House Loan Prediction with XGBoost and MLP

Data Analysis and prediction on Kaggle dataset: House Loan Data Analysis-Deep Learning
This repo provides algorithms for data preprocessing and model training for House Loan problem
Dataset: https://www.kaggle.com/datasets/deependraverma13/house-loan-data-analysis-deep-learning

Data Preprocessing

There are 122 columns in original dataset, including groundtruth ('TARGET' column), this repo used 20 feature columns and 1 'TARGET' column for groundtruth to train the model
Before running read_csv.py, please change the path of 'loan_data (1).csv' in line 7 to your local path
It will output two files to your current directory: train_data.csv and balanced_data.csv
The data preprocessing will present in both read_csv.py and train.py

Model Training

Please change the path of train_data.csv in line 10 to your local path
The hyperparameters used in train.py:
epoch = 10000
batch_size = 4
learning_rate=0.0001
optimizer: SGD

Inference

Please change the path of train_data.csv and state_dict model to your local path
test.py will output the accuracy and mean loss after forward pass the validation dataset to your model

XGBoost

xgboost_classifier.py provides the comparison between XGBoost and MLP method, using same set of training data
Please change the path of train_data.csv to your local path
end_to_end_xgboost.py provides the end to end script for XGBoost, by taking loan_data.csv as input
It will output all accuracy results for each number of features