path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73074345/cell_13 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, BaggingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
def score_model(model, X_t=X_train, X_v=X_valid, y_t=y_train, y_v=y_valid):
model.fit(X_t, y_t)
preds = model.predict(X_v)
return mean_absolute_error(y_v... | code |
73074345/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
X.drop(['target'], axis=1, inplace=T... | code |
73074345/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
y = X_full.target
y.head() | code |
73074345/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 |
73074345/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
X.drop(['target'], axis=1, inplace=T... | code |
73074345/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
X_full.head() | code |
73074345/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
y = X_full.target
X_full.info()
pri... | code |
325098/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoosti... | code |
325098/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
df = df[df['posteam'] == 'CHI']
df = df[df['DefensiveTeam'] == 'GB']
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayTy... | code |
325098/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
df = df[df['posteam'] == 'CHI']
df = df[df['DefensiveTeam'] == 'GB']
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayTy... | code |
1008127/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fig_code import plot_iris_knn
from fig_code import plot_iris_knn
plot_iris_knn() | code |
1008127/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
data = load_iris()
n_samples, n_features = data.data.shape
x_index = 1
y_index = 2
formatter = plt.FuncFormatter(lambda i, *args: data.target_names[int(i)])
plt.scatter(data.data[:, x_index], data.data[:, y_... | code |
1008127/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1008127/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import neighbors, datasets
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
data = load_iris()
n_samples, n_features = data.data.shape
x_index = 1
y_index = 2
formatter = plt.FuncFormatter(lambda i, *args: data.target_names[int(i)])
plt.color... | code |
1008127/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import Image
from IPython.display import Image
Image('http://scikit-learn.org/dev/_static/ml_map.png', width=800) | code |
1008127/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.datasets import load_iris
data = load_iris()
n_samples, n_features = data.data.shape
print(data.keys())
print(n_samples, n_features)
print(data.data.shape)
print(data.target.shape)
print(data.target_names)
print(data.feature_names) | code |
2025162/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'gra... | code |
2025162/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.describe() | code |
2025162/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'gra... | code |
2025162/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
import statsmodels.api as sm
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df... | code |
2025162/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.info() | code |
2025162/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax... | code |
2025162/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
f, ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2025162/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'gra... | code |
2025162/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'gra... | code |
2025162/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import scale
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
from scipy import stats | code |
2025162/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'gra... | code |
2025162/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', '... | code |
2025162/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'gra... | code |
2025162/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.head() | code |
329250/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
img = cv2.imread('../input/train_sm/set107_1.jpeg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img) | code |
329250/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
329250/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sub = pd.read_csv('../input/sample_submission.csv')
df_train = pd.read_csv('../input/train_sm/') | code |
329250/cell_5 | [
"text_plain_output_1.png"
] | import cv2
img = cv2.imread('../input/train_sm/set107_1.jpeg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img.shape | code |
129036266/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df.sample(3) | code |
129036266/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.describe() | code |
129036266/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2) | code |
129036266/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv') | code |
129036266/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df['Date Recorded'].values | code |
129036266/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px | code |
129036266/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df['OPM remarks'].value_counts() | code |
129036266/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df['Date Recorded'].values | code |
129036266/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.info() | code |
129036266/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df.info() | code |
129036266/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.describe(include='all') | code |
129040633/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_12.png",
"application_vnd.jupyter.stderr_output_8.png",
"application... | from skimage import io
from torchvision import datasets, transforms
import os
import pandas as pd
def fetch_dataset(path, attrs_name='lfw_attributes.txt', images_name='lfw-deepfunneled', dx=80, dy=80, dimx=64, dimy=64):
if not os.path.exists(images_name):
os.system('wget http://vis-www.cs.umass.edu/lfw/... | code |
129040633/cell_6 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 32
train_photos, val_photos, train_attrs, val_attrs = train_test_split(data, attrs, train_size=0.8, shuffle=False)
print('Training input shape: ', train_photos.shape)
d... | code |
129040633/cell_2 | [
"image_output_1.png"
] | import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device) | code |
129040633/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
from torch.autograd import Variable
from torchvision import datasets, transforms
from skimage import io
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data_utils
import torch
import matplotlib.pyplot as plt
import os
import pandas as pd
fr... | code |
129040633/cell_7 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 32
train_photos, val_photos, train_attrs, val_attrs = train_test_split(data, attrs, train_size=0.8, shuffle=False)
data_tr = torch.util... | code |
129040633/cell_16 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
from skimage import io
from sklearn.model_selection import train_test_split
from time import time
from torch.optim import lr_scheduler
from torchvision import datasets, transforms
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import numpy as np
impo... | code |
129040633/cell_12 | [
"text_plain_output_1.png"
] | from skimage import io
from sklearn.model_selection import train_test_split
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def fetch_dataset(path, attrs_name='... | code |
122248863/cell_4 | [
"text_plain_output_1.png"
] | import random
import random
list = []
for i in range(5):
list.append(random.randint(1, 10))
list.sort()
list = []
for i in range(1, 11, 2):
list.append(i)
buah = ['Anggur', 'Jambu', 'Apel', 'Pisang', 'Semangka']
print('List with Slicing = ', buah[2:5])
print('Panjang List ini =', len(list)) | code |
122248863/cell_2 | [
"text_plain_output_1.png"
] | import random
import random
list = []
for i in range(5):
list.append(random.randint(1, 10))
print('Contoh list acak :', list)
list.sort()
print('Lalu diurutkan :', list) | code |
122248863/cell_3 | [
"text_plain_output_1.png"
] | import random
import random
list = []
for i in range(5):
list.append(random.randint(1, 10))
list.sort()
list = []
for i in range(1, 11, 2):
list.append(i)
print('Contoh list dengan angka ganjil : \n', list) | code |
122248863/cell_5 | [
"text_plain_output_1.png"
] | mytupple = ((1, 2, 3, 4, 5, 6), ('A', 'N', 'G', 'G', 'U', 'R'))
for i in mytupple:
for j in i:
print(j) | code |
16129261/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
print('Question 2',... | code |
16129261/cell_20 | [
"text_plain_output_1.png"
] | print('The criterion of demographic parity allows us to examine whether the fraction of applicants getting loans is the same across groups.')
print('As the above data shows, the model estimates substantially higher default rates for minority applicants (4.6%) compared to non-minority applicants (0.1%).')
print('We also... | code |
16129261/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = ... | code |
16129261/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import numpy as np
import pandas as pd
import sklearn as sklearn
import sklearn.model_selection as sklearn_model_selection
import sklearn.ensemble as sklearn_ensemble
import os
print(os.listdir('../input')) | code |
16129261/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
print('Question 1:', '\n', '\n', 'Percentage of training set loans in default... | code |
16129261/cell_17 | [
"text_plain_output_1.png"
] | print('Question 10', '\n', '\n')
print('The loan granting scheme is group unaware. The model calculates the default probability of each applicants and then applies the same cut-off (50%) to all groups') | code |
16129261/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.... | code |
16129261/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = ... | code |
90105070/cell_4 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.... | code |
90105070/cell_6 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.... | code |
90105070/cell_8 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.... | code |
90105070/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.... | code |
2029345/cell_9 | [
"image_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predic... | code |
2029345/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', ... | code |
2029345/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
col_interest = ['ScreenPorch', 'MoSold', 'LotShape', 'SaleType', 'SaleCondition']
sa = data_train[col_interest]
sa.describe() | code |
2029345/cell_34 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metr... | code |
2029345/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metr... | code |
2029345/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType... | code |
2029345/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors | code |
2029345/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', ... | code |
2029345/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
print(data_train.columns) | code |
2029345/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType',... | code |
2029345/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType... | code |
2029345/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encod... | code |
2029345/cell_32 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metr... | code |
2029345/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.mode... | code |
2029345/cell_15 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, pre... | code |
2029345/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
data_train | code |
2029345/cell_17 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomF... | code |
2029345/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.mode... | code |
2029345/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predic... | code |
2029345/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.mode... | code |
2029345/cell_12 | [
"text_html_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
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBu... | code |
73067465/cell_25 | [
"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 # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preproce... | code |
73067465/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier
from sklearn.impute import KNNImputer,IterativeImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_te... | code |
73067465/cell_20 | [
"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 # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submit = pd.DataFrame(test['P... | code |
73067465/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 # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preproce... | code |
73067465/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 |
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