path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
90129163/cell_32 | [
"text_plain_output_1.png"
] | trn_data = name_ext(trn_data)
tst_data = name_ext(tst_data) | code |
90129163/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def one_hot(df, one_hot_categ):
for col in one_hot_categ:
tmp = pd.get_dummies(df[col], prefix=col)
df = pd.concat([df, tmp], axis=1)
df = df.drop(columns=one_hot_categ)
return df
trn_data = one_hot(trn_data, categorical_features_onehot)
tst_data = one_hot(tst_data, categorical_features_onehot)
trn_data.info(verbose=True) | code |
90129163/cell_68 | [
"text_plain_output_1.png",
"image_output_1.png"
] | sub['Transported'] = preds
sub.to_csv('submission_simple_split_03112022.csv', index=False) | code |
90129163/cell_62 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
val_preds = cls.predict(X_valid[features])
val_preds = val_preds.astype('bool')
accuracy = accuracy_score(val_preds, y_valid) | code |
90129163/cell_59 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from xgboost import XGBClassifier
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier | code |
90129163/cell_58 | [
"text_plain_output_1.png"
] | X_train.shape | code |
90129163/cell_28 | [
"text_plain_output_1.png"
] | trn_data = fill_missing(trn_data)
tst_data = fill_missing(tst_data) | code |
90129163/cell_78 | [
"text_plain_output_1.png"
] | scores = []
y_probs = []
for fold, (trn_id, val_id) in enumerate(folds.split(trn_data[features], trn_data[target_feature])):
X_train, y_train = (trn_data[features].iloc[trn_id], trn_data[target_feature].iloc[trn_id])
X_valid, y_valid = (trn_data[features].iloc[val_id], trn_data[target_feature].iloc[val_id])
model = XGBClassifier(**optuna_params)
model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric=['logloss'], early_stopping_rounds=50, verbose=False)
valid_pred = model.predict(X_valid)
valid_score = accuracy_score(y_valid, valid_pred)
print('Fold:', fold, 'Accuracy:', valid_score)
scores.append(valid_score)
y_probs.append(model.predict_proba(tst_data[features])) | code |
90129163/cell_8 | [
"text_plain_output_1.png"
] | pd.options.display.float_format = '{:,.2f}'.format
pd.set_option('display.max_columns', NCOLS)
pd.set_option('display.max_rows', NROWS) | code |
90129163/cell_15 | [
"text_plain_output_1.png"
] | def describe_categ(df):
for col in df.columns:
unique_samples = list(df[col].unique())
unique_values = df[col].nunique()
print(f' {col}: {unique_values} Unique Values, Data Sample >> {unique_samples[:5]}')
print(' ...')
return None | code |
90129163/cell_16 | [
"text_plain_output_1.png"
] | describe_categ(trn_data) | code |
90129163/cell_38 | [
"text_plain_output_1.png"
] | def route(df):
"""
Calculate a combination of origin and destinations, creates a new feature for training.
Args:
Returns:
"""
df['Route'] = df['HomePlanet'] + df['Destination']
return df | code |
90129163/cell_75 | [
"text_plain_output_1.png"
] | import optuna
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler | code |
90129163/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
def encode_categorical(train_df, test_df, categ_feat=categorical_features):
"""
"""
encoder_dict = {}
concat_data = pd.concat([trn_data[categ_feat], tst_data[categ_feat]])
for col in concat_data.columns:
print('Encoding: ', col, '...')
encoder = LabelEncoder()
encoder.fit(concat_data[col])
encoder_dict[col] = encoder
train_df[col + '_Enc'] = encoder.transform(train_df[col])
test_df[col + '_Enc'] = encoder.transform(test_df[col])
train_df = train_df.drop(columns=categ_feat, axis=1)
test_df = test_df.drop(columns=categ_feat, axis=1)
return (train_df, test_df) | code |
90129163/cell_66 | [
"text_plain_output_1.png"
] | plt.figure(figsize=(10, 7))
feature_importance(cls) | code |
90129163/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | describe_categ(tst_data) | code |
90129163/cell_35 | [
"text_plain_output_1.png"
] | trn_data = trn_data.merge(trn_relatives, how='left', on=['FamilyName'])
tst_data = tst_data.merge(tst_relatives, how='left', on=['FamilyName']) | code |
90129163/cell_77 | [
"text_plain_output_1.png"
] | optuna_params = {'n_estimators': 474, 'max_depth': 12, 'learning_rate': 0.17092496820170439, 'subsample': 0.8681931753955343, 'colsample_bytree': 0.6753406152924646, 'reg_lambda': 8.439432864212677, 'reg_alpha': 1.6521594249189673, 'gamma': 9.986385923158347, 'min_child_weight': 11, 'random_state': 69, 'objective': 'binary:logistic', 'tree_method': 'gpu_hist'} | code |
90129163/cell_43 | [
"text_plain_output_1.png"
] | trn_data = extract_group(trn_data)
tst_data = extract_group(tst_data) | code |
90129163/cell_31 | [
"text_plain_output_1.png"
] | def name_ext(df):
"""
Split the Name of the passenger into First and Family...
"""
df['FirstName'] = df['Name'].str.split(' ', expand=True)[0]
df['FamilyName'] = df['Name'].str.split(' ', expand=True)[1]
df.drop(columns=['Name'], inplace=True)
return df | code |
90129163/cell_46 | [
"text_plain_output_1.png"
] | numerical_features = ['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Total_Billed']
categorical_features = ['FirstName', 'FamilyName', 'CabinNum', 'TravelGroup']
categorical_features_onehot = ['HomePlanet', 'CryoSleep', 'CabinDeck', 'CabinSide', 'Destination', 'VIP']
target_feature = 'Transported' | code |
90129163/cell_24 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | analyse_categ_target(trn_data) | code |
90129163/cell_14 | [
"text_plain_output_1.png"
] | trn_data.describe() | code |
90129163/cell_53 | [
"text_plain_output_1.png"
] | trn_data.columns | code |
90129163/cell_10 | [
"text_plain_output_1.png"
] | trn_data = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
tst_data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
sub = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv') | code |
90129163/cell_27 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | def fill_missing(df):
"""
Fill nan values or missing data with mean or most commond value...
"""
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
numeric_tmp = df.select_dtypes(include=numerics)
categ_tmp = df.select_dtypes(exclude=numerics)
for col in numeric_tmp.columns:
print(col)
df[col] = df[col].fillna(value=df[col].mean())
for col in categ_tmp.columns:
print(col)
df[col] = df[col].fillna(value=df[col].mode()[0])
print('...')
return df | code |
90129163/cell_37 | [
"text_plain_output_1.png"
] | trn_data = cabin_separation(trn_data)
tst_data = cabin_separation(tst_data) | code |
90129163/cell_12 | [
"text_plain_output_1.png"
] | trn_data.info() | code |
90129163/cell_71 | [
"text_plain_output_1.png"
] | X_train, X_valid, y_train, y_valid = train_test_split(trn_data[features], trn_data[target_feature])
def objective(trial):
n_estimators = trial.suggest_int('n_estimators', 8, 2048)
max_depth = trial.suggest_int('max_depth', 2, 16)
learning_rate = trial.suggest_float('learning_rate', 0.01, 0.2)
subsample = trial.suggest_float('subsample', 0.5, 1)
colsample_bytree = trial.suggest_float('colsample_bytree', 0.5, 1)
reg_lambda = trial.suggest_float('reg_lambda', 1, 20)
reg_alpha = trial.suggest_float('reg_alpha', 0, 20)
gamma = trial.suggest_float('gamma', 0, 20)
min_child_weight = trial.suggest_int('min_child_weight', 0, 128)
clf = XGBClassifier(n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, reg_lambda=reg_lambda, reg_alpha=reg_alpha, gamma=gamma, min_child_weight=min_child_weight, random_state=69, objective='binary:logistic', tree_method='gpu_hist')
clf.fit(X_train, y_train)
valid_pred = clf.predict(X_valid)
score = accuracy_score(y_valid, valid_pred)
return score | code |
90129163/cell_70 | [
"text_plain_output_1.png"
] | import optuna | code |
90129163/cell_36 | [
"text_plain_output_1.png"
] | def cabin_separation(df):
"""
Split the Cabin name into Deck, Number and Side
"""
df['CabinDeck'] = df['Cabin'].str.split('/', expand=True)[0]
df['CabinNum'] = df['Cabin'].str.split('/', expand=True)[1]
df['CabinSide'] = df['Cabin'].str.split('/', expand=True)[2]
df.drop(columns=['Cabin'], inplace=True)
return df | code |
18140916/cell_6 | [
"text_plain_output_1.png"
] | from preprocess import DataPreprocessModule
data_preprocess_module = DataPreprocessModule(train_path='../input/hdb-resale-price-prediction/train.csv', test_path='../input/hdb-resale-price-prediction/test.csv')
X_train, X_val, X_test, y_train, y_val = data_preprocess_module.get_preprocessed_data()
print('Shape of X_train:', X_train.shape)
print('Shape of X_val:', X_val.shape)
print('Shape of X_test:', X_test.shape)
print('Shape of y_train:', y_train.shape)
print('Shape of y_val:', y_val.shape) | code |
18140916/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from preprocess import DataPreprocessModule
from sklearn.linear_model import (LinearRegression, Lasso,
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import numpy as np
import time
data_preprocess_module = DataPreprocessModule(train_path='../input/hdb-resale-price-prediction/train.csv', test_path='../input/hdb-resale-price-prediction/test.csv')
X_train, X_val, X_test, y_train, y_val = data_preprocess_module.get_preprocessed_data()
metric = lambda y1_real, y2_real: np.sqrt(mean_squared_error(y1_real, y2_real))
y_trfm = lambda y: np.expm1(y)
def get_score(model, X, y):
preds = model.predict(X)
preds = y_trfm(preds)
y = y_trfm(y)
return metric(preds, y)
pipeline_reg = data_preprocess_module.build_pipeline(LinearRegression())
pipeline_reg.fit(X_train, y_train)
get_score(pipeline_reg, X_val, y_val)
pipeline_lasso = data_preprocess_module.build_pipeline(Lasso())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
lasso = GridSearchCV(pipeline_lasso, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
lasso.fit(X_train, y_train)
get_score(lasso, X_val, y_val)
pipeline_ridge = data_preprocess_module.build_pipeline(Ridge())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
ridge = GridSearchCV(pipeline_ridge, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
ridge.fit(X_train, y_train)
get_score(ridge, X_val, y_val)
pipeline_elast = data_preprocess_module.build_pipeline(ElasticNet())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001], 'model__l1_ratio': [0.25, 0.5, 0.75]}
elast = GridSearchCV(pipeline_elast, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
elast.fit(X_train, y_train)
print('Time taken for hyperparameter tuning: {:.2f} min'.format((time.time() - time_start) / 60))
get_score(elast, X_val, y_val) | code |
18140916/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from preprocess import DataPreprocessModule
from sklearn.linear_model import (LinearRegression, Lasso,
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import numpy as np
import time
data_preprocess_module = DataPreprocessModule(train_path='../input/hdb-resale-price-prediction/train.csv', test_path='../input/hdb-resale-price-prediction/test.csv')
X_train, X_val, X_test, y_train, y_val = data_preprocess_module.get_preprocessed_data()
metric = lambda y1_real, y2_real: np.sqrt(mean_squared_error(y1_real, y2_real))
y_trfm = lambda y: np.expm1(y)
def get_score(model, X, y):
preds = model.predict(X)
preds = y_trfm(preds)
y = y_trfm(y)
return metric(preds, y)
pipeline_reg = data_preprocess_module.build_pipeline(LinearRegression())
pipeline_reg.fit(X_train, y_train)
get_score(pipeline_reg, X_val, y_val)
pipeline_lasso = data_preprocess_module.build_pipeline(Lasso())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
lasso = GridSearchCV(pipeline_lasso, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
lasso.fit(X_train, y_train)
get_score(lasso, X_val, y_val)
pipeline_ridge = data_preprocess_module.build_pipeline(Ridge())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
ridge = GridSearchCV(pipeline_ridge, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
ridge.fit(X_train, y_train)
get_score(ridge, X_val, y_val)
pipeline_elast = data_preprocess_module.build_pipeline(ElasticNet())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001], 'model__l1_ratio': [0.25, 0.5, 0.75]}
elast = GridSearchCV(pipeline_elast, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
elast.fit(X_train, y_train)
get_score(elast, X_val, y_val)
elast.best_params_ | code |
18140916/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from preprocess import DataPreprocessModule
from sklearn.linear_model import (LinearRegression, Lasso,
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import numpy as np
import time
data_preprocess_module = DataPreprocessModule(train_path='../input/hdb-resale-price-prediction/train.csv', test_path='../input/hdb-resale-price-prediction/test.csv')
X_train, X_val, X_test, y_train, y_val = data_preprocess_module.get_preprocessed_data()
metric = lambda y1_real, y2_real: np.sqrt(mean_squared_error(y1_real, y2_real))
y_trfm = lambda y: np.expm1(y)
def get_score(model, X, y):
preds = model.predict(X)
preds = y_trfm(preds)
y = y_trfm(y)
return metric(preds, y)
pipeline_reg = data_preprocess_module.build_pipeline(LinearRegression())
pipeline_reg.fit(X_train, y_train)
get_score(pipeline_reg, X_val, y_val)
pipeline_lasso = data_preprocess_module.build_pipeline(Lasso())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
lasso = GridSearchCV(pipeline_lasso, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
lasso.fit(X_train, y_train)
get_score(lasso, X_val, y_val)
pipeline_ridge = data_preprocess_module.build_pipeline(Ridge())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
ridge = GridSearchCV(pipeline_ridge, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
ridge.fit(X_train, y_train)
print('Time taken for hyperparameter tuning: {:.2f} min'.format((time.time() - time_start) / 60))
get_score(ridge, X_val, y_val) | code |
18140916/cell_10 | [
"text_plain_output_1.png"
] | from preprocess import DataPreprocessModule
from sklearn.metrics import mean_squared_error
import numpy as np
data_preprocess_module = DataPreprocessModule(train_path='../input/hdb-resale-price-prediction/train.csv', test_path='../input/hdb-resale-price-prediction/test.csv')
X_train, X_val, X_test, y_train, y_val = data_preprocess_module.get_preprocessed_data()
metric = lambda y1_real, y2_real: np.sqrt(mean_squared_error(y1_real, y2_real))
y_trfm = lambda y: np.expm1(y)
def get_score(model, X, y):
preds = model.predict(X)
preds = y_trfm(preds)
y = y_trfm(y)
return metric(preds, y)
pipeline_reg = data_preprocess_module.build_pipeline(LinearRegression())
pipeline_reg.fit(X_train, y_train)
get_score(pipeline_reg, X_val, y_val) | code |
18140916/cell_12 | [
"text_plain_output_1.png"
] | from preprocess import DataPreprocessModule
from sklearn.linear_model import (LinearRegression, Lasso,
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import numpy as np
import time
data_preprocess_module = DataPreprocessModule(train_path='../input/hdb-resale-price-prediction/train.csv', test_path='../input/hdb-resale-price-prediction/test.csv')
X_train, X_val, X_test, y_train, y_val = data_preprocess_module.get_preprocessed_data()
metric = lambda y1_real, y2_real: np.sqrt(mean_squared_error(y1_real, y2_real))
y_trfm = lambda y: np.expm1(y)
def get_score(model, X, y):
preds = model.predict(X)
preds = y_trfm(preds)
y = y_trfm(y)
return metric(preds, y)
pipeline_reg = data_preprocess_module.build_pipeline(LinearRegression())
pipeline_reg.fit(X_train, y_train)
get_score(pipeline_reg, X_val, y_val)
pipeline_lasso = data_preprocess_module.build_pipeline(Lasso())
params = {'model__alpha': [10, 1, 0.1, 0.01, 0.001]}
lasso = GridSearchCV(pipeline_lasso, params, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
time_start = time.time()
lasso.fit(X_train, y_train)
print('Time taken for hyperparameter tuning: {:.2f} min'.format((time.time() - time_start) / 60))
get_score(lasso, X_val, y_val) | code |
331878/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
df.loc[df['BasePay'] == 0.0] = 0.0
print(df['BasePay']) | code |
331878/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
print(df.dtypes) | code |
331878/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv') | code |
331878/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
df.loc[df['BasePay'] == 0.0] = 0.0
df1 = df.groupby(by=['Year', 'JobTitle'])['BasePay'].sum()
print(df1) | code |
331878/cell_1 | [
"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 |
331878/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
df.loc[df['BasePay'] == 0.0] = 0.0
print(df['BasePay']) | code |
331878/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
df.loc[df['BasePay'] == 0.0] = 0.0
df['BasePay'] = df['BasePay'].astype('float') | code |
331878/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
print(df.describe) | code |
331878/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
df.loc[df['BasePay'] == 0.0] = 0.0
df1 = df.groupby(by=['Year', 'JobTitle'])['BasePay'].sum() | code |
331878/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Salaries.csv')
df['BasePay'].fillna(0) | code |
128008988/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
plt.ticklabel_format(style='plain', axis='y')
c = df.groupby('category')[['price']].sum().reset_index()
c
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
p = df.groupby('payment_method')[['price']].sum().reset_index()
p
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
s = df.groupby('shopping_mall')[['price']].sum().reset_index()
s
plt.figure(figsize=(15, 5))
sns.barplot(x='shopping_mall', y='price', data=s)
plt.title('money spended in which mall')
plt.ylabel('amount spend')
plt.ticklabel_format(style='plain', axis='y')
plt.show() | code |
128008988/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
sns.barplot(x='gender', y='price', data=g)
plt.ylabel('total amount spend')
plt.title('amount spend by m/f on shopping')
plt.ticklabel_format(style='plain', axis='y')
plt.show() | code |
128008988/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
df.describe() | code |
128008988/cell_20 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
c = df.groupby('category')[['price']].sum().reset_index()
c
p = df.groupby('payment_method')[['price']].sum().reset_index()
p
s = df.groupby('shopping_mall')[['price']].sum().reset_index()
s | code |
128008988/cell_2 | [
"image_output_1.png"
] | import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') | code |
128008988/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
for col in df.describe(include='object').columns:
print(col)
print(df[col].unique())
print('--' * 50) | code |
128008988/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
plt.ticklabel_format(style='plain', axis='y')
c = df.groupby('category')[['price']].sum().reset_index()
c
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
p = df.groupby('payment_method')[['price']].sum().reset_index()
p
plt.ticklabel_format(style='plain', axis='y')
sns.barplot(x='payment_method', y='price', data=df, hue='gender')
plt.title('amount payed by which method by m/f')
plt.ticklabel_format(style='plain', axis='y')
plt.ylabel('amount spend')
plt.show() | code |
128008988/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df | code |
128008988/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
plt.ticklabel_format(style='plain', axis='y')
c = df.groupby('category')[['price']].sum().reset_index()
c
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
p = df.groupby('payment_method')[['price']].sum().reset_index()
p
sns.barplot(x='payment_method', y='price', data=p)
plt.title('amount payed by which method')
plt.ticklabel_format(style='plain', axis='y')
plt.ylabel('amount spend')
plt.show() | code |
128008988/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum() | code |
128008988/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
plt.ticklabel_format(style='plain', axis='y')
c = df.groupby('category')[['price']].sum().reset_index()
c
plt.figure(figsize=(10, 5))
sns.barplot(x='category', y='price', data=c)
plt.title('amount spent category wise')
plt.ylabel('amount spend')
plt.ticklabel_format(style='plain', axis='y')
plt.show() | code |
128008988/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
plt.ticklabel_format(style='plain', axis='y')
c = df.groupby('category')[['price']].sum().reset_index()
c
plt.ticklabel_format(style='plain', axis='y')
plt.figure(figsize=(10, 5))
sns.barplot(x='category', y='price', data=df, hue='gender')
plt.title('amount spent category wise by m/f')
plt.ylabel('amount spend')
plt.ticklabel_format(style='plain', axis='y')
plt.show() | code |
128008988/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
c = df.groupby('category')[['price']].sum().reset_index()
c
p = df.groupby('payment_method')[['price']].sum().reset_index()
p | code |
128008988/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g
c = df.groupby('category')[['price']].sum().reset_index()
c | code |
128008988/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
df.info() | code |
128008988/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df.drop(['invoice_no', 'customer_id', 'invoice_date'], axis=1, inplace=True)
df.isnull().sum()
g = df.groupby('gender')[['price']].sum().reset_index()
g | code |
128008988/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-shopping-dataset/customer_shopping_data.csv')
df | code |
49123033/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.dropna(subset=['Embarked'], inplace=True)
sns.pairplot(train_data[['Survived', 'Pclass', 'Age', 'Fare']], hue='Survived', palette='hls') | code |
49123033/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.head() | code |
49123033/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.head() | code |
49123033/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.info() | code |
49123033/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.info() | code |
49123033/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.head() | code |
49123033/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.head() | code |
49123033/cell_55 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
def impute_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 37
elif Pclass == 2:
return 29
else:
return 24
else:
return Age
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.dropna(subset=['Embarked'], inplace=True)
test_data.dropna(subset=['Embarked'], inplace=True)
X = train_data[['Pclass', 'Age', 'Fare', 'Sex', 'Embarked']]
y = train_data['Survived']
logmodel = LogisticRegression()
logmodel.fit(X_train, y_train)
predictions = logmodel.predict(X_test)
logmodel.score(X_train, y_train)
logmodel.score(X_test, y_test)
passengerId_test = test_data['PassengerId']
X_test = test_data[['Pclass', 'Age', 'Fare', 'Sex', 'Embarked']]
logmodel = LogisticRegression()
logmodel.fit(X, y)
predictions = logmodel.predict(X_test)
df_predictions = pd.DataFrame({'PassengerID': passengerId_test, 'Survived': predictions.astype(int)})
df_predictions.to_csv('logistic_regression_submission.csv', index=False) | code |
49123033/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data['Fare'].value_counts() | code |
49123033/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
sns.jointplot(x='Calc_Fare', y='Survived', data=train_data, color='#4CB391') | code |
49123033/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.dropna(subset=['Embarked'], inplace=True)
train_data.head() | code |
49123033/cell_54 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.dropna(subset=['Embarked'], inplace=True)
test_data.dropna(subset=['Embarked'], inplace=True)
X = train_data[['Pclass', 'Age', 'Fare', 'Sex', 'Embarked']]
y = train_data['Survived']
logmodel = LogisticRegression()
logmodel.fit(X_train, y_train)
predictions = logmodel.predict(X_test)
logmodel.score(X_train, y_train)
logmodel.score(X_test, y_test)
X_test = test_data[['Pclass', 'Age', 'Fare', 'Sex', 'Embarked']]
logmodel = LogisticRegression()
logmodel.fit(X, y)
predictions = logmodel.predict(X_test) | code |
49123033/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
logmodel = LogisticRegression()
logmodel.fit(X_train, y_train)
predictions = logmodel.predict(X_test)
print(classification_report(y_test, predictions)) | code |
49123033/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
logmodel = LogisticRegression()
logmodel.fit(X_train, y_train)
predictions = logmodel.predict(X_test)
logmodel.score(X_train, y_train)
logmodel.score(X_test, y_test) | code |
49123033/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 |
49123033/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
def impute_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 37
elif Pclass == 2:
return 29
else:
return 24
else:
return Age
train_data['Age'].fillna(train_data[['Age', 'Pclass']].apply(impute_age, axis=1)) | code |
49123033/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
sns.jointplot(x='Fare', y='Survived', data=train_data, color='#4CB391') | code |
49123033/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
logmodel = LogisticRegression()
logmodel.fit(X_train, y_train)
predictions = logmodel.predict(X_test)
logmodel.score(X_train, y_train) | code |
49123033/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data[train_data['Age'].isna()] | code |
49123033/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data['Survived'].value_counts() | code |
49123033/cell_47 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
logmodel = LogisticRegression()
logmodel.fit(X_train, y_train) | code |
49123033/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
sns.heatmap(train_data.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
49123033/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data['Embarked'].value_counts() | code |
49123033/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data.head() | code |
49123033/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.info() | code |
49123033/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.head() | code |
49123033/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data[train_data['Calc_Age'].isna()] | code |
49123033/cell_5 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
plt.figure(figsize=(12, 7))
sns.boxplot(x='Pclass', y='Age', data=train_data, palette='winter') | code |
49123033/cell_36 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.drop(['Calc_Age'], axis=1, inplace=True)
train_data.drop(['Calc_Fare'], axis=1, inplace=True)
train_data[train_data['Embarked'].isna()] | code |
34144083/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')
df.info() | code |
34144083/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import ppscore as pps
import seaborn as sns
df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')
df.isnull().sum() / len(df) * 100
for col in df.columns:
print(col, pps.score(df, col, 'Price')['ppscore']) | code |
34144083/cell_1 | [
"text_plain_output_1.png"
] | pip install ppscore | code |
34144083/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')
df.isnull().sum() / len(df) * 100 | code |
34144083/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')
df.isnull().sum() / len(df) * 100
sns.heatmap(df.corr()) | code |
34144083/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import ppscore as pps
import seaborn as sns
df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')
df.isnull().sum() / len(df) * 100
df_cat = df[['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname', 'Postcode']]
df_num = df[['Rooms', 'Distance', 'Bedroom2', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'Lattitude', 'Longtitude', 'Propertycount']]
df_date = pd.to_datetime(df['Date'])
y = df['Price']
for column in df_num.columns:
if df_num[column].isna().any() == True:
df_num.loc[:, column] = df_num[column].fillna(df_num[column].mean())
df_num.loc[:, column] = (df_num[column] - df_num[column].min()) / (df_num[column].max() - df_num[column].min())
df_num.describe().T | code |
34144083/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')
df[0:10].T | code |
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