This repository contains a machine learning model built to predict loan approval status. The model uses various applicant features to determine the likelihood of loan approval.

The goal of this project is to create a predictive model that classifies whether a loan will be approved or not based on specific applicant details. The dataset includes features such as applicant income, loan amount, credit history, and more. I used machine learning techniques to build and evaluate different models. The steps involved include descriptive analysis, data preprocessing, and model building using Logistic Regression and Random Forest algorithms.

The dataset used for this project includes the following features:

ApplicantIncome: The income of the loan applicant.

CoapplicantIncome: The income of the co-applicant (if any).

LoanAmount: The amount of the loan requested.

Loan_Amount_Term: The term of the loan in months.

Credit_History: Credit history of the applicant (1 if the applicant has a credit history, 0 otherwise).

Gender: Gender of the applicant.

Married: Marital status of the applicant.

Dependents: Number of dependents of the applicant.

Education: Educational level of the applicant.

Self_Employed: Whether the applicant is self-employed.

Property_Area: The area where the property is located (Urban, Semi-Urban, Rural).

Descriptive Analysis

• Explored the dataset structure and identified patterns or anomalies.
• Calculated and displayed the schema of the dataset.
• Printed the number of rows and columns.
• Displayed the first few rows of the dataframe.
• Calculated the percentage of missing values for each column.
• Calculated the mean, median, and mode for numerical columns.
• Identified outliers in the numerical columns.
• Plotted the distribution of numerical variables.
• Calculated and displayed the correlation matrix.
• Plotted scatter plots to analyze relationships between variables.

Data Processing

• Calculated the percentage of missing values for each column.
• Imputed missing values in categorical columns using mode.
• Imputed missing values in numerical columns using the median.
• Imputed missing values in the 'Credit_History' column using mode.
• Removed duplicate rows.
• Encoded categorical variables (Gender, Married, Education, Self_Employed, and Loan_Status).

Model Building

• Combined all features into a single feature vector using VectorAssembler.
• Built and evaluated a Logistic Regression model.
• Split the dataset into training and testing data.
• Trained the model on the training data.
• Evaluated model performance using accuracy, precision, recall, and ROC curves.
• Built and evaluated a Random Forest model.
• Split the dataset into training and testing data.
• Trained the model on the training data.
• Evaluated model performance using accuracy, precision, recall, F1-score, and ROC curve.