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
7,481
test = pd.read_csv(FOLDER+'test.csv') test.head()
Yes
4
45
Data_Extraction
load_from_csv
2,047,549
1
A
0
0
0
0
0
2
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
3
A
7,482
import numpy as np import pandas as pd import sklearn from matplotlib import pyplot as plt from scipy import stats as st import os import matplotlib.colors as mcolors
No
5
22
Environment
import_modules
2,056,678
1
A
0
0
0
0
0
7
ok
1
1
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}]
A
no applicable ML-specific findings
[]
["Import Checker"]
27
A
7,483
from sklearn.pipeline import make_pipeline from sklearn.ensemble import BaggingRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.neural_network import MLPRegressor from sklearn.ensemble import GradientBoostingRegressor from sklearn.ensemble import AdaBoostRegressor from sklearn.tree import Decis...
No
5
22
Environment
import_modules
2,056,678
1
A
0
0
0
0
0
8
ok
1
1
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}]
A
no applicable ML-specific findings
[]
["Import Checker"]
27
A
7,484
trainfilepath = "../input/atividade-3-pmr3508/train.csv" testfilepath = "../input/atividade-3-pmr3508/test.csv"
No
5
77
Other
define_variables
2,056,678
1
A
0
0
0
0
0
2
ok
3
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path d...
A
no applicable ML-specific findings
[]
["Hardcoded file path", "Import Checker"]
27
A
7,485
trainHouses = pd.read_csv(trainfilepath, sep=r'\\s*,\\s*', engine='python', na_values='?')<br>testHouses = pd.read_csv(testfilepath, sep=r'\\s*,\\s*', engine='python', na_values='?')
No
5
45
Data_Extraction
load_from_csv
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,486
states = pd.read_csv("../input/averagestatecoordinates/states.csv", sep=r\'\\s*,\\s*\', engine=\'python\', na_values=\'?\')<br>states.head()
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
7,487
from sklearn.neighbors import KNeighborsClassifier<br><br>knn = KNeighborsClassifier(n_neighbors=3, p=2)<br><br>#knn.fit(states.drop("state", axis="columns"), states[\'state\'])<br>knn.fit(states[["latitude", "longitude"]], states[\'state\'])<br><br><br>#trainHouses["state"] = knn.predict(trainHouses.drop(trainHouses.c...
Yes
3
7
Model_Train
train_model
null
1
A
0
null
0
0
0
null
invalid_python
0
0
[]
null
invalid_python
[]
[]
0
null
7,488
apple = (37.33182, -122.03118) SF = (37.783333, -122.416667) #trainHouses["distance_to_apple"] = np.sqrt((trainHouses["latitude"] - miami[0])**2 + (trainHouses["longitude"] - miami[1])**2) #d_to_apple = np.sqrt((trainHouses["latitude"] - apple[0])**2 + (trainHouses["longitude"] - apple[1])**2) d_to_apple = getDistance...
Yes
4
33
Visualization
distribution
2,056,678
1
A
2
0
0
0
10
12
ok
2
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection ...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Import Checker"]
27
A
7,489
LA = (35.0569, -118.25) Beverly_Hills = (34.073056, -118.399444) #d_to_LA = np.sqrt((trainHouses["latitude"] - LA[0])**2 + (trainHouses["longitude"] - LA[1])**2) d_to_LA = getDistanceFromLatLng(trainHouses["latitude"], trainHouses["longitude"], LA[0], LA[1]) #d_to_BH = np.sqrt((trainHouses["latitude"] - Beverly_Hills[...
Yes
4
33
Visualization
distribution
2,056,678
1
A
2
0
0
0
10
12
ok
2
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection ...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Import Checker"]
27
A
7,490
trainHouses["distance_to_SF"] = d_to_SF #testHouses["distance_to_SF"] = np.sqrt((testHouses["latitude"] - SF[0])**2 + (testHouses["longitude"] - SF[1])**2) testHouses["distance_to_SF"] = getDistanceFromLatLng(testHouses["latitude"], testHouses["longitude"], SF[0], SF[1]) trainHouses["distance_to_LA"] = d_to_LA #testHou...
No
5
8
Data_Transform
feature_engineering
2,056,678
2
B
3
0
0
0
15
8
ok
2
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection ...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Import Checker"]
27
B
7,491
trainHouses.shape
No
5
58
Debug
show_shape
2,056,678
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}]
A
no applicable ML-specific findings
[]
["Import Checker"]
27
A
7,492
trainHouses.head()
No
5
41
Debug
show_table
2,056,678
1
A
0
0
0
0
0
1
ok
2
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida...
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
["Import Checker"]
27
B
7,493
plt.hist(trainHouses["median_house_value"], bins=100) plt.show()
No
5
33
Visualization
distribution
2,056,678
1
A
0
0
0
0
0
2
ok
2
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection ...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Import Checker"]
27
A
7,494
XtrainHouses = trainHouses.drop(["Id", "median_house_value"], axis="columns") XtrainHousesB = trainHouses.drop(["Id", "median_house_value", "median_age", "total_rooms", "total_bedrooms", "population", "households"], axis="columns") YtrainHouses = trainHouses["median_house_value"] XtestHouses = testHouses.drop("Id", ax...
Yes
4
10
Data_Transform
drop_column
2,056,678
1
A
0
0
0
0
0
5
ok
5
5
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection ...
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", "Import Checker"]
27
B
7,495
#a = trainHouses[trainHouses["median_house_value"].transform(lambda x: x<=500000)] #a = trainHouses[trainHouses["median_house_value"] <=500000] plt.hist(trainHouses[trainHouses["median_house_value"] <=500000]["median_house_value"], bins=100) plt.show() culledTrainHouses = trainHouses[trainHouses["median_house_value"] <...
Yes
3
33
Visualization
distribution
2,056,678
1
A
1
0
0
0
5
5
ok
2
2
[{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 13"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection ...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Import Checker"]
27
A
7,496
import pandas as pd import sklearn import os import numpy as np import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_log_error from sklearn import linear_model from sklearn.ensemble import RandomFo...
Yes
4
22
Environment
import_modules
2,062,116
1
A
0
0
0
0
0
12
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
11
A
7,497
train_data = pd.read_csv('../input/train.csv', engine='python') test_data = pd.read_csv('../input/test.csv', engine='python')
No
5
45
Data_Extraction
load_from_csv
2,062,116
1
A
0
0
0
0
0
4
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."]
11
A
7,498
train_data.head()
No
5
41
Debug
show_table
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,499
train_data.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
7,500
train_data.info()
No
5
40
Debug
show_table_attributes
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,501
Xtrain = train_data Xtrain = Xtrain.drop('Id', axis=1) Xtrain = Xtrain.drop('median_house_value', axis=1) Ytrain = train_data['median_house_value']
Yes
4
21
Data_Transform
prepare_x_and_y
2,062,116
1
A
0
0
0
0
0
4
ok
4
4
[{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida...
B
Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}]
["Column Selection Checker"]
11
B
7,502
knn = KNeighborsRegressor(n_neighbors=10) knn.fit(Xtrain, Ytrain) scores = cross_val_score(knn, Xtrain, Ytrain, cv=10) Ypred = knn.predict(Xtrain) scores.mean()
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
5
ok
7
5
[{"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 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,503
rmsle(Ytrain, Ypred)
No
5
28
Model_Train
compute_train_metric
2,062,116
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
11
A
7,504
reg = linear_model.Lasso(alpha = 0.1) reg.fit(Xtrain, Ytrain) scores = cross_val_score(reg, Xtrain, Ytrain, cv=10) Ypred=reg.predict(Xtrain) scores.mean()
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
5
ok
7
5
[{"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 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,505
rid = linear_model.Ridge(alpha = 0.5) rid.fit(Xtrain, Ytrain) scores = cross_val_score(rid, Xtrain, Ytrain, cv=10) Ypred = rid.predict(Xtrain) scores.mean()
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
5
ok
7
5
[{"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 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,506
bay = linear_model.BayesianRidge() bay.fit(Xtrain, Ytrain) scores = cross_val_score(bay, Xtrain, Ytrain, cv=10) Ypred = bay.predict(Xtrain) scores.mean()
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
5
ok
7
5
[{"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 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,507
lars = linear_model.LassoLars(alpha = 0.1) lars.fit(Xtrain, Ytrain) scores = cross_val_score(lars, Xtrain, Ytrain, cv=10) Ypred = lars.predict(Xtrain) scores.mean()
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
5
ok
7
5
[{"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 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,508
california_sea=[(41.990352, -124.216535),(41.936725, -124.199048),(41.862157, -124.220161),(41.758672, -124.240793),(41.730317, -124.162807),(41.672629, -124.139878),(41.722746, -124.151351),(41.671813, -124.136762),(41.618963, -124.109252),(41.470737, -124.072740),(41.383226, -124.066948),(41.308172, -124.094492),(41....
No
5
77
Other
define_variables
2,062,116
1
A
0
0
0
0
0
19
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
11
A
7,509
train_data3 = train_data2<br>train_data3["rooms_per_household"] = train_data3["total_rooms"]/train_data3["households"]<br>train_data3["bedrooms_per_room"] = train_data3["total_bedrooms"]/train_data3["total_rooms"]<br>train_data3["population_per_household"] = train_data3["population"]/train_data3["households"]<br>train_...
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
7,510
d = 8 n = 100 boost = AdaBoostRegressor(DecisionTreeRegressor(max_depth=d), n_estimators=n) boost.fit(Xtrain3, Ytrain) Ypred = boost.predict(Xtrain3) boost_rmsle = rmsle(Ytrain, Ypred) print('error =', boost_rmsle)
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
8
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"}]
["Exception Handling Checker", "Magic number", "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
7,511
from sklearn.model_selection import train_test_split X_train, X_validation, y_train, y_validation = train_test_split(Xtrain3, Ytrain, train_size=0.7) from catboost import CatBoostRegressor model=CatBoostRegressor(iterations=200, depth=6, learning_rate=0.2, loss_function='RMSE') model.fit(Xtrain3, Ytrain,eval_set=(X_v...
Yes
2
7
Model_Train
train_model
2,062,116
1
A
1
0
0
0
5
8
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, Randomness Control Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,512
Xtest2 = test_data2.drop('Id', axis=1)
No
5
10
Data_Transform
drop_column
2,062,116
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
11
A
7,513
forest = RandomForestRegressor(max_depth=21, random_state=0, n_estimators=1000) forest.fit(Xtrain3, Ytrain) Ypred = forest.predict(Xtrain3) forest_rmsle = rmsle(Ytrain, Ypred) print('log error =', forest_rmsle) prediction = forest.predict(Xtest2)
Yes
3
7
Model_Train
train_model
2,062,116
1
A
0
0
0
0
0
6
ok
9
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, Cross-validation not detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,514
import numpy as np import pandas as pd import sklearn import matplotlib.pyplot as plt import os print(os.listdir("../input"))
No
5
88
Debug
list_files
2,074,747
1
A
0
0
0
0
0
6
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 15"}]
A
no applicable ML-specific findings
[]
["Hardcoded file path"]
8
A
7,515
train = pd.read_csv("../input/train.csv") test = pd.read_csv("../input/test.csv") Xtrain = train.drop(columns=["Id","median_house_value"]) Ytrain = train["median_house_value"]
Yes
3
45
Data_Extraction
load_from_csv
2,074,747
1
A
0
0
0
0
0
4
ok
5
4
[{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida...
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
["Column Selection Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."]
8
B
7,516
Xtrain.describe()
No
5
40
Debug
show_table_attributes
2,074,747
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}]
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
[]
8
B
7,517
YPredict.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
7,518
pd.DataFrame({"Id":ID_list,"median_house_value":YPredict}).to_csv("pred_R.csv",index=False)
No
5
25
Data_Export
save_to_csv
2,074,747
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
8
A
7,519
import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore")
No
5
23
Environment
set_options
2,076,170
1
A
0
0
0
0
0
6
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,520
data = pd.read_csv("../input/atividade-3-pmr3508/train.csv") data.head()
Yes
4
45
Data_Extraction
load_from_csv
2,076,170
1
A
0
0
0
0
0
2
ok
1
1
[{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/atividade-3-pmr3508/train.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 7"}]
A
no applicable ML-specific findings
[]
["Hardcoded file path"]
6
A
7,521
data.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
7,522
newData = data.drop(["Id","latitude","longitude"],axis=1)
No
5
10
Data_Transform
drop_column
2,076,170
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,523
newData["avg_rooms"] = newData.total_rooms/newData.households newData["avg_bedrooms"] = newData.total_bedrooms/newData.households newData["avg_inhabitants"] = newData.population/newData.households
No
5
8
Data_Transform
feature_engineering
2,076,170
1
A
0
0
0
0
0
3
ok
1
1
[{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}]
A
no applicable ML-specific findings
[]
["Column Selection Checker"]
6
A
7,524
from sklearn.neighbors import KNeighborsRegressor from sklearn.model_selection import GridSearchCV
No
5
22
Environment
import_modules
null
1
A
0
0
0
0
0
2
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
0
A
7,525
knn = KNeighborsRegressor() knn.get_params()
Yes
4
4
Model_Train
choose_model_class
null
1
A
0
0
0
0
0
2
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,526
param_grid = {"n_neighbors":[i for i in range(1,31)],"weights":["uniform","distance"],"p":[1,2]} grid = GridSearchCV(knn,param_grid,cv=10)
Yes
4
5
Hyperparam_Tuning
define_search_space
2,076,170
2
B
1
0
0
0
5
2
ok
3
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "...
A
no applicable ML-specific findings
[]
["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
6
B
7,527
grid.fit(xTrain,yTrain) print(grid.best_estimator_) print(grid.best_score_)
No
5
6
Hyperparam_Tuning
train_on_grid
2,076,170
1
A
0
0
0
0
0
3
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"]
6
B
7,528
ridge = Ridge() ridge.get_params()
Yes
4
4
Model_Train
choose_model_class
2,076,170
1
A
0
0
0
0
0
2
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,529
param_grid2 = {"alpha":np.linspace(0.5,10.5,101).tolist()} grid2 = GridSearchCV(ridge,param_grid2,cv=10)
Yes
4
5
Hyperparam_Tuning
define_search_space
2,076,170
1
A
0
0
0
0
0
2
ok
3
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "...
A
no applicable ML-specific findings
[]
["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
6
A
7,530
grid2.fit(xTrain,yTrain) print(grid2.best_estimator_) print(grid2.best_score_)
No
5
6
Hyperparam_Tuning
train_on_grid
2,076,170
1
A
0
0
0
0
0
3
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"]
6
B
7,531
from sklearn.linear_model import Lasso
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
7,532
lasso = Lasso() lasso.get_params()
Yes
4
4
Model_Train
choose_model_class
2,076,170
1
A
0
0
0
0
0
2
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,533
param_grid3 = {"alpha":np.linspace(0.5,5.5,51).tolist(),"normalize":[True,False]} grid3 = GridSearchCV(lasso,param_grid3,cv=10)
Yes
4
5
Hyperparam_Tuning
define_search_space
2,076,170
1
A
0
0
0
0
0
2
ok
3
3
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "...
A
no applicable ML-specific findings
[]
["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"]
6
A
7,534
grid3.fit(xTrain,yTrain) print(grid3.best_estimator_) print(grid3.best_score_)
No
5
6
Hyperparam_Tuning
train_on_grid
2,076,170
1
A
0
0
0
0
0
3
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"]
6
B
7,535
testRaw = pd.read_csv("../input/atividade-3-pmr3508/test.csv") ID_list = testRaw.Id.tolist() testRaw["avg_rooms"] = testRaw.total_rooms/testRaw.households testRaw["avg_bedrooms"] = testRaw.total_bedrooms/testRaw.households testRaw["avg_inhabitants"] = testRaw.population/testRaw.households testData = testRaw.drop(["Id",...
Yes
3
8
Data_Transform
feature_engineering
2,076,170
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": "general_ml", "framework": "General ML", "name": "Hardcoded file path d...
A
no applicable ML-specific findings
[]
["Column Selection Checker", "Hardcoded file path"]
6
A
7,536
knn.fit(xTrain,yTrain) pred_knn = knn.predict(testData).tolist()
Yes
4
7
Model_Train
train_model
2,076,170
1
A
0
0
0
0
0
2
ok
9
7
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear...
B
Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
6
B
7,537
pd.DataFrame({"Id":ID_list,"median_house_value":pred_knn}).to_csv("pred_knn.csv",index=False)
No
5
25
Data_Export
save_to_csv
2,076,170
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,538
ridge.fit(xTrain,yTrain) pred_ridge = ridge.predict(testData).tolist()
Yes
4
7
Model_Train
train_model
2,076,170
1
A
0
0
0
0
0
2
ok
9
7
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear...
B
Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
6
B
7,539
pd.DataFrame({"Id":ID_list,"median_house_value":pred_ridge}).to_csv("pred_ridge.csv",index=False)
No
5
25
Data_Export
save_to_csv
2,076,170
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,540
lasso.fit(xTrain,yTrain) pred_lasso = lasso.predict(testData).tolist()
Yes
4
7
Model_Train
train_model
2,076,170
1
A
0
0
0
0
0
2
ok
9
7
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear...
B
Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
6
B
7,541
pd.DataFrame({"Id":ID_list,"median_house_value":pred_lasso}).to_csv("pred_lasso.csv",index=False)
No
5
25
Data_Export
save_to_csv
2,076,170
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
6
A
7,542
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from geopy.distance import distance from geopy.distance import vincenty from sklearn.metrics import make_scorer from sklearn.model_selection import cross_validate from sklearn.preprocessing import MinMaxScaler from sklearn.mode...
No
5
22
Environment
import_modules
2,076,816
1
A
0
0
0
0
0
15
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
11
A
7,543
Train = pd.read_csv("../input/californianhouses/train.csv",<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="")<br>Test = pd.read_csv("../input/californianhouses/test.csv",<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="")
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
7,544
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
7,545
# Id no um critrio para estimar a varivel desejada<br>Train = Train.drop(columns=['Id'])
No
5
10
Data_Transform
drop_column
null
1
A
0
0
0
0
0
0
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,546
plt.title('Matriz de correlao')<br>sns.heatmap(mcorr)
No
5
80
Visualization
heatmap
null
1
A
0
0
0
1
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,547
scaler = MinMaxScaler() selected_columns = ['median_income', 'total_rooms','population','median_age'] SC = scaler.fit_transform(Train[selected_columns]) x_train, x_test, y_train, y_test = train_test_split(SC, Train['median_house_value'], test_size=0.20)
Yes
4
18
Data_Transform
normalization
2,076,816
1
A
0
0
0
0
0
4
ok
10
10
[{"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...
B
Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Pipeline Checker=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": "Pipeline Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}]
["Column Selection Checker", "Exception Handling Checker", "Potential data leakage: Preprocessing applied before train-test split (module-level code)", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,548
def rmsle(y_test, y_pred): return np.sqrt(np.mean((np.log(y_pred+1) - np.log(y_test+1))**2)) reg = LinearRegression() scorer = make_scorer(rmsle, greater_is_better=False) reg.fit(x_train, y_train) y_pred = reg.predict(x_test) print("RMSLE: " + str(rmsle(y_pred, y_test)))
Yes
3
7
Model_Train
train_model
null
1
A
0
1
0
0
0
7
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,549
param_grid = dict(n_neighbors=list(range(1,15))) neigh = KNeighborsClassifier() grid_obj = GridSearchCV(neigh, param_grid, scoring=scorer, cv=5) grid_obj.fit(x_train, y_train) grid_obj.best_params_
Yes
3
7
Model_Train
train_model
null
1
A
1
0
0
0
5
5
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,550
neigh = KNeighborsClassifier(n_neighbors=1) neigh.fit(x_train, y_train) y_pred = neigh.predict(x_test) print("RMSLE: " + str(rmsle(y_pred, y_test)))
Yes
3
7
Model_Train
train_model
null
1
A
0
0
0
0
0
4
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,551
las = Lasso() param_grid = dict(alpha=np.divide(list(range(1,100)),100)) grid_obj = GridSearchCV(las, param_grid, scoring=scorer, cv=5) grid_obj.fit(x_train, y_train) grid_obj.best_params_
Yes
2
7
Model_Train
train_model
null
1
A
1
0
0
0
5
5
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,552
las = Lasso(alpha=0.21) las.fit(x_train, y_train) y_pred = las.predict(x_test) print("RMSLE: " + str(rmsle(y_pred, y_test)))
Yes
3
7
Model_Train
train_model
null
1
A
0
0
0
0
0
4
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,553
rfc = RandomForestClassifier(n_estimators=50, max_depth=35, random_state=0) rfc.fit(x_train, y_train) y_pred = rfc.predict(x_test) print("RMSLE: " + str(rmsle(y_pred, y_test)))
Yes
3
7
Model_Train
train_model
2,076,816
1
A
0
0
0
0
0
4
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, Cross-validation not detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
11
B
7,554
dfTest = Test.drop(['longitude', 'latitude', 'households', 'total_bedrooms'], axis=1) dfTest.head()
Yes
4
10
Data_Transform
drop_column
2,076,816
1
A
0
0
0
0
0
2
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
11
A
7,555
dfTest.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
7,556
selected_model = rfc x_val_test = scaler.transform(dfTest[selected_columns]) y_val_test = selected_model.predict(x_val_test) dfSave = pd.DataFrame(data={"Id" : dfTest["Id"], "median_house_value" : y_val_test}) pd.DataFrame(dfSave[["Id", "median_house_value"]], columns = ["Id", "median_house_value"]).to_csv("Output.csv"...
Yes
3
25
Data_Export
save_to_csv
2,076,816
1
A
0
0
0
0
0
5
ok
5
4
[{"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...
B
No random seed setting detected=low
[{"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"]
11
B
7,557
# Importao das bibliotecas <br>import numpy as np<br>import pandas as pd<br>import matplotlib.pyplot as plt<br>%matplotlib inline<br>import warnings <br>import math<br>import seaborn as sns<br>
No
5
23
Environment
set_options
null
1
A
0
0
0
0
0
0
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,558
#Lendo a base de treino<br>traindata = pd.read_csv("../input/california-houses/train.csv",<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="?")
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
7,559
#Modificando a visalizao da base de treino<br>traindata.iloc[0:20,:]
No
5
14
Data_Transform
filter
null
1
A
0
0
0
0
0
0
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,560
traindata = traindata.drop(columns = ['Id'])
No
5
10
Data_Transform
drop_column
2,077,355
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}]
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
[]
4
B
7,561
traindata.info()
No
5
40
Debug
show_table_attributes
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,562
# Grficos de barra das variveis <br>traindata.hist(bins=50, figsize=(20,20))<br>plt.show()
No
5
33
Visualization
distribution
null
1
A
0
0
0
0
0
0
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,563
plt.figure(figsize=(6,6))<br>plt.title('Matriz de correlao')<br>sns.heatmap(traindata.corr(), annot=True, linewidths=0.1)
No
5
80
Visualization
heatmap
null
1
A
0
0
0
1
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,564
knn_trainX = traindata[["longitude","total_rooms","total_bedrooms","population","households","median_income"]]
No
5
12
Data_Transform
create_dataframe
2,077,355
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
4
A
7,565
from sklearn.neighbors import KNeighborsRegressor neighbor = KNeighborsRegressor(n_neighbors=2) neighbor.fit(knn_trainX,trainY) knn_predict = neighbor.predict(knn_trainX) df_knn = pd.DataFrame({'Y_real':trainY[:],'Y_pred':knn_predict[:]}) print(rmsle(df_knn.Y_real,df_knn.Y_pred))
Yes
2
7
Model_Train
train_model
2,077,355
1
A
0
0
0
0
0
6
ok
8
7
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear...
B
Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low
[{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}]
["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"]
4
B
7,566
def rooms_pop(row):<br> row['rooms_pop'] = row['total_rooms'] / row['population'] <br> return row<br>traindata = traindata.apply(rooms_pop, axis=1)<br>traindata = traindata.drop(['population'], axis=1)<br>plt.figure(figsize=(6,6))<br>plt.title('Matriz de correlao')<br>sns.heatmap(traindata.corr(), annot=True, lin...
Yes
3
80
Visualization
heatmap
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,567
def age_rooms(row):<br> row['age_rooms'] = row['median_age'] / row['total_rooms'] <br> return row<br>traindata = traindata.apply(age_rooms, axis=1)<br>traindata = traindata.drop(['median_age'], axis=1)<br>plt.figure(figsize=(6,6))<br>plt.title('Matriz de correlao')<br>sns.heatmap(traindata.corr(), annot=True, lin...
Yes
3
80
Visualization
heatmap
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,568
traindata = traindata.drop(columns = ['latitude','longitude'])
No
5
10
Data_Transform
drop_column
2,077,355
1
A
0
0
0
0
0
1
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}]
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
[]
4
B
7,569
newknn_trainX = traindata[["per_capita","total_bedrooms","rooms_pop","households","age_rooms"]]<br>neighbor = KNeighborsRegressor(n_neighbors=2)<br>neighbor.fit(newknn_trainX,trainY) <br>knn_predict = neighbor.predict(newknn_trainX)<br>df_knn = pd.DataFrame({\'Y_real\':trainY[:],\'Y_pred\':knn_predict[:]})<br>print(rms...
Yes
2
7
Model_Train
train_model
null
1
A
0
null
0
0
0
null
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,570
x_val_test = testX<br>y_val_test = neighbor.predict(x_val_test)<br><br>dfSave = pd.DataFrame(data={"Id" : testdata["Id"], "median_house_value" : y_val_test})<br>dfSave[\'Id\'] = dfSave[\'Id\'].astype(int)<br>pd.DataFrame(dfSave[["Id", "median_house_value"]], columns = ["Id", "median_house_value"]).to_csv("Output.csv", ...
Yes
3
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
7,571
%matplotlib inline<br><br>from matplotlib import pyplot as plt<br>import pandas as pd<br>import numpy as np<br><br>import os<br><br>df = pd.read_csv("../input/atividade-3-pmr3508/train.csv",<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="?")<br>df = df.replace(np.nan,\' \', regex=Tru...
No
3
45
Data_Extraction
load_from_csv
null
1
A
0
null
0
0
0
null
invalid_python
0
0
[]
null
invalid_python
[]
[]
0
null
7,572
df.describe()
No
5
40
Debug
show_table_attributes
null
1
A
0
0
0
0
0
1
no_ml_import
0
0
[]
null
no_ml_import
[]
[]
0
null
7,573
X["coord"] = X["latitude"].map(str) + ', ' + X["longitude"].map(str)
No
5
8
Data_Transform
feature_engineering
2,077,825
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
7
A
7,574
cities = pd.DataFrame(address.Id)<br>cities = cities.set_index(\'Id\')<br>cities[\'city\'] = \'\'<br><br>for index, row in address.iterrows():<br> city = row[\'location\'][-2]<br> row_id = row[\'Id\']<br> cities.at[row_id, \'city\'] = city.lstrip(\' \')<br><br>cities.to_csv("cities.csv", index=False)
Yes
4
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
7,575
cities = pd.read_csv("../input/california-prices/cities.csv",<br> sep=r\'\\s*"\\s*\',<br> engine=\'python\')<br>X[\'cities\'] = cities[\'city\']
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
7,576
calPrices = pd.read_csv("../input/california-prices/calif.csv",<br> sep=r\'\\s*,\\s*\\s*"\',<br> engine=\'python\')<br><br><br>calPrices = calPrices.replace(\'---\',\' \', regex=True)<br>calPrices.columns = [col.replace(\'"\', \'\') for col in calPrices.columns]<br>calPrices = calPrices.filter([\'Region N...
Yes
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
7,577
calPrices.head()
No
5
41
Debug
show_table
2,077,825
1
A
0
0
0
0
0
1
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
7
A
7,578
X['people_pb'] = X.population/X.total_bedrooms X['people_ph'] = X.population/X.households X['income_pr'] = X.median_income/X.total_rooms
No
5
8
Data_Transform
feature_engineering
2,077,825
1
A
0
0
0
0
0
3
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
7
A
7,579
X = X.replace('', np.NaN) X = X.replace(' ', np.NaN) X = X.dropna() X = X.drop(['Id','longitude','latitude','coord'], axis = 1)
Yes
4
17
Data_Transform
correct_missing_values
2,077,825
1
A
0
0
0
0
0
4
ok
0
0
[]
A
no applicable ML-specific findings
[]
[]
7
A
7,580
X_train = X.filter(['median_age', 'total_rooms','total_bedrooms', 'population', 'households', 'median_income', 'city_price', 'people_pb','people_ph', 'income_pr'], axis = 1) X_train.describe()
Yes
4
14
Data_Transform
filter
2,077,825
1
A
0
0
0
0
0
2
ok
1
1
[{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}]
B
Cross Validation Checker=low
[{"type": "Cross Validation Checker", "severity": "low"}]
[]
7
B