path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34150890/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layer... | code |
34150890/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from ... | code |
34150890/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd... | code |
34150890/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd... | code |
34150890/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenera... | code |
89142914/cell_2 | [
"text_html_output_1.png"
] | import os # os python utilities
import warnings
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from plotly.subplots import make_subplots
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
pri... | code |
89142914/cell_19 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
fg, ax =plt.subplots(1,2,figsize=(20,7))
ax[0].plot(data['Open'],label='Open',color='green')
ax[0].set_xlabel('Date',size=15)
ax[0].set_ylabel('Price',size=15)
ax[0]... | code |
89142914/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
data.head() | code |
89142914/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean... | code |
89142914/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
fg, ax = plt.subplots(1, 2, figsize=(20, 7))
ax[0].plot(data['Open'], label='Open', color='green')
ax[0].set_xlabel('Date', size=15)
ax[0].set_ylabel('Price', size=15)
ax[0].legend()
ax[1].plot(data['... | code |
89142914/cell_16 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))... | code |
89142914/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
data.head() | code |
89142914/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean... | code |
89142914/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
fig.show() | code |
89142914/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean... | code |
89142914/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
data.info() | code |
32063570/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.renam... | code |
32063570/cell_13 | [
"image_output_1.png"
] | from statsmodels.tools import add_constant
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
co... | code |
32063570/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(ax... | code |
32063570/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.renam... | code |
32063570/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum() | code |
32063570/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.renam... | code |
32063570/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import binarize
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sklearn
df = pd.read_csv('../input/framingham.... | code |
32063570/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.head() | code |
32063570/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
de... | code |
32063570/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import sklearn
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
sklearn.metrics.accuracy_score(y_test, y_pred) | code |
32063570/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import scipy.stats as st
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.mlab as mlab | code |
32063570/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
de... | code |
32063570/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve
from sklearn.preprocessing import binarize
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_cs... | code |
32063570/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
de... | code |
32063570/cell_14 | [
"text_plain_output_1.png"
] | from statsmodels.tools import add_constant
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
d... | code |
32063570/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(ax... | code |
32063570/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import binarize
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['... | code |
32063570/cell_12 | [
"text_plain_output_1.png"
] | from statsmodels.tools import add_constant
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = co... | code |
32063570/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
print('Total number of missing values:', count) | code |
90118273/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90118273/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90118273/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90118273/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90118273/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
90118273/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/... | code |
128005453/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
g = sns.pairplot(hrp, kind='reg', diag_kws={'color': 'red'})
g.fig.suptitle('Correlation of House rent prediction Dataset'... | code |
128005453/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Furnishing Status'].value_counts() | code |
128005453/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Area Locality'].value_counts() | code |
128005453/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.describe() | code |
128005453/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
g= sns.pairplot(hrp,kind="reg",diag_kws= {'color': 'red'})
g.fig.suptitle("Correlation of House re... | code |
128005453/cell_30 | [
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr() | code |
128005453/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes | code |
128005453/cell_29 | [
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary() | code |
128005453/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.head() | code |
128005453/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['City'].value_counts() | code |
128005453/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp['City'].value_counts().plot.pie() | code |
128005453/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape | code |
128005453/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp['Area Locality'].value_counts() | code |
128005453/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Floor'].value_counts() | code |
128005453/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna() | code |
128005453/cell_3 | [
"image_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.tail() | code |
128005453/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum() | code |
128005453/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
X.head() | code |
128005453/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Point of Contact'].value_counts() | code |
128005453/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
g= sns.pairplot(hrp,kind="reg",diag_kws= {'color': 'red'})
g.fig.suptitle("Correlation of House rent prediction Dataset",... | code |
128005453/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Area Type'].value_counts() | code |
128005453/cell_27 | [
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.param... | code |
128005453/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Tenant Preferred'].value_counts() | code |
128005453/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.info() | code |
105201509/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
#Converting a column of the dataframe to a list
genres_list = movies["genres"].head(10).to_list()
language_list = movies["language"].unique()
movies["budget"] = movies["budget"].astyp... | code |
105201509/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies | code |
105201509/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 |
105201509/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
movies.columns.to_list().index('imdb_score') | code |
105201509/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
Gross_amount = movies['gross'].sort_values(ascending=False)
Gross_amount.head(10) | code |
105201509/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
#Converting a column of the dataframe to a list
genres_list = movies["genres"].head(10).to_list()
language_list = movies["language"].unique()
movies["budget"] = movies["budget"].astyp... | code |
88086475/cell_21 | [
"text_plain_output_1.png"
] | # Generates metadata for test images.
path_to_test_metadata = "/kaggle/working/test.csv"
!echo "image,species,individual_id" > {path_to_test_metadata}
!ls {path_to_inputs}/test_images | sed "s/.jpg/.jpg,unknown,unknown/g" >> {path_to_test_metadata}
# Shows contents of generated metadata.
!head {path_to_test_metadata} | code |
88086475/cell_13 | [
"text_plain_output_1.png"
] | !head {path_to_inputs}/sample_submission.csv | code |
88086475/cell_25 | [
"text_plain_output_1.png"
] | # Installs required libraries.
!pip install numpy
!pip install pandas
!pip install keras
!pip install Pillow
!pip install imagehash
!pip install sewar
!pip install plotly | code |
88086475/cell_33 | [
"text_plain_output_1.png"
] | path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
print('Number of species:')
print(len(all_species))
pr... | code |
88086475/cell_44 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"image_output_17.png",
"image_output_30.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_28.png",
"text_plain_output_20.p... | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir... | code |
88086475/cell_41 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir... | code |
88086475/cell_11 | [
"text_plain_output_1.png"
] | path_to_inputs = "/kaggle/input/happy-whale-and-dolphin"
!ls {path_to_inputs} | code |
88086475/cell_19 | [
"text_plain_output_1.png"
] | !echo "Number of train_images:"
!ls {path_to_inputs}/train_images | cat -n | tail -1 | cut -f1
!echo ""
!echo "Number of test_images:"
!ls {path_to_inputs}/test_images | cat -n | tail -1 | cut -f1 | code |
88086475/cell_50 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir... | code |
88086475/cell_51 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir... | code |
88086475/cell_16 | [
"text_plain_output_1.png"
] | !ls {path_to_inputs}/train_images | head | code |
88086475/cell_17 | [
"text_plain_output_1.png"
] | !ls {path_to_inputs}/test_images | head | code |
88086475/cell_14 | [
"text_plain_output_1.png"
] | !head {path_to_inputs}/train.csv | code |
88086475/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir... | code |
88086475/cell_37 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir... | code |
17130551/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape | code |
17130551/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17130551/cell_8 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape
net.load_df(... | code |
17130551/cell_10 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape
net.load_df(... | code |
17130551/cell_5 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
show_widget = False
from clustergrammer2 import net
if show_widget == False:
print('\n-----------------------------------------------------')
print('>>> <<<')
print('>>> Please set show_widget to True to see widgets <<<')
pr... | code |
1005893/cell_6 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tflearn
df_trn = pd.read_csv('../input/train.csv')
df_tst = pd.read_csv('../input/test.csv')
x_trn = df_trn.ix[:, 1:].values
y_tr... | code |
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