path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
1008563/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any() | code |
1008563/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
sns.heatmap(df.corr(), vmax=0.8, square=True, annot=True, fmt='.2f') | code |
1008563/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job... | code |
1008563/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score,train_test_split
from sklearn.tree import DecisionTreeReg... | code |
1008563/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fi... | code |
1008563/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.describe() | code |
1008563/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(d... | code |
1008563/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fi... | code |
74070881/cell_9 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_error, accuracy_score
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playgr... | code |
74070881/cell_6 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"te... | import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_solution = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
X_test = test_data.copy()
X_test... | code |
74070881/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_solution = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
X_test = test_data.copy()
X_test... | code |
74070881/cell_7 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_error, accuracy_score
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playgr... | code |
74070881/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_solution = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
X_test = test_data.copy()
X_test... | code |
74070881/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_solution = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
X_test = test_data.copy()
X_test... | code |
74070881/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/sept-tps-5fold-stratified/sept_TPS_train_5_folds.csv')
test_data = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
sample_solution = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
X_test = test_data.copy()
X_test... | code |
33097350/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/uncover/regional_sources/the_belgian_institute_for_health/dataset-of-confirmed-cases-by-date-and-municipality.csv', encoding='ISO-8859-2')
df.dtypes | code |
33097350/cell_9 | [
"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
df = pd.read_csv('../input/uncover/regional_sources/the_belgian_institute_for_health/dataset-of-confirmed-cases-by-date-and-municipality.csv', encoding='ISO-8859-2')
plt.xticks(rotation=90)
... | code |
33097350/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/uncover/regional_sources/the_belgian_institute_for_health/dataset-of-confirmed-cases-by-date-and-municipality.csv', encoding='ISO-8859-2')
df.head() | code |
33097350/cell_2 | [
"text_html_output_2.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import feature_extraction, linear_model, model_selection, preprocessing
import plotly.graph_objs as go
import plotly.offline as py
import plotly.express as px
from plotly.offline import iplot
import seab... | code |
33097350/cell_11 | [
"text_plain_output_1.png"
] | df_grp_rl20 = df_grp_rl20.sort_values(by=['yearstart'], ascending=False) | code |
33097350/cell_7 | [
"application_vnd.jupyter.stderr_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
df = pd.read_csv('../input/uncover/regional_sources/the_belgian_institute_for_health/dataset-of-confirmed-cases-by-date-and-municipality.csv', encoding='ISO-8859-2')
sns.countplot(df['tx_rgn_... | code |
33097350/cell_5 | [
"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 plotly.express as px
df = pd.read_csv('../input/uncover/regional_sources/the_belgian_institute_for_health/dataset-of-confirmed-cases-by-date-and-municipality.csv', encoding='ISO-8859-2')
fig = px.bar(df[['cases', 'nis5']].sort_values('nis5... | code |
2009832/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
s... | code |
2009832/cell_4 | [
"text_plain_output_1.png"
] | Weather.head() | code |
2009832/cell_6 | [
"text_plain_output_1.png"
] | Weather['RAIN'].value_counts() | code |
2009832/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discr... | code |
2009832/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | Weather['RAIN'] = Weather['RAIN'].map(lambda i: 1 if i == True else 0)
Weather['RAIN'].value_counts() | code |
128011561/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from Bio import SeqIO
from tqdm import tqdm
import pandas as pd
def read_fasta(fastaPath):
fasta_sequences = SeqIO.parse(open(fastaPath), 'fasta')
ids = []
sequences = []
for fasta in fasta_sequences:
ids.append(fasta.id)
sequences.append(str(fasta.seq))
return pd.DataFrame({'Id':... | code |
329725/cell_4 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, ax... | code |
329725/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
print(df.head()) | code |
74041129/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
n_train = 700
X_train_class = train_df['Pclass'].values.reshape(-1, 1)
X_train_sex = train_df['Sex'].values.reshape(-1, 1)
X_train_age = train_df['Ag... | code |
74041129/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../inpu... | code |
74041129/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_df.describe() | code |
74041129/cell_11 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from keras.models import load_model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train... | code |
74041129/cell_18 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from keras.models import load_model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train... | code |
74041129/cell_15 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from keras.models import load_model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train... | code |
74041129/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from keras.models import load_model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train... | code |
74041129/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from keras.models import load_model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
train_df = pd.read_csv('../input/titanic/train... | code |
50213265/cell_13 | [
"text_html_output_2.png"
] | col = 'Q4'
v2 = df[col].value_counts().reset_index()
v2 = v2.rename(columns={col: 'count', 'index': col})
v2 = v2.sort_values(by='count', ascending=False)
plt.figure(figsize=(20, 8))
barplot = plt.bar(v2.Q4, v2['count'], color='red')
for bar in barplot:
yval = bar.get_height()
plt.text(bar.get_x() + bar.get_wid... | code |
50213265/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = data.iloc[1:, :]
print(df.shape)
df.head(3) | code |
50213265/cell_11 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = d... | code |
50213265/cell_7 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = data.iloc[1:, :]
init_notebook_mo... | code |
50213265/cell_8 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = data.iloc[1:, :]
init_notebook_mo... | code |
50213265/cell_16 | [
"text_html_output_1.png"
] | col = 'Q5'
v2 = df[col].value_counts().reset_index()
v2 = v2.rename(columns={col: 'count', 'index': col})
v2 = v2.sort_values(by='count', ascending=False)
plt.figure(figsize=(20, 8))
barplot = plt.bar(v2.Q5, v2['count'], color='green')
for bar in barplot:
yval = bar.get_height()
plt.text(bar.get_x() + bar.get_w... | code |
50213265/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = data.iloc[1:, :]
data.iloc[0, :].transpose() | code |
50213265/cell_14 | [
"text_html_output_2.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = d... | code |
50213265/cell_10 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objs as go
import plotly.graph_objs as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = d... | code |
50213265/cell_5 | [
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df = data.iloc[1:, :]
init_notebook_mode(connected=True)
col = 'Q1'
v1 = df[col].value_counts().reset_index()
v1 = v1.rename... | code |
90138657/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
... | code |
90138657/cell_13 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y) | code |
90138657/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
X_test... | code |
90138657/cell_6 | [
"text_html_output_1.png"
] | import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
with sns.plotting_context('notebook', font_scale=2.5):
g = sns.pairplot(dataset[['sqft_lot', 'sqft_above', 'price', 'sqft_living', 'bedrooms']], hue='bedrooms', palette='tab20', height=6)
g.set(xticklabels=[]) | code |
90138657/cell_2 | [
"text_plain_output_1.png"
] | dataset.columns | code |
90138657/cell_11 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
y.head() | code |
90138657/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
X_test = df.drop(columns=['... | code |
90138657/cell_1 | [
"text_html_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
dataset = pd.read_csv('../input/kc-house-data/kc_house_data.csv')
dataset.head() | code |
90138657/cell_7 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
len(df) | code |
90138657/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns | code |
90138657/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_ | code |
90138657/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_ | code |
90138657/cell_3 | [
"text_html_output_1.png"
] | dataset.columns
print(dataset.dtypes) | code |
90138657/cell_17 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_test = df.drop(columns=['price'])[:10]
X_test | code |
90138657/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y) | code |
90138657/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
... | code |
90138657/cell_10 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
len(X)
len(y) | code |
90138657/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X.head() | code |
90138657/cell_5 | [
"text_plain_output_1.png"
] | import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
sns.lmplot(x='price', y='sqft_living', data=df, ci=None) | code |
72087593/cell_6 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
df = pd.read_csv('../input/train-folds/train_folds.csv')
test_df = pd.read_csv('../input/30-days-of-ml/test.csv')
test_df.head... | code |
72087593/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 |
72087593/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train-folds/train_folds.csv')
test_df = pd.read_csv('../input/30-days-of-ml/test.csv')
test_df.head().T | code |
2029019/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
sales = pd.read_csv('../input/nyc-rolling-sales.csv', index_col=0)
sales.head(3) | code |
2029019/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
sales = pd.read_csv('../input/nyc-rolling-sales.csv', index_col=0)
df = sales[['SALE PRICE', 'TOTAL UNITS']].d... | code |
90105911/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t')
df.head() | code |
90105911/cell_30 | [
"image_output_1.png"
] | place = ['NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth']
place | code |
90105911/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t')
df.columns | code |
90105911/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/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 |
90105911/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_28 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t')
df.columns
df[['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']] | code |
90105911/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
plt.style.use('ggplot')
import warnings
warnings.fi... | code |
90105911/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t')
df.info() | code |
104126253/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklear... | code |
104126253/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklear... | code |
104126253/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.tre... | code |
104126253/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.tre... | code |
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