In this Notebook , We are going to solve the Loan Approval Prediction.This is a Classification problem in which we need to classify whether the loan will be approved or not.
- Introduction
- Business problem
- Importing Modules
- Dataset Analysis
- Handling Missing Values - Categorical & Numerical
- Outliers Detection & Handling
- Analysis Categorical Data with Target
- Data Preparation
- Handling Imbalance Data
- Creating Multiple Model & Choose The Ideal One
- Model Building
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.
The major aim of this notebook is to predict which of the customers will have their loan approved.
- LoanID = Unique Loan ID
- Gender = Male/ Female
- Married = Applicant married (Y/N)
- Dependents = Number of dependents
- Education = Applicant Education (Graduate/ Under Graduate)
- SelfEmployed = Self-employed (Y/N)
- ApplicantIncome = Applicant income
- CoapplicantIncome = Coapplicant income
- LoanAmount = Loan amount in thousands
- LoanAmountTerm = Term of the loan in months
- CreditHistory = Credit history
- PropertyArea= Urban/ Semi-Urban/ Rural
- LoanStatus = (Target) Loan approved (Y/N)
- Python
- Pandas, NumPy, Matplotlib, Seaborn
- Model : KNeighborsClassifier, SVC, DecisionTreeClassifier, LogisticRegression, GaussianNB, RandomForestClassifier
- Best Model Selection : LogisticRegression [ Logistic regression can be used for our model as its giving effective training testing accuracy ]
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Email : vikasvachheta.ds@gmail.com