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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
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+Test_Dataset.csv filter=lfs diff=lfs merge=lfs -text
+Train_Dataset.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+tags:
+- nbfi
+- tabular_classification
+- binary_classification
+pretty_name: Adult
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- default
+---
+# NBFI
+The [NBFI dataset](https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset) from the [Kaggle](https://www.kaggle.com/datasets).
+Client default prediction
+
+# Configurations and tasks
+- `default`, for binary classification of the individual's future default status.
+
+# Features
+|**Feature**                                    |**Type**       |
+|-----------------------------------------------|---------------|
+|`income`                                       | `float32`     |
+|`owns_a_car`                                   | `bool`        |
+|`owns_a_bike`                                  | `bool`        |
+|`has_an_active_loan`                           | `bool`        |
+|`owns_a_house`                                 | `bool`        |
+|`nr_children`                                  | `int8`        |
+|`credit`                                       | `float32`     |
+|`loan_annuity`                                 | `float32`     |
+|`accompanied_by`                               | `string`      |
+|`income_type`                                  | `string`      |
+|`education_level`                              | `float32`     |
+|`marital_status`                               | `float32`     |
+|`is_male`                                      | `bool`        |
+|`type_of_contract`                             | `string`      |
+|`type_of_housing`                              | `string`      |
+|`residence_density`                            | `float32`     |
+|`age_in_days`                                  | `int32`       |
+|`consecutive_days_of_employment`               | `int16`       |
+|`nr_days_since_last_registration_change`       | `int32`       |
+|`nr_days_since_last_document_change`           | `int32`       |
+|`owned_a_house_for_nr_days`                    | `int32`       |
+|`has_provided_a_mobile_number`                 | `bool`        |
+|`has_provided_a_home_number`                   | `bool`        |
+|`was_reachable_at_work`                        | `bool`        |
+|`job`                                          | `string`      |
+|`nr_family_members`                            | `int8`        |
+|`city_rating`                                  | `int8`        |
+|`weekday_of_application`                       | `int8`        |
+|`hour_of_application`                          | `float32`     |
+|`same_residence_and_home`                      | `bool`        |
+|`same_work_and_home`                           | `bool`        |
+|`score_1`                                      | `float32`     |
+|`score_2`                                      | `float32`     |
+|`score_3`                                      | `float32`     |
+|`nr_defaults_in_social_circle`                 | `int8`        |
+|`inquiries_in_last_year`                       | `float32`     |

Test_Dataset.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fce61c89339cc849186d4e9964c8d154c933359ea51e5c8d401c4312040aa137
+size 15229326

Train_Dataset.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bfaa92b0e2bf3cb523c2c287d00502328bdc19366cdb2a49486c58ce7cae5e33
+size 23193688

nbfi.py

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+"""NBFI: A Census Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_ORIGINAL_FEATURE_NAMES = [
+    "ID",
+    "Client_Income",
+    "Car_Owned",
+    "Bike_Owned",
+    "Active_Loan",
+    "House_Own",
+    "Child_Count",
+    "Credit_Amount",
+    "Loan_Annuity",
+    "Accompany_Client",
+    "Client_Income_Type",
+    "Client_Education",
+    "Client_Marital_Status",
+    "Client_Gender",
+    "Loan_Contract_Type",
+    "Client_Housing_Type",
+    "Population_Region_Relative",
+    "Age_Days",
+    "Employed_Days",
+    "Registration_Days",
+    "ID_Days",
+    "Own_House_Age",
+    "Mobile_Tag",
+    "Homephone_Tag",
+    "Workphone_Working",
+    "Client_Occupation",
+    "Client_Family_Members",
+    "Cleint_City_Rating",
+    "Application_Process_Day",
+    "Application_Process_Hour",
+    "Client_Permanent_Match_Tag",
+    "Client_Contact_Work_Tag",
+    "Type_Organization",
+    "Score_Source_1",
+    "Score_Source_2",
+    "Score_Source_3",
+    "Social_Circle_Default",
+    "Phone_Change",
+    "Credit_Bureau",
+    "Default"
+]
+_BASE_FEATURE_NAMES = [
+    "income",
+    "owns_a_car",
+    "owns_a_bike",
+    "has_an_active_loan",
+    "owns_a_house",
+    "nr_children",
+    "credit",
+    "loan_annuity",
+    "accompanied_by",
+    "income_type",
+    "education_level",
+    "marital_status",
+    "is_male",
+    "type_of_contract",
+    "type_of_housing",
+    "residence_density",
+    "age_in_days",
+    "consecutive_days_of_employment",
+    "nr_days_since_last_registration_change",
+    "nr_days_since_last_document_change",
+    "owned_a_house_for_nr_days",
+    "has_provided_a_mobile_number",
+    "has_provided_a_home_number",
+    "was_reachable_at_work",
+    "job",
+    "nr_family_members",
+    "city_rating",
+    "weekday_of_application",
+    "hour_of_application",
+    "same_residence_and_home",
+    "same_work_and_home",
+    "score_1",
+    "score_2",
+    "score_3",
+    "nr_defaults_in_social_circle",
+    "inquiries_in_last_year",
+    "has_defaulted"
+]
+features_types_per_config = {
+    "income": datasets.Value("float32"),
+    "owns_a_car": datasets.Value("bool"),
+    "owns_a_bike": datasets.Value("bool"),
+    "has_an_active_loan": datasets.Value("bool"),
+    "owns_a_house": datasets.Value("bool"),
+    "nr_children": datasets.Value("int8"),
+    "credit": datasets.Value("float32"),
+    "loan_annuity": datasets.Value("float32"),
+    "accompanied_by": datasets.Value("string"),
+    "income_type": datasets.Value("string"),
+    "education_level": datasets.Value("float32"),
+    "marital_status": datasets.Value("float32"),
+    "is_male": datasets.Value("bool"),
+    "type_of_contract": datasets.Value("string"),
+    "type_of_housing": datasets.Value("string"),
+    "residence_density": datasets.Value("float32"),
+    "age_in_days": datasets.Value("int32"),
+    "consecutive_days_of_employment": datasets.Value("int16"),
+    "nr_days_since_last_registration_change": datasets.Value("int32"),
+    "nr_days_since_last_document_change": datasets.Value("int32"),
+    "owned_a_house_for_nr_days": datasets.Value("int32"),
+    "has_provided_a_mobile_number": datasets.Value("bool"),
+    "has_provided_a_home_number": datasets.Value("bool"),
+    "was_reachable_at_work": datasets.Value("bool"),
+    "job": datasets.Value("string"),
+    "nr_family_members": datasets.Value("int8"),
+    "city_rating": datasets.Value("int8"),
+    "weekday_of_application": datasets.Value("int8"),
+    "hour_of_application": datasets.Value("float32"),
+    "same_residence_and_home": datasets.Value("bool"),
+    "same_work_and_home": datasets.Value("bool"),
+    "score_1": datasets.Value("float32"),
+    "score_2": datasets.Value("float32"),
+    "score_3": datasets.Value("float32"),
+    "nr_defaults_in_social_circle": datasets.Value("int8"),
+    "inquiries_in_last_year": datasets.Value("float32"),
+    "has_defaulted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+}
+_ENCODING_DICS = {}
+_EDUCATION_ENCODING = {
+    "Junior secondary": 0,
+    "Secondary": 1,
+    "Graduation dropout": 2,
+    "Graduation": 2,
+    "Post": 4
+}    
+
+DESCRIPTION = "NBFI dataset from default prediction."
+_HOMEPAGE = "https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset"
+_URLS = ("https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/nbfi/raw/main/Train_Dataset.csv", 
+    "test": "https://huggingface.co/datasets/mstz/nbfi/raw/main/Test_Dataset.csv"
+}
+
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class NBFIConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(NBFIConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class NBFI(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "default"
+    BUILDER_CONFIGS = [
+        NBFIConfig(name="default",
+                    description="NBFI for default binary classification.")
+    ]
+
+
+    def _info(self):
+        if self.config.name not in features_per_config:
+            raise ValueError(f"Unknown configuration: {self.config.name}")
+        
+        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
+                                    features=features_per_config[self.config.name])
+
+        return info
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        downloads = dl_manager.download_and_extract(urls_per_split)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}),
+        ]
+    
+    def _generate_examples(self, filepath: str):
+        if self.config.name == "default":
+            data = pandas.read_csv(filepath)
+            data = self.preprocess(data, config=self.config.name)
+
+            for row_id, row in data.iterrows():
+                data_row = dict(row)
+
+                yield row_id, data_row
+        else:
+            raise ValueError(f"Unknown config: {self.config.name}")
+
+
+
+    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        data.drop("ID", axis="columns", inplace=True)
+        data.drop("Own_House_Age", axis="columns", inplace=True)
+        data.drop("Type_Organization", axis="columns", inplace=True)
+        data.drop("Phone_Change", axis="columns", inplace=True)
+
+        data = data[~data.Client_Education.isna()]
+        data = data[~data.Client_Marital_Status.isna()]
+        data = data[~data.Client_Gender.isna()]
+        data = data[~data.Loan_Contract_Type.isna()]
+        data = data[~data.Client_Housing_Type.isna()]
+        data = data[~data.Age_Days.isna()]
+        data = data[~data.Employed_Days.isna()]
+        data = data[~data.Registration_Days.isna()]
+        data = data[~data.ID_Days.isna()]
+        data = data[~data.Cleint_City_Rating.isna()]
+        data = data[~data.Application_Process_Day.isna()]
+        data = data[~data.Application_Process_Hour.isna()]
+        data = data[~data.Client_Permanent_Match_Tag.isna()]
+        data = data[~data.Client_Contact_Work_Tag.isna()]
+        data.columns = _BASE_FEATURE_NAMES
+
+        data.iloc[:, "owns_a_car"] = data["owns_a_car"].apply(bool)
+        data.iloc[:, "owns_a_bike"] = data["owns_a_bike"].apply(bool)
+        data.iloc[:, "has_an_active_loan"] = data["has_an_active_loan"].apply(bool)
+        data.iloc[:, "owns_a_house"] = data["owns_a_house"].apply(bool)
+        data.iloc[:, "is_male"] = data["is_male"].apply(bool)
+        data.iloc[:, "has_provided_a_mobile_number"] = data["has_provided_a_mobile_number"].apply(bool)
+        data.iloc[:, "has_provided_a_home_number"] = data["has_provided_a_home_number"].apply(bool)
+        data.iloc[:, "was_reachable_at_work"] = data["was_reachable_at_work"].apply(bool)
+        data.iloc[:, "same_residence_and_home"] = data["same_residence_and_home"].apply(bool)
+        data.iloc[:, "same_work_and_home"] = data["same_work_and_home"].apply(bool)
+        
+        return data[list(features_types_per_config[config].keys())]
+