Datasets:
dimsmauls
/
code4ml-annotated-rated

Dataset card Data Studio Files
xet
 Community
1
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
5,478
data = pd.read_csv("../input/train.tsv",delimiter='\\t') data.shape'
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
5,479
adjectives = [] for i in range(0,data.shape[0]): pos_tagged = pos_tag(word_tokenize(data.iloc[i,2])) string = "" for j in range(0,len(pos_tagged)): if pos_tagged[j][1] in ("JJ","JJR", "JJS", "RB", "RBR", "RBS"): string = string + " " + pos_tagged[j][0] adjectives.append(string)
No
5
77
Other
define_variables
2,067,772
1
A
0
3
6
0
0
8
ok
2
2
[{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,480
data['Adjective Review'] = adjectives
No
5
8
Data_Transform
feature_engineering
2,067,772
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,481
data = data.drop("PhraseId",axis=1) data = data.drop("SentenceId",axis=1) data = data.drop("Phrase",axis=1) print(data.head())
No
4
10
Data_Transform
drop_column
2,067,772
1
A
0
0
0
0
0
4
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
5
A
5,482
predictors = data['Adjective Review'] predictors.shape
No
5
58
Debug
show_shape
2,067,772
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,483
response = data["Sentiment"] response.shape
No
5
58
Debug
show_shape
2,067,772
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,484
tv = TfidfVectorizer(min_df=0.0, max_df=1.0, ngram_range=(1,2),sublinear_tf=True,max_features=1000) tv_features = tv.fit_transform(predictors)
No
5
8
Data_Transform
feature_engineering
2,067,772
1
A
0
0
0
0
0
2
ok
6
6
[{"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
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}]
["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
5
D
5,485
print(tv_features.shape)
No
5
58
Debug
show_shape
2,067,772
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
5
A
5,486
train_predictors, test_predictors, train_response, test_response = train_test_split(tv_features, response, random_state = 0)
No
5
13
Data_Transform
split
2,067,772
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
5
B
5,487
print(train_predictors.shape) print(test_predictors.shape)
No
5
58
Debug
show_shape
2,067,772
1
A
0
0
0
0
0
2
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
5
A
5,488
from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(max_features = 10).fit(train_predictors, train_response)
No
5
7
Model_Train
train_model
2,067,772
3
C
2
0
0
0
10
2
ok
8
6
[{"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"}]
["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
5
C
5,489
predicted_test_response = model.predict(test_predictors)
No
5
48
Model_Evaluation
predict_on_test
2,067,772
1
A
0
0
0
0
0
1
ok
3
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"]
5
A
5,490
accuracy_score(test_response, predicted_test_response)
No
5
49
Model_Evaluation
compute_test_metric
2,067,772
1
A
0
0
0
0
0
1
ok
2
2
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "Unit Testing Checker"]
5
A
5,491
test = pd.read_csv("../input/test.tsv",delimiter='\\t') test.shape'
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
5,492
test['Adjective Review'] = test_adjectives
No
5
8
Data_Transform
feature_engineering
2,067,772
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,493
test = test.drop("SentenceId",axis=1) test = test.drop("Phrase",axis=1) print(test.head())
No
4
10
Data_Transform
drop_column
2,067,772
1
A
0
0
0
0
0
3
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
5
A
5,494
test_predictors = test['Adjective Review'] test_predictors.shape
No
5
58
Debug
show_shape
2,067,772
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,495
test_tv = TfidfVectorizer(min_df=0.0, max_df=1.0, ngram_range=(1,2),sublinear_tf=True,max_features=1000) test_tv_features = tv.fit_transform(test_predictors)
No
5
8
Data_Transform
feature_engineering
2,067,772
1
A
0
0
0
0
0
2
ok
6
6
[{"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
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}]
["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
5
D
5,496
test_response = model.predict(test_tv_features)
No
5
48
Model_Evaluation
predict_on_test
2,067,772
1
A
0
0
0
0
0
1
ok
3
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"]
5
A
5,497
len(test_response)
No
5
58
Debug
show_shape
2,067,772
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
5
A
5,498
test['Sentiment'] = test_response
No
5
8
Data_Transform
feature_engineering
2,067,772
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
5
A
5,499
test.head() test.shape
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
5,500
test = test.drop("Adjective Review",axis=1)
No
5
10
Data_Transform
drop_column
2,067,772
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
5
A
5,501
test.to_csv("Submission.csv", sep=',',index=False)'
No
5
25
Data_Export
save_to_csv
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,502
# 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
2,898,064
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 23"}]
A
no applicable ML-specific findings
[]
["Hardcoded file path"]
2
A
5,503
test_data = pd.read_csv("../input/test.tsv",delimiter='\\t') train_data = pd.read_csv("../input/train.tsv",delimiter="\\t")'
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
5,504
from keras.utils import np_utils
No
5
22
Environment
import_modules
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,506
model.fit(X, Y, epochs=10, validation_split=0.2)
No
5
7
Model_Train
train_model
2,898,064
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
2
A
5,507
Y_test = model.predict(X_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
5,508
submission = pd.DataFrame({'PhraseId' : test_data["PhraseId"], 'Sentiment' : Y_test})'
No
5
12
Data_Transform
create_dataframe
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,509
submission.to_csv("submission.csv", index=False)
No
5
25
Data_Export
save_to_csv
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,510
b"train_df=pd.read_csv('../input/train.tsv',sep='\\t')\ntest_df=pd.read_csv('../input/test.tsv',sep='\\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
5,511
train_df['token']=train_df.apply(tokenizer,axis=1) test_df['token']=test_df.apply(tokenizer,axis=1)
No
5
8
Data_Transform
feature_engineering
3,995,628
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
5,512
train_df['lemma']=train_df.apply(lemmatize,axis=1) test_df['lemma']=test_df.apply(lemmatize,axis=1)
No
5
78
Data_Transform
string_transform
null
1
A
0
0
0
0
0
2
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,513
#Loading the Data train= pd.read_csv('../input/airbnb-recruiting-new-user-bookings/train_users_2.csv.zip') test = pd.read_csv('../input/airbnb-recruiting-new-user-bookings/test_users.csv.zip')
No
5
45
Data_Extraction
load_from_csv
12,068,414
1
A
0
0
0
0
0
2
ok
4
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "general_ml", "framework": "Genera...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."]
11
A
5,514
train.signup_method.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,515
train.affiliate_channel.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,516
train.affiliate_provider.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,517
train.first_affiliate_tracked.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,518
train.first_device_type.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,519
train.first_browser.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,520
train.signup_app.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,521
countries.sort_values(by='distance_km')
No
5
9
Data_Transform
sort_values
12,068,414
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,522
a=train['country_destination'].value_counts() a=a.drop(['NDF','other'],axis=0) df_value_counts = pd.DataFrame(a) df_value_counts = a.reset_index() df_value_counts.columns = ['country_destination', 'value_counts'] # change column names mapdata= pd.merge(df_value_counts, countries,how= 'inner' , on='country_destinati...
Yes
3
12
Data_Transform
create_dataframe
12,068,414
1
A
0
0
0
0
0
8
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": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida...
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
["Column Selection Checker", "Dependent Threshold Checker"]
11
B
5,523
# Create a world map to show distributions of users import folium from folium.plugins import MarkerCluster #empty map world_map= folium.Map(tiles="cartodbpositron") marker_cluster = MarkerCluster().add_to(world_map) #for each coordinate, create circlemarker of user percent for i in range(len(mapdata)): lat = m...
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
5,524
a=train.groupby('country_destination').count().sort_values(by='id',ascending=True) a.id
Yes
5
9
Data_Transform
sort_values
12,068,414
1
A
0
0
0
0
0
2
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, 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"}]
["Dependent Threshold Checker"]
11
B
5,525
countries.dtypes
No
5
70
Debug
show_data_types
12,068,414
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,526
age_gender.drop(age_gender.index[0],inplace=True) age_gender
No
5
10
Data_Transform
drop_column
12,068,414
1
A
0
0
0
1
0
2
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 12"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold ...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "InPlace Checker"]
11
A
5,527
age_gender.country_destination.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,528
age_gender.age_bucket.unique()
No
5
57
Debug
show_unique_values
12,068,414
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,529
agebtw0n19= age_gender[(age_gender.age_bucket=='0-4') | (age_gender.age_bucket=='5-9') | (age_gender.age_bucket=='10-14')|(age_gender.age_bucket=='15-19')] agebtw49n34= age_gender[(age_gender.age_bucket=='45-49') | (age_gender.age_bucket=='40-44') | (age_gender.age_bucket=='35-39') | (age_gender.age_bucket=='30-34')] a...
No
4
13
Data_Transform
split
12,068,414
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
11
A
5,530
import seaborn as sns ax = sns.boxplot(x=agebtw20n29["population_in_thousands"])
No
5
33
Visualization
distribution
12,068,414
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
11
A
5,531
import seaborn as sns ax = sns.boxplot(x=agebtw99n50["population_in_thousands"])
No
5
33
Visualization
distribution
12,068,414
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": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
11
A
5,532
plt.figure(figsize=(20,5)) import seaborn as sns age_gender.sort_values("age_bucket", ascending=False,inplace=True) colors=sns.color_palette() sns.stripplot(x="age_bucket",y="population_in_thousands",data=age_gender,jitter=True,hue='country_destination',palette='pastel')'
No
5
81
Visualization
relationship
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,533
actionanditsdetail=pd.crosstab(age_gender.age_bucket, age_gender.country_destination,margins=False) from scipy.stats import chi2_contingency # defining the table stat, p, dof, expected = chi2_contingency(actionanditsdetail) # interpret p-value alpha = 0.05 print("p value is " + str(p)) if p <= alpha: \tprint('...
Yes
5
47
Model_Evaluation
statistical_test
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,534
actionanditsdetail=pd.crosstab(age_gender.country_destination, age_gender.gender,margins=False) from scipy.stats import chi2_contingency # defining the table stat, p, dof, expected = chi2_contingency(actionanditsdetail) # interpret p-value alpha = 0.05 print("p value is " + str(p)) if p <= alpha: \tprint('Depe...
Yes
5
47
Model_Evaluation
statistical_test
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,535
sessions=sessions.sort_values('user_id') sessions.rename(columns={"user_id": "id"},inplace=True) sessions.sort_values(by='id',inplace=True) sessions = sessions[(sessions['secs_elapsed'].notnull()) & (sessions['secs_elapsed'] > 0.0) ] sessions[(sessions['action_detail']=='booking')]'
Yes
3
9
Data_Transform
sort_values
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,536
groupedid=pd.DataFrame(sessions.groupby(['id'])['secs_elapsed'].sum()) groupedid['hour_elapsed']=groupedid['secs_elapsed'].div(3600).round(decimals=0) groupedid.drop('secs_elapsed',axis=1,inplace=True) groupedid.reset_index(inplace=True) groupedid[(groupedid['id'] == '6udv3scuxe') | (groupedid['id'] == 'yxf0sm9sbw') | ...
Yes
4
12
Data_Transform
create_dataframe
12,068,414
1
A
0
0
0
0
0
5
ok
3
3
[{"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 14"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker", "InPlace Checker"]
11
A
5,537
groupedid.hour_elapsed.describe()
No
5
40
Debug
show_table_attributes
12,068,414
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,538
actionndevicetype=pd.crosstab(sessions.action, sessions.device_type,margins=False) from scipy.stats import chi2_contingency # defining the table stat, p, dof, expected = chi2_contingency(actionndevicetype) # interpret p-value alpha = 0.05 print("p value is " + str(p)) if p <= alpha: \tprint('Dependent (reject ...
Yes
5
47
Model_Evaluation
statistical_test
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,539
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns print(train.country_destination.value_counts()) sns.countplot(train.country_destination)
Yes
3
72
EDA
count_values
12,068,414
1
A
0
0
0
0
0
5
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
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker"]
11
B
5,540
import pandas as pd #Loading the Data again train= pd.read_csv('../input/airbnb-recruiting-new-user-bookings/train_users_2.csv.zip') test = pd.read_csv('../input/airbnb-recruiting-new-user-bookings/test_users.csv.zip')
Yes
5
45
Data_Extraction
load_from_csv
12,068,414
1
A
0
0
0
0
0
3
ok
4
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "general_ml", "framework": "Genera...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."]
11
A
5,541
#missing data total = train.isnull().sum().sort_values(ascending=False) percent = (train.isnull().sum()/train.isnull().count()).sort_values(ascending=False) missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent']) missing_data.head(20)
Yes
2
11
Data_Transform
concatenate
null
1
A
0
0
0
0
0
4
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,542
train['first_affiliate_tracked'] = train['first_affiliate_tracked'].fillna('Unknown') test['first_affiliate_tracked'] = test['first_affiliate_tracked'].fillna('Unknown')
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
5,543
import seaborn as sns sns.boxplot(x=train.age)
Yes
5
33
Visualization
distribution
12,068,414
1
A
0
0
0
0
0
2
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,544
train.age.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
5,545
b"import numpy as np\n#train=train[(train.age > 14) & (train.age < 110)]# modele sadece 14 yandan byk ve 110 yandan kkleri dahil edebilirim.\n#test=test[(test.age > 14) & (test.age < 110)]\ntrain[(train.age < 14) & (train.age > 110)]=np.nan\ntest[(test.age < 14) & (test.age > 110)]=np.nan\ntrain['age'].fillna(train['ag...
Yes
5
17
Data_Transform
correct_missing_values
null
1
A
0
0
0
0
0
0
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,546
#Converting below columns as categories for plotting in graphs categorical_features = [ 'affiliate_channel', 'affiliate_provider', 'first_affiliate_tracked', 'first_browser', 'first_device_type', 'gender', 'language', 'signup_app', 'signup_method', 'signup_flow' ] for categorica...
No
5
16
Data_Transform
data_type_conversions
12,068,414
1
A
0
2
2
0
0
16
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": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida...
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
["Column Selection Checker", "Dependent Threshold Checker"]
11
B
5,547
train['date_account_created'] = pd.to_datetime(train['date_account_created']) train['date_first_booking'] = pd.to_datetime(train['date_first_booking']) train['timestamp_first_active'] = pd.to_datetime(train['timestamp_first_active'], format='%Y%m%d%H%M%S') train['timestamp_first_active'] = pd.to_datetime(train['timesta...
No
5
16
Data_Transform
data_type_conversions
12,068,414
1
A
0
0
0
0
0
7
ok
2
2
[{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Dependent Threshold Checker"]
11
A
5,548
import matplotlib.pyplot as plt import seaborn as sns # Use seaborn style defaults and set the default figure size df = train.groupby(['date_first_booking'])['country_destination'].count().reset_index() df.dropna(axis=0,inplace=True) import plotly.express as px fig = px.line(df, x='date_first_booking', y="country_des...
Yes
5
81
Visualization
relationship
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,549
train.drop(['date_first_booking'], axis=1,inplace=True) test.drop(['date_first_booking'], axis=1,inplace=True)
No
5
10
Data_Transform
drop_column
12,068,414
1
A
0
0
0
0
0
2
ok
4
3
[{"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 12"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": ...
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
["Dependent Threshold Checker", "InPlace Checker"]
11
B
5,550
#date_account_created dac = np.vstack(train.date_account_created.astype(str).apply(lambda x: list(map(int, x.split('-')))).values) train['dac_year'] = dac[:,0] train['dac_month'] = dac[:,1] train['dac_day'] = dac[:,2] train.drop(['date_account_created'], axis=1,inplace=True) #timestamp_first_active tfa = np.vstack(tra...
No
4
78
Data_Transform
string_transform
12,068,414
1
A
0
0
0
0
0
20
ok
13
7
[{"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 18"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": ...
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", "DataFrame Conversion Checker", "Dependent Threshold Checker", "InPlace Checker"]
11
B
5,551
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() train['gender']= le.fit_transform(train['gender']) train['signup_method']= le.fit_transform(train['signup_method']) train['first_affiliate_tracked']= le.fit_transform(train['first_affiliate_tracked']) train['signup_method']= le.fit_transform(trai...
Yes
5
20
Data_Transform
categorify
12,068,414
1
A
0
0
0
0
0
13
ok
31
11
[{"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": "Pipeline Che...
D
Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}]
["Column Selection Checker", "Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
D
5,552
le = LabelEncoder() test['gender']= le.fit_transform(test['gender']) test['signup_method']= le.fit_transform(test['signup_method']) test['first_affiliate_tracked']= le.fit_transform(test['first_affiliate_tracked']) test['signup_method']= le.fit_transform(test['signup_method']) test['language']= le.fit_transform(...
Yes
5
20
Data_Transform
categorify
12,068,414
1
A
0
0
0
0
0
12
ok
31
11
[{"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": "Pipeline Che...
D
Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}]
["Column Selection Checker", "Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
D
5,553
train.country_destination.replace('NDF',0,inplace=True) train.country_destination.replace('US',1,inplace=True) train.country_destination.replace('other',2,inplace=True) train.country_destination.replace('FR',3,inplace=True) train.country_destination.replace('CA',4,inplace=True) train.country_destination.replace('GB',5,...
No
5
20
Data_Transform
categorify
null
1
A
0
0
0
0
0
12
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,554
from sklearn.model_selection import train_test_split y=train['country_destination'] X=train.drop(['country_destination','id'],axis=1) from imblearn.combine import SMOTETomek # transform the dataset smotetomek = SMOTETomek(sampling_strategy='auto') # split the dataset into train and test sets X_train1, X_test1, y_train...
Yes
4
13
Data_Transform
split
12,068,414
1
A
0
0
0
0
0
7
ok
7
7
[{"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
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}]
["Column Selection Checker", "Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
5,555
x=pd.DataFrame(X_train) Y=pd.DataFrame(y_train) result = pd.concat([x, Y], axis=1, join='inner') sns.countplot(result.country_destination)
Yes
5
12
Data_Transform
create_dataframe
12,068,414
1
A
0
0
0
0
0
4
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,556
target_names = ['NDF', 'US', 'other', 'FR', 'CA', 'GB', 'ES', 'IT', 'PT', 'NL','DE', 'AU'] #Classifier from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report rf=RandomForestClas...
Yes
4
7
Model_Train
train_model
12,068,414
1
A
2
0
0
0
10
9
ok
11
9
[{"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"}]
["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
5,557
import pandas as pd feature_imp = pd.Series(rf.feature_importances_,index=feature_names).sort_values(ascending=False) feature_imp
Yes
5
79
Visualization
model_coefficients
12,068,414
1
A
0
0
0
1
0
3
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker"]
11
A
5,558
import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline # Creating a bar plot sns.barplot(x=feature_imp, y=feature_imp.index) # Add labels to your graph plt.xlabel('Feature Importance Score') plt.ylabel('Features') plt.title("Visualizing Important Features") plt.legend() plt.show()'
Yes
5
79
Visualization
model_coefficients
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,559
pred_country={0:"NDF", 1:"US", 2:"other", 3:"FR", 4:"CA", 5:"GB", 6:"ES", 7:"IT", 8:"PT", 9:"DE", 10:"NL", 11:"AU"}
No
3
77
Other
define_variables
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,560
from xgboost.sklearn import XGBClassifier xgb = XGBClassifier() xgb.fit(X_train,y_train) y_predxgb=xgb.predict(X_test1) print(classification_report(y_test1, y_predxgb, target_names=target_names))
Yes
3
7
Model_Train
train_model
12,068,414
1
A
0
0
0
0
0
5
ok
9
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"}]
["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
5,561
predictionsxgb=xgb.predict(test.drop(['id'],axis=1))
No
5
48
Model_Evaluation
predict_on_test
12,068,414
1
A
0
0
0
0
0
1
ok
3
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":...
A
no applicable ML-specific findings
[]
["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"]
11
A
5,563
from sklearn.neural_network import MLPClassifier #Generate prediction using Neural Net mlp = MLPClassifier(activation='identity', solver='sgd',learning_rate='adaptive', alpha=0.0001, batch_size='auto') mlp.fit(X_train,y_train) predsmlp = mlp.predict(X_test1) from sklearn import metrics print(classification_report(y_te...
Yes
3
7
Model_Train
train_model
12,068,414
2
B
2
0
0
0
10
6
ok
9
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"}]
["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
5,564
from sklearn.neural_network import MLPClassifier from sklearn.model_selection import GridSearchCV parameters = {'solver': ['lbfgs','sgd'], 'max_iter': [1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 ], 'alpha': 10.0 ** -np.arange(1, 10), 'hidden_layer_sizes':np.arange(10, 15), 'random_state':[0,1,2,3,4,5,6,7,8,...
Yes
4
6
Hyperparam_Tuning
train_on_grid
12,068,414
1
A
1
0
0
0
5
8
ok
9
6
[{"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 15"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
5,565
from sklearn.naive_bayes import ComplementNB cnb = ComplementNB() cnb.fit(X_train, y_train) y_predcnb=cnb.predict(X_test1) from sklearn import metrics print(classification_report(y_test1, y_predcnb, target_names=target_names))
Yes
4
49
Model_Evaluation
compute_test_metric
12,068,414
1
A
0
null
0
0
0
null
invalid_python
0
0
[]
null
invalid_python
[]
[]
11
null
5,566
import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from xgboost.sklearn import XGBClassifier #Loading the Data again train= pd.read_csv('../input/airbnb-recruiting-new-user-bookings/train_users_2.csv.zip') test = pd.read_csv('../input/airbnb-recruiting-new-user-bookings/test_users.cs...
Yes
2
7
Model_Train
train_model
12,068,414
1
A
0
null
0
0
0
null
invalid_python
0
0
[]
null
invalid_python
[]
[]
11
null
5,567
import pandas as pd from bs4 import BeautifulSoup import nltk from nltk.corpus import stopwords import re import numpy as np import time from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer
No
5
22
Environment
import_modules
4,770,480
1
A
0
0
0
0
0
9
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
4
A
5,568
train = pd.read_csv("../input/train.tsv", header = 0, delimiter = '\\t') test = pd.read_csv("../input/test.tsv", header = 0, delimiter = '\\t')'
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
5,569
def phrase_to_words(raw_phrase): #remove any html phrase_text = BeautifulSoup(raw_phrase).get_text() #remove non letters letters = re.sub("[^A-Za-z]", " ", phrase_text) #to lowercase lower_letters = letters.lower().split() #remove stopwords stop = set(stopwords.words(...
No
5
78
Data_Transform
string_transform
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,570
#First the train set num_phrase = train['Phrase'].size clean_train_phrase = [] for i in range(0, num_phrase): if( (i+1)%10000 == 0 ): print ("Review %d of %d\ " % ( i+1, num_phrase )) clean_train_phrase.append(phrase_to_words(train['Phrase'][i]))'
No
3
78
Data_Transform
string_transform
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,571
print("Making Bag of words") vectorizer = CountVectorizer(analyzer = "word", \\ tokenizer = None, \\ preprocessor = None, \\ stop_words = None, \\ max_features = 5000) train_data_features = vecto...
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
5,573
import time start = time.time() # Start time print("Training the Random Forest") from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(n_estimators=100, n_jobs = -1) rf = rf.fit(train_data_features, train['Sentiment']) # Get the end time and print how long the process took end = time.time()...
Yes
5
7
Model_Train
train_model
null
1
A
0
null
0
0
0
null
invalid_python
0
0
[]
null
invalid_python
[]
[]
0
null
5,574
test.columns.values
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
5,575
num_test_phrase = test['Phrase'].size clean_test_phrase = [] for i in range(0, num_test_phrase): if( (i+1)%10000 == 0 ): print ("Review %d of %d\ " % ( i+1, num_test_phrase )) clean_test_phrase.append(phrase_to_words(test['Phrase'][i]))'
No
4
78
Data_Transform
string_transform
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
5,576
#Get a bag of words for the test set, and convert to a numpy array test_data_features = vectorizer.transform(clean_test_phrase) test_data_features = test_data_features.toarray() #Use the random forest to make sentiment label predictions result = rf.predict(test_data_features) # Copy the results to a pandas dataframe ...
Yes
3
48
Model_Evaluation
predict_on_test
4,770,480
1
A
0
0
0
0
0
5
ok
6
5
[{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"]
4
B
5,577
train = pd.read_csv("../input/train.tsv", sep='\\t') test = pd.read_csv("../input/test.tsv", sep='\\t') #train = train[0:1000] train['Sentiment'] = train['Sentiment'].apply(str) '
Yes
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
5,578
train.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
5,579
train.Sentiment.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
5,580
import tensorflow as tf device_name = tf.test.gpu_device_name() if device_name != '/device:GPU:0': raise SystemError('GPU device not found') print('Found GPU at: {}'.format(device_name))
Yes
5
23
Environment
set_options
null
1
A
0
1
1
0
0
5
ok
1
1
[{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /device:GPU:0. 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"]
0
A