The objective of the work is to automate the loan eligibility process (real time) based on customer detail provided while filling online application form.
| 🪧 Vitrine.Dev | |
|---|---|
| ✨ Nome | Loan-Classification-Prediction-Competition-Case |
| 🏷️ Tecnologias | scikit-learn, numpy, matplotlib, pandas, Python |
| 🔥 Desafio | Problem statement link |
Features present in the bank data frame are customers's gender, marital status, level of education, number of dependents, income, loan amount, credit history and others.
This dateset was downloaded from AV website for practicing the eligibility of granting home loan. For further details please visit the website. https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/#ProblemStatement.
Build a binary classification model to predict the outcome:
- Exploratory data analysis (EDA).
- Feature engineering.
- Data preprocessing.
- Various models building and cross validation.
- Performance metric selection.
- Hiperparameters tuning.
- Model selection.