<jupyter_start><jupyter_text>Data Dictionary | Variable | Description   |  DataType ||:---------|:-------------:|----------:|| enrollee_id |  Unique ID for enrollee | int64    || city |    City code   |   object     ||city_development_index | Developement index of the city (scaled)| object ||gender | Gender ( Female, Male, Other )| object||relevent_experience | Relevent experience | object||enrolled_university | Type of University course enrolled if any | object ||education_level|  Education level | object || major_discipline | Major discipline | object || experience | Total experience in years | object || company_size | No of employees in current employer's company | object || company_type | Type of current employer | object || last_new_job | Difference in years between previous job and current job | object || training_hours | training hours completed | object || target | 0 – Not looking for job change, 1 – Looking for a job change | int64 |<jupyter_code># Funções personalizadas
train = pd.read_csv('train_jqd04QH.csv')
train.columns
train.isna().sum()
train.gender.value_counts()
train.shape
train.enrolled_university.value_counts()
train.company_size.value_counts()
train.info()
sns.countplot(train.gender)
plt.show()
train.enrolled_university.value_counts()
sns.countplot(train.enrolled_university)
plt.show()
train.experience.value_counts()
sns.countplot(train.experience)
plt.show()
train.major_discipline.value_counts()
sns.countplot(train.major_discipline)
plt.show()
sns.heatmap(train.corr(method = 'spearman'),annot = True)
plt.show()<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '170399', 'license': 'Apache-2.0', 'n_tokens_mistral': 496, 'n_tokens_neox': 461, 'n_words': 128, 'path': 'data/hr_analytics.ipynb', 'repo_name': 'jcr0ch4/hr_analytics', 'size': '1589'}