path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106210927/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train.info() | code |
106210927/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train.head(3) | code |
106210927/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
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR +... | code |
106210927/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import collections
import re
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.downloader.download('vader_lexicon')
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from sklearn.model_selection import train_test_split
imp... | code |
106210927/cell_10 | [
"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)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_test.head(3) | code |
106210927/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
sample_submission.info() | code |
331819/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | qplot(SepalLengthCm, SepalWidthCm, data=df, col=Species)
qplot(PetalLengthCm, PetalWidthCm, data=df, col=Species) | code |
331819/cell_2 | [
"text_html_output_1.png"
] | library(ggplot2)
library(readr)
df < -read_csv('../input/Iris.csv')
head(df) | code |
331819/cell_3 | [
"image_output_2.png",
"image_output_1.png"
] | qplot(SepalLengthCm, SepalWidthCm, data=df)
qplot(PetalLengthCm, PetalWidthCm, data=df) | code |
105202366/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum()
ds_data.head() | code |
105202366/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum() | code |
105202366/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data | code |
105202366/cell_23 | [
"text_plain_output_1.png"
] | len(x_test) | code |
105202366/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
naive = MultinomialNB()
naive.fit(x_train, y_train)
naive_score = naive.score(x_test, y_test)
naive_score | code |
105202366/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.info() | code |
105202366/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105202366/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
len(ds_data) | code |
105202366/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
naive = MultinomialNB()
naive.fit(x_train, y_train) | code |
105202366/cell_28 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
log = LogisticRegression()
log.fit(x_train, y_train)
log_score = log.score(x_test, y_test)
log_score | code |
105202366/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum()
sns.heatmap(ds_data.corr()) | code |
105202366/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data.head() | code |
105202366/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
log = LogisticRegression()
log.fit(x_train, y_train)
log_score = log.score(x_test, y_test)
log_score
naive = MultinomialNB()
naive.fit(x_train, y_train)
naive_score = naive.score(x_t... | code |
105202366/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.isnull().sum()
ds_data.info() | code |
105202366/cell_22 | [
"text_plain_output_1.png"
] | len(x_train) | code |
105202366/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
log = LogisticRegression()
log.fit(x_train, y_train) | code |
105202366/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ds_data = pd.read_csv('../input/data-science-job-salaries/ds_salaries.csv')
ds_data = ds_data.drop('Unnamed: 0', axis=1)
ds_data
ds_data.describe() | code |
2015269/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
... | code |
2015269/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2015269/cell_6 | [
"text_html_output_10.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_19.png",
"text_html_output_9.png",
"text_html_output_13.png",
"t... | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
... | code |
2015269/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
... | code |
2015269/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
x_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(x_columns):
... | code |
2015269/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
pyo.init_notebook_mode(connected=True)
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2015269/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.head() | code |
104115759/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum() | code |
104115759/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Mas... | code |
104115759/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
y_predictions = lr.predict(X_test)
lr.score(X_test, y_test) | code |
104115759/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data | code |
104115759/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df | code |
104115759/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Mas... | code |
104115759/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
predict_data = predict.iloc[:, :1]
predict_data
predict | code |
104115759/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.head() | code |
104115759/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns | code |
104115759/cell_32 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Mas... | code |
104115759/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict
len(predict) | code |
104115759/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns) | code |
104115759/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique() | code |
104115759/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes | code |
104115759/cell_38 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Mas... | code |
104115759/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T
df.isnull().sum()
df.nunique()
df.dtypes
X = df[['Mas... | code |
104115759/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.tail() | code |
104115759/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
predict = pd.read_csv('../input/al-majlis-ai-hackathon-investment-submission/submissionFile.csv')
predict | code |
104115759/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/al-majlis-ai-hackathon-investment/InvestmentDatasetMasked.csv')
df.columns
len(df.columns)
df.describe().T | code |
106198491/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1] | code |
106198491/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.describe()
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0]) | code |
106198491/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.columns
df.Name
df[['Name', 'Type 1', 'HP']]
df.iloc[0]
df.loc[df['Name'] == 'Ivysaur']
df.iloc[0, 1]
df.sort_values('HP', ascending=False)
df.sort_values(['HP', 'Attack'], ascending=[1, 0])
df['Total'] = df.iloc[:, 4:10].sum(axis=... | code |
106198491/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/pokemon-dataset/pokemon_data.csv')
df.head() | code |
105210749/cell_21 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.a... | code |
105210749/cell_25 | [
"text_html_output_1.png"
] | from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_t... | code |
105210749/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.info() | code |
105210749/cell_23 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_30 | [
"text_plain_output_1.png"
] | from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.door... | code |
105210749/cell_20 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_40 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_col... | code |
105210749/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_col... | code |
105210749/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum() | code |
105210749/cell_18 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_28 | [
"text_plain_output_1.png"
] | from word2number import w2n
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.door... | code |
105210749/cell_16 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_38 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
105210749/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.head() | code |
105210749/cell_17 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_14 | [
"text_html_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_10 | [
"text_html_output_1.png"
] | pip install word2number | code |
105210749/cell_37 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
105210749/cell_12 | [
"text_plain_output_1.png"
] | from word2number import w2n
import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.isna().sum()
from word2number import w2n
df.doornumber = df.doornumber.apply(w2n.word_to_num)
df.cylindernumber = df.cyl... | code |
105210749/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv', sep=',', encoding='utf-8')
pd.set_option('display.max_columns', None)
df.describe() | code |
2014525/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
print('Is this variable numeric?')
df['rating'].dtype == 'float' | code |
2014525/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
sns.regplot(df['calories'], df['dessert'], fit_reg=False) | code |
2014525/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df.head() | code |
2014525/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
sns.regplot(df['calories'], df['dessert']) | code |
2014525/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/epi_r.csv')
df = df[df['calories'] < 10000]
df.dropna(inplace=True)
print('Is this variable only integers?')
df['rating'].dtype == 'int' | code |
129016727/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['having_At_Symbol'].value_counts() | code |
129016727/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Google_Index'].value_counts() | code |
129016727/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset.head() | code |
129016727/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifi... | code |
129016727/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df = df.drop_duplicates()
df.shape
df['Result'... | code |
129016727/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
y_pred = LR.predict(X_test)
... | code |
129016727/cell_33 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegress... | code |
129016727/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df = df.drop_duplicates()
df.shape | code |
129016727/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['Result'].value_counts() | code |
129016727/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
LR = LogisticRegression()
LR.fit(X_train, y_train)
... | code |
129016727/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_feat... | code |
129016727/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
dataset['URLURL_Length'].value_counts() | code |
129016727/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/phishing-website-dataset/dataset.csv')
selected_features = ['URLURL_Length', 'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix', 'Page_Rank', 'Google_Index', 'Result']
df = dataset[selected_features]
df.head() | code |
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