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105216483/cell_15 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
dataset_train = data_path_train
churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'Yes']
not_churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'No']
churn_dataset_train['Churn'].value_counts() | code |
105216483/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
dataset_train = data_path_train
churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'Yes']
not_churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'No']
not_churn_dataset_train['Churn'].value_counts() | code |
105216483/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
dataset_train = data_path_train
churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'Yes']
not_churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'No']
new_dataset_train = not_churn_dataset_train.copy(deep=True)
for i in range(3):
new_dataset_train = new_dataset_train.append(churn_dataset_train)
new_dataset_train | code |
105216483/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [], 'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score = cross_val_score(model, X, y, scoring='f1')
new_result = {'Model': model.__class__.__name__, 'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean', 'Score Standard Deviation'])
dataset_train = data_path_train
dataset_test = data_path_test
churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'Yes']
not_churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'No']
new_dataset_train = not_churn_dataset_train.copy(deep=True)
for i in range(3):
new_dataset_train = new_dataset_train.append(churn_dataset_train)
new_dataset_train
dataset_train = new_dataset_train.sample(frac=1, random_state=42)
dataset_train['Churn'].value_counts()
encoded_train = pd.get_dummies(dataset_train, columns=['location_code'])
encoded_test = pd.get_dummies(dataset_test, columns=['location_code'])
encoded_train['Churn'] = encoded_train['Churn'].str.lower()
for col in ['intertiol_plan', 'voice_mail_plan', 'Churn']:
encoded_train[col] = encoded_train[col].map({'yes': 1, 'no': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_test[col] = encoded_test[col].map({'yes': 1, 'no': 0})
X = encoded_train.drop(columns=['Churn'])
y = encoded_train.Churn
scaler = StandardScaler()
stdscaled = X.copy(deep=True)
stdscaled[stdscaled.columns] = scaler.fit_transform(stdscaled[stdscaled.columns])
evaluate_for_models(models, stdscaled, y) | code |
105216483/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [], 'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score = cross_val_score(model, X, y, scoring='f1')
new_result = {'Model': model.__class__.__name__, 'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean', 'Score Standard Deviation'])
dataset_train = data_path_train
dataset_test = data_path_test
churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'Yes']
not_churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'No']
new_dataset_train = not_churn_dataset_train.copy(deep=True)
for i in range(3):
new_dataset_train = new_dataset_train.append(churn_dataset_train)
new_dataset_train
dataset_train = new_dataset_train.sample(frac=1, random_state=42)
dataset_train['Churn'].value_counts()
encoded_train = pd.get_dummies(dataset_train, columns=['location_code'])
encoded_test = pd.get_dummies(dataset_test, columns=['location_code'])
encoded_train['Churn'] = encoded_train['Churn'].str.lower()
for col in ['intertiol_plan', 'voice_mail_plan', 'Churn']:
encoded_train[col] = encoded_train[col].map({'yes': 1, 'no': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_test[col] = encoded_test[col].map({'yes': 1, 'no': 0})
encoded_test.tail() | code |
105216483/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
dataset_test = data_path_test
dataset_test.head() | code |
105216483/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
data_path_train = '/kaggle/input/cs-3110-mini-project/train.csv'
data_path_test = '/kaggle/input/cs-3110-mini-project/test.csv'
dataset_train = pd.read_csv(data_path_train)
dataset_test = pd.read_csv(data_path_test)
dataset_train.drop(['Unnamed: 20'], axis=1, inplace=True)
dataset_test.drop(['Unnamed: 19', 'Unnamed: 20'], axis=1, inplace=True)
subset_train = dataset_train.columns.drop('customer_id')
duplicates_droped_dataset_train = dataset_train.drop_duplicates(subset=subset_train)
subset_test = dataset_test.columns.drop('customer_id')
duplicates_droped_dataset_test = dataset_test.drop_duplicates(subset=subset_test)
nan_added_dataset_train = duplicates_droped_dataset_train.copy()
nan_added_dataset_test = duplicates_droped_dataset_test.copy()
nan_added_dataset_train.loc[(nan_added_dataset_train['total_day_min'] < 0) & (nan_added_dataset_train['total_day_calls'] < 0) & (nan_added_dataset_train['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
nan_added_dataset_test.loc[(nan_added_dataset_test['total_day_min'] < 0) & (nan_added_dataset_test['total_day_calls'] < 0) & (nan_added_dataset_test['total_day_charge'] < 0), ('total_day_min', 'total_day_calls', 'total_day_charge')] = 0
for col in ['total_day_min', 'total_day_calls', 'total_day_charge', 'total_eve_min', 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', 'total_night_calls', 'total_night_charge', 'total_intl_minutes', 'total_intl_calls']:
nan_added_dataset_train[col] = nan_added_dataset_train[col].abs()
nan_added_dataset_test[col] = nan_added_dataset_test[col].abs()
odm_handled_dataset_train = nan_added_dataset_train.copy()
odm_handled_dataset_test = nan_added_dataset_test.copy()
for col in ['account_length', 'location_code']:
odm_handled_dataset_train[col].fillna(odm_handled_dataset_train[col].median(), inplace=True)
odm_handled_dataset_test[col].fillna(odm_handled_dataset_test[col].median(), inplace=True)
odm_handled_dataset_train.loc[odm_handled_dataset_train['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_test.loc[odm_handled_dataset_test['intertiol_plan'].isnull(), 'intertiol_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] == 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['number_vm_messages'] != 0) & odm_handled_dataset_train.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] == 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'no'
odm_handled_dataset_test.loc[(odm_handled_dataset_test['number_vm_messages'] != 0) & odm_handled_dataset_test.voice_mail_plan.isnull(), 'voice_mail_plan'] = 'yes'
odm_handled_dataset_train.loc[(odm_handled_dataset_train['voice_mail_plan'] == 'no') & (odm_handled_dataset_train['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test['voice_mail_plan'] == 'no') & (odm_handled_dataset_test['number_vm_messages'] != 0), 'number_vm_messages'] = 0
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'yes') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_train.loc[odm_handled_dataset_train.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_train.loc[(odm_handled_dataset_train.voice_mail_plan == 'no') & odm_handled_dataset_train.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'yes') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = odm_handled_dataset_test.loc[odm_handled_dataset_test.voice_mail_plan == 'yes', 'number_vm_messages'].median()
odm_handled_dataset_test.loc[(odm_handled_dataset_test.voice_mail_plan == 'no') & odm_handled_dataset_test.number_vm_messages.isnull(), 'number_vm_messages'] = 0
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_train['total_day_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_train['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_train['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_train['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_train['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_train['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_train['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_train['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_train['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_train = odm_handled_dataset_train.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_train['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_train['customer_service_calls'].fillna(odm_handled_dataset_train['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.dropna(subset=['Churn'], inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_charge'])
odm_handled_dataset_test['total_day_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_day_min'])
odm_handled_dataset_test['total_day_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_day_min', 'total_day_charge'])
odm_handled_dataset_test['total_day_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_min'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_eve_min'])
odm_handled_dataset_test['total_eve_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_eve_min', 'total_eve_charge'])
odm_handled_dataset_test['total_eve_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_charge'])
odm_handled_dataset_test['total_night_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_night_minutes'])
odm_handled_dataset_test['total_night_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_night_minutes', 'total_night_charge'])
odm_handled_dataset_test['total_night_calls'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_minutes'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['location_code', 'total_intl_minutes'])
odm_handled_dataset_test['total_intl_charge'].ffill(inplace=True)
odm_handled_dataset_test = odm_handled_dataset_test.sort_values(['total_intl_minutes', 'total_intl_charge'])
odm_handled_dataset_test['total_intl_calls'].ffill(inplace=True)
odm_handled_dataset_test['customer_service_calls'].fillna(odm_handled_dataset_test['customer_service_calls'].median(), inplace=True)
odm_handled_dataset_train.loc[(odm_handled_dataset_train['total_intl_calls'] == 0) & (odm_handled_dataset_train['total_intl_minutes'] > 0) & (odm_handled_dataset_train['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_test.loc[(odm_handled_dataset_test['total_intl_calls'] == 0) & (odm_handled_dataset_test['total_intl_minutes'] > 0) & (odm_handled_dataset_test['total_intl_charge'] > 0), 'total_intl_calls'] = 1
odm_handled_dataset_train = odm_handled_dataset_train.sort_index()
odm_handled_dataset_test = odm_handled_dataset_test.sort_index()
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_min == 2283.9, 'total_day_min'] = 283.9
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_eve_min == 5186.4, 'total_eve_min'] = 186.4
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_minutes == 19700.0, 'total_night_minutes'] = 197.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_day_calls == 10700.0, 'total_day_calls'] = 107.0
odm_handled_dataset_train.loc[odm_handled_dataset_train.total_night_charge == 900.15, 'total_night_charge'] = 9.15
pre_processed_dataset_train = odm_handled_dataset_train
pre_processed_dataset_test = odm_handled_dataset_test
data_path_train = pre_processed_dataset_train
data_path_test = pre_processed_dataset_test
rs = 42
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), RandomForestClassifier(max_depth=40, n_estimators=1050, random_state=rs), SVC(max_iter=10000), LinearSVC(max_iter=10000), XGBClassifier(eval_metric='logloss', use_label_encoder=False), LogisticRegression(), GradientBoostingClassifier(random_state=rs), BaggingClassifier(XGBClassifier(eval_metric='logloss', use_label_encoder=False), random_state=rs), BaggingClassifier(GradientBoostingClassifier(random_state=rs), random_state=rs)]
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [], 'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score = cross_val_score(model, X, y, scoring='f1')
new_result = {'Model': model.__class__.__name__, 'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean', 'Score Standard Deviation'])
dataset_train = data_path_train
dataset_test = data_path_test
churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'Yes']
not_churn_dataset_train = dataset_train.loc[dataset_train.Churn == 'No']
new_dataset_train = not_churn_dataset_train.copy(deep=True)
for i in range(3):
new_dataset_train = new_dataset_train.append(churn_dataset_train)
new_dataset_train
dataset_train = new_dataset_train.sample(frac=1, random_state=42)
dataset_train['Churn'].value_counts()
encoded_train = pd.get_dummies(dataset_train, columns=['location_code'])
encoded_test = pd.get_dummies(dataset_test, columns=['location_code'])
encoded_train['Churn'] = encoded_train['Churn'].str.lower()
for col in ['intertiol_plan', 'voice_mail_plan', 'Churn']:
encoded_train[col] = encoded_train[col].map({'yes': 1, 'no': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_test[col] = encoded_test[col].map({'yes': 1, 'no': 0})
X = encoded_train.drop(columns=['Churn'])
y = encoded_train.Churn
scaler = StandardScaler()
stdscaled = X.copy(deep=True)
stdscaled[stdscaled.columns] = scaler.fit_transform(stdscaled[stdscaled.columns])
stdscaled.head() | code |
33095782/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1) | code |
33095782/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.datasets import imdb
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
X | code |
33095782/cell_1 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install --upgrade pandas-profiling
!pip install --upgrade hypertools
!pip install --upgrade pandas | code |
33095782/cell_7 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
import matplotlib.pyplot as plt
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
print('Review length: ')
result = list(map(len, X))
print('Mean %.2f words (%f)' % (np.mean(result), np.std(result)))
fig, ax = plt.subplots(figsize=(10, 5))
ax.set_title('Boxplot of review lenght')
ax.boxplot(result) | code |
33095782/cell_18 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1)
scores = model.evaluate(X_test, y_test, verbose=0)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Convolution1D(activation='relu', filters=vector_size, kernel_size=3, padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1)
scores = model.evaluate(X_test, y_test, verbose=0)
print('Accuracy: %.2f%%' % (scores[1] * 100)) | code |
33095782/cell_16 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1)
scores = model.evaluate(X_test, y_test, verbose=0)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Convolution1D(activation='relu', filters=vector_size, kernel_size=3, padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary()) | code |
33095782/cell_17 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1)
scores = model.evaluate(X_test, y_test, verbose=0)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Convolution1D(activation='relu', filters=vector_size, kernel_size=3, padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1) | code |
33095782/cell_14 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1)
scores = model.evaluate(X_test, y_test, verbose=0)
print('Accuracy: %.2f%%' % (scores[1] * 100)) | code |
33095782/cell_10 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
X_train[20] | code |
33095782/cell_12 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing import sequence
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)
model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary()) | code |
33095782/cell_5 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
import numpy as np
(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)
print('Training data: ')
print(X.shape)
print(y.shape)
print('Classes: ')
print(np.unique(y))
print('Number of words: ')
print(len(np.unique(np.hstack(X)))) | code |
105195844/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing.info() | code |
105195844/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum() | code |
105195844/cell_9 | [
"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)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.describe() | code |
105195844/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
from sklearn.preprocessing import LabelEncoder
ocean_le = LabelEncoder()
housing['ocean_proximity'] = ocean_le.fit_transform(housing['ocean_proximity'])
ocean_le.classes_ | code |
105195844/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housing.astype('float')
x = housing.copy()
x = x[x['total_bedrooms'] < 3000]
x.shape | code |
105195844/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing.hist(bins=50, figsize=(20, 15)) | code |
105195844/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing | code |
105195844/cell_26 | [
"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)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing.info() | code |
105195844/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
sns.heatmap(housing.isnull()) | code |
105195844/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
sns.heatmap(housing.isnull()) | code |
105195844/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 |
105195844/cell_7 | [
"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)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.head(5) | code |
105195844/cell_18 | [
"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)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum() | code |
105195844/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housing.astype('float')
x = housing.copy()
x[x['total_bedrooms'] >= 3000].shape | code |
105195844/cell_28 | [
"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)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housing.astype('float')
housing.info() | code |
105195844/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.info() | code |
105195844/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns | code |
105195844/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
total_bedroom_median = housing['total_bedrooms'].median()
total_bedroom_median | code |
105195844/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housing.astype('float')
x = housing.copy()
x = x[x['total_bedrooms'] < 3000]
x.shape
sns.scatterplot(x=x['total_bedrooms'], y=x['median_house_value'], color='brown') | code |
105195844/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housing.astype('float')
x = housing.copy()
sns.scatterplot(x=x['total_bedrooms'], y=x['median_house_value']) | code |
105195844/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape | code |
105195844/cell_36 | [
"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)
import seaborn as sns
housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing
housing.shape
housing.isnull().sum()
housing.columns
housing.isnull().sum()
housing = housing.astype('float')
x = housing.copy()
x = x[x['total_bedrooms'] < 3000]
x.shape
sns.boxplot(housing['total_bedrooms']) | code |
2025030/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Parch', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_y = data_train['Survived']
train_y.sample(4) | code |
2025030/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5) | code |
2025030/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5)
train_y = data_train['Survived']
train_y.sample(4)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
train_x['Sex'] = lb_make.fit_transform(train_x['Sex'])
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
model = GaussianNB()
model1 = SVC()
model2 = RandomForestClassifier()
model3 = GradientBoostingClassifier()
model.fit(train_x, train_y)
data_test['Family_Size'] = data_test['SibSp'] + data_test['Parch']
test_x = data_test[['Pclass', 'Sex', 'Family_Size']]
test_x['Sex'] = lb_make.fit_transform(test_x['Sex'])
test_x.sample(5)
y_model = model.predict(test_x)
Pa_id = data_test['PassengerId']
results = pd.DataFrame({'PassengerId': Pa_id, 'Survived': y_model})
results.sample(5) | code |
2025030/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
model = GaussianNB()
model1 = SVC()
model2 = RandomForestClassifier()
model3 = GradientBoostingClassifier()
models = [model, model1, model2, model3]
for i in models:
i.fit(xtrain, ytrain)
ypred = i.predict(xtest)
print(i, accuracy_score(ytest, ypred)) | code |
2025030/cell_11 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Embarked', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_19 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5) | code |
2025030/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5)
train_y = data_train['Survived']
train_y.sample(4)
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
model = GaussianNB()
model1 = SVC()
model2 = RandomForestClassifier()
model3 = GradientBoostingClassifier()
model.fit(train_x, train_y) | code |
2025030/cell_8 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Pclass', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_16 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5) | code |
2025030/cell_17 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
sns.barplot(x='Family_Size', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
train_x['Sex'] = lb_make.fit_transform(train_x['Sex'])
data_test['Family_Size'] = data_test['SibSp'] + data_test['Parch']
test_x = data_test[['Pclass', 'Sex', 'Family_Size']]
test_x['Sex'] = lb_make.fit_transform(test_x['Sex'])
test_x.sample(5) | code |
2025030/cell_24 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import train_test_split | code |
2025030/cell_22 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
data_train.sample(5)
train_x = data_train[['Pclass', 'Sex', 'Family_Size']]
train_x.sample(5)
from sklearn.preprocessing import LabelEncoder
lb_make = LabelEncoder()
train_x['Sex'] = lb_make.fit_transform(train_x['Sex']) | code |
2025030/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='SibSp', y='Survived', hue='Sex', data=data_train) | code |
2025030/cell_12 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.sample(5)
sns.barplot(x='Age', y='Survived', hue='Sex', data=data_train) | code |
73067972/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
engagement_data.rename(columns={'lp_id': 'LP ID'}, inplace=True)
merged = pd.merge(engagement_data, product_data, on='LP ID')
m = merged.groupby('Product Name')['engagement_index'].sum().sort_values(ascending=False).head(10)
plt.figure(figsize=(15, 6))
plt.bar(m.index, m.values, color=['#6930c3', '#5e60ce', '#0096c7', '#48cae4', '#ade8f4', '#ff7f51', '#ff9b54', '#ffbf69'])
plt.xlabel('Product Name')
plt.xticks(rotation=90)
plt.ylabel('Total page-load per 1000 students')
plt.title('Top 10 products with number of page-load per 1000 students') | code |
73067972/cell_9 | [
"image_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'District', 'District', 'District', 'District', 'Product', 'Product', 'Product', 'Product', 'Product', 'Product', 'Engagement', 'Engagement', 'Engagement', 'Engagement'], ['district_id', 'state', 'locale', 'pct_black/hispanic', 'pct_free/reduced', 'countyconnectionsratio', 'pptotalraw', 'LP ID', 'URL', 'Product Name', 'Provider/Company Name', 'Sector(s)', 'Primary Essential Function', 'time', 'lp_id', 'pct_access', 'engagement_index'], ['The unique identifier of the school district', 'The state where the district resides in', 'NCES locale classification that categorizes U.S. territory into four types of areas: City, Suburban, Town, and Rural.', 'Percentage of students in the districts identified as Black or Hispanic based on 2018-19 NCES data', 'Percentage of students in the districts eligible for free or reduced-price lunch based on 2018-19 NCES data', 'ratio (residential fixed high-speed connections over 200 kbps in at least one direction/households) based on the county level data from FCC From 477 (December 2018 version)', "Per-pupil total expenditure (sum of local and federal expenditure) from Edunomics Lab's National Education Resource Database on Schools (NERD$) project. The expenditure data are school-by-school, and we use the median value to represent the expenditure of a given school district.", 'The unique identifier of the product', 'Web Link to the specific product', 'Name of the specific product', 'Name of the product provider', 'Sector of education where the product is used', 'The basic function of the product. There are two layers of labels here. Products are first labeled as one of these three categories: ,<b>LC = Learning & Curriculum, CM = Classroom Management<b>, and <b>SDO = School & District Operations<b>. Each of these categories have multiple sub-categories with which the products were labeled', 'date in YYYY-MM-DD', 'The unique identifier of the product', 'Percentage of students in the district have at least one page-load event of a given product and on a given day', 'Total page-load events per one thousand students of a given product and on a given day']]
fig = go.Figure(data=[go.Table(columnorder=[1, 2, 3], columnwidth=[60, 80, 400], header=dict(values=[['<b>Dataset</b>'], ['<b>Columns Names</b>'], ['<b>Description</b>']], line_color='darkslategray', fill_color='royalblue', align=['left', 'center'], font=dict(color='white', size=15), height=40), cells=dict(values=values, line_color='darkslategray', fill=dict(color=['paleturquoise', 'white']), align=['left', 'center'], font_size=12, height=30))])
import pandas as pd
district_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(district_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[district_data['district_id'], district_data['state'], district_data['locale'], district_data['pct_black/hispanic'], district_data['pct_free/reduced'], district_data['county_connections_ratio'], district_data['pp_total_raw']], fill_color=[['white', 'lavender'] * len(district_data)], align='left'))])
product_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(product_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[product_data['LP ID'], product_data['URL'], product_data['Product Name'], product_data['Provider/Company Name'], product_data['Sector(s)'], product_data['Primary Essential Function']], fill_color=[['white', 'lavender'] * len(product_data)], align='left'))])
all_csv = []
for district in district_data['district_id']:
df = pd.read_csv(f'/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/{district}.csv')
df.insert(0, 'district_id', district)
all_csv.append(df)
engagement_data = pd.concat(all_csv)
district_data.dropna(subset=['state'], axis=0, inplace=True)
district_data.drop(columns=['pp_total_raw'], axis=1, inplace=True)
# states with number of school districts
a= district_data["state"].value_counts()
fig= px.scatter(a, x=a.index, y=a.values, size=a.values, color=a.index, hover_name= a.index, size_max=60,
title="States with number of school districts")
fig.update_layout()
fig.show()
locale = district_data['locale'].value_counts()
fig = px.scatter(locale, x=locale.index, y=locale.values, size=locale.values, color=locale.index, hover_name=locale.index, size_max=60, title='Locale with number of school districts')
fig.update_layout()
fig.show() | code |
73067972/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'District', 'District', 'District', 'District', 'Product', 'Product', 'Product', 'Product', 'Product', 'Product', 'Engagement', 'Engagement', 'Engagement', 'Engagement'], ['district_id', 'state', 'locale', 'pct_black/hispanic', 'pct_free/reduced', 'countyconnectionsratio', 'pptotalraw', 'LP ID', 'URL', 'Product Name', 'Provider/Company Name', 'Sector(s)', 'Primary Essential Function', 'time', 'lp_id', 'pct_access', 'engagement_index'], ['The unique identifier of the school district', 'The state where the district resides in', 'NCES locale classification that categorizes U.S. territory into four types of areas: City, Suburban, Town, and Rural.', 'Percentage of students in the districts identified as Black or Hispanic based on 2018-19 NCES data', 'Percentage of students in the districts eligible for free or reduced-price lunch based on 2018-19 NCES data', 'ratio (residential fixed high-speed connections over 200 kbps in at least one direction/households) based on the county level data from FCC From 477 (December 2018 version)', "Per-pupil total expenditure (sum of local and federal expenditure) from Edunomics Lab's National Education Resource Database on Schools (NERD$) project. The expenditure data are school-by-school, and we use the median value to represent the expenditure of a given school district.", 'The unique identifier of the product', 'Web Link to the specific product', 'Name of the specific product', 'Name of the product provider', 'Sector of education where the product is used', 'The basic function of the product. There are two layers of labels here. Products are first labeled as one of these three categories: ,<b>LC = Learning & Curriculum, CM = Classroom Management<b>, and <b>SDO = School & District Operations<b>. Each of these categories have multiple sub-categories with which the products were labeled', 'date in YYYY-MM-DD', 'The unique identifier of the product', 'Percentage of students in the district have at least one page-load event of a given product and on a given day', 'Total page-load events per one thousand students of a given product and on a given day']]
fig = go.Figure(data=[go.Table(columnorder=[1, 2, 3], columnwidth=[60, 80, 400], header=dict(values=[['<b>Dataset</b>'], ['<b>Columns Names</b>'], ['<b>Description</b>']], line_color='darkslategray', fill_color='royalblue', align=['left', 'center'], font=dict(color='white', size=15), height=40), cells=dict(values=values, line_color='darkslategray', fill=dict(color=['paleturquoise', 'white']), align=['left', 'center'], font_size=12, height=30))])
import pandas as pd
district_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(district_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[district_data['district_id'], district_data['state'], district_data['locale'], district_data['pct_black/hispanic'], district_data['pct_free/reduced'], district_data['county_connections_ratio'], district_data['pp_total_raw']], fill_color=[['white', 'lavender'] * len(district_data)], align='left'))])
fig.show()
product_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(product_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[product_data['LP ID'], product_data['URL'], product_data['Product Name'], product_data['Provider/Company Name'], product_data['Sector(s)'], product_data['Primary Essential Function']], fill_color=[['white', 'lavender'] * len(product_data)], align='left'))])
fig.show() | code |
73067972/cell_2 | [
"text_html_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'District', 'District', 'District', 'District', 'Product', 'Product', 'Product', 'Product', 'Product', 'Product', 'Engagement', 'Engagement', 'Engagement', 'Engagement'], ['district_id', 'state', 'locale', 'pct_black/hispanic', 'pct_free/reduced', 'countyconnectionsratio', 'pptotalraw', 'LP ID', 'URL', 'Product Name', 'Provider/Company Name', 'Sector(s)', 'Primary Essential Function', 'time', 'lp_id', 'pct_access', 'engagement_index'], ['The unique identifier of the school district', 'The state where the district resides in', 'NCES locale classification that categorizes U.S. territory into four types of areas: City, Suburban, Town, and Rural.', 'Percentage of students in the districts identified as Black or Hispanic based on 2018-19 NCES data', 'Percentage of students in the districts eligible for free or reduced-price lunch based on 2018-19 NCES data', 'ratio (residential fixed high-speed connections over 200 kbps in at least one direction/households) based on the county level data from FCC From 477 (December 2018 version)', "Per-pupil total expenditure (sum of local and federal expenditure) from Edunomics Lab's National Education Resource Database on Schools (NERD$) project. The expenditure data are school-by-school, and we use the median value to represent the expenditure of a given school district.", 'The unique identifier of the product', 'Web Link to the specific product', 'Name of the specific product', 'Name of the product provider', 'Sector of education where the product is used', 'The basic function of the product. There are two layers of labels here. Products are first labeled as one of these three categories: ,<b>LC = Learning & Curriculum, CM = Classroom Management<b>, and <b>SDO = School & District Operations<b>. Each of these categories have multiple sub-categories with which the products were labeled', 'date in YYYY-MM-DD', 'The unique identifier of the product', 'Percentage of students in the district have at least one page-load event of a given product and on a given day', 'Total page-load events per one thousand students of a given product and on a given day']]
fig = go.Figure(data=[go.Table(columnorder=[1, 2, 3], columnwidth=[60, 80, 400], header=dict(values=[['<b>Dataset</b>'], ['<b>Columns Names</b>'], ['<b>Description</b>']], line_color='darkslategray', fill_color='royalblue', align=['left', 'center'], font=dict(color='white', size=15), height=40), cells=dict(values=values, line_color='darkslategray', fill=dict(color=['paleturquoise', 'white']), align=['left', 'center'], font_size=12, height=30))])
fig.show() | code |
73067972/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'District', 'District', 'District', 'District', 'Product', 'Product', 'Product', 'Product', 'Product', 'Product', 'Engagement', 'Engagement', 'Engagement', 'Engagement'], ['district_id', 'state', 'locale', 'pct_black/hispanic', 'pct_free/reduced', 'countyconnectionsratio', 'pptotalraw', 'LP ID', 'URL', 'Product Name', 'Provider/Company Name', 'Sector(s)', 'Primary Essential Function', 'time', 'lp_id', 'pct_access', 'engagement_index'], ['The unique identifier of the school district', 'The state where the district resides in', 'NCES locale classification that categorizes U.S. territory into four types of areas: City, Suburban, Town, and Rural.', 'Percentage of students in the districts identified as Black or Hispanic based on 2018-19 NCES data', 'Percentage of students in the districts eligible for free or reduced-price lunch based on 2018-19 NCES data', 'ratio (residential fixed high-speed connections over 200 kbps in at least one direction/households) based on the county level data from FCC From 477 (December 2018 version)', "Per-pupil total expenditure (sum of local and federal expenditure) from Edunomics Lab's National Education Resource Database on Schools (NERD$) project. The expenditure data are school-by-school, and we use the median value to represent the expenditure of a given school district.", 'The unique identifier of the product', 'Web Link to the specific product', 'Name of the specific product', 'Name of the product provider', 'Sector of education where the product is used', 'The basic function of the product. There are two layers of labels here. Products are first labeled as one of these three categories: ,<b>LC = Learning & Curriculum, CM = Classroom Management<b>, and <b>SDO = School & District Operations<b>. Each of these categories have multiple sub-categories with which the products were labeled', 'date in YYYY-MM-DD', 'The unique identifier of the product', 'Percentage of students in the district have at least one page-load event of a given product and on a given day', 'Total page-load events per one thousand students of a given product and on a given day']]
fig = go.Figure(data=[go.Table(columnorder=[1, 2, 3], columnwidth=[60, 80, 400], header=dict(values=[['<b>Dataset</b>'], ['<b>Columns Names</b>'], ['<b>Description</b>']], line_color='darkslategray', fill_color='royalblue', align=['left', 'center'], font=dict(color='white', size=15), height=40), cells=dict(values=values, line_color='darkslategray', fill=dict(color=['paleturquoise', 'white']), align=['left', 'center'], font_size=12, height=30))])
import pandas as pd
district_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(district_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[district_data['district_id'], district_data['state'], district_data['locale'], district_data['pct_black/hispanic'], district_data['pct_free/reduced'], district_data['county_connections_ratio'], district_data['pp_total_raw']], fill_color=[['white', 'lavender'] * len(district_data)], align='left'))])
product_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(product_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[product_data['LP ID'], product_data['URL'], product_data['Product Name'], product_data['Provider/Company Name'], product_data['Sector(s)'], product_data['Primary Essential Function']], fill_color=[['white', 'lavender'] * len(product_data)], align='left'))])
all_csv = []
for district in district_data['district_id']:
df = pd.read_csv(f'/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/{district}.csv')
df.insert(0, 'district_id', district)
all_csv.append(df)
engagement_data = pd.concat(all_csv)
district_data.dropna(subset=['state'], axis=0, inplace=True)
district_data.drop(columns=['pp_total_raw'], axis=1, inplace=True)
a = district_data['state'].value_counts()
fig = px.scatter(a, x=a.index, y=a.values, size=a.values, color=a.index, hover_name=a.index, size_max=60, title='States with number of school districts')
fig.update_layout()
fig.show() | code |
73067972/cell_14 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | def custom_palette(custom_colors):
customPalette = sns.set_palette(sns.color_palette(custom_colors))
sns.palplot(sns.color_palette(custom_colors), size=0.8)
plt.tick_params(axis='both', labelsize=0, length=0)
import matplotlib.pyplot as plt
import seaborn as sns
red = ['#4f000b', '#720026', '#ce4257', '#ff7f51', '#ff9b54']
bo = ['#6930c3', '#5e60ce', '#0096c7', '#48cae4', '#ade8f4', '#ff7f51', '#ff9b54', '#ffbf69']
pink = ['#aa4465', '#dd2d4a', '#f26a8d', '#f49cbb', '#ffcbf2', '#e2afff', '#ff86c8', '#ffa3a5', '#ffbf81', '#e9b827', '#f9e576']
custom_palette(pink) | code |
73067972/cell_5 | [
"text_html_output_1.png"
] | from plotly.offline import plot, iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import plotly
import plotly.graph_objects as go
import plotly.express as px
values = [['District', 'District', 'District', 'District', 'District', 'District', 'District', 'Product', 'Product', 'Product', 'Product', 'Product', 'Product', 'Engagement', 'Engagement', 'Engagement', 'Engagement'], ['district_id', 'state', 'locale', 'pct_black/hispanic', 'pct_free/reduced', 'countyconnectionsratio', 'pptotalraw', 'LP ID', 'URL', 'Product Name', 'Provider/Company Name', 'Sector(s)', 'Primary Essential Function', 'time', 'lp_id', 'pct_access', 'engagement_index'], ['The unique identifier of the school district', 'The state where the district resides in', 'NCES locale classification that categorizes U.S. territory into four types of areas: City, Suburban, Town, and Rural.', 'Percentage of students in the districts identified as Black or Hispanic based on 2018-19 NCES data', 'Percentage of students in the districts eligible for free or reduced-price lunch based on 2018-19 NCES data', 'ratio (residential fixed high-speed connections over 200 kbps in at least one direction/households) based on the county level data from FCC From 477 (December 2018 version)', "Per-pupil total expenditure (sum of local and federal expenditure) from Edunomics Lab's National Education Resource Database on Schools (NERD$) project. The expenditure data are school-by-school, and we use the median value to represent the expenditure of a given school district.", 'The unique identifier of the product', 'Web Link to the specific product', 'Name of the specific product', 'Name of the product provider', 'Sector of education where the product is used', 'The basic function of the product. There are two layers of labels here. Products are first labeled as one of these three categories: ,<b>LC = Learning & Curriculum, CM = Classroom Management<b>, and <b>SDO = School & District Operations<b>. Each of these categories have multiple sub-categories with which the products were labeled', 'date in YYYY-MM-DD', 'The unique identifier of the product', 'Percentage of students in the district have at least one page-load event of a given product and on a given day', 'Total page-load events per one thousand students of a given product and on a given day']]
fig = go.Figure(data=[go.Table(columnorder=[1, 2, 3], columnwidth=[60, 80, 400], header=dict(values=[['<b>Dataset</b>'], ['<b>Columns Names</b>'], ['<b>Description</b>']], line_color='darkslategray', fill_color='royalblue', align=['left', 'center'], font=dict(color='white', size=15), height=40), cells=dict(values=values, line_color='darkslategray', fill=dict(color=['paleturquoise', 'white']), align=['left', 'center'], font_size=12, height=30))])
import pandas as pd
district_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(district_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[district_data['district_id'], district_data['state'], district_data['locale'], district_data['pct_black/hispanic'], district_data['pct_free/reduced'], district_data['county_connections_ratio'], district_data['pp_total_raw']], fill_color=[['white', 'lavender'] * len(district_data)], align='left'))])
product_data = pd.read_csv('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
fig = go.Figure(data=[go.Table(header=dict(values=list(product_data.columns), fill_color='paleturquoise', align='left'), cells=dict(values=[product_data['LP ID'], product_data['URL'], product_data['Product Name'], product_data['Provider/Company Name'], product_data['Sector(s)'], product_data['Primary Essential Function']], fill_color=[['white', 'lavender'] * len(product_data)], align='left'))])
all_csv = []
for district in district_data['district_id']:
df = pd.read_csv(f'/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/{district}.csv')
df.insert(0, 'district_id', district)
all_csv.append(df)
engagement_data = pd.concat(all_csv)
engagement_data.head() | code |
50232057/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe
lebron = df.loc[df['Player'] == 'Lebron James']
lebron
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
jordan['Date'] = pd.to_datetime(jordan['Date'])
sns.boxplot(jordan['Date'].dt.year, jordan['PTS'], color='red').set_title('Jordan')
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
kobe['Date'] = pd.to_datetime(kobe['Date'])
sns.boxplot(kobe['Date'].dt.year, kobe['PTS'], color='purple').set_title('Kobe')
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
lebron['Date'] = pd.to_datetime(lebron['Date'])
sns.boxplot(lebron['Date'].dt.year, lebron['PTS'], color='yellow').set_title('Lebron')
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
sns.lineplot(jordan['Date'].dt.year.apply(lambda x: x - 1983), jordan['PTS'], color='red')
sns.lineplot(kobe['Date'].dt.year.apply(lambda x: x - 1995), kobe['PTS'], color='purple')
sns.lineplot(lebron['Date'].dt.year.apply(lambda x: x - 2002), lebron['PTS'], color='yellow').set_title('Jordan = Red / Kobe = Purple / Lebron = Yellow')
ax.set(xlabel='NBA Career in Years', ylabel='Points Scored per Game') | code |
50232057/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
print(f"--- Jordan ---\nMin: {jordan['PTS'].min()} \nMax: {jordan['PTS'].max()} \nAvg: {round(jordan['PTS'].mean())}") | code |
50232057/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe | code |
50232057/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe
lebron = df.loc[df['Player'] == 'Lebron James']
lebron | code |
50232057/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe
lebron = df.loc[df['Player'] == 'Lebron James']
lebron
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
jordan['Date'] = pd.to_datetime(jordan['Date'])
sns.boxplot(jordan['Date'].dt.year, jordan['PTS'], color='red').set_title('Jordan')
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
kobe['Date'] = pd.to_datetime(kobe['Date'])
sns.boxplot(kobe['Date'].dt.year, kobe['PTS'], color='purple').set_title('Kobe')
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
lebron['Date'] = pd.to_datetime(lebron['Date'])
sns.boxplot(lebron['Date'].dt.year, lebron['PTS'], color='yellow').set_title('Lebron') | code |
50232057/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan | code |
50232057/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
jordan['Date'] = pd.to_datetime(jordan['Date'])
sns.boxplot(jordan['Date'].dt.year, jordan['PTS'], color='red').set_title('Jordan')
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
kobe['Date'] = pd.to_datetime(kobe['Date'])
sns.boxplot(kobe['Date'].dt.year, kobe['PTS'], color='purple').set_title('Kobe') | code |
50232057/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe
lebron = df.loc[df['Player'] == 'Lebron James']
lebron
print(f"--- Lebron ---\nMin: {lebron['PTS'].min()} \nMax: {lebron['PTS'].max()} \nAvg: {round(lebron['PTS'].mean())}") | code |
50232057/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims)
jordan['Date'] = pd.to_datetime(jordan['Date'])
sns.boxplot(jordan['Date'].dt.year, jordan['PTS'], color='red').set_title('Jordan') | code |
50232057/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
jordan = df.loc[df['Player'] == 'Michael Jordan']
jordan
kobe = df.loc[df['Player'] == 'Kobe Bryant']
kobe
print(f"--- Kobe ---\nMin: {kobe['PTS'].min()} \nMax: {kobe['PTS'].max()} \nAvg: {round(kobe['PTS'].mean())}") | code |
50232057/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('../input/michael-jordan-kobe-bryant-and-lebron-james-stats/allgames_stats.csv')
df.head() | code |
122260915/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy import ndimage
from skimage import color
import imageio
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
I = color.rgb2gray(I)
(plt.subplot(1, 2, 1), plt.imshow(I), plt.title('original image'))
(plt.subplot(1, 2, 2), plt.imshow(I, cmap='gray'), plt.title('gray version'))
side_box = 3
h_box = np.ones((side_box, side_box)) / side_box ** 2
sigma = 3
side_gauss = 33
[x, y] = np.meshgrid(np.arange(-side_gauss // 2, side_gauss // 2 + 1), np.arange(-side_gauss // 2, side_gauss // 2 + 1))
h_gauss = np.exp(-0.5 * (x ** 2 + y ** 2) / sigma ** 2) / (2 * np.pi * sigma ** 2)
h_gauss = h_gauss / np.sum(h_gauss)
temp = np.zeros((side_box, side_box))
temp[side_box // 2, side_box // 2] = 1
H_box = ndimage.uniform_filter(temp, side_box)
temp = np.zeros((side_gauss, side_gauss))
temp[side_gauss // 2, side_gauss // 2] = 1
H_gauss = ndimage.gaussian_filter(temp, sigma)
(plt.subplot(121), plt.imshow(h_box, cmap='gray', vmin=-1 / side_box ** 2, vmax=2 / side_box ** 2))
(plt.subplot(122), plt.imshow(H_box, cmap='gray', vmin=-1 / side_box ** 2, vmax=2 / side_box ** 2))
(plt.subplot(121), plt.imshow(h_gauss, cmap='gray'))
(plt.subplot(122), plt.imshow(H_gauss, cmap='gray'))
def box_car(side_box):
h_box = np.ones((side_box, side_box)) / side_box ** 2
return h_box
h_box_f = box_car(3)
def gauss(side_gauss, sigma):
[x, y] = np.meshgrid(np.arange(-side_gauss // 2, side_gauss // 2 + 1), np.arange(-side_gauss // 2, side_gauss // 2 + 1))
h_gauss = np.exp(-0.5 * (x ** 2 + y ** 2) / sigma ** 2) / (2 * np.pi * sigma ** 2)
h_gauss = h_gauss / np.sum(h_gauss)
return h_gauss
I_box = ndimage.convolve(I, h_box)
I_gauss = ndimage.convolve(I, h_gauss)
plt.figure()
(plt.subplot(131), plt.imshow(I, cmap='gray'), plt.title('originale'))
(plt.subplot(132), plt.imshow(I_box, cmap='gray'), plt.title('Box'))
(plt.subplot(133), plt.imshow(I_gauss, cmap='gray'), plt.title('Gauss'))
I_box_nd = ndimage.uniform_filter(I, side_box)
I_gauss_nd = ndimage.gaussian_filter(I, sigma=sigma)
plt.figure()
(plt.subplot(131), plt.imshow(I, cmap='gray', vmin=0, vmax=1), plt.title('originale'))
(plt.subplot(132), plt.imshow(I_box_nd, cmap='gray', vmin=0, vmax=1), plt.title('Box'))
(plt.subplot(133), plt.imshow(I_gauss_nd, cmap='gray', vmin=0, vmax=1), plt.title('Gauss')) | code |
122260915/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import ndimage
from skimage import color
import imageio
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
I = color.rgb2gray(I)
(plt.subplot(1, 2, 1), plt.imshow(I), plt.title('original image'))
(plt.subplot(1, 2, 2), plt.imshow(I, cmap='gray'), plt.title('gray version'))
side_box = 3
h_box = np.ones((side_box, side_box)) / side_box ** 2
sigma = 3
side_gauss = 33
[x, y] = np.meshgrid(np.arange(-side_gauss // 2, side_gauss // 2 + 1), np.arange(-side_gauss // 2, side_gauss // 2 + 1))
h_gauss = np.exp(-0.5 * (x ** 2 + y ** 2) / sigma ** 2) / (2 * np.pi * sigma ** 2)
h_gauss = h_gauss / np.sum(h_gauss)
temp = np.zeros((side_box, side_box))
temp[side_box // 2, side_box // 2] = 1
H_box = ndimage.uniform_filter(temp, side_box)
temp = np.zeros((side_gauss, side_gauss))
temp[side_gauss // 2, side_gauss // 2] = 1
H_gauss = ndimage.gaussian_filter(temp, sigma)
plt.figure()
(plt.subplot(121), plt.imshow(h_box, cmap='gray', vmin=-1 / side_box ** 2, vmax=2 / side_box ** 2))
(plt.subplot(122), plt.imshow(H_box, cmap='gray', vmin=-1 / side_box ** 2, vmax=2 / side_box ** 2))
plt.figure()
(plt.subplot(121), plt.imshow(h_gauss, cmap='gray'))
(plt.subplot(122), plt.imshow(H_gauss, cmap='gray')) | code |
122260915/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 |
122260915/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | from skimage import color
import imageio
import matplotlib.pyplot as plt
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
I = color.rgb2gray(I)
plt.figure()
(plt.subplot(1, 2, 1), plt.imshow(I), plt.title('original image'))
(plt.subplot(1, 2, 2), plt.imshow(I, cmap='gray'), plt.title('gray version')) | code |
122260915/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import ndimage
from skimage import color
import imageio
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
images = ['peppers.png', 'cameraman.tif', 'coins.png']
path = '/kaggle/input/lab-python/Immagini/'
I = imageio.imread(path + images[0])
if len(I.shape) == 3:
I = color.rgb2gray(I)
(plt.subplot(1, 2, 1), plt.imshow(I), plt.title('original image'))
(plt.subplot(1, 2, 2), plt.imshow(I, cmap='gray'), plt.title('gray version'))
side_box = 3
h_box = np.ones((side_box, side_box)) / side_box ** 2
sigma = 3
side_gauss = 33
[x, y] = np.meshgrid(np.arange(-side_gauss // 2, side_gauss // 2 + 1), np.arange(-side_gauss // 2, side_gauss // 2 + 1))
h_gauss = np.exp(-0.5 * (x ** 2 + y ** 2) / sigma ** 2) / (2 * np.pi * sigma ** 2)
h_gauss = h_gauss / np.sum(h_gauss)
temp = np.zeros((side_box, side_box))
temp[side_box // 2, side_box // 2] = 1
H_box = ndimage.uniform_filter(temp, side_box)
temp = np.zeros((side_gauss, side_gauss))
temp[side_gauss // 2, side_gauss // 2] = 1
H_gauss = ndimage.gaussian_filter(temp, sigma)
(plt.subplot(121), plt.imshow(h_box, cmap='gray', vmin=-1 / side_box ** 2, vmax=2 / side_box ** 2))
(plt.subplot(122), plt.imshow(H_box, cmap='gray', vmin=-1 / side_box ** 2, vmax=2 / side_box ** 2))
(plt.subplot(121), plt.imshow(h_gauss, cmap='gray'))
(plt.subplot(122), plt.imshow(H_gauss, cmap='gray'))
def box_car(side_box):
h_box = np.ones((side_box, side_box)) / side_box ** 2
return h_box
h_box_f = box_car(3)
def gauss(side_gauss, sigma):
[x, y] = np.meshgrid(np.arange(-side_gauss // 2, side_gauss // 2 + 1), np.arange(-side_gauss // 2, side_gauss // 2 + 1))
h_gauss = np.exp(-0.5 * (x ** 2 + y ** 2) / sigma ** 2) / (2 * np.pi * sigma ** 2)
h_gauss = h_gauss / np.sum(h_gauss)
return h_gauss
I_box = ndimage.convolve(I, h_box)
I_gauss = ndimage.convolve(I, h_gauss)
(plt.subplot(131), plt.imshow(I, cmap='gray'), plt.title('originale'))
(plt.subplot(132), plt.imshow(I_box, cmap='gray'), plt.title('Box'))
(plt.subplot(133), plt.imshow(I_gauss, cmap='gray'), plt.title('Gauss'))
I_box_nd = ndimage.uniform_filter(I, side_box)
I_gauss_nd = ndimage.gaussian_filter(I, sigma=sigma)
(plt.subplot(131), plt.imshow(I, cmap='gray', vmin=0, vmax=1), plt.title('originale'))
(plt.subplot(132), plt.imshow(I_box_nd, cmap='gray', vmin=0, vmax=1), plt.title('Box'))
(plt.subplot(133), plt.imshow(I_gauss_nd, cmap='gray', vmin=0, vmax=1), plt.title('Gauss'))
sides = [3, 19, 25, 33]
sigmas = [2, 7, 11, 15]
lista_box = []
lista_gauss = []
for s in sides:
h_box = np.ones((s, s)) / s ** 2
I_box = ndimage.convolve(I, h_box)
lista_box.append(I_box)
side_gauss = 33
[x, y] = np.meshgrid(np.arange(-side_gauss // 2, side_gauss // 2 + 1), np.arange(-side_gauss // 2, side_gauss // 2 + 1))
for sig in sigmas:
h_gauss = np.exp(-(x ** 2 + y ** 2) / 2 / sig ** 2) / (2 * np.pi * sig ** 2)
h_gauss = h_gauss / np.sum(h_gauss)
I_gauss = ndimage.convolve(I, h_gauss)
lista_gauss.append(I_gauss)
K = len(sides)
plt.figure()
(plt.subplot(1, K + 1, 1), plt.imshow(I, cmap='gray'))
for k in range(K):
(plt.subplot(1, K + 1, k + 2), plt.imshow(lista_box[k], cmap='gray'))
plt.figure()
(plt.subplot(1, K + 1, 1), plt.imshow(I, cmap='gray'))
for k in range(K):
(plt.subplot(1, K + 1, k + 2), plt.imshow(lista_gauss[k], cmap='gray')) | code |
1006144/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
df = gdp_stats.copy()
df['index'] = df['index'].astype(str).astype(int)
df = df.rename(columns={'index': 'title_year'})
df = df.rename(columns={'mean': 'growth_rate'})
sns.jointplot(x=df['growth_rate'], y=budget['budget']) | code |
1006144/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
sns.distplot(budget['budget']) | code |
1006144/cell_25 | [
"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)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
df = gdp_stats.copy()
df['index'] = df['index'].astype(str).astype(int)
df = df.rename(columns={'index': 'title_year'})
df = df.rename(columns={'mean': 'growth_rate'})
gross = movie[['title_year', 'gross']].dropna(axis=0)
gross = gross[gross['title_year'] > 1960]
temp = pd.concat([df['growth_rate'], gross['gross']], axis=1)
temp = temp.dropna()
temp.head() | code |
1006144/cell_23 | [
"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)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gross = movie[['title_year', 'gross']].dropna(axis=0)
gross = gross[gross['title_year'] > 1960]
gross.describe() | code |
1006144/cell_20 | [
"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)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
df = gdp_stats.copy()
df['index'] = df['index'].astype(str).astype(int)
df = df.rename(columns={'index': 'title_year'})
df = df.rename(columns={'mean': 'growth_rate'})
sns.jointplot(x='title_year', y='growth_rate', data=df) | code |
1006144/cell_26 | [
"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)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
df = gdp_stats.copy()
df['index'] = df['index'].astype(str).astype(int)
df = df.rename(columns={'index': 'title_year'})
df = df.rename(columns={'mean': 'growth_rate'})
gross = movie[['title_year', 'gross']].dropna(axis=0)
gross = gross[gross['title_year'] > 1960]
temp = pd.concat([df['growth_rate'], gross['gross']], axis=1)
temp = temp.dropna()
sns.set(style='darkgrid')
sns.lmplot(x='growth_rate', y='gross', data=temp) | code |
1006144/cell_2 | [
"text_plain_output_1.png",
"image_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 |
1006144/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
movie['budget'].max() | code |
1006144/cell_7 | [
"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)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
movie.describe() | code |
1006144/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
sns.distplot(gdp_stats['mean']) | code |
1006144/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp.describe() | code |
1006144/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
sns.jointplot(x='title_year', y='budget', data=budget) | code |
1006144/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
sns.jointplot(x='title_year', y='budget', data=budget) | code |
1006144/cell_17 | [
"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)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp_stats= gdp.describe().T
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
gdp_stats.head() | code |
1006144/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget.head() | code |
1006144/cell_22 | [
"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)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
df = gdp_stats.copy()
df['index'] = df['index'].astype(str).astype(int)
df = df.rename(columns={'index': 'title_year'})
df = df.rename(columns={'mean': 'growth_rate'})
sns.jointplot(x=df['growth_rate'], y=budget['budget']) | code |
1006144/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)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp_stats= gdp.describe().T
gdp_stats.head() | code |
1006144/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
movie = pd.read_csv('../input/movie_metadata.csv')
gdp_stats= gdp.describe().T
budget = movie[movie['budget'] < 300000000]
budget = budget[budget['budget'].isnull() == False]
budget = pd.concat([budget['title_year'], budget['budget']], axis=1)
budget = budget.dropna(axis=0)
budget = budget[budget['title_year'] > 1960]
gdp_stats = gdp_stats.reset_index()
gdp_stats = gdp_stats.dropna()
df = gdp_stats.copy()
df['index'] = df['index'].astype(str).astype(int)
df = df.rename(columns={'index': 'title_year'})
df = df.rename(columns={'mean': 'growth_rate'})
gross = movie[['title_year', 'gross']].dropna(axis=0)
gross = gross[gross['title_year'] > 1960]
temp = pd.concat([df['growth_rate'], gross['gross']], axis=1)
temp = temp.dropna()
temp['gross'] = temp['gross'] / 100000
temp.head() | code |
1006144/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gdp = pd.read_csv('../input/worldGDP_growth2.csv', encoding='ISO-8859-1')
gdp | code |
73083438/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.isnull().sum()
features = train.drop(['target'], axis=1)
list(test.columns) == list(features.columns) | code |
73083438/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.describe() | code |
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