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129011222/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRATEGY = 'median'
train.count() | code |
129011222/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots | code |
129011222/cell_36 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=2,
column_titles = ["Train Data", "Test Data",],
x_title = "Missing Values")
fig.show() | code |
72120326/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = test[my_cols].copy()
X_train = pd.get_dummies(X_train)
X_valid = pd.get_dummies(X_valid)
X_test = pd.get_dummies(X_test)
my_model = XGBRegressor(n_estimators=1, learning_rate=0.05, n_jobs=5)
my_model.fit(X_train, y_train)
predictions = my_model.predict(X_valid)
mae = mean_absolute_error(predictions, y_valid)
test_predictions = my_model.predict(X_test)
print(test_predictions) | code |
72120326/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = test[my_cols].copy()
X_train.head() | code |
72120326/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
train.columns | code |
72120326/cell_11 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = test[my_cols].copy()
X_train = pd.get_dummies(X_train)
X_valid = pd.get_dummies(X_valid)
X_test = pd.get_dummies(X_test)
my_model = XGBRegressor(n_estimators=1, learning_rate=0.05, n_jobs=5)
my_model.fit(X_train, y_train) | code |
72120326/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 |
72120326/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = test[my_cols].copy()
X_train = pd.get_dummies(X_train)
X_valid = pd.get_dummies(X_valid)
X_test = pd.get_dummies(X_test)
my_model = XGBRegressor(n_estimators=1, learning_rate=0.05, n_jobs=5)
my_model.fit(X_train, y_train)
predictions = my_model.predict(X_valid)
mae = mean_absolute_error(predictions, y_valid)
test_predictions = my_model.predict(X_test)
test_predictions = test_predictions.mean()
print(test_predictions) | code |
72120326/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = test[my_cols].copy()
X_train = pd.get_dummies(X_train)
X_valid = pd.get_dummies(X_valid)
X_test = pd.get_dummies(X_test)
my_model = XGBRegressor(n_estimators=1, learning_rate=0.05, n_jobs=5)
my_model.fit(X_train, y_train)
predictions = my_model.predict(X_valid)
print(predictions)
mae = mean_absolute_error(predictions, y_valid)
print(mae) | code |
72120326/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
train.head() | code |
122258112/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df
sensors = ['pm25', 'pm10', 'co', 'no2']
def plot_subplots(df, col_name, sensors):
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
for i, sensor in enumerate(sensors):
chart = df.groupby(col_name)[sensor].mean()
chart.plot(kind='bar', ax=ax[i], title=f'Sensor {sensor}')
plt.tight_layout()
plt.show()
plot_subplots(df, 'zabrana', sensors) | code |
122258112/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df | code |
122258112/cell_2 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df | code |
122258112/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122258112/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df
sensors = ['pm25', 'pm10', 'co', 'no2']
def plot_subplots(df, col_name, sensors):
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
for i, sensor in enumerate(sensors):
chart = df.groupby(col_name)[sensor].mean()
chart.plot(kind='bar', ax=ax[i], title=f'Sensor {sensor}')
plt.tight_layout()
plt.show()
plot_subplots(df, 'month', sensors) | code |
122258112/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df
sensors = ['pm25', 'pm10', 'co', 'no2']
def plot_subplots(df, col_name, sensors):
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
for i, sensor in enumerate(sensors):
chart = df.groupby(col_name)[sensor].mean()
chart.plot(kind='bar', ax=ax[i], title=f'Sensor {sensor}')
plt.tight_layout()
plt.show()
plot_subplots(df, 'year', sensors) | code |
122258112/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['UTC1'] = pd.to_datetime(df['UTC'], format='%m/%d/%Y %H:%M')
df['year'] = df['UTC1'].dt.year
df | code |
122258112/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df
sensors = ['pm25', 'pm10', 'co', 'no2']
def plot_subplots(df, col_name, sensors):
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
for i, sensor in enumerate(sensors):
chart = df.groupby(col_name)[sensor].mean()
chart.plot(kind='bar', ax=ax[i], title=f'Sensor {sensor}')
plt.tight_layout()
plt.show()
plot_subplots(df, 'covid', sensors) | code |
122258112/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df
sensors = ['pm25', 'pm10', 'co', 'no2']
def plot_subplots(df, col_name, sensors):
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
for i, sensor in enumerate(sensors):
chart = df.groupby(col_name)[sensor].mean()
chart.plot(kind='bar', ax=ax[i], title=f'Sensor {sensor}')
plt.tight_layout()
plt.show()
plot_subplots(df, 'day-night', sensors) | code |
122258112/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df
df['pm25'] = df.loc[:, ['n1-pm25', 'n2-pm25']].mean(axis=1)
df['pm10'] = df.loc[:, ['n1-pm10', 'n2-pm10']].mean(axis=1)
df['co'] = df.loc[:, ['n1-co-mg/m3', 'n3-co-mg/m3']].mean(axis=1)
df['no2'] = df.loc[:, ['n1-no2-ug/m3', 'n2-no2-ug/m3', 'n3-no2-ug/m3']].mean(axis=1)
df
sensors = ['pm25', 'pm10', 'co', 'no2']
def plot_subplots(df, col_name, sensors):
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
for i, sensor in enumerate(sensors):
chart = df.groupby(col_name)[sensor].mean()
chart.plot(kind='bar', ax=ax[i], title=f'Sensor {sensor}')
plt.tight_layout()
plt.show()
plot_subplots(df, 'grejna-negrejna', sensors) | code |
122258112/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda x: 'negrejna' if (x['month'] > 4 or (x['month'] == 4 and x['day'] >= 15)) and (x['month'] < 10 or (x['month'] == 10 and x['day'] <= 15)) else 'grejna', axis=1)
df['covid'] = df.apply(lambda x: 'no-covid' if (x['year'] < 2020 or x['year'] > 2021) or (x['year'] == 2020 and x['month'] < 3) or (x['year'] == 2021 and x['month'] > 3) else 'covid', axis=1)
df['zabrana'] = df.apply(lambda x: 'nema-zabrana' if x['year'] < 2020 or (x['year'] == 2020 and x['month'] < 2) or (x['year'] == 2020 and x['month'] == 2 and (x['day'] < 10)) else 'zabrana', axis=1)
df | code |
74052794/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data.info() | code |
74052794/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.describe() | code |
74052794/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.index)
data = data.reset_index(drop=True)
question_engine = data[data['Engine_capacity(cm3)'] < 200]
data = data.drop(question_engine.index)
data = data.reset_index(drop=True)
data | code |
74052794/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum() | code |
74052794/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.index)
data = data.reset_index(drop=True)
question_engine = data[data['Engine_capacity(cm3)'] < 200]
data = data.drop(question_engine.index)
data = data.reset_index(drop=True)
data
question_price = data[data['Price(euro)'] < 101]
data = data.drop(question_price.index)
data = data.reset_index(drop=True)
data.sort_values(by=['Year']).head(10) | code |
74052794/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
data.tail() | code |
74052794/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 |
74052794/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.index)
data = data.reset_index(drop=True)
question_engine = data[data['Engine_capacity(cm3)'] < 200]
question_engine.describe() | code |
74052794/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.index)
data = data.reset_index(drop=True)
question_engine = data[data['Engine_capacity(cm3)'] < 200]
data = data.drop(question_engine.index)
data = data.reset_index(drop=True)
data
question_price = data[data['Price(euro)'] < 101]
data = data.drop(question_price.index)
data = data.reset_index(drop=True)
data.sort_values(by=['Year']).head(10)
question_year = data[data.Year < 1980]
question_year.describe() | code |
74052794/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data.tail() | code |
74052794/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.head() | code |
74052794/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.index)
data = data.reset_index(drop=True)
question_engine = data[data['Engine_capacity(cm3)'] < 200]
data = data.drop(question_engine.index)
data = data.reset_index(drop=True)
data
question_price = data[data['Price(euro)'] < 101]
data = data.drop(question_price.index)
data = data.reset_index(drop=True)
data.describe() | code |
74052794/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
question_dist.describe() | code |
74052794/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.index)
data = data.reset_index(drop=True)
question_engine = data[data['Engine_capacity(cm3)'] < 200]
data = data.drop(question_engine.index)
data = data.reset_index(drop=True)
data
question_price = data[data['Price(euro)'] < 101]
question_price.describe() | code |
128027676/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
df['sale'] = df['sale'].str.rstrip('%').astype('float') / 100.0 | code |
128027676/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum() | code |
128027676/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df | code |
128027676/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
furniture_type = df['type'].value_counts()
plt.figure(figsize=(10, 15))
ax = sns.barplot(y=furniture_type.index[:10], x=furniture_type.values[:10])
for i in ax.containers:
ax.bar_label(i)
plt.title('The most popular furniture types', size=15)
plt.show() | code |
128027676/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df.info() | code |
128027676/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
furniture_type = df['type'].value_counts()
print(furniture_type)
print(len(furniture_type)) | code |
128027676/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
sns.heatmap(corr, annot=True, linewidth=0.5) | code |
128027676/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df | code |
128027676/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
for i in df.columns:
print(f'{i:15}: {df[i].nunique()} unique values') | code |
128027676/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
plt.figure(figsize=(12, 5))
sns.displot(df['rate'])
plt.suptitle('Distribution of the rate')
plt.show() | code |
128027676/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
plt.figure(figsize=(12, 5))
sns.kdeplot(df['delivery'], color='b', shade=True)
plt.suptitle('Distribution of the delivery')
plt.show() | code |
128027676/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
df.describe() | code |
128027676/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
furniture_type = df['type'].value_counts()
plt.figure(figsize=(10,15))
ax = sns.barplot(y = furniture_type.index[:10], x = furniture_type.values[:10])
for i in ax.containers:
ax.bar_label(i,)
plt.title("The most popular furniture types", size=15)
plt.show()
sns.scatterplot(x='price', y='delivery', data=df)
plt.xlabel('Price')
plt.ylabel('Delivery Price')
plt.title('Relationship between Furniture Price and Delivery Price')
plt.show() | code |
72091432/cell_13 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
labelIndexer = StringIndexer(inputCol='acceptability', outputCol='label')
buyIndexer = StringIndexer(inputCol='buying_price', outputCol='iBuyingPrice')
id2 = StringIndexer(inputCol='maintenance_price', outputCol='iMain')
id3 = StringIndexer(inputCol='number_of_doors', outputCol='iDoor')
id4 = StringIndexer(inputCol='carry_capacity', outputCol='iCarry')
id5 = StringIndexer(inputCol='trunk_size', outputCol='iTrunk')
id6 = StringIndexer(inputCol='safety', outputCol='iSefety')
indexedLabelTrain = labelIndexer.fit(train).transform(train)
df2 = buyIndexer.fit(indexedLabelTrain).transform(indexedLabelTrain)
df3 = id2.fit(df2).transform(df2)
df4 = id3.fit(df3).transform(df3)
df5 = id4.fit(df4).transform(df4)
df6 = id5.fit(df5).transform(df5)
df7 = id6.fit(df6).transform(df6)
assembler = VectorAssembler(inputCols=['iBuyingPrice', 'iMain', 'iDoor', 'iCarry', 'iTrunk', 'iSefety'], outputCol='features')
df8 = assembler.transform(df7)
decission_tree_classifier_model = DecisionTreeClassifier(labelCol='label', featuresCol='features', maxDepth=10)
decission_tree_classifier_model.fit(df8).transform(df8).select('car_id', 'prediction').show(2)
pipeline = Pipeline(stages=[buyIndexer, id2, id3, id4, id5, id6, assembler, decission_tree_classifier_model])
testSolution = pipeline.fit(indexedLabelTrain).transform(test).select('car_id', 'prediction')
labelsArray = ['unacc', 'acc', 'good', 'vgood']
testSolution = IndexToString(inputCol='prediction', outputCol='acceptability', labels=labelsArray).transform(testSolution)
testSolution.show(5) | code |
72091432/cell_9 | [
"text_plain_output_1.png"
] | from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
labelIndexer = StringIndexer(inputCol='acceptability', outputCol='label')
buyIndexer = StringIndexer(inputCol='buying_price', outputCol='iBuyingPrice')
id2 = StringIndexer(inputCol='maintenance_price', outputCol='iMain')
id3 = StringIndexer(inputCol='number_of_doors', outputCol='iDoor')
id4 = StringIndexer(inputCol='carry_capacity', outputCol='iCarry')
id5 = StringIndexer(inputCol='trunk_size', outputCol='iTrunk')
id6 = StringIndexer(inputCol='safety', outputCol='iSefety')
indexedLabelTrain = labelIndexer.fit(train).transform(train)
df2 = buyIndexer.fit(indexedLabelTrain).transform(indexedLabelTrain)
df3 = id2.fit(df2).transform(df2)
df4 = id3.fit(df3).transform(df3)
df5 = id4.fit(df4).transform(df4)
df6 = id5.fit(df5).transform(df5)
df7 = id6.fit(df6).transform(df6)
assembler = VectorAssembler(inputCols=['iBuyingPrice', 'iMain', 'iDoor', 'iCarry', 'iTrunk', 'iSefety'], outputCol='features')
df8 = assembler.transform(df7)
decission_tree_classifier_model = DecisionTreeClassifier(labelCol='label', featuresCol='features', maxDepth=10)
decission_tree_classifier_model.fit(df8).transform(df8).select('car_id', 'prediction').show(2) | code |
72091432/cell_6 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
test.show(5) | code |
72091432/cell_2 | [
"text_plain_output_1.png"
] | !pip install pyspark | code |
72091432/cell_11 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
labelIndexer = StringIndexer(inputCol='acceptability', outputCol='label')
buyIndexer = StringIndexer(inputCol='buying_price', outputCol='iBuyingPrice')
id2 = StringIndexer(inputCol='maintenance_price', outputCol='iMain')
id3 = StringIndexer(inputCol='number_of_doors', outputCol='iDoor')
id4 = StringIndexer(inputCol='carry_capacity', outputCol='iCarry')
id5 = StringIndexer(inputCol='trunk_size', outputCol='iTrunk')
id6 = StringIndexer(inputCol='safety', outputCol='iSefety')
indexedLabelTrain = labelIndexer.fit(train).transform(train)
df2 = buyIndexer.fit(indexedLabelTrain).transform(indexedLabelTrain)
df3 = id2.fit(df2).transform(df2)
df4 = id3.fit(df3).transform(df3)
df5 = id4.fit(df4).transform(df4)
df6 = id5.fit(df5).transform(df5)
df7 = id6.fit(df6).transform(df6)
assembler = VectorAssembler(inputCols=['iBuyingPrice', 'iMain', 'iDoor', 'iCarry', 'iTrunk', 'iSefety'], outputCol='features')
df8 = assembler.transform(df7)
decission_tree_classifier_model = DecisionTreeClassifier(labelCol='label', featuresCol='features', maxDepth=10)
decission_tree_classifier_model.fit(df8).transform(df8).select('car_id', 'prediction').show(2)
pipeline = Pipeline(stages=[buyIndexer, id2, id3, id4, id5, id6, assembler, decission_tree_classifier_model])
testSolution = pipeline.fit(indexedLabelTrain).transform(test).select('car_id', 'prediction')
testSolution.show() | code |
72091432/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 |
72091432/cell_7 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
labelIndexer = StringIndexer(inputCol='acceptability', outputCol='label')
buyIndexer = StringIndexer(inputCol='buying_price', outputCol='iBuyingPrice')
id2 = StringIndexer(inputCol='maintenance_price', outputCol='iMain')
id3 = StringIndexer(inputCol='number_of_doors', outputCol='iDoor')
id4 = StringIndexer(inputCol='carry_capacity', outputCol='iCarry')
id5 = StringIndexer(inputCol='trunk_size', outputCol='iTrunk')
id6 = StringIndexer(inputCol='safety', outputCol='iSefety')
indexedLabelTrain = labelIndexer.fit(train).transform(train)
df2 = buyIndexer.fit(indexedLabelTrain).transform(indexedLabelTrain)
df3 = id2.fit(df2).transform(df2)
df4 = id3.fit(df3).transform(df3)
df5 = id4.fit(df4).transform(df4)
df6 = id5.fit(df5).transform(df5)
df7 = id6.fit(df6).transform(df6)
df7.show(5) | code |
72091432/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
labelIndexer = StringIndexer(inputCol='acceptability', outputCol='label')
buyIndexer = StringIndexer(inputCol='buying_price', outputCol='iBuyingPrice')
id2 = StringIndexer(inputCol='maintenance_price', outputCol='iMain')
id3 = StringIndexer(inputCol='number_of_doors', outputCol='iDoor')
id4 = StringIndexer(inputCol='carry_capacity', outputCol='iCarry')
id5 = StringIndexer(inputCol='trunk_size', outputCol='iTrunk')
id6 = StringIndexer(inputCol='safety', outputCol='iSefety')
indexedLabelTrain = labelIndexer.fit(train).transform(train)
df2 = buyIndexer.fit(indexedLabelTrain).transform(indexedLabelTrain)
df3 = id2.fit(df2).transform(df2)
df4 = id3.fit(df3).transform(df3)
df5 = id4.fit(df4).transform(df4)
df6 = id5.fit(df5).transform(df5)
df7 = id6.fit(df6).transform(df6)
assembler = VectorAssembler(inputCols=['iBuyingPrice', 'iMain', 'iDoor', 'iCarry', 'iTrunk', 'iSefety'], outputCol='features')
df8 = assembler.transform(df7)
df8.show(5) | code |
72091432/cell_14 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
labelIndexer = StringIndexer(inputCol='acceptability', outputCol='label')
buyIndexer = StringIndexer(inputCol='buying_price', outputCol='iBuyingPrice')
id2 = StringIndexer(inputCol='maintenance_price', outputCol='iMain')
id3 = StringIndexer(inputCol='number_of_doors', outputCol='iDoor')
id4 = StringIndexer(inputCol='carry_capacity', outputCol='iCarry')
id5 = StringIndexer(inputCol='trunk_size', outputCol='iTrunk')
id6 = StringIndexer(inputCol='safety', outputCol='iSefety')
indexedLabelTrain = labelIndexer.fit(train).transform(train)
df2 = buyIndexer.fit(indexedLabelTrain).transform(indexedLabelTrain)
df3 = id2.fit(df2).transform(df2)
df4 = id3.fit(df3).transform(df3)
df5 = id4.fit(df4).transform(df4)
df6 = id5.fit(df5).transform(df5)
df7 = id6.fit(df6).transform(df6)
assembler = VectorAssembler(inputCols=['iBuyingPrice', 'iMain', 'iDoor', 'iCarry', 'iTrunk', 'iSefety'], outputCol='features')
df8 = assembler.transform(df7)
decission_tree_classifier_model = DecisionTreeClassifier(labelCol='label', featuresCol='features', maxDepth=10)
decission_tree_classifier_model.fit(df8).transform(df8).select('car_id', 'prediction').show(2)
pipeline = Pipeline(stages=[buyIndexer, id2, id3, id4, id5, id6, assembler, decission_tree_classifier_model])
testSolution = pipeline.fit(indexedLabelTrain).transform(test).select('car_id', 'prediction')
labelsArray = ['unacc', 'acc', 'good', 'vgood']
testSolution = IndexToString(inputCol='prediction', outputCol='acceptability', labels=labelsArray).transform(testSolution)
solutions = testSolution.select('car_id', 'acceptability')
solutions.show() | code |
72091432/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
spark = SparkSession.builder.getOrCreate()
train = spark.read.format('csv').load('/kaggle/input/dataset/train.csv', header='True', inferSchema='True')
test = spark.read.format('csv').load('/kaggle/input/dataset/test.csv', header='True', inferSchema='True')
train.show(5) | code |
1006176/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
features.head(n=20) | code |
1006176/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
classifiers = [('Linear SVC', LinearSVC()), ('Decision Tree', DecisionTreeClassifier()), ('Multinomial NB', MultinomialNB())]
random_score = float(max(n_survived, n_died)) / float(n_samples)
print('Random score: {:.4f}'.format(random_score))
for title, clf in classifiers:
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
print('{} score: {:.4f}'.format(title, score)) | code |
1006176/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006176/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
classifiers = [('Linear SVC', LinearSVC()), ('Decision Tree', DecisionTreeClassifier()), ('Multinomial NB', MultinomialNB())]
random_score = float(max(n_survived, n_died)) / float(n_samples)
for title, clf in classifiers:
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
scores = []
for max_depth in range(1, 10):
clf = DecisionTreeClassifier(max_depth=max_depth)
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
print('Max depth of {}: {:.4f}'.format(max_depth, score))
scores.append((max_depth, score)) | code |
1006176/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
classifiers = [('Linear SVC', LinearSVC()), ('Decision Tree', DecisionTreeClassifier()), ('Multinomial NB', MultinomialNB())]
random_score = float(max(n_survived, n_died)) / float(n_samples)
for title, clf in classifiers:
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
scores = []
for max_depth in range(1, 10):
clf = DecisionTreeClassifier(max_depth=max_depth)
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
scores.append((max_depth, score))
plt.plot(scores) | code |
1006176/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
print('Number of training samples: {}'.format(n_samples))
print('Number of features: {}'.format(n_features))
print('Number of survivors: {}'.format(n_survived))
print('Number of deaths: {}'.format(n_died)) | code |
1006176/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
processed_features.head(n=20) | code |
17103363/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
df_train.popularity.describe() | code |
17103363/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
for j, k in enumerate(df_train['genres'][:5]):
print(j, k) | code |
17103363/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train['revenue'].min() | code |
17103363/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
17103363/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
sns.distplot(df_train.popularity) | code |
17103363/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns) | code |
17103363/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
for j, k in enumerate(df_train['belongs_to_collection'][:5]):
print(j, k) | code |
17103363/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
df_train.revenue.describe() | code |
17103363/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
17103363/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum() | code |
17103363/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
sns.distplot(df_train.revenue) | code |
17103363/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any() | code |
17103363/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
sns.jointplot(x='budget', y='revenue', data=df_train, height=8, ratio=5, color='b')
plt.show() | code |
17103363/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
sns.jointplot(x='popularity', y='revenue', data=df_train, height=8, ratio=5, color='b')
plt.show() | code |
17103363/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.info() | code |
17103363/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
sns.jointplot(x='runtime', y='revenue', data=df_train, height=8, ratio=5, color='b')
plt.show() | code |
17103363/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
df_train['logRevenue'] = np.log1p(df_train['revenue'])
df_train[['release_month', 'release_day', 'release_year']] = df_train['release_date'].str.split('/', expand=True).replace(np.nan, -1).astype(int)
df_train.loc[(df_train['release_year'] <= 19) & (df_train['release_year'] < 100), 'release_year'] += 2000
df_train.loc[(df_train['release_year'] > 19) & (df_train['release_year'] < 100), 'release_year'] += 1900
releaseDate = pd.to_datetime(df_train['release_date'])
df_train['release_dayofweek'] = releaseDate.dt.dayofweek
df_train['release_quarter'] = releaseDate.dt.quarter
plt.figure(figsize=(25, 15))
sns.countplot(df_train['release_year'].sort_values())
plt.title('Movie Release count by Year', fontsize=25)
loc, labels = plt.xticks()
plt.xticks(fontsize=15, rotation=90)
plt.show() | code |
17103363/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
for j, k in enumerate(df_train['production_countries'][:10]):
print(j, k) | code |
17103363/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.drop(['belongs_to_collection'], axis=1)
df_train['logRevenue'] = np.log1p(df_train['revenue'])
sns.distplot(df_train['logRevenue']) | code |
17103363/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train['revenue'].max() | code |
17103363/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
print(df_train.shape, df_test.shape) | code |
18111717/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
drugTree = DecisionTreeClassifier(criterion='entropy', max_depth=4)
drugTree.fit(x_train, y_train) | code |
18111717/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/drug200.csv', delimiter=',')
df.dropna
df.head() | code |
18111717/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18111717/cell_18 | [
"text_html_output_1.png"
] | print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape) | code |
18111717/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/drug200.csv', delimiter=',')
df.dropna
from sklearn.tree import DecisionTreeClassifier
X = df[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X | code |
18111717/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
drugTree = DecisionTreeClassifier(criterion='entropy', max_depth=4)
drugTree.fit(x_train, y_train)
y_predict = drugTree.predict(x_test)
y_predict
from sklearn import metrics
import matplotlib.pyplot as plt
metrics.accuracy_score(y_test, y_predict) | code |
18111717/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
drugTree = DecisionTreeClassifier(criterion='entropy', max_depth=4)
drugTree.fit(x_train, y_train)
y_predict = drugTree.predict(x_test)
y_predict | code |
18111717/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/drug200.csv', delimiter=',')
df.dropna
from sklearn.tree import DecisionTreeClassifier
X = df[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X
X[:5] | code |
18111717/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/drug200.csv', delimiter=',')
df.dropna | code |
105193696/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)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
df_2019 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv')
df_tracking.columns
df_tracking.isnull().sum()
df_tracking.duplicated().sum()
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_tracking['month'].value_counts().plot(kind='bar', figsize=(16, 10)) | code |
105193696/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
df_2019 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv')
df_tracking.columns
df_tracking.isnull().sum()
df_tracking.duplicated().sum()
df_tracking['race_date'].value_counts() | code |
105193696/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
df_2019 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv')
df_tracking.columns
df_tracking.isnull().sum() | code |
105193696/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
df_2019 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv')
df_start.columns
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_start | code |
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