path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
128012943/cell_28 | [
"image_output_1.png"
] | x_test.shape | code |
128012943/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure['sex'].value_counts() | code |
128012943/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
sns.displot(data=df_insure['expenses'])
plt.show() | code |
128012943/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.groupby('smoker')['bmi'].mean().plot(kind='bar')
plt.ylabel('Average BMI')
plt.show() | code |
128012943/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_array = ohe.fit_transform(x_train[['sex', 'smoker', 'region']]).toarray()
x_test_array = ohe.transform(x_test[['sex', 'smoker', 'region']]).toarray()
x_trainfeatures = pd.DataFrame(x_train_array, columns=['male', 'smokes', 'northwest', 'southeast', 'southwest'])
x_trainfeatures
x_testfeatures = pd.DataFrame(x_test_array, columns=['male', 'smokes', 'northwest', 'southeast', 'southwest'])
x_testfeatures
x_train_new = pd.concat([x_train, x_trainfeatures.set_axis(x_train.index)], axis=1)
x_train_new
x_train_new.drop(['sex', 'smoker', 'region'], axis=1, inplace=True)
x_train_new | code |
128012943/cell_31 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_array = ohe.fit_transform(x_train[['sex', 'smoker', 'region']]).toarray()
x_test_array = ohe.transform(x_test[['sex', 'smoker', 'region']]).toarray()
ohe.categories_ | code |
128012943/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
sns.displot(data=df_insure['bmi'])
plt.show() | code |
128012943/cell_27 | [
"image_output_1.png"
] | x_test.head() | code |
128012943/cell_37 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_array = ohe.fit_transform(x_train[['sex', 'smoker', 'region']]).toarray()
x_test_array = ohe.transform(x_test[['sex', 'smoker', 'region']]).toarray()
x_trainfeatures = pd.DataFrame(x_train_array, columns=['male', 'smokes', 'northwest', 'southeast', 'southwest'])
x_trainfeatures
x_testfeatures = pd.DataFrame(x_test_array, columns=['male', 'smokes', 'northwest', 'southeast', 'southwest'])
x_testfeatures
x_train_new = pd.concat([x_train, x_trainfeatures.set_axis(x_train.index)], axis=1)
x_train_new
x_test_new = pd.concat([x_test, x_testfeatures.set_axis(x_test.index)], axis=1)
x_test_new
x_test_new.drop(['sex', 'smoker', 'region'], axis=1, inplace=True)
x_test_new | code |
128012943/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.info() | code |
128012943/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_array = ohe.fit_transform(x_train[['sex', 'smoker', 'region']]).toarray()
x_test_array = ohe.transform(x_test[['sex', 'smoker', 'region']]).toarray()
x_trainfeatures = pd.DataFrame(x_train_array, columns=['male', 'smokes', 'northwest', 'southeast', 'southwest'])
x_trainfeatures
x_testfeatures = pd.DataFrame(x_test_array, columns=['male', 'smokes', 'northwest', 'southeast', 'southwest'])
x_testfeatures
x_train_new = pd.concat([x_train, x_trainfeatures.set_axis(x_train.index)], axis=1)
x_train_new
x_test_new = pd.concat([x_test, x_testfeatures.set_axis(x_test.index)], axis=1)
x_test_new | code |
318069/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
dfc.groupby(dfc.index.year).sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False) | code |
318069/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
print("Attack with most victims happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \n'%s' \n" % (most_victim.City.values[0], most_victim.index.strftime('%B %d,%Y')[0], most_victim.Killed, most_victim.Injured, most_victim.Victims, '%s' % most_victim.Description.values[0]))
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
print("Attack with the most deaths happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \n'%s' \n" % (most_killed.City.values[0], most_killed.index.strftime('%B %d,%Y')[0], most_killed.Killed, most_killed.Injured, most_killed.Victims, '%s' % most_killed.Description.values[0]))
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
print("Attack with the most injuries happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \n'%s' \n" % (most_injuries.City.values[0], most_injuries.index.strftime('%B %d,%Y')[0], most_injuries.Killed, most_injuries.Injured, most_injuries.Victims, '%s' % most_injuries.Description.values[0])) | code |
318069/cell_26 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
dfc.groupby(dfc.index.year).sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False)
killedbyday = dfc.groupby([dfc.index.map(lambda x: x.weekday), dfc.index.year], sort=True).agg({'Killed': 'sum'})
rcParams['figure.figsize'] = (20, 10)
killedbyday.unstack(level=0).plot(kind='bar', subplots=False)
killedbyday.unstack(level=1).plot(kind='bar', subplots=False) | code |
318069/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
print('%s is ranked %.0f with %d attacks resulting to %d deaths and %d injuries' % (country, country_rank, country_attacks, country_killed, country_injured)) | code |
318069/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True) | code |
318069/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.City.value_counts().plot(kind='bar', figsize=(17, 7))
plt.title('Number of attacks by city') | code |
318069/cell_27 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric_only=True, ascending=False).loc[country]
country_attacks = df.Country.value_counts()[country]
country_killed = dfc.Killed.sum()
country_injured = dfc.Injured.sum()
dfc.groupby('City').sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)
most_victim = dfc.sort_values(by='Victims', ascending=False).head(1)
most_killed = dfc.sort_values(by='Killed', ascending=False).head(1)
most_injuries = dfc.sort_values(by='Injured', ascending=False).head(1)
dfc.groupby(dfc.index.year).sum()[['Victims', 'Killed', 'Injured']].sort_values(by='Victims', ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False)
killedbyday = dfc.groupby([dfc.index.map(lambda x: x.weekday), dfc.index.year], sort=True).agg({'Killed': 'sum'})
rcParams['figure.figsize'] = (20, 10)
killedbymonth = dfc.groupby([dfc.index.map(lambda x: x.month), dfc.index.year], sort=True).agg({'Killed': 'sum'})
rcParams['figure.figsize'] = (20, 10)
killedbymonth.unstack(level=0).plot(kind='bar', subplots=False)
killedbymonth.unstack(level=1).plot(kind='bar', subplots=False) | code |
106209369/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
import lightgbm as lgbm
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv')
def get_dataset(df, target):
L = df.shape[0]
cv_test1 = df.iloc[:round(L / 5), :]
cv_test2 = df.iloc[round(L / 5):round(2 * L / 5), :]
cv_test3 = df.iloc[round(2 * L / 5):round(3 * L / 5), :]
cv_test4 = df.iloc[round(3 * L / 5):round(4 * L / 5), :]
cv_test5 = df.iloc[round(4 * L / 5):, :]
cv_train1 = pd.concat([cv_test2, cv_test3, cv_test4, cv_test5], axis=0)
cv_train2 = pd.concat([cv_test1, cv_test3, cv_test4, cv_test5], axis=0)
cv_train3 = pd.concat([cv_test1, cv_test2, cv_test4, cv_test5], axis=0)
cv_train4 = pd.concat([cv_test1, cv_test2, cv_test3, cv_test5], axis=0)
cv_train5 = pd.concat([cv_test1, cv_test2, cv_test3, cv_test5], axis=0)
T1 = target.iloc[0:round(L / 5)]
T2 = target.iloc[round(L / 5):round(2 * L / 5)]
T3 = target.iloc[round(2 * L / 5):round(3 * L / 5)]
T4 = target.iloc[round(3 * L / 5):round(4 * L / 5)]
T5 = target.iloc[round(4 * L / 5):]
t1 = pd.concat([T2, T3, T4, T5], axis=0)
t2 = pd.concat([T1, T3, T4, T5], axis=0)
t3 = pd.concat([T1, T2, T4, T5], axis=0)
t4 = pd.concat([T1, T2, T3, T5], axis=0)
t5 = pd.concat([T1, T2, T3, T4], axis=0)
cv_test = [cv_test1, cv_test2, cv_test3, cv_test4, cv_test5]
cv_train = [cv_train1, cv_train2, cv_train3, cv_train4, cv_train5]
T = [T1, T2, T3, T4, T5]
t = [t1, t2, t3, t4, t5]
return (cv_test, cv_train, T, t)
val = get_dataset(x_train, y_train)
def scorer(A):
score = 0
for i in range(0, 5):
model = lgbm.LGBMRegressor(n_estimators=100, random_state=42)
model.fit(A[1][i], np.array(A[3][i]))
pred = model.predict(A[0][i])
from sklearn.metrics import r2_score
S = r2_score(pred, A[2][i])
score = score + S
return score / 5
all_score = scorer(val)
all_score | code |
106209369/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv')
x_train.head() | code |
106209369/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv') | code |
106209369/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
import lightgbm as lgbm
import numpy as np
import pandas as pd
import warnings
import warnings
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv')
def get_dataset(df, target):
L = df.shape[0]
cv_test1 = df.iloc[:round(L / 5), :]
cv_test2 = df.iloc[round(L / 5):round(2 * L / 5), :]
cv_test3 = df.iloc[round(2 * L / 5):round(3 * L / 5), :]
cv_test4 = df.iloc[round(3 * L / 5):round(4 * L / 5), :]
cv_test5 = df.iloc[round(4 * L / 5):, :]
cv_train1 = pd.concat([cv_test2, cv_test3, cv_test4, cv_test5], axis=0)
cv_train2 = pd.concat([cv_test1, cv_test3, cv_test4, cv_test5], axis=0)
cv_train3 = pd.concat([cv_test1, cv_test2, cv_test4, cv_test5], axis=0)
cv_train4 = pd.concat([cv_test1, cv_test2, cv_test3, cv_test5], axis=0)
cv_train5 = pd.concat([cv_test1, cv_test2, cv_test3, cv_test5], axis=0)
T1 = target.iloc[0:round(L / 5)]
T2 = target.iloc[round(L / 5):round(2 * L / 5)]
T3 = target.iloc[round(2 * L / 5):round(3 * L / 5)]
T4 = target.iloc[round(3 * L / 5):round(4 * L / 5)]
T5 = target.iloc[round(4 * L / 5):]
t1 = pd.concat([T2, T3, T4, T5], axis=0)
t2 = pd.concat([T1, T3, T4, T5], axis=0)
t3 = pd.concat([T1, T2, T4, T5], axis=0)
t4 = pd.concat([T1, T2, T3, T5], axis=0)
t5 = pd.concat([T1, T2, T3, T4], axis=0)
cv_test = [cv_test1, cv_test2, cv_test3, cv_test4, cv_test5]
cv_train = [cv_train1, cv_train2, cv_train3, cv_train4, cv_train5]
T = [T1, T2, T3, T4, T5]
t = [t1, t2, t3, t4, t5]
return (cv_test, cv_train, T, t)
val = get_dataset(x_train, y_train)
def scorer(A):
score = 0
for i in range(0, 5):
model = lgbm.LGBMRegressor(n_estimators=100, random_state=42)
model.fit(A[1][i], np.array(A[3][i]))
pred = model.predict(A[0][i])
from sklearn.metrics import r2_score
S = r2_score(pred, A[2][i])
score = score + S
return score / 5
all_score = scorer(val)
all_score
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning)
check = x_train
max_score = 1
all_score = scorer(val)
drop = list()
while max_score >= all_score:
col_list = list()
score_list = list()
for col in check.columns:
col_list.append(col)
temp = check.drop(col, axis=1)
B = get_dataset(temp, y_train)
s = scorer(B)
score_list.append(s)
val_df = pd.DataFrame(list(zip(col_list, score_list)), columns=['col_list', 'score_list'])
val_df = val_df.sort_values(by='score_list', ascending=False)
max_col = val_df.iloc[0, 0]
max_score = val_df.iloc[0, 1]
if max_score >= all_score:
drop.append(max_col)
all_score = max_score
check = check.drop(max_col, axis=1)
def auto_selector(x_train, y_train):
def get_dataset(df, target):
L = df.shape[0]
cv_test1 = df.iloc[:round(L / 5), :]
cv_test2 = df.iloc[round(L / 5):round(2 * L / 5), :]
cv_test3 = df.iloc[round(2 * L / 5):round(3 * L / 5), :]
cv_test4 = df.iloc[round(3 * L / 5):round(4 * L / 5), :]
cv_test5 = df.iloc[round(4 * L / 5):, :]
cv_train1 = pd.concat([cv_test2, cv_test3, cv_test4, cv_test5], axis=0)
cv_train2 = pd.concat([cv_test1, cv_test3, cv_test4, cv_test5], axis=0)
cv_train3 = pd.concat([cv_test1, cv_test2, cv_test4, cv_test5], axis=0)
cv_train4 = pd.concat([cv_test1, cv_test2, cv_test3, cv_test5], axis=0)
cv_train5 = pd.concat([cv_test1, cv_test2, cv_test3, cv_test5], axis=0)
T1 = target.iloc[0:round(L / 5)]
T2 = target.iloc[round(L / 5):round(2 * L / 5)]
T3 = target.iloc[round(2 * L / 5):round(3 * L / 5)]
T4 = target.iloc[round(3 * L / 5):round(4 * L / 5)]
T5 = target.iloc[round(4 * L / 5):]
t1 = pd.concat([T2, T3, T4, T5], axis=0)
t2 = pd.concat([T1, T3, T4, T5], axis=0)
t3 = pd.concat([T1, T2, T4, T5], axis=0)
t4 = pd.concat([T1, T2, T3, T5], axis=0)
t5 = pd.concat([T1, T2, T3, T4], axis=0)
cv_test = [cv_test1, cv_test2, cv_test3, cv_test4, cv_test5]
cv_train = [cv_train1, cv_train2, cv_train3, cv_train4, cv_train5]
T = [T1, T2, T3, T4, T5]
t = [t1, t2, t3, t4, t5]
return (cv_test, cv_train, T, t)
val = get_dataset(x_train, y_train)
def scorer(A):
score = 0
for i in range(0, 5):
model = lgbm.LGBMRegressor(n_estimators=100, random_state=42)
model.fit(A[1][i], np.array(A[3][i]))
pred = model.predict(A[0][i])
from sklearn.metrics import r2_score
S = r2_score(pred, A[2][i])
score = score + S
return score / 5
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning)
check = x_train
max_score = 1
all_score = scorer(val)
drop = list()
while max_score >= all_score:
col_list = list()
score_list = list()
for col in check.columns:
col_list.append(col)
temp = check.drop(col, axis=1)
B = get_dataset(temp, y_train)
s = scorer(B)
score_list.append(s)
val_df = pd.DataFrame(list(zip(col_list, score_list)), columns=['col_list', 'score_list'])
val_df = val_df.sort_values(by='score_list', ascending=False)
max_col = val_df.iloc[0, 0]
max_score = val_df.iloc[0, 1]
if max_score >= all_score:
drop.append(max_col)
all_score = max_score
check = check.drop(max_col, axis=1)
return (x_train.drop(drop, axis=1), y_train)
final_df = auto_selector(x_train, y_train)
(final_df[0].shape, final_df[1].shape) | code |
2041508/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.head()
spotify.shape
spotify.dtypes | code |
2041508/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe2 = globe.groupby('Artist').agg({'Streams': 'sum'})
top_globe2 = top_globe2.sort_values(['Streams'], ascending=False)
top_globe2['country'] = 'Globe'
top_usa2 = usa.groupby('Artist').agg({'Streams': 'sum'})
top_usa2 = top_usa2.sort_values(['Streams'], ascending=False)
top_usa2['country'] = 'USA'
top_great_britain2 = great_britain.groupby('Artist').agg({'Streams': 'sum'})
top_great_britain2 = top_great_britain2.sort_values(['Streams'], ascending=False)
top_great_britain2['country'] = 'Great Britain'
top_mexico2 = mexico.groupby('Artist').agg({'Streams': 'sum'})
top_mexico2 = top_mexico2.sort_values(['Streams'], ascending=False)
top_mexico2['country'] = 'Mexico'
top_taiwan2 = taiwan.groupby('Artist').agg({'Streams': 'sum'})
top_taiwan2 = top_taiwan2.sort_values(['Streams'], ascending=False)
top_taiwan2['country'] = 'Taiwan'
top_singapore2 = singapore.groupby('Artist').agg({'Streams': 'sum'})
top_singapore2 = top_singapore2.sort_values(['Streams'], ascending=False)
top_singapore2['country'] = 'Singapore'
top_globe2 = top_globe2[0:3]
top_usa2 = top_usa2[0:3]
top_great_britain2 = top_great_britain2[0:3]
top_mexico2 = top_mexico2[0:3]
top_taiwan2 = top_taiwan2[0:3]
top_singapore2 = top_singapore2[0:3]
top_all_merged2 = top_globe2.append([top_usa2, top_great_britain2, top_mexico2, top_taiwan2, top_singapore2]) | code |
2041508/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import plotly.graph_objs as go
import plotly.plotly as py
import plotly.plotly as py
import plotly.plotly as py
from plotly.graph_objs import *
trace1 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [2.54, 1.45, 2.47, 1.75, 2.11, 2.7], 'name': 'Shape of You', 'type': 'bar', 'uid': 'd81641', 'visible': True, 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:593809'}
trace2 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [1.48, 0, 1.4, 0, 0, 1.48], 'name': 'Despacito - Remix', 'type': 'bar', 'uid': 'c15c84', 'visible': True, 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:724a8c'}
trace3 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [0, 0, 0, 0, 1.97, 1.78], 'name': 'Something Just Like This', 'type': 'bar', 'uid': '1dbc1b', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:31cdb8'}
trace4 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [1.3, 0, 0, 2.22, 0, 0], 'name': 'Despacito (Featuring Daddy Yankee)', 'type': 'bar', 'uid': 'c6b042', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:4583e2'}
trace5 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [0, 0, 0, 2.16, 0, 0], 'name': 'Me Rehúso', 'type': 'bar', 'uid': 'be7d95', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:b9ea50'}
trace6 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [0, 1.2, 0, 0, 0, 0], 'name': 'Mask Off', 'type': 'bar', 'uid': '60d6b8', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:989da6'}
trace7 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [0, 0, 0, 0, 1.23, 0], 'name': '演員', 'type': 'bar', 'uid': 'f912b1', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:cd61ac'}
trace8 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [0, 0, 1.67, 0, 0, 0], 'name': 'Castle on the Hill', 'type': 'bar', 'uid': 'c01a7b', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:083ede'}
trace9 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [0, 1.51, 0, 0, 0, 0], 'name': 'HUMBLE.', 'type': 'bar', 'uid': 'd9ea4a', 'xsrc': 'sweetmusicality:7:ff97bb', 'ysrc': 'sweetmusicality:7:1008dc'}
data = [trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9]
layout = {'annotations': [{'x': 1.09648221896, 'y': 0.671878877124, 'font': {'size': 21}, 'showarrow': False, 'text': '<b>Song</b>', 'xanchor': 'middle', 'xref': 'paper', 'yanchor': 'bottom', 'yref': 'paper'}], 'autosize': True, 'barmode': 'stack', 'font': {'size': 18}, 'hovermode': 'closest', 'legend': {'x': 1.01935845381, 'y': 0.673239347844, 'borderwidth': 0, 'orientation': 'v', 'traceorder': 'normal'}, 'margin': {'b': 80}, 'title': '<b>Top 3 Streamed Songs on Spotify from Jan 2017 - Aug 2017 by Country</b>', 'titlefont': {'size': 28}, 'xaxis': {'autorange': False, 'domain': [0, 1.01], 'range': [-0.5, 5.51343670089], 'side': 'bottom', 'title': '<b>Country</b>', 'type': 'category'}, 'yaxis': {'anchor': 'x', 'autorange': False, 'domain': [-0.01, 1], 'range': [0, 6.66421250763], 'title': '<b>% this song was streamed in its country</b>', 'type': 'linear'}}
fig = go.Figure(data=data, layout=layout)
py.iplot(fig) | code |
2041508/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe2 = globe.groupby('Artist').agg({'Streams': 'sum'})
top_globe2 = top_globe2.sort_values(['Streams'], ascending=False)
top_globe2['country'] = 'Globe'
top_usa2 = usa.groupby('Artist').agg({'Streams': 'sum'})
top_usa2 = top_usa2.sort_values(['Streams'], ascending=False)
top_usa2['country'] = 'USA'
top_great_britain2 = great_britain.groupby('Artist').agg({'Streams': 'sum'})
top_great_britain2 = top_great_britain2.sort_values(['Streams'], ascending=False)
top_great_britain2['country'] = 'Great Britain'
top_mexico2 = mexico.groupby('Artist').agg({'Streams': 'sum'})
top_mexico2 = top_mexico2.sort_values(['Streams'], ascending=False)
top_mexico2['country'] = 'Mexico'
top_taiwan2 = taiwan.groupby('Artist').agg({'Streams': 'sum'})
top_taiwan2 = top_taiwan2.sort_values(['Streams'], ascending=False)
top_taiwan2['country'] = 'Taiwan'
top_singapore2 = singapore.groupby('Artist').agg({'Streams': 'sum'})
top_singapore2 = top_singapore2.sort_values(['Streams'], ascending=False)
top_singapore2['country'] = 'Singapore'
top_globe2['prop'] = top_globe2['Streams'] / sum(top_globe2['Streams']) * 100
top_usa2['prop'] = top_usa2['Streams'] / sum(top_usa2['Streams']) * 100
top_great_britain2['prop'] = top_great_britain2['Streams'] / sum(top_great_britain2['Streams']) * 100
top_mexico2['prop'] = top_mexico2['Streams'] / sum(top_mexico2['Streams']) * 100
top_taiwan2['prop'] = top_taiwan2['Streams'] / sum(top_taiwan2['Streams']) * 100
top_singapore2['prop'] = top_singapore2['Streams'] / sum(top_singapore2['Streams']) * 100 | code |
2041508/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe2 = globe.groupby('Artist').agg({'Streams': 'sum'})
top_globe2 = top_globe2.sort_values(['Streams'], ascending=False)
top_globe2['country'] = 'Globe'
top_usa2 = usa.groupby('Artist').agg({'Streams': 'sum'})
top_usa2 = top_usa2.sort_values(['Streams'], ascending=False)
top_usa2['country'] = 'USA'
top_great_britain2 = great_britain.groupby('Artist').agg({'Streams': 'sum'})
top_great_britain2 = top_great_britain2.sort_values(['Streams'], ascending=False)
top_great_britain2['country'] = 'Great Britain'
top_mexico2 = mexico.groupby('Artist').agg({'Streams': 'sum'})
top_mexico2 = top_mexico2.sort_values(['Streams'], ascending=False)
top_mexico2['country'] = 'Mexico'
top_taiwan2 = taiwan.groupby('Artist').agg({'Streams': 'sum'})
top_taiwan2 = top_taiwan2.sort_values(['Streams'], ascending=False)
top_taiwan2['country'] = 'Taiwan'
top_singapore2 = singapore.groupby('Artist').agg({'Streams': 'sum'})
top_singapore2 = top_singapore2.sort_values(['Streams'], ascending=False)
top_singapore2['country'] = 'Singapore'
top_globe2 = top_globe2[0:3]
top_usa2 = top_usa2[0:3]
top_great_britain2 = top_great_britain2[0:3]
top_mexico2 = top_mexico2[0:3]
top_taiwan2 = top_taiwan2[0:3]
top_singapore2 = top_singapore2[0:3]
del top_globe2['Streams']
del top_usa2['Streams']
del top_great_britain2['Streams']
del top_mexico2['Streams']
del top_taiwan2['Streams']
del top_singapore2['Streams'] | code |
2041508/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe = top_globe[0:3]
top_usa = top_usa[0:3]
top_great_britain = top_great_britain[0:3]
top_mexico = top_mexico[0:3]
top_taiwan = top_taiwan[0:3]
top_singapore = top_singapore[0:3]
top_all_merged = top_globe.append([top_usa, top_great_britain, top_mexico, top_taiwan, top_singapore])
top_all_merged = top_all_merged.reset_index()
all_songs = top_all_merged['Track Name'].value_counts()
all_songs = all_songs.reset_index()
len(top_all_merged['Track Name'].value_counts()) | code |
2041508/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes | code |
2041508/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe = top_globe[0:3]
top_usa = top_usa[0:3]
top_great_britain = top_great_britain[0:3]
top_mexico = top_mexico[0:3]
top_taiwan = top_taiwan[0:3]
top_singapore = top_singapore[0:3]
top_all_merged = top_globe.append([top_usa, top_great_britain, top_mexico, top_taiwan, top_singapore])
top_all_merged = top_all_merged.reset_index() | code |
2041508/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe = top_globe[0:3]
top_usa = top_usa[0:3]
top_great_britain = top_great_britain[0:3]
top_mexico = top_mexico[0:3]
top_taiwan = top_taiwan[0:3]
top_singapore = top_singapore[0:3]
top_all_merged = top_globe.append([top_usa, top_great_britain, top_mexico, top_taiwan, top_singapore]) | code |
2041508/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe2 = globe.groupby('Artist').agg({'Streams': 'sum'})
top_globe2 = top_globe2.sort_values(['Streams'], ascending=False)
top_globe2['country'] = 'Globe'
top_usa2 = usa.groupby('Artist').agg({'Streams': 'sum'})
top_usa2 = top_usa2.sort_values(['Streams'], ascending=False)
top_usa2['country'] = 'USA'
top_great_britain2 = great_britain.groupby('Artist').agg({'Streams': 'sum'})
top_great_britain2 = top_great_britain2.sort_values(['Streams'], ascending=False)
top_great_britain2['country'] = 'Great Britain'
top_mexico2 = mexico.groupby('Artist').agg({'Streams': 'sum'})
top_mexico2 = top_mexico2.sort_values(['Streams'], ascending=False)
top_mexico2['country'] = 'Mexico'
top_taiwan2 = taiwan.groupby('Artist').agg({'Streams': 'sum'})
top_taiwan2 = top_taiwan2.sort_values(['Streams'], ascending=False)
top_taiwan2['country'] = 'Taiwan'
top_singapore2 = singapore.groupby('Artist').agg({'Streams': 'sum'})
top_singapore2 = top_singapore2.sort_values(['Streams'], ascending=False)
top_singapore2['country'] = 'Singapore'
top_globe2 = top_globe2[0:3]
top_usa2 = top_usa2[0:3]
top_great_britain2 = top_great_britain2[0:3]
top_mexico2 = top_mexico2[0:3]
top_taiwan2 = top_taiwan2[0:3]
top_singapore2 = top_singapore2[0:3] | code |
2041508/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe2 = globe.groupby('Artist').agg({'Streams': 'sum'})
top_globe2 = top_globe2.sort_values(['Streams'], ascending=False)
top_globe2['country'] = 'Globe'
top_usa2 = usa.groupby('Artist').agg({'Streams': 'sum'})
top_usa2 = top_usa2.sort_values(['Streams'], ascending=False)
top_usa2['country'] = 'USA'
top_great_britain2 = great_britain.groupby('Artist').agg({'Streams': 'sum'})
top_great_britain2 = top_great_britain2.sort_values(['Streams'], ascending=False)
top_great_britain2['country'] = 'Great Britain'
top_mexico2 = mexico.groupby('Artist').agg({'Streams': 'sum'})
top_mexico2 = top_mexico2.sort_values(['Streams'], ascending=False)
top_mexico2['country'] = 'Mexico'
top_taiwan2 = taiwan.groupby('Artist').agg({'Streams': 'sum'})
top_taiwan2 = top_taiwan2.sort_values(['Streams'], ascending=False)
top_taiwan2['country'] = 'Taiwan'
top_singapore2 = singapore.groupby('Artist').agg({'Streams': 'sum'})
top_singapore2 = top_singapore2.sort_values(['Streams'], ascending=False)
top_singapore2['country'] = 'Singapore' | code |
2041508/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
spotify['Region'].value_counts()
len(spotify['Region'].value_counts()) | code |
2041508/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe = top_globe[0:3]
top_usa = top_usa[0:3]
top_great_britain = top_great_britain[0:3]
top_mexico = top_mexico[0:3]
top_taiwan = top_taiwan[0:3]
top_singapore = top_singapore[0:3] | code |
2041508/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv') | code |
2041508/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe = top_globe[0:3]
top_usa = top_usa[0:3]
top_great_britain = top_great_britain[0:3]
top_mexico = top_mexico[0:3]
top_taiwan = top_taiwan[0:3]
top_singapore = top_singapore[0:3]
del top_globe['Streams']
del top_usa['Streams']
del top_great_britain['Streams']
del top_mexico['Streams']
del top_taiwan['Streams']
del top_singapore['Streams'] | code |
2041508/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe2 = globe.groupby('Artist').agg({'Streams': 'sum'})
top_globe2 = top_globe2.sort_values(['Streams'], ascending=False)
top_globe2['country'] = 'Globe'
top_usa2 = usa.groupby('Artist').agg({'Streams': 'sum'})
top_usa2 = top_usa2.sort_values(['Streams'], ascending=False)
top_usa2['country'] = 'USA'
top_great_britain2 = great_britain.groupby('Artist').agg({'Streams': 'sum'})
top_great_britain2 = top_great_britain2.sort_values(['Streams'], ascending=False)
top_great_britain2['country'] = 'Great Britain'
top_mexico2 = mexico.groupby('Artist').agg({'Streams': 'sum'})
top_mexico2 = top_mexico2.sort_values(['Streams'], ascending=False)
top_mexico2['country'] = 'Mexico'
top_taiwan2 = taiwan.groupby('Artist').agg({'Streams': 'sum'})
top_taiwan2 = top_taiwan2.sort_values(['Streams'], ascending=False)
top_taiwan2['country'] = 'Taiwan'
top_singapore2 = singapore.groupby('Artist').agg({'Streams': 'sum'})
top_singapore2 = top_singapore2.sort_values(['Streams'], ascending=False)
top_singapore2['country'] = 'Singapore'
top_globe2 = top_globe2[0:3]
top_usa2 = top_usa2[0:3]
top_great_britain2 = top_great_britain2[0:3]
top_mexico2 = top_mexico2[0:3]
top_taiwan2 = top_taiwan2[0:3]
top_singapore2 = top_singapore2[0:3]
top_all_merged2 = top_globe2.append([top_usa2, top_great_britain2, top_mexico2, top_taiwan2, top_singapore2])
top_all_merged2 = top_all_merged2.reset_index() | code |
2041508/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore'
top_globe['prop'] = top_globe['Streams'] / sum(top_globe['Streams']) * 100
top_usa['prop'] = top_usa['Streams'] / sum(top_usa['Streams']) * 100
top_great_britain['prop'] = top_great_britain['Streams'] / sum(top_great_britain['Streams']) * 100
top_mexico['prop'] = top_mexico['Streams'] / sum(top_mexico['Streams']) * 100
top_taiwan['prop'] = top_taiwan['Streams'] / sum(top_taiwan['Streams']) * 100
top_singapore['prop'] = top_singapore['Streams'] / sum(top_singapore['Streams']) * 100 | code |
2041508/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg'] | code |
2041508/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[spotify.Region == 'global']
usa = spotify[spotify.Region == 'us']
great_britain = spotify[spotify.Region == 'gb']
mexico = spotify[spotify.Region == 'mx']
taiwan = spotify[spotify.Region == 'tw']
singapore = spotify[spotify.Region == 'sg']
top_globe = globe.groupby('Track Name').agg({'Streams': 'sum'})
top_globe = top_globe.sort_values(['Streams'], ascending=False)
top_globe['country'] = 'Globe'
top_usa = usa.groupby('Track Name').agg({'Streams': 'sum'})
top_usa = top_usa.sort_values(['Streams'], ascending=False)
top_usa['country'] = 'USA'
top_great_britain = great_britain.groupby('Track Name').agg({'Streams': 'sum'})
top_great_britain = top_great_britain.sort_values(['Streams'], ascending=False)
top_great_britain['country'] = 'Great Britain'
top_mexico = mexico.groupby('Track Name').agg({'Streams': 'sum'})
top_mexico = top_mexico.sort_values(['Streams'], ascending=False)
top_mexico['country'] = 'Mexico'
top_taiwan = taiwan.groupby('Track Name').agg({'Streams': 'sum'})
top_taiwan = top_taiwan.sort_values(['Streams'], ascending=False)
top_taiwan['country'] = 'Taiwan'
top_singapore = singapore.groupby('Track Name').agg({'Streams': 'sum'})
top_singapore = top_singapore.sort_values(['Streams'], ascending=False)
top_singapore['country'] = 'Singapore' | code |
121154376/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
model = sm.OLS.from_formula('Income ~ Mortgage', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary() | code |
121154376/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.head() | code |
121154376/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
model = sm.OLS.from_formula('Income ~ Mortgage', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
df.rename(columns={'ID': 'ID', 'Age': 'Age', 'Experience': 'Experience', 'Income': 'Income', 'ZIP Code': 'ZIP Code', 'Family': 'Family', 'CCAvg': 'CCAvg', 'Education': 'Education', 'Mortgage': 'Mortgage', 'Personal Loan': 'PersonalLoan', 'Securities Account': 'SecuritiesAccount', 'CD Account': 'CDAccount', 'Online': 'Online', 'CreditCard': 'CreditCard'}, inplace=True)
df['CDAccount'].replace({0: 'No', 1: 'Yes'}, inplace=True)
df['PersonalLoan'].replace({0: 'No', 1: 'Yes'}, inplace=True)
df['Education'].replace({1: 'Undergrad', 2: 'Graduate', 3: 'Advanced/Professional'}, inplace=True)
df['Family'].replace({1: 'One', 2: 'Two', 3: 'Three', 4: 'Four'}, inplace=True)
categ_features = ['CDAccount', 'PersonalLoan', 'Education', 'Family']
for i in categ_features:
x = 'Income ~' + i
model = sm.OLS.from_formula(x, data=df)
corr = model.fit()
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
best = -1000
for i in range(5, 600, 5):
model = DecisionTreeRegressor(random_state=1, max_leaf_nodes=i)
income = model.fit(trainX, trainy)
predicted_values = income.predict(valX)
mae = -mean_absolute_error(valy, predicted_values)
if mae > best:
best = mae
x = 'model = DecisionTreeRegressor(random_state = 1, max_leaf_nodes = i)\nincome = model.fit(trainX, trainy)\npredicted_values = income.predict(valX)\nmae = mean_absolute_error(valy, predicted_values)'
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
best = -1000
for i in range(5, 700, 10):
model = RandomForestRegressor(random_state=1, max_leaf_nodes=i)
income = model.fit(trainX, trainy)
predicted_values = income.predict(valX)
mae = -mean_absolute_error(valy, predicted_values)
if mae > best:
best = mae
print(-best) | code |
121154376/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary() | code |
121154376/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
model = sm.OLS.from_formula('Income ~ Mortgage', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
df.rename(columns={'ID': 'ID', 'Age': 'Age', 'Experience': 'Experience', 'Income': 'Income', 'ZIP Code': 'ZIP Code', 'Family': 'Family', 'CCAvg': 'CCAvg', 'Education': 'Education', 'Mortgage': 'Mortgage', 'Personal Loan': 'PersonalLoan', 'Securities Account': 'SecuritiesAccount', 'CD Account': 'CDAccount', 'Online': 'Online', 'CreditCard': 'CreditCard'}, inplace=True)
df['CDAccount'].replace({0: 'No', 1: 'Yes'}, inplace=True)
df['PersonalLoan'].replace({0: 'No', 1: 'Yes'}, inplace=True)
df['Education'].replace({1: 'Undergrad', 2: 'Graduate', 3: 'Advanced/Professional'}, inplace=True)
df['Family'].replace({1: 'One', 2: 'Two', 3: 'Three', 4: 'Four'}, inplace=True)
categ_features = ['CDAccount', 'PersonalLoan', 'Education', 'Family']
for i in categ_features:
x = 'Income ~' + i
model = sm.OLS.from_formula(x, data=df)
corr = model.fit()
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
best = -1000
for i in range(5, 600, 5):
model = DecisionTreeRegressor(random_state=1, max_leaf_nodes=i)
income = model.fit(trainX, trainy)
predicted_values = income.predict(valX)
mae = -mean_absolute_error(valy, predicted_values)
if mae > best:
best = mae
print(-best)
x = 'model = DecisionTreeRegressor(random_state = 1, max_leaf_nodes = i)\nincome = model.fit(trainX, trainy)\npredicted_values = income.predict(valX)\nmae = mean_absolute_error(valy, predicted_values)' | code |
121154376/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns | code |
121154376/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
model = sm.OLS.from_formula('Income ~ Mortgage', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
df.rename(columns={'ID': 'ID', 'Age': 'Age', 'Experience': 'Experience', 'Income': 'Income', 'ZIP Code': 'ZIP Code', 'Family': 'Family', 'CCAvg': 'CCAvg', 'Education': 'Education', 'Mortgage': 'Mortgage', 'Personal Loan': 'PersonalLoan', 'Securities Account': 'SecuritiesAccount', 'CD Account': 'CDAccount', 'Online': 'Online', 'CreditCard': 'CreditCard'}, inplace=True)
df['CDAccount'].replace({0: 'No', 1: 'Yes'}, inplace=True)
df['PersonalLoan'].replace({0: 'No', 1: 'Yes'}, inplace=True)
df['Education'].replace({1: 'Undergrad', 2: 'Graduate', 3: 'Advanced/Professional'}, inplace=True)
df['Family'].replace({1: 'One', 2: 'Two', 3: 'Three', 4: 'Four'}, inplace=True)
categ_features = ['CDAccount', 'PersonalLoan', 'Education', 'Family']
for i in categ_features:
x = 'Income ~' + i
model = sm.OLS.from_formula(x, data=df)
corr = model.fit()
print(corr.summary())
print('\n\n\n\n\n') | code |
121154376/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
sns.heatmap(np.round(df.corr(), 2), vmin=-1, vmax=1, annot=True, annot_kws={'fontsize': 5, 'fontweight': 'bold'}) | code |
128031395/cell_42 | [
"text_plain_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
y_predict = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
threshold = 0.25
pred_s = rf_model.predict_proba(X_valid)
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_valid, predicted)
accuracy | code |
128031395/cell_9 | [
"image_output_1.png"
] | 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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
import seaborn as sn
import matplotlib.pyplot as plt
hm = sn.heatmap(df.corr().round(3), cmap='YlGnBu')
plt.show() | code |
128031395/cell_25 | [
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i, s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show() | code |
128031395/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df.info() | code |
128031395/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy | code |
128031395/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test) | code |
128031395/cell_44 | [
"text_plain_output_1.png"
] | from eli5.sklearn import PermutationImportance
from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve
import eli5
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
y_predict = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
threshold = 0.25
pred_s = rf_model.predict_proba(X_valid)
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_valid, predicted)
accuracy
import eli5
from eli5.sklearn import PermutationImportance
perm = PermutationImportance(rf_model, random_state=1).fit(X_valid, y_valid)
eli5.show_weights(perm, feature_names=X_valid.columns.tolist()) | code |
128031395/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
#
# Print the confusion matrix using Matplotlib
#
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i, s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show() | code |
128031395/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
y_predict = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy | code |
128031395/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128031395/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include=np.number).transpose()
missing = df.isnull().sum() * 100 / len(df)
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc, df_missing], axis=1) | code |
128031395/cell_51 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
y_predict = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
threshold = 0.25
pred_s = rf_model.predict_proba(X_valid)
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_valid, predicted)
accuracy
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy | code |
128031395/cell_28 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
y_predict = rf_model.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_predict) | code |
128031395/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
df.corr().round(3)
X = df.drop('Class', axis=1)
y = df['Class']
df_majority = df[df.Class == 0]
df_minority = df[df.Class == 1]
df_majority_downsampled = resample(df_majority, replace=False, n_samples=100000)
df_up_down_sampled = pd.concat([df_minority, df_majority_downsampled])
df_up_down_sampled.shape | code |
128031395/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns | code |
128031395/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
df.corr().round(3)
X = df.drop('Class', axis=1)
y = df['Class']
df_majority = df[df.Class == 0]
df_minority = df[df.Class == 1]
df_majority_downsampled = resample(df_majority, replace=False, n_samples=100000)
df_up_down_sampled = pd.concat([df_minority, df_majority_downsampled])
df_up_down_sampled.shape
df_up_down_sampled['Class'].value_counts() | code |
128031395/cell_35 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))
pyplot.plot([0, 1], [0, 1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.', label='Logistic')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
pyplot.show() | code |
128031395/cell_43 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve
import shap
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
y_predict = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
threshold = 0.25
pred_s = rf_model.predict_proba(X_valid)
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_valid, predicted)
accuracy
import shap
explainer = shap.TreeExplainer(rf_model)
shap_values = explainer.shap_values(X_valid)
shap.summary_plot(shap_values[1], X_valid) | code |
128031395/cell_31 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
#
# Print the confusion matrix using Matplotlib
#
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
#
# Print the confusion matrix using Matplotlib
#
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i, s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show() | code |
128031395/cell_24 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy | code |
128031395/cell_10 | [
"text_plain_output_1.png"
] | 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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
df.corr().round(3) | code |
128031395/cell_37 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve
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
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.number).transpose()
missing= ((df.isnull().sum())*100)/(len(df))
df_missing = pd.DataFrame(missing, columns=['missing%'])
pd.concat([df_desc,df_missing],axis=1)
#heatmap
import seaborn as sn
import matplotlib.pyplot as plt
hm=sn.heatmap(df.corr().round(3), cmap="YlGnBu")
plt.show()
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
#
# Print the confusion matrix using Matplotlib
#
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
#
# Print the confusion matrix using Matplotlib
#
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
#
# Print the confusion matrix using Matplotlib
#
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.metrics import confusion_matrix
conf_matrix = confusion_matrix(y_true=y_test, y_pred=predicted)
fig, ax = plt.subplots(figsize=(7.5, 7.5))
ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)
for i in range(conf_matrix.shape[0]):
for j in range(conf_matrix.shape[1]):
ax.text(x=j, y=i, s=conf_matrix[i, j], va='center', ha='center', size='xx-large')
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show() | code |
128031395/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing | code |
128031395/cell_36 | [
"text_plain_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
accuracy
threshold = 0.5
predicted = (pred_s[:, 1] >= threshold).astype('int')
threshold = 0.25
predicted = (pred_s[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test)
lr_probs = classifier.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
from numpy import sqrt
from sklearn.metrics import roc_curve
from numpy import argmax
from matplotlib import pyplot
yhat_prob = classifier.predict_proba(X_test)
yhat = yhat_prob[:, 1]
fpr, tpr, thresholds = roc_curve(y_test, yhat)
gmeans = sqrt(tpr * (1 - fpr))
ix = argmax(gmeans)
threshold = 0.3
predicted = (yhat_prob[:, 1] >= threshold).astype('int')
accuracy = accuracy_score(y_test, predicted)
accuracy | code |
128016199/cell_4 | [
"text_plain_output_1.png"
] | !yolo task=detect mode=predict model=/kaggle/working//runs/detect/train/weights/best.pt conf=0.25 source=/kaggle/input/detect-pv/detect_pv/test/images save=True | code |
128016199/cell_2 | [
"text_plain_output_1.png"
] | !yolo task=detect mode=train model=yolov8l.pt data=/kaggle/input/datayaml/data.yaml epochs=120 plots=True | code |
128016199/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | # Pip install method (recommended)
!pip install ultralytics==8.0.20
from IPython import display
display.clear_output()
import ultralytics
ultralytics.checks() | code |
128016199/cell_3 | [
"text_plain_output_1.png"
] | !yolo task=detect mode=val model=/kaggle/working/runs/detect/train/weights/best.pt data=/kaggle/input/datayaml/data.yaml | code |
128016199/cell_5 | [
"image_output_11.png",
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"image_output_24.png",
"text_plain_output_43.png",
"image_output_46.png",
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"image_output_37.png",
"text_plain_output_16.png",
"image_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"image_output_27.png",
"image_output_6.png",
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"text_plain_output_34.png",
"image_output_45.png",
"text_plain_output_42.png",
"text_plain_output_23.png",
"image_output_12.png",
"text_plain_output_28.png",
"image_output_22.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_39.png",
"image_output_3.png",
"image_output_29.png",
"image_output_44.png",
"image_output_43.png",
"text_plain_output_19.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"image_output_33.png",
"image_output_50.png",
"image_output_15.png",
"image_output_49.png",
"image_output_9.png",
"image_output_19.png",
"image_output_38.png",
"image_output_26.png",
"text_plain_output_46.png"
] | from IPython import display
from IPython.display import Image, display
import glob
import glob
from IPython.display import Image, display
for image_path in glob.glob('/kaggle/working/runs/detect/predict/*.jpg')[:50]:
display(Image(filename=image_path, width=300))
print('\n') | code |
106198653/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
plt.figure(figsize=(15, 10))
sns.histplot(data=train_df, x='Age', bins=20, hue='Transported', color=sns.color_palette('flare', as_cmap=True), binwidth=1, kde=True)
plt.title('Distribution de la variable Age', fontsize=18)
plt.xlabel('Age', fontsize=18)
plt.ylabel('Nombre de passagers', fontsize=18) | code |
106198653/cell_25 | [
"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
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
exp_feats = ["RoomService", "FoodCourt", "ShoppingMall", "Spa", "VRDeck"]
fig=plt.figure(figsize=(10,20))
for i, var_name in enumerate(exp_feats):
ax=fig.add_subplot(5,2,2*i+1)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=False, hue='Transported')
ax.set_title(var_name)
ax=fig.add_subplot(5,2,2*i+2)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=True, hue='Transported')
plt.ylim([0,100])
ax.set_title(var_name)
fig.tight_layout()
plt.show()
cat_feats = ["HomePlanet", "CryoSleep", "Destination", "VIP"]
fig = plt.figure(figsize=(10, 20))
sns.set(font_scale=1.2)
for i, var_name in enumerate(cat_feats):
ax = fig.add_subplot(4, 1, i + 1)
sns.countplot(data=train_df, x=var_name, axes=ax, hue="Transported")
ax.set_title(var_name)
fig.tight_layout()
plt.show()
train_df['Total_expense'] = train_df.iloc[:, -7:-2].sum(axis=1)
train_df['Spent_money'] = train_df['Total_expense'].apply(lambda x: False if x == 0 else True)
test_df['Total_expense'] = test_df.iloc[:, -7:-2].sum(axis=1)
test_df['Spent_money'] = test_df['Total_expense'].apply(lambda x: False if x == 0 else True)
train_df.loc[train_df['Family_size'] > 100, 'Family_size'] = np.nan
test_df.loc[test_df['Family_size'] > 100, 'Family_size'] = np.nan
fig = plt.figure(figsize=(30, 15))
sns.set(font_scale=2)
sns.countplot(data=train_df, x='Spent_money', hue='Transported')
plt.xlabel('Spent_money', fontsize=40)
plt.ylabel('Nombre de passagers', fontsize=40) | code |
106198653/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
import missingno as msno
msno.matrix(train_df)
msno.matrix(test_df) | code |
106198653/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
!pip install miceforest
!pip install missingpy
import sklearn
from sklearn import preprocessing | code |
106198653/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(space_torr) | code |
106198653/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
exp_feats = ["RoomService", "FoodCourt", "ShoppingMall", "Spa", "VRDeck"]
fig=plt.figure(figsize=(10,20))
for i, var_name in enumerate(exp_feats):
ax=fig.add_subplot(5,2,2*i+1)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=False, hue='Transported')
ax.set_title(var_name)
ax=fig.add_subplot(5,2,2*i+2)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=True, hue='Transported')
plt.ylim([0,100])
ax.set_title(var_name)
fig.tight_layout()
plt.show()
cat_feats = ['HomePlanet', 'CryoSleep', 'Destination', 'VIP']
fig = plt.figure(figsize=(10, 20))
sns.set(font_scale=1.2)
for i, var_name in enumerate(cat_feats):
ax = fig.add_subplot(4, 1, i + 1)
sns.countplot(data=train_df, x=var_name, axes=ax, hue='Transported')
ax.set_title(var_name)
fig.tight_layout()
plt.show() | code |
106198653/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
exp_feats = ["RoomService", "FoodCourt", "ShoppingMall", "Spa", "VRDeck"]
fig=plt.figure(figsize=(10,20))
for i, var_name in enumerate(exp_feats):
ax=fig.add_subplot(5,2,2*i+1)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=False, hue='Transported')
ax.set_title(var_name)
ax=fig.add_subplot(5,2,2*i+2)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=True, hue='Transported')
plt.ylim([0,100])
ax.set_title(var_name)
fig.tight_layout()
plt.show()
cat_feats = ["HomePlanet", "CryoSleep", "Destination", "VIP"]
fig = plt.figure(figsize=(10, 20))
sns.set(font_scale=1.2)
for i, var_name in enumerate(cat_feats):
ax = fig.add_subplot(4, 1, i + 1)
sns.countplot(data=train_df, x=var_name, axes=ax, hue="Transported")
ax.set_title(var_name)
fig.tight_layout()
plt.show()
train_df['Total_expense'] = train_df.iloc[:, -7:-2].sum(axis=1)
train_df['Spent_money'] = train_df['Total_expense'].apply(lambda x: False if x == 0 else True)
test_df['Total_expense'] = test_df.iloc[:, -7:-2].sum(axis=1)
test_df['Spent_money'] = test_df['Total_expense'].apply(lambda x: False if x == 0 else True)
train_df['Name'].fillna('Anonymous Anonymous', inplace=True)
test_df['Name'].fillna('Anonymous Anonymous', inplace=True)
train_df['Last_name'] = train_df['Name'].apply(lambda x: x.split(' ')[-1] if type(x) == str else None)
test_df['Last_name'] = test_df['Name'].apply(lambda x: x.split(' ')[-1] if type(x) == str else None)
train_df.loc[train_df['Family_size'] > 100, 'Family_size'] = np.nan
test_df.loc[test_df['Family_size'] > 100, 'Family_size'] = np.nan
fig = plt.figure(figsize=(30, 15))
sns.set(font_scale=2)
sns.countplot(data=train_df, x="Spent_money", hue="Transported")
plt.xlabel("Spent_money", fontsize=40)
plt.ylabel("Nombre de passagers", fontsize=40)
train_df.Cabin.fillna('Z/6666/Z', inplace=True)
test_df.Cabin.fillna('Z/6666/Z', inplace=True)
train_df['Cabin_num'] = train_df['Cabin'].apply(lambda x: x.split('/')[1]).astype(int)
test_df['Cabin_num'] = test_df['Cabin'].apply(lambda x: x.split('/')[1]).astype(int)
train_df['Deck'] = train_df['Cabin'].apply(lambda x: x[0])
train_df['Side'] = train_df['Cabin'].apply(lambda x: x[-1])
test_df['Deck'] = test_df['Cabin'].apply(lambda x: x[0])
test_df['Side'] = test_df['Cabin'].apply(lambda x: x[-1])
train_df.Cabin.replace('Z/6666/Z', np.nan, inplace=True)
test_df.Cabin.replace('Z/6666/Z', np.nan, inplace=True)
train_df.Deck.replace('Z', np.nan, inplace=True)
test_df.Deck.replace('Z', np.nan, inplace=True)
train_df.Side.replace('Z', np.nan, inplace=True)
test_df.Side.replace('Z', np.nan, inplace=True)
fig = plt.figure(figsize=(15, 10))
sns.set(font_scale=1.2)
sns.histplot(data=train_df, x='Cabin_num', hue='Transported', binwidth=50)
plt.xlim([0, 2000])
plt.xlabel('Numéro de cabine', fontsize=18)
plt.ylabel('Nombre de passagers', fontsize=18) | code |
106198653/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
exp_feats = ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']
fig = plt.figure(figsize=(10, 20))
for i, var_name in enumerate(exp_feats):
ax = fig.add_subplot(5, 2, 2 * i + 1)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=False, hue='Transported')
ax.set_title(var_name)
ax = fig.add_subplot(5, 2, 2 * i + 2)
sns.histplot(data=train_df, x=var_name, axes=ax, bins=30, kde=True, hue='Transported')
plt.ylim([0, 100])
ax.set_title(var_name)
fig.tight_layout()
plt.show() | code |
106198653/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
plt.figure(figsize=(12, 8))
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
plt.pie(train_df.Transported.value_counts().values, labels=train_df.Transported.value_counts().index, colors=colors, explode=explode, startangle=0, shadow=True, autopct='%.1f%%')
plt.title('Distribution de la variable cible', fontsize=18) | code |
106198653/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
import missingno as msno
msno.matrix(train_df) | code |
105178234/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.head(3) | code |
105178234/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape | code |
105178234/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df[df['target'] == 0][['num_char', 'NUm_words', 'Num_sentence']].describe() | code |
105178234/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df['target'].value_counts() | code |
105178234/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df.head() | code |
105178234/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df[['num_char', 'NUm_words', 'Num_sentence']].describe() | code |
105178234/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape | code |
105178234/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
import matplotlib.pyplot as plt
sns.pairplot(df, hue='target') | code |
105178234/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 6))
sns.histplot(df[df['target'] == 0]['num_char'], color='green')
sns.histplot(df[df['target'] == 1]['num_char'], color='red') | code |
105178234/cell_26 | [
"text_plain_output_1.png"
] | import nltk
import nltk
nltk.download('punkt') | code |
105178234/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.info() | code |
105178234/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum() | code |
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