id int64 0 7.95k | code_block stringlengths 1 52.9k | too_long stringclasses 2
values | marks int64 1 5 | graph_vertex_id float64 1 88 | graph_vertex_class stringclasses 12
values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
values | mlscent_ml_rating_basis stringclasses 52
values | mlscent_ml_findings_json stringclasses 50
values | mlscent_ignored_findings_json stringclasses 199
values | context_line_count int64 0 33 | combined_rating stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5,683 | from keras.models import Sequential
from keras.layers import LSTM,Dense,Conv1D,MaxPool1D
from keras.layers.embeddings import Embedding | No | 5 | 22 | Environment | import_modules | 11,266,342 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,684 | model.fit(X_train,Y_train,
batch_size=256,
epochs=10,
verbose=2) | No | 5 | 7 | Model_Train | train_model | 11,266,342 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,685 | test = pd.read_csv("/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip",sep="\\t")
test' | 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 |
5,686 | x_test = []
x_test = test['Phrase']
print(type(x_train))
x_test = np.asarray(x_test)
print(type(x_test))
x_test.shape | No | 5 | 21 | Data_Transform | prepare_x_and_y | 11,266,342 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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 |
5,687 | x_test = [rev.replace(",","").replace(".","").lower() for rev in x_test] | No | 5 | 78 | Data_Transform | string_transform | 11,266,342 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,688 | predicts = model.predict(X_test,
batch_size=256,
verbose=2) | No | 5 | 48 | Model_Evaluation | predict_on_test | 11,266,342 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,689 | predicts.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 |
5,690 | out = pd.DataFrame(data=test.PhraseId,columns=['PhraseId'])
out | No | 5 | 12 | Data_Transform | create_dataframe | 11,266,342 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,691 | out['Sentiment'] = preds_final
out | No | 5 | 55 | Data_Export | prepare_output | 11,266,342 | 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"] | 2 | A |
5,692 | out.to_csv('../working/submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 11,266,342 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../working/submission.csv. 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 |
5,693 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | No | 4 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 9 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 13"}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcode... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
5,694 | b"\ndf_train = pd.read_csv(train_zip_file,sep='\\t')\ndf_train.head()" | No | 4 | 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 |
5,695 | b"\ndf_test = pd.read_csv(test_zip_file, sep='\\t')\ndf_test.head()\n" | No | 4 | 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 |
5,696 | import string
from nltk.corpus import stopwords
| 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 |
5,697 | def cleanup_message(message):
messsage_pun = [ char for char in message if char not in string.punctuation]
message_punc_join = ''.join(messsage_pun)
message_punc_join_clean = [ word for word in message_punc_join.split() if word.lower() not in stopwords.words('english')]
return message_punc_join_clean | No | 5 | 78 | Data_Transform | string_transform | 11,354,257 | 1 | A | 0 | 3 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: cleanup_message. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 5"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 4 | A |
5,698 | from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(analyzer=cleanup_message)
train_vector = vectorizer.fit_transform(df_train['Phrase']) | No | 4 | 22 | Environment | import_modules | 11,354,257 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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 |
5,699 | from sklearn.naive_bayes import MultinomialNB | 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 |
5,700 | multinomialnb = MultinomialNB() | No | 5 | 4 | Model_Train | choose_model_class | 11,354,257 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
5,701 | labels = df_train['Sentiment'].values | No | 5 | 77 | Other | define_variables | 11,354,257 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 4 | null |
5,702 | multinomialnb.fit(train_vector,labels) | No | 5 | 7 | Model_Train | train_model | 11,354,257 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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 |
5,703 | pharaseid = df_test["PhraseId"] | No | 5 | 77 | Other | define_variables | 11,354,257 | 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"] | 4 | A |
5,704 | pharaseid | No | 5 | 53 | Other | something_strange | 11,354,257 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
5,705 | y1 = multinomialnb.predict(test_vector) | No | 5 | 48 | Model_Evaluation | predict_on_test | 11,354,257 | 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 |
5,706 | y1 | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,707 | dataframe=pd.DataFrame(y1, columns=['Sentiment']) | No | 5 | 12 | Data_Transform | create_dataframe | 11,354,257 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
5,708 | dataframe | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,709 | result = pd.concat([pharaseid, dataframe], axis=1) | No | 5 | 11 | Data_Transform | concatenate | 11,354,257 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
5,710 | result.to_csv('sentiment_submission.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 11,354,257 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
5,712 | b"train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', '\\t')" | No | 5 | 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 |
5,713 | b"test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip','\\t')" | No | 5 | 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 |
5,714 | train.isnull().sum() #no missing values | No | 5 | 39 | EDA | count_missing_values | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"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": "general_ml", "framework": "General ML", "name... | 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"}] | [] | 3 | B |
5,715 | test.isnull().sum() #no missing values | No | 5 | 39 | EDA | count_missing_values | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,716 | import seaborn as sns
sns.set()
sns.countplot(x='Sentiment',data=train) | No | 4 | 33 | Visualization | distribution | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,717 | #drop unecessary columns
train.drop(['PhraseId','SentenceId'],inplace = True,axis='columns') | No | 5 | 10 | Data_Transform | drop_column | 12,981,020 | 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 5"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Chec... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["InPlace Checker"] | 3 | B |
5,718 | from keras.preprocessing.text import text_to_word_sequence
#convert sentences to tokenized words
for i in range(len(train['Phrase'])):
train['Phrase'][i] = text_to_word_sequence(train['Phrase'][i])
| No | 5 | 78 | Data_Transform | string_transform | 12,981,020 | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | ok | 2 | 2 | [{"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": "Column Select... | A | no applicable ML-specific findings | [] | ["Chain Indexing", "Column Selection Checker"] | 3 | A |
5,719 | from keras.preprocessing.text import Tokenizer
#convert tokenized words to numeric form required for model building
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(train['Phrase'])
train['Phrase'] = tokenizer.texts_to_sequences(train['Phrase']) | No | 5 | 8 | Data_Transform | feature_engineering | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
5,720 | submission = pd.DataFrame()
submission['PhraseId'] = test['PhraseId'] | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,721 | test.drop(['PhraseId''SentenceId']) | No | 5 | 10 | Data_Transform | drop_column | 12,981,020 | 1 | A | 1 | 0 | 0 | 0 | 1 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,722 | X = train_copy
y = pd.get_dummies(train['Sentiment']) | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,723 | from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3) | No | 5 | 13 | Data_Transform | split | 12,981,020 | 2 | B | 1 | 0 | 0 | 0 | 5 | 2 | ok | 2 | 2 | [{"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"}] | [] | 3 | B |
5,724 | train_history=model.fit(x=X_train,y=y_train,batch_size=64,epochs=10,verbose=2,validation_data=(X_val,y_val)) | No | 5 | 7 | Model_Train | train_model | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"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"] | 3 | B |
5,725 | import matplotlib.pyplot as plt
plt.figure(figsize=(5,5))
plt.plot(train_history.history['accuracy'],'r',label='Training accuracy')
plt.plot(train_history.history['val_accuracy'],'b',label='Validation accuracy')
plt.legend() | No | 5 | 35 | Visualization | learning_history | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,726 | prediction = model.predict(test_copy) | No | 5 | 48 | Model_Evaluation | predict_on_test | 12,981,020 | 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 |
5,727 | final_prediction = [np.argmax(i) for i in prediction] | No | 5 | 77 | Other | define_variables | 12,981,020 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 4"}] | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | [] | 3 | C |
5,728 | submission['Sentiment'] = final_prediction
submission.head() | No | 4 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,729 | submission.to_csv('../working/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 |
5,730 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | No | 5 | 88 | Debug | list_files | 2,110,762 | 1 | A | 0 | 0 | 0 | 0 | 0 | 14 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 32"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 10 | A |
5,731 | dataset = pd.read_csv("../input/train.csv", names=['Store','Dept','Date','weeklySales','isHoliday'],sep=',', header=0)
features = pd.read_csv("../input/features.csv",sep=',', header=0,
names=['Store','Date','Temperature','Fuel_Price','MarkDown1','MarkDown2','MarkDown3','MarkDown4',
... | No | 4 | 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 |
5,732 | def scatter(dataset, column):
plt.figure()
plt.scatter(dataset[column] , dataset['weeklySales'])
plt.ylabel('weeklySales')
plt.xlabel(column) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,733 | scatter(dataset, 'Fuel_Price')
scatter(dataset, 'Size')
scatter(dataset, 'CPI')
scatter(dataset, 'Type')
scatter(dataset, 'isHoliday')
scatter(dataset, 'Unemployment')
scatter(dataset, 'Temperature')
scatter(dataset, 'Store')
scatter(dataset, 'Dept') | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,734 | def calculate_error(test_y, predicted, weights):
return mean_absolute_error(test_y, predicted, sample_weight=weights) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,735 | dataset_test = pd.get_dummies(dataset_test, columns=["Type"])
dataset_test[['MarkDown1','MarkDown2','MarkDown3','MarkDown4', 'MarkDown5']] = dataset_test[['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5']].fillna(0)
dataset_test = dataset_test.fillna(0)
column_date = dataset_test['Date']
dataset_test['Month'... | No | 4 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,736 | predicted_test = best_model.predict(dataset_test) | 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 |
5,737 | dataset_test['weeklySales'] = predicted_test
dataset_test['Date'] = column_date
dataset_test['id'] = dataset_test['Store'].astype(str) + '_' + dataset_test['Dept'].astype(str) + '_' + dataset_test['Date'].astype(str)
dataset_test = dataset_test[['id', 'weeklySales']]
dataset_test = dataset_test.rename(columns={'id': ... | No | 3 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,738 | dataset_test.to_csv('output.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 |
5,739 | train=pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/train.csv")
train.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 6,213,142 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"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": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Hardcoded file path"] | 3 | B |
5,740 | test=pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/test.csv")
test.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 6,213,142 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/test.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 |
5,741 | y=train["Weekly_Sales"] | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,742 | train["Date"]=train["Date"].astype("datetime64")
test["Date"]=test["Date"].astype("datetime64")
train.dtypes | No | 4 | 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 |
5,743 | #train["day of week"]=train["Date"].dt.dayofweek
#test["day of week"]=test["Date"].dt.dayofweek
train["month"]=train["Date"].dt.month
test["month"]=test["Date"].dt.month
train["day"]=train["Date"].dt.day
test["day"]=test["Date"].dt.day
train["year"]=train["Date"].dt.year
test["year"]=test["Date"].dt.year | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,745 | import matplotlib.pyplot as plt
import seaborn as sns
A,B=plt.subplots(1,1,figsize=(10,10))
sns.boxplot(train["month"],train["Weekly_Sales"]) | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,746 | train=pd.merge(train,store)
test=pd.merge(test,store) | 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 |
5,747 | train["Type"].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 |
5,748 | train["Type"]=train["Type"].replace({"A":1,"B":2,"C":3})
test["Type"]=test["Type"].replace({"A":1,"B":2,"C":3}) | 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 |
5,749 | train=train.drop(["Weekly_Sales","Date"],axis=1)
train.head() | No | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,750 | tesst=test.drop(["Date"],axis=1)
test.head() | No | 5 | 10 | Data_Transform | drop_column | 6,213,142 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,751 | #from lightgbm import LGBMRegressor
#lgb=LGBMRegressor()
#lgb.fit(train,y)
from sklearn.ensemble import RandomForestRegressor
rf=RandomForestRegressor(n_estimators=100, n_jobs=-1)
rf.fit(train,y) | No | 5 | 7 | Model_Train | train_model | null | 3 | C | 3 | 0 | 0 | 0 | 15 | 3 | ok | 6 | 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"] | 0 | C |
5,752 | sample=pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/sampleSubmission.csv")
sample.head() | No | 4 | 7 | Model_Train | train_model | 6,213,142 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/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 |
5,753 | sample["Weekly_Sales"]=value | 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 |
5,754 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | No | 4 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,755 | pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/sampleSubmission.csv').head() | No | 5 | 45 | Data_Extraction | load_from_csv | 6,457,885 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 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."] | 17 | A |
5,756 | X_train = pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/stores.csv')
features = pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/features.csv')
X_train = X_train.merge(stores, on='Stor... | No | 4 | 45 | Data_Extraction | load_from_csv | 6,457,885 | 2 | B | 3 | 0 | 0 | 0 | 15 | 7 | ok | 11 | 9 | [{"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 22"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | 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", "Data Augmentation Checker", "Hardcoded file path", "InPlace Checker", "Logging Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 17 | B |
5,757 | X_train['Date'].dtype | No | 5 | 70 | Debug | show_data_types | 6,457,885 | 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"] | 17 | A |
5,758 | train=pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/train.csv.zip")
train.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 8,322,473 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"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": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Hardcoded file path"] | 3 | B |
5,759 | test=pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/test.csv.zip")
test.head() | Yes | 5 | 45 | Data_Extraction | load_from_csv | 8,322,473 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/test.csv.zip. 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 |
5,760 | tesst=test.drop(["Date"],axis=1)
tesst.head() | Yes | 3 | 10 | Data_Transform | drop_column | 8,322,473 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,761 | sample=pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/sampleSubmission.csv.zip")
sample.head() | Yes | 4 | 7 | Model_Train | train_model | 8,322,473 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/sampleSubmission.csv.zip. 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 |
5,762 | sample.to_csv("20200309.csv",index=False) | No | 5 | 25 | Data_Export | save_to_csv | 8,322,473 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,763 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | Yes | 5 | 88 | Debug | list_files | 8,666,356 | 1 | A | 0 | 2 | 3 | 0 | 0 | 7 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 15"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
5,764 | train = pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/train.csv.zip",parse_dates=["Date"])
test = pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/test.csv.zip",parse_dates=["Date"]) | No | 5 | 45 | Data_Extraction | load_from_csv | 8,666,356 | 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."] | 3 | A |
5,765 | add = pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/features.csv.zip")
add.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/features.csv.zip. 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 |
5,766 | store = pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/stores.csv")
store | Yes | 5 | 45 | Data_Extraction | load_from_csv | 8,666,356 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/stores.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 |
5,767 | import seaborn as sns
import matplotlib.pyplot as plt
ax = sns.barplot(x="Type", y="Size", hue="Type", data=store)
sns.catplot(x="Type", kind="count", palette="ch:.25", data=store) | No | 5 | 33 | Visualization | distribution | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,768 | train = pd.merge(train,store,on = "Store",how="left")
test = pd.merge(test,store,on = "Store",how="left") | No | 5 | 32 | Data_Transform | merge | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,769 | train["year"] = train["Date"].dt.year
train["month"] = train["Date"].dt.month
train["day"] = train["Date"].dt.day
train["week"] = train["Date"].dt.week
test["year"] = test["Date"].dt.year
test["month"] = test["Date"].dt.month
test["day"] = test["Date"].dt.day
test["week"] = test["Date"].dt.week | No | 5 | 8 | Data_Transform | feature_engineering | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | 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 |
5,770 | train["Type"] = train["Type"].replace({"A":0,"B":1,"C":2})
test["Type"] = test["Type"].replace({"A":0,"B":1,"C":2}) | No | 5 | 20 | Data_Transform | categorify | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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 |
5,771 | def wmae(y_pred, targ, holiday_week):
sumOfWeights = 0
sumofCol_B_X_Col_E = 0
for i in range(0, len(y_pred)):
weight = 0
if holiday_week[i]:
weight = 5
else:
weight = 1
Col_B_X_Col_E = abs(targ[i] - y_pred[i])*weight
sumOfWeights... | No | 5 | 84 | Other | not_enough_vertices | 8,666,356 | 1 | A | 3 | 3 | 4 | 0 | 6 | 14 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Magic number detected... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Magic number", "Missing function docstring"] | 3 | A |
5,772 | train.groupby("IsHoliday")["Weekly_Sales"].median() | No | 3 | 40 | Debug | show_table_attributes | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"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": "general_ml", "framework": "General ML", "name... | 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"}] | [] | 3 | B |
5,773 | plt.figure(figsize=(10,6))
sns.barplot(train["IsHoliday"],train["Weekly_Sales"]) | No | 5 | 33 | Visualization | distribution | 8,666,356 | 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 |
5,774 | train.groupby("Type")["Weekly_Sales"].mean() | No | 4 | 40 | Debug | show_table_attributes | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"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": "general_ml", "framework": "General ML", "name... | 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"}] | [] | 3 | B |
5,775 | plt.figure(figsize=(10,6))
sns.barplot(train["Type"],train["Weekly_Sales"]) | No | 5 | 33 | Visualization | distribution | 8,666,356 | 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 |
5,776 | plt.figure(figsize=(10,6))
sns.boxplot(train["year"],train["Weekly_Sales"],showfliers=False) | No | 5 | 75 | Visualization | time_series | 8,666,356 | 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 |
5,777 | plt.figure(figsize=(10,6))
sns.boxplot(train["Store"],train["Weekly_Sales"],showfliers=False) | No | 5 | 33 | Visualization | distribution | 8,666,356 | 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 |
5,778 | plt.figure(figsize=(10,6))
sns.boxplot(train["Dept"],train["Weekly_Sales"],showfliers=False) | No | 5 | 33 | Visualization | distribution | 8,666,356 | 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 |
5,779 | feature = ["Store","Dept","year","month","day","week","IsHoliday", "Size"] | No | 5 | 77 | Other | define_variables | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,780 | X_train = train[feature]
X_test = test[feature] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 8,666,356 | 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 |
5,781 | X_train | 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 |
5,782 | y_train = train["Weekly_Sales"] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 8,666,356 | 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"] | 3 | A |
5,783 | from sklearn.model_selection import train_test_split
X_train1, X_test1, y_train1, y_test1 = train_test_split(X_train,y_train, test_size=0.2) | Yes | 5 | 13 | Data_Transform | split | 8,666,356 | 2 | B | 1 | 0 | 0 | 0 | 5 | 2 | ok | 2 | 2 | [{"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"}] | [] | 3 | B |
5,784 | %%time
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=10,n_jobs=4)
| Yes | 5 | 4 | Model_Train | choose_model_class | 8,666,356 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
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