siddharthiyervarma / loan-approval-prediction Goto Github PK

Project Overview:

Objective:

The primary objective of this project is to leverage decision tree-based models for predicting credit approval. The project aims to develop robust algorithms capable of assessing loan eligibility based on a set of features typically found in credit applications. By employing a comprehensive workflow, including data cleaning, feature engineering, and the implementation of various tree-based models, the project strives to provide accurate and interpretable predictions to aid financial institutions in making informed decisions.

Dataset:

The dataset used for this project contains information relevant to credit applications, encompassing both categorical and numerical features. Features such as income, employment status, credit history, and other pertinent variables serve as the basis for training and evaluating the predictive models.

Machine Learning, Data Cleaning and Processing, Model Building, Feature Engineering, Data Visualization and Model Evaluation

• @siddharthiyervarma

1. Data Cleaning and Processing:

-Importing necessary libraries for data manipulation and analysis.

-Loading and exploring the credit dataset to understand its structure and characteristics.

-Identifying and handling missing values, restructuring features, and ensuring data integrity.

-Converting categorical variables into numerical format through techniques such as one-hot encoding.

2. Model Building:

-Splitting the dataset into training and testing sets to facilitate model evaluation.

-Building an initial decision tree model to predict credit approval.

-Visualizing the decision tree to interpret the decision-making process.

-Implementing pruning techniques to optimize model performance and avoid overfitting.

-Identifying and evaluating the importance of features for better model interpretation.

-Exploring ensemble methods such as bagging, boosting, and random forests for improved prediction accuracy.

3. Evaluation and Interpretation:

-Assessing model performance using metrics like accuracy, precision, recall, and F1 score.

-Generating confusion matrices to gain insights into the true positive and false positive predictions.

-Analyzing the interpretability of decision tree-based models in the context of credit approval.