path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90150781/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.info() | code |
90150781/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_counts().plot(kind='bar'... | code |
90150781/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df['class'].value_counts() | code |
90150781/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_counts().plot(kind='bar'... | code |
90150781/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_counts().plot(kind='bar'... | code |
90150781/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_counts().plot(kind='bar'... | code |
90150781/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_c... | code |
90150781/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.describe() | code |
90150781/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns | code |
90150781/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df1 = pd.read_csv('/kaggle/input/flight-price-prediction/business.csv')
df2 = pd.read_csv('/kaggle/input/flight-price-prediction/economy.csv')
print(df1.shape)
print(df2.shape) | code |
105201476/cell_11 | [
"text_plain_output_1.png"
] | from fastai.tabular.all import df_shrink
from time import sleep
import gc
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files_static = [f'/kaggle/input/cccscicandmal2020/StaticAnalysis/{f}' for f in os.listdir('/kaggle/input/cccsc... | code |
105201476/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:
if 'StaticAnalysis' in dirname:
print(os.path.join(dirname, filename)) | code |
105201476/cell_7 | [
"text_plain_output_1.png"
] | from fastai.tabular.all import df_shrink
from time import sleep
import gc
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files_static = [f'/kaggle/input/cccscicandmal2020/StaticAnalysis/{f}' for f in os.listdir('/kaggle/input/cccsc... | code |
105201476/cell_5 | [
"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"
] | !ls -lath /kaggle/input/cccscicandmal2020/StaticAnalysis | code |
329772/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
... | code |
329772/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
... | code |
329772/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
... | code |
329772/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
... | code |
33098146/cell_4 | [
"text_html_output_1.png"
] | import os
import pandas as pd
base_path = '/kaggle'
if os.path.exists(base_path):
input_path = os.path.join(base_path, 'input', 'nlp-getting-started')
output_path = os.path.join(base_path, 'working')
else:
base_path = 'data'
input_path = base_path
output_path = os.path.join(base_path, 'submissions... | code |
33098146/cell_1 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | # Upgrade packages for work with new Pandas version
!pip install --upgrade pandas-profiling
!pip install --upgrade hypertools
!pip install --upgrade pandas | code |
90109387/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_44 | [
"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",... | from sklearn.decomposition import PCA
import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axi... | code |
90109387/cell_55 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axi... | code |
90109387/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
test_data | code |
90109387/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_41 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
train_data.shape | code |
90109387/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_50 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_da... | code |
90109387/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data | code |
90109387/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_3 | [
"text_plain_output_1.png"
] | 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
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection ... | code |
90109387/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_46 | [
"image_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axi... | code |
90109387/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_data.drop('id', axis=1, inplace=True)
train_data.drop('id'... | code |
90109387/cell_53 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_da... | code |
90109387/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
90109387/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
train_data.shape
train_data.head() | code |
90109387/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
train_data | code |
90109387/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/tabular-playground-series-mar-2021/train.csv')
test_data = pd.read_csv('../input/tabular-playground-series-mar-2021/test.csv')
all_data = pd.concat([train_data, test_data])
all_data
train_data.shape
all_da... | code |
1004405/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import re
import re
import nltk
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
english_stemmer = nltk.stem.SnowballStemmer('english')
from sklearn.model_selection import train_test_split
from sklearn.feature_ext... | code |
1004405/cell_13 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(df['building_id']) | code |
1004405/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
df.columns
num_feats = ['bathrooms', 'bedrooms', 'latitude', 'longitude', 'price', 'num_photos', 'num_features', 'num_description_words', 'created_year', 'created_month', 'created_day', 'buildin... | code |
1004405/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
clf = RandomForestClassifier(n_estimators=1500)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pred)
from sklearn.ensemble import BaggingClas... | code |
1004405/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.neighbors import KNeighborsClassifier
clf = RandomForestClassifier(n_estimators=1500)... | code |
1004405/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
print(df.shape) | code |
1004405/cell_2 | [
"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 |
1004405/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
df['street_address'].value_counts().plot(kind='hist', bins=50) | code |
1004405/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
df.head() | code |
1004405/cell_32 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
clf = RandomForestClassifier(n_estimators=1500)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_... | code |
1004405/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
clf = RandomForestClassifier(n_estimators=1500)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pred) | code |
1004405/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
print(df.shape) | code |
1004405/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(df['building_id'])
df['building_id'] = le.fit_transform(df['building_id'])
df['build... | code |
1004405/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../... | code |
1004405/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
clf = RandomForestClassifier(n_estimators=1500)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_v... | code |
1004405/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
df.columns | code |
1004405/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../input/train.json', 'r'))
print(df['building_id'].value_counts().nlargest(50)) | code |
1004405/cell_37 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../... | code |
1004405/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_json(open('../... | code |
128027861/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd
df = pd.read_csv('/kaggle/input/airways-customer-data/filtered_customer_booking.csv', index_col=0)
df = df.reset_index(drop=True)
df_final = df
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(handle_unknown='ignore')
encode... | code |
128027861/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams.update({'font.size': 14})
d... | code |
128027861/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams.update({'font.size': 14}) | code |
128027861/cell_45 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEnco... | code |
128027861/cell_18 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
df = pd.read_csv('/kaggle/input/airways-customer-data/filtered_customer_booking.csv', index_col=0)
df = df.reset_index(drop=True)
df_final = df
from sklearn.preprocessing import OneHotEncoder
encod... | code |
128027861/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from yellowbrick.classifier import ConfusionMatrix
clf_rf = RandomForestClassifier(max_depth=50, min_samples_split=5, random_state=0)
cm = ConfusionMatrix(clf_rf, classes=[0, 1])
cm.fit(X_train, y_train)
cm.score(X_train, y_train) | code |
128027861/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Sta... | code |
128027861/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/airways-customer-data/filtered_customer_booking.csv', index_col=0)
df = df.reset_index(drop=True)
df | code |
17109112/cell_21 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import matplotlib.pyplot as plt
import numpy as np
import os
import torch
import torchvision
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.No... | code |
17109112/cell_4 | [
"image_output_1.png"
] | import sys
import sys
sys.version | code |
17109112/cell_34 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import matplotlib.pyplot as plt
import numpy as np
import os
import torch
import torchvision
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.No... | code |
17109112/cell_23 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
model_vgg = models.vgg16(pretrained=True) | code |
17109112/cell_33 | [
"text_plain_output_1.png"
] | import json
import json
fpath = '../input/imagenet-class-index/imagenet_class_index.json'
with open(fpath) as f:
class_dict = json.load(f)
dic_imagenet = [class_dict[str(i)][1] for i in range(len(class_dict))]
print([dic_imagenet[i] for i in preds_try.data]) | code |
17109112/cell_20 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import matplotlib.pyplot as plt
import numpy as np
import os
import torch
import torchvision
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.No... | code |
17109112/cell_29 | [
"image_output_1.png"
] | from torchvision import models, transforms, datasets
import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
inputs_try.shape
model_vgg = models.vgg16(pretrained=True)
inputs_try, lables_try = (inputs_try.to(device), labels_try.to(device))
model_vgg = model_vgg.to(de... | code |
17109112/cell_39 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
import torch
import torch.nn as nn
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.... | code |
17109112/cell_48 | [
"text_plain_output_1.png"
] | predictions, all_proba, all_classes = test_model(model_vgg, load_test, size=dset_sizes['valid']) | code |
17109112/cell_11 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), normalize])
dsets = {x: dataset... | code |
17109112/cell_19 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import matplotlib.pyplot as plt
import numpy as np
import os
import torchvision
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([transforms... | code |
17109112/cell_7 | [
"text_plain_output_1.png"
] | import os
data_dir = '../input/dogscats/dogscats/dogscats/'
print(os.listdir('../input/dogscats/dogscats/dogscats/')) | code |
17109112/cell_49 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import matplotlib.pyplot as plt
import numpy as np
import os
import torch
import torch.nn as nn
import torchvision
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
no... | code |
17109112/cell_32 | [
"text_plain_output_1.png"
] | vals_try | code |
17109112/cell_28 | [
"image_output_1.png"
] | from torchvision import models, transforms, datasets
import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
inputs_try.shape
model_vgg = models.vgg16(pretrained=True)
inputs_try, lables_try = (inputs_try.to(device), labels_try.to(device))
model_vgg = model_vgg.to(de... | code |
17109112/cell_15 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format... | code |
17109112/cell_16 | [
"text_plain_output_1.png"
] | labels_try | code |
17109112/cell_3 | [
"image_output_1.png"
] | import torch
torch.__version__ | code |
17109112/cell_17 | [
"text_plain_output_1.png"
] | inputs_try.shape | code |
17109112/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from torchvision import models, transforms, datasets
import os
import torch
import torch.nn as nn
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.... | code |
17109112/cell_46 | [
"text_plain_output_1.png"
] | train_model(model_vgg, load_train, size=dset_sizes['train'], epochs=2, optimizer=optimizer_vgg) | code |
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