path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129023624/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
test_df | code |
129023624/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 |
129023624/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape | code |
129023624/cell_45 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-la... | code |
129023624/cell_28 | [
"text_plain_output_1.png"
] | !pip install sentencepiece | code |
129023624/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count | code |
129023624/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
trai... | code |
129023624/cell_47 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from tra... | code |
129023624/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, KFold, GridSearchCV
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion... | code |
129023624/cell_35 | [
"text_plain_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
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 ... | code |
129023624/cell_43 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-la... | code |
129023624/cell_31 | [
"text_plain_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE) | code |
129023624/cell_46 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix,precision_recall_fscore_support
fro... | code |
129023624/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'],... | code |
129023624/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'],... | code |
129023624/cell_53 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from tra... | code |
129023624/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'],... | code |
129023624/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'],... | code |
129023624/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
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 ... | code |
73099194/cell_9 | [
"text_html_output_1.png"
] | dicom_test = Dicom()
dicom_test.exec('test') | code |
73099194/cell_11 | [
"text_plain_output_1.png"
] | dicom_test = Dicom()
dicom_test.exec('test')
dicom_test.df.head() | code |
73099194/cell_8 | [
"text_html_output_1.png"
] | dicom_train = Dicom()
dicom_train.exec('train') | code |
73099194/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | code | |
73099194/cell_10 | [
"text_plain_output_1.png"
] | dicom_train = Dicom()
dicom_train.exec('train')
dicom_train.df.head() | code |
73099194/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pydicom import dcmread
from pydicom import dcmread
data_dir = '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification'
fpath = data_dir + '/train/00000/FLAIR/Image-1.dcm'
ds = dcmread(fpath)
print(ds) | code |
50225023/cell_21 | [
"text_plain_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collecti... | code |
50225023/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.f... | code |
50225023/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counte... | code |
50225023/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
train.head() | code |
50225023/cell_19 | [
"image_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collecti... | code |
50225023/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
print('There are {} rows and {} columns in train'.format(train.shape[0], train.shape[1]))
print('There are {} rows and {} columns in test'.format(test.shape[0], test.shape[1])) | code |
50225023/cell_16 | [
"image_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.f... | code |
50225023/cell_17 | [
"image_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.f... | code |
50225023/cell_10 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.f... | code |
90126740/cell_9 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.describe(include='all') | code |
90126740/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique() | code |
90126740/cell_19 | [
"image_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
import matplotlib.pyplot as plt
import pandas as pd
import scipy.cluster.hierarchy as sch
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique()
X = dataset.iloc[:, 1:3].values.round(2)
i... | code |
90126740/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset | code |
90126740/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import scipy.cluster.hierarchy as sch
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique()
X = dataset.iloc[:, 1:3].values.round(2)
import scipy.cluster.hierarchy as sch
den = sch.dendro... | code |
90126740/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique()
X = dataset.iloc[:, 1:3].values.round(2)
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClu... | code |
90126740/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum() | code |
105204136/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_9 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_6 | [
"text_html_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_7 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_16 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
105204136/cell_14 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearch... | code |
89136081/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv... | code |
89136081/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv... | code |
89136081/cell_4 | [
"image_output_1.png"
] | pip install openpyxl | code |
89136081/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_18 | [
"image_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_24 | [
"image_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
89136081/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv... | code |
89136081/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athlet... | code |
34143777/cell_13 | [
"text_plain_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequent... | code |
34143777/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 |
34143777/cell_7 | [
"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)
train = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_... | code |
34143777/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequent... | code |
34143777/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test... | code |
34143777/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequent... | code |
34143777/cell_12 | [
"text_plain_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequent... | code |
105203731/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.corr()
sns.heatmap(df.corr(), annot=True) | code |
105203731/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
y_train_pred = lr_model.predict(x_train_sm)
x_test_sm = sm.add_constant(x_test)
y_test_pr... | code |
105203731/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape | code |
105203731/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
... | code |
105203731/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.corr()
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()... | code |
105203731/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/random-linear-regression/train.csv')
df.shape
plt.scatter(df['x'], df['y'])
plt.show() | code |
105203731/cell_11 | [
"text_plain_output_1.png"
] | x_train.shape | code |
105203731/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
... | code |
105203731/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
sns.regplot(x='x', y='y', data=df) | code |
105203731/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.corr() | code |
105203731/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary() | code |
105203731/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
... | code |
105203731/cell_14 | [
"image_output_1.png"
] | import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params | code |
105203731/cell_12 | [
"text_html_output_1.png"
] | import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm | code |
105203731/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.head() | code |
33098715/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.figure(figsize=(18, 8))
... | code |
33098715/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.describe().transpose() | code |
33098715/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum() | code |
33098715/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
data['model'].value_counts().head() | code |
33098715/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 |
33098715/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
data.head() | code |
33098715/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
print('Unique car brands: ', data['brand'].nunique())
print('Unique car models: ', d... | code |
33098715/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.info() | code |
33098715/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
33098715/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xtick... | code |
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