path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104121949/cell_19 | [
"text_plain_output_1.png"
] | age2 = {'a': 3, 'b': 6, 'c': 9}
age2 | code |
104121949/cell_8 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks['Venkat'] | code |
104121949/cell_15 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
print(a) | code |
104121949/cell_17 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age['Rohit'] = 18
age | code |
104121949/cell_14 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
age | code |
104121949/cell_22 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age.pop('c') | code |
104121949/cell_10 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
for i in marks:
print(i) | code |
104121949/cell_27 | [
"text_plain_output_1.png"
] | n = int(input('Enter the number'))
d = {}
for i in range(1, 1 + n):
d[i] = i * i
print(d) | code |
104121949/cell_12 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks.keys()
marks.values() | code |
104121949/cell_5 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
age['Venkat'] | code |
128010513/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', ... | code |
128010513/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
data.hist(bins=10, figsize=(15, 10), xlabelsize=7, ylabelsize=7) | code |
128010513/cell_6 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes | code |
128010513/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
print(IQR)
upper = data[~(data > Q3 + 1.5 * IQR)].... | code |
128010513/cell_19 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
... | from scipy import stats
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score, jaccard_score, mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt #visualisat... | code |
128010513/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
data.describe() | code |
128010513/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id',... | code |
128010513/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.head() | code |
128010513/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #visualisation
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft... | code |
128010513/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = [... | code |
128010513/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape | code |
88077915/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)
import pandas as pd
train_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/train.csv')
test_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/test.csv')
def check_data(data):
new_da... | code |
88077915/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 |
88077915/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)
import pandas as pd
train_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/train.csv')
test_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/test.csv')
print(train_data.info())
print(t... | code |
88077915/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/train.csv')
test_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/test.csv')
print(train_data.info())
print(t... | code |
128023079/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.colab import files
from google.colab import files
files.upload() | code |
128023079/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import re
import pandas as pd
import librosa
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.ensemble import Ad... | code |
128023079/cell_3 | [
"text_plain_output_1.png"
] | ! pip install -q kaggle | code |
17138453/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader, Dataset
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
labels = pd.read_csv('../input/train.csv')
sub = pd.read_csv('../in... | code |
17138453/cell_5 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
labels = pd.read_csv('../input/train.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train_path = '../input/train/train/'
test_path = '../input/test/test/'
dtrain, dval = train_test_split(labels, stratify=labels.has_cactus, test_size=... | code |
88091034/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data[... | code |
88091034/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data[... | code |
88091034/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]']... | code |
88091034/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]']... | code |
88091034/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data[... | code |
88091034/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]']... | code |
88091034/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]']... | code |
88091034/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data[... | code |
88091034/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data.head() | code |
88091034/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * dat... | code |
88091034/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data[... | code |
50240953/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
test_data.is... | code |
50240953/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head() | code |
50240953/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum() | code |
50240953/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
test_data.de... | code |
50240953/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
test_data['E... | code |
50240953/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.s... | code |
50240953/cell_5 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import neighbors
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
... | code |
128029205/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
features = list(X_train.columns)
target = list(y_train.unique())
fig, ax = plt.subplots(figsize=(12, 8))
plot_tree(gin... | code |
128029205/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.shape | code |
128029205/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'pe... | code |
128029205/cell_30 | [
"image_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
g... | code |
128029205/cell_20 | [
"text_plain_output_1.png"
] | features = list(X_train.columns)
target = list(y_train.unique())
y_train.unique() | code |
128029205/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
g... | code |
128029205/cell_26 | [
"text_html_output_1.png"
] | import math
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal... | code |
128029205/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.shape
X = data.drop(['Drug'], axis=1)
y = data['Drug']
data.describe() | code |
128029205/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
confusion_matrix(y_test, y_pred) | code |
128029205/cell_28 | [
"text_plain_output_1.png"
] | import math
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal... | code |
128029205/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
print(f'Predicted Values : {y_pred}') | code |
128029205/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.head() | code |
128029205/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred) * 100
print(f'accuracy : {accurac... | code |
128029205/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'pe... | code |
128029205/cell_14 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train) | code |
128029205/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.shape
X = data.drop(['Drug'], axis=1)
y = data['Drug']
len(data['Drug'].unique()) | code |
17096225/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
X = X.reshape(X.shape[0], 28, 28)
X = X / 255.0
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.set_size_inches(9, 9)
for i, img in enumerate(X):
if i + 1 > 3 * 3:
break
plt.subplot(3, 3, i + 1)
plt.imshow(img)
plt.show() | code |
17096225/cell_4 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
train = df.drop(['label'], axis=1)
train.head() | code |
17096225/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('../input/train.csv')
train = df.drop(['label'], axis=1)
labels = df['label']
labels.head() | code |
17096225/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.head() | code |
17096225/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17096225/cell_18 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
17096225/cell_15 | [
"text_html_output_1.png"
] | import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dro... | code |
17096225/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
X = X.reshape(X.shape[0], 28, 28)
X = X / 255.0
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.set_size_inches(9, 9)
for i, img in enumerate(X):
if i + 1 > 3 * 3:
break
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28))... | code |
17096225/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_csv('../input/train.csv')
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, acti... | code |
17096225/cell_12 | [
"text_html_output_1.png"
] | import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dro... | code |
130020397/cell_4 | [
"text_plain_output_1.png"
] | import requests
url = 'https://www.trendyol.com/cep-telefonu-x-c103498?pi=6?'
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36 OPR/98.0.0.0'}
page = requests.get(url, headers=header)
print(page) | code |
130020397/cell_5 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import requests
url = 'https://www.trendyol.com/cep-telefonu-x-c103498?pi=6?'
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safa... | code |
73072707/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain | code |
73072707/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/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 |
73072707/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull... | code |
73072707/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
73072707/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
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