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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
74067279/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
genders = {'male': 1, 'female': 0}
general['sex_id'] = general['sex'].apply(lambda x: genders.get(x))
general['relatives'] = general['parch'] + general['sibsp']
embarkments = {'S': 1, 'C': 2, 'Q': 3}
general['embarked_id'] = general['embarked'].apply(lambda x: embarkments.get(x))
general['is_single'] = general['relatives'].apply(lambda x: 1 if x == 0 else 0)
decks = {'U': 0, 'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'T': 8}
general['deck_id'] = general['cabin'].fillna('U').apply(lambda c: decks.get(c[0]))
general['title'] = general['name'].apply(lambda x: re.search(' ([A-Za-z]+)\\.', x).group(1).lower())
general['cabin_id'] = general['cabin'].str.extract('(\\d+)').astype('float').fillna(0)
general['title_id'] = LabelEncoder().fit_transform(general['title'])
general.loc[general['deck_id'] == 0, 'deck_id'] = np.nan
general['deck_id'] = general.groupby(['pclass'])['deck_id'].transform(lambda x: x.fillna(x.median()))
general.info() | code |
74067279/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
genders = {'male': 1, 'female': 0}
general['sex_id'] = general['sex'].apply(lambda x: genders.get(x))
general['relatives'] = general['parch'] + general['sibsp']
embarkments = {'S': 1, 'C': 2, 'Q': 3}
general['embarked_id'] = general['embarked'].apply(lambda x: embarkments.get(x))
general['is_single'] = general['relatives'].apply(lambda x: 1 if x == 0 else 0)
decks = {'U': 0, 'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'T': 8}
general['deck_id'] = general['cabin'].fillna('U').apply(lambda c: decks.get(c[0]))
general['title'] = general['name'].apply(lambda x: re.search(' ([A-Za-z]+)\\.', x).group(1).lower())
general['cabin_id'] = general['cabin'].str.extract('(\\d+)').astype('float').fillna(0)
general['title_id'] = LabelEncoder().fit_transform(general['title'])
general.loc[general['deck_id'] == 0, 'deck_id'] = np.nan
general['deck_id'] = general.groupby(['pclass'])['deck_id'].transform(lambda x: x.fillna(x.median()))
train_filled = general.head(891)
test_filled = general.tail(418)
test_filled = test_filled.drop('survived', 1)
train_filled.info()
test_final = test_filled[['passengerid', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_id', 'relatives', 'embarked_id', 'is_single', 'deck_id', 'cabin_id', 'title_id']]
train_final = train_filled[['passengerid', 'survived', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_id', 'relatives', 'embarked_id', 'is_single', 'deck_id', 'cabin_id', 'title_id']]
train_final1 = train_final.drop('survived', axis=1)
train_final1.info() | code |
74067279/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
general.info() | code |
74067279/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
genders = {'male': 1, 'female': 0}
general['sex_id'] = general['sex'].apply(lambda x: genders.get(x))
general['relatives'] = general['parch'] + general['sibsp']
embarkments = {'S': 1, 'C': 2, 'Q': 3}
general['embarked_id'] = general['embarked'].apply(lambda x: embarkments.get(x))
general['is_single'] = general['relatives'].apply(lambda x: 1 if x == 0 else 0)
decks = {'U': 0, 'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'T': 8}
general['deck_id'] = general['cabin'].fillna('U').apply(lambda c: decks.get(c[0]))
general['title'] = general['name'].apply(lambda x: re.search(' ([A-Za-z]+)\\.', x).group(1).lower())
general['cabin_id'] = general['cabin'].str.extract('(\\d+)').astype('float').fillna(0)
general['title_id'] = LabelEncoder().fit_transform(general['title'])
general.loc[general['deck_id'] == 0, 'deck_id'] = np.nan
general['deck_id'] = general.groupby(['pclass'])['deck_id'].transform(lambda x: x.fillna(x.median()))
train_filled = general.head(891)
test_filled = general.tail(418)
test_filled = test_filled.drop('survived', 1)
train_filled.info()
test_final = test_filled[['passengerid', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_id', 'relatives', 'embarked_id', 'is_single', 'deck_id', 'cabin_id', 'title_id']]
train_final = train_filled[['passengerid', 'survived', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_id', 'relatives', 'embarked_id', 'is_single', 'deck_id', 'cabin_id', 'title_id']]
train_final1 = train_final.drop('survived', axis=1)
alg_test = RandomForestClassifier(random_state=1, n_estimators=350, min_samples_split=6, min_samples_leaf=2)
alg_test.fit(train_final1, train_final['survived'])
predict_y = alg_test.predict(train_final1)
metrics.accuracy_score(train_final['survived'], predict_y) | code |
74067279/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
genders = {'male': 1, 'female': 0}
general['sex_id'] = general['sex'].apply(lambda x: genders.get(x))
general['relatives'] = general['parch'] + general['sibsp']
embarkments = {'S': 1, 'C': 2, 'Q': 3}
general['embarked_id'] = general['embarked'].apply(lambda x: embarkments.get(x))
general['is_single'] = general['relatives'].apply(lambda x: 1 if x == 0 else 0)
decks = {'U': 0, 'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'T': 8}
general['deck_id'] = general['cabin'].fillna('U').apply(lambda c: decks.get(c[0]))
general['title'] = general['name'].apply(lambda x: re.search(' ([A-Za-z]+)\\.', x).group(1).lower())
general['cabin_id'] = general['cabin'].str.extract('(\\d+)').astype('float').fillna(0)
general['title_id'] = LabelEncoder().fit_transform(general['title'])
general.loc[general['deck_id'] == 0, 'deck_id'] = np.nan
general['deck_id'] = general.groupby(['pclass'])['deck_id'].transform(lambda x: x.fillna(x.median()))
train_filled = general.head(891)
test_filled = general.tail(418)
test_filled = test_filled.drop('survived', 1)
train_filled.info()
test_final = test_filled[['passengerid', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_id', 'relatives', 'embarked_id', 'is_single', 'deck_id', 'cabin_id', 'title_id']]
train_final = train_filled[['passengerid', 'survived', 'pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_id', 'relatives', 'embarked_id', 'is_single', 'deck_id', 'cabin_id', 'title_id']]
train_final1 = train_final.drop('survived', axis=1)
alg_test = RandomForestClassifier(random_state=1, n_estimators=350, min_samples_split=6, min_samples_leaf=2)
alg_test.fit(train_final1, train_final['survived'])
predict_y = alg_test.predict(train_final1)
metrics.accuracy_score(train_final['survived'], predict_y)
alg_test = RandomForestClassifier(random_state=1, n_estimators=350, min_samples_split=6, min_samples_leaf=2)
alg_test.fit(train_final1, train_final['survived'])
predictions = alg_test.predict(test_final)
submission = pd.DataFrame({'PassengerId': test_final['passengerid'], 'Survived': predictions})
submission['Survived'] = submission['Survived'].astype(int)
print(submission.head(20))
print(example.head(20))
print(submission.info(5))
print(example.info(5)) | code |
74067279/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
genders = {'male': 1, 'female': 0}
general['sex_id'] = general['sex'].apply(lambda x: genders.get(x))
general['relatives'] = general['parch'] + general['sibsp']
embarkments = {'S': 1, 'C': 2, 'Q': 3}
general['embarked_id'] = general['embarked'].apply(lambda x: embarkments.get(x))
general['is_single'] = general['relatives'].apply(lambda x: 1 if x == 0 else 0)
decks = {'U': 0, 'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'T': 8}
general['deck_id'] = general['cabin'].fillna('U').apply(lambda c: decks.get(c[0]))
general['title'] = general['name'].apply(lambda x: re.search(' ([A-Za-z]+)\\.', x).group(1).lower())
general['cabin_id'] = general['cabin'].str.extract('(\\d+)').astype('float').fillna(0)
general['title_id'] = LabelEncoder().fit_transform(general['title'])
general.info() | code |
74067279/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
example = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.columns = train.columns.str.lower()
test.columns = test.columns.str.lower()
general = pd.concat([train, test], axis=0)
general['fare'] = general.groupby('pclass')['fare'].transform(lambda x: x.fillna(x.median()))
general['embarked'] = general.groupby(['pclass'])['embarked'].transform(lambda x: x.fillna(x.value_counts().idxmax()))
general['age'] = general.groupby(['pclass', 'parch', 'sibsp'])['age'].transform(lambda x: x.fillna(x.median()))
general['age'] = general.groupby(['pclass'])['age'].transform(lambda x: x.fillna(x.median()))
genders = {'male': 1, 'female': 0}
general['sex_id'] = general['sex'].apply(lambda x: genders.get(x))
general['relatives'] = general['parch'] + general['sibsp']
embarkments = {'S': 1, 'C': 2, 'Q': 3}
general['embarked_id'] = general['embarked'].apply(lambda x: embarkments.get(x))
general['is_single'] = general['relatives'].apply(lambda x: 1 if x == 0 else 0)
decks = {'U': 0, 'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'T': 8}
general['deck_id'] = general['cabin'].fillna('U').apply(lambda c: decks.get(c[0]))
general['title'] = general['name'].apply(lambda x: re.search(' ([A-Za-z]+)\\.', x).group(1).lower())
general['cabin_id'] = general['cabin'].str.extract('(\\d+)').astype('float').fillna(0)
general['title_id'] = LabelEncoder().fit_transform(general['title'])
general.loc[general['deck_id'] == 0, 'deck_id'] = np.nan
general['deck_id'] = general.groupby(['pclass'])['deck_id'].transform(lambda x: x.fillna(x.median()))
train_filled = general.head(891)
test_filled = general.tail(418)
test_filled = test_filled.drop('survived', 1)
train_filled.info() | code |
89128640/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
import cupy as np
from sklearn.model_selection import cross_val_score
import numpy | code |
73071065/cell_9 | [
"text_html_output_1.png"
] | from scipy.stats.mstats import winsorize
from sklearn.linear_model import TweedieRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
import numpy as np
import pandas as pd
X_train = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test = pd.read_csv('../input/30-days-of-ml/test.csv')
y_train = X_train.target
X_train = X_train.set_index('id').drop('target', axis='columns')
X_test = X_test.set_index('id')
y_stratified = pd.cut(y_train.rank(method='first'), bins=10, labels=False)
categoricals = [item for item in X_train.columns if 'cat' in item]
dummies = pd.get_dummies(X_train.append(X_test)[categoricals])
X_train[dummies.columns] = dummies.iloc[:len(X_train), :]
X_test[dummies.columns] = dummies.iloc[len(X_train):, :]
del dummies
important_features = ['cat8_E', 'cont0', 'cont5', 'cont7', 'cont8', 'cat1_A', 'cont2', 'cont13', 'cont3', 'cont10', 'cont1', 'cont9', 'cont11', 'cat1', 'cat8_C', 'cont6', 'cont12', 'cat5', 'cat3_C', 'cont4', 'cat8']
X_train = X_train[important_features]
X_test = X_test[important_features]
folds = 10
skf = StratifiedKFold(n_splits=folds, shuffle=True, random_state=0)
predictions = np.zeros(len(X_test))
score = list()
for k, (train_idx, val_idx) in enumerate(skf.split(X_train, y_stratified)):
ss = StandardScaler()
X = ss.fit_transform(X_train.iloc[train_idx, :]).astype(np.float32)
Xv = ss.transform(X_train.iloc[val_idx, :]).astype(np.float32)
Xt = ss.transform(X_test).astype(np.float32)
y_train_w = np.array(winsorize(y_train[train_idx], [0.002, 0.0]))
glm = TweedieRegressor(power=1, alpha=0.0001, max_iter=10000)
glm.fit(X, y_train_w)
val_preds = glm.predict(Xv)
val_rmse = mean_squared_error(y_true=y_train[val_idx], y_pred=val_preds, squared=False)
print(f'Fold {k} RMSE: {val_rmse:0.5f}')
predictions += glm.predict(Xt).ravel()
score.append(val_rmse)
predictions /= folds
print(f'CV RMSE {np.mean(score):0.5f} ({np.std(score):0.5f})') | code |
73071065/cell_11 | [
"text_plain_output_1.png"
] | from scipy.stats.mstats import winsorize
from sklearn.linear_model import TweedieRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
import numpy as np
import pandas as pd
X_train = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test = pd.read_csv('../input/30-days-of-ml/test.csv')
y_train = X_train.target
X_train = X_train.set_index('id').drop('target', axis='columns')
X_test = X_test.set_index('id')
y_stratified = pd.cut(y_train.rank(method='first'), bins=10, labels=False)
categoricals = [item for item in X_train.columns if 'cat' in item]
dummies = pd.get_dummies(X_train.append(X_test)[categoricals])
X_train[dummies.columns] = dummies.iloc[:len(X_train), :]
X_test[dummies.columns] = dummies.iloc[len(X_train):, :]
del dummies
important_features = ['cat8_E', 'cont0', 'cont5', 'cont7', 'cont8', 'cat1_A', 'cont2', 'cont13', 'cont3', 'cont10', 'cont1', 'cont9', 'cont11', 'cat1', 'cat8_C', 'cont6', 'cont12', 'cat5', 'cat3_C', 'cont4', 'cat8']
X_train = X_train[important_features]
X_test = X_test[important_features]
folds = 10
skf = StratifiedKFold(n_splits=folds, shuffle=True, random_state=0)
predictions = np.zeros(len(X_test))
score = list()
for k, (train_idx, val_idx) in enumerate(skf.split(X_train, y_stratified)):
ss = StandardScaler()
X = ss.fit_transform(X_train.iloc[train_idx, :]).astype(np.float32)
Xv = ss.transform(X_train.iloc[val_idx, :]).astype(np.float32)
Xt = ss.transform(X_test).astype(np.float32)
y_train_w = np.array(winsorize(y_train[train_idx], [0.002, 0.0]))
glm = TweedieRegressor(power=1, alpha=0.0001, max_iter=10000)
glm.fit(X, y_train_w)
val_preds = glm.predict(Xv)
val_rmse = mean_squared_error(y_true=y_train[val_idx], y_pred=val_preds, squared=False)
predictions += glm.predict(Xt).ravel()
score.append(val_rmse)
predictions /= folds
submission = pd.DataFrame({'id': X_test.index, 'target': predictions})
submission.to_csv('submission.csv', index=False)
submission | code |
128011630/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.shape
df_train.dropna(inplace=True)
df_train.shape
df_train.duplicated().sum()
df_train.describe(include='all') | code |
128011630/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum() | code |
128011630/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.shape
df_train.dropna(inplace=True)
df_train.shape
df_train.duplicated().sum()
df_train.dtypes
for i, j in df_train.dtypes.items():
if j == 'object':
df_train[i] = df_train[i].astype('category')
df_train.dtypes | code |
128011630/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.shape
df_train.dropna(inplace=True)
df_train.shape
df_train.duplicated().sum()
df_train.dtypes | code |
128011630/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.shape
df_train.dropna(inplace=True)
df_train.shape
df_train.duplicated().sum() | code |
128011630/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
df_train.describe(include='all') | code |
128011630/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum() | code |
128011630/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.shape
df_train.dropna(inplace=True)
df_train.shape | code |
128011630/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.decomposition import PCA | code |
128011630/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.shape | code |
128011630/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
for i in df_train.columns:
print(i) | code |
128011630/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum() | code |
128011630/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.isnull().sum()
df_train.drop(['BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond'], axis=1, inplace=True)
pd.options.display.min_rows = 500
pd.set_option('display.max_columns', None)
df_train.loc[:, 'BsmtExposure':'Electrical'].isnull().sum()
df_train.drop(['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'], axis=1, inplace=True)
df_train.describe(include='all') | code |
128011630/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum() | code |
128011630/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape | code |
1003108/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import numpy as np
import operator
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
files_changed_per_commit = df.groupby(['author_dt', 'commit_hash'])['filename'].agg('count').reset_index().sort_values('author_dt', ascending=True)
files_changed_per_commit = pd.Series(files_changed_per_commit['filename'].values, index=files_changed_per_commit['author_dt'])
files_changed_per_utc_offset = df.groupby('commit_utc_offset_hours')['filename'].agg('count').reset_index().sort_values('filename', ascending=False)
n_authors_by_offset = df.groupby('commit_utc_offset_hours')['author_id'].nunique().reset_index().sort_values('author_id', ascending=False)
from collections import Counter
import operator
n_rows = 10000.0
subject_words = []
for row_number, row in df.ix[0:n_rows].iterrows():
ws = row['subject'].split(' ')
subject_words = subject_words + [w.lower() for w in ws]
words = []
counts = []
for word, count in sorted(Counter(subject_words).items(), key=operator.itemgetter(1), reverse=True):
words.append(word)
counts.append(count)
from wordcloud import WordCloud
wordcloud = WordCloud().generate(' '.join(subject_words))
plt.figure(figsize=(12, 8))
plt.imshow(wordcloud)
plt.axis('off') | code |
1003108/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
files_changed_per_commit = df.groupby(['author_dt', 'commit_hash'])['filename'].agg('count').reset_index().sort_values('author_dt', ascending=True)
files_changed_per_commit = pd.Series(files_changed_per_commit['filename'].values, index=files_changed_per_commit['author_dt'])
files_changed_per_utc_offset = df.groupby('commit_utc_offset_hours')['filename'].agg('count').reset_index().sort_values('filename', ascending=False)
sns.barplot(x='commit_utc_offset_hours', y='filename', data=files_changed_per_utc_offset) | code |
1003108/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
commits_series.resample('M').mean().plot(title='number of commits on monthly resampled data') | code |
1003108/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
t.plot(title='lines of code added') | code |
1003108/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1003108/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
files_changed_per_commit = df.groupby(['author_dt', 'commit_hash'])['filename'].agg('count').reset_index().sort_values('author_dt', ascending=True)
files_changed_per_commit = pd.Series(files_changed_per_commit['filename'].values, index=files_changed_per_commit['author_dt'])
files_changed_per_commit.plot(title='number files changed per commit') | code |
1003108/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np
import operator
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
files_changed_per_commit = df.groupby(['author_dt', 'commit_hash'])['filename'].agg('count').reset_index().sort_values('author_dt', ascending=True)
files_changed_per_commit = pd.Series(files_changed_per_commit['filename'].values, index=files_changed_per_commit['author_dt'])
files_changed_per_utc_offset = df.groupby('commit_utc_offset_hours')['filename'].agg('count').reset_index().sort_values('filename', ascending=False)
n_authors_by_offset = df.groupby('commit_utc_offset_hours')['author_id'].nunique().reset_index().sort_values('author_id', ascending=False)
from collections import Counter
import operator
n_rows = 10000.0
subject_words = []
for row_number, row in df.ix[0:n_rows].iterrows():
ws = row['subject'].split(' ')
subject_words = subject_words + [w.lower() for w in ws]
words = []
counts = []
for word, count in sorted(Counter(subject_words).items(), key=operator.itemgetter(1), reverse=True):
words.append(word)
counts.append(count)
wcdf = pd.DataFrame({'word': words, 'count': counts})
sns.barplot(y='word', x='count', data=wcdf[0:20]) | code |
1003108/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
commits_series.plot(title='number of commits on original time series') | code |
1003108/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
t = pd.Series(time_df['diff'].values, index=time_df['author_dt'])
commits_over_time = df.groupby('author_dt')['commit_hash'].nunique().reset_index().sort_values('author_dt', ascending=True)
commits_series = pd.Series(commits_over_time['commit_hash'].values, index=commits_over_time['author_dt'])
files_changed_per_commit = df.groupby(['author_dt', 'commit_hash'])['filename'].agg('count').reset_index().sort_values('author_dt', ascending=True)
files_changed_per_commit = pd.Series(files_changed_per_commit['filename'].values, index=files_changed_per_commit['author_dt'])
files_changed_per_utc_offset = df.groupby('commit_utc_offset_hours')['filename'].agg('count').reset_index().sort_values('filename', ascending=False)
n_authors_by_offset = df.groupby('commit_utc_offset_hours')['author_id'].nunique().reset_index().sort_values('author_id', ascending=False)
sns.barplot(x='commit_utc_offset_hours', y='author_id', data=n_authors_by_offset) | code |
1003108/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
df.head() | code |
1003108/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_timestamp', 'author_dt'])[['n_additions', 'n_deletions']].agg(np.sum).reset_index().sort_values('author_timestamp', ascending=True)
time_df['diff'] = time_df['n_additions'] - time_df['n_deletions']
time_df.head() | code |
128031687/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape | code |
128031687/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.head() | code |
128031687/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes | code |
128031687/cell_34 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
data.describe() | code |
128031687/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.info() | code |
128031687/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
plt.figure(figsize=(12, 10))
sns.countplot(data.region)
plt.xticks(rotation=90) | code |
128031687/cell_40 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
sns.countplot(data=data, x='department')
plt.show() | code |
128031687/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum() | code |
128031687/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
plt.xticks(rotation=90)
sns.countplot(data=data, x='education')
plt.show() | code |
128031687/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
data['department'].value_counts().head(10).plot(kind='pie', autopct='%1.1f%%', figsize=(10, 10)).legend() | code |
128031687/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
print('lenght of data is', len(data)) | code |
128031687/cell_52 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
plt.xticks(rotation=90)
sns.countplot(data=data, x='gender')
plt.show() | code |
128031687/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
plt.xticks(rotation=90)
data['region'].value_counts().head(10).plot(kind='pie', autopct='%1.1f%%', figsize=(10, 10), startangle=0).legend() | code |
128031687/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
plt.xticks(rotation=90)
data['education'].value_counts().head(7).plot(kind='pie', autopct='%1.1f%%', figsize=(10, 10), startangle=0).legend() | code |
128031687/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
print('Count of rows in the data is: ', len(data)) | code |
128031687/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.tail() | code |
128031687/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns | code |
128031687/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
print('Count of columns in the data is: ', len(data.columns)) | code |
128031687/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
plt.xticks(rotation=90)
data['gender'].value_counts().head(7).plot(kind='pie', autopct='%1.1f%%', figsize=(10, 10), startangle=0).legend() | code |
128031687/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1)) | code |
128031687/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
data.hist(figsize=(20, 20), bins=20, color='#107009AA')
plt.title('Numeric Features Distribution')
plt.show() | code |
105186524/cell_13 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91] | code |
105186524/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
youtube.info() | code |
105186524/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count | code |
105186524/cell_11 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index | code |
105186524/cell_19 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91]
column_names = ['title', 'trending_date']
duplicates2 = youtube.duplicated(subset=column_names, keep=False)
youtube[duplicates2].sort_values(by='title')
youtube = youtube.drop_duplicates(['title', 'trending_date'], keep='first')
youtube[youtube['title'] == '13 Reasons Why: Season 2 | Official Trailer [HD] | Netflix']
youtube['trending_date'] = pd.to_datetime(youtube['trending_date'], format='%y.%d.%m')
youtube['publish_time'] = pd.to_datetime(youtube['publish_time'])
youtube.describe() | code |
105186524/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 |
105186524/cell_7 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100 | code |
105186524/cell_18 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91]
column_names = ['title', 'trending_date']
duplicates2 = youtube.duplicated(subset=column_names, keep=False)
youtube[duplicates2].sort_values(by='title')
youtube = youtube.drop_duplicates(['title', 'trending_date'], keep='first')
youtube[youtube['title'] == '13 Reasons Why: Season 2 | Official Trailer [HD] | Netflix']
youtube['trending_date'] = pd.to_datetime(youtube['trending_date'], format='%y.%d.%m')
youtube['publish_time'] = pd.to_datetime(youtube['publish_time'])
youtube.info() | code |
105186524/cell_15 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91]
column_names = ['title', 'trending_date']
duplicates2 = youtube.duplicated(subset=column_names, keep=False)
youtube[duplicates2].sort_values(by='title') | code |
105186524/cell_16 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91]
column_names = ['title', 'trending_date']
duplicates2 = youtube.duplicated(subset=column_names, keep=False)
youtube[duplicates2].sort_values(by='title')
youtube['title'].loc[34137] | code |
105186524/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
youtube.head() | code |
105186524/cell_17 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91]
column_names = ['title', 'trending_date']
duplicates2 = youtube.duplicated(subset=column_names, keep=False)
youtube[duplicates2].sort_values(by='title')
youtube = youtube.drop_duplicates(['title', 'trending_date'], keep='first')
youtube[youtube['title'] == '13 Reasons Why: Season 2 | Official Trailer [HD] | Netflix'] | code |
105186524/cell_14 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
youtube.loc[pd.isna(youtube['description']), :].index
youtube = youtube.fillna('no description available for this video')
youtube.loc[91]
youtube.info() | code |
105186524/cell_10 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.sum()
total_missing / total_cells * 100
indices = list(np.where(youtube['description'].isnull())[0])
indices | code |
122244852/cell_2 | [
"text_html_output_1.png"
] | !pip install mlflow dagshub | code |
122244852/cell_7 | [
"text_plain_output_1.png"
] | import mlflow
mlflow.set_tracking_uri('https://dagshub.com/ChiragChauhan4579/MLflow-integration.mlflow')
mlflow.set_experiment(experiment_name='wine-quality') | code |
122244852/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.head() | code |
122244852/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split, GridSearchCV
import mlflow
import numpy as np
mlflow.set_tracking_uri('https://dagshub.com/ChiragChauhan4579/MLflow-integration.mlflow')
mlflow.set_experiment(experiment_name='wine-quality')
y_train.value_counts()
alpha = 0.6
l1_ratio = 0.9
with mlflow.start_run():
mlflow.set_tag('model', 'elastic-net')
mlflow.log_param('alpha', alpha)
mlflow.log_param('l1_ratio', l1_ratio)
lr = ElasticNet(alpha=alpha, l1_ratio=alpha)
lr.fit(x_train, y_train)
y_pred = lr.predict(x_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
mlflow.log_metric('rmse', rmse)
mlflow.log_metric('mae', mae)
mlflow.log_metric('r2', r2)
mlflow.sklearn.log_model(lr, 'elastic-net-base')
rf = RandomForestRegressor()
with mlflow.start_run():
mlflow.set_tag('hyperparameter tuning', 'random forest')
params = [{'n_estimators': [100, 250, 500, 1000], 'max_depth': list(range(3, 7)), 'max_features': list(range(0, 14))}]
reg = GridSearchCV(rf, params, cv=10, scoring='neg_mean_absolute_error')
reg.fit(x_train, y_train)
y_pred = reg.predict(x_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
mlflow.log_metric('rmse', rmse)
mlflow.log_metric('mae', mae)
mlflow.log_metric('r2', r2)
mlflow.log_param('n_estimators', reg.best_params_['n_estimators'])
mlflow.log_param('max_leaf_nodes', reg.best_params_['max_depth'])
mlflow.sklearn.log_model(reg.best_estimator_, 'best_rf_model') | code |
122244852/cell_10 | [
"text_plain_output_1.png"
] | y_train.value_counts() | code |
122244852/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import mlflow
import numpy as np
mlflow.set_tracking_uri('https://dagshub.com/ChiragChauhan4579/MLflow-integration.mlflow')
mlflow.set_experiment(experiment_name='wine-quality')
y_train.value_counts()
alpha = 0.6
l1_ratio = 0.9
with mlflow.start_run():
mlflow.set_tag('model', 'elastic-net')
mlflow.log_param('alpha', alpha)
mlflow.log_param('l1_ratio', l1_ratio)
lr = ElasticNet(alpha=alpha, l1_ratio=alpha)
lr.fit(x_train, y_train)
y_pred = lr.predict(x_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
mlflow.log_metric('rmse', rmse)
mlflow.log_metric('mae', mae)
mlflow.log_metric('r2', r2)
mlflow.sklearn.log_model(lr, 'elastic-net-base')
print(f'Elastic net Params: alpha: {alpha}, l1_ratio: {l1_ratio}')
print(f'Elastic net metric: rmse:{rmse}, mae:{mae},r2:{r2}') | code |
122244852/cell_5 | [
"text_plain_output_1.png"
] | import dagshub
import dagshub
dagshub.init('MLflow-integration', 'ChiragChauhan4579', mlflow=True) | code |
73070733/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
test.head() | code |
73070733/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
X = train.drop(['target'], axis=1)
X.head() | code |
73070733/cell_18 | [
"text_html_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_validate, cross_val_predict
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
X = train.drop(['target'], axis=1)
categorical_cols = [col for col in X.columns if 'cat' in col]
numerical_cols = [col for col in X.columns if 'cont' in col]
numerical_transformer = StandardScaler()
categorical_transformer = OrdinalEncoder(handle_unknown='ignore', dtype=np.int)
preprocessor = ColumnTransformer(transformers=[('num', numerical_transformer, numerical_cols), ('cat', categorical_transformer, categorical_cols)])
model = LGBMRegressor(device='gpu', gpu_platform_id=0, gpu_device_id=0)
pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
results = cross_validate(pipeline, X=X, y=y, cv=kfold, scoring='neg_root_mean_squared_error', n_jobs=-1)
print('RMSE: %f (%f)' % (-results['test_score'].mean(), results['test_score'].std())) | code |
330183/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.info() | code |
330183/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
print('Number of observations in the training set: %d (%d%%)' % (n_train, ratio * 100))
print('Number of observations in the test set: %d (%d%%)' % (n_test, (1 - ratio) * 100)) | code |
330183/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
df_titanic_na.head() | code |
330183/cell_83 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
forest.fit(X_train, y_train) | code |
330183/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=0.8, square=True) | code |
330183/cell_90 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape
null_age = df_titanic_ml.Age.isnull()
df_titanic_ml[null_age].shape
df_titanic_ml = df_titanic_ml[np.invert(null_age)]
forest = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
forest.fit(X_train, y_train)
def forest_metrics(X_test, y_test, clf):
f_preds = clf.predict_proba(X_test)[:, 1]
f_fpr, f_tpr, _ = metrics.roc_curve(y_test, f_preds)
fig, ax = plt.subplots()
ax.plot(f_fpr, f_tpr)
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
print("Model Accuracy: %.1f%%" % (clf.score(X_test,y_test) * 100))
print ("Model ROC AUC: %.1f%%" % (metrics.roc_auc_score(y_test, f_preds)*100))
print("ROC Curve")
ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
print(metrics.classification_report(y_test, forest.predict(X_test))) | code |
330183/cell_87 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape
null_age = df_titanic_ml.Age.isnull()
df_titanic_ml[null_age].shape
df_titanic_ml = df_titanic_ml[np.invert(null_age)]
forest = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
forest.fit(X_train, y_train)
def forest_metrics(X_test, y_test, clf):
f_preds = clf.predict_proba(X_test)[:, 1]
f_fpr, f_tpr, _ = metrics.roc_curve(y_test, f_preds)
fig, ax = plt.subplots()
ax.plot(f_fpr, f_tpr)
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
print("Model Accuracy: %.1f%%" % (clf.score(X_test,y_test) * 100))
print ("Model ROC AUC: %.1f%%" % (metrics.roc_auc_score(y_test, f_preds)*100))
print("ROC Curve")
ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
forest_metrics(X_test, y_test, forest) | code |
330183/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
survived = df_titanic_na.Survived == 1
died = df_titanic_na.Survived == 0
g = sns.distplot(df_titanic_na.Embarked, color="darkgreen", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticklabels(["Cherbourg", "", "Queenstown", "", "Southampton"])
sns.distplot(df_titanic_na[survived].Age, color='darkgreen', hist_kws={'alpha': 0.3})
sns.distplot(df_titanic_na[died].Age, color='darkred', hist_kws={'alpha': 0.3}) | code |
330183/cell_73 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape
null_age = df_titanic_ml.Age.isnull()
df_titanic_ml[null_age].shape
df_titanic_ml = df_titanic_ml[np.invert(null_age)]
df_titanic_ml.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_ml.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
emb_dummies = pd.get_dummies(df_titanic_ml.Embarked, prefix='Embarked')
df_titanic_ml = df_titanic_ml.join(emb_dummies)
df_titanic_ml.drop('Embarked', axis=1, inplace=True)
df_titanic_ml.head() | code |
330183/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
survived = df_titanic_na.Survived == 1
died = df_titanic_na.Survived == 0
g = sns.distplot(df_titanic_na.Embarked, color='darkgreen', hist_kws={'alpha': 0.3}, kde=None)
g.set_xticklabels(['Cherbourg', '', 'Queenstown', '', 'Southampton']) | code |
330183/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
survived = df_titanic_na.Survived == 1
died = df_titanic_na.Survived == 0
g = sns.distplot(df_titanic_na.Embarked, color="darkgreen", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticklabels(["Cherbourg", "", "Queenstown", "", "Southampton"])
g = sns.factorplot(x="Pclass", y="Survived", hue="Sex", data=df_titanic_na,
size=6, kind="bar", palette="muted", ci=None)
g.despine(left=True)
g.set_ylabels("Survival Probability")
g.set_xlabels("Passenger Class")
g = sns.factorplot(x='Embarked', y='Survived', hue='Pclass', data=df_titanic_na, size=6, kind='bar', palette='muted', ci=None)
g.despine(left=True)
g.set_ylabels('Survival Probability')
g.set_xlabels('Embarkation Port')
g.set_xticklabels(['Cherbourg', 'Queenstown', 'Southampton']) | code |
330183/cell_64 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape
null_age = df_titanic_ml.Age.isnull()
df_titanic_ml[null_age].shape | code |
330183/cell_89 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape
null_age = df_titanic_ml.Age.isnull()
df_titanic_ml[null_age].shape
df_titanic_ml = df_titanic_ml[np.invert(null_age)]
forest = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
forest.fit(X_train, y_train)
def forest_metrics(X_test, y_test, clf):
f_preds = clf.predict_proba(X_test)[:, 1]
f_fpr, f_tpr, _ = metrics.roc_curve(y_test, f_preds)
fig, ax = plt.subplots()
ax.plot(f_fpr, f_tpr)
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
print("Model Accuracy: %.1f%%" % (clf.score(X_test,y_test) * 100))
print ("Model ROC AUC: %.1f%%" % (metrics.roc_auc_score(y_test, f_preds)*100))
print("ROC Curve")
ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
print(metrics.confusion_matrix(y_test, forest.predict(X_test))) | code |
330183/cell_68 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape
null_age = df_titanic_ml.Age.isnull()
df_titanic_ml[null_age].shape
df_titanic_ml = df_titanic_ml[np.invert(null_age)]
df_titanic_ml.info() | code |
330183/cell_62 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
df_titanic_ml = df_titanic.copy()
df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna('Southampton')
df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape | code |
330183/cell_80 | [
"text_html_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier | code |
330183/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10) | code |
330183/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color="red", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticks([0,1])
g.autoscale()
g.set_xticklabels(["Dead", "Survived"])
corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked']].corr()
f, ax = plt.subplots(figsize=(10, 7))
sns.heatmap(corrmat, vmax=.8, square=True)
survived = df_titanic_na.Survived == 1
died = df_titanic_na.Survived == 0
g = sns.distplot(df_titanic_na.Embarked, color="darkgreen", hist_kws={"alpha": 0.3}, kde=None)
g.set_xticklabels(["Cherbourg", "", "Queenstown", "", "Southampton"])
g = sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=df_titanic_na, size=6, kind='bar', palette='muted', ci=None)
g.despine(left=True)
g.set_ylabels('Survival Probability')
g.set_xlabels('Passenger Class') | code |
330183/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_titanic.dropna()
df_titanic_na.Sex = df_titanic.Sex.map({'female': 0, 'male': 1})
df_titanic_na.Embarked = df_titanic.Embarked.map({'C': 0, 'Q': 1, 'S': 2})
g = sns.distplot(df_titanic_na.Survived, color='red', hist_kws={'alpha': 0.3}, kde=None)
g.set_xticks([0, 1])
g.autoscale()
g.set_xticklabels(['Dead', 'Survived']) | code |
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