path
stringlengths 13
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|---|---|---|---|
74040768/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/pizza-price-prediction/pizza_v1.csv'
df = pd.read_csv(path)
df.info() | code |
74040768/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/pizza-price-prediction/pizza_v1.csv'
df = pd.read_csv(path)
df.info() | code |
74040768/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/pizza-price-prediction/pizza_v1.csv'
df = pd.read_csv(path)
df.info() | code |
74040768/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
import numpy as np
def run_model(model, X_train, X_test, y_train, y_test):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
train_accuracy = model.score(X_train, y_train)
test_accuracy = model.score(X_test, y_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
k_values = [1, 5, 10]
for n in k_values:
model = KNeighborsRegressor(n_neighbors=n)
run_model(model, X_train, X_test, y_train, y_test)
model = DecisionTreeRegressor()
run_model(model, X_train, X_test, y_train, y_test)
trees = [10, 50, 100, 200, 500]
for n in trees:
model = RandomForestRegressor(n_estimators=n)
run_model(model, X_train, X_test, y_train, y_test)
model = GradientBoostingRegressor()
run_model(model, X_train, X_test, y_train, y_test) | code |
74040768/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
path = '../input/pizza-price-prediction/pizza_v1.csv'
df = pd.read_csv(path)
plt.figure(figsize=(10, 6))
sns.swarmplot(x='size', y='price_rupiah', data=df)
plt.show() | code |
74040768/cell_36 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
import numpy as np
def run_model(model, X_train, X_test, y_train, y_test):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
train_accuracy = model.score(X_train, y_train)
test_accuracy = model.score(X_test, y_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
k_values = [1, 5, 10]
for n in k_values:
model = KNeighborsRegressor(n_neighbors=n)
run_model(model, X_train, X_test, y_train, y_test)
model = DecisionTreeRegressor()
run_model(model, X_train, X_test, y_train, y_test) | code |
90108018/cell_4 | [
"text_plain_output_1.png"
] | cmd="./DeepFaceLab/main.py train --training-data-src-dir ./data_src/aligned --training-data-dst-dir ./data_dst/aligned --model-dir ./model --model SAEHD "
cmd+=" --pretraining-data-dir ./pretrain --saving-time 15 --silent-start "
!python $cmd | code |
88075619/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | x = [[2], [5], [7], [8]]
x
x_transpose = np.array(x).T.tolist()
x_transpose | code |
88075619/cell_4 | [
"text_plain_output_1.png"
] | A = [[1, 3, 21, 2, 0], [50, 5, -9, 0, 0], [54, -7, 11, 10, 0]]
A | code |
88075619/cell_7 | [
"text_plain_output_1.png"
] | x = [[2], [5], [7], [8]]
x | code |
88075619/cell_5 | [
"text_plain_output_1.png"
] | A = [[1, 3, 21, 2, 0], [50, 5, -9, 0, 0], [54, -7, 11, 10, 0]]
A
A = [[1, 3], [50, 5], [54, -7], [2, 4]]
A | code |
122256157/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df.head() | code |
122256157/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred)
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
f1_score(y_test, y_pred)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
f1_score(y_test, y_pred)
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred)
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df = df.drop('Booking_ID', axis=1)
features = ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests']
target = 'booking_status'
le = LabelEncoder()
for col in df.columns:
if df[col].dtype == 'object':
df[col] = le.fit_transform(df[col])
for col in df.columns:
df[col] = pd.to_numeric(df[col], downcast='unsigned')
plt.gcf().set_size_inches(20, 20)
sm = SMOTE(random_state=42)
X, y = sm.fit_resample(df[features], df[target])
sns.countplot(y) | code |
122256157/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred)
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
f1_score(y_test, y_pred)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
f1_score(y_test, y_pred)
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)
f1_score(y_test, y_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
f1_score(y_test, y_pred)
nn = MLPClassifier()
nn.fit(X_train, y_train)
y_pred = nn.predict(X_test)
f1_score(y_test, y_pred)
y_pred = rf.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred)) | code |
122256157/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df = df.drop('Booking_ID', axis=1)
features = ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests']
target = 'booking_status'
df.info() | code |
122256157/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred)
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
f1_score(y_test, y_pred)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_15 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df = df.drop('Booking_ID', axis=1)
features = ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests']
target = 'booking_status'
le = LabelEncoder()
for col in df.columns:
if df[col].dtype == 'object':
df[col] = le.fit_transform(df[col])
for col in df.columns:
df[col] = pd.to_numeric(df[col], downcast='unsigned')
sns.countplot(x=target, data=df) | code |
122256157/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred)
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
f1_score(y_test, y_pred)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
f1_score(y_test, y_pred)
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)
f1_score(y_test, y_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
f1_score(y_test, y_pred)
nn = MLPClassifier()
nn.fit(X_train, y_train)
y_pred = nn.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df = df.drop('Booking_ID', axis=1)
features = ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests']
target = 'booking_status'
le = LabelEncoder()
for col in df.columns:
if df[col].dtype == 'object':
df[col] = le.fit_transform(df[col])
for col in df.columns:
df[col] = pd.to_numeric(df[col], downcast='unsigned')
sns.heatmap(df.corr(), annot=True)
plt.gcf().set_size_inches(20, 20) | code |
122256157/cell_24 | [
"image_output_1.png"
] | from sklearn.metrics import f1_score
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred) | code |
122256157/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df = df.drop('Booking_ID', axis=1)
features = ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests']
target = 'booking_status'
le = LabelEncoder()
for col in df.columns:
if df[col].dtype == 'object':
df[col] = le.fit_transform(df[col])
df.head() | code |
122256157/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-reservations-classification-dataset/Hotel Reservations.csv')
df = df.drop('Booking_ID', axis=1)
features = ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'market_segment_type', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests']
target = 'booking_status'
le = LabelEncoder()
for col in df.columns:
if df[col].dtype == 'object':
df[col] = le.fit_transform(df[col])
for col in df.columns:
df[col] = pd.to_numeric(df[col], downcast='unsigned')
df.info() | code |
122256157/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
f1_score(y_test, y_pred)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
f1_score(y_test, y_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
f1_score(y_test, y_pred)
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
y_pred = xgb.predict(X_test)
f1_score(y_test, y_pred)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
f1_score(y_test, y_pred)
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)
f1_score(y_test, y_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
f1_score(y_test, y_pred) | code |
333377/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn
data = pd.read_csv('../input/Iris.csv')
import seaborn
# Calculate correlation coefficient
cols = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm']
cm = np.corrcoef(data[cols].values.T)
heatmap = seaborn.heatmap(cm, cbar=True,
annot=True,
square=True,
fmt='.2f',
annot_kws={'size': 15},
yticklabels=cols,
xticklabels=cols)
seaborn.set(style='dark')
seaborn.reset_orig()
seaborn.pairplot(data.drop('Id', axis=1), hue='Species', size=2.2, diag_kind='kde') | code |
333377/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm') | code |
333377/cell_6 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn
data = pd.read_csv('../input/Iris.csv')
import seaborn
cols = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm']
cm = np.corrcoef(data[cols].values.T)
heatmap = seaborn.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 15}, yticklabels=cols, xticklabels=cols) | code |
333377/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.head() | code |
333377/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
333377/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn
data = pd.read_csv('../input/Iris.csv')
import seaborn
# Calculate correlation coefficient
cols = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm']
cm = np.corrcoef(data[cols].values.T)
heatmap = seaborn.heatmap(cm, cbar=True,
annot=True,
square=True,
fmt='.2f',
annot_kws={'size': 15},
yticklabels=cols,
xticklabels=cols)
seaborn.pairplot(data.drop('Id', axis=1), hue='Species', size=2.2, diag_kind='kde') | code |
333377/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn
data = pd.read_csv('../input/Iris.csv')
import seaborn
# Calculate correlation coefficient
cols = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm']
cm = np.corrcoef(data[cols].values.T)
heatmap = seaborn.heatmap(cm, cbar=True,
annot=True,
square=True,
fmt='.2f',
annot_kws={'size': 15},
yticklabels=cols,
xticklabels=cols)
seaborn.set(style='dark')
seaborn.pairplot(data.drop('Id', axis=1), hue='Species', size=2.2, diag_kind='kde') | code |
333377/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn
data = pd.read_csv('../input/Iris.csv')
import seaborn
seaborn.pairplot(data.drop('Id', axis=1), hue='Species', size=2) | code |
73087587/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
product_reorder = prior_order.groupby('product_id').mean()[['reordered']].reset_index()
product_reorder.sort_values('reordered', ascending=False, inplace=True)
product_reorder = pd.merge(product_reorder, products, on='product_id', how='left')
product_reorder = pd.merge(product_reorder, departments, on='department_id', how='left')
user_reorder = prior_order.groupby('user_id').mean()[['reordered']].reset_index()
user_reorder.sort_values('reordered', ascending=False, inplace=True)
prior_order.query('user_id == 26489') | code |
73087587/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
product_count.head(10) | code |
73087587/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
sns.barplot(x='product_count', y='product_name', data=product_count.head(10)) | code |
73087587/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
prior_order_unique['days_since_prior_order'].describe() | code |
73087587/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
plt.figure(figsize=(20, 5))
sns.countplot(x='order_hour_of_day', data=prior_order_unique) | code |
73087587/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
sns.countplot(x='order_dow', data=prior_order_unique) | code |
73087587/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
product_reorder = prior_order.groupby('product_id').mean()[['reordered']].reset_index()
product_reorder.sort_values('reordered', ascending=False, inplace=True)
product_reorder = pd.merge(product_reorder, products, on='product_id', how='left')
product_reorder = pd.merge(product_reorder, departments, on='department_id', how='left')
user_reorder = prior_order.groupby('user_id').mean()[['reordered']].reset_index()
user_reorder.sort_values('reordered', ascending=False, inplace=True)
user_reorder.head(5) | code |
73087587/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
plt.figure(figsize=(20, 5))
sns.distplot(prior_order_unique['days_since_prior_order'], kde=False, bins=30, color='blue') | code |
73087587/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
print('【train】', train.shape)
display(train.head())
print('【test】', test.shape)
display(test.head())
print('【train_prior_order】', train_prior_order.shape)
display(train_prior_order.head())
print('【test_prior_order】', test_prior_order.shape)
display(test_prior_order.head())
print('【products】', products.shape)
display(products.head())
print('【departments】', departments.shape)
display(departments.head()) | code |
73087587/cell_38 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
product_reorder = prior_order.groupby('product_id').mean()[['reordered']].reset_index()
product_reorder.sort_values('reordered', ascending=False, inplace=True)
product_reorder = pd.merge(product_reorder, products, on='product_id', how='left')
product_reorder = pd.merge(product_reorder, departments, on='department_id', how='left')
sns.barplot(x='reordered', y='product_name', data=product_reorder.head(10)) | code |
73087587/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import gc
from tqdm.notebook import tqdm
from sklearn.model_selection import GroupKFold
from sklearn.metrics import roc_auc_score
from sklearn.metrics import f1_score
import lightgbm as lgb
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') | code |
73087587/cell_17 | [
"text_plain_output_5.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
sns.countplot(x='reordered', data=prior_order) | code |
73087587/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', 50)
pd.set_option('display.max_rows', 50)
train = pd.read_csv('/kaggle/input/marketing-dsg/train.csv')
test = pd.read_csv('/kaggle/input/marketing-dsg/test.csv')
train_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/train_prior_order.csv')
test_prior_order = pd.read_csv('/kaggle/input/marketing-dsg/test_prior_order.csv')
products = pd.read_csv('/kaggle/input/marketing-dsg/products.csv')
departments = pd.read_csv('/kaggle/input/marketing-dsg/departments.csv')
sample_submission = pd.read_csv('/kaggle/input/marketing-dsg/sample_submission.csv')
prior_order = train_prior_order.append(test_prior_order)
prior_order.shape
prior_order = pd.merge(prior_order, products, on='product_id', how='left')
prior_order = pd.merge(prior_order, departments, on='department_id', how='left')
prior_order_unique = prior_order.drop_duplicates('order_id')
product_count = prior_order.groupby('product_id').count()[['order_id']].reset_index().rename(columns={'order_id': 'product_count'})
product_count.sort_values('product_count', ascending=False, inplace=True)
product_count = pd.merge(product_count, products, on='product_id', how='left')
product_count = pd.merge(product_count, departments, on='department_id', how='left')
product_reorder = prior_order.groupby('product_id').mean()[['reordered']].reset_index()
product_reorder.sort_values('reordered', ascending=False, inplace=True)
product_reorder = pd.merge(product_reorder, products, on='product_id', how='left')
product_reorder = pd.merge(product_reorder, departments, on='department_id', how='left')
product_reorder.head(10) | code |
16137891/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
plt.style.use('_classic_test')
plt.xticks(rotation=90)
plt.style.use('seaborn-white')
plt.style.use('seaborn-white')
plt.figure(figsize=(10, 8))
sns.boxplot(x='Category', y='Rating', palette='rainbow', data=df)
plt.xticks(rotation=90) | code |
16137891/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
plt.style.use('_classic_test')
plt.figure(figsize=(10, 8))
sns.countplot(df['Installs'])
plt.xticks(rotation=90) | code |
16137891/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
costlier = df.sort_values(by='Price', ascending=False)[['App', 'Price']].head(20)
costlier | code |
16137891/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.head() | code |
16137891/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
plt.style.use('_classic_test')
plt.xticks(rotation=90)
plt.style.use('seaborn-white')
plt.style.use('seaborn-white')
plt.xticks(rotation=90)
plt.figure(figsize=(20, 10))
sns.boxplot(x='Genres', y='Rating', palette='hsv', data=df)
plt.xticks(rotation=90) | code |
16137891/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
costlier = df.sort_values(by="Price", ascending=False)[["App", "Price"]].head(20)
costlier
apps = df[df['Reviews'] >= 200]
# Top 10 Apps
top_apps = apps.sort_values(by=["Rating", "Reviews", "Installs"], ascending=False)[["App", "Rating", "Reviews"]].head(10)
top_apps
apps.sort_values(by='Reviews', ascending=False)[['App', 'Reviews', 'Rating']].head(15) | code |
16137891/cell_20 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
df[['Category', 'Rating']].groupby('Category', as_index=False).mean().sort_values('Rating', ascending=False).head(10) | code |
16137891/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df.info() | code |
16137891/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
costlier = df.sort_values(by="Price", ascending=False)[["App", "Price"]].head(20)
costlier
apps = df[df['Reviews'] >= 200]
top_apps = apps.sort_values(by=['Rating', 'Reviews', 'Installs'], ascending=False)[['App', 'Rating', 'Reviews']].head(10)
top_apps | code |
16137891/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
print('Various Categories:\n')
print(df['Category'].value_counts())
print('\nVarious Genres:\n')
print(df['Genres'].value_counts()) | code |
16137891/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
def pie_plot(cnt, colors, text):
labels = list(cnt.index)
values = list(cnt.values)
trace = go.Pie(labels=labels,
values=values,
hoverinfo='value+percent',
title=text,
textinfo='label',
hole=.4,
textposition='inside',
marker=dict(colors = colors,
),
)
return trace
types = df["Type"].value_counts()
trace = pie_plot(types, ['#09efef', '#b70333'], "Results")
py.iplot([trace], filename='Type')
content = df['Content Rating'].value_counts()
trace = pie_plot(content, ['yellow', 'cyan', 'purple', 'red'], 'Content')
py.iplot([trace], filename='Content') | code |
16137891/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
def pie_plot(cnt, colors, text):
labels = list(cnt.index)
values = list(cnt.values)
trace = go.Pie(labels=labels, values=values, hoverinfo='value+percent', title=text, textinfo='label', hole=0.4, textposition='inside', marker=dict(colors=colors))
return trace
types = df['Type'].value_counts()
trace = pie_plot(types, ['#09efef', '#b70333'], 'Results')
py.iplot([trace], filename='Type') | code |
16137891/cell_3 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.offline as py
import plotly.graph_objs as go
from plotly import tools
py.init_notebook_mode(connected=True)
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
16137891/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
costlier = df.sort_values(by="Price", ascending=False)[["App", "Price"]].head(20)
costlier
apps = df[df['Reviews'] >= 200]
# Top 10 Apps
top_apps = apps.sort_values(by=["Rating", "Reviews", "Installs"], ascending=False)[["App", "Rating", "Reviews"]].head(10)
top_apps
apps.sort_values(by='Installs', ascending=False)[['App', 'Installs', 'Rating']].head(15) | code |
16137891/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
plt.style.use('_classic_test')
plt.xticks(rotation=90)
plt.style.use('seaborn-white')
sns.pairplot(df) | code |
16137891/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
df[['Genres', 'Rating']].groupby('Genres', as_index=False).mean().sort_values('Rating', ascending=False).head(10) | code |
16137891/cell_27 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
costlier = df.sort_values(by="Price", ascending=False)[["App", "Price"]].head(20)
costlier
apps = df[df['Reviews'] >= 200]
apps.head() | code |
16137891/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum()
df = df.dropna(axis=0)
df.drop_duplicates('App', inplace=True)
plt.style.use('_classic_test')
plt.figure(figsize=(10, 8))
plt.hist(df['Rating'], bins=100) | code |
16137891/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/googleplaystore.csv')
df.isnull().sum() | code |
73087852/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5) | code |
73087852/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
seed = 42
random.seed(seed)
np.random.seed(seed)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cat = train[['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']]
ohe = OneHotEncoder().fit_transform(cat).toarray()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5)
cont_array = cont.to_numpy()
cont_array.shape
target = train['target']
target
X = np.hstack((ohe, cont_array))
X.shape
y = target.to_numpy()
y.shape
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
test_size = 0.2
df = train[train.columns[1:]]
test_bound = int(df.shape[0] * (1 - test_size))
train_df = df[:test_bound]
test_df = df[test_bound:]
X_train = train_df[train_df.columns[:-1]]
y_train = train_df['target']
X_test = test_df[test_df.columns[:-1]]
y_test = test_df['target']
(X_train.shape, y_train.shape, X_test.shape, y_test.shape) | code |
73087852/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.head(10) | code |
73087852/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
seed = 42
random.seed(seed)
np.random.seed(seed)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cat = train[['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']]
ohe = OneHotEncoder().fit_transform(cat).toarray()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5)
cont_array = cont.to_numpy()
cont_array.shape
target = train['target']
target
X = np.hstack((ohe, cont_array))
X.shape
y = target.to_numpy()
y.shape
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
from sklearn.metrics import mean_squared_error
def rmse(y_true, y_pred):
return mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5
from sklearn.linear_model import LinearRegression
linreg = LinearRegression()
linreg.fit(X_train, y_train)
pred = linreg.predict(X_test)
rmse(y_test, pred) | code |
73087852/cell_30 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
seed = 42
random.seed(seed)
np.random.seed(seed)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cat = train[['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']]
ohe = OneHotEncoder().fit_transform(cat).toarray()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5)
cont_array = cont.to_numpy()
cont_array.shape
target = train['target']
target
X = np.hstack((ohe, cont_array))
X.shape
y = target.to_numpy()
y.shape
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
from sklearn.metrics import mean_squared_error
def rmse(y_true, y_pred):
return mean_squared_error(y_true=y_true, y_pred=y_pred) ** 0.5
from sklearn.linear_model import LinearRegression
linreg = LinearRegression()
linreg.fit(X_train, y_train)
pred = linreg.predict(X_test)
rmse(y_test, pred)
test_size = 0.2
df = train[train.columns[1:]]
test_bound = int(df.shape[0] * (1 - test_size))
train_df = df[:test_bound]
test_df = df[test_bound:]
X_train = train_df[train_df.columns[:-1]]
y_train = train_df['target']
X_test = test_df[test_df.columns[:-1]]
y_test = test_df['target']
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
params = {'depth': 7, 'iterations': 111, 'learning_rate': 0.07, 'l2_leaf_reg': 0.36}
model = CatBoostRegressor(**params, random_state=seed, cat_features=['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9'], loss_function='RMSE')
model.fit(train.drop(columns=['id', 'target']), train['target'], verbose=False)
pred = model.predict(X_test)
rmse(y_test, pred) | code |
73087852/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
test.head(10) | code |
73087852/cell_29 | [
"text_plain_output_1.png"
] | params = {'depth': 7, 'iterations': 111, 'learning_rate': 0.07, 'l2_leaf_reg': 0.36}
params | code |
73087852/cell_19 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
seed = 42
random.seed(seed)
np.random.seed(seed)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cat = train[['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']]
ohe = OneHotEncoder().fit_transform(cat).toarray()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5)
cont_array = cont.to_numpy()
cont_array.shape
target = train['target']
target
X = np.hstack((ohe, cont_array))
X.shape
y = target.to_numpy()
y.shape
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape) | code |
73087852/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 |
73087852/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum() | code |
73087852/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
test.isna().sum().sum() | code |
73087852/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
target = train['target']
target | code |
73087852/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import random
seed = 42
random.seed(seed)
np.random.seed(seed)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cat = train[['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']]
ohe = OneHotEncoder().fit_transform(cat).toarray()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5)
cont_array = cont.to_numpy()
cont_array.shape
X = np.hstack((ohe, cont_array))
X.shape | code |
73087852/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5) | code |
73087852/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
target = train['target']
target
y = target.to_numpy()
y.shape | code |
73087852/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.corr().style.background_gradient(cmap='coolwarm').set_precision(5)
cont_array = cont.to_numpy()
cont_array.shape | code |
73087852/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cat = train[['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']]
cat.head(5) | code |
73087852/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns
train.isna().sum().sum()
cont = train[['cont0', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']]
cont.head(5) | code |
73087852/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv').head(5)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
train.columns | code |
90111149/cell_4 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.getcwd()) | code |
90111149/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import math
import random
import pickle
import itertools
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, label_ranking_average_precision_score, label_ranking_loss, coverage_error
from sklearn.utils import shuffle
from scipy.signal import resample
import matplotlib.pyplot as plt
np.random.seed(42)
import pickle
from sklearn.preprocessing import OneHotEncoder
from keras.models import Model
from keras.layers import Input, Dense, Conv1D, MaxPooling1D, Softmax, Add, Flatten, Activation
from keras import backend as K
from keras.optimizers import Adam
from keras.callbacks import LearningRateScheduler, ModelCheckpoint
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
import math
import random
import pickle
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
np.random.seed(42)
import tensorflow as tf
import tensorflow.keras as keras | code |
90111149/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.signal import resample
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils import resample
import pandas as pd
import pandas as pd
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)
mit_test_data = pd.read_csv('../input/heartbeat/mitbih_test.csv', header=None)
mit_train_data = pd.read_csv('../input/heartbeat/mitbih_train.csv', header=None)
from sklearn.utils import resample
df_1 = mit_train_data[mit_train_data[187] == 1]
df_2 = mit_train_data[mit_train_data[187] == 2]
df_3 = mit_train_data[mit_train_data[187] == 3]
df_4 = mit_train_data[mit_train_data[187] == 4]
df_0 = mit_train_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_2_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_3_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_4_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
df_11 = mit_test_data[mit_train_data[187] == 1]
df_22 = mit_test_data[mit_train_data[187] == 2]
df_33 = mit_test_data[mit_train_data[187] == 3]
df_44 = mit_test_data[mit_train_data[187] == 4]
df_00 = mit_test_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_11_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_22_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_33_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_44_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
test_df = pd.concat([df_00, df_11_upsample, df_22_upsample, df_33_upsample, df_44_upsample])
equilibre = train_df[187].value_counts()
test = pd.read_csv('../input/heartbeat/mitbih_test.csv', header=None)
test = test.iloc[0, 0:len(test.T) - 1]
test = pd.DataFrame(test)
from sklearn.preprocessing import MinMaxScaler
values = test.values
scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(values)
normalized = scaler.transform(values)
normalized = pd.DataFrame(normalized)
normalized | code |
90111149/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 |
90111149/cell_7 | [
"text_plain_output_1.png"
] | from scipy.signal import resample
from sklearn.utils import resample
import pandas as pd
import pandas as pd
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)
mit_test_data = pd.read_csv('../input/heartbeat/mitbih_test.csv', header=None)
mit_train_data = pd.read_csv('../input/heartbeat/mitbih_train.csv', header=None)
from sklearn.utils import resample
df_1 = mit_train_data[mit_train_data[187] == 1]
df_2 = mit_train_data[mit_train_data[187] == 2]
df_3 = mit_train_data[mit_train_data[187] == 3]
df_4 = mit_train_data[mit_train_data[187] == 4]
df_0 = mit_train_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_2_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_3_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_4_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
df_11 = mit_test_data[mit_train_data[187] == 1]
df_22 = mit_test_data[mit_train_data[187] == 2]
df_33 = mit_test_data[mit_train_data[187] == 3]
df_44 = mit_test_data[mit_train_data[187] == 4]
df_00 = mit_test_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_11_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_22_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_33_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_44_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
test_df = pd.concat([df_00, df_11_upsample, df_22_upsample, df_33_upsample, df_44_upsample])
equilibre = train_df[187].value_counts()
print(equilibre) | code |
90111149/cell_8 | [
"text_plain_output_1.png"
] | from scipy.signal import resample
from sklearn.utils import resample
import pandas as pd
import pandas as pd
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)
mit_test_data = pd.read_csv('../input/heartbeat/mitbih_test.csv', header=None)
mit_train_data = pd.read_csv('../input/heartbeat/mitbih_train.csv', header=None)
from sklearn.utils import resample
df_1 = mit_train_data[mit_train_data[187] == 1]
df_2 = mit_train_data[mit_train_data[187] == 2]
df_3 = mit_train_data[mit_train_data[187] == 3]
df_4 = mit_train_data[mit_train_data[187] == 4]
df_0 = mit_train_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_2_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_3_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_4_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
df_11 = mit_test_data[mit_train_data[187] == 1]
df_22 = mit_test_data[mit_train_data[187] == 2]
df_33 = mit_test_data[mit_train_data[187] == 3]
df_44 = mit_test_data[mit_train_data[187] == 4]
df_00 = mit_test_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_11_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_22_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_33_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_44_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
test_df = pd.concat([df_00, df_11_upsample, df_22_upsample, df_33_upsample, df_44_upsample])
equilibre = train_df[187].value_counts()
print('ALL Train data')
print('Type\tCount')
print(mit_train_data[187].value_counts())
print('-------------------------')
print('ALL Test data')
print('Type\tCount')
print(mit_test_data[187].value_counts())
print('ALL Balanced Train data')
print('Type\tCount')
print(train_df[187].value_counts())
print('-------------------------')
print('ALL Balanced Test data')
print('Type\tCount')
print(train_df[187].value_counts()) | code |
90111149/cell_14 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from scipy.signal import resample
from sklearn.utils import resample
import pandas as pd
import pandas as pd
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)
mit_test_data = pd.read_csv('../input/heartbeat/mitbih_test.csv', header=None)
mit_train_data = pd.read_csv('../input/heartbeat/mitbih_train.csv', header=None)
from sklearn.utils import resample
df_1 = mit_train_data[mit_train_data[187] == 1]
df_2 = mit_train_data[mit_train_data[187] == 2]
df_3 = mit_train_data[mit_train_data[187] == 3]
df_4 = mit_train_data[mit_train_data[187] == 4]
df_0 = mit_train_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_2_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_3_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_4_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
df_11 = mit_test_data[mit_train_data[187] == 1]
df_22 = mit_test_data[mit_train_data[187] == 2]
df_33 = mit_test_data[mit_train_data[187] == 3]
df_44 = mit_test_data[mit_train_data[187] == 4]
df_00 = mit_test_data[mit_train_data[187] == 0].sample(n=20000, random_state=42)
df_11_upsample = resample(df_1, replace=True, n_samples=20000, random_state=123)
df_22_upsample = resample(df_2, replace=True, n_samples=20000, random_state=124)
df_33_upsample = resample(df_3, replace=True, n_samples=20000, random_state=125)
df_44_upsample = resample(df_4, replace=True, n_samples=20000, random_state=126)
test_df = pd.concat([df_00, df_11_upsample, df_22_upsample, df_33_upsample, df_44_upsample])
equilibre = train_df[187].value_counts()
from keras.utils import to_categorical
print('--- X ---')
X = train_df.loc[:, mit_train_data.columns != 187]
print(X.head())
print(X.info())
print('--- Y ---')
y = train_df.loc[:, mit_train_data.columns == 187]
y = to_categorical(y)
print('--- testX ---')
testX = test_df.loc[:, mit_test_data.columns != 187]
print(testX.head())
print(testX.info())
print('--- testy ---')
testy = test_df.loc[:, mit_test_data.columns == 187]
testy = to_categorical(testy) | code |
2028270/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
red_df = wine_df[(wine_df['cheaper'] == 'yes') & (wine_df['quality'] == 'yes') & (wine_df['reds'] == 'yes')]
red_df['variety'].value_counts().plot(kind='bar') | code |
2028270/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.head() | code |
2028270/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
red_df = wine_df[(wine_df['cheaper'] == 'yes') & (wine_df['quality'] == 'yes') & (wine_df['reds'] == 'yes')]
red_df['country'].value_counts().plot(kind='bar') | code |
2028270/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
sns.heatmap(wine_df.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
2028270/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
wine_df.head() | code |
2028270/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
red_df = wine_df[(wine_df['cheaper'] == 'yes') & (wine_df['quality'] == 'yes') & (wine_df['reds'] == 'yes')]
red_df.head() | code |
2028270/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
wine_df = pd.read_csv('../input/winemag-data_first150k.csv')
wine_df.drop('region_1', axis=1, inplace=True)
wine_df.drop('region_2', axis=1, inplace=True)
wine_df.drop('description', axis=1, inplace=True)
wine_df.drop('designation', axis=1, inplace=True)
newdf = wine_df[(wine_df['cheaper'] == 'yes') & (wine_df['quality'] == 'yes')]
sns.countplot(x='country', data=newdf) | code |
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