path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18137853/cell_5 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from tqdm import tqdm, tqdm_notebook
import matplotlib.patches as patches
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g.... | code |
106213588/cell_5 | [
"text_plain_output_1.png"
] | import torch
class Var:
"""simple variable container"""
def __init__(self, value):
self.v = value
self.grad = 0
self.dag = None
def __add__(self, other):
"""Overloads + operator"""
if not isinstance(other, Var):
other = Var(other)
result = Var(sel... | code |
32068950/cell_13 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
from sklearn.linear_model import RidgeCV
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['C... | code |
32068950/cell_9 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Count... | code |
32068950/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Country')
countries.head() | code |
32068950/cell_20 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
from sklearn.linear_model import RidgeCV
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['C... | code |
32068950/cell_11 | [
"text_html_output_1.png"
] | from collections import OrderedDict
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
c... | code |
32068950/cell_19 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Count... | code |
32068950/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))
from collections import OrderedDict
from sklearn.linear_model import RidgeCV
from sklearn.ensemble import ExtraTreesRegressor
f... | code |
32068950/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Country')
train_data = pd.read_csv('/kaggle/input/covid19... | code |
32068950/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Country')
train_data = pd.read_csv('/kaggle/input/covid19... | code |
32068950/cell_15 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Count... | code |
32068950/cell_17 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['Country'] = countries.Country.str.lower()
countries = countries.set_index('Count... | code |
32068950/cell_12 | [
"text_html_output_1.png"
] | from collections import OrderedDict
from sklearn.linear_model import RidgeCV
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
N_FEATURES = 5
countries = pd.read_csv('/kaggle/input/countries-of-the-world/countries of the world.csv', decimal=',')
countries['C... | code |
105180497/cell_25 | [
"image_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
105180497/cell_29 | [
"text_plain_output_1.png"
] | from statsmodels.formula.api import ols
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 statsmodels.api as sm
train_data = pd.read_csv('../input/house-prices-advanced-regressio... | code |
105180497/cell_26 | [
"text_plain_output_1.png"
] | from statsmodels.formula.api import ols
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 statsmodels.api as sm
train_data = pd.read_csv('../input/house-prices-advanced-regressio... | code |
105180497/cell_19 | [
"text_plain_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
105180497/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 |
105180497/cell_7 | [
"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)
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.head() | code |
105180497/cell_18 | [
"text_plain_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
105180497/cell_28 | [
"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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices... | code |
105180497/cell_15 | [
"text_html_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
105180497/cell_17 | [
"text_plain_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
105180497/cell_14 | [
"text_plain_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
105180497/cell_22 | [
"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 # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices... | code |
105180497/cell_12 | [
"text_html_output_1.png"
] | 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_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/t... | code |
90137984/cell_9 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from numpy import linspace
from pandas import read_csv
from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split, Ra... | code |
90137984/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import read_csv
from pandas import read_csv
train = read_csv('/kaggle/input/telecom-churn-case-study-hackathon-gc1/train.csv')
train = train.drop(['id', 'circle_id'], axis=1)
test = read_csv('/kaggle/input/telecom-churn-case-study-hackathon-gc1/test.csv')
sample = read_csv('/kaggle/input/telecom-churn-case... | code |
90137984/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import read_csv
from pandas import read_csv
train = read_csv('/kaggle/input/telecom-churn-case-study-hackathon-gc1/train.csv')
train = train.drop(['id', 'circle_id'], axis=1)
test = read_csv('/kaggle/input/telecom-churn-case-study-hackathon-gc1/test.csv')
sample = read_csv('/kaggle/input/telecom-churn-case... | code |
105175580/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree... | code |
105175580/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train.describe() | code |
105175580/cell_11 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import graphviz
tra... | code |
105175580/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | pred = clf.predict(test)
pred
predict = clf.predict_proba(test)
predict | code |
105175580/cell_3 | [
"text_plain_output_1.png"
] | train.info() | code |
105175580/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn import tree
from sklearn import tree
train.dtypes
pred = clf.predict(test)
pred
predict = clf.predict_proba(test)
predict
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
x = train.drop('TARGET', axis=1)
y = train.TARGET
clf = tree.DecisionTreeClassifi... | code |
105175580/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassi... | code |
105175580/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train.dtypes | code |
73078742/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axes[0])
ax1 = df.posttest.plot.kde(ax=axes[0])
ax1.legend(['Pretest', 'Posttest'... | code |
73078742/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16, 10), ax=axes[0])
ax1 = df.posttest.plot.kde(ax=axes[0])
ax1.legend(['Pretest', 'Posttest... | code |
73078742/cell_20 | [
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.feature_selection import RFE
from sklearn.metrics import explained_variance_score
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.... | code |
73078742/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axes[0])
ax1 = df.posttest.plot.kde(ax=axes[0])
ax1.legend(['Pretest', 'Posttest'... | code |
73078742/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df | code |
73078742/cell_11 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axes[0])
ax1 = df.posttest.plot.kde(ax=axes[0]... | code |
73078742/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axes[0])
ax1 = df.posttest.plot.kde(ax=axes[0])
ax1.legend(['Pretest', 'Posttest'... | code |
73078742/cell_15 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.feature_selection import RFE
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes =... | code |
73078742/cell_16 | [
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.feature_selection import RFE
from sklearn.metrics import explained_variance_score
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.... | code |
73078742/cell_17 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.feature_selection import RFE
from sklearn.metrics import explained_variance_score
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.... | code |
73078742/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axe... | code |
73078742/cell_12 | [
"image_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axe... | code |
73078742/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df
plt.style.use('ggplot')
fig, axes = plt.subplots(nrows=2)
ax1 = df.pretest.plot.kde(figsize=(16,10), ax=axes[0])
ax1 = df.posttest.plot.kde(ax=axes[0])
ax1.legend(['Pretest', 'Posttest'... | code |
18159704/cell_4 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import numpy as np
train_data1 = glob('../input/fruits-360_dataset/fruits-360/Training/Raspberry/*')
train_data2 = glob('../input/fruits-360_dataset/fruits-360/Training/Pomelo Sweetie/*')
test_data1 = glob('../input/fruits-360_dataset/fruits-360/Test/Raspberry/*')
test_data2 = glob('... | code |
18159704/cell_2 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
train_data1 = glob('../input/fruits-360_dataset/fruits-360/Training/Raspberry/*')
train_data2 = glob('../input/fruits-360_dataset/fruits-360/Training/Pomelo Sweetie/*')
test_data1 = glob('../input/fruits-360_dataset/fruits-360/Test/Raspberry/*')
test_data2 = glob('../input/fruits-360_... | code |
18159704/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import minmax_scale
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Sequential
from keras.layers import Dense
from g... | code |
18159704/cell_3 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import numpy as np
train_data1 = glob('../input/fruits-360_dataset/fruits-360/Training/Raspberry/*')
train_data2 = glob('../input/fruits-360_dataset/fruits-360/Training/Pomelo Sweetie/*')
test_data1 = glob('../input/fruits-360_dataset/fruits-360/Test/Raspberry/*')
test_data2 = glob('... | code |
18159704/cell_5 | [
"text_plain_output_1.png"
] | from glob import glob
from keras.layers import Dense
from keras.models import Sequential
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
import cv2
import numpy as np
train_data1 = glob('../input/fruits-360_dataset/fruits-360/Training/Raspberry/*')
trai... | code |
18135845/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_csv('../input/Amazon.... | code |
18135845/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_c... | code |
18135845/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_c... | code |
18135845/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_c... | code |
18135845/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_c... | code |
18135845/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_c... | code |
18135845/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_csv('../input/Amazon.... | code |
18135845/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt
MSFT = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN = pd.read_csv('../input/Amazon.csv', header=None)
MSFT['AdjClose'] = pd.read_csv('../input/Microsoft.csv', header=None)
AMZN['AdjClose'] = pd.read_c... | code |
17122172/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from gensim.models import KeyedVectors, Word2Vec
from matplotlib import pyplot
from nltk.tokenize import RegexpTokenizer
from sklearn.decomposition import PCA
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
forum_posts = pd.read_csv('../input/meta-kaggle/ForumMessages.csv')['Message'].astype... | code |
17122172/cell_5 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors, Word2Vec
from nltk.tokenize import RegexpTokenizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
forum_posts = pd.read_csv('../input/meta-kaggle/ForumMessages.csv')['Message'].astype('str')
tokenizer = RegexpTokenizer('\\w+')
data_tokenized = [w.lower... | code |
72106102/cell_13 | [
"text_plain_output_1.png"
] | missing_values = X_train_full.isnull().sum()
cat_cols = [col for col in X_train_full.columns if X_train_full[col].dtype == 'object' and X_train_full[col].nunique() < 10]
print('Number of unique category for each categorical Feature')
for cols in cat_cols:
print(f'{cols}: {X_train_full[cols].nunique()}') | code |
72106102/cell_9 | [
"text_plain_output_1.png"
] | X_train_full.head() | code |
72106102/cell_11 | [
"text_html_output_1.png"
] | print(f'Shape of training data: {X_train_full.shape}')
missing_values = X_train_full.isnull().sum()
print(missing_values[missing_values > 0]) | code |
72106102/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
X_full = pd.read_csv('../in... | code |
72106102/cell_10 | [
"text_html_output_1.png"
] | X_train_full.describe() | code |
128016720/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)... | code |
128016720/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
pipe = make_pipeline(StandardScaler(), KNeighborsClassifier())
param_grid = {'kneighborsclassifier__n_neighbors': range(1, ... | code |
128016720/cell_11 | [
"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
data = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
data['label'].sort_values() | code |
128016720/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 |
128016720/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
i... | code |
128016720/cell_28 | [
"image_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
pipe = make_pipeline(StandardScaler(), KNeighborsClassifier())
param_grid = {'kneighborsclassifier__n_neighbors': range(1, ... | code |
128016720/cell_8 | [
"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
data = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
plt.figure(figsize=(8, 6))
sns.heatmap(data.corr(), cmap='coolwarm') | code |
128016720/cell_15 | [
"text_plain_output_1.png"
] | print(f'Training data shape: {X_train.shape}')
print(f'Training labels shape: {y_train.shape}')
print(f'Test data shape: {X_test.shape}')
print(f'Test labels shape: {y_test.shape}') | code |
128016720/cell_17 | [
"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 random
import random
import seaborn as sns
data = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
import random
classes = ['T-shirt', 'Trouser', 'Pullover', 'Dress',... | code |
128016720/cell_10 | [
"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
data = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
if data.isnull().values.any():
print('There is something missing in the data')
else:
prin... | code |
128016720/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/fashionmnist/fashion-mnist_train.csv')
data.head() | code |
50244608/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
employees.describe() | code |
50244608/cell_9 | [
"image_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape | code |
50244608/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees['Attrition'].value_counts() | code |
50244608/cell_4 | [
"text_plain_output_1.png"
] | pip install -U plotly | code |
50244608/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(... | code |
50244608/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(['EmployeeCount', 'StandardHours'... | code |
50244608/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from plotly... | code |
50244608/cell_40 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(... | code |
50244608/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(['EmployeeCount', 'StandardHours'... | code |
50244608/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum() | code |
50244608/cell_7 | [
"image_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.head() | code |
50244608/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
employees['EducationField'].unique() | code |
50244608/cell_8 | [
"image_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.info() | code |
50244608/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
employees['Attrition'].unique() | code |
50244608/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
employees['OverTime'].unique() | code |
50244608/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(... | code |
50244608/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
employees['Over18'].unique() | code |
50244608/cell_43 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
employees = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.shape
employees.dtypes
employees.isnull().sum()
employees.duplicated().sum()
sns.set_style('darkgrid')
employees.drop(... | code |
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