path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
18108547/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
import seaborn as sns
sns.set()
df['Growth_Left'] = df['Potential'] - df['Overall']
sns.lineplot(x='Growth_Left', y='Value_Num', data=df) | code |
18108547/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from learntools.core import *
y = df.Overall
features = ['Age', 'Value_Num', 'Wage_Num', 'Potential']
X = df[features]
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
ml_model = DecisionTreeRegressor(random_state=1)
ml_model.fit(train_X, train_y)
val_predictions = ml_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(train_X, train_y)
rf_val_predictions = rf_model.predict(val_X)
rf_val_mae = mean_absolute_error(rf_val_predictions, val_y)
print('Validation MAE for Random Forest Model: {:,.0f}'.format(rf_val_mae))
print(train_X)
print(train_y)
print(val_X)
print(val_predictions)
print(val_y) | code |
18108547/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
import seaborn as sns
sns.set()
df['Growth_Left'] = df['Potential'] - df['Overall']
sns.lineplot(x='Growth_Left', y='Wage_Num', data=df) | code |
18108547/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df.head() | code |
18108547/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18108547/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"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/data.csv')
list(df.columns)
print(type(df['Age'][0]))
print(type(df['Nationality'][0]))
print(type(df['Overall'][0]))
print(type(df['Potential'][0]))
print(type(df['Value'][0]))
print(type(df['Wage'][0])) | code |
18108547/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum() | code |
18108547/cell_32 | [
"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 = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
top_100 = df[:100]
top_100.shape | code |
18108547/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
import seaborn as sns
sns.set()
df['Growth_Left'] = df['Potential'] - df['Overall']
sns.lineplot(x='Age', y='Value_Num', data=df) | code |
18108547/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum() | code |
18108547/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from learntools.core import *
y = df.Overall
features = ['Age', 'Value_Num', 'Wage_Num', 'Potential']
X = df[features]
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
ml_model = DecisionTreeRegressor(random_state=1)
ml_model.fit(train_X, train_y)
val_predictions = ml_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print('Validation MAE when using a Decision Tree: {:,.0f}'.format(val_mae))
print(train_X)
print(train_y)
print(val_X)
print(val_predictions)
print(val_y) | code |
18108547/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 = pd.read_csv('../input/data.csv')
df.head() | code |
18108547/cell_35 | [
"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 = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
top_100 = df[:100]
top_100.shape
club_100_plots = top_100['Club'].value_counts()
club_100_plots.plot(kind='bar') | code |
18108547/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
import seaborn as sns
sns.set()
sns.lineplot(x='Overall', y='Value_Num', data=df) | code |
18108547/cell_14 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape | code |
18108547/cell_22 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
foot_plots = df['Preferred Foot'].value_counts()
foot_plots.plot(kind='bar') | code |
18108547/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
import seaborn as sns
sns.set()
df['Growth_Left'] = df['Potential'] - df['Overall']
sns.lineplot(x='Age', y='Wage_Num', data=df) | code |
18108547/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/data.csv')
list(df.columns) | code |
18108547/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
elif last_char == 'M':
num = num * 1000000
return num
df['Wage_Num'] = df.apply(lambda row: clean_d(row['Wage']), axis=1)
df['Value_Num'] = df.apply(lambda row: clean_d(row['Value']), axis=1)
df.shape
df.isna().sum()
df = df.dropna(axis=0, subset=['Preferred Foot'])
df.isna().sum()
import seaborn as sns
sns.set()
df['Growth_Left'] = df['Potential'] - df['Overall']
sns.pairplot(df, vars=['Age', 'Overall', 'Wage_Num', 'Value_Num', 'Potential', 'Growth_Left']) | code |
2000084/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
result = pd.read_csv('../input/catboost1223/catboost1223.csv')
result.head() | code |
2000084/cell_2 | [
"text_plain_output_1.png"
] | !pwd | code |
32065345/cell_9 | [
"text_plain_output_2.png",
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['REASON'].astype('category').cat.categories | code |
32065345/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape | code |
32065345/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/hmeq-data/hmeq.csv')
df.shape
df.sample(30) | code |
32065345/cell_2 | [
"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 |
32065345/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['REASON'].astype('category').cat.codes | code |
32065345/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T | code |
32065345/cell_18 | [
"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)
import seaborn as sn
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df.dropna(thresh=8, inplace=True)
df.REASON.fillna('DebtCon', inplace=True)
df.JOB.fillna('Other', inplace=True)
for col in df.columns:
if df[col].dtype == 'object':
df[col] = df[col].astype('category').cat.codes
import seaborn as sn
import matplotlib.pyplot as plt
plt.figure(figsize=(18, 18))
corrMatrix = df[df['DEBTINC'].notnull()].corr()
sn.heatmap(corrMatrix, annot=True)
plt.show() | code |
32065345/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
print('Rows : ', df.shape[0])
print('Columns : ', df.shape[1])
print('\nFeatures : \n', df.columns.tolist())
print('\nMissing values : ', df.isnull().sum().values.sum())
print('\nUnique values : \n', df.nunique())
print('\nPorcentagem Missing: \n', df.isna().mean().round(4) * 100) | code |
32065345/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df.dropna(thresh=8, inplace=True)
df.REASON.fillna('DebtCon', inplace=True)
df.JOB.fillna('Other', inplace=True)
print('INFO : ', df.info())
print('Rows : ', df.shape[0])
print('Columns : ', df.shape[1])
print('\nFeatures : \n', df.columns.tolist())
print('\nMissing values : ', df.isnull().sum().values.sum())
print('\nUnique values : \n', df.nunique())
print('\nPorcentagem Missing: \n', df.isna().mean().round(4) * 100) | code |
32065345/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.info() | code |
32065345/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df.dropna(thresh=8, inplace=True)
df.REASON.fillna('DebtCon', inplace=True)
df.JOB.fillna('Other', inplace=True)
for col in df.columns:
if df[col].dtype == 'object':
df[col] = df[col].astype('category').cat.codes
print('INFO : ', df.info())
print('Rows : ', df.shape[0])
print('Columns : ', df.shape[1])
print('\nFeatures : \n', df.columns.tolist())
print('\nMissing values : ', df.isnull().sum().values.sum())
print('\nUnique values : \n', df.nunique())
print('\nTotal Missing: \n', df.isna().sum())
print('\nPorcentagem Missing: \n', df.isna().mean().round(4) * 100) | code |
32065345/cell_10 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['JOB'].astype('category').cat.categories | code |
32065345/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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['JOB'].astype('category').cat.codes | code |
32065345/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/hmeq-data/hmeq.csv')
df.shape
df.head() | code |
88096283/cell_21 | [
"text_plain_output_1.png"
] | dt = create_model('dt') | code |
88096283/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):
return df.isnull().values.sum()
detect_NaNs(df)
df.iloc[:, :-1].columns
def replace_Nans(df_temp, columns, replacement):
for col in columns:
df[col] = df[col].fillna(replacement)
return df_temp
df = replace_Nans(df, df.iloc[:, :-1].columns, -5)
test_df = replace_Nans(test_df, df.iloc[:, :-1].columns, -5)
def reduce_mem_usage(df, verbose=True):
numerics = ['int8','int16', 'int32', 'int64', 'float16', 'float32', 'float64']
start_mem = df.memory_usage().sum() / 1024**2
for col in df.columns:
col_type = df[col].dtypes
if col_type in numerics:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
if c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
end_mem = df.memory_usage().sum() / 1024**2
if verbose:
print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))
return df
test_df = reduce_mem_usage(test_df)
df = reduce_mem_usage(df)
df.dtypes
from pycaret.classification import *
setup(data=df.copy(), target='Class', silent=True, normalize=True, session_id=42, create_clusters=False, remove_perfect_collinearity=False, polynomial_features=False, fix_imbalance=False, fold=10)
display() | code |
88096283/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes | code |
88096283/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install pycaret | code |
88096283/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):
return df.isnull().values.sum()
detect_NaNs(df)
df.iloc[:, :-1].columns
def replace_Nans(df_temp, columns, replacement):
for col in columns:
df[col] = df[col].fillna(replacement)
return df_temp
df = replace_Nans(df, df.iloc[:, :-1].columns, -5)
test_df = replace_Nans(test_df, df.iloc[:, :-1].columns, -5)
def reduce_mem_usage(df, verbose=True):
numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']
start_mem = df.memory_usage().sum() / 1024 ** 2
for col in df.columns:
col_type = df[col].dtypes
if col_type in numerics:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
end_mem = df.memory_usage().sum() / 1024 ** 2
if verbose:
print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))
return df
test_df = reduce_mem_usage(test_df)
df = reduce_mem_usage(df)
df.dtypes | code |
88096283/cell_24 | [
"text_html_output_1.png"
] | model = dt
plot_model(dt, plot='confusion_matrix') | code |
88096283/cell_22 | [
"text_html_output_1.png"
] | dt = create_model('dt')
dt = tune_model(dt, optimize='Precision') | code |
88096283/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):
print('NaNs in data: ', df.isnull().values.sum())
return df.isnull().values.sum()
detect_NaNs(df) | code |
88096283/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):
return df.isnull().values.sum()
detect_NaNs(df)
df.iloc[:, :-1].columns | code |
88096283/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
sns.countplot(df['Class'])
print(len(df.loc[df['Class'] == 0]) / len(df) * 100, '%') | code |
33105697/cell_4 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
import os
img_size = 32
def process_image(img_file):
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (img_size, img_size))
return img
start = time.time()
X_data = []
Y_data = []
for j in range(10):
print('Load folder c{}'.format(j))
path = os.path.join('../input/state-farm-distracted-driver-detection/imgs/train', 'c' + str(j), '*.jpg')
files = glob.glob(path)
X_data.extend(Parallel(n_jobs=2)((delayed(process_image)(im_file) for im_file in files)))
Y_data.extend([j] * len(files))
end = time.time() - start
print('Time: %.2f seconds' % end) | code |
33105697/cell_6 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
img_size = 32
def process_image(img_file):
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (img_size, img_size))
return img
start = time.time()
X_data = []
Y_data = []
for j in range(10):
path = os.path.join('../input/state-farm-distracted-driver-detection/imgs/train', 'c' + str(j), '*.jpg')
files = glob.glob(path)
X_data.extend(Parallel(n_jobs=2)((delayed(process_image)(im_file) for im_file in files)))
Y_data.extend([j] * len(files))
end = time.time() - start
X_data = np.array(X_data)
Y_data = np.array(Y_data)
np.random.shuffle(X_data)
np.random.shuffle(Y_data)
X_data = X_data[0:1000]
Y_data = Y_data[0:1000]
plt.imshow(X_data[0], cmap='gray')
plt.show()
print(Y_data[0]) | code |
33105697/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from joblib import Parallel, delayed
from torch.utils.data import Dataset, DataLoader
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
img_size = 32
def process_image(img_file):
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (img_size, img_size))
return img
start = time.time()
X_data = []
Y_data = []
for j in range(10):
path = os.path.join('../input/state-farm-distracted-driver-detection/imgs/train', 'c' + str(j), '*.jpg')
files = glob.glob(path)
X_data.extend(Parallel(n_jobs=2)((delayed(process_image)(im_file) for im_file in files)))
Y_data.extend([j] * len(files))
end = time.time() - start
X_data = np.array(X_data)
Y_data = np.array(Y_data)
np.random.shuffle(X_data)
np.random.shuffle(Y_data)
X_data = X_data[0:1000]
Y_data = Y_data[0:1000]
X_data = torch.Tensor(X_data)
X_data = X_data.flatten(start_dim=1)
X_data = X_data.numpy()
Y_data = np.reshape(Y_data, (-1, 1))
class Drivers_dataset(Dataset):
def __init__(self, df):
rows = df.shape[0]
self.imgnp = df.iloc[:rows, 0:img_size * img_size].values
self.labels = df.iloc[:rows, img_size * img_size].values
self.rows = rows
def __len__(self):
return self.rows
def __getitem__(self, idx):
image = torch.tensor(self.imgnp[idx], dtype=torch.float) / 255
image = image.view(1, img_size, img_size)
label = self.labels[idx]
return (image, label)
trainset = np.append(X_train, np.reshape(Y_train, (-1, 1)), axis=1)
testset = np.append(X_test, np.reshape(Y_test, (-1, 1)), axis=1)
testset = pd.DataFrame(data=testset)
trainset = pd.DataFrame(data=trainset)
trainset = Drivers_dataset(trainset)
testset = Drivers_dataset(testset)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=True, num_workers=2)
train_data_iter = iter(trainloader)
test_data_iter = iter(testloader)
dataiter = iter(trainloader)
images, labels = dataiter.next()
(images.size(), labels.size())
for data in trainloader:
inputs, labels = data
print(inputs.shape)
print(labels.shape)
print(labels.data)
break | code |
33105697/cell_14 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
from torch.utils.data import Dataset, DataLoader
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
img_size = 32
def process_image(img_file):
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (img_size, img_size))
return img
start = time.time()
X_data = []
Y_data = []
for j in range(10):
path = os.path.join('../input/state-farm-distracted-driver-detection/imgs/train', 'c' + str(j), '*.jpg')
files = glob.glob(path)
X_data.extend(Parallel(n_jobs=2)((delayed(process_image)(im_file) for im_file in files)))
Y_data.extend([j] * len(files))
end = time.time() - start
X_data = np.array(X_data)
Y_data = np.array(Y_data)
np.random.shuffle(X_data)
np.random.shuffle(Y_data)
X_data = X_data[0:1000]
Y_data = Y_data[0:1000]
X_data = torch.Tensor(X_data)
X_data = X_data.flatten(start_dim=1)
X_data = X_data.numpy()
Y_data = np.reshape(Y_data, (-1, 1))
class Drivers_dataset(Dataset):
def __init__(self, df):
rows = df.shape[0]
self.imgnp = df.iloc[:rows, 0:img_size * img_size].values
self.labels = df.iloc[:rows, img_size * img_size].values
self.rows = rows
def __len__(self):
return self.rows
def __getitem__(self, idx):
image = torch.tensor(self.imgnp[idx], dtype=torch.float) / 255
image = image.view(1, img_size, img_size)
label = self.labels[idx]
return (image, label)
trainset = np.append(X_train, np.reshape(Y_train, (-1, 1)), axis=1)
testset = np.append(X_test, np.reshape(Y_test, (-1, 1)), axis=1)
testset = pd.DataFrame(data=testset)
trainset = pd.DataFrame(data=trainset)
trainset = Drivers_dataset(trainset)
testset = Drivers_dataset(testset)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=True, num_workers=2)
train_data_iter = iter(trainloader)
test_data_iter = iter(testloader)
dataiter = iter(trainloader)
images, labels = dataiter.next()
(images.size(), labels.size()) | code |
106193702/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.info() | code |
106193702/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns | code |
106193702/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum() | code |
106193702/cell_56 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes | code |
106193702/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
df['Bare_Nuclei'].isna().sum() | code |
106193702/cell_44 | [
"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)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (30, 25)
correlation = df.corr()
correlation['Class'].sort_values(ascending=False) | code |
106193702/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes | code |
106193702/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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.head() | code |
106193702/cell_74 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')
(X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_test.isnull().sum()
cols = X_train.columns
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = pd.DataFrame(X_train, columns=[cols])
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train) | code |
106193702/cell_76 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')
(X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_test.isnull().sum()
cols = X_train.columns
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = pd.DataFrame(X_train, columns=[cols])
X_test = pd.DataFrame(X_test, columns=[cols])
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
y_pred | code |
106193702/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
df['Bare_Nuclei'].unique() | code |
106193702/cell_39 | [
"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)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (30, 25)
df.plot(kind='hist', bins=10, subplots=True, layout=(5, 2), sharex=False, sharey=False) | code |
106193702/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum() | code |
106193702/cell_65 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_test.isnull().sum()
X_test.isnull().sum()
X_test.head() | code |
106193702/cell_48 | [
"text_plain_output_1.png",
"image_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 = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (30, 25)
correlation = df.corr()
import seaborn as sns
a = sns.heatmap(correlation, square=True, annot=True, fmt='.2f', linecolor='white')
a.set_xticklabels(a.get_xticklabels(), rotation=90)
a.set_yticklabels(a.get_yticklabels(), rotation=30) | code |
106193702/cell_61 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum() | code |
106193702/cell_54 | [
"text_html_output_1.png"
] | (X_train.shape, X_test.shape) | code |
106193702/cell_72 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')
(X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_test.isnull().sum()
cols = X_train.columns
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = pd.DataFrame(X_train, columns=[cols])
X_train.head() | code |
106193702/cell_64 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_train.head() | code |
106193702/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 |
106193702/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape | code |
106193702/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
df['Class'].value_counts() / np.float(len(df)) | code |
106193702/cell_62 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_test.isnull().sum()
X_test.isnull().sum() | code |
106193702/cell_59 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
for col in X_train.columns:
if X_train[col].isnull().mean() > 0:
print(col, round(X_train[col].isnull().mean(), 4)) | code |
106193702/cell_58 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_test.isnull().sum() | code |
106193702/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
df['Bare_Nuclei'].value_counts() | code |
106193702/cell_78 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')
(X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_test.isnull().sum()
cols = X_train.columns
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = pd.DataFrame(X_train, columns=[cols])
X_test = pd.DataFrame(X_test, columns=[cols])
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
y_pred
knn.predict_proba(X_test)[:, 0]
knn.predict_proba(X_test)[:, 1] | code |
106193702/cell_80 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')
(X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_test.isnull().sum()
cols = X_train.columns
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = pd.DataFrame(X_train, columns=[cols])
X_test = pd.DataFrame(X_test, columns=[cols])
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
y_pred
from sklearn.metrics import accuracy_score
print('Model accuracy score: {0:0.4f}'.format(accuracy_score(y_test, y_pred))) | code |
106193702/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
for var in df.columns:
print(df[var].value_counts()) | code |
106193702/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
print(round(df.describe(), 2)) | code |
106193702/cell_77 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')
(X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_test.isnull().sum()
cols = X_train.columns
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = pd.DataFrame(X_train, columns=[cols])
X_test = pd.DataFrame(X_test, columns=[cols])
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
y_pred
knn.predict_proba(X_test)[:, 0] | code |
106193702/cell_43 | [
"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)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (30, 25)
correlation = df.corr()
correlation | code |
106193702/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum()
df.isna().sum()
df['Class'].value_counts() | code |
106193702/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.drop('Id', axis=1, inplace=True)
df.dtypes
df.isnull().sum() | code |
106193702/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']
df.columns = col_names
df.columns
df.head() | code |
311188/cell_4 | [
"text_plain_output_1.png"
] | 50 + 100 | code |
311188/cell_6 | [
"text_plain_output_1.png"
] | 100 + 200 | code |
311188/cell_1 | [
"text_plain_output_1.png"
] | 1 + 1 | code |
311188/cell_3 | [
"text_plain_output_1.png"
] | 20 + 30 | code |
311188/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2 * np.pi * t)
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.savefig('test.png')
plt.show() | code |
128005164/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
log_r = LogisticRegression(random_state=0)
log_r.fit(X_train, y_train)
y_pred_lr = log_r.predict(X_test)
accuracy_score(y_test, y_pred_lr)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred_lr)) | code |
128005164/cell_4 | [
"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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.head() | code |
128005164/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred_dt = dt.predict(X_test)
accuracy_score(y_test, y_pred_dt) | code |
128005164/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
log_r = LogisticRegression(random_state=0)
log_r.fit(X_train, y_train)
y_pred_lr = log_r.predict(X_test)
accuracy_score(y_test, y_pred_lr) | code |
128005164/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)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.describe() | code |
128005164/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.ensemble import RandomForestClassifier
rf_c = RandomForestClassifier(n_estimators=10, criterion='entropy')
rf_c.fit(X_train, y_train)
y_pred_rf_c = rf_c.predict(X_test)
accuracy_score(y_test, y_pred_rf_c) | code |
128005164/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
log_r = LogisticRegression(random_state=0)
log_r.fit(X_train, y_train) | code |
128005164/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 |
128005164/cell_7 | [
"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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
pd.concat([train, test], axis=0).isnull().sum() | code |
128005164/cell_18 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns
cat_columns = train.select_dtypes(exclude=['number']).columns.tolist()
cat_columns
X = train.drop(['Survived', 'PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)
y = train['Survived']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print('X_train:', X_train.shape)
print('y_train:', y_train.shape)
print('X_test:', X_test.shape)
print('y_test:', y_test.shape) | code |
128005164/cell_8 | [
"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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns | code |
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