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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,141 | firstfatality.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 |
1,142 | firstfatality.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 |
1,143 | firstconfirmed.columns = ['Province_State','Country_Region','FirstCaseDate']
firstfatality.columns = ['Province_State','Country_Region','FirstFatalityDate'] | No | 5 | 61 | Data_Transform | rename_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,144 | df = df.merge(firstconfirmed, left_on=['Province_State','Country_Region'],right_on=['Province_State','Country_Region'],how='left')
df = df.merge(firstfatality, left_on=['Province_State','Country_Region'],right_on=['Province_State','Country_Region'],how='left') | No | 5 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,145 | df.head(40) | 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 |
1,146 | df_test = df_test.merge(firstconfirmed, left_on=['Province_State','Country_Region'],right_on=['Province_State','Country_Region'],how='left')
df_test = df_test.merge(firstfatality, left_on=['Province_State','Country_Region'],right_on=['Province_State','Country_Region'],how='left') | No | 5 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,147 | df_test.head(40) | 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 |
1,148 | df['Date']=pd.to_datetime(df['Date'], infer_datetime_format=True)
df['FirstCaseDate']=pd.to_datetime(df['FirstCaseDate'], infer_datetime_format=True)
df['FirstFatalityDate']=pd.to_datetime(df['FirstFatalityDate'], infer_datetime_format=True) | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,149 | df_test['Date']=pd.to_datetime(df_test['Date'], infer_datetime_format=True)
df_test['FirstCaseDate']=pd.to_datetime(df_test['FirstCaseDate'], infer_datetime_format=True)
df_test['FirstFatalityDate']=pd.to_datetime(df_test['FirstFatalityDate'], infer_datetime_format=True) | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,150 | df['days_first_case']=(df['Date']-df['FirstCaseDate']).dt.days
df['days_first_fatality']=(df['Date']-df['FirstFatalityDate']).dt.days | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,151 | df['days_first_case'] | 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 |
1,152 | df_test['days_first_case']=(df_test['Date']-df_test['FirstCaseDate']).dt.days
df_test['days_first_fatality']=(df_test['Date']-df_test['FirstFatalityDate']).dt.days | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,153 | df_test['days_first_case'] | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,154 | df['days_first_case']=np.where(df['days_first_case']<0,0,df['days_first_case'].fillna(0))
df['days_first_fatality']=np.where(df['days_first_fatality']<0,0,df['days_first_fatality'].fillna(0)) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,155 | df_test['days_first_case']=np.where(df_test['days_first_case']<0,0,df_test['days_first_case'].fillna(0))
df_test['days_first_fatality']=np.where(df_test['days_first_fatality']<0,0,df_test['days_first_fatality'].fillna(0)) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,156 | df.tail() | 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 |
1,157 | df[df['Country_Region']=='Brazil'].tail() | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,158 | df_test[df_test['Country_Region']=='Brazil'].tail() | No | 4 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,159 | add_datepart(df,'Date',drop=False) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,160 | add_datepart(df_test,'Date',drop=False) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,161 | external = pd.read_csv('/kaggle/input/covid19-week2-external-data/external_data.csv',sep=';',decimal=',') | 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 |
1,162 | external.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 |
1,163 | df = df.merge(external, left_on='key',right_on='key',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 |
1,164 | df_test = df_test.merge(external, left_on='key',right_on='key',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 |
1,165 | list(df) | 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 |
1,166 | df['Confirmedlast43'] = df['ConfirmedCases'].shift(43)
df['Fatalitieslast43'] = df['Fatalities'].shift(43) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,167 | df['is_valid'] = np.where(df['Date']<'2020-03-29', False, True) | No | 5 | 8 | Data_Transform | feature_engineering | 8,682,800 | 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"] | 2 | A |
1,168 | df.groupby('is_valid').size() | No | 5 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,169 | df['ConfirmedLog'] = np.log(df['ConfirmedCases']+1)
df['FatalitiesLog'] = np.log(df['Fatalities']+1) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,170 | cat_vars = ['Province_State','Country_Region','province_flag']
cont_vars = ['Elapsed',
'days_first_case',
'days_first_fatality',
'pop_density',
'population',
'area',
'lat_min',
'lat_max',
'lon_min',
'lon... | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 49 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,171 | procs = [FillMissing, Categorify, Normalize]
splits = ColSplitter('is_valid')(df) | No | 2 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,172 | splits | 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 |
1,174 | to = TabularPandas(df, procs, cat_names=cat_vars, cont_names=cont_vars, y_names=dep_vars, y_block=RegressionBlock(), splits=splits) | 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 |
1,175 | to | 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 |
1,176 | cats.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 |
1,177 | conts.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 |
1,178 | y.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 |
1,179 | dls.c | 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 |
1,180 | learn.load('/kaggle/input/covid19-week2-external-data/best18_2day') | No | 5 | 30 | Model_Train | load_pretrained | 8,682,800 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/covid19-week2-external-data/best18_2day. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
1,181 | preds, y = learn.get_preds() | 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 |
1,182 | np.exp(preds)-1 | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,183 | np.exp(y)-1 | 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 |
1,184 | raw_test_preds = learn.get_preds(dl=dl) | 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 |
1,185 | raw_test_preds[0] | No | 4 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,186 | raw_test_preds[1] | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,187 | preds = np.exp(to_np(raw_test_preds[0]))-1 | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,188 | preds[:40] | 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 |
1,189 | df['ConfirmedCases'].head(40) | 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 |
1,190 | preds = pd.DataFrame(preds)
preds.columns = ['pred_confirmed'] | No | 4 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,191 | preds.tail() | 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 |
1,192 | df = pd.concat([df, preds], axis=1) | 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 |
1,193 | preds_confirmed_test = np.exp(to_np(raw_test_preds[0]))-1 | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,194 | preds_confirmed_test[:40] | 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 |
1,195 | learn.load('/kaggle/input/covid19-week2-external-data/fat_best2_2day') | No | 5 | 30 | Model_Train | load_pretrained | 8,682,800 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/covid19-week2-external-data/fat_best2_2day. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
1,196 | preds | 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 |
1,197 | y | 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 |
1,198 | preds_confirmed_test = pd.DataFrame(preds_confirmed_test) | 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 |
1,199 | preds_confirmed_test.columns=['ConfirmedCases'] | No | 5 | 61 | Data_Transform | rename_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,200 | preds_confirmed_test.tail() | 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 |
1,201 | preds_confirmed_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 |
1,202 | df_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 |
1,203 | df_test = pd.concat([df_test, preds_confirmed_test], axis=1) | 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 |
1,204 | df_test.tail(40) | 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 |
1,205 | df_test['pred_confirmed']=df_test['ConfirmedCases'] | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,206 | preds_fatalities_test = np.exp(to_np(raw_test_preds[0]))-1 | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,207 | preds_fatalities_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 |
1,208 | preds_fatalities_test = pd.DataFrame(preds_fatalities_test) | 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 |
1,209 | preds_fatalities_test.columns=['Fatalities'] | No | 5 | 61 | Data_Transform | rename_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,210 | preds_fatalities_test.tail() | 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 |
1,211 | preds_fatalities_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 |
1,212 | df_test = pd.concat([df_test, preds_fatalities_test], axis=1) | 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 |
1,213 | df_test[df_test['Country_Region']=='Brazil'].head(20) | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,214 | sub_ex = df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/submission.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 |
1,215 | sub_ex.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 |
1,216 | sub = df_test[['ForecastId','ConfirmedCases','Fatalities']] | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,217 | sub.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 |
1,218 | sub_ex.tail() | 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 |
1,219 | sub.tail() | 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 |
1,220 | import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore') | No | 2 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
1,221 | df_train = pd.read_csv("../input/covid19-global-forecasting-week-2/train.csv")
print(df_train.shape)
df_train.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,222 | df_test = pd.read_csv("../input/covid19-global-forecasting-week-2/test.csv")
print(df_test.shape)
df_test.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,223 | # concat train and test
df_traintest = pd.concat([df_train, df_test])
print(df_train.shape, df_test.shape, df_traintest.shape) | No | 3 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,224 | # process date
df_traintest['Date'] = pd.to_datetime(df_traintest['Date'])
df_traintest['day'] = df_traintest['Date'].apply(lambda x: x.dayofyear).astype(np.int16)
df_traintest.head() | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,225 | # concat Country/Region and Province/State
def func(x):
try:
x_new = x['Country_Region'] + "/" + x['Province_State']
except:
x_new = x['Country_Region']
return x_new
df_traintest['place_id'] = df_traintest.apply(lambda x: func(x), axis=1)
df_traintest.head()' | 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 |
1,226 | df_traintest[(df_traintest['day']>=day_before_public-3) & (df_traintest['place_id']=='China/Hubei')].head() | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,227 | # concat lat and long
df_latlong = pd.read_csv("../input/smokingstats/df_Latlong.csv")
df_latlong.head() | No | 4 | 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 |
1,228 | # concat Country/Region and Province/State
def func(x):
try:
x_new = x['Country/Region'] + "/" + x['Province/State']
except:
x_new = x['Country/Region']
return x_new
df_latlong['place_id'] = df_latlong.apply(lambda x: func(x), axis=1)
df_latlong = df_latlong[df_latlong['place_id'].d... | 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 |
1,229 | df_traintest = pd.merge(df_traintest, df_latlong[['place_id', 'Lat', 'Long']], on='place_id', how='left')
df_traintest.head() | No | 5 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,230 | print(pd.isna(df_traintest['Lat']).sum()) # count Nan
df_traintest[pd.isna(df_traintest['Lat'])].head() | No | 3 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,231 |
data = pd.read_csv( '/kaggle/input/restaurant-revenue-prediction/train.csv.zip')
test_data = pd.read_csv('/kaggle/input/restaurant-revenue-prediction/test.csv.zip')
data.head()
| No | 4 | 45 | Data_Extraction | load_from_csv | 10,424,951 | 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."] | 8 | A |
1,232 |
data.describe()
| No | 5 | 40 | Debug | show_table_attributes | 10,424,951 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
1,233 | data.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 |
1,234 | data['City Group'].unique() | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,235 | data['City'].unique() | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,236 | #Creating a flag for each type of restaurant
data['Type_IL'] = np.where(data['Type'] == 'IL', 1, 0)
data['Type_FC'] = np.where(data['Type'] == 'FC', 1, 0)
data['Type_DT'] = np.where(data['Type'] == 'DT', 1, 0)
#Creating a flag for 'Big Cities'
data['Big_Cities'] = np.where(data['City Group'] == 'Big Cities', 1, 0)
#C... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 2 | 0 | 0 | 0 | 16 | 18 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,237 |
from sklearn import model_selection
from sklearn import linear_model
X = data.drop(['Id', 'revenue'], axis=1)
Y = data.revenue
| No | 5 | 21 | Data_Transform | prepare_x_and_y | 10,424,951 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
1,238 |
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn import metrics
def check_rmse(X, Y, alpha):
RMSE_lasso = []
RMSE_ridge = []
for i in alpha:
lasso = Lasso(alpha=i)
lasso.fit(X, Y)
ridge = Ridge(alpha=i)
ridge.fit(X, Y)
... | No | 5 | 84 | Other | not_enough_vertices | 10,424,951 | 1 | A | 2 | 2 | 1 | 0 | 4 | 14 | ok | 12 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "Missing function docstring", "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 |
1,239 | plt.figure()
plt.plot(alpha, RMSE_lasso, 'o-', color="r", label="RMSE_lasso")
plt.legend(loc='best')
plt.show()' | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,240 | lasso = Lasso(alpha=5.5)
lasso.fit(X, Y)
metrics.mean_squared_error(Y, lasso.predict(X)) | No | 4 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,241 |
model = Lasso(alpha=5.5)
model.fit(X, Y)
test_predicted = pd.DataFrame()
test_predicted['Id'] = test_data.Id
test_predicted['Prediction'] = model.predict(test_data.drop('Id', axis=1))
test_predicted.to_csv('submission-lasso-5.5.csv', index=False)
test_predicted.describe()
| No | 3 | 28 | Model_Train | compute_train_metric | 10,424,951 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 11 | 8 | [{"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", "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 |
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