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608 | from sklearn.model_selection import GridSearchCV
from xgboost import XGBRegressor
gsearch = GridSearchCV(estimator=XGBRegressor(random_state=my_randome_state),
param_grid = parameters,
scoring='neg_root_mean_squared_error',
n_jobs=4,cv=5, verbose=7)
... | No | 4 | 6 | Hyperparam_Tuning | train_on_grid | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 11"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | A |
609 | best_n_estimators = gsearch.best_params_.get('n_estimators')
best_learning_rate = gsearch.best_params_.get('learning_rate')
best_max_depth = gsearch.best_params_.get('max_depth')
best_max_gamma = gsearch.best_params_.get('gamma')
best_max_eta = gsearch.best_params_.get('eta')
best_max_reg_alpha = gsearch.best_para... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 11,097,956 | 1 | A | 0 | 0 | 0 | 7 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
610 | final_model = XGBRegressor(n_estimators=best_n_estimators,
random_state=my_randome_state,
learning_rate=best_learning_rate,
max_depth=best_max_depth,
gamma=best_max_gamma,
eta=best_m... | No | 4 | 7 | Model_Train | train_model | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 4 | 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 |
611 | preds_test = final_model.predict(normed_test_data) | No | 5 | 48 | Model_Evaluation | predict_on_test | 11,097,956 | 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 |
612 | # zip
import zipfile
with zipfile.ZipFile("/kaggle/input/restaurant-revenue-prediction/test.csv.zip") as zf:
zf.extractall()
with zipfile.ZipFile("/kaggle/input/restaurant-revenue-prediction/train.csv.zip") as zf:
zf.extractall()
' | No | 3 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
613 | #
df_train = pd.read_csv("train.csv")
df_test = pd.read_csv("test.csv")
df_submission = pd.read_csv("/kaggle/input/restaurant-revenue-prediction/sampleSubmission.csv")' | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
614 | df_test | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
617 | #
corrmat = df_train.corr()
# corrmat' | 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 |
618 | #
y_train = df_train["revenue"]
del df_train["revenue"]' | No | 4 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
619 | #
df_all = pd.concat([df_train, df_test], axis=0) # axis=0 : ' | No | 5 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
620 | # OpenDate
# timestamp
df_all["Open Date"] = pd.to_datetime(df_all["Open Date"])
df_all["Year"] = df_all["Open Date"].dt.year
df_all["Month"] = df_all["Open Date"].dt.month
df_all["Day"] = df_all["Open Date"].dt.day' | 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 |
621 | dftrain['Open Date'] = dftrain['Open Date'].apply(lambda x: pd.to_datetime(x))
dftest['Open Date'] = dftest['Open Date'].apply(lambda x: pd.to_datetime(x))
def calc_days(dtime):
now_time = pd.to_datetime('2015-01-01')
result = now_time - dtime
return int(result.total_seconds()/3600//24)
dftrain['days_since_... | No | 3 | 8 | Data_Transform | feature_engineering | 6,470,191 | 1 | A | 1 | 1 | 0 | 0 | 12 | 8 | ok | 5 | 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": "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", "Missing function docstring"] | 3 | B |
622 | dftrain['revenue'] = dftrain['revenue'].astype(int) | No | 5 | 16 | Data_Transform | data_type_conversions | 6,470,191 | 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"] | 3 | B |
626 |
for col in dftrain.columns:
if (dftrain[col].dtype == int) | (dftrain[col].dtype == float):
if col not in ['P2','P3', 'P7','revenue']:
dftrain[col] = dftrain[col].map(lambda x:np.log1p(x))
print (col)
dftest[col] = dftest[col].map(lambda x: np.log1p(x))
| No | 5 | 20 | Data_Transform | categorify | 6,470,191 | 1 | A | 0 | 3 | 6 | 0 | 0 | 6 | 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"] | 3 | B |
628 | pd.get_dummies(dftrain).columns | No | 5 | 71 | Debug | show_columns | 6,470,191 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
629 | #TFI (tab food investments) has provided a dataset with 137 restaurants in the training set, and a test set of 100000 restaurants..
data = pd.read_csv('../input/restaurant-revenue-prediction/train.csv')
test_data = pd.read_csv('../input/restaurant-revenue-prediction/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 5,729,566 | 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."] | 10 | A |
630 | data.describe() | 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 |
631 | data.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 |
632 | pd.get_dummies(dftest).columns[50:] | No | 5 | 53 | Other | something_strange | 6,470,191 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
633 | #log transforming dftrain revenue
dftrain['revenue']= dftrain.revenue.apply(lambda x: np.log1p(x)) | No | 5 | 8 | Data_Transform | feature_engineering | 6,470,191 | 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"] | 3 | B |
634 | dftrain1 = pd.get_dummies(dftrain, drop_first = True)
dftest1 = pd.get_dummies(dftest,drop_first = True) | No | 5 | 20 | Data_Transform | categorify | 6,470,191 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
635 | cat_cols = ['City', 'Open Date', 'City'] | No | 3 | 10 | Data_Transform | drop_column | 6,470,191 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
636 | final_pred = model.predict(dftest1) | No | 2 | 10 | Data_Transform | drop_column | 6,470,191 | 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"] | 3 | A |
637 | sampledf = pd.read_csv('/kaggle/input/restaurant-revenue-prediction/sampleSubmission.csv')
| No | 2 | 7 | Model_Train | train_model | 6,470,191 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/restaurant-revenue-prediction/sampleSubmission.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
638 | sampledf.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 |
639 | sub = pd.DataFrame() | No | 5 | 12 | Data_Transform | create_dataframe | 6,470,191 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
640 | sub['Id'] = sampledf.Id
sub['Prediction'] = np.expm1(final_pred) | No | 5 | 55 | Data_Export | prepare_output | 6,470,191 | 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"] | 3 | A |
641 | sub.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 |
642 | train = pd.read_csv("../input/restaurant-revenue-prediction/train.csv.zip") | No | 5 | 45 | Data_Extraction | load_from_csv | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/restaurant-revenue-prediction/train.csv.zip. 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"] | 4 | A |
643 | train.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 |
644 | train["City"].value_counts() | No | 5 | 72 | EDA | count_values | 11,611,498 | 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"] | 4 | B |
645 | train["City"] = (train["City"]=="stanbul").astype(np.int)' | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
646 | train["City Group"].value_counts() | No | 5 | 72 | EDA | count_values | 11,611,498 | 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"] | 4 | B |
647 | train["City Group"] = l.fit_transform(train["City Group"]) | No | 3 | 16 | Data_Transform | data_type_conversions | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 6 | 6 | [{"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... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | D |
648 | train["Type"].value_counts() | No | 5 | 72 | EDA | count_values | 11,611,498 | 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"] | 4 | B |
649 | train["Type"] = l.fit_transform(train["Type"]) | No | 5 | 20 | Data_Transform | categorify | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 6 | 6 | [{"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... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | D |
650 | train["year"]=0
for i in range(len(train["Open Date"])):
a=train["Open Date"][i].split("/")
train["year"][i]=a[2]
train["month"]=0
for i in range(len(train["Open Date"])):
a=train["Open Date"][i].split("/")
train["month"][i]=a[0]
train["day_No"]=0
for i in range(len(train["Open Date"])... | No | 5 | 8 | Data_Transform | feature_engineering | 11,611,498 | 1 | A | 1 | 3 | 3 | 0 | 12 | 12 | ok | 9 | 5 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexing", "fix": "Use single indexing or .loc[], .iloc[] methods.", "benefits": "Enhances code readability and prevents performance issues.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexin... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Chain Indexing", "Column Selection Checker", "Hardcoded file path"] | 4 | B |
651 | test = pd.read_csv("../input/restaurant-revenue-prediction/test.csv.zip") | No | 5 | 45 | Data_Extraction | load_from_csv | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/restaurant-revenue-prediction/test.csv.zip. 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"] | 4 | A |
652 | 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 |
653 | test["City"] = (test["City"]=="stanbul").astype(np.int)
test["Type"] = l.fit_transform(test["Type"])
test["City Group"] = l.fit_transform(test["City Group"])' | No | 4 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
654 | test["year"]=0
for i in range(len(test["Open Date"])):
a=test["Open Date"][i].split("/")
test["year"][i]=a[2]
test["month"]=0
for i in range(len(test["Open Date"])):
a=test["Open Date"][i].split("/")
test["month"][i]=a[0]
test["day_No"]=0
for i in range(len(test["Open Date"])):
... | No | 5 | 8 | Data_Transform | feature_engineering | 11,611,498 | 1 | A | 1 | 3 | 3 | 0 | 12 | 12 | ok | 7 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexing", "fix": "Use single indexing or .loc[], .iloc[] methods.", "benefits": "Enhances code readability and prevents performance issues.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexin... | A | no applicable ML-specific findings | [] | ["Chain Indexing", "Column Selection Checker", "Hardcoded file path"] | 4 | A |
655 | x_train = train.drop(columns=["Id","revenue","Open Date"],axis=1)
y_train = train["revenue"]
x_test = test.drop(columns=["Id","Open Date"],axis=1)
x_train.shape,x_test.shape,y_train.shape | No | 5 | 21 | Data_Transform | prepare_x_and_y | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 4 | B |
656 | xtrain = s.fit_transform(x_train)
x_train = pd.DataFrame(x_train,columns=x_train.columns)
xtest = s.fit_transform(x_test)
x_test = pd.DataFrame(x_test,columns=x_test.columns) | No | 4 | 18 | Data_Transform | normalization | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"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"] | 4 | D |
657 | x_train.shape,x_test.shape,y_train.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 |
658 | from xgboost import XGBRegressor
model = XGBRegressor()
model.fit(x_train,y_train)
y_pred = model.predict(x_test) | No | 1 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
659 | s = pd.read_csv("../input/restaurant-revenue-prediction/sampleSubmission.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/restaurant-revenue-prediction/sampleSubmission.csv. 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"] | 4 | A |
660 | s.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 |
661 | f = pd.DataFrame(f) | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
662 | f.to_csv("submission.csv",index=False) | No | 5 | 25 | Data_Export | save_to_csv | 11,611,498 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
663 | f.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 |
664 | df=pd.read_csv("/kaggle/input/restaurant-revenue-prediction/train.csv.zip")
df.head() | No | 3 | 45 | Data_Extraction | load_from_csv | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/restaurant-revenue-prediction/train.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 8 | A |
665 | df.describe() | 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 |
666 | import matplotlib.pyplot as plt
import seaborn as sns | 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 |
667 | 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 |
668 | df=df.drop('Id',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 |
669 | 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 |
670 | df['Open Date']=pd.to_datetime(df['Open Date'])
df | No | 5 | 16 | Data_Transform | data_type_conversions | 11,656,525 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 8 | A |
671 | df['month']=[x.month for x in df['Open Date']]
df['year']=[x.year for x in df['Open Date']] | No | 5 | 8 | Data_Transform | feature_engineering | 11,656,525 | 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"] | 8 | A |
672 | df
| No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
673 | df.groupby('month')['revenue'].mean() | No | 5 | 60 | Data_Transform | groupby | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
674 | sns.barplot('month','revenue',data=df) | No | 5 | 33 | Visualization | distribution | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
675 | df=df.drop('Open Date',axis=1)
df['Type'].value_counts()
ty={'FC':0,'IL':1,'DT':2}
df['Type']=df['Type'].map(ty) | No | 3 | 8 | Data_Transform | feature_engineering | 11,656,525 | 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"] | 8 | A |
676 | df['City Group'].value_counts() | No | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
677 | cg={'Big Cities':0,'Other':1}
df['City Group']=df['City Group'].map(cg) | No | 5 | 20 | Data_Transform | categorify | 11,656,525 | 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"] | 8 | A |
678 | df['City'].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 |
679 | x=0
c={}
for i in df['City'].unique():
c.update({i:x})
x=x+1
| No | 5 | 53 | Other | something_strange | 11,656,525 | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | 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"] | 8 | A |
680 | c | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
681 | from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor | No | 5 | 22 | Environment | import_modules | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
682 | from sklearn.metrics import mean_absolute_error ,mean_squared_error,r2_score | No | 5 | 22 | Environment | import_modules | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
683 | dr=DecisionTreeRegressor()
dr=dr.fit(X_train,Y_train)
pred=dr.predict(X_test)
s=mean_absolute_error(Y_test,pred)
s1=mean_squared_error(Y_test,pred)
s2=r2_score(Y_test,pred)
print("The MAE with the DecisionTreeRegressor is: "+str(s))
print("The MsE with the DecisionTreeRegressor is: "+str(s1))
print("The R2_Score with t... | No | 4 | 49 | Model_Evaluation | compute_test_metric | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | 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"] | 8 | B |
684 | r=RandomForestRegressor()
r=r.fit(X_train,Y_train)
pred=r.predict(X_test)
s=mean_absolute_error(Y_test,pred)
s1=mean_squared_error(Y_test,pred)
s2=r2_score(Y_test,pred)
print("The MAE with the RandomForestRegressor is: "+str(s))
print("The MsE with the RandomForestRegressor is: "+str(s1))
print("The R2_Score with the R... | No | 4 | 49 | Model_Evaluation | compute_test_metric | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 10 | 8 | [{"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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting 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"] | 8 | B |
685 | x=XGBRegressor()
x=dr.fit(X_train,Y_train)
pred=x.predict(X_test)
s=mean_absolute_error(Y_test,pred)
s1=mean_squared_error(Y_test,pred)
s2=r2_score(Y_test,pred)
print("The MAE with the XGBRegressor is: "+str(s))
print("The MsE with the XGBRegressor is: "+str(s1))
print("The R2_Score with the XGBRegressor is: "+str(s2)) | Yes | 3 | 7 | Model_Train | train_model | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | 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"] | 8 | B |
686 | df_t=pd.read_csv("/kaggle/input/restaurant-revenue-prediction/test.csv.zip")
df_t.head()
i_d=df_t['Id']
df_t=df_t.drop('Id',axis=1)' | Yes | 3 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
687 | df_t['Open Date']=pd.to_datetime(df_t['Open Date']) | Yes | 5 | 16 | Data_Transform | data_type_conversions | 11,656,525 | 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"] | 8 | A |
688 | df_t['month']=[x.month for x in df_t['Open Date']]
df_t['year']=[x.year for x in df_t['Open Date']] | Yes | 5 | 8 | Data_Transform | feature_engineering | 11,656,525 | 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"] | 8 | A |
689 | df_t=df_t.drop('Open Date',axis=1)
df_t['Type'].value_counts()
ty={'FC':0,'IL':1,'DT':2}
df_t['Type']=df_t['Type'].map(ty)
cg={'Big Cities':0,'Other':1}
df_t['City Group']=df_t['City Group'].map(cg)
x=0
c={}
for i in df_t['City'].unique():
c.update({i:x})
x=x+1
df_t['City']=df_t['City'].map(c) | Yes | 5 | 8 | Data_Transform | feature_engineering | 11,656,525 | 1 | A | 0 | 1 | 1 | 0 | 0 | 12 | 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"] | 8 | A |
690 | df_t.head() | No | 2 | 45 | Data_Extraction | load_from_csv | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
691 | df_t.dropna | No | 5 | 17 | Data_Transform | correct_missing_values | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
692 | df_t['Type']=df_t['Type'].fillna(0) | No | 5 | 17 | Data_Transform | correct_missing_values | 11,656,525 | 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"] | 8 | A |
693 | df_t.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 |
694 |
p=k.predict(df_t)
| No | 5 | 48 | Model_Evaluation | predict_on_test | 11,656,525 | 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"] | 8 | A |
695 | sub=pd.read_csv("/kaggle/input/restaurant-revenue-prediction/sampleSubmission.csv")
sub['Id']=i_d' | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
696 | sub.to_csv("Submission1.csv",index=False) | No | 5 | 25 | Data_Export | save_to_csv | 11,656,525 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
697 | b"import pandas as pd\n\ntrain = pd.read_csv('/kaggle/input/restaurant-revenue-prediction/train.csv.zip')\ntest = pd.read_csv('/kaggle/input/restaurant-revenue-prediction/test.csv.zip')\n\n# Id\ntrain_Id = train.Id\ntest_Id = test.Id\n\n# Id\ntrain.drop('Id', axis=1, inplace=True)\ntest.drop('Id', axis=1, inplace=True)... | No | 3 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
698 | #importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# import seaborn as sns
import seaborn as sns; sns.set(style="ticks", color_codes=True)
from datetime import datetime
from scipy import stats
from scipy.stats import norm, skew
from sklearn.preprocessing import StandardScal... | No | 4 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
699 | print('Size of train data', train.shape)
print('Size of test data', test.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 |
700 | train.describe(include='O') | 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 |
702 | train["revenue"].describe() | 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 |
703 | #revenueQ-Q
#
fig = plt.figure(figsize=(10, 4))
plt.subplots_adjust(wspace=0.4)
#
ax = fig.add_subplot(1, 2, 1)
sns.distplot(train['revenue'], ax=ax)
# QQ
ax2 = fig.add_subplot(1, 2, 2)
stats.probplot(train['revenue'], plot=ax2)
plt.show()
#
print(train['revenue'].describe())
print("-----------------------------... | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
704 | # dataframe
df = train.copy()
#log(x+1)
df['revenue'] = np.log1p(df['revenue'])
# (0, 1)
scaler=StandardScaler()
df['revenue']=scaler.fit_transform(df[['revenue']])
#
fig = plt.figure(figsize=(10, 4))
plt.subplots_adjust(wspace=0.4)
#
ax = fig.add_subplot(1, 2, 1)
sns.distplot(df['revenue'], ax=ax)
# QQ
ax2 = fi... | Yes | 2 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
705 | # dataframe
df = train.copy()
# (0, 1)
scaler=StandardScaler()
df['revenue']=scaler.fit_transform(df[['revenue']])
#
fig = plt.figure(figsize=(10, 4))
plt.subplots_adjust(wspace=0.4)
#
ax = fig.add_subplot(1, 2, 1)
sns.distplot(df['revenue'], ax=ax)
# QQ
ax2 = fig.add_subplot(1, 2, 2)
stats.probplot(df['revenue'... | Yes | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
706 | # dataframe
df = train.copy()
# Min-Max((1, 0))
scaler=MinMaxScaler()
df['revenue']=scaler.fit_transform(df[['revenue']])
#
fig = plt.figure(figsize=(10, 4))
plt.subplots_adjust(wspace=0.4)
#
ax = fig.add_subplot(1, 2, 1)
sns.distplot(df['revenue'], ax=ax)
# QQ
ax2 = fig.add_subplot(1, 2, 2)
stats.probplot(df['re... | Yes | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
707 | b"# \n# Open Date\ntrain['pd_date'] = pd.to_datetime(train['Open Date'], format='%m/%d/%Y')\n# \ntrain['Open_Year'] = train['pd_date'].dt.strftime('%Y')\n# \ntrain['Open_Month'] = train['pd_date'].dt.strftime('%m')\n\ntrain = train.drop('pd_date',axis=1)\ntrain = train.drop('Open Date',axis=1)" | Yes | 3 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
708 | b"# \n# Open Date\ntest['pd_date'] = pd.to_datetime(test['Open Date'], format='%m/%d/%Y')\n# \ntest['Open_Year'] = test['pd_date'].dt.strftime('%Y')\n# \ntest['Open_Month'] = test['pd_date'].dt.strftime('%m')\n\ntest = test.drop('pd_date',axis=1)\ntest = test.drop('Open Date',axis=1)" | Yes | 3 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
709 | train.dtypes.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 |
710 | b"#\ncats = list(train.select_dtypes(include=['object']).columns)\nnums = list(train.select_dtypes(exclude=['object']).columns)\nprint(f'categorical variables: {cats}')\nprint(f'numerical variables: {nums}')" | No | 3 | 37 | EDA | count_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
711 | train.nunique(axis=0) | No | 5 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
712 | columns = len(nominal_list)/2+1
fig = plt.figure(figsize=(30, 20))
plt.subplots_adjust(hspace=0.6, wspace=0.4)
for i in range(len(nominal_list)):
ax = fig.add_subplot(columns, 2, i+1)
sns.countplot(x=nominal_list[i], data=train, ax=ax)
plt.xticks(rotation=45)
plt.show() | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
713 | columns = len(num_list)/3+1
fig = plt.figure(figsize=(30, 40))
plt.subplots_adjust(hspace=0.6, wspace=0.4)
for i in range(len(num_list)):
ax = fig.add_subplot(columns, 3, i+1)
train[num_list[i]].hist(ax=ax)
ax2 = train[num_list[i]].plot.kde(ax=ax, secondary_y=True,title=num_list[i])
ax2.set_ylim(0)
... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
714 | columns = len(nominal_list)/2+1
fig = plt.figure(figsize=(20, 10))
plt.subplots_adjust(hspace=0.6, wspace=0.4)
for i in range(len(nominal_list)):
ax = fig.add_subplot(columns, 2, i+1)
#
sns.boxplot(x=nominal_list[i], y=train.revenue, data=train, ax=ax)
plt.xticks(rotation=45)
#
# sns.ba... | 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 |
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