path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
73064231/cell_9 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[0].plot(x_values, y_values)
ax[0].scatter(X, Y, c='b');
ax[0].title.set_text('Рис. 1')
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11], [18], [19], [20], [21], [22], [23], [24]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 25, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[1].plot(x_values, y_values)
ax[1].scatter(X, Y, c='b');
ax[1].title.set_text('Рис. 2')
def sigmoid(x):
return 1 / (1 + np.exp(-x))
fig, ax = plt.subplots(1, 2, figsize=(15,5))
x_values = np.linspace(-5, 5, 100)
y_values = [sigmoid(x) for x in x_values]
ax[0].plot(x_values, y_values);
X = np.array([[-1],[-2],[-3],[-4],[-8],[-9],[5],[8], [12], [13], [14], [15]])
Y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
x_values = np.linspace(-10, 25, 100)
y_values = [sigmoid(x) for x in x_values]
ax[1].plot(x_values, y_values);
ax[1].scatter(X, [sigmoid(x) for x in X], c='r');
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
def if_y_1(hx):
return -np.log(hx)
x_values = np.linspace(0.001, 1, 100)
y_values = [if_y_1(hx) for hx in x_values]
ax[0].plot(x_values, y_values)
ax[0].title.set_text('If y = 1')
ax[0].set_xlabel('h(x) - sigmoid output')
ax[0].set_ylabel('Cost')
def if_y_0(hx):
return -np.log(1 - hx)
x_values = np.linspace(0, 0.999, 100)
y_values = [if_y_0(hx) for hx in x_values]
ax[1].plot(x_values, y_values)
ax[1].title.set_text('If y = 0')
ax[1].set_xlabel('h(x) - sigmoid output')
ax[1].set_ylabel('Cost') | code |
73064231/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[0].plot(x_values, y_values)
ax[0].scatter(X, Y, c='b');
ax[0].title.set_text('Рис. 1')
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11], [18], [19], [20], [21], [22], [23], [24]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 25, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[1].plot(x_values, y_values)
ax[1].scatter(X, Y, c='b');
ax[1].title.set_text('Рис. 2')
def sigmoid(x):
return 1 / (1 + np.exp(-x))
fig, ax = plt.subplots(1, 2, figsize=(15,5))
x_values = np.linspace(-5, 5, 100)
y_values = [sigmoid(x) for x in x_values]
ax[0].plot(x_values, y_values);
X = np.array([[-1],[-2],[-3],[-4],[-8],[-9],[5],[8], [12], [13], [14], [15]])
Y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
x_values = np.linspace(-10, 25, 100)
y_values = [sigmoid(x) for x in x_values]
ax[1].plot(x_values, y_values);
ax[1].scatter(X, [sigmoid(x) for x in X], c='r');
# hx - sigmoid values between (0, 1)
fig, ax = plt.subplots(1, 2, figsize=(15,5))
def if_y_1(hx):
return -np.log(hx)
x_values = np.linspace(0.001, 1, 100)
y_values = [if_y_1(hx) for hx in x_values]
ax[0].plot(x_values, y_values)
ax[0].title.set_text('If y = 1');
ax[0].set_xlabel('h(x) - sigmoid output')
ax[0].set_ylabel('Cost')
def if_y_0(hx):
return -np.log(1 - hx)
x_values = np.linspace(0, 0.999, 100)
y_values = [if_y_0(hx) for hx in x_values]
ax[1].plot(x_values, y_values)
ax[1].title.set_text('If y = 0');
ax[1].set_xlabel('h(x) - sigmoid output')
ax[1].set_ylabel('Cost');
class LogisticRegression:
def __init__(self, lr=0.1, n_iters=1000):
self.lr = lr
self.n_iters = n_iters
self.weights = None
self.bias = None
def fit(self, X, y):
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.bias = 0
for _ in range(self.n_iters):
linear_model = np.dot(X, self.weights) + self.bias
hx = self._sigmoid(linear_model)
dw = (X.T * (hx - y)).T.mean(axis=0)
db = (hx - y).mean(axis=0)
self.weights -= self.lr * dw
self.bias -= self.lr * db
def predict(self, X):
linear_model = np.dot(X, self.weights) + self.bias
y_predicted = self._sigmoid(linear_model)
y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]
return y_predicted_cls
def _sigmoid(self, x):
return 1 / (1 + np.exp(-x))
fig, ax = plt.subplots(1, 2, figsize=(15,5))
color = ['blue' if l == 0 else 'green' for l in y_train]
ax[0].scatter(X_train[:, 0], X_train[:, 1], c=color, label='1')
ax[0].title.set_text('Train sample')
color = ['blue' if l == 0 else 'green' for l in y_test]
ax[1].scatter(X_test[:, 0], X_test[:, 1], c=color, label='1');
ax[1 ].title.set_text('Test sample')
model = LogisticRegression()
model.fit(X_train, y_train)
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
x_values = np.linspace(X_train[:, 0].min(), X_train[:, 0].max(), 100)
y_values = [(-model.bias - model.weights[0] * x) / model.weights[1] for x in x_values]
color = ['blue' if l == 0 else 'green' for l in y_train]
ax[0].scatter(X_train[:, 0], X_train[:, 1], c=color, label='1')
ax[0].plot(x_values, y_values)
ax[0].title.set_text('Train sample, accuracy: {}'.format(accuracy_score(y_train, model.predict(X_train))))
x_values = np.linspace(X_test[:, 0].min(), X_test[:, 0].max(), 100)
y_values = [(-model.bias - model.weights[0] * x) / model.weights[1] for x in x_values]
color = ['blue' if l == 0 else 'green' for l in y_test]
ax[1].scatter(X_test[:, 0], X_test[:, 1], c=color, label='1')
ax[1].plot(x_values, y_values)
ax[1].title.set_text('Test sample, accuracy: {}'.format(accuracy_score(y_test, model.predict(X_test)))) | code |
73064231/cell_7 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[0].plot(x_values, y_values)
ax[0].scatter(X, Y, c='b');
ax[0].title.set_text('Рис. 1')
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11], [18], [19], [20], [21], [22], [23], [24]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 25, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[1].plot(x_values, y_values)
ax[1].scatter(X, Y, c='b');
ax[1].title.set_text('Рис. 2')
def sigmoid(x):
return 1 / (1 + np.exp(-x))
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
x_values = np.linspace(-5, 5, 100)
y_values = [sigmoid(x) for x in x_values]
ax[0].plot(x_values, y_values)
X = np.array([[-1], [-2], [-3], [-4], [-8], [-9], [5], [8], [12], [13], [14], [15]])
Y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
x_values = np.linspace(-10, 25, 100)
y_values = [sigmoid(x) for x in x_values]
ax[1].plot(x_values, y_values)
ax[1].scatter(X, [sigmoid(x) for x in X], c='r') | code |
73064231/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[0].plot(x_values, y_values)
ax[0].scatter(X, Y, c='b');
ax[0].title.set_text('Рис. 1')
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11], [18], [19], [20], [21], [22], [23], [24]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 25, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[1].plot(x_values, y_values)
ax[1].scatter(X, Y, c='b');
ax[1].title.set_text('Рис. 2')
def sigmoid(x):
return 1 / (1 + np.exp(-x))
fig, ax = plt.subplots(1, 2, figsize=(15,5))
x_values = np.linspace(-5, 5, 100)
y_values = [sigmoid(x) for x in x_values]
ax[0].plot(x_values, y_values);
X = np.array([[-1],[-2],[-3],[-4],[-8],[-9],[5],[8], [12], [13], [14], [15]])
Y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
x_values = np.linspace(-10, 25, 100)
y_values = [sigmoid(x) for x in x_values]
ax[1].plot(x_values, y_values);
ax[1].scatter(X, [sigmoid(x) for x in X], c='r');
# hx - sigmoid values between (0, 1)
fig, ax = plt.subplots(1, 2, figsize=(15,5))
def if_y_1(hx):
return -np.log(hx)
x_values = np.linspace(0.001, 1, 100)
y_values = [if_y_1(hx) for hx in x_values]
ax[0].plot(x_values, y_values)
ax[0].title.set_text('If y = 1');
ax[0].set_xlabel('h(x) - sigmoid output')
ax[0].set_ylabel('Cost')
def if_y_0(hx):
return -np.log(1 - hx)
x_values = np.linspace(0, 0.999, 100)
y_values = [if_y_0(hx) for hx in x_values]
ax[1].plot(x_values, y_values)
ax[1].title.set_text('If y = 0');
ax[1].set_xlabel('h(x) - sigmoid output')
ax[1].set_ylabel('Cost');
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
color = ['blue' if l == 0 else 'green' for l in y_train]
ax[0].scatter(X_train[:, 0], X_train[:, 1], c=color, label='1')
ax[0].title.set_text('Train sample')
color = ['blue' if l == 0 else 'green' for l in y_test]
ax[1].scatter(X_test[:, 0], X_test[:, 1], c=color, label='1')
ax[1].title.set_text('Test sample') | code |
73064231/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
X = np.array([[1], [2], [3], [4], [8], [9], [10], [11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[0].plot(x_values, y_values)
ax[0].scatter(X, Y, c='b')
ax[0].title.set_text('Рис. 1')
X = np.array([[1], [2], [3], [4], [8], [9], [10], [11], [18], [19], [20], [21], [22], [23], [24]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 25, 100)
y_values = [model.intercept_ + x * model.coef_[0] for x in x_values]
ax[1].plot(x_values, y_values)
ax[1].scatter(X, Y, c='b')
ax[1].title.set_text('Рис. 2') | code |
106194820/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data
new_data = pd.merge(new_data, d_sal_tra, how='inner', on='item_id')
new_data
new_data = pd.merge(new_data, d_shop, how='inner', on='shop_id')
new_data
dx = ['shop_id', 'item_id']
x = new_data[dx]
y = new_data['item_cnt_day']
(x, y) | code |
106194820/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
print('items categorical null values', d_item_cat.isnull().sum().sum())
print('items null values', d_items.isnull().sum().sum())
print('sales train null values', d_sal_tra.isnull().sum().sum())
print('test data null values', d_test.isnull().sum().sum()) | code |
106194820/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_shop.head() | code |
106194820/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data
new_data = pd.merge(new_data, d_sal_tra, how='inner', on='item_id')
new_data
new_data = pd.merge(new_data, d_shop, how='inner', on='shop_id')
new_data | code |
106194820/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_items.head() | code |
106194820/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test | code |
106194820/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data
new_data = pd.merge(new_data, d_sal_tra, how='inner', on='item_id')
new_data | code |
106194820/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 |
106194820/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sam_sub.head() | code |
106194820/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data | code |
106194820/cell_28 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data
new_data = pd.merge(new_data, d_sal_tra, how='inner', on='item_id')
new_data
new_data = pd.merge(new_data, d_shop, how='inner', on='shop_id')
new_data
dx = ['shop_id', 'item_id']
x = new_data[dx]
y = new_data['item_cnt_day']
(x, y)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=20)
(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
lr = LinearRegression()
lr.fit(x_train, y_train)
pred = lr.predict(x_test)
pred | code |
106194820/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_sal_tra.head() | code |
106194820/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
print('items categorical unique values', d_item_cat.nunique())
print('items unique values', d_items.nunique())
print('sales train unique values', d_sal_tra.nunique())
print('test data unique values', d_test.nunique()) | code |
106194820/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data | code |
106194820/cell_24 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data
new_data = pd.merge(new_data, d_sal_tra, how='inner', on='item_id')
new_data
new_data = pd.merge(new_data, d_shop, how='inner', on='shop_id')
new_data
dx = ['shop_id', 'item_id']
x = new_data[dx]
y = new_data['item_cnt_day']
(x, y)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=20)
(x_train.shape, x_test.shape, y_train.shape, y_test.shape) | code |
106194820/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
d_test.head() | code |
106194820/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
d_sal_tra = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_train.csv')
d_shop = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
d_test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv')
dt_test = d_test.drop(labels='ID', axis=1)
dt_test
new_data = d_sal_tra.copy()
new_data
new_data = pd.merge(d_item_cat, d_items, how='inner', on='item_category_id')
new_data
new_data = pd.merge(new_data, d_sal_tra, how='inner', on='item_id')
new_data
new_data = pd.merge(new_data, d_shop, how='inner', on='shop_id')
new_data
dx = ['shop_id', 'item_id']
x = new_data[dx]
y = new_data['item_cnt_day']
(x, y)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=20)
(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
lr = LinearRegression()
lr.fit(x_train, y_train) | code |
106194820/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_item_cat.head() | code |
130026009/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
sns.set()
df = pd.read_csv('/kaggle/input/eurovision-2023-betting-odds/data/spotify/2023-04-13-spotify-streaming.csv')
top_artists = df[['artist', 'popularity']]
top_artists
plt.figure(figsize=(10, 6))
sns.barplot(x='popularity', y='artist', data=top_artists.head(10), palette='viridis')
plt.title('top_artists')
plt.xlabel('popularity')
plt.ylabel('artist')
plt.show() | code |
130026009/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/eurovision-2023-betting-odds/data/spotify/2023-04-13-spotify-streaming.csv')
df.head() | code |
130026009/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 |
130026009/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
sns.set()
df = pd.read_csv('/kaggle/input/eurovision-2023-betting-odds/data/spotify/2023-04-13-spotify-streaming.csv')
plt.figure(figsize=(12, 12))
sns.lineplot(x='popularity', y='song', data=df.head(10), color='blue')
plt.title('Top ten popular songs')
plt.xlabel('popularity')
plt.ylabel('song')
plt.show() | code |
130026009/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
import seaborn as sns
sns.set() | code |
104120390/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='marital') | code |
104120390/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='default') | code |
104120390/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
bdata.marital.value_counts().plot(kind='bar')
plt.title('Marital Status Distribution')
plt.ylabel('Number of people')
plt.xlabel('Marital Status') | code |
104120390/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='education') | code |
104120390/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='job') | code |
104120390/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts() | code |
104120390/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
palette = sns.color_palette('bright')
for col in bdata.select_dtypes('object'):
palette_color = sns.color_palette('bright')
bdata[col].value_counts().plot.pie(colors=palette_color, autopct='%.0f%%')
plt.figure() | code |
104120390/cell_2 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104120390/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='y') | code |
104120390/cell_7 | [
"image_output_5.png",
"image_output_4.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts().plot(kind='bar')
plt.title('Job Distribution')
plt.xlabel('Jobs')
plt.ylabel('Number of People') | code |
104120390/cell_8 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts() | code |
104120390/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='housing') | code |
104120390/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.head() | code |
104120390/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
sns.kdeplot(data=bdata, x='age', hue='loan') | code |
104120390/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
bdata.education.value_counts().plot(kind='bar')
plt.title('Education Distribution')
plt.xlabel('Education Level')
plt.ylabel('Number of People') | code |
104120390/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
palette = sns.color_palette('bright')
for col in bdata.select_dtypes('float64'):
plt.figure()
sns.distplot(bdata[col], bins=7, color=random.choice(palette)) | code |
104120390/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)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.describe() | code |
90128350/cell_21 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.kdeplot(data=data_stroke1, x='age', color='red', fill=True)
sns.kdeplot(data=data_stroke0, x='age', color='green', fill=True) | code |
90128350/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
label = ['No', 'Yes']
value_label = data_orgin['stroke'].value_counts()
explode = [0.1, 0.05]
fig, ax = plt.subplots()
ax.pie(value_label, explode=explode, labels=label, autopct='%1.2f%%', shadow=True)
ax.set(title='Stroke') | code |
90128350/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique() | code |
90128350/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
data_orgin[data_orgin['hypertension'] == 1].stroke.value_counts() | code |
90128350/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.barplot(data=data_orgin, x='ever_married', y='stroke') | code |
90128350/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.barplot(data=data_orgin, x='work_type', y='stroke') | code |
90128350/cell_20 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
sns.displot(x=data_orgin['age'], kde=True) | code |
90128350/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.describe() | code |
90128350/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
#Exploring data with stroke
label=['No','Yes']
value_label=data_orgin['stroke'].value_counts()
explode=[0.1,0.05]
fig,ax=plt.subplots()
ax.pie(value_label,explode=explode,labels=label,autopct='%1.2f%%',shadow=True)
ax.set(title='Stroke');
fig,ax = plt.subplots()
ax.bar(label,value_label)
ax.set(title="Stroke",ylabel='Count');
categorical = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']
label_0 = ['Female', 'Male', 'Other']
label_1 = ['Yes', 'No']
label_2 = ['Private', 'Self-employed', 'children', 'Govt_job', 'Never_worked']
label_3 = ['Urban', 'Rural']
label_4 = ['Never smoked', 'Unknown', 'Formerly smoked', 'Smokes']
value_gender = data_orgin[categorical[0]].value_counts()
value_married = data_orgin[categorical[1]].value_counts()
value_worked = data_orgin[categorical[2]].value_counts()
value_res = data_orgin[categorical[3]].value_counts()
value_smoking = data_orgin[categorical[4]].value_counts()
all_label=[label_0,label_1,label_2,label_3,label_4]
all_sum=[value_gender,value_married,value_worked,value_res,value_smoking]
attri=['Gender','Ever_married','Work_type','Residence_type','Smoking_status']
fig=plt.figure(figsize=(20,15))
for i in range(1,6):
fig.add_subplot(2,3,i)
plt.bar(all_label[i-1],all_sum[i-1])
plt.xlabel(attri[i-1])
data_orgin.groupby(['gender'])['stroke'].value_counts()
label = ['Yes', 'No']
num_marr = data_orgin['ever_married'].value_counts()
explode = [0.07, 0.05]
fig, ax = plt.subplots()
ax.pie(num_marr, explode=explode, labels=label, startangle=90, autopct='%1.2f%%', shadow=True)
ax.set(title='Ever_married') | code |
90128350/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.kdeplot(data=data_stroke1, x='bmi', color='green', fill=True)
sns.kdeplot(data=data_stroke0, x='bmi', color='blue', fill=True) | code |
90128350/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
data_orgin[data_orgin['heart_disease'] == 0].stroke.value_counts() | code |
90128350/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.head() | code |
90128350/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
sns.countplot(x=data_orgin['gender'], hue=data_orgin['stroke']) | code |
90128350/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin['stroke'].value_counts() | code |
90128350/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts() | code |
90128350/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.countplot(data=data_orgin, x='work_type') | code |
90128350/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
#Exploring data with stroke
label=['No','Yes']
value_label=data_orgin['stroke'].value_counts()
explode=[0.1,0.05]
fig,ax=plt.subplots()
ax.pie(value_label,explode=explode,labels=label,autopct='%1.2f%%',shadow=True)
ax.set(title='Stroke');
fig,ax = plt.subplots()
ax.bar(label,value_label)
ax.set(title="Stroke",ylabel='Count');
categorical = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']
label_0 = ['Female', 'Male', 'Other']
label_1 = ['Yes', 'No']
label_2 = ['Private', 'Self-employed', 'children', 'Govt_job', 'Never_worked']
label_3 = ['Urban', 'Rural']
label_4 = ['Never smoked', 'Unknown', 'Formerly smoked', 'Smokes']
value_gender = data_orgin[categorical[0]].value_counts()
value_married = data_orgin[categorical[1]].value_counts()
value_worked = data_orgin[categorical[2]].value_counts()
value_res = data_orgin[categorical[3]].value_counts()
value_smoking = data_orgin[categorical[4]].value_counts()
all_label = [label_0, label_1, label_2, label_3, label_4]
all_sum = [value_gender, value_married, value_worked, value_res, value_smoking]
attri = ['Gender', 'Ever_married', 'Work_type', 'Residence_type', 'Smoking_status']
fig = plt.figure(figsize=(20, 15))
for i in range(1, 6):
fig.add_subplot(2, 3, i)
plt.bar(all_label[i - 1], all_sum[i - 1])
plt.xlabel(attri[i - 1]) | code |
90128350/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.info() | code |
90128350/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.barplot(data=data_orgin, x='Residence_type', y='stroke') | code |
90128350/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.barplot(data=data_orgin, x='hypertension', y='stroke') | code |
90128350/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
categorical = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']
for i in range(len(categorical)):
print(data_orgin[categorical[i]].value_counts()) | code |
90128350/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
#Exploring data with stroke
label=['No','Yes']
value_label=data_orgin['stroke'].value_counts()
explode=[0.1,0.05]
fig,ax=plt.subplots()
ax.pie(value_label,explode=explode,labels=label,autopct='%1.2f%%',shadow=True)
ax.set(title='Stroke');
fig, ax = plt.subplots()
ax.bar(label, value_label)
ax.set(title='Stroke', ylabel='Count') | code |
90128350/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.barplot(data=data_orgin, x='heart_disease', y='stroke') | code |
90128350/cell_37 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
data_orgin.groupby(['gender'])['stroke'].value_counts()
sns.set_theme()
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
data_stroke1 = data_orgin[data_orgin['stroke'] == 1]
data_stroke0 = data_orgin[data_orgin['stroke'] == 0]
sns.kdeplot(data=data_stroke1, x='avg_glucose_level', color='orange', fill=True)
sns.kdeplot(data=data_stroke0, x='avg_glucose_level', color='blue', fill=True) | code |
90128350/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum()
numerical = ['age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi']
data_orgin[numerical].hist(bins=30, figsize=(20, 15)) | code |
90128350/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, roc_auc_score, classification_report
from sklearn.metrics import roc_curve
from imblearn.under_sampling import NearMiss
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV
data_orgin = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data_orgin.nunique()
data_orgin.isnull().sum() | code |
129022563/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
train_null[0] | code |
129022563/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
print(test.isna().sum().sort_values(ascending=False)) | code |
129022563/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
print(train.isna().sum().sort_values(ascending=False)) | code |
129022563/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count() | code |
129022563/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
test.describe() | code |
129022563/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
print(FEATURES) | code |
129022563/cell_44 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
fig = make_subplots(rows = 1,
cols = 2,
column_titles = ["Train Data","Test Data"],
x_title = "Missing Values")
fig.show()
train_null.index
fig.add_trace(go.Bar(x=train_null[0], y=train_null.index, orientation='h'), 1, 1)
fig.show() | code |
129022563/cell_55 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False)
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
missing_train_row = train.isna().sum(axis=1)
missing_train_row = pd.DataFrame(missing_train_row.value_counts() / train.shape[0]).reset_index()
missing_train_row.value_counts() | code |
129022563/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
test_null = test_null.sort_values(by=0, ascending=False)
test_null | code |
129022563/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
submission.head() | code |
129022563/cell_41 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=2, column_titles=['Train Data', 'Test Data'], x_title='Missing Values')
fig.show() | code |
129022563/cell_54 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False)
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
missing_train_row = train.isna().sum(axis=1)
missing_train_row = pd.DataFrame(missing_train_row.value_counts() / train.shape[0]).reset_index()
missing_train_row | code |
129022563/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
print(f'Number of rows in train data: {train.shape[0]}')
print(f'Number of columns in train data: {train.shape[1]}')
print(f'Number of values in train data: {train.count().sum()}')
print(f'Number of missing values in train data: {sum(train.isna().sum())}') | code |
129022563/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
print(f'Number of rows in test data: {test.shape[0]}')
print(f'Number of columns in test data: {test.shape[1]}')
print(f'Number of values in test data: {test.count().sum()}')
print(f'Number of missing values in test data: {sum(test.isna().sum())}') | code |
129022563/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False)
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
missing_train_row = train.isna().sum(axis=1)
missing_train_row = pd.DataFrame(missing_train_row.value_counts() / train.shape[0]).reset_index()
train.isna().sum(axis=1).unique() | code |
129022563/cell_52 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False)
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
missing_train_row = train.isna().sum(axis=1)
missing_train_row = pd.DataFrame(missing_train_row.value_counts() / train.shape[0]).reset_index()
train.isna().sum(axis=1).unique()
train.shape[0]
train.isna().sum(axis=1).value_counts() | code |
129022563/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 |
129022563/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.head() | code |
129022563/cell_45 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
test_null = test_null.sort_values(by=0, ascending=False)
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
fig = make_subplots(rows = 1,
cols = 2,
column_titles = ["Train Data","Test Data"],
x_title = "Missing Values")
fig.show()
train_null.index
fig.add_trace(go.Bar(x=train_null[0], y=train_null.index, orientation='h'), 1, 1)
fig.add_trace(go.Bar(x=test_null[0], y=test_null.index, orientation='h'), 1, 2)
fig.show() | code |
129022563/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False)
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
missing_train_row = train.isna().sum(axis=1)
missing_train_row = pd.DataFrame(missing_train_row.value_counts() / train.shape[0]).reset_index()
missing_train_row | code |
129022563/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
test.head() | code |
129022563/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False) | code |
129022563/cell_51 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train.iloc[:, :-1].describe().T.sort_values(by='std', ascending=False)
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
missing_train_row = train.isna().sum(axis=1)
missing_train_row = pd.DataFrame(missing_train_row.value_counts() / train.shape[0]).reset_index()
train.isna().sum(axis=1).unique()
train.shape[0] | code |
129022563/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape | code |
129022563/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.describe() | code |
129022563/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder | code |
129022563/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
test.isna().sum() | code |
129022563/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
test.isna().sum()
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(test.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
train_null.index | code |
129022563/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'median'
train.shape
train.count()
train.isna().sum()
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = [col for col in train.columns if col != TARGET]
RANDOM_STATE = 12
train | code |
Subsets and Splits