path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73080198/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.hist(train["target"],
b... | code |
73080198/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
train.describe() | code |
73080198/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
predictions_base = pd.read_csv('/kaggle/input/submissionstevenferrercsv/submissionStevenF... | code |
73080198/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', low_memory=False)
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', low_memory=False)
fig, ax = plt.subplots(figsize=(12, 6))
bars = ax.hist(train['target'], bins=40, range=(0, 11), color='orange... | code |
2041151/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
X_train = np.array(train[['Age', 'Fare', 'Parch', 'SibSp', 'P... | code |
122250913/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count() | code |
122250913/cell_25 | [
"text_plain_output_1.png"
] | from scipy.stats import ttest_1samp, shapiro, levene, ttest_ind, mannwhitneyu, \
import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.... | code |
122250913/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.head() | code |
122250913/cell_26 | [
"text_plain_output_1.png"
] | from scipy.stats import ttest_1samp, shapiro, levene, ttest_ind, mannwhitneyu, \
import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.... | code |
122250913/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.info() | code |
122250913/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T | code |
122250913/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_24 | [
"text_plain_output_1.png"
] | from scipy.stats import ttest_1samp, shapiro, levene, ttest_ind, mannwhitneyu, \
import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.... | code |
122250913/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
122250913/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df_ = pd.read_csv('/kaggle/input/fast-food-marketing-campaign-ab-test/WA_Marketing-Campaign.csv')
df = df_.copy()
df.describe().T
df.groupby('Promotion')['Promotion'].count()
df_age_sales = df.groupby(['MarketID', 'LocationID']).agg({'AgeOfStore': 'mean', 'SalesInThousands':... | code |
2022426/cell_13 | [
"text_plain_output_1.png"
] | from keras.preprocessing import text, sequence
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
max_features = 20000
maxlen = 100
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
list_sentences_train = train['comment_text'].fillna('unknown')... | code |
2022426/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.layers import Dense, Embedding, Input,GRU
from keras.models import Model
max_features = 20000
maxlen = 100
def cnn_rnn():
embed_size = 256
inp = Input(shape=(maxlen,))
main = Embedding(max_features, embed_siz... | code |
2022426/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.head(10))
list_sentences_train = train['comment_text'].fillna('unknown').values
list_classes = ['toxic', 'severe_toxic', 'obscene', '... | code |
2022426/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Dense, Embedding, Input, GRU
from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.preprocessing import text, sequence
from keras.callbacks import EarlyStopping, ModelCheckpoint | code |
2022426/cell_11 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.layers import Dense, Embedding, Input,GRU
from keras.models import Model
from sklearn.model_selection import train_test_split
import numpy as np
import numpy a... | code |
2022426/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2022426/cell_15 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.layers import Dense, Embedding, Input,GRU
from keras.models import Model
from keras.preprocessing import text, sequence
from sklearn.model_selection import trai... | code |
2022426/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.layers import Dense, Embedding, Input,GRU
from keras.models import Model
from keras.preprocessing import text, sequence
from sklearn.model_selection import trai... | code |
2022426/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.layers import Dense, Embedding, Input,GRU
from keras.models import Model
from sklearn.model_selection import train_test_split
import numpy as np
import numpy a... | code |
2022426/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
list_sentences_train = train['comm... | code |
2022426/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPool1D, Dropout, concatenate
from keras.layers import Dense, Embedding, Input,GRU
from keras.models import Model
from sklearn.model_selection import train_test_split
import numpy as np
import numpy a... | code |
2022426/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
list_sentences_train = train['comment_text'].fillna('unknown').values
list_classes = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'id... | code |
122262044/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardSc... | code |
122262044/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_tes... | code |
122262044/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
... | code |
122262044/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/predicting-credit-card-customer-attrition-with-m/BankChurners.csv')
df = df.rename(columns={'CLIENTNUM': 'Client_Num', 'Dependent_count': 'Dependent_Count', 'Months_on_book': 'Months_on_Book', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mo... | code |
122262044/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import ... | code |
122262044/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
... | code |
122262044/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
... | code |
122262044/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_tes... | code |
122262044/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_tes... | code |
122262044/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_tes... | code |
122262044/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.model_selection import cross_val_predict
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(random_state=42)
dt.fit(X_train, y_train)
y_predtr... | code |
122262044/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_tes... | code |
122262044/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
... | code |
122262044/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_tes... | code |
122262044/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/predicting-credit-card-customer-attrition-with-m/BankChurners.csv')
df.info() | code |
129016882/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
cat_cols ... | code |
129016882/cell_9 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
dataset.d... | code |
129016882/cell_4 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.head() | code |
129016882/cell_6 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
print(f'nu... | code |
129016882/cell_2 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.head() | code |
129016882/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
cat_cols ... | code |
129016882/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') | code |
129016882/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
print(f'n... | code |
129016882/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
cat_cols ... | code |
129016882/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
dataset.i... | code |
129016882/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicat... | code |
129016882/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicat... | code |
129016882/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset.head() | code |
129016882/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset[dataset.duplicated()]
cat_cols ... | code |
129016882/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicat... | code |
129016882/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
dataset.isna().sum()
duplicated_entries_df = dataset... | code |
129016882/cell_5 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/arrests-for-marijuana-possession/Arrests.csv')
dataset.drop(dataset.columns[0], axis=1, inplace=True)
dataset['year'] = pd.to_datetime(dataset['year'], format='%Y').dt.year
print('number of empty records in each features : ')
dataset.isna().sum() | code |
18110097/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png"
] | import datetime as dt
import pandas as pd
def astype_cat(dd, cols):
for col in cols:
if isinstance(col, tuple):
col, idx1, idx2 = col
for idx in range(idx1, idx2 + 1):
full_col = col + str(idx)
dd[full_col] = dd[full_col].astype('category')
e... | code |
18110097/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.metrics import normalized_mutual_info_score, mutual_info_score
from tqdm import tqdm_notebook as tqdm
from itertools import combinations
import seaborn as sns
from functools import partial
import os
prin... | code |
18110097/cell_11 | [
"text_html_output_1.png"
] | from functools import partial
from itertools import combinations
from sklearn.metrics import normalized_mutual_info_score, mutual_info_score
import datetime as dt
import pandas as pd
import seaborn as sns
def astype_cat(dd, cols):
for col in cols:
if isinstance(col, tuple):
col, idx1, idx2... | code |
18110097/cell_8 | [
"text_html_output_1.png",
"image_output_1.png"
] | from functools import partial
from itertools import combinations
from sklearn.metrics import normalized_mutual_info_score, mutual_info_score
import datetime as dt
import pandas as pd
import seaborn as sns
def astype_cat(dd, cols):
for col in cols:
if isinstance(col, tuple):
col, idx1, idx2... | code |
18110097/cell_10 | [
"text_plain_output_1.png"
] | from itertools import combinations
from sklearn.metrics import normalized_mutual_info_score, mutual_info_score
import datetime as dt
import pandas as pd
def astype_cat(dd, cols):
for col in cols:
if isinstance(col, tuple):
col, idx1, idx2 = col
for idx in range(idx1, idx2 + 1):
... | code |
105213782/cell_42 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt #plotting
import numpy as np
import pandas as pd
import seaborn as sns #visualization
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=... | code |
105213782/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plotting
import pandas as pd
import seaborn as sns #visualization
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
sns.countplot(data=data, x='stroke')
plt.s... | code |
105213782/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['gen... | code |
105213782/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum() | code |
105213782/cell_56 | [
"image_output_1.png"
] | from sklearn import tree
dt_classifier = DecisionTreeClassifier()
dt_classifier.fit(X_train, Y_train) | code |
105213782/cell_34 | [
"text_html_output_1.png"
] | Y_test | code |
105213782/cell_33 | [
"text_html_output_1.png"
] | Y_train | code |
105213782/cell_76 | [
"image_output_1.png"
] | from sklearn.svm import SVC
from sklearn.svm import SVC
svm_classifier = SVC()
svm_classifier.fit(X_train, Y_train) | code |
105213782/cell_40 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, f1_score,accuracy_score #metrics
from sklearn.metrics import roc_auc_score, roc_curve #metrics
import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-data... | code |
105213782/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[data['stroke'] == 1])
no_strokes = data[data.stroke == 0].index
random_indices = np.... | code |
105213782/cell_26 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[data['stroke'] == 1])
no_strokes = data[data.stroke == 0].index
random_indices = np.... | code |
105213782/cell_48 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()... | code |
105213782/cell_72 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, roc_curve #metrics
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_baye... | code |
105213782/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum() | code |
105213782/cell_60 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, f1_score,accuracy_score #metrics
from sklearn.metrics import roc_auc_score, roc_curve #metrics
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
data = pd.read_csv... | code |
105213782/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
data.info() | code |
105213782/cell_50 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, f1_score,accuracy_score #metrics
from sklearn.metrics import roc_auc_score, roc_curve #metrics
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
data = pd.read_csv... | code |
105213782/cell_52 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt #plotting
import numpy as np
import pandas as pd
import seaborn as sns #visualization
data = pd.read_csv('../input/stroke-prediction-dataset/healthcar... | code |
105213782/cell_32 | [
"text_plain_output_1.png"
] | X_test | code |
105213782/cell_68 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-... | code |
105213782/cell_62 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, roc_curve #metrics
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.py... | code |
105213782/cell_58 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()... | code |
105213782/cell_28 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[data['stroke'] == 1])
no_strokes = data[data.stroke == 0].index
random_indices = np.... | code |
105213782/cell_78 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd
data = p... | code |
105213782/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['gen... | code |
105213782/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['gen... | code |
105213782/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[data['stroke'] == 1])
no_stroke... | code |
105213782/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['gen... | code |
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