path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
1008693/cell_34 | [
"text_plain_output_1.png"
] | from scipy import interp
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import panda... | code |
1008693/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sea... | code |
1008693/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
employees... | code |
1008693/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
employees['salary'] = pd.factorize(employees['salary'])[0]
... | code |
1008693/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
plt.subplots(figsize=(8, 8))
sns.heatmap(correlation_matrix, vmax=0.8, square=True)
plt.show() | code |
1008693/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
employees['salary'] = pd.factorize(employees['salary'])[0]
employees['sales'] = pd.factorize(em... | code |
1008693/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
employees = pd.read_csv('../input/HR_comma_sep.csv')
employees.head() | code |
1008693/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
employees['salary'] = pd.factorize(employees['salary'])[0]
... | code |
1008693/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
employees['sales'].unique() | code |
1008693/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
employees.shape
employees.mean()
import seaborn as sns
correlation_matrix = employees.corr()
employees['salary'].unique() | code |
1008693/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | employees.shape
employees.mean() | code |
128027378/cell_13 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df... | code |
128027378/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027378/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.head() | code |
128027378/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027378/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', ... | code |
128027378/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128027378/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027378/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027378/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, recall_score
from sklearn.svm import SVC
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, recall_score
svm_clf = SVC()
svm_clf.fit(X_train_res, y_train_res)
svm_clf_preds = svm_clf.predict(X_test_res)
print('SVM Classifier accuracy on validation data ... | code |
128027378/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, recall_score
from xgboost import XGBClassifier
from xgboost import XGBClassifier
xgb_clf = XGBClassifier(early_stopping_rounds=3)
xgb_clf.fit(X_train_res, y_train_res, eval_set=[(X_test_res, y_test_res)])
xgb_clf_preds = xgb_clf.predict(X_test_res)
print('Accuracy of XGBoos... | code |
34124545/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
data_paid[data_paid['price'] == '200']['subject'].value_counts() | code |
34124545/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.describe() | code |
34124545/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
data_paid[data_paid['engagement'] == 1.0] | code |
34124545/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
sns.set_style('ticks')
fi... | code |
34124545/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.sort_values(by='... | code |
34124545/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
sns.set_style('ticks')
fi... | code |
34124545/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
len(data['course_title'].value_counts()) | code |
34124545/cell_39 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
data_paid_10 = data_paid.sort_values(by='num_subscribers', ascending=False)[0:10]... | code |
34124545/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.sort_values(by='num_subscribers', ascending=False)
data_free['subject'].value_counts() | code |
34124545/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.head() | code |
34124545/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape | code |
34124545/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False) | code |
34124545/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.sort_values(by='... | code |
34124545/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
sns.set_style('ticks')
fi... | code |
34124545/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.head() | code |
34124545/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.head() | code |
34124545/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.sort_values(by='num_subscribers', ascending=False) | code |
34124545/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.sort_values(by='... | code |
34124545/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
import re
data[data['course_title'].str.contains('Data') == True] | code |
34124545/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
data_paid['subject'].value_counts() | code |
34124545/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_free = data[data['is_paid'] == False]
data_free.shape | code |
34124545/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
data_paid[data_paid['num_subscribers'] == max(data_paid['num_subscribers'])] | code |
34124545/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape | code |
34124545/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_free = data[data['is_paid'] == False]
data_free.shape
data_free.sort_values(by='... | code |
34124545/cell_36 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('../input/udemy-courses/clean_dataset.csv')
data.shape
data_paid = data[data['is_paid'] == True]
data_paid.shape
data_paid.sort_values(by='num_subscribers', ascending=False)
data_paid[data_paid['num_lectures'] == max(data_paid['num_lectures'])] | code |
32069437/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape | code |
32069437/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)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10) | code |
32069437/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 |
32069437/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10)
menu.loc[menu.Sugars.idxmax()].Item
menu.set_index('Item').loc['Egg McMuffin', 'Calories']
menu.Category.value_counts()
menu.gro... | code |
32069437/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10)
menu.loc[menu.Sugars.idxmax()].Item | code |
32069437/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10)
menu.loc[menu.Sugars.idxmax()].Item
menu.set_index('Item').loc['Egg McMuffin', 'Calories']
menu.Category.value_counts()
menu.gro... | code |
32069437/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10)
menu.loc[menu.Sugars.idxmax()].Item
menu.set_index('Item').loc['Egg McMuffin', 'Calories']
menu.Category.value_counts()
menu.gro... | code |
32069437/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10)
menu.loc[menu.Sugars.idxmax()].Item
menu.set_index('Item').loc['Egg McMuffin', 'Calories'] | code |
32069437/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu = pd.read_csv('/kaggle/input/nutrition-facts/menu.csv')
menu.shape
menu.sort_values('Serving Size').tail(10)
menu.loc[menu.Sugars.idxmax()].Item
menu.set_index('Item').loc['Egg McMuffin', 'Calories']
menu.Category.value_counts() | code |
72066220/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
y = data.Species
data.drop(['Id', 'Species'], axis=1, inplace=True)
data.shape | code |
72066220/cell_25 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
y = data.Species
data.drop(['Id', 'Species'], axis=1, inplace=True)
data.shape
x = data.iloc[:].values
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11... | code |
72066220/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
y = data.Species
data.drop(['Id', 'Species'], axis=1, inplace=True)
data.shape
x = data.iloc[:].values
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters... | code |
72066220/cell_6 | [
"image_output_1.png"
] | import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
print('Total Species: ', data.Species.nunique())
print(data.Species.unique()) | code |
72066220/cell_19 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
y = data.Species
data.drop(['Id', 'Species'], axis=1, inplace=True)
data.shape
x = data.iloc[:].values
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11... | code |
72066220/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
y = data.Species
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
y_data = encoder.fit_transform(y)
y_data | code |
72066220/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
y = data.Species
data.drop(['Id', 'Species'], axis=1, inplace=True)
data.head() | code |
72066220/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/iris/Iris.csv'
data = pd.read_csv(path)
data.head() | code |
130026088/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
sns.set()
(sns.get_dataset_names(), len(sns.get_dataset_names()))
healthexp = sns.load_dataset('healthexp')
healthexp
top_spending_countrys = healthexp[['Country', 'Life_Expectancy']]
top_spending_countrys
... | code |
130026088/cell_4 | [
"text_html_output_1.png"
] | import seaborn as sns
import seaborn as sns
sns.set()
(sns.get_dataset_names(), len(sns.get_dataset_names())) | code |
130026088/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 |
130026088/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
sns.set()
(sns.get_dataset_names(), len(sns.get_dataset_names()))
healthexp = sns.load_dataset('healthexp')
healthexp
top_spending_countrys = healthexp[['Country', 'Life_Expectancy']]
top_spending_countrys
... | code |
130026088/cell_3 | [
"text_plain_output_1.png"
] | import seaborn as sns
import seaborn as sns
sns.set() | code |
130026088/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
sns.set()
(sns.get_dataset_names(), len(sns.get_dataset_names()))
healthexp = sns.load_dataset('healthexp')
healthexp
top_spending_countrys = healthexp[['Country', 'Life_Expectancy']]
top_spending_countrys
... | code |
130026088/cell_5 | [
"image_output_1.png"
] | import seaborn as sns
import seaborn as sns
sns.set()
(sns.get_dataset_names(), len(sns.get_dataset_names()))
healthexp = sns.load_dataset('healthexp')
healthexp | code |
72070182/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train_data_file_path = '../input/30-days-of-ml/train.csv'
test_data_file_path = '../input/30-days-of-ml/test.csv'
df_train = pd.read_csv(train_data_file_path, index_col=0)
df_test = pd.read_csv(test_data_file_path, index_col=0)
df_train.head() | code |
72070182/cell_6 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train_data_file_path = '../input/30-days-of-ml/train.csv'
test_data_file_path = '../input/30-days-of-ml/test.csv'
df_train = pd.read_csv(train_data_file_path, index_col=0)
df_test = pd.read_csv(test_data_file_path, index_col=0)
y = df_train['targe... | code |
72070182/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
model = RandomForestRegressor(random_state=1)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
print(mean_squared_error(y_valid, preds_valid, squared=False)) | code |
72070182/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_data_file_path = '../input/30-days-of-ml/train.csv'
test_data_file_path = '../input/30-days-of-ml/test.csv'
df_train = pd.read_csv(train_data_file_path, index_col=0)
df_test = pd.read_csv(test_data_file_path, index_col=0)
y = df_train['target']
features = df_train.drop(['target'], axis=1)
f... | code |
105189181/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum() | code |
105189181/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape | code |
105189181/cell_34 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_23 | [
"text_html_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
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns | code |
105189181/cell_40 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=True)
data.isnull().sum() | code |
105189181/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/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 |
105189181/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.info() | code |
105189181/cell_32 | [
"text_html_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
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data.describe() | code |
105189181/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data.Industry.unique... | code |
105189181/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_22 | [
"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
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum() | code |
105189181/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
105189181/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.shape | code |
105189181/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.head(5) | code |
105189181/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/world-wide-unicorn-startups/World_Wide_Unicorn_Startups.csv')
data.shape
data.columns
data.isnull().sum()
data.duplicated().sum()
data.dropna(inplace=Tru... | code |
89143018/cell_21 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.regression import LinearRegression
from pyspark.ml.regression import LinearRegression
from pyspark.sql imp... | code |
89143018/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import str... | code |
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