path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89141106/cell_82 | [
"text_plain_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_51 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_62 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_59 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_58 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_78 | [
"text_plain_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainType': 'Tipo de dor', 'RestingBP': 'Pressão', 'Cholesterol': 'Colesterol', 'FastingBS': 'Glicemia', 'RestingECG': 'Eletro', 'MaxHR': 'BPM', 'ExerciseA... | code |
89141106/cell_75 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_66 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_93 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': '... | code |
89141106/cell_105 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
rfc = RandomForestClassifier(n_jobs=-1, n_estimators=500, max_depth=70, max_features=2, random_state=0)
knn = KNeighborsClassifier(n_neighbors=5, algorithm='kd_tree', weights='uniform', n... | code |
89141106/cell_27 | [
"image_output_1.png"
] | import pandas as pd
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainType': 'Tipo de dor', 'RestingBP': 'Pressão', 'Cholesterol': 'Colesterol', 'FastingBS': 'Glicemia', 'RestingECG': 'Eletro', 'MaxHR': 'BPM', 'ExerciseA... | code |
89141106/cell_12 | [
"image_output_1.png"
] | import pandas as pd
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados | code |
89141106/cell_71 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_70 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
18104935/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fi... | code |
18104935/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fig.add_subplot(2, 2, 1)
ax2=fig.add_subplot(2,2,2)
ax3=fig... | code |
18104935/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fig.add_subplot(2, 2, 1)
ax2=fig.add_subplot(2,2,2)
ax3=fig.add_subplot(2,2,3)
ax4... | code |
18104935/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fi... | code |
18104935/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fig.add_subplot(2, 2, 1)
ax2=fig.add_subplot(2,2,2)
ax3=fig... | code |
18104935/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/train.csv'
df = pd.read_csv(path)
df.head() | code |
18104935/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fi... | code |
18104935/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fig.add_subplot(2, 2, 1)
ax2=fig.add_subplot(2,2,2)
ax3=fig... | code |
18104935/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18104935/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns | code |
18104935/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fi... | code |
18104935/cell_8 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.... | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2... | code |
18104935/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fig.add_subplot(2, 2, 1)
ax2=fig.add_subplot(2,2,2)
ax3=fig... | code |
18104935/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '../input/train.csv'
df = pd.read_csv(path)
df.columns
fig = plt.figure(2)
ax1 = fi... | code |
130009993/cell_2 | [
"text_plain_output_1.png"
] | !pip install pandasai | code |
130009993/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode, iplot, plot
import plotly as py
init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
import math
import seaborn as sns
from pandas_profiling import ProfileReport... | code |
130009993/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandasai import PandasAI
from pandasai.llm.openai import OpenAI | code |
34119091/cell_42 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mi... | code |
34119091/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.info() | code |
34119091/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mileage = np.sqrt(data['m... | code |
34119091/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
log_mileage.skew() | code |
34119091/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
sns.distplot(data['mileage'], hist=True) | code |
34119091/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mileage = np.sqrt(data['m... | code |
34119091/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage | code |
34119091/cell_41 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mileage = np.sqrt(data['m... | code |
34119091/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
data['mileage'].skew() | code |
34119091/cell_7 | [
"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 |
34119091/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mileage = np.sqrt(data['m... | code |
34119091/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
import seaborn as sns
data['price'].hist(grid=False) | code |
34119091/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
data['price'].skew() | code |
34119091/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
sns.distplot(data['price'], hist=True) | code |
34119091/cell_35 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mileage = np.sqrt(data['m... | code |
34119091/cell_46 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mileage = np.sqrt(data['m... | code |
34119091/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape | code |
34119091/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data.head(2) | code |
34119091/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.head(2) | code |
34119091/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv')
data = data.drop(['Unnamed: 0'], axis=1)
data.shape
log_mileage = np.log(data['mileage'])
log_mileage
sqrt_mi... | code |
73082288/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/train.csv')
train_df.drop(train_df[(train_df.target == 0) & (train_df.standard_error == 0)].index, inplace=True)
train_df.reset_index(drop=True, inplace=True)
test_df = pd.read_csv('... | code |
73082288/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/train.csv')
train_df.drop(train_df[(train_df.target == 0) & (train_df.standard_error == 0)].index, inplace=True)
train_df.reset_index(drop=True, inplace=True)
test_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/test.csv')
su... | code |
73082288/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
impor... | code |
73082288/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/train.csv')
train_df.drop(train_df[(train_df.target == 0) & (train_df.standard_error == 0)].index, inplace=True)
train_df.reset_index(drop=True, inplace=True)
test_df = pd.read_csv('... | code |
73082288/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/train.csv')
train_df.drop(train_df[(train_df.target == 0) & (train_df.standard_error == 0)].index, inplace=True)
train_df.reset_index(drop=True, inplace=True)
test_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/test.csv')
su... | code |
73082288/cell_10 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/train.csv')
train_df.drop(train_df[(train_df.target == 0) & (train_df.standard_error == 0)].index, inplace=True)
trai... | code |
73082288/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import pandas as pd
import re
train_df = pd.read_csv('/kaggle/input/commonlitreadabilityprize/train.csv')
train_df.drop(train_df[(train_df.target == 0) & (train_df.standard_error == 0)].index, inplace=True)
train_df.reset_index(drop=True, inplace=True)
test_df = pd.read... | code |
72085616/cell_63 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_21 | [
"text_plain_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
test_cols_with_missing = []
for col in X_train.columns:
if X_train[col].isnull().any():
test_cols_with_missing.append(col)
test_cols_with_missing
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
... | code |
72085616/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape
melb_predictors.dtypes
X = melb_pred... | code |
72085616/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
data.head() | code |
72085616/cell_57 | [
"text_plain_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
X_train_full.shape
X_valid_full.shape
X_valid_full.columns
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
cols_with_missing
X_train_full.drop(cols_with_missing, axis=... | code |
72085616/cell_56 | [
"text_plain_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
X_train_full.shape
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
cols_with_missing | code |
72085616/cell_79 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
... | code |
72085616/cell_33 | [
"text_html_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
cols_with_missing | code |
72085616/cell_20 | [
"text_plain_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
test_cols_with_missing = []
for col in X_train.columns:
if X_train[col].isnull().any():
test_cols_with_missing.append(col)
test_cols_with_missing
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
... | code |
72085616/cell_76 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_w... | code |
72085616/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_29 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape
data.shape
y = data.Price
y.shape | code |
72085616/cell_41 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
... | code |
72085616/cell_61 | [
"text_plain_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
X_train_full.shape
X_valid_full.shape
X_valid_full.columns
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
cols_with_missing
X_train_full.drop(cols_with_missing, axis=... | code |
72085616/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape | code |
72085616/cell_54 | [
"text_plain_output_1.png"
] | X_valid_full.shape
X_valid_full.columns
X_valid_full.head() | code |
72085616/cell_72 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
... | code |
72085616/cell_67 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_69 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_52 | [
"text_plain_output_1.png"
] | X_valid_full.shape | code |
72085616/cell_64 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape | code |
72085616/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape
melb_predictors.dtypes
X = melb_pred... | code |
72085616/cell_18 | [
"text_plain_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
test_cols_with_missing = []
for col in X_train.columns:
if X_train[col].isnull().any():
test_cols_with_missing.append(col)
test_cols_with_missing | code |
72085616/cell_51 | [
"text_plain_output_1.png"
] | X_train_full.shape | code |
72085616/cell_68 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_59 | [
"text_html_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing
X_train_full.shape
X_valid_full.shape
X_valid_full.columns
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
cols_with_missing
X_train_full.drop(cols_with_missing, axis=... | code |
72085616/cell_28 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape
melb_predictors.dtypes | code |
72085616/cell_38 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_75 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape
data.shape | code |
72085616/cell_66 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_17 | [
"text_html_output_1.png"
] | cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
cols_with_missing | code |
72085616/cell_35 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_77 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
... | code |
72085616/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
def score_dataset(X_train, X_valid, y_train, y_valid):
model = RandomForestRegressor(n_estimators=10, random_state=0... | code |
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