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sequencelengths 1
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|---|---|---|---|
72073997/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
X_train.head() | code |
72073997/cell_38 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
y_preds_tree | code |
72073997/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
for_reg = RandomForestRegressor()
for_reg.fit(X_train_scaled, y_train)
y_preds_for = for_reg.predict(X_val_scaled)
xgb_reg = xgb.XGBRegressor(gpu_id=0, tree_method='gpu_hist')
xgb_reg.fit(X_train_scaled, y_train)
y_preds_xgb = xgb_reg.predict(X_val_scaled)
print('RMSE for XGBoost Regressor: ', np.sqrt(mse(y_val, y_preds_xgb))) | code |
72073997/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
X_train.info() | code |
72073997/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
sub_lr = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
target_pred_ridge = ridge.predict(X_test_scaled)
sub_ridge = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sub_ridge['target'] = target_pred_ridge
sub_ridge.to_csv('sub_ridge.csv', index=False)
sub_ridge.head() | code |
72073997/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
sub_lr = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
sub_ridge = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
for_reg = RandomForestRegressor()
for_reg.fit(X_train_scaled, y_train)
y_preds_for = for_reg.predict(X_val_scaled)
target_pred_for = for_reg.predict(X_test_scaled)
target_pred_for
sub_for = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sub_for['target'] = target_pred_for
sub_for.head() | code |
72073997/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
print('RMSE of Ridge Regression: ', np.sqrt(mse(y_val, y_preds_ridge))) | code |
72073997/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
print('Training Data Shape: ', train.shape)
print('Testing Data Shape: ', test.shape) | code |
72073997/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
target_pred_lr = lr.predict(X_test_scaled)
target_pred_lr | code |
72073997/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
print('RMSE for Decision Tree Regressor: ', np.sqrt(mse(y_val, y_preds_tree))) | code |
72073997/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.info() | code |
32069487/cell_4 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
import missingno as msno
msno.matrix(p) | code |
32069487/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.head() | code |
32069487/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 |
32069487/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
import missingno as msno
msno.matrix(p)
msno.matrix(p) | code |
32069487/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.head() | code |
32069487/cell_15 | [
"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 sns
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p['deaths'] = p['deaths'].fillna(0)
p['recovered'] = p['recovered'].fillna(0)
p['tests'] = np.where(p['tests'].isnull(), p['casecount'], p['tests'])
p.drop(p.columns[[4, 30, 31, 32, 33, 34, 35]], axis=1, inplace=True)
import seaborn as sns
import matplotlib.pyplot as plt
y = p[['pop0to4_2019', 'pop5to9_2019', 'pop10to14_2019', 'pop15to19_2019', 'pop20to24_2019', 'pop25to29_2019', 'pop30to34_2019', 'pop35to39_2019', 'pop40to44_2019', 'pop45to49_2019', 'pop50to54_2019', 'pop55to59_2019', 'pop60to64_2019', 'pop65to69_2019', 'pop70to74_2019', 'pop75to79_2019', 'pop80to84_2019', 'pop85older']]
plt.xticks(np.arange(0, 34, 2))
plt.figure(figsize=(40, 30))
plt.subplots_adjust(hspace=1.0)
j = 1
for i in y.columns:
plt.subplot(4, 5, j)
sns.barplot(p['deaths'], p[i])
plt.ylabel(i)
plt.xlabel('deaths')
j += 1 | code |
32069487/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any() | code |
32069487/cell_14 | [
"text_plain_output_1.png",
"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)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p['deaths'] = p['deaths'].fillna(0)
p['recovered'] = p['recovered'].fillna(0)
p['tests'] = np.where(p['tests'].isnull(), p['casecount'], p['tests'])
p.drop(p.columns[[4, 30, 31, 32, 33, 34, 35]], axis=1, inplace=True)
import seaborn as sns
import matplotlib.pyplot as plt
y = p[['pop0to4_2019', 'pop5to9_2019', 'pop10to14_2019', 'pop15to19_2019', 'pop20to24_2019', 'pop25to29_2019', 'pop30to34_2019', 'pop35to39_2019', 'pop40to44_2019', 'pop45to49_2019', 'pop50to54_2019', 'pop55to59_2019', 'pop60to64_2019', 'pop65to69_2019', 'pop70to74_2019', 'pop75to79_2019', 'pop80to84_2019', 'pop85older']]
plt.figure(figsize=(20, 20))
plt.bar('deaths', 'pop0to4_2019', data=p)
plt.bar('deaths', 'pop5to9_2019', data=p)
plt.bar('deaths', 'pop10to14_2019', data=p)
plt.bar('deaths', 'pop15to19_2019', data=p)
plt.bar('deaths', 'pop20to24_2019', data=p)
plt.bar('deaths', 'pop25to29_2019', data=p)
plt.bar('deaths', 'pop30to34_2019', data=p)
plt.bar('deaths', 'pop35to39_2019', data=p)
plt.bar('deaths', 'pop40to44_2019', data=p)
plt.bar('deaths', 'pop45to49_2019', data=p)
plt.bar('deaths', 'pop50to54_2019', data=p, color='black')
plt.bar('deaths', 'pop55to59_2019', data=p, color='maroon')
plt.bar('deaths', 'pop60to64_2019', data=p, color='greenyellow')
plt.bar('deaths', 'pop65to69_2019', data=p, color='floralwhite')
plt.bar('deaths', 'pop70to74_2019', data=p, color='indigo')
plt.bar('deaths', 'pop75to79_2019', data=p, color='crimson')
plt.bar('deaths', 'pop80to84_2019', data=p, color='lightpink')
plt.bar('deaths', 'pop85older_2019', data=p, color='slategrey')
plt.xticks(np.arange(0, 34, 2))
plt.xlabel('Deaths')
plt.ylabel('Population categories distribution')
plt.title('DEATHS PER POPULATION')
plt.legend(y, loc='best') | code |
32069487/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.head() | code |
32069487/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.drop(p.columns[[4, 30, 31, 32, 33, 34, 35]], axis=1, inplace=True)
p.head() | code |
32069487/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
p = pd.read_csv('/kaggle/input/uncover/UNCOVER/canadian_outbreak_tracker/canada-cumulative-case-count-by-new-hybrid-regional-health-boundaries.csv')
p.isnull().any()
p.info() | code |
49126654/cell_11 | [
"text_html_output_1.png",
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
games = pd.read_csv('../input/nfl-big-data-bowl-2021/games.csv')
players = pd.read_csv('../input/nfl-big-data-bowl-2021/players.csv')
plays = pd.read_csv('../input/nfl-big-data-bowl-2021/plays.csv')
week_1 = pd.read_csv('../input/nfl-big-data-bowl-2021/week1.csv')
week_2 = pd.read_csv('../input/nfl-big-data-bowl-2021/week2.csv')
week_3 = pd.read_csv('../input/nfl-big-data-bowl-2021/week3.csv')
week_4 = pd.read_csv('../input/nfl-big-data-bowl-2021/week4.csv')
week_5 = pd.read_csv('../input/nfl-big-data-bowl-2021/week5.csv')
week_6 = pd.read_csv('../input/nfl-big-data-bowl-2021/week6.csv')
week_7 = pd.read_csv('../input/nfl-big-data-bowl-2021/week7.csv')
week_8 = pd.read_csv('../input/nfl-big-data-bowl-2021/week8.csv')
week_9 = pd.read_csv('../input/nfl-big-data-bowl-2021/week9.csv')
week_10 = pd.read_csv('../input/nfl-big-data-bowl-2021/week10.csv')
week_11 = pd.read_csv('../input/nfl-big-data-bowl-2021/week11.csv')
week_12 = pd.read_csv('../input/nfl-big-data-bowl-2021/week12.csv')
week_13 = pd.read_csv('../input/nfl-big-data-bowl-2021/week13.csv')
week_14 = pd.read_csv('../input/nfl-big-data-bowl-2021/week14.csv')
week_15 = pd.read_csv('../input/nfl-big-data-bowl-2021/week15.csv')
week_16 = pd.read_csv('../input/nfl-big-data-bowl-2021/week16.csv')
week_17 = pd.read_csv('../input/nfl-big-data-bowl-2021/week17.csv')
all_col = list(games.columns) + list(plays.columns) + list(players.columns) + list(week_1.columns)
week_1 = week_1[(week_1['event'] == 'ball_snap') & (week_1['position'] == 'CB')]
week1 = pd.merge(week_1, plays, how='inner', on=['gameId', 'playId'])
def calculate_distance(absYdline, x):
distance = abs(absYdline - x)
return distance
week1['distance'] = week1.apply(lambda y: calculate_distance(y['absoluteYardlineNumber'], y['x']), axis=1)
week1 = week1[week1['distance'] <= 20]
week1 = week1.drop_duplicates(subset=['playId'])
x = week1['distance']
y = week1['offensePlayResult']
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
plt.figure(figsize=(20, 10))
plt.plot(x, y, '.')
plt.plot(x, slope * x + intercept, '#EB6E1F')
x_list = x.tolist()
y_list = y.tolist()
plt.xlabel('distance')
plt.ylabel('yards gained by offense')
plt.show()
summary_frame = pd.DataFrame(index=['slope', 'intercept', 'r_value', 'p_value', 'std_err'])
summary_stats = ['{:.7f}'.format(slope), '{:.7f}'.format(intercept), '{:.7f}'.format(r_value), '{:.7f}'.format(p_value), '{:.7f}'.format(std_err)]
summary_frame['summary stats'] = summary_stats
summary_frame | code |
49126654/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
games = pd.read_csv('../input/nfl-big-data-bowl-2021/games.csv')
players = pd.read_csv('../input/nfl-big-data-bowl-2021/players.csv')
plays = pd.read_csv('../input/nfl-big-data-bowl-2021/plays.csv')
week_1 = pd.read_csv('../input/nfl-big-data-bowl-2021/week1.csv')
week_2 = pd.read_csv('../input/nfl-big-data-bowl-2021/week2.csv')
week_3 = pd.read_csv('../input/nfl-big-data-bowl-2021/week3.csv')
week_4 = pd.read_csv('../input/nfl-big-data-bowl-2021/week4.csv')
week_5 = pd.read_csv('../input/nfl-big-data-bowl-2021/week5.csv')
week_6 = pd.read_csv('../input/nfl-big-data-bowl-2021/week6.csv')
week_7 = pd.read_csv('../input/nfl-big-data-bowl-2021/week7.csv')
week_8 = pd.read_csv('../input/nfl-big-data-bowl-2021/week8.csv')
week_9 = pd.read_csv('../input/nfl-big-data-bowl-2021/week9.csv')
week_10 = pd.read_csv('../input/nfl-big-data-bowl-2021/week10.csv')
week_11 = pd.read_csv('../input/nfl-big-data-bowl-2021/week11.csv')
week_12 = pd.read_csv('../input/nfl-big-data-bowl-2021/week12.csv')
week_13 = pd.read_csv('../input/nfl-big-data-bowl-2021/week13.csv')
week_14 = pd.read_csv('../input/nfl-big-data-bowl-2021/week14.csv')
week_15 = pd.read_csv('../input/nfl-big-data-bowl-2021/week15.csv')
week_16 = pd.read_csv('../input/nfl-big-data-bowl-2021/week16.csv')
week_17 = pd.read_csv('../input/nfl-big-data-bowl-2021/week17.csv')
all_col = list(games.columns) + list(plays.columns) + list(players.columns) + list(week_1.columns)
week_1 = week_1[(week_1['event'] == 'ball_snap') & (week_1['position'] == 'CB')]
week1 = pd.merge(week_1, plays, how='inner', on=['gameId', 'playId'])
def calculate_distance(absYdline, x):
distance = abs(absYdline - x)
return distance
week1['distance'] = week1.apply(lambda y: calculate_distance(y['absoluteYardlineNumber'], y['x']), axis=1)
week1 = week1[week1['distance'] <= 20]
week1 = week1.drop_duplicates(subset=['playId'])
week1.corr('kendall') | code |
16144430/cell_21 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import torch
import torchvision
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
ship = {1: 'Cargo', 2: 'Military', 3: 'Carrier', 4: 'Cruise', 5: 'Tankers'}
def imshow(img, title):
npimg = img.numpy()
plt.axis('off')
def show_batch_images(dataloader):
images, labels = next(iter(dataloader))
img = torchvision.utils.make_grid(images)
show_batch_images(trainfull_loader) | code |
16144430/cell_30 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
tr, val = train_test_split(train_data.category, stratify=train_data.category, test_size=0.15, random_state=10)
training_batchsize = 16
num_workers = 8
train_sampler = SubsetRandomSampler(list(tr.index))
valid_sampler = SubsetRandomSampler(list(val.index))
len(list(tr.index)) | code |
16144430/cell_33 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import torch
import torchvision
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
ship = {1: 'Cargo', 2: 'Military', 3: 'Carrier', 4: 'Cruise', 5: 'Tankers'}
def imshow(img, title):
npimg = img.numpy()
plt.axis('off')
def show_batch_images(dataloader):
images, labels = next(iter(dataloader))
img = torchvision.utils.make_grid(images)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
from torchvision import models
import torch.optim as optim
num_classes = 5
Training_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), transforms.RandomAffine(degrees=15, translate=(0.3, 0.3), scale=(0.5, 1.5), shear=None, resample=False, fillcolor=0), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
tr, val = train_test_split(train_data.category, stratify=train_data.category, test_size=0.15, random_state=10)
training_batchsize = 16
num_workers = 8
train_sampler = SubsetRandomSampler(list(tr.index))
valid_sampler = SubsetRandomSampler(list(val.index))
traindataset = ShipDataLoader('../input/train/train.csv', 'train', Training_transforms, '../input/train/images', map_img_class_dict)
train_loader = torch.utils.data.DataLoader(traindataset, batch_size=training_batchsize, sampler=train_sampler, num_workers=num_workers)
show_batch_images(train_loader) | code |
16144430/cell_44 | [
"text_plain_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
model_ft.fc.in_features | code |
16144430/cell_40 | [
"text_plain_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
print('Number of trainable parameters: ', sum((p.numel() for p in model_ft.parameters() if p.requires_grad))) | code |
16144430/cell_39 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
print(model_ft) | code |
16144430/cell_41 | [
"text_plain_output_1.png"
] | from torchvision import models
model_ft = models.resnet50(pretrained=True)
for name, child in model_ft.named_children():
print(name) | code |
16144430/cell_49 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
from torchvision import models
import numpy as np
import os
import pandas as pd
import random
import torch
import torch.nn as nn
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
from torchvision import models
import torch.optim as optim
num_classes = 5
Training_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), transforms.RandomAffine(degrees=15, translate=(0.3, 0.3), scale=(0.5, 1.5), shear=None, resample=False, fillcolor=0), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
tr, val = train_test_split(train_data.category, stratify=train_data.category, test_size=0.15, random_state=10)
training_batchsize = 16
num_workers = 8
train_sampler = SubsetRandomSampler(list(tr.index))
valid_sampler = SubsetRandomSampler(list(val.index))
traindataset = ShipDataLoader('../input/train/train.csv', 'train', Training_transforms, '../input/train/images', map_img_class_dict)
train_loader = torch.utils.data.DataLoader(traindataset, batch_size=training_batchsize, sampler=train_sampler, num_workers=num_workers)
dataiter = iter(train_loader)
images, labels = dataiter.next()
model_ft = models.resnet50(pretrained=True)
model_ft.fc.in_features
from collections import OrderedDict
fc = nn.Sequential(nn.Linear(model_ft.fc.in_features, 720), nn.ReLU(), nn.Dropout(0.5), nn.Linear(720, 256), nn.ReLU(), nn.Dropout(0.4), nn.Linear(256, 64), nn.ReLU(), nn.Dropout(0.3), nn.Linear(64, 5), nn.Softmax(dim=1))
model_ft.fc = fc
out = model_ft(images)
out | code |
16144430/cell_38 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
from torchvision import models
import torch.optim as optim
num_classes = 5
Training_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), transforms.RandomAffine(degrees=15, translate=(0.3, 0.3), scale=(0.5, 1.5), shear=None, resample=False, fillcolor=0), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
tr, val = train_test_split(train_data.category, stratify=train_data.category, test_size=0.15, random_state=10)
training_batchsize = 16
num_workers = 8
train_sampler = SubsetRandomSampler(list(tr.index))
valid_sampler = SubsetRandomSampler(list(val.index))
traindataset = ShipDataLoader('../input/train/train.csv', 'train', Training_transforms, '../input/train/images', map_img_class_dict)
train_loader = torch.utils.data.DataLoader(traindataset, batch_size=training_batchsize, sampler=train_sampler, num_workers=num_workers)
dataiter = iter(train_loader)
images, labels = dataiter.next()
print(images.shape)
print(images[1].shape)
print(labels[1]) | code |
16144430/cell_47 | [
"text_plain_output_1.png"
] | from torchvision import models
import torch.nn as nn
model_ft = models.resnet50(pretrained=True)
model_ft.fc.in_features
from collections import OrderedDict
fc = nn.Sequential(nn.Linear(model_ft.fc.in_features, 720), nn.ReLU(), nn.Dropout(0.5), nn.Linear(720, 256), nn.ReLU(), nn.Dropout(0.4), nn.Linear(256, 64), nn.ReLU(), nn.Dropout(0.3), nn.Linear(64, 5), nn.Softmax(dim=1))
model_ft.fc = fc
print(model_ft) | code |
16144430/cell_43 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
from torchvision import models
import torch.optim as optim
num_classes = 5
num_classes | code |
16144430/cell_31 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
tr, val = train_test_split(train_data.category, stratify=train_data.category, test_size=0.15, random_state=10)
training_batchsize = 16
num_workers = 8
train_sampler = SubsetRandomSampler(list(tr.index))
valid_sampler = SubsetRandomSampler(list(val.index))
len(list(val.index)) | code |
16144430/cell_24 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import random
import torch
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
from torchvision import models
import torch.optim as optim
num_classes = 5 | code |
16144430/cell_22 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import torch
import torchvision
transform_ship = transforms.Compose([transforms.ToTensor()])
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
map_img_class_dict = {k: v for k, v in zip(train_data.image, train_data.category)}
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
from PIL import Image
class ShipDataLoader(torch.utils.data.DataLoader):
def __init__(self, CSVfolder, process='train', transform=transforms.Compose([transforms.Resize(size=(224, 224)), transforms.ToTensor()]), imgFolder='../input/train/images/', labelsDict={}, y_labels=list(train_data.category)):
self.process = process
self.imgFolder = imgFolder
self.CSVfolder = CSVfolder
self.y = y_labels
self.FileList = pd.read_csv(self.CSVfolder)['image'].tolist()
self.transform = transform
self.labelsDict = labelsDict
if self.process == 'train':
self.labels = [labelsDict[i] for i in self.FileList]
else:
self.labels = [0 for i in range(len(self.FileList))]
def __len__(self):
return len(self.FileList)
def __getitem__(self, idx):
file_name = self.FileList[idx]
image_data = self.pil_loader(self.imgFolder + '/' + file_name)
if self.transform:
image_data = self.transform(image_data)
if self.process == 'train':
label = self.y[idx]
else:
label = file_name
return (image_data, label)
def pil_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
training_batchsize = 5
full_data = ShipDataLoader(base_dir + '/train/train.csv', process='train', imgFolder=base_dir + '/train/images', labelsDict=map_img_class_dict)
trainfull_loader = torch.utils.data.DataLoader(full_data, batch_size=training_batchsize, shuffle=True)
ship = {1: 'Cargo', 2: 'Military', 3: 'Carrier', 4: 'Cruise', 5: 'Tankers'}
def imshow(img, title):
npimg = img.numpy()
plt.axis('off')
def show_batch_images(dataloader):
images, labels = next(iter(dataloader))
img = torchvision.utils.make_grid(images)
show_batch_images(trainfull_loader) | code |
16144430/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import torch
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
train_data.head() | code |
16144430/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import random
import torch
SEED = 200
base_dir = '../input/'
def seed_everything(seed=SEED):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(SEED)
train_data = pd.read_csv(base_dir + '/train/train.csv')
test_data = pd.read_csv(base_dir + '/test_ApKoW4T.csv')
test_data.head() | code |
128000719/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
test = a[11]
test | code |
128000719/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[5] | code |
128000719/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[3] | code |
128000719/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG | code |
128000719/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
RR = a[9]
RR['Team'] = 'RR'
RR.rename(columns={'2022 Squad RR': 'Players'}, inplace=True)
RR | code |
128000719/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final | code |
128000719/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[8] | code |
128000719/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[9] | code |
128000719/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final | code |
128000719/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[0] | code |
128000719/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI
final = final.append(MI, ignore_index=True)
final | code |
128000719/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[11] | code |
128000719/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final | code |
128000719/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
test = a[11]
test.rename(columns={'Base Price IN ₹ (CR.)': 'Base Price'}, inplace=True)
test.rename(columns={'Player': 'Players'}, inplace=True)
test.drop('Base Price IN $ (000)', axis=1, inplace=True)
test = test[['Players', 'Base Price', 'TYPE', 'COST IN ₹ (CR.)', 'Cost IN $ (000)', '2021 Squad', 'Team']]
test | code |
128000719/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR | code |
128000719/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI
final = final.append(MI, ignore_index=True)
final
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB
final = final.append(RCB, ignore_index=True)
final
RR = a[9]
RR['Team'] = 'RR'
RR.rename(columns={'2022 Squad RR': 'Players'}, inplace=True)
RR
final = final.append(RR, ignore_index=True)
final
SRH = a[10]
SRH['Team'] = 'SRH'
SRH.rename(columns={'2022 Squad SRH': 'Players'}, inplace=True)
SRH
final = final.append(SRH, ignore_index=True)
final
test = a[11]
test.rename(columns={'Base Price IN ₹ (CR.)': 'Base Price'}, inplace=True)
test.rename(columns={'Player': 'Players'}, inplace=True)
test.drop('Base Price IN $ (000)', axis=1, inplace=True)
test = test[['Players', 'Base Price', 'TYPE', 'COST IN ₹ (CR.)', 'Cost IN $ (000)', '2021 Squad', 'Team']]
final = final.append(test, ignore_index=True)
final
final.info() | code |
128000719/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[1] | code |
128000719/cell_45 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
test = a[11]
test.rename(columns={'Base Price IN ₹ (CR.)': 'Base Price'}, inplace=True)
test.rename(columns={'Player': 'Players'}, inplace=True)
test.drop('Base Price IN $ (000)', axis=1, inplace=True)
test | code |
128000719/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI
final = final.append(MI, ignore_index=True)
final
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB
final = final.append(RCB, ignore_index=True)
final
RR = a[9]
RR['Team'] = 'RR'
RR.rename(columns={'2022 Squad RR': 'Players'}, inplace=True)
RR
final = final.append(RR, ignore_index=True)
final
SRH = a[10]
SRH['Team'] = 'SRH'
SRH.rename(columns={'2022 Squad SRH': 'Players'}, inplace=True)
SRH
final = final.append(SRH, ignore_index=True)
final
test = a[11]
test.rename(columns={'Base Price IN ₹ (CR.)': 'Base Price'}, inplace=True)
test.rename(columns={'Player': 'Players'}, inplace=True)
test.drop('Base Price IN $ (000)', axis=1, inplace=True)
test = test[['Players', 'Base Price', 'TYPE', 'COST IN ₹ (CR.)', 'Cost IN $ (000)', '2021 Squad', 'Team']]
final = final.append(test, ignore_index=True)
final | code |
128000719/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI
final = final.append(MI, ignore_index=True)
final
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB
final = final.append(RCB, ignore_index=True)
final | code |
128000719/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI | code |
128000719/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT | code |
128000719/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC | code |
128000719/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final | code |
128000719/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI
final = final.append(MI, ignore_index=True)
final
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB
final = final.append(RCB, ignore_index=True)
final
RR = a[9]
RR['Team'] = 'RR'
RR.rename(columns={'2022 Squad RR': 'Players'}, inplace=True)
RR
final = final.append(RR, ignore_index=True)
final
SRH = a[10]
SRH['Team'] = 'SRH'
SRH.rename(columns={'2022 Squad SRH': 'Players'}, inplace=True)
SRH
final = final.append(SRH, ignore_index=True)
final | code |
128000719/cell_3 | [
"text_html_output_1.png"
] | !pip install openpyxl | code |
128000719/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[4] | code |
128000719/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final
DC = a[3]
DC['Team'] = 'DC'
DC.rename(columns={'2022 Squad DC': 'Players'}, inplace=True)
DC
final = final.append(DC, ignore_index=True)
final
KKR = a[4]
KKR['Team'] = 'KKR'
KKR.rename(columns={'2022 Squad KKR': 'Players'}, inplace=True)
KKR
final = final.append(KKR, ignore_index=True)
final
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK
final = final.append(PBK, ignore_index=True)
final
LSG = a[6]
LSG['Team'] = 'LSG'
LSG.rename(columns={'2022 Squad LSG': 'Players'}, inplace=True)
LSG
final = final.append(LSG, ignore_index=True)
final
MI = a[7]
MI['Team'] = 'MI'
MI.rename(columns={'2022 Squad MI': 'Players'}, inplace=True)
MI
final = final.append(MI, ignore_index=True)
final
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB
final = final.append(RCB, ignore_index=True)
final
RR = a[9]
RR['Team'] = 'RR'
RR.rename(columns={'2022 Squad RR': 'Players'}, inplace=True)
RR
final = final.append(RR, ignore_index=True)
final | code |
128000719/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
RCB = a[8]
RCB['Team'] = 'RCB'
RCB.rename(columns={'2022 Squad RCB': 'Players'}, inplace=True)
RCB | code |
128000719/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[6] | code |
128000719/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
PBK = a[5]
PBK['Team'] = 'PBK'
PBK.rename(columns={'2022 Squad PBKS': 'Players'}, inplace=True)
PBK | code |
128000719/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK | code |
128000719/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[7] | code |
128000719/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
SRH = a[10]
SRH['Team'] = 'SRH'
SRH.rename(columns={'2022 Squad SRH': 'Players'}, inplace=True)
SRH | code |
128000719/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
GT = a[1]
GT['Team'] = 'GT'
GT.rename(columns={'2022 Squad GT': 'Players'}, inplace=True)
GT
CSK = a[2]
CSK['Team'] = 'CSK'
CSK.rename(columns={'2022 Squad CSK': 'Players'}, inplace=True)
CSK
final = GT.append(CSK, ignore_index=True)
final | code |
128000719/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a | code |
128000719/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
url = 'https://www.news18.com/cricketnext/ipl-auction-2022/'
a = pd.read_html(url)
a
a[10] | code |
17132420/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df = df.drop(['Bedroom2', 'Rooms v Bedroom2'], 1)
df = df[df['BuildingArea'] != 0]
df = df[df['YearBuilt'] > 1835]
df.isnull().sum()
df.dropna(inplace=True)
# Build Histogram to visualise price distribution
num_bins = 50
n, bins, patches = plt.hist(df.Price, num_bins, color='b', alpha=0.5, histtype = 'bar', ec = 'black')
plt.ylabel ('Frequency')
plt.xlabel ('Price ($)')
plt.xlim([0, 6000000])
plt.title ('Histogram House Prices')
plt.show()
plt.savefig('Histogram.png')
df.select_dtypes(['float64', 'int64']).columns
pair_plot = sns.pairplot(df[['Rooms', 'Price', 'Distance', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'Propertycount', 'YearBuilt', 'Type']], hue='Type')
pair_plot.savefig('Pairplot.png') | code |
17132420/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df = df.drop(['Bedroom2', 'Rooms v Bedroom2'], 1)
df = df[df['BuildingArea'] != 0]
df = df[df['YearBuilt'] > 1835]
df.isnull().sum()
df.dropna(inplace=True)
# Build Histogram to visualise price distribution
num_bins = 50
n, bins, patches = plt.hist(df.Price, num_bins, color='b', alpha=0.5, histtype = 'bar', ec = 'black')
plt.ylabel ('Frequency')
plt.xlabel ('Price ($)')
plt.xlim([0, 6000000])
plt.title ('Histogram House Prices')
plt.show()
plt.savefig('Histogram.png')
# Determine Numerical Values
df.select_dtypes(['float64', 'int64']).columns
# Pairplot variables to visualise inter-variable relationships
pair_plot = sns.pairplot(df[['Rooms', 'Price', 'Distance', 'Bathroom', 'Car', 'Landsize','BuildingArea', 'Propertycount', 'YearBuilt', 'Type']], hue = 'Type')
pair_plot.savefig('Pairplot.png')
df.info() | code |
17132420/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df = df.drop(['Bedroom2', 'Rooms v Bedroom2'], 1)
df = df[df['BuildingArea'] != 0]
df = df[df['YearBuilt'] > 1835]
df.isnull().sum()
df.dropna(inplace=True)
# Build Histogram to visualise price distribution
num_bins = 50
n, bins, patches = plt.hist(df.Price, num_bins, color='b', alpha=0.5, histtype = 'bar', ec = 'black')
plt.ylabel ('Frequency')
plt.xlabel ('Price ($)')
plt.xlim([0, 6000000])
plt.title ('Histogram House Prices')
plt.show()
plt.savefig('Histogram.png')
# Determine Numerical Values
df.select_dtypes(['float64', 'int64']).columns
# Pairplot variables to visualise inter-variable relationships
pair_plot = sns.pairplot(df[['Rooms', 'Price', 'Distance', 'Bathroom', 'Car', 'Landsize','BuildingArea', 'Propertycount', 'YearBuilt', 'Type']], hue = 'Type')
pair_plot.savefig('Pairplot.png')
fig, ax = plt.subplots(figsize=(15, 15))
heat_map = sns.heatmap(df[df['Type'] == 'h'].corr(), cmap='jet', annot=True)
plt.savefig('Heatmap.png') | code |
17132420/cell_33 | [
"image_output_1.png"
] | from scipy import stats
from sklearn import metrics
from sklearn.ensemble import GradientBoostingRegressor
import numpy as np
max_r2 = 0
for i in np.linspace(0.1, 1, 50):
gbr = GradientBoostingRegressor(learning_rate=i)
gbr.fit(x_train, y_train)
y_pred = gbr.predict(x_test)
print('For learning rate i: %0.2f' % i)
print('Gradient Boosting Regression MAE: %0.5f' % metrics.mean_absolute_error(y_test, y_pred))
print('Gradient Boosting MSE:%0.5f' % metrics.mean_squared_error(y_test, y_pred))
print('Gradient Boosting RMSE:%0.5f' % np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
print('Gradient Boosting R^2: %0.5f' % metrics.explained_variance_score(y_test, y_pred))
print('---------------------------------')
if metrics.explained_variance_score(y_test, y_pred) > max_r2:
max_r2 = metrics.explained_variance_score(y_test, y_pred)
max_i = i
y_pred_gbr = y_pred
se_gbr = stats.sem(y_pred_gbr)
print('Max R^2 is: %0.5f' % max_r2, 'with learning rate: %0.2f' % max_i) | code |
17132420/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df.info() | code |
17132420/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df = df.drop(['Bedroom2', 'Rooms v Bedroom2'], 1)
df.describe().transpose() | code |
17132420/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df = df.drop(['Bedroom2', 'Rooms v Bedroom2'], 1)
df = df[df['BuildingArea'] != 0]
df = df[df['YearBuilt'] > 1835]
df.isnull().sum()
df.dropna(inplace=True)
num_bins = 50
n, bins, patches = plt.hist(df.Price, num_bins, color='b', alpha=0.5, histtype='bar', ec='black')
plt.ylabel('Frequency')
plt.xlabel('Price ($)')
plt.xlim([0, 6000000])
plt.title('Histogram House Prices')
plt.show()
plt.savefig('Histogram.png') | code |
17132420/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df.head(100) | code |
17132420/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
change_objects = ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname']
for colname in change_objects:
df[colname] = df[colname].astype('category')
change_numeric = ['Postcode']
for colname in change_numeric:
df[colname] = df[colname].astype('category')
df['Date'] = pd.to_datetime(df['Date'])
df['Rooms v Bedroom2'] = df['Rooms'] - df['Bedroom2']
df = df.drop(['Bedroom2', 'Rooms v Bedroom2'], 1)
df = df[df['BuildingArea'] != 0]
df = df[df['YearBuilt'] > 1835]
df.isnull().sum() | code |
17132420/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
df.info() | code |
17132420/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import GradientBoostingRegressor
df = pd.read_csv('../input/Melbourne_housing_FULL.csv')
df.info() | code |
130008558/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
dataset = df.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(dataset)
print(scaled[:10]) | code |
130008558/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
len(df[df['Passengers'] == 0]) | code |
130008558/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
print(df[df.isnull().any(axis=1)][null_columns].head()) | code |
130008558/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
print('Total rows: {}'.format(len(df)))
df.head() | code |
130008558/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
dataset = df.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(dataset)
train_size = int(len(scaled) * 0.7)
test_size = len(scaled - train_size)
train, test = (scaled[0:train_size, :], scaled[train_size:len(scaled), :])
print('train: {}\ntest: {}'.format(len(train), len(test))) | code |
130008558/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
dataset = df.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(dataset)
print('Min', np.min(scaled))
print('Max', np.max(scaled)) | code |
130008558/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.plot() | code |
130008558/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
print(df.head())
df.plot() | code |
130008558/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum() | code |
130008558/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import GRU, Dense
from keras.layers import LSTM
from keras import callbacks
from keras import optimizers
import pandas as pd
import tensorflow as tf
import numpy as np | code |
130008558/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum() | code |
130008558/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum() | code |
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