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32068084/cell_28 | [
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB | code |
32068084/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import decomposition
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train) | code |
32068084/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import decomposition
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_ | code |
32068084/cell_35 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb
ETC = ExtraTreesClassifier(n_estimators=100)
ETC.fit(X_train, y_train)
y_pred = ETC.predict(X_valid)
acc_ETC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_ETC | code |
32068084/cell_31 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC | code |
32068084/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import decomposition
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB | code |
32068084/cell_37 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb
ETC = ExtraTreesClassifier(n_estimators=100)
ETC.fit(X_train, y_train)
y_pred = ETC.predict(X_valid)
acc_ETC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_ETC
LG = LogisticRegression(solver='lbfgs', multi_class='multinomial')
LG.fit(X_train, y_train)
y_pred = LG.predict(X_valid)
acc_LG = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LG | code |
32068084/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
train_df_final.shape | code |
104131375/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.isnull().sum()
data2.drop(data2.iloc[:, 4:11], axis=1, inplace=True)
data2.drop(['ID'], axis=1) | code |
104131375/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
data.head() | code |
104131375/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
import matplotlib.pyplot as plt
plt.plot(genre)
plt.xlabel('Anime')
plt.ylabel('Number')
plt.title('Top 10 Animes')
plt.xticks(rotation=70)
plt.grid(True)
plt.show() | code |
104131375/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.isnull().sum()
data2.drop(data2.iloc[:, 4:11], axis=1, inplace=True)
data2.head() | code |
104131375/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
data.info() | code |
104131375/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
data.Anime.value_counts().head(10) | code |
104131375/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape | code |
104131375/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.info() | code |
104131375/cell_17 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.isnull().sum() | code |
104131375/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.head() | code |
104131375/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
genre | code |
104131375/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape | code |
2022777/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
class_pivot = train.pivot_table(index='Pclass', values='Survived')
family_cols = ['SibSp', 'Parch', 'Survived']
family = train[family_cols].copy()
family['familysize'] = family[['SibSp', 'Parch']].sum(axis=1)
familySize = family[['SibSp', 'Parch']].sum(axis=1)
family['isalone'] = np.where(familySize >= 1, 1, 0)
family_pivot = family.pivot_table(index='familysize', values='Survived')
isalone_pivot = family.pivot_table(index='isalone', values='Survived')
train['Fare'] = train['Fare'].fillna(train['Fare'].mean())
train['Embarked'] = train['Embarked'].fillna('S')
holdout['Fare'] = holdout['Fare'].fillna(train['Fare'].mean())
holdout['Embarked'] = holdout['Embarked'].fillna('S')
train['Age'] = train['Age'].fillna(-0.5)
holdout['Age'] = holdout['Age'].fillna(-0.5)
train.head(2)
holdout.head(2) | code |
2022777/cell_6 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
class_pivot = train.pivot_table(index='Pclass', values='Survived')
family_cols = ['SibSp', 'Parch', 'Survived']
family = train[family_cols].copy()
family['familysize'] = family[['SibSp', 'Parch']].sum(axis=1)
familySize = family[['SibSp', 'Parch']].sum(axis=1)
family['isalone'] = np.where(familySize >= 1, 1, 0)
family_pivot = family.pivot_table(index='familysize', values='Survived')
isalone_pivot = family.pivot_table(index='isalone', values='Survived')
train['Fare'] = train['Fare'].fillna(train['Fare'].mean())
train['Embarked'] = train['Embarked'].fillna('S')
holdout['Fare'] = holdout['Fare'].fillna(train['Fare'].mean())
holdout['Embarked'] = holdout['Embarked'].fillna('S')
train['Age'] = train['Age'].fillna(-0.5)
holdout['Age'] = holdout['Age'].fillna(-0.5)
cuts = [-1, 0, 5, 12, 18, 35, 60, 100]
labels = ['Missing', 'Infant', 'Child', 'Teenager', 'Young Adult', 'Adult', 'Senior']
train['Age_categories'] = pd.cut(train['Age'], cuts, labels=labels)
holdout['Age_categories'] = pd.cut(holdout['Age'], cuts, labels=labels)
fare_cuts = [-1, 12, 50, 100, 1000]
fare_labels = ['0-12', '12-50', '50-100', '100+']
train['Fare_categories'] = pd.cut(train['Fare'], fare_cuts, labels=fare_labels)
holdout['Fare_categories'] = pd.cut(holdout['Fare'], fare_cuts, labels=fare_labels)
train['Cabin_type'] = train['Cabin'].str[0]
train['Cabin_type'] = train['Cabin_type'].fillna('Unknown')
train = train.drop('Cabin', axis=1)
holdout['Cabin_type'] = holdout['Cabin'].str[0]
holdout['Cabin_type'] = holdout['Cabin_type'].fillna('Unknown')
holdout = holdout.drop('Cabin', axis=1)
titles = {'Mr': 'Mr', 'Mme': 'Mrs', 'Ms': 'Mrs', 'Mrs': 'Mrs', 'Master': 'Master', 'Mlle': 'Miss', 'Miss': 'Miss', 'Capt': 'Officer', 'Col': 'Officer', 'Major': 'Officer', 'Dr': 'Officer', 'Rev': 'Officer', 'Jonkheer': 'Royalty', 'Don': 'Royalty', 'Sir': 'Royalty', 'Countess': 'Royalty', 'Dona': 'Royalty', 'Lady': 'Royalty'}
train_titles = train['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)
train['Title'] = train_titles.map(titles)
holdout_titles = holdout['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)
holdout['Title'] = holdout_titles.map(titles)
familySize_train = train[['SibSp', 'Parch']].sum(axis=1)
train['isalone'] = np.where(familySize_train >= 1, 1, 0)
familySize_holdout = holdout[['SibSp', 'Parch']].sum(axis=1)
holdout['isalone'] = np.where(familySize_holdout >= 1, 1, 0)
def get_dummies(df, column_name):
dummies = pd.get_dummies(df[column_name], prefix=column_name)
df = pd.concat([df, dummies], axis=1)
return df
columnNames = ['Age_categories', 'Pclass', 'Sex', 'Fare_categories', 'Title', 'Cabin_type', 'Embarked']
for column in columnNames:
dummies_train = pd.get_dummies(train[column], prefix=column)
train = pd.concat([train, dummies_train], axis=1)
dummies_holdout = pd.get_dummies(holdout[column], prefix=column)
holdout = pd.concat([holdout, dummies_holdout], axis=1)
columns = ['Age_categories_Missing', 'Age_categories_Infant', 'Age_categories_Child', 'Age_categories_Teenager', 'Age_categories_Young Adult', 'Age_categories_Adult', 'Age_categories_Senior', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Fare_categories_0-12', 'Fare_categories_12-50', 'Fare_categories_50-100', 'Fare_categories_100+', 'Title_Master', 'Title_Miss', 'Title_Mr', 'Title_Mrs', 'Title_Officer', 'Title_Royalty', 'Cabin_type_A', 'Cabin_type_B', 'Cabin_type_C', 'Cabin_type_D', 'Cabin_type_E', 'Cabin_type_F', 'Cabin_type_G', 'Cabin_type_Unknown', 'isalone']
def get_model(df, features):
train_X = df[features]
train_y = df['Survived']
cv = ShuffleSplit(n_splits=10, test_size=0.3, train_size=0.6, random_state=0)
model_params = [{'name': 'RandomForestClassifier', 'estimator': RandomForestClassifier(random_state=0), 'hyperparameters': {'n_estimators': [20, 25, 35, 40, 45, 50, 55, 60, 65, 70, 75], 'criterion': ['entropy', 'gini'], 'max_features': ['log2', 'sqrt'], 'min_samples_leaf': [1, 5, 8], 'min_samples_split': [2, 3, 5]}}, {'name': 'DecisionTreeClassifier', 'estimator': tree.DecisionTreeClassifier(), 'hyperparameters': {'criterion': ['entropy', 'gini'], 'max_depth': [None, 2, 4, 6, 8, 10, 12, 14, 16], 'min_samples_split': [2, 3, 4, 5, 10, 0.03, 0.05, 0.1], 'max_features': [None, 'auto'], 'min_samples_leaf': [1, 2, 3, 4, 5, 10, 12, 0.5, 0.03, 0.05, 0.1]}}, {'name': 'KernelSVMClassifier', 'estimator': SVC(random_state=0), 'hyperparameters': {'kernel': ['rbf'], 'C': np.logspace(-9, 3, 13), 'gamma': np.logspace(-9, 3, 13)}}, {'name': 'KNeighborsClassifier', 'estimator': KNeighborsClassifier(), 'hyperparameters': {'n_neighbors': range(1, 20, 2), 'weights': ['distance', 'uniform'], 'algorithm': ['ball_tree', 'kd_tree', 'brute'], 'p': [1, 2]}}, {'name': 'LogisticRegressionClassifier', 'estimator': LogisticRegression(), 'hyperparameters': {'solver': ['newton-cg', 'lbfgs', 'liblinear']}}]
models = []
for model in model_params:
print(model['name'])
grid = GridSearchCV(model['estimator'], param_grid=model['hyperparameters'], cv=10)
grid.fit(train_X, train_y)
model_att = {'model': grid.best_estimator_, 'best_params': grid.best_params_, 'best_score': grid.best_score_, 'grid': grid}
models.append(model_att)
print('Evaluated model and its params: ')
print(grid.best_params_)
print(grid.best_score_)
return models
def ann_model(df, features):
classifier = Sequential()
classifier.add(Dense(input_dim=len(features), units=15, activation='relu', kernel_initializer='uniform'))
classifier.add(Dense(units=15, kernel_initializer='uniform', activation='relu'))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
train_X = df[features]
train_y = df['Survived']
classifier.fit(np.array(train_X), np.array(train_y), batch_size=10, epochs=100)
return classifier
def get_features(df, columns, model=None):
newDf = df.copy()
newDf = newDf.select_dtypes(['number'])
newDf = newDf.dropna(axis=1, how='any')
all_X = newDf[columns]
all_y = df['Survived']
cv = StratifiedShuffleSplit(n_splits=10, test_size=0.3, train_size=0.6, random_state=0)
if model == None:
classifier = tree.DecisionTreeClassifier(criterion='entropy', max_depth=10, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_samples_leaf=10, min_samples_split=3)
else:
classifier = model
selector = RFECV(classifier, scoring='roc_auc', cv=cv, step=1)
selector.fit(all_X, all_y)
rfecv_columns = all_X.columns[selector.support_]
return rfecv_columns
models = get_model(train, columns)
best_grid = models[0]['grid']
best_classifier = models[0]['model']
best_params = models[0]['best_params']
rfecv_features = get_features(train, columns, best_classifier)
print(len(rfecv_features))
print(rfecv_features)
models = get_model(train, rfecv_features)
best_classifier = models[0]['model']
predictions = best_classifier.predict(holdout[rfecv_features])
sub = {'PassengerId': holdout['PassengerId'], 'Survived': predictions}
submission = pd.DataFrame(sub)
submission.to_csv(path_or_buf='Submission.csv', index=False, header=True) | code |
2022777/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
train.describe()
holdout.describe() | code |
2022777/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.feature_selection
from sklearn.feature_selection import RFECV
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import minmax_scale
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.model_selection import ShuffleSplit
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn import tree
from subprocess import check_output
print(check_output(['ls', '.']).decode('utf8')) | code |
2022777/cell_3 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
gender_pivot.plot.bar()
plt.show()
class_pivot = train.pivot_table(index='Pclass', values='Survived')
class_pivot.plot.bar()
plt.show()
family_cols = ['SibSp', 'Parch', 'Survived']
family = train[family_cols].copy()
family['familysize'] = family[['SibSp', 'Parch']].sum(axis=1)
familySize = family[['SibSp', 'Parch']].sum(axis=1)
family['isalone'] = np.where(familySize >= 1, 1, 0)
family_pivot = family.pivot_table(index='familysize', values='Survived')
isalone_pivot = family.pivot_table(index='isalone', values='Survived')
isalone_pivot.plot.bar(ylim=(0, 1), yticks=np.arange(0, 1, 0.1))
family_pivot.plot.bar(ylim=(0, 1), yticks=np.arange(0, 1, 0.1))
plt.show() | code |
2022777/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
class_pivot = train.pivot_table(index='Pclass', values='Survived')
family_cols = ['SibSp', 'Parch', 'Survived']
family = train[family_cols].copy()
family['familysize'] = family[['SibSp', 'Parch']].sum(axis=1)
familySize = family[['SibSp', 'Parch']].sum(axis=1)
family['isalone'] = np.where(familySize >= 1, 1, 0)
family_pivot = family.pivot_table(index='familysize', values='Survived')
isalone_pivot = family.pivot_table(index='isalone', values='Survived')
train['Fare'] = train['Fare'].fillna(train['Fare'].mean())
train['Embarked'] = train['Embarked'].fillna('S')
holdout['Fare'] = holdout['Fare'].fillna(train['Fare'].mean())
holdout['Embarked'] = holdout['Embarked'].fillna('S')
train['Age'] = train['Age'].fillna(-0.5)
holdout['Age'] = holdout['Age'].fillna(-0.5)
cuts = [-1, 0, 5, 12, 18, 35, 60, 100]
labels = ['Missing', 'Infant', 'Child', 'Teenager', 'Young Adult', 'Adult', 'Senior']
train['Age_categories'] = pd.cut(train['Age'], cuts, labels=labels)
holdout['Age_categories'] = pd.cut(holdout['Age'], cuts, labels=labels)
fare_cuts = [-1, 12, 50, 100, 1000]
fare_labels = ['0-12', '12-50', '50-100', '100+']
train['Fare_categories'] = pd.cut(train['Fare'], fare_cuts, labels=fare_labels)
holdout['Fare_categories'] = pd.cut(holdout['Fare'], fare_cuts, labels=fare_labels)
train['Cabin_type'] = train['Cabin'].str[0]
train['Cabin_type'] = train['Cabin_type'].fillna('Unknown')
train = train.drop('Cabin', axis=1)
holdout['Cabin_type'] = holdout['Cabin'].str[0]
holdout['Cabin_type'] = holdout['Cabin_type'].fillna('Unknown')
holdout = holdout.drop('Cabin', axis=1)
titles = {'Mr': 'Mr', 'Mme': 'Mrs', 'Ms': 'Mrs', 'Mrs': 'Mrs', 'Master': 'Master', 'Mlle': 'Miss', 'Miss': 'Miss', 'Capt': 'Officer', 'Col': 'Officer', 'Major': 'Officer', 'Dr': 'Officer', 'Rev': 'Officer', 'Jonkheer': 'Royalty', 'Don': 'Royalty', 'Sir': 'Royalty', 'Countess': 'Royalty', 'Dona': 'Royalty', 'Lady': 'Royalty'}
train_titles = train['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)
train['Title'] = train_titles.map(titles)
holdout_titles = holdout['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)
holdout['Title'] = holdout_titles.map(titles)
familySize_train = train[['SibSp', 'Parch']].sum(axis=1)
train['isalone'] = np.where(familySize_train >= 1, 1, 0)
familySize_holdout = holdout[['SibSp', 'Parch']].sum(axis=1)
holdout['isalone'] = np.where(familySize_holdout >= 1, 1, 0)
def get_dummies(df, column_name):
dummies = pd.get_dummies(df[column_name], prefix=column_name)
df = pd.concat([df, dummies], axis=1)
return df
columnNames = ['Age_categories', 'Pclass', 'Sex', 'Fare_categories', 'Title', 'Cabin_type', 'Embarked']
for column in columnNames:
dummies_train = pd.get_dummies(train[column], prefix=column)
train = pd.concat([train, dummies_train], axis=1)
dummies_holdout = pd.get_dummies(holdout[column], prefix=column)
holdout = pd.concat([holdout, dummies_holdout], axis=1)
train.head(5)
holdout.head(5)
print(holdout.columns) | code |
48166874/cell_7 | [
"text_plain_output_1.png"
] | from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL')
model = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL') | code |
48166874/cell_14 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png"
] | from datasets import load_dataset
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL')
model = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL')
def get_sql(query):
input_text = 'translate English to SQL: %s </s>' % query
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
valid_dataset = load_dataset('wikisql', split='validation')
for idx in random.sample(range(len(valid_dataset)), 200):
print(f"Text: {valid_dataset[idx]['question']}")
print(f"Pred SQL: {get_sql(valid_dataset[idx]['question'])}")
print(f"True SQL: {valid_dataset[idx]['sql']['human_readable']}\n") | code |
48166874/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset
valid_dataset = load_dataset('wikisql', split='validation') | code |
48166874/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoModelWithLMHead, AutoTokenizer
from datasets import load_dataset
import random, warnings
warnings.filterwarnings('ignore') | code |
2007202/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train.groupby('Initial')['Age'].mean() | code |
2007202/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
sns.barplot(x='Embarked', y='Survived', hue='Sex', data=train) | code |
2007202/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['Pclass', 'Survived']].groupby(['Pclass']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
2007202/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train.groupby('Initial')['Age'].mean()
train.loc[train.Age.isnull() & (train.Initial == 'Mr'), 'Age'] = 33
train.loc[train.Age.isnull() & (train.Initial == 'Mrs'), 'Age'] = 36
train.loc[train.Age.isnull() & (train.Initial == 'Master'), 'Age'] = 5
train.loc[train.Age.isnull() & (train.Initial == 'Miss'), 'Age'] = 22
train.loc[train.Age.isnull() & (train.Initial == 'Other'), 'Age'] = 46
test.loc[test.Age.isnull() & (test.Initial == 'Mr'), 'Age'] = 33
test.loc[test.Age.isnull() & (test.Initial == 'Mrs'), 'Age'] = 36
test.loc[test.Age.isnull() & (test.Initial == 'Master'), 'Age'] = 5
test.loc[test.Age.isnull() & (test.Initial == 'Miss'), 'Age'] = 22
test.loc[test.Age.isnull() & (test.Initial == 'Other'), 'Age'] = 46
sns.distplot(train['Fare'], bins=50) | code |
2007202/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
print(categorical)
train[categorical].describe() | code |
2007202/cell_29 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train.groupby('Initial')['Age'].mean()
train.loc[train.Age.isnull() & (train.Initial == 'Mr'), 'Age'] = 33
train.loc[train.Age.isnull() & (train.Initial == 'Mrs'), 'Age'] = 36
train.loc[train.Age.isnull() & (train.Initial == 'Master'), 'Age'] = 5
train.loc[train.Age.isnull() & (train.Initial == 'Miss'), 'Age'] = 22
train.loc[train.Age.isnull() & (train.Initial == 'Other'), 'Age'] = 46
test.loc[test.Age.isnull() & (test.Initial == 'Mr'), 'Age'] = 33
test.loc[test.Age.isnull() & (test.Initial == 'Mrs'), 'Age'] = 36
test.loc[test.Age.isnull() & (test.Initial == 'Master'), 'Age'] = 5
test.loc[test.Age.isnull() & (test.Initial == 'Miss'), 'Age'] = 22
test.loc[test.Age.isnull() & (test.Initial == 'Other'), 'Age'] = 46
train['Fare'].sort_values().unique() | code |
2007202/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train.groupby('Initial')['Age'].mean()
train.loc[train.Age.isnull() & (train.Initial == 'Mr'), 'Age'] = 33
train.loc[train.Age.isnull() & (train.Initial == 'Mrs'), 'Age'] = 36
train.loc[train.Age.isnull() & (train.Initial == 'Master'), 'Age'] = 5
train.loc[train.Age.isnull() & (train.Initial == 'Miss'), 'Age'] = 22
train.loc[train.Age.isnull() & (train.Initial == 'Other'), 'Age'] = 46
test.loc[test.Age.isnull() & (test.Initial == 'Mr'), 'Age'] = 33
test.loc[test.Age.isnull() & (test.Initial == 'Mrs'), 'Age'] = 36
test.loc[test.Age.isnull() & (test.Initial == 'Master'), 'Age'] = 5
test.loc[test.Age.isnull() & (test.Initial == 'Miss'), 'Age'] = 22
test.loc[test.Age.isnull() & (test.Initial == 'Other'), 'Age'] = 46
train = train.drop(['Ticket', 'Name'], axis=1)
test = test.drop(['Ticket', 'Name'], axis=1)
train.head() | code |
2007202/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train.groupby('Initial')['Age'].mean()
train.loc[train.Age.isnull() & (train.Initial == 'Mr'), 'Age'] = 33
train.loc[train.Age.isnull() & (train.Initial == 'Mrs'), 'Age'] = 36
train.loc[train.Age.isnull() & (train.Initial == 'Master'), 'Age'] = 5
train.loc[train.Age.isnull() & (train.Initial == 'Miss'), 'Age'] = 22
train.loc[train.Age.isnull() & (train.Initial == 'Other'), 'Age'] = 46
test.loc[test.Age.isnull() & (test.Initial == 'Mr'), 'Age'] = 33
test.loc[test.Age.isnull() & (test.Initial == 'Mrs'), 'Age'] = 36
test.loc[test.Age.isnull() & (test.Initial == 'Master'), 'Age'] = 5
test.loc[test.Age.isnull() & (test.Initial == 'Miss'), 'Age'] = 22
test.loc[test.Age.isnull() & (test.Initial == 'Other'), 'Age'] = 46
train = train.drop(['Ticket', 'Name'], axis=1)
test = test.drop(['Ticket', 'Name'], axis=1)
sns.barplot(x='Age', y='Survived', hue='Sex', data=train) | code |
2007202/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['Parch', 'Survived']].groupby(['Parch']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train['Initial'].replace(['Mlle', 'Mme', 'Ms', 'Dr', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Miss', 'Miss', 'Miss', 'Mr', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
test['Initial'].replace(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme'], ['Mr', 'Mrs', 'Miss', 'Master', 'Mr', 'Other', 'Mr', 'Mrs'], inplace=True)
print(train['Initial'].unique())
print(test['Initial'].unique()) | code |
2007202/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum() | code |
2007202/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
print(train['Initial'].unique())
print(test['Initial'].unique()) | code |
2007202/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2007202/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.') | code |
2007202/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
sns.barplot(x='Pclass', y='Survived', hue='Sex', data=train) | code |
2007202/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['Sex', 'Survived']].groupby(['Sex']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['SibSp', 'Survived']].groupby(['SibSp']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.info() | code |
2007202/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test:
test['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
train.groupby('Initial')['Age'].mean()
train.loc[train.Age.isnull() & (train.Initial == 'Mr'), 'Age'] = 33
train.loc[train.Age.isnull() & (train.Initial == 'Mrs'), 'Age'] = 36
train.loc[train.Age.isnull() & (train.Initial == 'Master'), 'Age'] = 5
train.loc[train.Age.isnull() & (train.Initial == 'Miss'), 'Age'] = 22
train.loc[train.Age.isnull() & (train.Initial == 'Other'), 'Age'] = 46
test.loc[test.Age.isnull() & (test.Initial == 'Mr'), 'Age'] = 33
test.loc[test.Age.isnull() & (test.Initial == 'Mrs'), 'Age'] = 36
test.loc[test.Age.isnull() & (test.Initial == 'Master'), 'Age'] = 5
test.loc[test.Age.isnull() & (test.Initial == 'Miss'), 'Age'] = 22
test.loc[test.Age.isnull() & (test.Initial == 'Other'), 'Age'] = 46
bins = (0, 5, 12, 18, 25, 35, 60, 120)
group_names = ['Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
categories = pd.cut(train['Age'], bins, labels=group_names)
train['Age'] = categories
bins = (0, 5, 12, 18, 25, 35, 60, 120)
group_names = ['Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
categories = pd.cut(test['Age'], bins, labels=group_names)
test['Age'] = categories
group_names = ['1Q', '2Q', '3Q', '4Q']
quartiles = pd.qcut(train['Fare'], 4, labels=group_names)
train['Fare'] = quartiles
group_names = ['1Q', '2Q', '3Q', '4Q']
quartiles_test = pd.qcut(test['Fare'], 4, labels=group_names)
test['Fare'] = quartiles
train = train.drop(['Ticket', 'Name'], axis=1)
test = test.drop(['Ticket', 'Name'], axis=1)
pd.crosstab(index=train['Embarked'], columns='count') | code |
32063079/cell_8 | [
"image_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/')
master_df = pd.read_csv(path / 'covid_19_data.csv')
recovered_df = pd.read_csv(path / 'time_series_covid_19_recovered.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
deaths_df = pd.read_csv(path / 'time_series_covid_19_deaths.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
confirmed_df = pd.read_csv(path / 'time_series_covid_19_confirmed.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
sorted_country_list = confirmed_df.sort_values(by=confirmed_df.columns[-1], ascending=False).index.to_list()
@interact(country=sorted_country_list, threshold=(0, 1000, 10))
def log_lin_visualise(country, threshold=100):
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
lr = LinearRegression(normalize=True)
lr.fit(X=x.reshape(-1, 1), y=y)
y_fitted = lr.predict(X=x.reshape(-1, 1))
print(f'r2_score = {round(r2_score(y, y_fitted), 2)}')
print(f'mean_squared_error = {round(mean_squared_error(y, y_fitted), 2)}')
plt.figure(figsize=(10, 5))
plt.plot(x, y, label='Actual')
plt.plot(x, y_fitted, label='Linear Regression')
plt.xlabel(f'Days Since {threshold}th Case')
plt.ylabel('Natural Logarithm of Confirmed Cases')
plt.legend()
plt.title(country)
plt.show()
plt.close() | code |
32063079/cell_15 | [
"text_plain_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/')
master_df = pd.read_csv(path / 'covid_19_data.csv')
recovered_df = pd.read_csv(path / 'time_series_covid_19_recovered.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
deaths_df = pd.read_csv(path / 'time_series_covid_19_deaths.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
confirmed_df = pd.read_csv(path / 'time_series_covid_19_confirmed.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
sorted_country_list = confirmed_df.sort_values(by=confirmed_df.columns[-1], ascending=False).index.to_list()
@interact(country=sorted_country_list, threshold=(0, 1000, 10))
def log_lin_visualise(country, threshold=100):
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
lr = LinearRegression(normalize=True)
lr.fit(X=x.reshape(-1, 1), y=y)
y_fitted = lr.predict(X=x.reshape(-1, 1))
plt.close()
country = 'US'
threshold = 100
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
with pm.Model() as unpooled_model:
α = pm.Normal(name='α', mu=int(np.log(threshold)), sd=10)
β = pm.Normal(name='β')
σ = pm.HalfNormal(name='σ', sd=10)
μ = pm.Deterministic(name='μ', var=α + β * x)
pm.Normal(name=country, mu=μ, sd=σ, observed=y)
pm.model_to_graphviz(unpooled_model)
with unpooled_model:
prior = pm.sample_prior_predictive()
trace = pm.sample()
pred = pm.sample_posterior_predictive(trace)
unpooled = az.from_pymc3(trace=trace, prior=prior, posterior_predictive=pred)
prior_vars = ['α', 'β', 'σ']
unpooled
az.summary(unpooled)
az.plot_trace(data=unpooled, var_names=prior_vars) | code |
32063079/cell_16 | [
"text_html_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/')
master_df = pd.read_csv(path / 'covid_19_data.csv')
recovered_df = pd.read_csv(path / 'time_series_covid_19_recovered.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
deaths_df = pd.read_csv(path / 'time_series_covid_19_deaths.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
confirmed_df = pd.read_csv(path / 'time_series_covid_19_confirmed.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
sorted_country_list = confirmed_df.sort_values(by=confirmed_df.columns[-1], ascending=False).index.to_list()
@interact(country=sorted_country_list, threshold=(0, 1000, 10))
def log_lin_visualise(country, threshold=100):
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
lr = LinearRegression(normalize=True)
lr.fit(X=x.reshape(-1, 1), y=y)
y_fitted = lr.predict(X=x.reshape(-1, 1))
plt.close()
country = 'US'
threshold = 100
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
with pm.Model() as unpooled_model:
α = pm.Normal(name='α', mu=int(np.log(threshold)), sd=10)
β = pm.Normal(name='β')
σ = pm.HalfNormal(name='σ', sd=10)
μ = pm.Deterministic(name='μ', var=α + β * x)
pm.Normal(name=country, mu=μ, sd=σ, observed=y)
pm.model_to_graphviz(unpooled_model)
with unpooled_model:
prior = pm.sample_prior_predictive()
trace = pm.sample()
pred = pm.sample_posterior_predictive(trace)
unpooled = az.from_pymc3(trace=trace, prior=prior, posterior_predictive=pred)
prior_vars = ['α', 'β', 'σ']
unpooled
az.summary(unpooled)
az.plot_posterior(data=unpooled, var_names=prior_vars, group='posterior') | code |
32063079/cell_3 | [
"image_output_1.png"
] | from pathlib import Path
import cufflinks as cf
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/') | code |
32063079/cell_17 | [
"image_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/')
master_df = pd.read_csv(path / 'covid_19_data.csv')
recovered_df = pd.read_csv(path / 'time_series_covid_19_recovered.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
deaths_df = pd.read_csv(path / 'time_series_covid_19_deaths.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
confirmed_df = pd.read_csv(path / 'time_series_covid_19_confirmed.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
sorted_country_list = confirmed_df.sort_values(by=confirmed_df.columns[-1], ascending=False).index.to_list()
@interact(country=sorted_country_list, threshold=(0, 1000, 10))
def log_lin_visualise(country, threshold=100):
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
lr = LinearRegression(normalize=True)
lr.fit(X=x.reshape(-1, 1), y=y)
y_fitted = lr.predict(X=x.reshape(-1, 1))
plt.close()
country = 'US'
threshold = 100
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
with pm.Model() as unpooled_model:
α = pm.Normal(name='α', mu=int(np.log(threshold)), sd=10)
β = pm.Normal(name='β')
σ = pm.HalfNormal(name='σ', sd=10)
μ = pm.Deterministic(name='μ', var=α + β * x)
pm.Normal(name=country, mu=μ, sd=σ, observed=y)
pm.model_to_graphviz(unpooled_model)
with unpooled_model:
prior = pm.sample_prior_predictive()
trace = pm.sample()
pred = pm.sample_posterior_predictive(trace)
unpooled = az.from_pymc3(trace=trace, prior=prior, posterior_predictive=pred)
prior_vars = ['α', 'β', 'σ']
unpooled
az.summary(unpooled)
az.plot_posterior(data=unpooled, var_names=prior_vars, group='prior') | code |
32063079/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/')
master_df = pd.read_csv(path / 'covid_19_data.csv')
recovered_df = pd.read_csv(path / 'time_series_covid_19_recovered.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
deaths_df = pd.read_csv(path / 'time_series_covid_19_deaths.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
confirmed_df = pd.read_csv(path / 'time_series_covid_19_confirmed.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
sorted_country_list = confirmed_df.sort_values(by=confirmed_df.columns[-1], ascending=False).index.to_list()
@interact(country=sorted_country_list, threshold=(0, 1000, 10))
def log_lin_visualise(country, threshold=100):
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
lr = LinearRegression(normalize=True)
lr.fit(X=x.reshape(-1, 1), y=y)
y_fitted = lr.predict(X=x.reshape(-1, 1))
plt.close()
country = 'US'
threshold = 100
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
with pm.Model() as unpooled_model:
α = pm.Normal(name='α', mu=int(np.log(threshold)), sd=10)
β = pm.Normal(name='β')
σ = pm.HalfNormal(name='σ', sd=10)
μ = pm.Deterministic(name='μ', var=α + β * x)
pm.Normal(name=country, mu=μ, sd=σ, observed=y)
pm.model_to_graphviz(unpooled_model)
with unpooled_model:
prior = pm.sample_prior_predictive()
trace = pm.sample()
pred = pm.sample_posterior_predictive(trace)
unpooled = az.from_pymc3(trace=trace, prior=prior, posterior_predictive=pred)
prior_vars = ['α', 'β', 'σ']
unpooled
az.summary(unpooled) | code |
32063079/cell_12 | [
"text_plain_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/')
master_df = pd.read_csv(path / 'covid_19_data.csv')
recovered_df = pd.read_csv(path / 'time_series_covid_19_recovered.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
deaths_df = pd.read_csv(path / 'time_series_covid_19_deaths.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
confirmed_df = pd.read_csv(path / 'time_series_covid_19_confirmed.csv').drop(columns=['Lat', 'Long']).groupby('Country/Region').sum()
sorted_country_list = confirmed_df.sort_values(by=confirmed_df.columns[-1], ascending=False).index.to_list()
@interact(country=sorted_country_list, threshold=(0, 1000, 10))
def log_lin_visualise(country, threshold=100):
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
lr = LinearRegression(normalize=True)
lr.fit(X=x.reshape(-1, 1), y=y)
y_fitted = lr.predict(X=x.reshape(-1, 1))
plt.close()
country = 'US'
threshold = 100
y = confirmed_df.filter(items=[country], axis=0).values.squeeze(0)
y = np.log(y[y > threshold])
x = np.arange(1, y.shape[0] + 1)
with pm.Model() as unpooled_model:
α = pm.Normal(name='α', mu=int(np.log(threshold)), sd=10)
β = pm.Normal(name='β')
σ = pm.HalfNormal(name='σ', sd=10)
μ = pm.Deterministic(name='μ', var=α + β * x)
pm.Normal(name=country, mu=μ, sd=σ, observed=y)
pm.model_to_graphviz(unpooled_model)
with unpooled_model:
prior = pm.sample_prior_predictive()
trace = pm.sample()
pred = pm.sample_posterior_predictive(trace) | code |
1002861/cell_4 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
import tensorflow as tf
import numpy as np
video_lvl_record = '../input/video_level/train-1.tfrecord'
frame_lvl_record = '../input/frame_level/train-1.tfrecord'
vid_ids = []
labels = []
mean_rgb = []
mean_audio = []
for example in tf.python_io.tf_record_iterator(video_lvl_record):
tf_example = tf.train.Example.FromString(example)
vid_ids.append(tf_example.features.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8'))
labels.append(tf_example.features.feature['labels'].int64_list.value)
mean_rgb.append(tf_example.features.feature['mean_rgb'].float_list.value)
mean_audio.append(tf_example.features.feature['mean_audio'].float_list.value)
n = 20
from collections import Counter
label_mapping = pd.Series.from_csv('../input/label_names.csv', header=0)
label_dict = label_mapping.to_dict()
top_n = Counter([item for sublist in labels for item in sublist]).most_common(n)
top_n_labels = [int(i[0]) for i in top_n]
top_n_label_count = [int(i[1]) for i in top_n]
top_n_label_names = [label_dict[x] for x in top_n_labels]
top_labels = pd.DataFrame(data=top_n_labels, columns=['label_num'])
top_labels['count'] = top_n_label_count
top_labels['label_name'] = top_n_label_names
top_labels = top_labels.drop('label_num', axis=1)
import seaborn as sns
import matplotlib.pyplot as plt
ax = sns.barplot(x='label_name', y='count', data=top_labels)
ax.set(xlabel='Label Name', ylabel='Label Count')
ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)
_ = plt.show() | code |
1002861/cell_6 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import numpy as np
video_lvl_record = '../input/video_level/train-1.tfrecord'
frame_lvl_record = '../input/frame_level/train-1.tfrecord'
vid_ids = []
labels = []
mean_rgb = []
mean_audio = []
for example in tf.python_io.tf_record_iterator(video_lvl_record):
tf_example = tf.train.Example.FromString(example)
vid_ids.append(tf_example.features.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8'))
labels.append(tf_example.features.feature['labels'].int64_list.value)
mean_rgb.append(tf_example.features.feature['mean_rgb'].float_list.value)
mean_audio.append(tf_example.features.feature['mean_audio'].float_list.value)
test = labels[:5]
label_test = [item for sublist in test for item in sublist]
label_test | code |
1002861/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1002861/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import numpy as np
video_lvl_record = '../input/video_level/train-1.tfrecord'
frame_lvl_record = '../input/frame_level/train-1.tfrecord'
vid_ids = []
labels = []
mean_rgb = []
mean_audio = []
for example in tf.python_io.tf_record_iterator(video_lvl_record):
tf_example = tf.train.Example.FromString(example)
vid_ids.append(tf_example.features.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8'))
labels.append(tf_example.features.feature['labels'].int64_list.value)
mean_rgb.append(tf_example.features.feature['mean_rgb'].float_list.value)
mean_audio.append(tf_example.features.feature['mean_audio'].float_list.value) | code |
17131741/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Price'] = df.Price.str.replace('$', '').replace(' ', '')
df['Price'] = df.Price.astype(np.float64)
df = df.set_index(df['Date'])
df = df.drop('Date', 1)
df['Revenue'] = df.Price * df.Sales
df = df[['Revenue', 'Temperature', 'Rainfall', 'Flyers']]
df.head() | code |
128033347/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.reshape(-1, 1)
ytrain = train.y.values.reshape(-1, 1)
lm = LinearRegression()
lm.fit(xtrain, ytrain)
xtest = test.x.values.reshape(-1, 1)
ytest = test.y.values.reshape(-1, 1)
prediction = lm.predict(xtest)
from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(ytest, prediction))
print('MSE:', metrics.mean_squared_error(ytest, prediction))
print('RMSE:', np.sqrt(metrics.mean_squared_error(ytest, prediction))) | code |
128033347/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
train.info() | code |
128033347/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
sns.scatterplot(train, x='x', y='y') | code |
128033347/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.reshape(-1, 1)
ytrain = train.y.values.reshape(-1, 1)
lm = LinearRegression()
lm.fit(xtrain, ytrain)
xtest = test.x.values.reshape(-1, 1)
ytest = test.y.values.reshape(-1, 1)
prediction = lm.predict(xtest)
sns.distplot(ytest - prediction, bins=50) | code |
128033347/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 |
128033347/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.info() | code |
128033347/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.reshape(-1, 1)
ytrain = train.y.values.reshape(-1, 1)
lm = LinearRegression()
lm.fit(xtrain, ytrain)
xtest = test.x.values.reshape(-1, 1)
ytest = test.y.values.reshape(-1, 1)
prediction = lm.predict(xtest)
plt.scatter(prediction, ytest)
plt.xlabel = 'predection'
plt.ylabel = 'y test' | code |
128033347/cell_15 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.reshape(-1, 1)
ytrain = train.y.values.reshape(-1, 1)
lm = LinearRegression()
lm.fit(xtrain, ytrain)
print(lm.coef_) | code |
128033347/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns | code |
128033347/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.reshape(-1, 1)
ytrain = train.y.values.reshape(-1, 1)
lm = LinearRegression()
lm.fit(xtrain, ytrain) | code |
128033347/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test.describe() | code |
73097245/cell_13 | [
"text_html_output_1.png"
] | from numpy.linalg import norm
from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
myData = np.array([0.0 for i in range(671 * 9066)])
mydf = pd.DataFrame(myData.reshape(671, -1))
mydf.columns = columns
mydf.index = rows
mydf
for i in range(100004):
mydf.loc[ratings.loc[i, 'userId'], ratings.loc[i, 'movieId']] = ratings.loc[i, 'rating']
mydf
user_list = list(mydf.index)
movie_list = list(mydf.columns)
from scipy.sparse import coo_matrix
R = coo_matrix(mydf.values)
M, N = R.shape
K = 3
P = np.random.rand(M, K)
Q = np.random.rand(K, N)
R.data
R.row
R.col
from numpy.linalg import norm
def error(R, P, Q, lamda=0.02):
ratings = R.data
rows = R.row
cols = R.col
e = 0
for ui in range(len(ratings)):
rui = ratings[ui]
u = rows[ui]
i = cols[ui]
if rui > 0:
e = e + pow(rui - np.dot(P[u, :], Q[:, i]), 2) + lamda * (pow(norm(P[u, :]), 2) + pow(norm(Q[:, i]), 2))
return e
error(R, P, Q) | code |
73097245/cell_9 | [
"text_plain_output_1.png"
] | from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
myData = np.array([0.0 for i in range(671 * 9066)])
mydf = pd.DataFrame(myData.reshape(671, -1))
mydf.columns = columns
mydf.index = rows
mydf
for i in range(100004):
mydf.loc[ratings.loc[i, 'userId'], ratings.loc[i, 'movieId']] = ratings.loc[i, 'rating']
mydf
user_list = list(mydf.index)
movie_list = list(mydf.columns)
from scipy.sparse import coo_matrix
R = coo_matrix(mydf.values)
print('R Shape::', R.shape)
print('R Columns::', R.col)
print('R Rows::', R.row) | code |
73097245/cell_6 | [
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
ratings | code |
73097245/cell_11 | [
"text_plain_output_1.png"
] | from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
myData = np.array([0.0 for i in range(671 * 9066)])
mydf = pd.DataFrame(myData.reshape(671, -1))
mydf.columns = columns
mydf.index = rows
mydf
for i in range(100004):
mydf.loc[ratings.loc[i, 'userId'], ratings.loc[i, 'movieId']] = ratings.loc[i, 'rating']
mydf
user_list = list(mydf.index)
movie_list = list(mydf.columns)
from scipy.sparse import coo_matrix
R = coo_matrix(mydf.values)
M, N = R.shape
K = 3
P = np.random.rand(M, K)
Q = np.random.rand(K, N)
R.data
R.row
R.col | code |
73097245/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 |
73097245/cell_7 | [
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
myData = np.array([0.0 for i in range(671 * 9066)])
mydf = pd.DataFrame(myData.reshape(671, -1))
mydf.columns = columns
mydf.index = rows
mydf
for i in range(100004):
mydf.loc[ratings.loc[i, 'userId'], ratings.loc[i, 'movieId']] = ratings.loc[i, 'rating']
mydf | code |
73097245/cell_3 | [
"text_html_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
print(len(rows))
print(len(columns))
columns = np.sort(columns) | code |
73097245/cell_14 | [
"text_html_output_1.png"
] | from numpy.linalg import norm
from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
myData = np.array([0.0 for i in range(671 * 9066)])
mydf = pd.DataFrame(myData.reshape(671, -1))
mydf.columns = columns
mydf.index = rows
mydf
for i in range(100004):
mydf.loc[ratings.loc[i, 'userId'], ratings.loc[i, 'movieId']] = ratings.loc[i, 'rating']
mydf
user_list = list(mydf.index)
movie_list = list(mydf.columns)
from scipy.sparse import coo_matrix
R = coo_matrix(mydf.values)
M, N = R.shape
K = 3
P = np.random.rand(M, K)
Q = np.random.rand(K, N)
R.data
R.row
R.col
from numpy.linalg import norm
def error(R, P, Q, lamda=0.02):
ratings = R.data
rows = R.row
cols = R.col
e = 0
for ui in range(len(ratings)):
rui = ratings[ui]
u = rows[ui]
i = cols[ui]
if rui > 0:
e = e + pow(rui - np.dot(P[u, :], Q[:, i]), 2) + lamda * (pow(norm(P[u, :]), 2) + pow(norm(Q[:, i]), 2))
return e
rmse = np.sqrt(error(R, P, Q) / len(R.data))
rmse | code |
73097245/cell_5 | [
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns = np.sort(columns)
myData = np.array([0.0 for i in range(671 * 9066)])
mydf = pd.DataFrame(myData.reshape(671, -1))
mydf.columns = columns
mydf.index = rows
mydf | code |
17118943/cell_23 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import pandas as pd
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
dfBoston = pd.read_csv('../input/boston_mm_tab.csv', header=None)
dfBoston
X = dfBoston[np.arange(0, 13)]
y = dfBoston[13]
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.RandomUniform(seed=1)
simple_sgd = K.optimizers.SGD(lr=0.01)
model = K.models.Sequential()
model.add(K.layers.Dense(units=10, input_dim=13, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=10, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=1, kernel_initializer=init, activation=None))
model.compile(loss='mean_squared_error', optimizer=simple_sgd, metrics=['mse'])
batch_size = 8
max_epochs = 500
h = model.fit(X_train, y_train, batch_size=batch_size, epochs=max_epochs, shuffle=True, verbose=1)
y_pred = model.predict(X_train)
y_d = np.array(y_train).reshape(-1, 1)
results = abs(y_pred - y_d) < np.abs(0.15 * y_d)
results
acc = np.sum(results) / len(results)
y_pred = model.predict(X_test)
y_d = np.array(y_test).reshape(-1, 1)
results = abs(y_pred - y_d) < np.abs(0.15 * y_d)
results
acc = np.sum(results) / len(results)
eval = model.evaluate(X_train, y_train, verbose=0)
eval = model.evaluate(X_test, y_test, verbose=0)
np.set_printoptions(precision=4)
unknown = np.full(shape=(1, 13), fill_value=0.6, dtype=np.float32)
unknown[0][3] = -1.0
predicted = model.predict(unknown)
print('Usando o modelo para previsão de preço médio de casa para as caracteristicas: ')
print(unknown)
print('\nO preço médio será [dolares]: ')
print(predicted * 10000) | code |
17118943/cell_19 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import pandas as pd
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
dfBoston = pd.read_csv('../input/boston_mm_tab.csv', header=None)
dfBoston
X = dfBoston[np.arange(0, 13)]
y = dfBoston[13]
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.RandomUniform(seed=1)
simple_sgd = K.optimizers.SGD(lr=0.01)
model = K.models.Sequential()
model.add(K.layers.Dense(units=10, input_dim=13, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=10, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=1, kernel_initializer=init, activation=None))
model.compile(loss='mean_squared_error', optimizer=simple_sgd, metrics=['mse'])
batch_size = 8
max_epochs = 500
h = model.fit(X_train, y_train, batch_size=batch_size, epochs=max_epochs, shuffle=True, verbose=1)
y_pred = model.predict(X_train)
y_d = np.array(y_train).reshape(-1, 1)
results = abs(y_pred - y_d) < np.abs(0.15 * y_d)
results
acc = np.sum(results) / len(results)
y_pred = model.predict(X_test)
y_d = np.array(y_test).reshape(-1, 1)
results = abs(y_pred - y_d) < np.abs(0.15 * y_d)
results
acc = np.sum(results) / len(results)
eval = model.evaluate(X_train, y_train, verbose=0)
print('Erro médio do conjunto de treinamento {0:.4f}'.format(eval[0]))
eval = model.evaluate(X_test, y_test, verbose=0)
print('Erro médio do conjunto de teste {0:.4f}'.format(eval[0])) | code |
17118943/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import keras as K
import numpy as np
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.RandomUniform(seed=1)
simple_sgd = K.optimizers.SGD(lr=0.01)
model = K.models.Sequential()
model.add(K.layers.Dense(units=10, input_dim=13, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=10, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=1, kernel_initializer=init, activation=None))
model.compile(loss='mean_squared_error', optimizer=simple_sgd, metrics=['mse'])
batch_size = 8
max_epochs = 500
print('Iniciando treinamento... ')
h = model.fit(X_train, y_train, batch_size=batch_size, epochs=max_epochs, shuffle=True, verbose=1)
print('Treinamento finalizado \n') | code |
17118943/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import keras as K
import tensorflow as tf
import pandas as pd
import seaborn as sns
import os
from matplotlib import pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | code |
17118943/cell_17 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import pandas as pd
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
dfBoston = pd.read_csv('../input/boston_mm_tab.csv', header=None)
dfBoston
X = dfBoston[np.arange(0, 13)]
y = dfBoston[13]
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.RandomUniform(seed=1)
simple_sgd = K.optimizers.SGD(lr=0.01)
model = K.models.Sequential()
model.add(K.layers.Dense(units=10, input_dim=13, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=10, kernel_initializer=init, activation='tanh'))
model.add(K.layers.Dense(units=1, kernel_initializer=init, activation=None))
model.compile(loss='mean_squared_error', optimizer=simple_sgd, metrics=['mse'])
batch_size = 8
max_epochs = 500
h = model.fit(X_train, y_train, batch_size=batch_size, epochs=max_epochs, shuffle=True, verbose=1)
y_pred = model.predict(X_train)
y_d = np.array(y_train).reshape(-1, 1)
results = abs(y_pred - y_d) < np.abs(0.15 * y_d)
results
acc = np.sum(results) / len(results)
print('Taxa de acerto do conjunto de treinamento (%): {0:.4f}'.format(acc * 100))
y_pred = model.predict(X_test)
y_d = np.array(y_test).reshape(-1, 1)
results = abs(y_pred - y_d) < np.abs(0.15 * y_d)
results
acc = np.sum(results) / len(results)
print('Taxa de acerto do conjunto de teste (%): {0:.4f}'.format(acc * 100)) | code |
72086533/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.columns if c not in ('id', 'loss', 'kfold')]
df_train[useful_features] | code |
72086533/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.columns if c not in ('id', 'loss', 'kfold')]
df_train[useful_features]
from xgboost import XGBRegressor
import numpy as np
final_preds = []
for fold in range(5):
xtrain = df_train[df_train.kfold != fold]
xvalid = df_train[df_train.kfold == fold]
ytrain = xtrain['loss']
xtrain = xtrain[useful_features]
yvalid = xvalid['loss']
xvalid = xvalid[useful_features]
model = XGBRegressor(n_estimators=500, random_state=fold)
model.fit(xtrain, ytrain, early_stopping_rounds=5, eval_set=[(xvalid, yvalid)], verbose=False)
preds_valid = model.predict(xvalid)
test = df_test[useful_features]
test_preds = model.predict(test)
final_preds.append(test_preds)
print(mean_squared_error(yvalid, preds_valid, squared=False)) | code |
72086533/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
sample_submission.head() | code |
2011179/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].str.contains(ind_animation)
animation0 = ~movies['genres'].str.contains(ind_animation)
children1 = movies['genres'].str.contains(ind_children)
children0 = ~movies['genres'].str.contains(ind_children)
both = movies[animation1 & children1]
just_anim = movies[animation1 & children0]
just_chil = movies[animation0 & children1]
just_anim_plt = just_anim[['rating', 'year']]
just_anim_plt = just_anim_plt.groupby(['year'], as_index=False).mean()
just_anim_plt.head(15) | code |
2011179/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.head() | code |
2011179/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].str.contains(ind_animation)
animation0 = ~movies['genres'].str.contains(ind_animation)
children1 = movies['genres'].str.contains(ind_children)
children0 = ~movies['genres'].str.contains(ind_children)
both = movies[animation1 & children1]
just_anim = movies[animation1 & children0]
just_chil = movies[animation0 & children1]
print('The dataset which includes both Animation and Children genres has {0} rows.'.format(len(both)))
print('The dataset which includes just Animation genre has {0} rows.'.format(len(just_anim)))
print('The dataset which includes just Children genre has {0} rows.'.format(len(just_chil))) | code |
2011179/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
tags.head() | code |
2011179/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].str.contains(ind_animation)
animation0 = ~movies['genres'].str.contains(ind_animation)
children1 = movies['genres'].str.contains(ind_children)
children0 = ~movies['genres'].str.contains(ind_children)
both = movies[animation1 & children1]
just_anim = movies[animation1 & children0]
just_chil = movies[animation0 & children1]
just_chil.head() | code |
2011179/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
ratings.head() | code |
2011179/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].str.contains(ind_animation)
animation0 = ~movies['genres'].str.contains(ind_animation)
children1 = movies['genres'].str.contains(ind_children)
children0 = ~movies['genres'].str.contains(ind_children)
both = movies[animation1 & children1]
just_anim = movies[animation1 & children0]
just_chil = movies[animation0 & children1]
just_anim.head() | code |
2011179/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any() | code |
2011179/cell_3 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2011179/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].str.contains(ind_animation)
animation0 = ~movies['genres'].str.contains(ind_animation)
children1 = movies['genres'].str.contains(ind_children)
children0 = ~movies['genres'].str.contains(ind_children)
both = movies[animation1 & children1]
just_anim = movies[animation1 & children0]
just_chil = movies[animation0 & children1]
both.head() | code |
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