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stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
2008232/cell_11 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').to_julian_date()
df.DISCOVERY_DATE = pd.to_datetime(df.DISCOVERY_DATE - epoch, unit='D')
df.CONT_DATE = pd.to_datetime(df.CONT_DATE - epoch, unit='D')
df.index = pd.to_datetime(df.DISCOVERY_DATE)
df_wa = df[df.STATE == 'WA']
# analysis for yearly burn area
y=df_wa.FIRE_SIZE.resample('AS').sum().fillna(0)
ax = y.plot(kind='bar',figsize=(10,6))
# set xaxis major labels
# Make most of the ticklabels empty so the labels don't get too crowded
ticklabels = ['']*len(y.index)
# Every 4th ticklable shows the month and day
#ticklabels[::5] = [item.strftime('%b %d') for item in y.index[::4]]
# Every 12th ticklabel includes the year
#ticklabels[::5] = [item.strftime('%b %d\n%Y') for item in y.index[::5]]
ticklabels[::1] = [item.strftime('%Y') for item in y.index[::1]]
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.gcf().autofmt_xdate()
plt.xlabel('Year')
plt.ylabel('Acres Burned');
plt.title('Acres Burned by Year');
# Extract the data we're interested in
lat = df_wa['LATITUDE'].values
lon = df_wa['LONGITUDE'].values
fsize = df_wa['FIRE_SIZE'].values
# Draw the map background
fig = plt.figure(figsize=(17, 10))
m = Basemap(projection='mill',llcrnrlon=-124. ,llcrnrlat=45.3,urcrnrlon=-117 ,urcrnrlat=49.1, resolution = 'h', epsg = 4269)
# do not know how to download the following background image with kaggel kernel, so I had to
# comment out the command
#m.arcgisimage(service='World_Physical_Map', xpixels = 5000, verbose= False)
m.drawcoastlines(color='blue')
m.drawcountries(color='blue')
m.drawstates(color='blue')
# scatter plot
m.scatter(lon, lat, latlon=True,
c=np.log10(fsize), s=fsize*.01,
cmap='Set1', alpha=0.5)
# create colorbar and legend
plt.colorbar(label=r'$\log_{10}({\rm Size Acres})$',fraction=0.02, pad=0.04)
plt.clim(3, 7)
cause = df_wa.STAT_CAUSE_DESCR.value_counts()
fig, ax = plt.subplots(figsize=(10, 10))
ax.pie(x=cause, labels=cause.index, rotatelabels=False, autopct='%.2f%%')
plt.title('Fire Cause Distribution') | code |
2008232/cell_7 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').to_julian_date()
df.DISCOVERY_DATE = pd.to_datetime(df.DISCOVERY_DATE - epoch, unit='D')
df.CONT_DATE = pd.to_datetime(df.CONT_DATE - epoch, unit='D')
df.index = pd.to_datetime(df.DISCOVERY_DATE)
df_wa = df[df.STATE == 'WA']
# analysis for yearly burn area
y=df_wa.FIRE_SIZE.resample('AS').sum().fillna(0)
ax = y.plot(kind='bar',figsize=(10,6))
# set xaxis major labels
# Make most of the ticklabels empty so the labels don't get too crowded
ticklabels = ['']*len(y.index)
# Every 4th ticklable shows the month and day
#ticklabels[::5] = [item.strftime('%b %d') for item in y.index[::4]]
# Every 12th ticklabel includes the year
#ticklabels[::5] = [item.strftime('%b %d\n%Y') for item in y.index[::5]]
ticklabels[::1] = [item.strftime('%Y') for item in y.index[::1]]
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.gcf().autofmt_xdate()
plt.xlabel('Year')
plt.ylabel('Acres Burned');
plt.title('Acres Burned by Year');
lat = df_wa['LATITUDE'].values
lon = df_wa['LONGITUDE'].values
fsize = df_wa['FIRE_SIZE'].values
fig = plt.figure(figsize=(17, 10))
m = Basemap(projection='mill', llcrnrlon=-124.0, llcrnrlat=45.3, urcrnrlon=-117, urcrnrlat=49.1, resolution='h', epsg=4269)
m.drawcoastlines(color='blue')
m.drawcountries(color='blue')
m.drawstates(color='blue')
m.scatter(lon, lat, latlon=True, c=np.log10(fsize), s=fsize * 0.01, cmap='Set1', alpha=0.5)
plt.colorbar(label='$\\log_{10}({\\rm Size Acres})$', fraction=0.02, pad=0.04)
plt.clim(3, 7) | code |
2008232/cell_16 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').to_julian_date()
df.DISCOVERY_DATE = pd.to_datetime(df.DISCOVERY_DATE - epoch, unit='D')
df.CONT_DATE = pd.to_datetime(df.CONT_DATE - epoch, unit='D')
df.index = pd.to_datetime(df.DISCOVERY_DATE)
df_wa = df[df.STATE == 'WA']
# analysis for yearly burn area
y=df_wa.FIRE_SIZE.resample('AS').sum().fillna(0)
ax = y.plot(kind='bar',figsize=(10,6))
# set xaxis major labels
# Make most of the ticklabels empty so the labels don't get too crowded
ticklabels = ['']*len(y.index)
# Every 4th ticklable shows the month and day
#ticklabels[::5] = [item.strftime('%b %d') for item in y.index[::4]]
# Every 12th ticklabel includes the year
#ticklabels[::5] = [item.strftime('%b %d\n%Y') for item in y.index[::5]]
ticklabels[::1] = [item.strftime('%Y') for item in y.index[::1]]
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.gcf().autofmt_xdate()
plt.xlabel('Year')
plt.ylabel('Acres Burned');
plt.title('Acres Burned by Year');
# Extract the data we're interested in
lat = df_wa['LATITUDE'].values
lon = df_wa['LONGITUDE'].values
fsize = df_wa['FIRE_SIZE'].values
# Draw the map background
fig = plt.figure(figsize=(17, 10))
m = Basemap(projection='mill',llcrnrlon=-124. ,llcrnrlat=45.3,urcrnrlon=-117 ,urcrnrlat=49.1, resolution = 'h', epsg = 4269)
# do not know how to download the following background image with kaggel kernel, so I had to
# comment out the command
#m.arcgisimage(service='World_Physical_Map', xpixels = 5000, verbose= False)
m.drawcoastlines(color='blue')
m.drawcountries(color='blue')
m.drawstates(color='blue')
# scatter plot
m.scatter(lon, lat, latlon=True,
c=np.log10(fsize), s=fsize*.01,
cmap='Set1', alpha=0.5)
# create colorbar and legend
plt.colorbar(label=r'$\log_{10}({\rm Size Acres})$',fraction=0.02, pad=0.04)
plt.clim(3, 7)
cause = df_wa.STAT_CAUSE_DESCR.value_counts()
# plot pie chart for cause distribution
fig,ax = plt.subplots(figsize=(10,10))
ax.pie(x=cause,labels=cause.index,rotatelabels=False, autopct='%.2f%%');
plt.title('Fire Cause Distribution');
# group cause colume in 2 year segments
df_wa_cause = df_wa.groupby(pd.Grouper(key='DISCOVERY_DATE', freq='2AS'))['STAT_CAUSE_DESCR'].value_counts()
ticklabels = ['1992 - 1993','1994 - 1995','1996 - 1997','1998 - 1999','2000 - 2001','2002 - 2003','2004 - 2005',
'2006 - 2007','2008 - 2009','2010 - 2011','2012 - 2013','2014 - 2015']
df_wa_cause
# Fire Cause Distribution 2 Year Windows
df_wa_cause_us = df_wa_cause.unstack()
ax = df_wa_cause_us.plot(kind='bar',x=df_wa_cause_us.index,stacked=True,figsize=(10,6))
plt.title('Fire Cause Distribution 2 Year Window')
plt.xlabel('2 Year Window')
plt.ylabel('Number Fires')
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
ax.yaxis.grid(False,'minor') # turn off minor tic grid lines
ax.yaxis.grid(True,'major') # turn on major tic grid lines;
plt.gcf().autofmt_xdate()
fig = plt.figure()
fig.set_figheight(10)
fig.set_figwidth(15)
plt.subplots_adjust(hspace=0.5)
plt.subplot(211)
plt.title('Lightning Caused')
plt.xlabel('Fire Size')
plt.grid()
plt.ylabel('Number Wildfires')
plt.hist(df_wa[df_wa['STAT_CAUSE_DESCR'] == 'Equipment Use']['FIRE_SIZE'], bins=20, bottom=0.1)
plt.semilogy()
plt.subplot(212)
plt.title('Equipment Use Caused')
plt.xlabel('Fire Size')
plt.ylabel('Number Wildfires')
plt.grid()
plt.hist(df_wa[df_wa['STAT_CAUSE_DESCR'] == 'Lightning']['FIRE_SIZE'], bins=20, bottom=0.1)
plt.semilogy() | code |
2008232/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').to_julian_date()
df.DISCOVERY_DATE = pd.to_datetime(df.DISCOVERY_DATE - epoch, unit='D')
df.CONT_DATE = pd.to_datetime(df.CONT_DATE - epoch, unit='D')
df.index = pd.to_datetime(df.DISCOVERY_DATE)
df_wa = df[df.STATE == 'WA']
y = df_wa.FIRE_SIZE.resample('AS').sum().fillna(0)
ax = y.plot(kind='bar', figsize=(10, 6))
ticklabels = [''] * len(y.index)
ticklabels[::1] = [item.strftime('%Y') for item in y.index[::1]]
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.gcf().autofmt_xdate()
plt.xlabel('Year')
plt.ylabel('Acres Burned')
plt.title('Acres Burned by Year') | code |
16164281/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
target_count = dataset.target.value_counts()
print('Class 0:', target_count[0])
print('Class 1:', target_count[1])
print('Proportion:', round(target_count[0] / target_count[1], 2), ': 1')
target_count.plot(kind='bar', title='Count (target)') | code |
16164281/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np # linear algebra
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])
pipeline_1.fit(X_train, y_train)
from sklearn.svm import SVC
pipeline_svm = Pipeline([('scl', StandardScaler()), ('clf', SVC(kernel='linear', C=0.05, random_state=1))])
pipeline_svm.fit(X_train, y_train)
skf = StratifiedKFold(random_state=1, n_splits=2)
resultados = []
for train, test in skf.split(X_train, y_train):
pipeline_1.fit(X_train[train], y_train[train])
resultado = pipeline_1.score(X_train[test], y_train[test])
resultados.append(resultado)
from sklearn.model_selection import cross_val_score
resultados = cross_val_score(estimator=pipeline_1, X=X_train, y=y_train, cv=5, n_jobs=1)
print('CV accuracy scores: %s' % resultados)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(resultados), np.std(resultados))) | code |
16164281/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])
pipeline_1.fit(X_train, y_train)
from sklearn.svm import SVC
pipeline_svm = Pipeline([('scl', StandardScaler()), ('clf', SVC(kernel='linear', C=0.05, random_state=1))])
pipeline_svm.fit(X_train, y_train)
skf = StratifiedKFold(random_state=1, n_splits=2)
resultados = []
for train, test in skf.split(X_train, y_train):
pipeline_1.fit(X_train[train], y_train[train])
resultado = pipeline_1.score(X_train[test], y_train[test])
resultados.append(resultado)
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=1, ratio=1.0)
X_train_res, y_train_res = sm.fit_sample(X_train, y_train)
pipeline_1.fit(X_train_res, y_train_res)
print('Resultado: %.3f' % pipeline_1.score(X_test, y_test))
showConfusionMat(pipeline_1, X_test, y_test)
pipeline_svm.fit(X_train_res, y_train_res)
print('Resultado: %.3f' % pipeline_svm.score(X_test, y_test))
showConfusionMat(pipeline_svm, X_test, y_test) | code |
16164281/cell_19 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
import xgboost as xgb
gbm = xgb.XGBClassifier(max_depth=4, n_estimators=300, learning_rate=0.1)
gbm.fit(X_train_res, y_train_res)
from sklearn.model_selection import GridSearchCV
pipe = Pipeline([('gbm', xgb.XGBClassifier())])
param_grid = [{'gbm__max_depth': [3, 4, 5], 'gbm__n_estimators': [250, 300, 350], 'gbm__learning_rate': [0.2, 0.1, 0.5]}]
gs = GridSearchCV(estimator=pipe, param_grid=param_grid, scoring='accuracy', cv=5, n_jobs=-1)
gs = gs.fit(X_train_res, y_train_res)
showConfusionMat(gs, X_test, y_test) | code |
16164281/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline | code |
16164281/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])
pipeline_1.fit(X_train, y_train)
from sklearn.svm import SVC
pipeline_svm = Pipeline([('scl', StandardScaler()), ('clf', SVC(kernel='linear', C=0.05, random_state=1))])
pipeline_svm.fit(X_train, y_train)
skf = StratifiedKFold(random_state=1, n_splits=2)
resultados = []
for train, test in skf.split(X_train, y_train):
pipeline_1.fit(X_train[train], y_train[train])
resultado = pipeline_1.score(X_train[test], y_train[test])
resultados.append(resultado)
print('Entrenamiento:')
print(y_train)
print('Test:')
print(y_test)
showConfusionMat(pipeline_1, X_test, y_test)
showConfusionMat(pipeline_svm, X_test, y_test) | code |
16164281/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
import xgboost as xgb
gbm = xgb.XGBClassifier(max_depth=4, n_estimators=300, learning_rate=0.1)
gbm.fit(X_train_res, y_train_res)
from sklearn.model_selection import GridSearchCV
pipe = Pipeline([('gbm', xgb.XGBClassifier())])
param_grid = [{'gbm__max_depth': [3, 4, 5], 'gbm__n_estimators': [250, 300, 350], 'gbm__learning_rate': [0.2, 0.1, 0.5]}]
gs = GridSearchCV(estimator=pipe, param_grid=param_grid, scoring='accuracy', cv=5, n_jobs=-1)
gs = gs.fit(X_train_res, y_train_res)
print(gs.best_score_)
print(gs.best_params_) | code |
16164281/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
import xgboost as xgb
gbm = xgb.XGBClassifier(max_depth=4, n_estimators=300, learning_rate=0.1)
gbm.fit(X_train_res, y_train_res)
print('Resultado: %.3f' % gbm.score(X_test, y_test))
showConfusionMat(gbm, X_test, y_test) | code |
16164281/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
target_count = dataset.target.value_counts()
import xgboost as xgb
gbm = xgb.XGBClassifier(max_depth=4, n_estimators=300, learning_rate=0.1)
gbm.fit(X_train_res, y_train_res)
print(list(dataset.columns.values[0:-1]))
print(X[0])
print(list(gbm.feature_importances_)) | code |
16164281/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])
pipeline_1.fit(X_train, y_train)
print('Resultado: %.3f' % pipeline_1.score(X_test, y_test))
from sklearn.svm import SVC
pipeline_svm = Pipeline([('scl', StandardScaler()), ('clf', SVC(kernel='linear', C=0.05, random_state=1))])
pipeline_svm.fit(X_train, y_train)
print('Resultado: %.3f' % pipeline_svm.score(X_test, y_test)) | code |
16164281/cell_14 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
from sklearn.ensemble import RandomForestClassifier
rmfc = RandomForestClassifier(n_estimators=100)
rmfc = rmfc.fit(X_train_res, y_train_res)
print('Resultado: %.3f' % rmfc.score(X_test, y_test))
showConfusionMat(rmfc, X_test, y_test) | code |
16164281/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
import xgboost as xgb
gbm = xgb.XGBClassifier(max_depth=4, n_estimators=300, learning_rate=0.1)
gbm.fit(X_train_res, y_train_res)
from sklearn.model_selection import GridSearchCV
pipe = Pipeline([('gbm', xgb.XGBClassifier())])
param_grid = [{'gbm__max_depth': [3, 4, 5], 'gbm__n_estimators': [250, 300, 350], 'gbm__learning_rate': [0.2, 0.1, 0.5]}]
gs = GridSearchCV(estimator=pipe, param_grid=param_grid, scoring='accuracy', cv=5, n_jobs=-1)
gs = gs.fit(X_train_res, y_train_res)
from sklearn.model_selection import GridSearchCV
pipe_gbm = Pipeline([('gbm', xgb.XGBClassifier())])
param_grid = [{'gbm__max_depth': [3, 4, 5], 'gbm__n_estimators': [250, 300, 350], 'gbm__learning_rate': [0.2, 0.1, 0.5]}]
gs2 = GridSearchCV(estimator=pipe_gbm, param_grid=param_grid, scoring='f1', cv=5, n_jobs=-1)
gs2 = gs2.fit(X_train_res, y_train_res)
print(gs2.best_score_)
print(gs2.best_params_)
showConfusionMat(gs2, X_test, y_test) | code |
16164281/cell_12 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.drop(['ip_hash', 'fecha', 'lang', 'country'], axis=1, inplace=True)
X, y = (dataset.iloc[:, 0:-1].values, dataset.iloc[:, -1].values)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt
def showConfusionMat(pipe, X_test, y_test):
y_pred = pipe.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
df_cm = pd.DataFrame(confmat, ['F', 'T'], ['F', 'T'])
sn.set(font_scale=1.4)
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])
pipeline_1.fit(X_train, y_train)
from sklearn.svm import SVC
pipeline_svm = Pipeline([('scl', StandardScaler()), ('clf', SVC(kernel='linear', C=0.05, random_state=1))])
pipeline_svm.fit(X_train, y_train)
skf = StratifiedKFold(random_state=1, n_splits=2)
resultados = []
for train, test in skf.split(X_train, y_train):
pipeline_1.fit(X_train[train], y_train[train])
resultado = pipeline_1.score(X_train[test], y_train[test])
resultados.append(resultado)
from sklearn.model_selection import cross_val_score
resultados = cross_val_score(estimator=pipeline_1, X=X_train, y=y_train, cv=5, n_jobs=1)
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=1, ratio=1.0)
X_train_res, y_train_res = sm.fit_sample(X_train, y_train)
pipeline_1.fit(X_train_res, y_train_res)
pipeline_svm.fit(X_train_res, y_train_res)
from sklearn.model_selection import cross_val_score
resultados = cross_val_score(estimator=pipeline_1, X=X_train_res, y=y_train_res, cv=5)
print('CV accuracy scores: %s' % resultados)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(resultados), np.std(resultados))) | code |
16164281/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])
pipeline_1.fit(X_train, y_train)
from sklearn.svm import SVC
pipeline_svm = Pipeline([('scl', StandardScaler()), ('clf', SVC(kernel='linear', C=0.05, random_state=1))])
pipeline_svm.fit(X_train, y_train)
skf = StratifiedKFold(random_state=1, n_splits=2)
resultados = []
for train, test in skf.split(X_train, y_train):
pipeline_1.fit(X_train[train], y_train[train])
resultado = pipeline_1.score(X_train[test], y_train[test])
resultados.append(resultado)
print(resultado) | code |
73067458/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
## Correlations
correlations = X_full[my_features].corr()
f, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(correlations, square=True, cbar=True, annot=True, vmax=.9);
## Box Plot for Outliers
fig = plt.figure(figsize=(18,6))
sns.boxplot(data=X_full[numeric_features], orient="h", palette="Set2");
plt.xticks(fontsize= 14)
plt.title('Box plot of numerical columns', fontsize=16);
plt.xticks(fontsize=14)
from scipy import stats
def treatoutliers(df=None, columns=None, factor=1.5, method='IQR', treatment='cap'):
for column in columns:
if method == 'STD':
permissable_std = factor * df[column].std()
col_mean = df[column].mean()
floor, ceil = (col_mean - permissable_std, col_mean + permissable_std)
elif method == 'IQR':
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
floor, ceil = (Q1 - factor * IQR, Q3 + factor * IQR)
if treatment == 'remove':
df = df[(df[column] >= floor) & (df[column] <= ceil)]
elif treatment == 'cap':
df[column] = df[column].clip(floor, ceil)
return df
for colName in [['target', 'cont0', 'cont6', 'cont8']]:
X_full = treatoutliers(df=X_full, columns=colName, treatment='cap')
plt.xticks(fontsize=14)
fig = plt.figure(figsize=(18, 6))
sns.boxplot(data=X_full[numeric_features], orient='h', palette='Set2')
plt.xticks(fontsize=14)
plt.title('Box plot of numerical columns after handling Outliers', fontsize=16) | code |
73067458/cell_9 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
print('categorical_features:', categorical_features)
print('numeric_features:', numeric_features)
print('my_features:', my_features)
numeric_features.remove('target')
print('numeric_features minus target column:', numeric_features) | code |
73067458/cell_25 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
categorical_features = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() <= 15 and X_train_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
print('categorical_features', categorical_features)
print('numeric_features', numeric_features)
print('my_features', my_features) | code |
73067458/cell_23 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
from scipy import stats
def treatoutliers(df=None, columns=None, factor=1.5, method='IQR', treatment='cap'):
for column in columns:
if method == 'STD':
permissable_std = factor * df[column].std()
col_mean = df[column].mean()
floor, ceil = (col_mean - permissable_std, col_mean + permissable_std)
elif method == 'IQR':
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
floor, ceil = (Q1 - factor * IQR, Q3 + factor * IQR)
if treatment == 'remove':
df = df[(df[column] >= floor) & (df[column] <= ceil)]
elif treatment == 'cap':
df[column] = df[column].clip(floor, ceil)
return df
for colName in [['target', 'cont0', 'cont6', 'cont8']]:
X_full = treatoutliers(df=X_full, columns=colName, treatment='cap')
X_full.dropna(axis=0, subset=['target'], inplace=True)
y = X_full['target']
X_full.drop(['target'], axis=1, inplace=True)
X_full.head() | code |
73067458/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
## Correlations
correlations = X_full[my_features].corr()
f, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(correlations, square=True, cbar=True, annot=True, vmax=.9);
## Box Plot for Outliers
fig = plt.figure(figsize=(18,6))
sns.boxplot(data=X_full[numeric_features], orient="h", palette="Set2");
plt.xticks(fontsize= 14)
plt.title('Box plot of numerical columns', fontsize=16);
plt.xticks(fontsize=14)
from scipy import stats
def treatoutliers(df=None, columns=None, factor=1.5, method='IQR', treatment='cap'):
for column in columns:
if method == 'STD':
permissable_std = factor * df[column].std()
col_mean = df[column].mean()
floor, ceil = (col_mean - permissable_std, col_mean + permissable_std)
elif method == 'IQR':
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
floor, ceil = (Q1 - factor * IQR, Q3 + factor * IQR)
if treatment == 'remove':
df = df[(df[column] >= floor) & (df[column] <= ceil)]
elif treatment == 'cap':
df[column] = df[column].clip(floor, ceil)
return df
for colName in [['target', 'cont0', 'cont6', 'cont8']]:
X_full = treatoutliers(df=X_full, columns=colName, treatment='cap')
sns.boxplot(data=X_full[['target']], orient='h', palette='Set2')
plt.xticks(fontsize=14)
plt.title('Box plot of target column after handling Outliers', fontsize=16) | code |
73067458/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.info()
print('*' * 100)
X_full.isnull().sum() | code |
73067458/cell_29 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
categorical_features = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() <= 15 and X_train_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
X_train = X_train_full[my_features]
X_valid = X_valid_full[my_features]
X_test = X_test_full[my_features]
X_train.shape | code |
73067458/cell_11 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
X_full[numeric_features].hist(figsize=(24, 12)) | code |
73067458/cell_19 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
from scipy import stats
def treatoutliers(df=None, columns=None, factor=1.5, method='IQR', treatment='cap'):
for column in columns:
if method == 'STD':
permissable_std = factor * df[column].std()
col_mean = df[column].mean()
floor, ceil = (col_mean - permissable_std, col_mean + permissable_std)
elif method == 'IQR':
Q1 = df[column].quantile(0.25)
Q3 = df[column].quantile(0.75)
IQR = Q3 - Q1
floor, ceil = (Q1 - factor * IQR, Q3 + factor * IQR)
if treatment == 'remove':
print(treatment, column)
df = df[(df[column] >= floor) & (df[column] <= ceil)]
elif treatment == 'cap':
print(treatment, column)
df[column] = df[column].clip(floor, ceil)
return df
for colName in [['target', 'cont0', 'cont6', 'cont8']]:
X_full = treatoutliers(df=X_full, columns=colName, treatment='cap')
X_full.info() | code |
73067458/cell_32 | [
"text_plain_output_1.png"
] | rans = 42
def log_transform(x):
return np.log(x + 1)
transformer = FunctionTransformer(log_transform)
numerical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(transformers=[('num', numerical_transformer, numeric_features), ('cat', categorical_transformer, categorical_features)], remainder='passthrough') | code |
73067458/cell_28 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
categorical_features = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() <= 15 and X_train_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
X_train = X_train_full[my_features]
X_valid = X_valid_full[my_features]
X_test = X_test_full[my_features]
X_train.describe(include='all') | code |
73067458/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
X_full.describe(include='all') | code |
73067458/cell_15 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
## Correlations
correlations = X_full[my_features].corr()
f, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(correlations, square=True, cbar=True, annot=True, vmax=.9);
fig = plt.figure(figsize=(18, 6))
sns.boxplot(data=X_full[numeric_features], orient='h', palette='Set2')
plt.xticks(fontsize=14)
plt.title('Box plot of numerical columns', fontsize=16) | code |
73067458/cell_3 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
print('Tensor Flow:', tf.__version__)
print('SciKit Learn:', sk.__version__)
print('Pandas:', pd.__version__)
print('Numpy:', np.__version__)
print('Seaborn:', sns.__version__)
print('MatPlot Library:', mpl.__version__)
print('XG Boost:', xgb.__version__) | code |
73067458/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
## Correlations
correlations = X_full[my_features].corr()
f, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(correlations, square=True, cbar=True, annot=True, vmax=.9);
## Box Plot for Outliers
fig = plt.figure(figsize=(18,6))
sns.boxplot(data=X_full[numeric_features], orient="h", palette="Set2");
plt.xticks(fontsize= 14)
plt.title('Box plot of numerical columns', fontsize=16);
sns.boxplot(data=X_full[['target']], orient='h', palette='Set2')
plt.xticks(fontsize=14)
plt.title('Box plot of target column', fontsize=16) | code |
73067458/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
correlations = X_full[my_features].corr()
f, ax = plt.subplots(figsize=(12, 12))
sns.heatmap(correlations, square=True, cbar=True, annot=True, vmax=0.9) | code |
73067458/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
categorical_features = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() <= 15 and X_train_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
X_train = X_train_full[my_features]
X_valid = X_valid_full[my_features]
X_test = X_test_full[my_features]
X_train.head() | code |
73067458/cell_37 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
result_df = pd.DataFrame({'Actual': y_valid, 'Predicted': preds_valid, 'Diff': preds_valid - y_valid})
result_df['Diff'].round().value_counts() | code |
73067458/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull().sum()
categorical_features = [cname for cname in X_full.columns if X_full[cname].nunique() <= 15 and X_full[cname].dtype == 'object']
numeric_features = [cname for cname in X_full.columns if X_full[cname].dtype in ['int64', 'float64']]
my_features = categorical_features + numeric_features
numeric_features.remove('target')
X_full[numeric_features].hist(figsize=(24, 12), log=True) | code |
73067458/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.head() | code |
73067458/cell_36 | [
"text_html_output_1.png"
] | clf = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])
final_model = clf.fit(X_train, y_train)
preds_valid = final_model.predict(X_valid)
print('MAE:', mean_absolute_error(y_valid, preds_valid))
print('RMSE:', mean_squared_error(y_valid, preds_valid, squared=False)) | code |
32062582/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_test.head() | code |
32062582/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
df_world.head() | code |
32062582/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print('Minimum date from test set: {}'.format(df_test['Date'].min()))
print('Maximum date from test set: {}'.format(df_test['Date'].max())) | code |
32062582/cell_26 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
df_map = df_train.copy()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date', 'Country_Region'], as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_train[df_train.Country_Region == 'India'].Date.min()
def create_features(df):
df['day'] = df['Date'].dt.day
df['month'] = df['Date'].dt.month
df['dayofweek'] = df['Date'].dt.dayofweek
df['dayofyear'] = df['Date'].dt.dayofyear
df['quarter'] = df['Date'].dt.quarter
df['weekofyear'] = df['Date'].dt.weekofyear
return df
df_train = create_features(df_train)
columns = ['day', 'month', 'dayofweek', 'dayofyear', 'quarter', 'weekofyear', 'Province_State', 'Country_Region', 'ConfirmedCases', 'Fatalities']
df_train = df_train[columns]
df_dev = df_dev[columns]
df_train.Province_State.fillna('NaN', inplace=True)
df_test.Province_State.fillna('NaN', inplace=True)
df_test = create_features(df_test)
columns = ['day', 'month', 'dayofweek', 'dayofyear', 'quarter', 'weekofyear']
df_train.dtypes
submission = []
for country in df_train.Country_Region.unique():
df_train1 = df_train[df_train['Country_Region'] == country]
for state in df_train1.Province_State.unique():
df_train2 = df_train1[df_train1['Province_State'] == state]
df_train3 = df_train2.drop(['Country_Region', 'Province_State'], axis=1)
train = df_train3.values
X_train, y_train = (train[:, :-2], train[:, -2:])
model1 = XGBRegressor(random_state=1, n_estimators=1000)
model1.fit(X_train, y_train[:, 0])
model2 = XGBRegressor(random_state=1, n_estimators=1000)
model2.fit(X_train, y_train[:, 1])
df_test1 = df_test[(df_test['Country_Region'] == country) & (df_test['Province_State'] == state)]
ForecastId = df_test1.ForecastId.values
df_test2 = df_test1[columns]
y_pred1 = model1.predict(df_test2.values).astype(int)
y_pred2 = model2.predict(df_test2.values).astype(int)
for i in range(len(y_pred1)):
d = {'ForecastId': ForecastId[i], 'ConfirmedCases': y_pred1[i], 'Fatalities': y_pred2[i]}
submission.append(d)
len(submission) | code |
32062582/cell_19 | [
"text_html_output_2.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
df_map = df_train.copy()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date', 'Country_Region'], as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_train[df_train.Country_Region == 'India'].Date.min()
def create_features(df):
df['day'] = df['Date'].dt.day
df['month'] = df['Date'].dt.month
df['dayofweek'] = df['Date'].dt.dayofweek
df['dayofyear'] = df['Date'].dt.dayofyear
df['quarter'] = df['Date'].dt.quarter
df['weekofyear'] = df['Date'].dt.weekofyear
return df
df_train = create_features(df_train)
df_train.head() | code |
32062582/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
display(df_train.head())
display(df_train.describe())
display(df_train.info()) | code |
32062582/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
df_map = df_train.copy()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date', 'Country_Region'], as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_train[df_train.Country_Region == 'India'].Date.min() | code |
32062582/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
df_map = df_train.copy()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date', 'Country_Region'], as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_train[df_train.Country_Region == 'India'].Date.min()
def create_features(df):
df['day'] = df['Date'].dt.day
df['month'] = df['Date'].dt.month
df['dayofweek'] = df['Date'].dt.dayofweek
df['dayofyear'] = df['Date'].dt.dayofyear
df['quarter'] = df['Date'].dt.quarter
df['weekofyear'] = df['Date'].dt.weekofyear
return df
df_train = create_features(df_train)
columns = ['day', 'month', 'dayofweek', 'dayofyear', 'quarter', 'weekofyear', 'Province_State', 'Country_Region', 'ConfirmedCases', 'Fatalities']
df_train = df_train[columns]
df_dev = df_dev[columns]
df_train.Province_State.fillna('NaN', inplace=True)
df_test.Province_State.fillna('NaN', inplace=True)
df_test = create_features(df_test)
columns = ['day', 'month', 'dayofweek', 'dayofyear', 'quarter', 'weekofyear']
df_train.dtypes | code |
32062582/cell_10 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
fig = go.Figure(data=[go.Bar(name='Cases', x=df_world['Date'], y=df_world['Daily Cases']), go.Bar(name='Deaths', x=df_world['Date'], y=df_world['Daily Deaths'])])
fig.update_layout(barmode='overlay', title='Worldwide daily Case and Death count')
fig.show() | code |
32062582/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fatalities']
for i in range(1, len(df_world)):
df.loc[i, 'Daily Cases'] = df.loc[i, 'ConfirmedCases'] - df.loc[i - 1, 'ConfirmedCases']
df.loc[i, 'Daily Deaths'] = df.loc[i, 'Fatalities'] - df.loc[i - 1, 'Fatalities']
df.loc[0, 'Daily Cases'] = 0
df.loc[0, 'Daily Deaths'] = 0
return df
df_world = df_train.copy()
df_world = df_world.groupby('Date', as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_world = add_daily_measures(df_world)
df_map = df_train.copy()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date', 'Country_Region'], as_index=False)['ConfirmedCases', 'Fatalities'].sum()
df_map.head() | code |
32062582/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print('Minimum date from training set: {}'.format(df_train['Date'].min()))
print('Maximum date from training set: {}'.format(df_train['Date'].max())) | code |
128019479/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
Data_train.isnull().sum().sum()
Data_test.isnull().sum().sum()
Data_train.corr()
feature_name = list(Data_train.columns[1:-1])
Data_train.drop('id', inplace=True, axis=1)
id = Data_test['id']
Data_test.drop('id', inplace=True, axis=1)
Data_train.skew()
feature_name = ['MedInc', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'MedHouseVal']
for i in range(len(feature_name)):
q1 = Data_train[feature_name[i]].quantile(0.25)
q2 = Data_train[feature_name[i]].quantile(0.75)
Data_train[feature_name[i]] = np.where(Data_train[feature_name[i]] < q1, q1, Data_train[feature_name[i]])
Data_train[feature_name[i]] = np.where(Data_train[feature_name[i]] > q2, q2, Data_train[feature_name[i]])
Data_train.skew() | code |
128019479/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape
Data_test.columns
Data_test.isnull().sum().sum() | code |
128019479/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.describe() | code |
128019479/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
Data_train.isnull().sum().sum()
Data_test.isnull().sum().sum()
Data_train.corr()
feature_name = list(Data_train.columns[1:-1])
Data_train.drop('id', inplace=True, axis=1)
id = Data_test['id']
Data_test.drop('id', inplace=True, axis=1)
Data_train.skew()
feature_name = ['MedInc', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'MedHouseVal']
for i in range(len(feature_name)):
q1 = Data_train[feature_name[i]].quantile(0.25)
q2 = Data_train[feature_name[i]].quantile(0.75)
Data_train[feature_name[i]] = np.where(Data_train[feature_name[i]] < q1, q1, Data_train[feature_name[i]])
Data_train[feature_name[i]] = np.where(Data_train[feature_name[i]] > q2, q2, Data_train[feature_name[i]])
Data_train.skew()
Data_test.skew()
feature_name = ['MedInc', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']
for i in range(len(feature_name)):
q1 = Data_test[feature_name[i]].quantile(0.25)
q2 = Data_test[feature_name[i]].quantile(0.75)
Data_test[feature_name[i]] = np.where(Data_test[feature_name[i]] < q1, q1, Data_test[feature_name[i]])
Data_test[feature_name[i]] = np.where(Data_test[feature_name[i]] > q2, q2, Data_test[feature_name[i]])
Data_test.skew() | code |
128019479/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
Data_train.isnull().sum().sum()
Data_test.isnull().sum().sum()
Data_train.corr()
feature_name = list(Data_train.columns[1:-1])
Data_train.drop('id', inplace=True, axis=1)
id = Data_test['id']
Data_test.drop('id', inplace=True, axis=1)
Data_train.skew() | code |
128019479/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape | code |
128019479/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error | code |
128019479/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns | code |
128019479/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape
Data_test.columns | code |
128019479/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.corr() | code |
128019479/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.corr()
sns.heatmap(Data_train.corr(), cmap='hot') | code |
128019479/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.corr()
feature_name = list(Data_train.columns[1:-1])
plt.figure(figsize=(15, 15))
for i in range(len(feature_name)):
plt.subplot(4, 4, i + 1)
sns.scatterplot(x=Data_train[feature_name[i]], y=Data_train['MedHouseVal']) | code |
128019479/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.hist(figsize=(30, 30)) | code |
128019479/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
Data_train.isnull().sum().sum()
Data_test.isnull().sum().sum()
Data_train.corr()
feature_name = list(Data_train.columns[1:-1])
Data_train.drop('id', inplace=True, axis=1)
id = Data_test['id']
Data_test.drop('id', inplace=True, axis=1)
Data_test.skew() | code |
128019479/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape
Data_test.columns
Data_test.describe() | code |
128019479/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum() | code |
128019479/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape | code |
89140329/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
data['extracted_body'][10] | code |
89140329/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
data.head() | code |
89140329/cell_25 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
len(STOPWORDS) | code |
89140329/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data['Label'].value_counts() | code |
89140329/cell_34 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
data.head() | code |
89140329/cell_29 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
word_cloud_plot(subset=data[data['Label'] == 0]) | code |
89140329/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
data['extracted_body'][10].split() | code |
89140329/cell_41 | [
"text_html_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
data['extracted_body_tk'] = data['extracted_body'].apply(lambda x: x.split())
from nltk.stem import WordNetLemmatizer
stopwords = list(set(STOPWORDS))
lemmatizer = WordNetLemmatizer()
data['extracted_body_tk_lm'] = data['extracted_body_tk'].apply(lambda x: ' '.join([lemmatizer.lemmatize(elem) for elem in x if elem not in stopwords]))
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(max_features=1000)
X = vectorizer.fit_transform(data['extracted_body_tk_lm'])
df_bow_sklearn = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names())
df_bow_sklearn.shape | code |
89140329/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
print('#----------------------------------------------------#')
print(df1.head(3))
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
print('#----------------------------------------------------#')
print(df2.head(3))
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
print('#----------------------------------------------------#')
print(df3.head(3))
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
print(data.shape)
data.head(20) | code |
89140329/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
data['extracted_body'][10] | code |
89140329/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89140329/cell_7 | [
"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)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data['len'] = data['Body'].apply(lambda x: len(str(x)))
data
print('Quantiles of data : ')
print(data['len'].quantile([0, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 1]), '\n')
fig = plt.figure(figsize=(10, 7))
plt.ylim(-50, 5000)
plt.boxplot(data['len']) | code |
89140329/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
data['extracted_body'][10] | code |
89140329/cell_38 | [
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
data['extracted_body_tk'] = data['extracted_body'].apply(lambda x: x.split())
from nltk.stem import WordNetLemmatizer
stopwords = list(set(STOPWORDS))
lemmatizer = WordNetLemmatizer()
data['extracted_body_tk_lm'] = data['extracted_body_tk'].apply(lambda x: ' '.join([lemmatizer.lemmatize(elem) for elem in x if elem not in stopwords]))
data[['extracted_body_tk', 'extracted_body_tk_lm']][10:11] | code |
89140329/cell_35 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
data['extracted_body_tk'] = data['extracted_body'].apply(lambda x: x.split())
data.head() | code |
89140329/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
word_cloud_plot(subset=data[data['Label'] == 1]) | code |
89140329/cell_24 | [
"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 re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
plt.title('Bar plot of Ham vs SPam frequencies in the data')
plt.xlabel('Labels')
plt.ylabel('Frequency')
plt.bar(['Ham', 'Spam'], [count_ham, count_spam], color='green') | code |
89140329/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
data['extracted_body'][10] | code |
89140329/cell_37 | [
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
data["len"] = data["Body"].apply(lambda x: len(str(x)))
data
#------------------------------------------------------------------
print("Quantiles of data : ")
print(data["len"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1]),"\n")
#------------------------------------------------------------------
fig = plt.figure(figsize =(10, 7))
plt.ylim(-50,5000)
#plt.hist(data["len"])
plt.boxplot(data["len"])
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
count_ham = len(data) - data['Label'].sum()
count_spam = data['Label'].sum()
subset = data.copy()
def word_cloud_plot(subset):
comment_words = ''
stopwords = set(STOPWORDS)
for val in data['extracted_body']:
val = str(val)
tokens = val.split()
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += ' '.join(tokens) + ' '
wordcloud = WordCloud(width=800, height=800, background_color='white', stopwords=stopwords, min_font_size=6).generate(comment_words)
plt.axis('off')
plt.tight_layout(pad=0)
data['extracted_body_tk'] = data['extracted_body'].apply(lambda x: x.split())
from nltk.stem import WordNetLemmatizer
stopwords = list(set(STOPWORDS))
lemmatizer = WordNetLemmatizer()
data['extracted_body_tk_lm'] = data['extracted_body_tk'].apply(lambda x: ' '.join([lemmatizer.lemmatize(elem) for elem in x if elem not in stopwords]))
data.head() | code |
89140329/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes
import re
data['extracted_body'] = data['Body'].apply(lambda x: re.sub('http\\S+', '', str(x)))
import re
data['extracted_body'] = data['extracted_body'].apply(lambda x: re.sub('\\W+', ' ', x))
data.head() | code |
89140329/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/lingSpam.csv')
df3 = df3[['Body', 'Label']]
data = df1.append(df2).append(df3)
data = data.reset_index(drop=True)
data.dtypes | code |
17135990/cell_13 | [
"text_html_output_1.png"
] | from IPython.display import display
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw.SalePrice = np.log(df_raw.SalePrice)
add_datepart(df_raw, 'saledate')
df_raw.saleYear.head() | code |
17135990/cell_20 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw = pd.read_feather('tmp/bulldozers-raw') | code |
17135990/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17135990/cell_11 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
display_all(df_raw.describe(include='all').T) | code |
17135990/cell_1 | [
"text_plain_output_1.png"
] | !pip install fastai==0.7.0
!pip install torchtext==0.2.3 | code |
17135990/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !ls {PATH} | code |
17135990/cell_18 | [
"text_plain_output_1.png"
] | from IPython.display import display
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw.SalePrice = np.log(df_raw.SalePrice)
add_datepart(df_raw, 'saledate')
df_raw.UsageBand.cat.categories
df_raw.UsageBand.cat.set_categories(['High', 'Medium', 'Low'], ordered=True, inplace=True)
df_raw.UsageBand = df_raw.UsageBand.cat.codes
display_all(df_raw.isnull().sum().sort_index() / len(df_raw)) | code |
17135990/cell_15 | [
"text_html_output_1.png"
] | from IPython.display import display
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw.SalePrice = np.log(df_raw.SalePrice)
add_datepart(df_raw, 'saledate')
df_raw.UsageBand.cat.categories | code |
17135990/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
m = RandomForestRegressor(n_jobs=-1)
m.fit(df, y)
m.score(df, y) | code |
17135990/cell_10 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
display_all(df_raw.tail().T) | code |
121150522/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.info() | code |
121150522/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/spaceship-titanic/train.csv')
df.pivot_table(index='CryoSleep', columns='Transported', aggfunc={'Transported': 'count'})
df_count = df[['Age']].apply(pd.value_counts)
df_count.plot(kind='bar', color='Orange', figsize=(12, 12))
plt.xticks(rotation=85)
plt.title('Most Common Ages')
plt.show() | code |
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