path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72107191/cell_43 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.spl... | code |
72107191/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.spl... | code |
72107191/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
129010375/cell_21 | [
"text_plain_output_1.png"
] | import os
image_path = '/content/dataset/semantic_drone_dataset/original_images'
mask_path = '/content/dataset/semantic_drone_dataset/label_images_semantic'
length = len(os.listdir(image_path))
train_dataset_len = int(length * 0.7)
val_dataset_len = length - train_dataset_len
train_dataset = DroneDataset(image_path,... | code |
129010375/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/content/class_dict_seg.csv')
classes = labels.name.values.tolist()
print(classes) | code |
129010375/cell_4 | [
"image_output_1.png"
] | !pip install kaggle | code |
129010375/cell_33 | [
"text_plain_output_1.png"
] | from torch.nn import CrossEntropyLoss
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
from torchvision.models.segmentation.deeplabv3 import DeepLabHead
from torchvision.models.segmentation.fcn import FCNHead
from tqdm import tqdm
from tqdm.notebo... | code |
129010375/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/content/class_dict_seg.csv')
labels.head() | code |
129010375/cell_18 | [
"text_plain_output_1.png"
] | import torchvision
torchvision.models.segmentation.DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1.transforms() | code |
129010375/cell_22 | [
"text_plain_output_1.png"
] | import os
image_path = '/content/dataset/semantic_drone_dataset/original_images'
mask_path = '/content/dataset/semantic_drone_dataset/label_images_semantic'
length = len(os.listdir(image_path))
train_dataset_len = int(length * 0.7)
val_dataset_len = length - train_dataset_len
train_dataset = DroneDataset(image_path,... | code |
129010375/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/content/class_dict_seg.csv')
len(labels) | code |
32072152/cell_13 | [
"text_plain_output_1.png"
] | from scipy.stats import loguniform, uniform, randint
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import RandomizedSearchCV, KFold
import numpy as np
import pandas as pd
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import RandomizedSearchCV,... | code |
32072152/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
len(df)
cluster_count = df['cluster'].value_counts().sort_values()
ax = cluster_count.plot(kind='bar', figsize=(15, 5))
ax.set_xticks([])
ax.set_xlabel('Cluster id')
ax.set_ylabel('Count')
ax.grid(True) | code |
32072152/cell_8 | [
"image_output_1.png"
] | import numpy as np
embeddings = np.load('/kaggle/input/biowordvec-precomputed-cord19/biowordvec.npy')
embeddings.shape | code |
32072152/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import loguniform, uniform, randint
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import RandomizedSearchCV, KFold
import numpy as np
import pandas as pd
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import RandomizedSearchCV,... | code |
32072152/cell_12 | [
"text_plain_output_1.png"
] | from scipy.stats import loguniform, uniform, randint
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import RandomizedSearchCV, KFold
import numpy as np
import pandas as pd
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import RandomizedSearchCV,... | code |
32072152/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
len(df) | code |
49118067/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
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=... | code |
49118067/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM'... | code |
49118067/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None, delim_whitespace=True, names=names)
print(data_df... | code |
49118067/cell_34 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
49118067/cell_23 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO',... | code |
49118067/cell_20 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read... | code |
49118067/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None, delim_whitespace=True, names=names)
data_df.isnul... | code |
49118067/cell_29 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO',... | code |
49118067/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 |
49118067/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None, delim_whitespace=True, names=names)
data_df.isnul... | code |
49118067/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None,... | code |
49118067/cell_32 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # ... | code |
49118067/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None, delim_whitespace=True, names=names)
data_df.isnul... | code |
49118067/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr_all = LinearRegression()
lr_all.fit(X_train, y_train)
y_pred1 = lr_all.predict(X_test)
lr_all.intercept_ | code |
49118067/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None, delim_whitespace=True, names=names)
data_df.head(... | code |
49118067/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None,... | code |
49118067/cell_35 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
import numpy as np # linear algebra
import pandas as pd # data proces... | code |
49118067/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO',... | code |
49118067/cell_10 | [
"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
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=... | code |
49118067/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g.... | code |
49118067/cell_37 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
import matplotlib.py... | code |
49118067/cell_12 | [
"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
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=... | code |
49118067/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data_df = pd.read_csv('/kaggle/input/boston-house-prices/housing.csv', header=None, delim_whitespace=True, names=names)
data_df.info(... | code |
49118067/cell_36 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
import matplotlib.py... | code |
90148331/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::... | code |
90148331/cell_20 | [
"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_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../inpu... | code |
90148331/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', engine='python', hea... | code |
90148331/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', engine='python', hea... | code |
90148331/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90148331/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', engine='python', hea... | code |
90148331/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', engine='python', hea... | code |
90148331/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)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', engine='python', hea... | code |
90148331/cell_15 | [
"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_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../inpu... | code |
90148331/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', engine='python', hea... | code |
90148331/cell_22 | [
"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_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', engine='python', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../inpu... | code |
88098284/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Custome... | code |
88098284/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_23 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
df.isnull().sum() | code |
88098284/cell_30 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
df.isnull().sum()
df_coded = df.copy()
df_coded = df_coded.replace({'AnnualIncomeClass': {'Low Income': 0, 'Middle Income':... | code |
88098284/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.ensemble import BalancedRandomForestClassifier, BalancedBaggingClassifier
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction... | code |
88098284/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.head() | code |
88098284/cell_40 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(col... | code |
88098284/cell_39 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(col... | code |
88098284/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.ensemble import BalancedRandomForestClassifier, BalancedBaggingClassifier
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction... | code |
88098284/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(colu... | code |
88098284/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage... | code |
88098284/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe(... | code |
88098284/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.info() | code |
88098284/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe()
ax = sns.countplot(data=df, x='Churn')
percentage = df['Churn'].value... | code |
88098284/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Custome... | code |
88098284/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tour-travels-customer-churn-prediction/Customertravel.csv')
df.rename(columns={'Target': 'Churn'}, inplace=True)
df.groupby('Churn').describe() | code |
33101127/cell_21 | [
"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
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum()
playstore.loc[playstore['Reviews'] == '3.0M', 'Reviews'] = 3000000
playstore.loc[... | code |
33101127/cell_23 | [
"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
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum()
playstore.loc[playstore['Reviews'] == '3.0M', 'Reviews'] = 3000000
playstore.loc[... | code |
33101127/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.info() | code |
33101127/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
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum()
playstore.loc[playstore['Reviews'] == '3.0M', 'Reviews'] = 3000000
playstore.loc[... | code |
33101127/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 |
33101127/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
for col in playstore.columns:
print('{} has {} unique values'.format(col, len(playstore[col].unique()))) | code |
33101127/cell_16 | [
"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
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum()
playstore.loc[playstore['Reviews'] == '3.0M', 'Reviews'] = 3000000
playstore.loc[... | code |
33101127/cell_24 | [
"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
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum()
playstore.loc[playstore['Reviews'] == '3.0M', 'Reviews'] = 3000000
playstore.loc[... | code |
33101127/cell_14 | [
"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)
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum()
playstore.loc[playstore['Reviews'] == '3.0M', 'Reviews'] = 3000000
playstore.loc[playstore['Size'] == 'V... | code |
33101127/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.isnull().sum() | code |
33101127/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
playstore = pd.read_csv('/kaggle/input/google-play-store-apps/googleplaystore.csv')
playstore.head() | code |
34139893/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
from sklearn.compose import ColumnTransformer
from sklearn.metrics import confusion_matrix, classification_report, roc_curve
from sklearn.pipeline import Pipeline
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv')
submission_df = pd.read_csv('/kaggle/input/t... | code |
34139893/cell_9 | [
"text_html_output_1.png"
] | from sklearn import model_selection
from sklearn.compose import ColumnTransformer
from sklearn.metrics import confusion_matrix, classification_report, roc_curve
from sklearn.pipeline import Pipeline
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv')
submission_df = pd.read_csv('/kaggle/input/t... | code |
34139893/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/test.csv')
ids = pd.read_csv('/kaggle/input/titanic/test.csv')
df.head() | code |
34139893/cell_2 | [
"text_html_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 |
129018802/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
dados = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
dados.drop('Student ID', axis=1, inplace=True) | code |
129018802/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
dados = pd.read_csv('/kaggle/input/student-performance-in-mathematics/exams.csv')
dados.head() | code |
106192280/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum() | code |
106192280/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == ... | code |
106192280/cell_30 | [
"image_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_genre = pd.DataFrame({'Genre': ['Female', 'Male'], 'Genre_code': [0, 1]})
df_genre.head() | code |
106192280/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.columns
df_cust.isna().sum()
df_cust.isnull().sum()
df_cust_male = df_cust[df_cust['Genre'] == 'Male']
df_cust_female = df_cust[df_cust['Genre'] == 'Female']
plt.figure(f... | code |
106192280/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_cust = pd.read_csv('../input/customer-segmentation-dataset/Mall_Customers.csv')
df_cust.describe() | code |
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