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values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
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values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7,681 | from sklearn.ensemble import RandomForestRegressor | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,682 | rf = RandomForestRegressor(random_state=42, n_jobs=-1) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,683 | rf.fit(train[feats], train['rentals']) | No | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,684 | train_preds = rf.predict(train[feats]) | No | 5 | 27 | Model_Train | predict_on_train | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,685 | mean_squared_error(train['rentals'], train_preds)**(1/2) | No | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,686 | valid_preds = rf.predict(valid[feats]) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,687 | mean_squared_error(valid['rentals'], valid_preds)**(1/2) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 2,146,505 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
7,688 | test[['datetime', 'count']].to_csv('rf.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,689 | # Prepare the train data
train_data = process_data(train_images, TRAIN_DIR, isTrain=True)
X = np.array([i[0] for i in train_data]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
y = np.array([i[1] for i in train_data]) | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,690 | plot_accuracy_and_loss(history) | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,691 | import numpy as np<br>import pandas as pd<br>import keras<br>from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder<br>from sklearn.model_selection import train_test_split<br>from sklearn.metrics import f1_score<br>from matplotlib import pyplot as plt<br>import os<br>import re<br>print(os.listdir... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,692 | df = pd.read_csv('../input/2TWH_train.csv', index_col='IDNum')
for col in df:
if col[0] == ' ':
df = df.rename(columns={col : col[1:]})
dt = pd.read_csv('../input/test.csv', index_col='IDNum')
for col in dt:
if col[0] == ' ':
dt = dt.rename(columns={col : col[1:]}) | Yes | 3 | 45 | Data_Extraction | load_from_csv | 2,172,412 | 1 | A | 0 | 4 | 6 | 0 | 0 | 8 | ok | 5 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 6 | A |
7,693 | df['Source IP'] = df['Source IP'].apply(to_int)
df['Destination IP'] = df['Destination IP'].apply(to_int)
df['Timestamp'] = df['Timestamp'].apply(to_int)
df['Flow Bytes/s'] = df['Flow Bytes/s'].astype(float)
df['Flow Packets/s'] = df['Flow Packets/s'].astype(float)
dt['Source IP'] = dt['Source IP'].apply(to_int)
dt[... | No | 5 | 16 | Data_Transform | data_type_conversions | 2,172,412 | 3 | C | 4 | 0 | 0 | 0 | 32 | 10 | ok | 10 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker", "Hardcoded file path"] | 6 | C |
7,694 | df = df.replace([np.inf, 'Infinity', 'infinity', 'inf'], 2**31-1)
df = df.replace([np.nan, np.inf, 'NaN'], 0)
dt = dt.replace([np.inf, 'Infinity', 'infinity', 'inf'], 2**31-1)
dt = dt.replace([np.nan, 'NaN'], 0) | Yes | 4 | 17 | Data_Transform | correct_missing_values | 2,172,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 6 | A |
7,695 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)
ohe = OneHotEncoder(categories='auto')
y_train_ohe = ohe.fit_transform(y_train.reshape(-1, 1))
y_test_ohe = ohe.fit_transform(y_test.reshape(-1, 1))
y_ohe = ohe.fit_transform(y.reshape(-1, 1))
scaler = StandardScaler()
X_scale =... | Yes | 3 | 13 | Data_Transform | split | 2,172,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 17 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | B | Cross Validation Checker=low, Cross-validation not detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | B |
7,696 | from keras import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(units=20, activation='relu', input_dim=X.shape[1]))
model.add(Dense(units=10, activation='relu'))
model.add(Dense(units=3, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentro... | No | 5 | 4 | Model_Train | choose_model_class | 2,172,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 6 | A |
7,697 | history = model.fit(X_train_scale,
y_train_ohe,
epochs=1000,
batch_size=8192,
validation_data=(X_test_scale, y_test_ohe)) | No | 5 | 7 | Model_Train | train_model | 2,172,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | B |
7,698 | import matplotlib.pyplot as plt
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.figure(figsize = (10, 10))
plt.semilogy(epochs, loss, 'bo', label='Training loss')
plt.semilogy(epochs, val_loss, 'red', label='Validation loss')
plt.title('Training and validation ... | No | 5 | 35 | Visualization | learning_history | 2,172,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 6 | A |
7,699 | plt.clf()
history_dict = history.history
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
plt.figure(figsize = (10, 10))
plt.semilogy(epochs, acc_values, 'bo', label='Training acc')
plt.semilogy(epochs, val_acc_values, 'red', label='Validation acc')
plt.title('Training and validation accuracy')... | No | 5 | 35 | Visualization | learning_history | 2,172,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 6 | A |
7,700 | # Load packages
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
from random import shuffle
import os, gc, time, cv2, random, math
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
####################
# Global Constants #
#########... | No | 4 | 77 | Other | define_variables | 2,180,506 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 1 | null |
7,701 | # Plotting loss and accuracy for the model
def plot_accuracy_and_loss(history):
eval_res = pd.DataFrame(history.history)
f, ax = plt.subplots(1,2, figsize=(18,5))
for i, c in enumerate(['acc', 'loss']):
ax[i].plot(eval_res[[c]], label=f'Training {c}')
ax[i].plot(eval_res[[f'val_{c}']], label... | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 1 | 2 | 1 | 0 | 5 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,702 | import os
print(os.listdir("../input")) | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,703 | import pandas as pd
from pathlib import Path
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,704 | data_dir = Path('../input')
train_df = pd.read_csv(data_dir / 'train.csv')
test_df = pd.read_csv(data_dir / 'test.csv')
sample_submission = pd.read_csv(data_dir / 'sampleSubmission.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,705 | print(train_df.shape)
train_df.head() | Yes | 3 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,706 | print(test_df.shape)
test_df.head() | Yes | 3 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,707 | features = ['open', 'high', 'low', 'close', 'volume', 'trades', 'macd',
'macd_hist', 'macd_signal', 'adx', 'di_plus', 'di_minus',
'rsi', 'cci', 'adl'] | No | 5 | 77 | Other | define_variables | 2,186,697 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
7,708 | X_train = train_df[features]
y_train = train_df['y']
X_test = test_df[features] | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,709 | scaler = StandardScaler(with_std=False)
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test) | No | 5 | 18 | Data_Transform | normalization | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,710 | regressor = RandomForestRegressor(n_estimators=200, random_state=42, n_jobs=-1,
max_depth=7)
regressor.fit(X_train, y_train) | No | 5 | 7 | Model_Train | train_model | 2,186,697 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 5 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
7,711 | y_test = regressor.predict(X_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,186,697 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
7,712 | sample_submission.to_csv('submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,713 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
print(os.listdir("../input")) | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 5"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
7,714 | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression as LR
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchC... | No | 5 | 22 | Environment | import_modules | 2,189,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 10 | A |
7,715 | import warnings
warnings.filterwarnings("ignore") | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,716 | data = pd.read_csv('../input/train.csv')
target = data.pop('y')
data_test = pd.read_csv('../input/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 2,189,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 10 | A |
7,717 | out_df = pd.DataFrame(np.concatenate(preds_df), columns=['id','expected'])
out_df.id = out_df.id.astype('int')
out_df.to_csv('by_asset_lgbm.csv',index = False) | Yes | 4 | 25 | Data_Export | save_to_csv | 2,189,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 10 | A |
7,718 | out_df = pd.DataFrame(np.concatenate(grid_preds_df), columns=['id','expected'])
out_df.id = out_df.id.astype('int')
out_df.to_csv('grid_by_asset.csv',index = False) | Yes | 4 | 25 | Data_Export | save_to_csv | 2,189,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 10 | A |
7,719 | out_df = pd.DataFrame(np.concatenate(preds_df), columns=['id','expected'])
out_df.id = out_df.id.astype('int')
out_df.to_csv('Elastic_net.csv',index = False) | Yes | 4 | 25 | Data_Export | save_to_csv | 2,189,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 10 | A |
7,720 | data_dir = Path('../input')
train_df = pd.read_csv(data_dir / 'train.csv')
test_df = pd.read_csv(data_dir / 'test.csv')
sample_submission = pd.read_csv(data_dir / 'sampleSubmission.csv')
train_df['d'] = train_df['close'] / train_df['open']
test_df['d'] = test_df['close'] / test_df['open'] | Yes | 4 | 45 | Data_Extraction | load_from_csv | 2,191,512 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection C... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Hardcoded file path", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 10 | A |
7,721 |
features = ['asset','di_minus',
'rsi', 'cci','volume']
features = ['asset', 'open', 'high', 'low', 'close', 'volume', 'trades', 'macd',
'macd_hist', 'macd_signal', 'adx', 'di_plus', 'di_minus',
'rsi', 'cci', 'adl'] | No | 5 | 77 | Other | define_variables | 2,191,512 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 10 | A |
7,722 | X_train = train_df[features]
y_train = train_df[['asset', 'y']]
X_test = test_df[features] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 2,191,512 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 10 | A |
7,723 | from numpy import column_stack
scaler = StandardScaler()
X_train = pd.DataFrame(data=scaler.fit_transform(train_df[features]), columns=features)
X_train['asset'] = train_df['asset']
X_test = pd.DataFrame(data=scaler.transform(test_df[features]), columns=features)
X_test['asset'] = test_df['asset'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 2,191,512 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 12 | 10 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | D |
7,724 | regressor = TssRegressor()
regressor.fit(X_train, y_train)
y_test = [item[1] for item in regressor.predict(X_test)]
y_test[:12] = [0]*12
sample_submission['expected'] = y_test | Yes | 3 | 7 | Model_Train | train_model | 2,191,512 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 10 | 9 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection C... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
7,725 | import pylab
import calendar
import numpy as np
import pandas as pd
import seaborn as sn
from scipy import stats
import missingno as msno
from datetime import datetime
import matplotlib.pyplot as plt
import warnings
pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,726 | dailyData = pd.read_csv("../input/train.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,727 | dailyData.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,728 | dailyData.head(2) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,729 | dailyData.dtypes | No | 5 | 70 | Debug | show_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,730 | dailyData["date"] = dailyData.datetime.apply(lambda x : x.split()[0])<br>dailyData["hour"] = dailyData.datetime.apply(lambda x : x.split()[1].split(":")[0])<br>dailyData["weekday"] = dailyData.date.apply(lambda dateString : calendar.day_name[datetime.strptime(dateString,"%Y-%m-%d").weekday()])<br>dailyData["month"] = d... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,731 | dailyData = dailyData.drop(["datetime"],axis=1) | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,732 | fig, axes = plt.subplots(nrows=2,ncols=2)<br>fig.set_size_inches(12, 10)<br>sn.boxplot(data=dailyData,y="count",orient="v",ax=axes[0][0])<br>sn.boxplot(data=dailyData,y="count",x="season",orient="v",ax=axes[0][1])<br>sn.boxplot(data=dailyData,y="count",x="hour",orient="v",ax=axes[1][0])<br>sn.boxplot(data=dailyData,y="... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,733 | dailyDataWithoutOutliers = dailyData[np.abs(dailyData["count"]-dailyData["count"].mean())<=(3*dailyData["count"].std())] | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,734 | print ("Shape Of The Before Ouliers: ",dailyData.shape)
print ("Shape Of The After Ouliers: ",dailyDataWithoutOutliers.shape) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,735 | dataTrain = pd.read_csv("../input/train.csv")
dataTest = pd.read_csv("../input/test.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,736 | data = dataTrain.append(dataTest)
data.reset_index(inplace=True)
data.drop('index',inplace=True,axis=1) | Yes | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,737 | data["date"] = data.datetime.apply(lambda x : x.split()[0])
data["hour"] = data.datetime.apply(lambda x : x.split()[1].split(":")[0]).astype("int")
data["year"] = data.datetime.apply(lambda x : x.split()[0].split("-")[0])
data["weekday"] = data.date.apply(lambda dateString : datetime.strptime(dateString,"%Y-%m-%d").wee... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,738 | categoricalFeatureNames = ["season","holiday","workingday","weather","weekday","month","year","hour"]<br>numericalFeatureNames = ["temp","humidity","windspeed","atemp"]<br>dropFeatures = [\'casual\',"count","datetime","date","registered"] | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,739 | for var in categoricalFeatureNames:
data[var] = data[var].astype("category") | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,740 | dataTrain = dataTrain.drop(dropFeatures,axis=1)
dataTest = dataTest.drop(dropFeatures,axis=1) | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,741 | from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
import warnings
pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Initialize logistic regression model
lModel = LinearRegre... | Yes | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | ok | 8 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | B |
7,742 | from sklearn.ensemble import GradientBoostingRegressor
gbm = GradientBoostingRegressor(n_estimators=4000,alpha=0.01); ### Test 0.41
yLabelsLog = np.log1p(yLabels)
gbm.fit(dataTrain,yLabelsLog)
preds = gbm.predict(X= dataTrain)
print ("RMSLE Value For Gradient Boost: ",rmsle(np.exp(yLabelsLog),np.exp(preds),False)) | Yes | 3 | 7 | Model_Train | train_model | null | 2 | B | 2 | 0 | 0 | 0 | 10 | 6 | ok | 9 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | B |
7,743 | predsTest = gbm.predict(X= dataTest)
fig,(ax1,ax2)= plt.subplots(ncols=2)
fig.set_size_inches(12,5)
sn.distplot(yLabels,ax=ax1,bins=50)
sn.distplot(np.exp(predsTest),ax=ax2,bins=50) | No | 5 | 56 | Visualization | plot_predictions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,744 | submission = pd.DataFrame({<br> "datetime": datetimecol,<br> "count": [max(0, x) for x in np.exp(predsTest)]<br> })<br>submission.to_csv(\'bike_predictions_gbm_separate_without_fe.csv\', index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,745 | import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
%matplotlib inline | No | 5 | 23 | Environment | set_options | 2,213,549 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,746 | train = pd.read_csv('../input/training/training.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,747 | train.dropna(inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,748 | train.tail(3) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,749 | test = pd.read_csv('../input/test/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,750 | train.shape, test.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,751 | x = np.stack(train.Image)[..., None] | No | 5 | 11 | Data_Transform | concatenate | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,752 | x.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,753 | x_t = np.stack(test.Image)[..., None] | No | 5 | 11 | Data_Transform | concatenate | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,754 | x_t.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,755 | from IPython.display import SVG
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPool2D, Flatten, LeakyReLU, ELU
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.utils.vis_utils import model_to_dot | No | 5 | 22 | Environment | import_modules | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,765 | np.random.seed(777)
model10 = Sequential()
model10.add(Conv2D(filters = 64, kernel_size = (5,5), padding = 'Same', activation = 'relu', input_shape = (96, 96, 1)))
model10.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model10.add(Dropout(0.3))
model10.add(Conv2D(filters = 32, kernel_size = (4,4), padding = 'Same', ... | No | 5 | 4 | Model_Train | choose_model_class | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,775 | MODEL_DIR = '../model/' | No | 5 | 77 | Other | define_variables | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,776 | modelpath = '../model/{epoch:02d}-{val_loss:4f}.hdf5' | No | 5 | 77 | Other | define_variables | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,786 | predict = model10.predict(x) | No | 5 | 27 | Model_Train | predict_on_train | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,787 | train_loss = history.history['loss']
val_loss = history.history['val_loss']
x_len = np.arange(len(train_loss))
plt.plot(x_len, train_loss, marker='.', c='red', label='Train_loss')
plt.plot(x_len, val_loss, marker='.', c='blue', label='Val_loss')
plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel(... | No | 5 | 35 | Visualization | learning_history | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,788 | y_t = model10.predict(x_t) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,789 | look_id = pd.read_csv('../input/IdLookupTable.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,790 | look_id.info() | No | 5 | 40 | Debug | show_table_attributes | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,791 | look_id.drop('Location', axis=1, inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,792 | look_id[['RowId','Location']].to_csv('Predict.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 2,213,549 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,798 | from xgboost import XGBClassifier
import xgboost as xgb | No | 5 | 22 | Environment | import_modules | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,799 | #for scaling
from sklearn.preprocessing import StandardScaler | No | 5 | 22 | Environment | import_modules | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,800 | data = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
| No | 5 | 45 | Data_Extraction | load_from_csv | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 2 | A |
7,801 | print(data.shape) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,802 | data.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,803 | df=pd.concat([df,Date,Time],axis=1)
#df
#-----------test data------------------
df_test=pd.concat([df_test,Date_test,Time_test],axis=1)
| No | 5 | 11 | Data_Transform | concatenate | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,804 | df=df.drop(labels=['Dates'],axis=1)
#-----------test data------------------
df_test=df_test.drop(labels=['Dates'],axis=1)
| No | 5 | 10 | Data_Transform | drop_column | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,805 | df.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,806 | df["rot60_X"]=(0.5) * df["Y"] + (1.732/2) * df["X"]
df["rot60_Y"]=0.5 * df["Y"] - (1.732/2) * df["X"]
df_test["rot60_X"]=(0.5) * df_test["Y"] + (1.732/2) * df_test["X"]
df_test["rot60_Y"]=0.5 * df_test["Y"] - (1.732/2) * df_test["X"]
df["rot45_X"]=0.707 * df["Y"] + 0.707 * df["X"]
df["rot45_Y"]=0.707 * df["Y"] - 0.... | No | 5 | 8 | Data_Transform | feature_engineering | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,807 | df["radial60"]=np.sqrt(np.power(df[\'rot60_X\'],2) + np.power(df[\'rot60_Y\'],2))<br><br>df_test["radial60"]=np.sqrt(np.power(df_test[\'rot60_X\'],2) + np.power(df_test[\'rot60_Y\'],2)) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,808 | df=df.drop(labels='rot60_X',axis=1)
df_test=df_test.drop(labels='rot60_X',axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,809 | df=df.drop(labels='rot60_Y',axis=1)
df_test=df_test.drop(labels='rot60_Y',axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,810 | df=df.drop(labels='Second',axis=1)
df_test=df_test.drop(labels='Second',axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,811 | df['Minute']=df['Minute'].apply(lambda x:int(x))
df['Minute']=df['Minute'].apply(lambda x : 'low' if x <31 else 'high')
df_test['Minute']=df_test['Minute'].apply(lambda x:int(x))
df_test['Minute']=df_test['Minute'].apply(lambda x : 'low' if x <31 else 'high')
| No | 5 | 8 | Data_Transform | feature_engineering | 2,236,557 | 1 | A | 0 | 2 | 2 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,812 | df['DayOfWeek']= df['DayOfWeek'].apply(lambda x : 'WeekHigh' if x in ('Wednesday','Friday') else ('WeekMed' if x in ('Tuesday','Thursday','Saturday') else 'WeekLow'))
df_test['DayOfWeek']= df_test['DayOfWeek'].apply(lambda x : 'WeekHigh' if x in ('Wednesday','Friday') else ('WeekMed' if x in ('Tuesday','Thursday','Sa... | No | 5 | 8 | Data_Transform | feature_engineering | 2,236,557 | 3 | C | 2 | 4 | 6 | 0 | 10 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | C |
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