To predict whether the customer is eligible for applying a loan application based on various parameters such as gender, maritial status, income, education, employment, loan amount, loan term, and credit history given by user on the website hosted using Flask.
The outcome is a discrete value of 0(not eligible) or 1(eligible). So Logistic Regression is best choice to classify the customer between two classes.
The dataset is consist of 12 columns of which 1 consists of output and one is ID no which of no use.
- The dataset is first loaded in jupyter notebook for analysis, cleaning and training of data using Logistic Regression.
- The model after training, is stored using pickle library as loan_model.pkl.
- Now come to the main part of the project, the app.py file.
- First created some simple html file along with some quick and easy css for frontend interface.
- Then in the app.py file, I have created two routes, one for displaying home page where the user inputs the required information.
- This information is fetched from home.html file to main program using following code snippet:
user_inputs.append(int(request.form.get(value)))Here, request function is used to fetch the data from form in the html with passing the name variable in place of value above, like,
<p class="appincome">
Applicant's Income *:
<input type="number" name="app_income" required>
</p>In above html to capture the input for applicant's income, the name is given "app_income", so to fetch the input, the python snippet would be,
input = request.form.get('app_income')- Later the inputs are converted to numpy array and passed to the model to predict.
- Based on the output, according jinja2 syntax is sent to html file to display.
