Correct team, BuildersLab framing, pretty_name; add badges

README.md

@@ -9,19 +9,19 @@ tags:
 - Machine-learning
 - Risk-prediction
 - Finance
-pretty_name: "LendingClub Loan Data (2007–2018)"
+pretty_name: "LendingClub Loan Data (2007-2018)"
 size_categories:
 - 1M<n<10M
 ---
 
-# LendingClub Loan Data (2007–2018)
+# LendingClub Loan Data (2007-2018)
 
 [![License: CC0-1.0](https://img.shields.io/badge/License-CC0%201.0-lightgrey)](https://creativecommons.org/publicdomain/zero/1.0/) [![BuildersLab](https://img.shields.io/badge/BuildersLab-builderslab.dev-F05A24)](https://builderslab.dev/) [![GitHub repo](https://img.shields.io/badge/GitHub-Credit--Risk--Default-181717?logo=github)](https://github.com/BuildersLab/Credit-Risk-Default)
 
-Personal loan applications and originations from LendingClub (2007–2018), sourced from Kaggle. Used by [BuildersLab](https://github.com/BuildersLab)'s **Credit Risk Default** project to build an explainable model that predicts **loan application defaults** — not a credit card product — for a fictional bank case study.
+Personal loan applications and originations from LendingClub (2007-2018), sourced from Kaggle. Used by [BuildersLab](https://github.com/BuildersLab)'s **Credit Risk Default** project to build an explainable model that predicts **loan application defaults**, not a credit card product, for a fictional bank case study.
 
 - **Project repo:** https://github.com/BuildersLab/Credit-Risk-Default
-- **Source:** [Kaggle — All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club) (`wordsforthewise/lending-club`)
+- **Source:** [Kaggle: All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club) (`wordsforthewise/lending-club`)
 - **License:** CC0 1.0 Universal (Public Domain)
 
 ## About the project
@@ -34,17 +34,17 @@ This dataset backs **Credit Risk Default**: an explainable credit-risk platform
 
 | Name | Role |
 |---|---|
-| Nafisat Ibrahim, Marienne Dosso | Project Lead |
-| Bintou Ba, Marienne Dosso, Lynda Allepo | Data Science |
-| Divyanshi Kashyap | ML Engineer |
+| Nafisat Ibrahim | Data Scientist, Project Lead |
+| Marienne Dosso | Data Scientist |
+| Bintou Ba | Data Scientist |
 
 ## Data Dictionary
 
 Two project-generated summary files under `data_dictionary/`, produced while cleaning
-the raw LendingClub CSV (see [`notebooks/00_cleaning.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/main/notebooks/00_cleaning.ipynb)) — not Kaggle's raw column list.
+the raw LendingClub CSV (see [`notebooks/00_cleaning.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/main/notebooks/00_cleaning.ipynb)), not Kaggle's raw column list.
 
-- **`data_dictionary_summary.csv`** — one row per column: `Variable`, `Description`
+- **`data_dictionary_summary.csv`**: one row per column: `Variable`, `Description`
   (LendingClub's official column definition), `Missing Values` (count),
   `Percentage (%)`, and `Missingness Label` (e.g. "Very High (95-100%)").
-- **`missing_values_summary.csv`** — missing-value counts and percentages per column,
+- **`missing_values_summary.csv`**: missing-value counts and percentages per column,
   used to identify and drop columns with structural missingness before modeling.