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3,718 | df['month'] = df['datetime'].dt.month
df.head() | No | 4 | 8 | Data_Transform | feature_engineering | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,719 | df['week'] = df['datetime'].dt.week
df.head() | No | 5 | 8 | Data_Transform | feature_engineering | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,720 | df['hour'] = df['datetime'].dt.hour
df.head() | No | 4 | 8 | Data_Transform | feature_engineering | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,721 | df['year'] = df['datetime'].dt.year
df.head() | No | 4 | 8 | Data_Transform | feature_engineering | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,722 | df['day'] = df['datetime'].dt.day
df.head() | No | 4 | 8 | Data_Transform | feature_engineering | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,723 | df = df.drop(labels='datetime', axis=1)
df.head() | No | 4 | 10 | Data_Transform | drop_column | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,724 | fig, ax = plt.subplots(1, 2, figsize=(12, 4))
names = ['1', '2', '3', '4']
values = df['season'][df['year'] == 2011].value_counts()
ax[0].bar(names, values)
values = df['season'][df['year'] == 2012].value_counts()
ax[1].bar(names, values)
fig.suptitle('Seasons in 2011 & 2012'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,725 | spring_2011 = int(df['season'][df['season'] == 1][df['year'] == 2011].value_counts())
summer_2011 = int(df['season'][df['season'] == 2][df['year'] == 2011].value_counts())
fall_2011 = int(df['season'][df['season'] == 3][df['year'] == 2011].value_counts())
winter_2011 = int(df['season'][df['season'] == 4][df['year'] == ... | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,726 | fig, ax = plt.subplots(1, 2, figsize=(12, 4))
names = ['0', '1']
values = df['holiday'][df['year'] == 2011].value_counts()
ax[0].bar(names, values)
values = df['holiday'][df['year'] == 2012].value_counts()
ax[1].bar(names, values)
fig.suptitle('Holidays in 2011 & 2012'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,727 | # metric to optimize
from sklearn.metrics import mean_squared_error
from sklearn.metrics import make_scorer
scorer = make_scorer(lambda y_test, predictions: np.sqrt(mean_squared_error(y_test, predictions))) | Yes | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"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": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 0 | A |
3,728 | no_holiday_2011 = int(df['holiday'][df['holiday'] == 0][df['year'] == 2011].value_counts())
holiday_2011 = int(df['holiday'][df['holiday'] == 1][df['year'] == 2011].value_counts())
no_holiday_2012 = int(df['holiday'][df['holiday'] == 0][df['year'] == 2012].value_counts())
holiday_2012 = int(df['holiday'][df['holiday'] ... | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,729 | training_set = pd.read_csv('../input/train.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/train.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 10"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 9 | A |
3,730 | training_set.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 |
3,731 | fig, ax = plt.subplots(1, 2, figsize=(12, 4))
names = ['0', '1']
values = df['workingday'][df['year'] == 2011].value_counts()
ax[0].bar(names, values)
values = df['workingday'][df['year'] == 2012].value_counts()
ax[1].bar(names, values)
fig.suptitle('Working day in 2011 & 2012'); | No | 4 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,732 | no_workingday_2011 = int(df['workingday'][df['workingday'] == 0][df['year'] == 2011].value_counts())
workingday_2011 = int(df['workingday'][df['workingday'] == 1][df['year'] == 2011].value_counts())
no_workingday_2012 = int(df['workingday'][df['workingday'] == 0][df['year'] == 2012].value_counts())
workingday_2012 = in... | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,733 | fig, ax = plt.subplots(1, 2, figsize=(12, 4))
names_2011 = ['1', '2', '3']
names_2012 = ['1', '2', '3', '4']
values = df['weather'][df['year'] == 2011].value_counts()
ax[0].bar(names_2011, values)
values = df['weather'][df['year'] == 2012].value_counts()
ax[1].bar(names_2012, values)
fig.suptitle('Weather in 2011 &... | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,734 | weather_2011_1 = df['weather'][df['weather'] == 1][df['year'] == 2011].value_counts()
weather_2011_2 = df['weather'][df['weather'] == 2][df['year'] == 2011].value_counts()
weather_2011_3 = df['weather'][df['weather'] == 3][df['year'] == 2011].value_counts()
weather_2012_1 = df['weather'][df['weather'] == 1][df['year']... | No | 5 | 72 | EDA | count_values | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 26 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,735 | plt.hist(df['temp'][df['year'] == 2011], alpha=0.5, label='2011')
plt.hist(df['temp'][df['year'] == 2012], alpha=0.5, label='2012')
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,736 | training_set.plot(x = 'datetime', y = 'casual') | No | 5 | 75 | Visualization | time_series | 1,813,404 | 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"}] | [] | 9 | B |
3,737 | training_set.plot(x = 'datetime', y = 'registered') | No | 5 | 75 | Visualization | time_series | 1,813,404 | 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"}] | [] | 9 | B |
3,738 | plt.hist(df['atemp'][df['year'] == 2011], alpha=0.5, label='2011')
plt.hist(df['atemp'][df['year'] == 2012], alpha=0.5, label='2012')
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,739 | plt.hist(df['humidity'][df['year'] == 2011], alpha=0.5, label='2011')
plt.hist(df['humidity'][df['year'] == 2012], alpha=0.5, label='2012')
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,740 | plt.hist(df['windspeed'][df['year'] == 2011], alpha=0.5, label='2011')
plt.hist(df['windspeed'][df['year'] == 2012], alpha=0.5, label='2012')
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,741 | plt.hist(df['dow'][df['year'] == 2011], alpha=0.5, label='2011', bins=7)
plt.hist(df['dow'][df['year'] == 2012], alpha=0.5, label='2012', bins=7)
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,742 | plt.hist(df['month'][df['year'] == 2011], alpha=0.5, label='2011', bins=12)
plt.hist(df['month'][df['year'] == 2012], alpha=0.5, label='2012', bins=12)
plt.legend(loc='upper right'); | No | 5 | 75 | Visualization | time_series | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,743 | plt.hist(df['week'][df['year'] == 2011], alpha=0.5, label='2011', bins=52)
plt.hist(df['week'][df['year'] == 2012], alpha=0.5, label='2012', bins=52)
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,744 | plt.hist(df['hour'][df['year'] == 2011], alpha=0.5, label='2011', bins=24)
plt.hist(df['hour'][df['year'] == 2012], alpha=0.5, label='2012', bins=24)
plt.legend(loc='upper right'); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,745 | plt.hist(df['day'][df['year'] == 2011], alpha=0.5, label='2011', bins=31)
plt.hist(df['day'][df['year'] == 2012], alpha=0.5, label='2012', bins=31)
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.legend(loc='upper right'); | No | 5 | 75 | Visualization | time_series | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,746 | names = ['2011', '2012']
values = df['year'].value_counts()
plt.bar(names, values); | No | 5 | 33 | Visualization | distribution | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,747 | count_2011 = df['year'][df['year'] == 2011].count()
count_2012 = df['year'][df['year'] == 2012].count()
print('2011: {}'.format(count_2011))
print('2012: {}'.format(count_2012)) | No | 5 | 72 | EDA | count_values | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,748 | cor_mat = df[:].corr()
mask = np.array(cor_mat)
mask[np.tril_indices_from(mask)] = False
fig = plt.gcf()
fig.set_size_inches(30,12)
sns.heatmap(data=cor_mat, mask=mask, square=True, annot=True, cbar=True); | No | 5 | 80 | Visualization | heatmap | 2,165,156 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
3,749 | corr = training_set.corr()
fig, ax = plt.subplots(figsize=(30, 30))
ax.matshow(corr)
for (i, j), z in np.ndenumerate(corr):
ax.text(j, i, '{:0.1f}'.format(z), ha='center', va='center',
bbox=dict(boxstyle='round', facecolor='white', edgecolor='0.3'))
plt.xticks(range(len(corr.columns)), corr.columns);
... | No | 5 | 80 | Visualization | heatmap | 1,813,404 | 1 | A | 1 | 1 | 1 | 0 | 5 | 8 | 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"}] | [] | 9 | B |
3,750 | from sklearn.model_selection import train_test_split
# Basic preprocessing which applies to all regression techniques (dependent variable: casual)
data = training_set.drop(columns = ['datetime', 'atemp', 'registered', 'count'])
X_train, X_test, y_train, y_test = train_test_split(data, data.casual, test_size=0.2, rand... | Yes | 3 | 13 | Data_Transform | split | 1,813,404 | 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": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | [] | 9 | B |
3,751 | # Preprocessing for linear regression
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train_norm = scaler.fit_transform(X_train)
X_test_norm = scaler.transform(X_test)
one_hot = OneHotEncoder(categorical_features = [0, 1, 2, 3]) #season, holida... | Yes | 4 | 20 | Data_Transform | categorify | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 13 | 9 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | 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"}] | ["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"] | 9 | D |
3,752 | 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 |
3,753 | from sklearn.model_selection import cross_val_score
casual_model = Lasso()
scores = cross_val_score(casual_model, X_train_norm, y_train, cv=5, scoring = scorer)
scores | Yes | 5 | 84 | Other | not_enough_vertices | 1,813,404 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 9 | A |
3,754 | casual_model.fit(X_train_norm, y_train) | No | 5 | 7 | Model_Train | train_model | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 9 | B |
3,755 | # Same thing for the second variable
# Basic preprocessing which applies to all regression techniques (dependent variable: casual)
data = training_set.drop(columns = ['datetime', 'atemp', 'casual', 'count'])
X_train, X_test, y_train, y_test = train_test_split(data, data.registered, test_size=0.2, random_state = RANDOM... | Yes | 4 | 13 | Data_Transform | split | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | [] | 9 | B |
3,756 | # Preprocessing for linear regression
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train_norm = scaler.fit_transform(X_train)
X_test_norm = scaler.transform(X_test)
one_hot = OneHotEncoder... | Yes | 4 | 20 | Data_Transform | categorify | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 13 | 9 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | 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"}] | ["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"] | 9 | D |
3,757 | from sklearn.linear_model import Lasso
from sklearn.model_selection import cross_val_score
registered_model = Lasso()
scores = cross_val_score(registered_model, X_train_norm, y_train, cv=5, scoring = scorer)
scores | Yes | 5 | 28 | Model_Train | compute_train_metric | 1,813,404 | 1 | A | 0 | 0 | 0 | 1 | 0 | 5 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 9 | A |
3,758 | registered_model.fit(X_train_norm, y_train) | No | 5 | 7 | Model_Train | train_model | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 9 | B |
3,759 | # Final prediction of the baseline models, as I am not going to tweak them, I will move directly to the test data
test_dataset = pd.read_csv("../input/test.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 12"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 9 | A |
3,760 | test_data = test_dataset.drop(columns = ['datetime', 'atemp'])
test_data = scaler.transform(test_data)
test_data = one_hot.transform(test_data) | Yes | 5 | 20 | Data_Transform | categorify | 1,813,404 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 9 | B |
3,761 | casual = casual_model.predict(test_data)
registered = registered_model.predict(test_data)
total = casual + registered | No | 4 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,762 | test_dataset[test_dataset['count'] < 0] | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,763 | test_dataset.loc[test_dataset['count'] < 0, 'count'] = 0 | No | 2 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,764 | test_dataset[test_dataset['count'] <= 0] | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,765 | test_dataset[['datetime', 'count']].to_csv('result.csv', index = False) | No | 4 | 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 |
3,766 | # Ignore the warnings
import warnings
warnings.filterwarnings('always')
warnings.filterwarnings('ignore')
# data visualisation and manipulation
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
import seaborn as sns
import missingno as msno
#configure
# sets matplotli... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
3,767 | train=pd.read_csv(r'../input/train.csv')
test=pd.read_csv(r'../input/test.csv')
df=train.copy()
test_df=test.copy()
df.head() | Yes | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,768 | df.columns.unique() | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,769 | df.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,770 | msno.matrix(df) | No | 5 | 34 | Visualization | missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,771 | df.season.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 |
3,772 | sns.factorplot(x='season',data=df,kind='count',size=5,aspect=1.5) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,773 | df.holiday.value_counts()
sns.factorplot(x='holiday',data=df,kind='count',size=5,aspect=1) | Yes | 5 | 33 | Visualization | distribution | 2,200,876 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
3,774 | df.workingday.value_counts()
sns.factorplot(x='workingday',data=df,kind='count',size=5,aspect=1) | Yes | 5 | 33 | Visualization | distribution | 2,200,876 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
3,775 | # 1-> spring
# 2-> summer
# 3-> fall
# 4-> winter
sns.factorplot(x='weather',data=df,kind='count',size=5,aspect=1) | No | 5 | 33 | Visualization | distribution | 2,200,876 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
3,776 | sns.boxplot(data=df[['temp',
'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count']])
fig=plt.gcf()
fig.set_size_inches(10,10) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,777 | df.temp.unique()
fig,axes=plt.subplots(2,2)
axes[0,0].hist(x="temp",data=df,edgecolor="black",linewidth=2,color='#ff4125')
axes[0,0].set_title("Variation of temp")
axes[0,1].hist(x="atemp",data=df,edgecolor="black",linewidth=2,color='#ff4125')
axes[0,1].set_title("Variation of atemp")
axes[1,0].hist(x="windspeed",data=... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,778 | cor_mat= df[:].corr()
mask = np.array(cor_mat)
mask[np.tril_indices_from(mask)] = False
fig=plt.gcf()
fig.set_size_inches(30,12)
sns.heatmap(data=cor_mat,mask=mask,square=True,annot=True,cbar=True) | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,779 | season=pd.get_dummies(df['season'],prefix='season')
df=pd.concat([df,season],axis=1)
df.head()
season=pd.get_dummies(test_df['season'],prefix='season')
test_df=pd.concat([test_df,season],axis=1)
test_df.head() | Yes | 4 | 20 | Data_Transform | categorify | 2,200,876 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
3,780 | weather=pd.get_dummies(df['weather'],prefix='weather')
df=pd.concat([df,weather],axis=1)
df.head()
weather=pd.get_dummies(test_df['weather'],prefix='weather')
test_df=pd.concat([test_df,weather],axis=1)
test_df.head() | Yes | 4 | 20 | Data_Transform | categorify | 2,200,876 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
3,781 | df.drop(['season','weather'],inplace=True,axis=1)
df.head()
test_df.drop(['season','weather'],inplace=True,axis=1)
test_df.head() | Yes | 4 | 10 | Data_Transform | drop_column | 2,200,876 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 1 | [{"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 3"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "... | A | no applicable ML-specific findings | [] | ["InPlace Checker"] | 2 | A |
3,782 | df["hour"] = [t.hour for t in pd.DatetimeIndex(df.datetime)]
df["day"] = [t.dayofweek for t in pd.DatetimeIndex(df.datetime)]
df["month"] = [t.month for t in pd.DatetimeIndex(df.datetime)]
df['year'] = [t.year for t in pd.DatetimeIndex(df.datetime)]
df['year'] = df['year'].map({2011:0, 2012:1})
df.head()' | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,783 | test_df["hour"] = [t.hour for t in pd.DatetimeIndex(test_df.datetime)]
test_df["day"] = [t.dayofweek for t in pd.DatetimeIndex(test_df.datetime)]
test_df["month"] = [t.month for t in pd.DatetimeIndex(test_df.datetime)]
test_df['year'] = [t.year for t in pd.DatetimeIndex(test_df.datetime)]
test_df['year'] = test_df['yea... | Yes | 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 |
3,784 | df.drop('datetime',axis=1,inplace=True)
df.head() | Yes | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,785 | df.drop(['casual','registered'],axis=1,inplace=True)
df.head() | Yes | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,786 | sns.factorplot(x="hour",y="count",data=df,kind='bar',size=5,aspect=1.5)' | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,787 | sns.factorplot(x="month",y="count",data=df,kind='bar',size=5,aspect=1.5)' | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,788 | sns.factorplot(x="year",y="count",data=df,kind='bar',size=5,aspect=1.5)' | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,789 | sns.factorplot(x="day",y='count',kind='bar',data=df,size=5,aspect=1)' | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,790 | plt.scatter(x="temp",y="count",data=df,color='#ff4125')' | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,791 | new_df=df.copy()
new_df.temp.describe()
new_df['temp_bin']=np.floor(new_df['temp'])//5
new_df['temp_bin'].unique()
# now we can visualize as follows
sns.factorplot(x="temp_bin",y="count",data=new_df,kind='bar')' | No | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,792 | df.columns.to_series().groupby(df.dtypes).groups | No | 3 | 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 |
3,793 | x_train,x_test,y_train,y_test=train_test_split(df.drop('count',axis=1),df['count'],test_size=0.25,random_state=42) | No | 4 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,794 | models=[RandomForestRegressor(),AdaBoostRegressor(),BaggingRegressor(),SVR(),KNeighborsRegressor()]
model_names=['RandomForestRegressor','AdaBoostRegressor','BaggingRegressor','SVR','KNeighborsRegressor']
rmsle=[]
d={}
for model in range (len(models)):
clf=models[model]
clf.fit(x_train,y_train)
test_pred=cl... | Yes | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 1 | 1 | 1 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,795 | rmsle_frame=pd.DataFrame(d)
rmsle_frame | No | 4 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,796 | sns.factorplot(y='Modelling Algo',x='RMSLE',data=rmsle_frame,kind='bar',size=5,aspect=2) | No | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,797 | sns.factorplot(x='Modelling Algo',y='RMSLE',data=rmsle_frame,kind='point',size=5,aspect=2) | No | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,798 | no_of_test=[500]
params_dict={'n_estimators':no_of_test,'n_jobs':[-1],'max_features':["auto",'sqrt','log2']}
clf_rf=GridSearchCV(estimator=RandomForestRegressor(),param_grid=params_dict,scoring='neg_mean_squared_log_error')
clf_rf.fit(x_train,y_train)
pred=clf_rf.predict(x_test)
print((np.sqrt(mean_squared_log_error(pr... | Yes | 3 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,799 | clf_rf.best_params_ | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,800 | n_neighbors=[]
for i in range (0,50,5):
if(i!=0):
n_neighbors.append(i)
params_dict={'n_neighbors':n_neighbors,'n_jobs':[-1]}
clf_knn=GridSearchCV(estimator=KNeighborsRegressor(),param_grid=params_dict,scoring='neg_mean_squared_log_error')
clf_knn.fit(x_train,y_train)
pred=clf_knn.predict(x_test)
print((np.... | Yes | 4 | 6 | Hyperparam_Tuning | train_on_grid | 2,200,876 | 1 | A | 0 | 2 | 3 | 0 | 0 | 9 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
3,801 | clf_knn.best_params_ | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,802 | pred=clf_rf.predict(test_df.drop('datetime',axis=1))
d={'datetime':test['datetime'],'count':pred}
ans=pd.DataFrame(d)
ans.to_csv('answer.csv',index=False) | Yes | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,803 | # train .
train = pd.read_csv("../input/train.csv")
# train .
print(train.shape)
# train 5 .
train.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 |
3,804 | # test
test = pd.read_csv("../input/test.csv")
# test .
print(test.shape)
# test 5 .
test.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 |
3,805 | # .
# train["datetime"].dt.year
# .
# String .
# Datetime .
# datetime .
train["datetime"] = pd.to_datetime(train["datetime"])' | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,806 | # train .
print(train.shape)
# " " .
train["datetime-year"] = train["datetime"].dt.year
train["datetime-month"] = train["datetime"].dt.month
train["datetime-day"] = train["datetime"].dt.day
train["datetime-hour"] = train["datetime"].dt.hour
train["datetime-minute"] = train["datetime"].dt.minute
train["datet... | Yes | 4 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,807 | # test datetime Type String datetime .
test["datetime"] = pd.to_datetime(test["datetime"])' | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,808 | # train .
print(test.shape)
# datetime .
test["datetime-year"] = test["datetime"].dt.year
test["datetime-month"] = test["datetime"].dt.month
test["datetime-day"] = test["datetime"].dt.day
test["datetime-hour"] = test["datetime"].dt.hour
test["datetime-minute"] = test["datetime"].dt.minute
test["datetim... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,809 | import seaborn as sns
# .
%matplotlib inline' | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,810 | sns.barplot(data=train, x="weather", y="count") | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,811 | sns.lmplot(data=train, x="temp", y="atemp") | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,812 | sns.distplot(train["windspeed"]) | No | 5 | 33 | Visualization | distribution | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 3 | A |
3,813 | sns.barplot(data=train, x="datetime-year", y="count") | No | 5 | 75 | Visualization | time_series | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,814 | sns.barplot(data=train, x="datetime-month", y="count") | No | 5 | 75 | Visualization | time_series | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,815 | sns.barplot(data=train, x="datetime-day", y="count") | No | 5 | 75 | Visualization | time_series | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,816 | sns.barplot(data=train, x="datetime-hour", y="count") | No | 5 | 75 | Visualization | time_series | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,817 | sns.barplot(data=train, x="datetime-minute", y="count") | No | 5 | 75 | Visualization | time_series | 2,719,562 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
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