.plot(X, y_pred)
plt.scatter(X, y) | code |
105186076/cell_14 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_)
y_pred = (78.35 * X + 0).reshape(4)
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
y_pred1 = (78.35 * X + b1).reshape(4)
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2
y_pred2 = (78.35 * X + b2).reshape(4)
plt.figure(figsize=(18, 6))
plt.scatter(X, y)
plt.plot(X, reg.predict(X), color='green', label='OLS')
plt.plot(X, y_pred2, color='#ffb347', label='b2 = {}_updated@step2'.format(b2))
plt.plot(X, y_pred1, color='#f8b878', label='b1 = {}_updated@step1'.format(b1))
plt.plot(X, y_pred, color='#ffa500', label='b = 0_initial_random')
plt.legend()
plt.show() | code |
105186076/cell_10 | [
"image_output_1.png"
] | X.ravel()[:5] | code |
105186076/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_)
y_pred = (78.35 * X + 0).reshape(4)
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
y_pred1 = (78.35 * X + b1).reshape(4)
plt.figure(figsize=(18, 6))
plt.scatter(X, y)
plt.plot(X, reg.predict(X), color='green', label='OLS')
plt.plot(X, y_pred1, color='#ffdab9', label='b1 = {}_updated'.format(b1))
plt.plot(X, y_pred, color='#ffa500', label='b = 0_initial_random')
plt.legend()
plt.show() | code |
105186076/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_) | code |
105207876/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts() | code |
105207876/cell_4 | [
"text_plain_output_1.png"
] | import glob as glob
len(glob.glob('../input/jpg-images/train_jpg/*jpg')) | code |
105207876/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
meta_vir.head() | code |
105207876/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts()
labels = np.array(y)
labels.shape
X_train, X_valid, y_train, y_valid = train_test_split(image_files_list, labels, random_state=20, test_size=0.3, stratify=labels)
(len(X_train), len(X_valid), y_train.shape, y_valid.shape) | code |
105207876/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape | code |
105207876/cell_8 | [
"image_output_1.png"
] | import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
y['overall'] = y['C1'] + y['C2'] + y['C3'] + y['C4'] + y['C5'] + y['C6'] + y['C7']
y['z_total'] = y['overall'].apply(lambda x: x if x == 0 else 1)
y['z_total'].value_counts() | code |
105207876/cell_17 | [
"text_plain_output_1.png"
] | model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=5) | code |
105207876/cell_14 | [
"text_html_output_1.png"
] | from torchvision import datasets,models,transforms
import PIL
import glob as glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
len(glob.glob('../input/jpg-images/train_jpg/*jpg'))
len(glob.glob('../input/jpg-images/test_jpg/*jpg'))
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts()
labels = np.array(y)
labels.shape
train_transforms = Compose([LoadImage(image_only=True), transforms.ToTensor(), EnsureChannelFirst(), ScaleIntensity(), RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True), RandFlip(spatial_axis=0, prob=0.5), RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5)])
val_transforms = Compose([LoadImage(image_only=True), transforms.ToTensor(), EnsureChannelFirst(), ScaleIntensity()])
image_files_list = glob.glob('../input/jpg-images/train_jpg/*.jpg')
plt.subplots(3, 3, figsize=(8, 8))
for i in range(9):
im = PIL.Image.open(image_files_list[i])
arr = np.array(im)
plt.subplot(3, 3, i + 1)
plt.imshow(arr, cmap='gray')
plt.tight_layout()
plt.show() | code |
105207876/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
meta_vir = pd.read_csv('../input/rsna-2022-spine-fracture-detection-metadata/meta_train_with_vertebrae.csv')
y = meta_vir.iloc[:3408, 9:]
y.shape
del y['overall']
y.z_total.value_counts()
labels = np.array(y)
labels.shape | code |
105207876/cell_5 | [
"text_plain_output_1.png"
] | import glob as glob
len(glob.glob('../input/jpg-images/train_jpg/*jpg'))
len(glob.glob('../input/jpg-images/test_jpg/*jpg')) | code |
1001162/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
x = np.arange(0, 5, 1)
y = np.sin(x)
fig, ax = plt.subplots()
ind = np.arange(len(student_data.studytime.unique()))
ax.bar(ind, student_data.studytime) | code |
1001162/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape | code |
1001162/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
print(student_data[student_data.sex == 'F'].sex.count())
print(student_data[student_data.sex == 'M'].sex.count()) | code |
1001162/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1001162/cell_7 | [
"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)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
plt.hist(student_data.studytime) | code |
1001162/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes
x = np.arange(0, 5, 1)
y = np.sin(x)
plt.plot(student_data.studytime) | code |
1001162/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data | code |
1001162/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
student_data = pd.read_csv('../input/student-mat.csv')
student_data.shape
student_data.dtypes | code |
16113958/cell_21 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts()
df.Gender.value_counts() | code |
16113958/cell_13 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique() | code |
16113958/cell_9 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :] | code |
16113958/cell_4 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df.info() | code |
16113958/cell_23 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts()
df.Gender.value_counts()
df.Place.value_counts() | code |
16113958/cell_20 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts() | code |
16113958/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum() | code |
16113958/cell_26 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts()
df.Gender.value_counts()
df.Place.value_counts()
df = df.drop(columns='BCHC Requested Methodology')
df.columns | code |
16113958/cell_11 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns | code |
16113958/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16113958/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('../input/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :] | code |
16113958/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.head() | code |
16113958/cell_17 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts() | code |
16113958/cell_24 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts()
df.Gender.value_counts()
df.Place.value_counts()
df['BCHC Requested Methodology'].value_counts() | code |
16113958/cell_14 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts() | code |
16113958/cell_22 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts()
df.Gender.value_counts()
df['Race/ Ethnicity'].value_counts() | code |
16113958/cell_27 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df.Indicator.nunique()
df.Indicator.value_counts()
df = df.drop(columns='Indicator')
df.Year.value_counts()
df = df[~df.Year.str.contains('-')]
df.Year.value_counts()
df.Gender.value_counts()
df.Place.value_counts()
df = df.drop(columns='BCHC Requested Methodology')
df.columns
df.Source.value_counts() | code |
16113958/cell_12 | [
"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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)]
df.duplicated().sum()
df.iloc[12320:12325, :]
df = df.drop_duplicates()
df.iloc[12320:12325, :]
df.columns
df['Indicator Category'].value_counts() | code |
16113958/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/Big_Cities_Health_Data_Inventory.csv')
df[df.duplicated(keep=False)] | code |
88082047/cell_21 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from pycaret.classification import * | code |
88082047/cell_13 | [
"text_html_output_1.png"
] | !pip install autoviz | code |
88082047/cell_9 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/data_dict.csv')
df_personal.head() | code |
88082047/cell_4 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88082047/cell_30 | [
"image_output_1.png"
] | nb = create_model('nb')
plot_model(nb, plot='pr') | code |
88082047/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | pip install pycaret --ignore-installed llvmlite numba | code |
88082047/cell_29 | [
"text_html_output_1.png"
] | nb = create_model('nb')
plot_model(nb, plot='auc') | code |
88082047/cell_19 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | from autoviz.AutoViz_Class import AutoViz_Class
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/data_dict.csv')
AV = AutoViz_Class()
url1 = '../input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv'
df_viz = pd.read_csv(url1, index_col=None, thousands=',')
target = 'injury'
dft = AV.AutoViz(depVar=target, dfte=df_viz, header=0, verbose=0, lowess=False, chart_format='svg', max_rows_analyzed=1500000, max_cols_analyzed=300, filename='', sep=',') | code |
88082047/cell_28 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | nb = create_model('nb')
plot_model(nb, plot='confusion_matrix') | code |
88082047/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/data_dict.csv')
url1 = '../input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv'
df_viz = pd.read_csv(url1, index_col=None, thousands=',')
print(df_viz.shape)
df_viz.head() | code |
88082047/cell_31 | [
"image_output_1.png"
] | nb = create_model('nb')
optimize_threshold(nb) | code |
88082047/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/data_dict.csv')
df1 = df_personal.drop(['treatment', 'weight', 'year'], axis=1)
model = setup(df1, target='injury', silent=True, session_id=1) | code |
88082047/cell_14 | [
"text_plain_output_1.png"
] | from autoviz.AutoViz_Class import AutoViz_Class | code |
88082047/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_house = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_HOUSEHOLD_VICTIMIZATION_1993-2014.csv')
df_personal = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/NCVS_PERSONAL_VICTIMIZATION_1993-2014.csv')
df_dict = pd.read_csv('/kaggle/input/bjs-crime-victim-statisticse/data_dict.csv')
df_personal.info() | code |
88082047/cell_27 | [
"text_html_output_1.png"
] | nb = create_model('nb') | code |
73091762/cell_13 | [
"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)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
df.head(5) | code |
73091762/cell_20 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
import re
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
import plotly.graph_objects as go
import math
df_type = pd.DataFrame(df['type'].value_counts())
df_type
fig = go.Figure(data=go.Bar(x=df_type.index, y=df_type['type']))
'{:.2f}'.format(13.95)
df_country = pd.DataFrame(df['country'].value_counts())
# Let's examine how many countries we have
df_country.shape
# we will be only visualize for top 15 countries
df_country.sort_values(by = ["country"], ascending = False, inplace = True)
df_count15 = df_country.head(15)
from plotly.subplots import make_subplots
# Let's do ead for the df_country
fig = go.Figure(data=go.Bar(x = df_count15.index, y = df_count15["country"]))
fig = make_subplots(rows=1, cols=2, column_widths=[0.7, 0.3])
fig.add_trace(go.Bar(x=df_count15.index, y=df_count15["country"]),
row=1, col=1)
fig.add_trace(go.Scatter(x=df_count15.index, y=df_count15["country"]),
row=1, col=2)
fig.show()
df_rating = pd.DataFrame(df['rating'].value_counts())
fig = go.Figure(data=[go.Pie(labels=df_rating.index, values=df_rating['rating'])])
import re
merged_cat = ''
for i in df['listed_in']:
merged_cat += i
merged_cat += '@'
merged = re.split(', |&|@', merged_cat)
merged = [i.strip() for i in merged]
list_value = pd.DataFrame(merged).value_counts()
list_value = pd.DataFrame(list_value)
list_value.columns = ['list']
type(list(list_value.index)[0][0])
a = [i[0] for i in list(list_value.index)]
a
fig = go.Figure(data=go.Bar(x=a, y=list_value['list']))
fig.show() | code |
73091762/cell_6 | [
"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)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.head(10) | code |
73091762/cell_2 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
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 |
73091762/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)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns | code |
73091762/cell_19 | [
"text_html_output_2.png",
"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)
import re
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
import re
merged_cat = ''
for i in df['listed_in']:
merged_cat += i
merged_cat += '@'
merged = re.split(', |&|@', merged_cat)
merged = [i.strip() for i in merged]
print(f'in total we have {df.shape[0]} tv series and movies and overall it is defined by {len(merged)} which accounts for {len(merged) / df.shape[0]} per show') | code |
73091762/cell_7 | [
"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)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape | code |
73091762/cell_18 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
import plotly.graph_objects as go
import math
df_type = pd.DataFrame(df['type'].value_counts())
df_type
fig = go.Figure(data=go.Bar(x=df_type.index, y=df_type['type']))
'{:.2f}'.format(13.95)
df_country = pd.DataFrame(df['country'].value_counts())
# Let's examine how many countries we have
df_country.shape
# we will be only visualize for top 15 countries
df_country.sort_values(by = ["country"], ascending = False, inplace = True)
df_count15 = df_country.head(15)
from plotly.subplots import make_subplots
# Let's do ead for the df_country
fig = go.Figure(data=go.Bar(x = df_count15.index, y = df_count15["country"]))
fig = make_subplots(rows=1, cols=2, column_widths=[0.7, 0.3])
fig.add_trace(go.Bar(x=df_count15.index, y=df_count15["country"]),
row=1, col=1)
fig.add_trace(go.Scatter(x=df_count15.index, y=df_count15["country"]),
row=1, col=2)
fig.show()
df_rating = pd.DataFrame(df['rating'].value_counts())
df_rating.head()
fig = go.Figure(data=[go.Pie(labels=df_rating.index, values=df_rating['rating'])])
fig.show() | code |
73091762/cell_8 | [
"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)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.head() | code |
73091762/cell_15 | [
"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)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
import plotly.graph_objects as go
import math
df_type = pd.DataFrame(df['type'].value_counts())
df_type
fig = go.Figure(data=go.Bar(x=df_type.index, y=df_type['type']))
'{:.2f}'.format(13.95)
df_country = pd.DataFrame(df['country'].value_counts())
df_country.head() | code |
73091762/cell_17 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
import plotly.graph_objects as go
import math
df_type = pd.DataFrame(df['type'].value_counts())
df_type
fig = go.Figure(data=go.Bar(x=df_type.index, y=df_type['type']))
'{:.2f}'.format(13.95)
df_country = pd.DataFrame(df['country'].value_counts())
# Let's examine how many countries we have
df_country.shape
# we will be only visualize for top 15 countries
df_country.sort_values(by = ["country"], ascending = False, inplace = True)
df_count15 = df_country.head(15)
from plotly.subplots import make_subplots
fig = go.Figure(data=go.Bar(x=df_count15.index, y=df_count15['country']))
fig = make_subplots(rows=1, cols=2, column_widths=[0.7, 0.3])
fig.add_trace(go.Bar(x=df_count15.index, y=df_count15['country']), row=1, col=1)
fig.add_trace(go.Scatter(x=df_count15.index, y=df_count15['country']), row=1, col=2)
fig.show() | code |
73091762/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)
import plotly.graph_objects as go
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.columns
df = df[['type', 'title', 'country', 'month_added', 'day_added', 'year_added', 'release_year', 'rating', 'duration', 'listed_in', 'description', 'director', 'cast']]
import plotly.graph_objects as go
import math
df_type = pd.DataFrame(df['type'].value_counts())
df_type
fig = go.Figure(data=go.Bar(x=df_type.index, y=df_type['type']))
fig.show()
'{:.2f}'.format(13.95)
print(f" Netflix has more {float(df_type.loc['Movie'] / df_type.loc['TV Show'])} times more movie than TV Shows.") | code |
73091762/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)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.shape
df.drop(columns=['show_id'], inplace=True)
df.drop(columns=['null1'], inplace=True)
df['day_added'] = df['day_added'].str.replace(',', '')
df.head() | code |
129024743/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
reg = MLPRegressor(max_iter=300)
reg.fit(X_train, y_train)
reg.score(X_test, y_test) | code |
129024743/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
digits = datasets.load_digits()
X, y = datasets.load_digits(return_X_y=True)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = datasets.load_digits(return_X_y=True)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.datasets import load_digits
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import numpy as np
X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
y = y.astype(np.uint8)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129024743/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
digits = datasets.load_digits()
plt.figure(1, figsize=(3, 3))
plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show() | code |
129024743/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test) | code |
129024743/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
reg = MLPRegressor(max_iter=300)
reg.fit(X_train, y_train)
reg.score(X_test, y_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X2_train = scaler_mm.fit_transform(X2_train)
X2_test = scaler_mm.transform(X2_test)
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
reg = MLPRegressor(max_iter=300)
reg.fit(X2_train, y2_train)
reg.score(X2_test, y2_test) | code |
129024743/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129024743/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129024743/cell_38 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
from sklearn import datasets
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
digits = datasets.load_digits()
X, y = datasets.load_digits(return_X_y=True)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = datasets.load_digits(return_X_y=True)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.datasets import load_digits
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import numpy as np
X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
y = y.astype(np.uint8)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = datasets.load_digits(return_X_y=True)
y_binary = [1 if i == 0 else 0 for i in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.datasets import load_digits
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import numpy as np
X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
y = y.astype(np.uint8)
y_binary = [1 if i == 0 else 0 for i in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129024743/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.datasets import fetch_openml
X2, y2 = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False) | code |
129024743/cell_31 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
digits = datasets.load_digits()
X, y = datasets.load_digits(return_X_y=True)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = datasets.load_digits(return_X_y=True)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129024743/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test) | code |
129024743/cell_36 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn.datasets import fetch_openml
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
digits = datasets.load_digits()
X, y = datasets.load_digits(return_X_y=True)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)
clf = MLPClassifier(random_state=1)
clf.fit(X2_train, y2_train)
clf.score(X2_test, y2_test)
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = datasets.load_digits(return_X_y=True)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.datasets import load_digits
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import numpy as np
X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
y = y.astype(np.uint8)
y_binary = [1 if val % 2 != 0 else 0 for val in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = datasets.load_digits(return_X_y=True)
y_binary = [1 if i == 0 else 0 for i in y]
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
clf = MLPClassifier(random_state=1)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
105206572/cell_13 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm['Recency'] = rfm['Recent_date'].apply(lambda x: (dt.date(2011, 12, 31) - x).days)
rfm = rfm[['CustomerID', 'Recency', 'Frequency', 'Monetory']]
rfm['R'] = pd.qcut(rfm['Recency'], 5, labels=[5, 4, 3, 2, 1])
rfm['F'] = pd.qcut(rfm['Frequency'], 5, labels=[1, 2, 3, 4, 5])
rfm['M'] = pd.qcut(rfm['Monetory'], 5, labels=[1, 2, 3, 4, 5])
rfm['RFM_Score'] = rfm['R'].astype(str) + rfm['F'].astype(str) + rfm['M'].astype(str)
rfm.head() | code |
105206572/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm.head() | code |
105206572/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df.head() | code |
105206572/cell_11 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm['Recency'] = rfm['Recent_date'].apply(lambda x: (dt.date(2011, 12, 31) - x).days)
rfm = rfm[['CustomerID', 'Recency', 'Frequency', 'Monetory']]
rfm.head() | code |
105206572/cell_19 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm['Recency'] = rfm['Recent_date'].apply(lambda x: (dt.date(2011, 12, 31) - x).days)
rfm = rfm[['CustomerID', 'Recency', 'Frequency', 'Monetory']]
rfm['R'] = pd.qcut(rfm['Recency'], 5, labels=[5, 4, 3, 2, 1])
rfm['F'] = pd.qcut(rfm['Frequency'], 5, labels=[1, 2, 3, 4, 5])
rfm['M'] = pd.qcut(rfm['Monetory'], 5, labels=[1, 2, 3, 4, 5])
rfm['RFM_Score'] = rfm['R'].astype(str) + rfm['F'].astype(str) + rfm['M'].astype(str)
seg_map = {'[1-2][1-2]': 'Hibernating', '[1-2][3-4]': 'At Risk', '[1-2][5]': "Can't Loose", '3[1-2]': 'about to sleep', '33': 'Attention', '[3-4][4-5]': 'Loyal Customers', '41': 'Promosing', '51': 'New Customers', '[4-5][2-3]': 'Potential ', '5[4-5]': 'Champions'}
rfm['Segment'] = rfm['R'].astype(str) + rfm['F'].astype(str)
rfm['Segment'] = rfm['Segment'].replace(seg_map, regex=True)
rfm1 = rfm.groupby('Segment', as_index=False).agg(No_Cxs=('CustomerID', 'count'), Recency_mean=('Recency', 'mean'), Frequency_mean=('Frequency', 'mean'), Monetory_mean=('Monetory', 'mean')).sort_values('No_Cxs', ascending=False)
rfm1 | code |
105206572/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 |
105206572/cell_8 | [
"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
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
df.head() | code |
105206572/cell_15 | [
"text_html_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm['Recency'] = rfm['Recent_date'].apply(lambda x: (dt.date(2011, 12, 31) - x).days)
rfm = rfm[['CustomerID', 'Recency', 'Frequency', 'Monetory']]
rfm['R'] = pd.qcut(rfm['Recency'], 5, labels=[5, 4, 3, 2, 1])
rfm['F'] = pd.qcut(rfm['Frequency'], 5, labels=[1, 2, 3, 4, 5])
rfm['M'] = pd.qcut(rfm['Monetory'], 5, labels=[1, 2, 3, 4, 5])
rfm['RFM_Score'] = rfm['R'].astype(str) + rfm['F'].astype(str) + rfm['M'].astype(str)
seg_map = {'[1-2][1-2]': 'Hibernating', '[1-2][3-4]': 'At Risk', '[1-2][5]': "Can't Loose", '3[1-2]': 'about to sleep', '33': 'Attention', '[3-4][4-5]': 'Loyal Customers', '41': 'Promosing', '51': 'New Customers', '[4-5][2-3]': 'Potential ', '5[4-5]': 'Champions'}
rfm['Segment'] = rfm['R'].astype(str) + rfm['F'].astype(str)
rfm['Segment'] = rfm['Segment'].replace(seg_map, regex=True)
rfm.head() | code |
105206572/cell_17 | [
"text_html_output_1.png"
] | import datetime as dt
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
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm['Recency'] = rfm['Recent_date'].apply(lambda x: (dt.date(2011, 12, 31) - x).days)
rfm = rfm[['CustomerID', 'Recency', 'Frequency', 'Monetory']]
rfm['R'] = pd.qcut(rfm['Recency'], 5, labels=[5, 4, 3, 2, 1])
rfm['F'] = pd.qcut(rfm['Frequency'], 5, labels=[1, 2, 3, 4, 5])
rfm['M'] = pd.qcut(rfm['Monetory'], 5, labels=[1, 2, 3, 4, 5])
rfm['RFM_Score'] = rfm['R'].astype(str) + rfm['F'].astype(str) + rfm['M'].astype(str)
seg_map = {'[1-2][1-2]': 'Hibernating', '[1-2][3-4]': 'At Risk', '[1-2][5]': "Can't Loose", '3[1-2]': 'about to sleep', '33': 'Attention', '[3-4][4-5]': 'Loyal Customers', '41': 'Promosing', '51': 'New Customers', '[4-5][2-3]': 'Potential ', '5[4-5]': 'Champions'}
rfm['Segment'] = rfm['R'].astype(str) + rfm['F'].astype(str)
rfm['Segment'] = rfm['Segment'].replace(seg_map, regex=True)
plt.rcParams['figure.figsize'] = [20, 15]
plt.rcParams['figure.autolayout'] = True
f, axes = plt.subplots(3, 1)
sns.barplot(x='Segment', y='Recency', data=rfm, ax=axes[0], ci=None)
sns.barplot(x='Segment', y='Frequency', data=rfm, ax=axes[1], ci=None)
sns.barplot(x='Segment', y='Monetory', data=rfm, ax=axes[2], ci=None) | code |
105206572/cell_10 | [
"text_plain_output_1.png"
] | import datetime as dt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape
df = df[df['Country'] == 'Germany']
df['Value'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.DatetimeIndex(df['InvoiceDate']).date
rfm = df.groupby('CustomerID', as_index=False).agg(Recent_date=('InvoiceDate', 'max'), Frequency=('InvoiceNo', 'count'), Monetory=('Value', 'sum'))
rfm['Recency'] = rfm['Recent_date'].apply(lambda x: (dt.date(2011, 12, 31) - x).days)
rfm.head() | code |
105206572/cell_5 | [
"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
import numpy as np
import seaborn as sns
import time, warnings
import datetime as dt
import squarify
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/customer-trans/data.csv', encoding='ISO-8859-1', dtype={'CustomerID': str, 'InvoiceNo': str})
df.shape | code |
90103033/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train) | code |
90103033/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
model.predict(X_test)
model.score(X_test, y_test) | code |
90103033/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape | code |
90103033/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean() | code |
90103033/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
df_new = df[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
dummy_salary = pd.get_dummies(df_new.salary, prefix='salary')
dummy_salary.head() | code |
90103033/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = df[df.left == 1]
left.shape
retained = df[df.left == 0]
retained.shape
df.groupby('left').mean()
df_new = df[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
dummy_salary = pd.get_dummies(df_new.salary, prefix='salary')
df_new_with_dummy = pd.concat([df_new, dummy_salary], axis='columns')
df_new_with_dummy.head() | code |
90103033/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
df.head() | code |
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