kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
585,608 | learn.save('resnet152-1' )<load_pretrained> | ohe = preprocessing.LabelEncoder()
ohe.fit(pd.unique(X.Embarked ).astype(str))
X.Embarked = ohe.transform(X.Embarked.astype(str))
xText.Embarked = ohe.transform(xText.Embarked.astype(str))
finalTest.Embarked = ohe.transform(finalTest.Embarked.astype(str))
print(ohe.classes_)
| Titanic - Machine Learning from Disaster |
585,608 | learn.load('resnet152-1');<train_model> | X['dependent'] = np.add(X.SibSp, X.Parch)
xText['dependent'] = np.add(xText.SibSp, xText.Parch)
finalTest['dependent'] = np.add(finalTest.SibSp, finalTest.Parch)
X.Age.fillna(X.Age.mean() , inplace=True)
xText.Age.fillna(xText.Age.mean() , inplace=True)
finalTest.Age.fillna(finalTest.Age.mean() , inplace=True)
X.... | Titanic - Machine Learning from Disaster |
585,608 | learn.fit_one_cycle(3, slice(1e-6,1e-5))<save_model> | Titanic - Machine Learning from Disaster | |
585,608 | learn.save('resnet152-2' )<load_pretrained> | Titanic - Machine Learning from Disaster | |
585,608 | learn.load('resnet152-2');<normalization> | desicionTreeclf = DecisionTreeRegressor(criterion='mse',max_leaf_nodes=8, random_state=0,max_depth=6, min_samples_leaf=5)
desicionTreeclf = desicionTreeclf.fit(X, Y)
| Titanic - Machine Learning from Disaster |
585,608 | <prepare_output><EOS> | isSurvived = pd.DataFrame({'Survived' :(desicionTreeclf.predict(xText)) ,'PassengerId' :(X_test['PassengerId'])})
finalIsSurvived = pd.DataFrame({'Survived' :(desicionTreeclf.predict(finalTest)) ,'PassengerId' :(test['PassengerId'])})
isSurvived.Survived =isSurvived.Survived.round(0)
isSurvived.Survived =isSurvived.... | Titanic - Machine Learning from Disaster |
481,342 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<train_model> | %matplotlib inline
plt.style.use('fivethirtyeight')
warnings.filterwarnings('ignore')
train = pd.read_csv('.. /input/train.csv')
test = pd.read_csv('.. /input/test.csv')
merged = train.append(test)
| Titanic - Machine Learning from Disaster |
481,342 | learn.fit_one_cycle(4, 2e-4 )<save_model> | merged['PTitle'] = merged['PTitle'].replace(['Lady', 'the Countess','Mlle', 'Ms', 'Mme', 'Dona','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer'], 'Rare')
merged[['Sex','PTitle','Pclass','Survived']].groupby(['Sex','PTitle','Pclass'] ).mean() | Titanic - Machine Learning from Disaster |
481,342 | learn.save('resnet152-3' )<load_pretrained> | merged.loc[merged['FamilySize'] == 1, 'Fsize'] = 'Alone'
merged.loc[(merged['FamilySize'] > 1)&(merged['FamilySize'] < 5), 'Fsize'] = 'Small'
merged.loc[merged['FamilySize'] >4, 'Fsize'] = 'Large'
fem_analysis = merged[merged['Sex'] == 'female']
fem_analysis[['PTitle','Pclass','Survived','Fsize']].groupby(['Pclass','PT... | Titanic - Machine Learning from Disaster |
481,342 | learn.load('resnet152-3');<train_model> | fem2_analysis = fem_analysis[(fem_analysis['Pclass'] == 3)&
(fem_analysis['Fsize'] != 'Large')]
fem2_analysis[['PTitle','Embarked','Survived']].groupby(['PTitle','Embarked'] ).mean() | Titanic - Machine Learning from Disaster |
481,342 | learn.fit_one_cycle(6, 2e-5 )<save_model> | fem2_analysis[['PTitle','Embarked','Survived']].groupby(['PTitle','Embarked'] ).count() | Titanic - Machine Learning from Disaster |
481,342 | learn.save('resnet152-4' )<load_pretrained> | men_analysis = merged[merged['Sex'] == 'male']
men_analysis[['PTitle','Pclass','Survived','Fsize']].groupby(['PTitle','Pclass','Fsize'] ).mean() | Titanic - Machine Learning from Disaster |
481,342 | learn.load('resnet152-4');<load_pretrained> | men2_analysis = men_analysis[(men_analysis['Pclass'] == 1)&
(men_analysis['PTitle'] != 'Master')&
(men_analysis['Fsize'] != 'Large')]
men2_analysis['HasCabin'] = men2_analysis['Cabin'].notnull()
men2_analysis[['PTitle','HasCabin','Survived']].groupby(['PTitle','HasCabin'] ).mean() | Titanic - Machine Learning from Disaster |
481,342 | learn.load('resnet152-4');<predict_on_test> | merged['Group'] = merged['Sex']
merged.loc[(merged['Sex'] == 'female')&
(merged['Fsize'] == 'Large')&
(merged['Pclass'] == 3), 'Group'] = 'Females, large family, class 3'
merged.loc[(merged['PTitle'] == 'Miss')&
(merged['Fsize'] != 'Large')&
(merged['Pclass'] == 3)&
(merged['Embarked'] == 'S'), 'Group'] = 'Miss, c... | Titanic - Machine Learning from Disaster |
481,342 | preds, _ = learn.get_preds(ds_type=DatasetType.Test )<load_from_csv> | merged['Predict'] = 1
merged.loc[merged['Group'] == 'male', 'Predict'] = 0
merged.loc[merged['Group'] == 'Females, large family, class 3', 'Predict'] = 0
merged.loc[merged['Group'] == 'Miss, class 3, embarked Southampton', 'Predict'] = 0
merged.loc[merged['Group'] == 'Masters in Pclass 1&2 or small families', 'Predict'... | Titanic - Machine Learning from Disaster |
481,342 | <data_type_conversions><EOS> | my_solution = pd.DataFrame({'PassengerId': test_analysed['PassengerId'],
'Survived':test_analysed['Predict']})
my_solution.to_csv('submission.csv', index = False ) | Titanic - Machine Learning from Disaster |
9,815,155 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<prepare_output> | display.Image('https://raw.githubusercontent.com/Dutta-SD/Images_Unsplash/master/Kaggle/dorian-mongel-5Rgr_zI7pBw-unsplash.jpg', width = 3000, height = 500 ) | Titanic - Machine Learning from Disaster |
9,815,155 | submission['diagnosis'] = preds
submission.head()<set_options> | import pandas as pd
import numpy as np | Titanic - Machine Learning from Disaster |
9,815,155 | warnings.simplefilter(action='ignore', category=FutureWarning )<save_to_csv> | train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv' ) | Titanic - Machine Learning from Disaster |
9,815,155 | submission.to_csv('submission.csv', index = False )<import_modules> | PassengerID = test_data.PassengerId
train_data.drop(['Name', 'Ticket', 'Cabin', 'PassengerId'], inplace=True, axis=1)
test_data.drop(['Name', 'Ticket', 'Cabin', 'PassengerId'], inplace=True, axis=1)
test_data.head() | Titanic - Machine Learning from Disaster |
9,815,155 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns<load_from_csv> | print(train_data.isnull().any() ) | Titanic - Machine Learning from Disaster |
9,815,155 | train = pd.read_csv('.. /input/train_V2.csv')
test = pd.read_csv('.. /input/test_V2.csv' )<drop_column> | test_data.isnull().any() | Titanic - Machine Learning from Disaster |
9,815,155 | train['totalDistance']=train['rideDistance']+train['walkDistance']+train['swimDistance']
train['killsWithoutMoving']=(( train['kills']>0)&(train['totalDistance']==0))
train['headshot_rate']=train['headshotKills']/train['kills']
train['playersJoined'] = train.groupby('matchId')['matchId'].transform('count')
print(train... | y = train_data.Survived
X = train_data.drop(['Survived'], axis = 1)
print(y.head())
print(X.head() ) | Titanic - Machine Learning from Disaster |
9,815,155 | train['killsNorm'] = train['kills']*(( 100-train['playersJoined'])/100 + 1)
train['headshotKillsNorm'] = train['headshotKills']*(( 100-train['playersJoined'])/100 + 1)
train['killPlaceNorm'] = train['killPlace']*(( 100-train['playersJoined'])/100 + 1)
train['killPointsNorm'] = train['killPoints']*(( 100-train['playe... | import matplotlib.pyplot as plt
import seaborn as sns | Titanic - Machine Learning from Disaster |
9,815,155 | target='winPlacePerc'
features=list(train.columns)
features.remove("Id")
features.remove("matchId")
features.remove("groupId")
features.remove("matchType")
features.remove("killsWithoutMoving")
features.remove("headshot_rate")
features.remove(target)
sample = 500000
df_sample = train.sample(sample)
x_train=df_... | X_train, X_test, y_train, y_test = X, test_data, y, None | Titanic - Machine Learning from Disaster |
9,815,155 | random_seed=1
sample = 500000
x_train,x_val,y_train,y_val=train_test_split(x_train,y_train,test_size=0.1,random_state=random_seed)
model=RandomForestRegressor(n_estimators=70,min_samples_leaf=3,max_features=0.5,n_jobs=-1 )<train_model> | X_train.reset_index(drop=True, inplace=True)
X_test.reset_index(drop=True, inplace=True)
y_train.reset_index(drop=True, inplace=True)
y_train.reset_index(drop=True, inplace=True)
X_train.info() | Titanic - Machine Learning from Disaster |
9,815,155 | %%time
model.fit(x_train,y_train )<compute_test_metric> | s =(X_train.dtypes=='object')
categorical_cols = list(s[s].index)
numerical_cols = [ i for i in X_train.columns if not i in categorical_cols ]
numerical_cols | Titanic - Machine Learning from Disaster |
9,815,155 | print('mae train:',mean_absolute_error(model.predict(x_train),y_train))
print('mae train:',mean_absolute_error(model.predict(x_val),y_val))<feature_engineering> | nm_imputer = KNNImputer()
X_train_numerical = pd.DataFrame(nm_imputer.fit_transform(X_train[numerical_cols]),
columns = numerical_cols)
X_test_numerical = pd.DataFrame(nm_imputer.transform(X_test[numerical_cols]),
columns = numerical_cols)
| Titanic - Machine Learning from Disaster |
9,815,155 | test['totalDistance']=test['rideDistance']+test['walkDistance']+test['swimDistance']
test['headshot_rate']=test['headshotKills']/test['kills']
test['playersJoined'] = test.groupby('matchId')['matchId'].transform('count')
test['killsNorm'] = test['kills']*(( 100-test['playersJoined'])/100 + 1)
test['headshotKillsNorm'... | X_train = X_train.drop(numerical_cols, axis = 1)
X_test = X_test.drop(numerical_cols, axis = 1)
X_train = X_train.join(X_train_numerical)
X_test = X_test.join(X_test_numerical)
X_train.isnull().any() | Titanic - Machine Learning from Disaster |
9,815,155 | x_test=test[features]
pred=model.predict(x_test)
test['winPlacePerc']=pred
submission=test[['Id','winPlacePerc']]
submission.head()<save_to_csv> | nm_imputer = SimpleImputer(strategy='most_frequent')
X_train_numerical = pd.DataFrame(nm_imputer.fit_transform(X_train[categorical_cols]),
columns = categorical_cols)
X_test_numerical = pd.DataFrame(nm_imputer.transform(X_test[categorical_cols]),
columns = categorical_cols ) | Titanic - Machine Learning from Disaster |
9,815,155 | submission.to_csv('submission.csv',index=False )<import_modules> | X_train = X_train.drop(categorical_cols, axis = 1)
X_test = X_test.drop(categorical_cols, axis = 1)
X_train = X_train.join(X_train_numerical)
X_test = X_test.join(X_test_numerical)
X_train.isnull().any() | Titanic - Machine Learning from Disaster |
9,815,155 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt<load_from_csv> | OH_encoder = OneHotEncoder(handle_unknown = 'ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[categorical_cols]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(X_test[categorical_cols]))
OH_cols_train.index = X_train.index
OH_cols_test.index = X_test.index
num_X_train = X_train.d... | Titanic - Machine Learning from Disaster |
9,815,155 | develop_mode = False
if develop_mode:
df_train = reduce_mem_usage(pd.read_csv('.. /input/pubg-finish-placement-prediction/train_V2.csv', nrows=5000))
df_test = reduce_mem_usage(pd.read_csv('.. /input/pubg-finish-placement-prediction/test_V2.csv'))
else:
df_train = reduce_mem_usage(pd.read_csv('.. /input/pubg-finish-pla... | X_train_2, X_val, y_train_2, y_val = train_test_split(X_train, y_train, test_size = 0.2, random_state = 10 ) | Titanic - Machine Learning from Disaster |
9,815,155 | print('The sizes of the datasets are:')
print('Training Dataset: ', df_train.shape)
print('Testing Dataset: ', df_test.shape )<set_options> | from tensorflow import keras | Titanic - Machine Learning from Disaster |
9,815,155 | warnings.filterwarnings('ignore' )<load_from_csv> | from keras import Sequential
from keras.layers import BatchNormalization, Dense
| Titanic - Machine Learning from Disaster |
9,815,155 | def feature_engineering(is_train=True,debug=True):
test_idx = None
if is_train:
print("processing train.csv")
if debug == True:
df = pd.read_csv('.. /input/pubg-finish-placement-prediction/train_V2.csv', nrows=10000)
else:
df = pd.read_csv('.. /input/pubg-finish-placement-prediction/train_V2.csv')
df = df[df['maxPla... | model = Sequential()
model.add(Dense(128, activation = 'relu', input_shape =(10,)))
model.add(BatchNormalization())
model.add(Dense(64, activation = 'relu'))
model.add(BatchNormalization())
model.add(Dense(8, activation = 'relu'))
model.add(BatchNormalization())
model.add(Dense(1, activation = 'sigmoid'))
model.sum... | Titanic - Machine Learning from Disaster |
9,815,155 | X_train, y_train, train_columns, _ = feature_engineering(True,False)
X_test, _, _ , test_idx = feature_engineering(False,True )<drop_column> | model.compile(optimizer='adam',
loss=keras.losses.BinaryCrossentropy() ,
metrics = ['accuracy'] ) | Titanic - Machine Learning from Disaster |
9,815,155 | X_train =reduce_mem_usage(X_train)
X_test = reduce_mem_usage(X_test )<train_model> | history = model.fit(
X_train_2,
y_train_2,
batch_size=32,
epochs=20,
validation_data=(X_val, y_val)
) | Titanic - Machine Learning from Disaster |
9,815,155 | LR_model = LinearRegression(n_jobs=4, normalize=True)
LR_model.fit(X_train,y_train )<compute_test_metric> | model.compile(optimizer='adam',
loss=keras.losses.BinaryCrossentropy() ,
metrics = ['accuracy'])
history = model.fit(
X_train,
y_train,
batch_size=32,
epochs=20
) | Titanic - Machine Learning from Disaster |
9,815,155 | LR_model.score(X_train,y_train )<predict_on_test> | y_preds = model.predict_classes(X_test ) | Titanic - Machine Learning from Disaster |
9,815,155 | <feature_engineering><EOS> | file_name = "MyTitanicSubmission.csv"
y_pred_series = pd.Series(y_preds.flatten() , name = 'Survived')
file = pd.concat([PassengerID, y_pred_series], axis = 1)
file.to_csv(file_name, index = False ) | Titanic - Machine Learning from Disaster |
6,950,793 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<save_to_csv> | print(f'matplotlib: {matplotlib.__version__}')
print(f'sklearn : {sklearn.__version__}' ) | Titanic - Machine Learning from Disaster |
6,950,793 | df_test['winPlacePerc'] = y_pred_test
submission = df_test[['Id', 'winPlacePerc']]
submission.to_csv('submission.csv', index=False )<load_from_csv> | print(f'pandas version: {pd.__version__}')
test = pd.read_csv(".. /input/titanic/test.csv")
train = pd.read_csv(".. /input/titanic/train.csv" ) | Titanic - Machine Learning from Disaster |
6,950,793 | train = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/train_V2.csv')
test = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/test_V2.csv' )<drop_column> | def format_data(data):
data = pd.get_dummies(data, columns=['Sex','Embarked'])
data = data.drop(['Name','Ticket','Cabin'], axis=1)
data.fillna(data.mean() , inplace=True)
if 'Survived' in data.columns:
data_y = data['Survived']
data_x = data.drop(['Survived'], axis=1)
return data_x, data_y
else:
return data
train_x... | Titanic - Machine Learning from Disaster |
6,950,793 | train.drop(2744604,inplace=True )<concatenate> | model = RandomForestClassifier(random_state=1)
model.fit(train_x, train_y); | Titanic - Machine Learning from Disaster |
6,950,793 | all_data = pd.concat([train, test] )<groupby> | X_train, X_test, y_train, y_test = train_test_split(train_x, train_y,
test_size=0.2,
random_state=42)
model.fit(X_train, y_train)
def summary_stats(x,y):
pred = model.predict(x)
f1 = f1_score(pred, y)
acc = model.score(x, y)
print(f" F1 score: {f1}")
print(f" Accuracy: {acc}")
print(f"Training:")
summary_stats(... | Titanic - Machine Learning from Disaster |
6,950,793 | all_data['playersJoined'] = all_data.groupby('matchId')['matchId'].transform('count' )<drop_column> | search_pars = {
'n_estimators': [10, 30, 100, 300, 1000],
'max_features': [0.25, 0.5, 0.75, 1.0],
'criterion' : ['gini', 'entropy']
} | Titanic - Machine Learning from Disaster |
6,950,793 | all_data.drop(columns = ['Id','groupId','matchId'],inplace=True )<categorify> | rf_model = RandomForestClassifier(random_state=1)
clf = GridSearchCV(rf_model, search_pars)
clf.fit(X_train, y_train); | Titanic - Machine Learning from Disaster |
6,950,793 | all_data['killsNorm'] = all_data['kills']*(( 100-all_data['playersJoined'])/100+1)
all_data['damageDealtNorm'] = all_data['damageDealt']*(( 100-all_data['playersJoined'])/100+1)
all_data['totalDistance'] = all_data['rideDistance'] + all_data['walkDistance'] + all_data['swimDistance']
all_data['headshot_rate'] = all_d... | model = clf.best_estimator_
model.fit(train_x, train_y); | Titanic - Machine Learning from Disaster |
6,950,793 | train = all_data.iloc[:4446965]
test = all_data.iloc[4446965:]<drop_column> | print("Final model training results:")
summary_stats(train_x, train_y ) | Titanic - Machine Learning from Disaster |
6,950,793 | train.drop(train[(train['kills']>1)&(train['totalDistance']==0)].index,inplace=True )<drop_column> | pred_test = model.predict(test_x ) | Titanic - Machine Learning from Disaster |
6,950,793 | train.drop(train[train['kills']>20].index,inplace=True )<prepare_x_and_y> | submission.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
6,950,793 | X = train.drop(columns=['winPlacePerc'])
Y = train['winPlacePerc']<prepare_x_and_y> | submission.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
10,512,499 |
<import_modules> | import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from numpy import savetxt | Titanic - Machine Learning from Disaster |
10,512,499 | from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from IPython.display import display
from sklearn import metrics
import lightgbm as lgb
from sklearn.metrics import accuracy_score<split> | base_train = pd.read_csv("/kaggle/input/titanic/train.csv")
base_train = base_train[['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'Survived']]
del base_train['Name']
del base_train['Ticket']
del base_train['Cabin']
base_test = pd.read_csv("/kaggle/input/titani... | Titanic - Machine Learning from Disaster |
10,512,499 | seed = 7
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed )<create_dataframe> | imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')
imputer = imputer.fit(base_train.iloc[:, 3:4])
base_train.iloc[:, 3:4] = imputer.transform(base_train.iloc[:, 3:4])
base_test.iloc[:, 3:4] = imputer.transform(base_test.iloc[:, 3:4] ) | Titanic - Machine Learning from Disaster |
10,512,499 | d_train = lgb.Dataset(X_train, label=y_train)
params = {}
params['objective'] = 'regression'
params['metric'] = 'mae'<train_model> | labelencoder = LabelEncoder()
base_train['Embarked'] = labelencoder.fit_transform(base_train['Embarked'].astype(str))
base_test['Embarked'] = labelencoder.fit_transform(base_test['Embarked'].astype(str))
base_train.iloc[:, 2] = labelencoder.fit_transform(base_train.iloc[:, 2])
base_test.iloc[:, 2] = labelencoder.fit_t... | Titanic - Machine Learning from Disaster |
10,512,499 | model = lgb.train(params, d_train )<predict_on_test> | scaler = StandardScaler()
base_train.iloc[:, 1:8] = scaler.fit_transform(base_train.iloc[:, 1:8])
base_test.iloc[:, 1:8] = scaler.fit_transform(base_test.iloc[:, 1:8] ) | Titanic - Machine Learning from Disaster |
10,512,499 | y_pred=model.predict(X_test )<compute_test_metric> | base_test[base_test==np.inf]=np.nan
base_test.fillna(base_test.mean() , inplace=True ) | Titanic - Machine Learning from Disaster |
10,512,499 | mae = mean_absolute_error(y_test, y_pred)
print("MAE: {}".format(mae))<create_dataframe> | predictors_train = base_train.iloc[:, 0:8].values
class_train = base_train.iloc[:, 8].values | Titanic - Machine Learning from Disaster |
10,512,499 | d_train_full = lgb.Dataset(X, label=Y)
params = {}
params['objective'] = 'regression'
params['metric'] = 'mae'<train_model> | classificador = RandomForestClassifier(n_estimators = 40, criterion = 'entropy', random_state = 0)
classificador.fit(predictors_train, class_train)
predictions = classificador.predict(base_test ) | Titanic - Machine Learning from Disaster |
10,512,499 | model_full = lgb.train(params, d_train_full )<load_from_csv> | output = pd.DataFrame({'PassengerId': base_test.PassengerId, 'Survived': predictions})
output.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
467,142 | X_submit = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/test_V2.csv' )<drop_column> | train = pd.read_csv(".. /input/titanic/train.csv")
test = pd.read_csv(".. /input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
467,142 | test.drop(columns=['winPlacePerc'], inplace = True )<predict_on_test> | def missing_percentage(df):
total = df.isnull().sum().sort_values(ascending = False)
percent = round(df.isnull().sum().sort_values(ascending = False)/len(df)*100,2)
return pd.concat([total, percent], axis=1, keys=['Total','Percent'] ) | Titanic - Machine Learning from Disaster |
467,142 | y_pred_submit=model_full.predict(test )<concatenate> | %timeit -r2 -n10 missing_percentage(train ) | Titanic - Machine Learning from Disaster |
467,142 | submission = pd.concat([X_submit,pd.Series(y_pred_submit, name='winPlacePerc')], axis=1 )<predict_on_test> | missing_percentage(train ) | Titanic - Machine Learning from Disaster |
467,142 | submission['pred_winPlacePerc'] = submission.iloc[:,-1]
def adjust_pred(x):
space = 1/(x.maxPlace-1)
return round(x.pred_winPlacePerc / space)* space
submission['adj_winPlacePerc'] = adjust_pred(submission)
submission.head()<prepare_output> | %%timeit -r2 -n10
missing_percentage(test ) | Titanic - Machine Learning from Disaster |
467,142 | submission = submission.loc[:,['Id','adj_winPlacePerc']]
submission.columns = ['Id','winPlacePerc']
submission.head()<split> | missing_percentage(test ) | Titanic - Machine Learning from Disaster |
467,142 |
<train_model> | def percent_value_counts(df, feature):
percent = pd.DataFrame(round(df.loc[:,feature].value_counts(dropna=False, normalize=True)*100,2))
total = pd.DataFrame(df.loc[:,feature].value_counts(dropna=False))
total.columns = ["Total"]
percent.columns = ['Percent']
return pd.concat([total, percent], axis = 1)
| Titanic - Machine Learning from Disaster |
467,142 |
<save_to_csv> | percent_value_counts(train, 'Embarked' ) | Titanic - Machine Learning from Disaster |
467,142 | submission.to_csv('submission.csv', index=False )<save_to_csv> | percent_value_counts(train, 'Embarked' ) | Titanic - Machine Learning from Disaster |
467,142 | submission.to_csv('submission.csv', index=False )<load_from_csv> | train[train.Embarked.isnull() ] | Titanic - Machine Learning from Disaster |
467,142 |
%matplotlib inline
warnings.filterwarnings("ignore")
train = pd.read_csv('.. /input/pubg-finish-placement-prediction/train_V2.csv')
test = pd.read_csv('.. /input/pubg-finish-placement-prediction/test_V2.csv')
y_t = 'winPlacePerc'
train.drop(2744604, inplace=True)
train['playersJoined'] = train.groupby('matchId')[... | train.Embarked.fillna("C", inplace=True ) | Titanic - Machine Learning from Disaster |
467,142 | s = 500000
train = train.sample(s)
k = list(train.columns)
k.remove("Id")
k.remove("matchId")
k.remove("groupId")
y_ = np.array(train[y_t])
k.remove(y_t)
x_ = train[k]
x_test = test[k]
random_seed=1
x_train, x_train_test, y_train, y_train_test = train_test_split(x_, y_, test_size = 0.1, random_state=random_seed)... | print("Train Cabin missing: " + str(train.Cabin.isnull().sum() /len(train.Cabin)))
print("Test Cabin missing: " + str(test.Cabin.isnull().sum() /len(test.Cabin)) ) | Titanic - Machine Learning from Disaster |
467,142 | %%time
md.fit(x_train, y_train)
print('train_MAE: ', mean_absolute_error(md.predict(x_train), y_train))
print('test_MAE: ', mean_absolute_error(md.predict(x_train_test), y_train_test))<create_dataframe> | survivers = train.Survived
train.drop(["Survived"],axis=1, inplace=True)
all_data = pd.concat([train,test], ignore_index=False)
all_data.Cabin.fillna("N", inplace=True ) | Titanic - Machine Learning from Disaster |
467,142 | importance = pd.DataFrame({'cols':x_train.columns, 'importance':md.feature_importances_} ).sort_values('importance', ascending=False)
print(importance[:20] )<save_to_csv> | all_data.Cabin = [i[0] for i in all_data.Cabin] | Titanic - Machine Learning from Disaster |
467,142 | %%time
p = md.predict(x_test)
test['winPlacePerc'] = p
submission = test[['Id', 'winPlacePerc']]
submission.to_csv('submission.csv', index=False )<import_modules> | percent_value_counts(all_data, "Cabin" ) | Titanic - Machine Learning from Disaster |
467,142 | import os
import warnings
import gc
import time
from tqdm import tqdm
import pandas as pd
import numpy as np
from sklearn.metrics import log_loss
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
from itertools import product
from sklearn.model_selection import train_test_spl... | all_data.groupby("Cabin")['Fare'].mean().sort_values() | Titanic - Machine Learning from Disaster |
467,142 | train = pd.get_dummies(train,columns=['matchType'] )<correct_missing_values> | def cabin_estimator(i):
a = 0
if i<16:
a = "G"
elif i>=16 and i<27:
a = "F"
elif i>=27 and i<38:
a = "T"
elif i>=38 and i<47:
a = "A"
elif i>= 47 and i<53:
a = "E"
elif i>= 53 and i<54:
a = "D"
elif i>=54 and i<116:
a = 'C'
else:
a = "B"
return a
| Titanic - Machine Learning from Disaster |
467,142 | train =train.dropna()<categorify> | with_N = all_data[all_data.Cabin == "N"]
without_N = all_data[all_data.Cabin != "N"] | Titanic - Machine Learning from Disaster |
467,142 | test = pd.get_dummies(test,columns=['matchType'] )<prepare_x_and_y> | with_N['Cabin'] = with_N.Fare.apply(lambda x: cabin_estimator(x))
all_data = pd.concat([with_N, without_N], axis=0)
all_data.sort_values(by = 'PassengerId', inplace=True)
train = all_data[:891]
test = all_data[891:]
train['Survived'] = survivers | Titanic - Machine Learning from Disaster |
467,142 | y_train =train['winPlacePerc']
x_train =train.drop(['Id','groupId','matchId','winPlacePerc'],axis=1)
<prepare_x_and_y> | test[test.Fare.isnull() ] | Titanic - Machine Learning from Disaster |
467,142 | X_train = x_train.values
Y_train = y_train.values<split> | missing_value = test[(test.Pclass == 3)&
(test.Embarked == "S")&
(test.Sex == "male")].Fare.mean()
test.Fare.fillna(missing_value, inplace=True ) | Titanic - Machine Learning from Disaster |
467,142 | validation_size = 0.20
seed = 1000
X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=validation_size, random_state=seed )<train_model> | print("Train age missing value: " + str(( train.Age.isnull().sum() /len(train)) *100)+str("%"))
print("Test age missing value: " + str(( test.Age.isnull().sum() /len(test)) *100)+str("%")) | Titanic - Machine Learning from Disaster |
467,142 | train_data = lgb.Dataset(data=X_train, label=Y_train)
valid_data = lgb.Dataset(data=X_valid, label=Y_valid)
params = {
'num_leaves': 144,
"metric" : "mae",
'learning_rate': 0.1,
'n_estimators': 800,
'max_depth':13,
'max_bin':55,
'bagging_fraction':0.8,
'bagging_freq':5,
'feature_fraction':0.9
}
lgb_model = lgb.train(... | train[train.Fare > 280] | Titanic - Machine Learning from Disaster |
467,142 | X_test = test.drop(["Id", "groupId", "matchId"], axis = 1 )<save_to_csv> | train['Sex'] = train.Sex.apply(lambda x: 0 if x == "female" else 1)
test['Sex'] = test.Sex.apply(lambda x: 0 if x == "female" else 1 ) | Titanic - Machine Learning from Disaster |
467,142 | Y_test = lgb_model.predict(X_test)
submission = pd.DataFrame({
"Id": test['Id'],
"winPlacePerc": Y_test
})
submission.to_csv('submission.csv', index=False )<save_to_csv> | survived_summary = train.groupby("Survived")
survived_summary.mean().reset_index() | Titanic - Machine Learning from Disaster |
467,142 | submission = pd.read_csv(".. /input/submission/submission.csv")
submission.to_csv('submission.csv', index=False )<define_variables> | survived_summary = train.groupby("Sex")
survived_summary.mean().reset_index() | Titanic - Machine Learning from Disaster |
467,142 | def get_sample(df,n):
idxs = sorted(np.random.permutation(len(df)) [:n])
return df.iloc[idxs].copy()
def proc_df(df, y_fld, skip_flds=None, do_scale=False, na_dict=None,
preproc_fn=None, max_n_cat=None, subset=None, mapper=None):
if not skip_flds: skip_flds=[]
if subset: df = get_sample(df,subset)
df = df.copy()
... | survived_summary = train.groupby("Pclass")
survived_summary.mean().reset_index() | Titanic - Machine Learning from Disaster |
467,142 | %load_ext autoreload
%autoreload 2
%matplotlib inline
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
<load_from_csv> | pd.DataFrame(abs(train.corr() ['Survived'] ).sort_values(ascending = False)) | Titanic - Machine Learning from Disaster |
467,142 | train = pd.read_csv(".. /input/pubg-finish-placement-prediction/train_V2.csv")
test = pd.read_csv(".. /input/pubg-finish-placement-prediction/test_V2.csv" )<filter> | male_mean = train[train['Sex'] == 1].Survived.mean()
female_mean = train[train['Sex'] == 0].Survived.mean()
print("Male survival mean: " + str(male_mean))
print("female survival mean: " + str(female_mean))
print("The mean difference between male and female survival rate: " + str(female_mean - male_mean)) | Titanic - Machine Learning from Disaster |
467,142 | train[train['winPlacePerc'].isnull() ]<drop_column> | male = train[train['Sex'] == 1]
female = train[train['Sex'] == 0]
m_mean_samples = []
f_mean_samples = []
for i in range(50):
m_mean_samples.append(np.mean(random.sample(list(male['Survived']),50,)))
f_mean_samples.append(np.mean(random.sample(list(female['Survived']),50,)))
print(f"Male mean sample mean: {round(np.m... | Titanic - Machine Learning from Disaster |
467,142 | train.drop(2744604, inplace=True )<filter> | train['name_length'] = [len(i)for i in train.Name]
test['name_length'] = [len(i)for i in test.Name]
def name_length_group(size):
a = ''
if(size <=20):
a = 'short'
elif(size <=35):
a = 'medium'
elif(size <=45):
a = 'good'
else:
a = 'long'
return a
train['nLength_group'] = train['name_length'].map(name_length_group)
tes... | Titanic - Machine Learning from Disaster |
467,142 | train[train['winPlacePerc'].isnull() ]<feature_engineering> | train["title"] = [i.split('.')[0] for i in train.Name]
train["title"] = [i.split(',')[1] for i in train.title]
| Titanic - Machine Learning from Disaster |
467,142 | train['totalDistance'] = train['walkDistance'] + train['rideDistance'] + train['swimDistance']
train['healsAndBoosts'] = train['heals']+train['boosts']<categorify> | test['title'] = [i.split('.')[0].split(',')[1].strip() for i in test.Name]
| Titanic - Machine Learning from Disaster |
467,142 | train['team'] = [1 if i>50 else 2 if(i>25 & i<=50)else 4 for i in train['numGroups']]
train['playersJoined'] = train.groupby('matchId')['matchId'].transform('count' )<feature_engineering> | train["title"] = [i.replace('Ms', 'Miss')for i in train.title]
train["title"] = [i.replace('Mlle', 'Miss')for i in train.title]
train["title"] = [i.replace('Mme', 'Mrs')for i in train.title]
train["title"] = [i.replace('Dr', 'rare')for i in train.title]
train["title"] = [i.replace('Col', 'rare')for i in train.title]
tr... | Titanic - Machine Learning from Disaster |
467,142 | train['killsNorm'] = train['kills']*(( 100-train['playersJoined'])/100 + 1)
train['damageDealtNorm'] = train['damageDealt']*(( 100-train['playersJoined'])/100 + 1)
train['maxPlaceNorm'] = train['maxPlace']*(( 100-train['playersJoined'])/100 + 1)
train['matchDurationNorm'] = train['matchDuration']*(( 100-train['playe... | train['family_size'] = train.SibSp + train.Parch+1
test['family_size'] = test.SibSp + test.Parch+1 | Titanic - Machine Learning from Disaster |
467,142 | train['headshot_rate'] = train['headshotKills'] / train['kills']
train['headshot_rate'] = train['headshot_rate'].fillna(0 )<drop_column> | train['family_group'] = train['family_size'].map(family_group)
test['family_group'] = test['family_size'].map(family_group ) | Titanic - Machine Learning from Disaster |
467,142 | train['killsWithoutMoving'] =(( train['kills'] > 0)&(train['totalDistance'] == 0))
train.drop(train[train['killsWithoutMoving'] == True].index, inplace=True )<filter> | train['is_alone'] = [1 if i<2 else 0 for i in train.family_size]
test['is_alone'] = [1 if i<2 else 0 for i in test.family_size] | Titanic - Machine Learning from Disaster |
467,142 | train[train['roadKills'] > 10]<drop_column> | train.Ticket.value_counts().sample(10 ) | Titanic - Machine Learning from Disaster |
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