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2,469 | test=pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
test['Date']=pd.to_datetime(test['Date'],format='%Y-%m-%d')
store_id=test.Store.unique()[0]
store_test_rows=test[test['Store']==store_id]
store_test_rows['Date'].min(),store_test_rows['Date'].max() | No | 3 | 16 | Data_Transform | data_type_conversions | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | 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": "general_ml", "framework": "General ML", "name": "Hardcoded file path d... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Hardcoded file path"] | 9 | A |
2,470 | print(test.isna().sum())
print(store.isna().sum()) | No | 5 | 39 | EDA | count_missing_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,471 | store['Promo2SinceWeek']=store['Promo2SinceWeek'].fillna(0)
store['Promo2SinceYear']=store['Promo2SinceYear'].fillna(store['Promo2SinceYear'].mode().loc[0])
store['PromoInterval']=store['PromoInterval'].fillna(store['PromoInterval'].mode().iloc[0]) | No | 5 | 17 | Data_Transform | correct_missing_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 9 | A |
2,472 | store['CompetitionDistance']=store['CompetitionDistance'].fillna(store['CompetitionDistance'].max())
store['CompetitionOpenSinceMonth']=store['CompetitionOpenSinceMonth'].fillna(store['CompetitionOpenSinceMonth'].mode().iloc[0])
store['CompetitionOpenSinceYear']=store['CompetitionOpenSinceYear'].fillna(store['Competiti... | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,473 | data_merged=data.merge(store,on='Store',how='left')
print(data.shape)
print(data_merged.shape) | No | 3 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,474 | data_merged.isna().sum() | No | 5 | 39 | EDA | count_missing_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,475 | data_merged['Date']=pd.to_datetime(data_merged['Date'],format='%Y-%m-%d')
data_merged['day']=data_merged['Date'].dt.day
data_merged['year']=data_merged['Date'].dt.year
data_merged['month']=data_merged['Date'].dt.month | No | 4 | 8 | Data_Transform | feature_engineering | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 9 | A |
2,476 | data_merged.info() | No | 5 | 40 | Debug | show_table_attributes | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,477 | data_merged.StoreType.value_counts() | No | 5 | 72 | EDA | count_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,478 | data_merged.StateHoliday.value_counts() | No | 5 | 72 | EDA | count_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,479 | data_merged['StateHoliday']=data_merged['StateHoliday'].map({'0':0,0:0,'a':1,'b':2,'c':3})
data_merged['StateHoliday']=data_merged['StateHoliday'].astype(int) | No | 4 | 20 | Data_Transform | categorify | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 9 | A |
2,480 | data_merged.Assortment.value_counts() | No | 5 | 72 | EDA | count_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,481 | data_merged['Assortment']=data_merged['Assortment'].map({'a':0,'b':1,'c':2})
data_merged['Assortment']=data_merged['Assortment'].astype(int) | No | 4 | 20 | Data_Transform | categorify | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 9 | A |
2,482 | data_merged['PromoInterval'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 9 | A |
2,483 | data_merged['PromoInterval']=data_merged['PromoInterval'].map({'Jan,Apr,Jul,Oct':1,'Feb,May,Aug,Nov':2,'Mar,Jun,Sept,Dec':3})
data_merged['PromoInterval']=data_merged['PromoInterval'].astype(int) | No | 4 | 20 | Data_Transform | categorify | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 9 | A |
2,484 | data_merged['StoreType'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 9 | A |
2,485 | data_merged['StoreType']=data_merged['StoreType'].map({'a':1,'b':2,'c':3,'d':4})
data_merged['StoreType']=data_merged['StoreType'].astype(int) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,486 | X=data_merged.drop('Sales',axis=1)
y=data_merged.Sales | No | 5 | 21 | Data_Transform | prepare_x_and_y | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,487 | X=data_merged.drop(['Sales','Date','Customers'],axis=1)
y=data_merged.Sales
X_train, X_test, y_train, y_test = train_test_split(X, np.log(y+1), test_size=0.2, random_state=1) | No | 4 | 21 | Data_Transform | prepare_x_and_y | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"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": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 9 | B |
2,488 | X_train.shape,X_test.shape,y_train.shape,y_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 |
2,489 | from sklearn.tree import DecisionTreeRegressor as DTR
from sklearn.ensemble import RandomForestRegressor as RFR | No | 5 | 22 | Environment | import_modules | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,490 | model_dtr=DTR(max_depth=11,random_state=1).fit(X_train,y_train)
y_pred=model_dtr.predict(X_test) | Yes | 3 | 7 | Model_Train | train_model | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Randomness Control Checker", "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"] | 9 | B |
2,491 | y_test_inv=np.exp(y_test)-1
y_pred_inv=np.exp(y_pred)-1
print('RMSE',np.sqrt(mean_squared_error(y_test_inv,y_pred_inv)))
print('Accuracy',r2_score(y_test_inv,y_pred_inv)) | No | 2 | 48 | Model_Evaluation | predict_on_test | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"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": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 9 | A |
2,492 | def ToWeight(y):
w = np.zeros(y.shape, dtype=float)
ind = y != 0
w[ind] = 1./(y[ind]**2)
return w
def rmspe(y, yhat):
w = ToWeight(y)
rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
return rmspe
y_test_inv=np.exp(y_test)-1
y_pred_inv=np.exp(y_pred)-1
rmse_test = np.sqrt(mean_squared_error(y_... | No | 5 | 49 | Model_Evaluation | compute_test_metric | 6,511,403 | 1 | A | 1 | 2 | 0 | 0 | 10 | 14 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 18"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Che... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "Missing function docstring"] | 9 | C |
2,493 | from sklearn.model_selection import GridSearchCV | 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 |
2,500 | model_dtr.feature_importances_ | No | 5 | 79 | Visualization | model_coefficients | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
2,501 | store_avg_cust=data.groupby(['Store'])[['Customers']].mean().reset_index().astype(int)
test_1=test.merge(store_avg_cust,on='Store',how='left')
test.shape,test_1.shape
test_merged=test_1.merge(store,on='Store',how='left')
test_merged['Open']=test['Open'].fillna(1)
test_merged['Date']=pd.to_datetime(test_merged['Date'],f... | Yes | 2 | 32 | Data_Transform | merge | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | 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"] | 9 | A |
2,502 | test_pred=model_dtr.predict(test_merged[data_merged.columns.drop(['Sales','Date','Customers'])])
test_pred_inv=np.exp(test_pred)-1
submission_predicted=pd.DataFrame({'Id':test['Id'],'Sales':test_pred_inv})
submission_predicted.to_csv('submission.csv',index=False)
submission_predicted.head() | Yes | 4 | 48 | Model_Evaluation | predict_on_test | 6,511,403 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 7 | 5 | [{"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": "Unit Testing... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 9 | B |
2,503 | from sklearn.ensemble import AdaBoostRegressor | 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 |
2,504 | import seaborn as sns
import matplotlib.pyplot as plt | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,505 | store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
data = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 6,511,414 | 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."] | 6 | A |
2,506 | print(data.shape,store.shape) | No | 5 | 58 | Debug | show_shape | 6,511,414 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,507 | data=pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
store=pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
test=pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
print(data.shape)
print(store.shape)
print(test.shape) | No | 4 | 45 | Data_Extraction | load_from_csv | 6,511,456 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 4 | 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."] | 5 | A |
2,508 | train=pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test=pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store=pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 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."] | 7 | A |
2,509 | print(train.shape)
print(test.shape)
print(store.shape) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,510 | train['StateHoliday'].value_counts() | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,511 | train.describe()[['Sales','Customers']] | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,512 | train.describe()[['Sales','Customers']].loc['mean'] | No | 5 | 40 | Debug | show_table_attributes | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"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": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 7 | B |
2,513 | train.describe()[['Sales','Customers']].loc['min'] | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,514 | train.describe()[['Sales','Customers']].loc['max'] | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,515 | train['Store'].value_counts().head(20) | No | 5 | 41 | Debug | show_table | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 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": "Cross Valida... | 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"] | 7 | B |
2,516 | train['Store'].value_counts().tail(20) | No | 5 | 41 | Debug | show_table | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 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": "Cross Valida... | 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"] | 7 | B |
2,517 | train['DayOfWeek'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 7 | B |
2,518 | train['Open'].value_counts() | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,519 | train['Promo'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 7 | B |
2,520 | train['Date']=pd.to_datetime(train['Date'],format='%Y-%m-%d') | No | 5 | 16 | Data_Transform | data_type_conversions | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,521 | store.isna().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,522 | store1=train[train['Store']==1] | No | 5 | 14 | Data_Transform | filter | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,523 | store1.head() | No | 5 | 41 | Debug | show_table | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,524 | store1.shape | No | 5 | 58 | Debug | show_shape | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,525 | store1.resample('1d',on='Date')['Sales'].sum().plot.line(figsize=(15,5)) | No | 5 | 75 | Visualization | time_series | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,526 | store1[store1['Sales']==0] | No | 5 | 14 | Data_Transform | filter | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,527 | test_store1=test[test['Store']==1]
test_store1['Date']=pd.to_datetime(test_store1['Date'],format='%Y-%m-%d') | No | 5 | 16 | Data_Transform | data_type_conversions | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 7 | A |
2,528 | test_store1['Date'].min(),test_store1['Date'].max() | No | 5 | 40 | Debug | show_table_attributes | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,529 | test_store1['Open'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,530 | import seaborn as sns
import matplotlib.pyplot as plt
plt.hist(store1['Sales']) | Yes | 3 | 22 | Environment | import_modules | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 7 | A |
2,531 | store[store['Store']==1].T | 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 |
2,532 | store=pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/rossmann-store-sales/store.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 8"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 7 | A |
2,533 | store['Promo2SinceWeek']=store['Promo2SinceWeek'].fillna(0) | 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 |
2,534 | store['Promo2SinceYear']=store['Promo2SinceYear'].fillna(store['Promo2SinceYear'].mode().iloc[0]) | No | 5 | 17 | Data_Transform | correct_missing_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,535 | store['PromoInterval']=store['PromoInterval'].fillna(store['PromoInterval'].mode().iloc[0]) | No | 5 | 17 | Data_Transform | correct_missing_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,536 | df=train.merge(store,on='Store',how='left') | No | 5 | 32 | Data_Transform | merge | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"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": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 7 | B |
2,537 | print(train.shape)
print(df.shape) | No | 5 | 58 | Debug | show_shape | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,538 | df.isna().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,539 | df.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,540 | df['day']=df['Date'].dt.day
df['year']=df['Date'].dt.year
df['month']=df['Date'].dt.month | No | 5 | 8 | Data_Transform | feature_engineering | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 7 | A |
2,542 | #Dummies: StateHoliday,StoreType,Assortment,PromoInterval
df['StateHoliday']=df['StateHoliday'].apply(lambda x:'0' if x==0 or x=='0' else x) | No | 5 | 8 | Data_Transform | feature_engineering | 6,511,492 | 1 | A | 0 | 2 | 2 | 0 | 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"] | 7 | A |
2,543 | df['StateHoliday']=df['StateHoliday'].map({'0':0,'a':1,'b':2,'c':3})
df['StateHoliday']=df['StateHoliday'].astype(int) | No | 4 | 20 | Data_Transform | categorify | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 7 | A |
2,544 | df['StoreType'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,545 | df['StoreType']=df['StoreType'].map({'a':0,'b':1,'c':2,'d':3}) | No | 5 | 20 | Data_Transform | categorify | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,546 | df['Assortment']=df['Assortment'].map({'a':0,'b':1,'c':2}) | No | 5 | 20 | Data_Transform | categorify | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,547 | df['Assortment']=df['Assortment'].astype(int) | No | 5 | 16 | Data_Transform | data_type_conversions | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,548 | df['PromoInterval'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,549 | df['PromoInterval']=df['PromoInterval'].map({'Jan,Apr,Jul,Oct':0,'Feb,May,Aug,Nov':1,'Mar,Jun,Sept,Dec':2}) | No | 5 | 20 | Data_Transform | categorify | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,550 | df['PromoInterval']=df['PromoInterval'].astype(int) | No | 5 | 16 | Data_Transform | data_type_conversions | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,551 | df=df.drop('Date',1) | No | 5 | 10 | Data_Transform | drop_column | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,552 | y=np.log1p(df['Sales']) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,553 | X=df.drop(['Sales','Customers'],1) | No | 5 | 10 | Data_Transform | drop_column | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,554 | from sklearn.model_selection import train_test_split
X_train,X_val,y_train,y_val=train_test_split(X,y,random_state=1,test_size=0.2) | No | 5 | 13 | Data_Transform | split | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"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": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 7 | B |
2,555 | y.plot.hist() | No | 5 | 33 | Visualization | distribution | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,556 | from sklearn.tree import DecisionTreeRegressor
dt=DecisionTreeRegressor(max_depth=11,random_state=1).fit(X_train,y_train) | No | 3 | 7 | Model_Train | train_model | 6,511,492 | 2 | B | 1 | 0 | 0 | 0 | 5 | 2 | 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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
2,557 | y_pred_val=dt.predict(X_val) | No | 5 | 48 | Model_Evaluation | predict_on_test | 6,511,492 | 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"] | 7 | A |
2,558 | from sklearn.metrics import r2_score,mean_squared_error
print(r2_score(y_val,y_pred_val))
print(np.sqrt(mean_squared_error(y_val,y_pred_val))) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"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": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 7 | A |
2,559 | y_val_exp=np.exp(y_val)-1
y_pred_val_exp=np.exp(y_pred_val)-1
np.sqrt(mean_squared_error(y_val_exp,y_pred_val_exp)) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,560 | r2_score(y_val_exp,y_pred_val_exp) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"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": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 7 | A |
2,561 | draw_tree(dt,X_train.columns) | No | 5 | 84 | Other | not_enough_vertices | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,562 | print(dt.feature_importances_)
plt.barh(y=X_train.columns,width=dt.feature_importances_)
plt.show() | No | 5 | 79 | Visualization | model_coefficients | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,563 | avg_cust=df.groupby(['Store'])[['Customers']].mean().astype(int)
test1=test.merge(avg_cust,on='Store',how='left') | Yes | 3 | 60 | Data_Transform | groupby | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,564 | test.shape,test1.shape | No | 5 | 58 | Debug | show_shape | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,565 | test_merged=test1.merge(store,on='Store',how='left') | No | 5 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,566 | test1.shape,test_merged.shape | No | 5 | 58 | Debug | show_shape | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,567 | test_merged.head() | 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 |
2,568 | test_merged['Open'].fillna(1,inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker",... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 7 | A |
2,569 | test_merged.isna().sum() | No | 5 | 39 | EDA | count_missing_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,570 | test_merged['Date']=pd.to_datetime(test_merged['Date'],format='%Y-%m-%d')
test_merged['day']=test_merged['Date'].dt.day
test_merged['month']=test_merged['Date'].dt.month
test_merged['year']=test_merged['Date'].dt.year
| No | 5 | 8 | Data_Transform | feature_engineering | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 7 | A |
2,571 | test_merged=test_merged.drop('Date',1) | No | 5 | 10 | Data_Transform | drop_column | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,572 | test_merged['StateHoliday']=test_merged['StateHoliday'].apply(lambda x:'0' if x==0 or x=='0' else x) | No | 5 | 8 | Data_Transform | feature_engineering | 6,511,492 | 1 | A | 0 | 2 | 2 | 0 | 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"] | 7 | A |
2,573 | test_merged['StateHoliday'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,574 | test_merged['StoreType'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
2,575 | test_merged['Assortment'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 7 | A |
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