itisWasp / loan-prediction

This repo is for derived from a competition from analytics vidhya for predicting loan using the data given.

loan-prediction

This repo is for derived from a competition from analytics vidhya for predicting loan using the data given.

*Loan Predicting using the dataset from Analytics Vidhya Hackathon.

• Optimized Logistic Regressio, Decision Trees, and Random Forest Regressors using GridsearchCV to reach the best model.

EDA

I looked at the distributions of the data and the value counts for the various categorical variables.

Model Building

First, I transformed the categorical variables into dummy variables. I also split the data into train and tests sets with a test size of 20%.

I tried three different models and evaluated them using Accuracy Score. I chose Accuracy_Score because it is relatively easy to interpret and outliers aren’t particularly bad in for this type of model.

I tried three different models:

• ** Logistic Regression** – Baseline for the model
• Decision Trees – The problem at hand is a classification problem.
• Random Forest – Again, with the sparsity associated with the data, I thought that this would be a good fit.

Model performance

The Random Forest model far outperformed the other approaches on the test and validation sets.

• Random Forest : accuracy_score = 0.7950819672131147
• Logistic Regression: accuracy_score = 0.7886178861788617
• Decision Trees: accuracy_score = 0.6885245901639344