This project is an End-to-End Data Science project which predicts whether a person is eligible for loan base on the given dataset. The dataset has been taken from Kaggle- Loan Eligibility Dataset
The purpose of this project is to build an ML model that predicts whether a person is eligible for loan base on the given dataset.
- Exploratory Data Analysis
- Data Visualization
- Data Preprocessing and Data Scalling
- Predictive Modeling
- Hyperparameter Tuning
- Model Evaluation
- Pipelining and Feature engineering
- Python(Jupyter Notebook)
- EDA and Data Visualization Tools:-
numpy,pandasmatplotlib,seaborn
- Modelling Tools:-
LogisticRegression,KNeighborsClassifier,RandomForestClassifier,GaussianNB,SGDClassifier,DecisionTreeClassifier,SVC
- Data preprocessing:-
train_test_split,StratifiedShuffleSplit
- Pipeline and Scaling:-
Pipeline,StandardScaler
- Hyperparameter Tuning:-
GridSearchCV,cross_val_score
- Model Evaluation Tools:-
confusion_matrix,classification_report,precision_score,recall_score,f1_score,plot_roc_curve
Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban, and rural areas. Customer-first applies for a home loan after that company validates the customer eligibility for a loan.
The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History, and others. To automate this process, they have given a problem to identify the customer's segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.
- Clone this repo (for help see this tutorial).
- Raw Data is being kept here within this repo.
- Data processing/transformation scripts are being kept here.
- You can connect with me on Linkedin.
- Feel free to contact team leads with any questions or if you are interested in contributing!