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https://github.com/esvs2202/credit-card-lead-prediction-model

A classification model built using Gradient Boosting classifier algorithm and deployed using flask framework, gunicorn and Heroku.
https://github.com/esvs2202/credit-card-lead-prediction-model

banking-application exploratory-data-analysis flask-application gunicorn-web-server heroku-deployment jupyter-notebook machine-learning-algorithms pycharm-ide python3

Last synced: 8 days ago
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A classification model built using Gradient Boosting classifier algorithm and deployed using flask framework, gunicorn and Heroku.

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README 

        
# Credit-Card-Lead-Prediction-Model

Hello all 😊, 



This is one of my personal projects in my Data Science journey and the data source:- https://www.kaggle.com/sajidhussain3/jobathon-may-2021-credit-card-lead-prediction?select=train.csv and conducted this as a job a thon by Analytics Vidhya during May 2021.- https://datahack.analyticsvidhya.com/contest/job-a-thon-2/#MySubmissions



I built a classification model to predict whether a particular bank customer is a lead for offering him/her a credit card or not, using Gradient Boosting Classifier algorithm. Achieved training accuracy as 0.8050 and validation accuracy as 0.8036.



After building the model, first I deployed it in my local system using flask framework and then deployed in Heroku ( a PaaS ) using gunicorn (a python Web Server Gateway Interface (WSGI) HTTP server).



`Brief Description`:



Problem Statement: 



Happy Customer Bank is a mid-sized private bank that deals in all kinds of banking products, like Savings accounts, Current accounts, investment products, credit products, among other offerings.



The bank also cross-sells products to its existing customers and to do so they use different kinds of communication like tele-calling, e-mails, recommendations on net banking, mobile banking, etc. 



In this case, the Happy Customer Bank wants to cross sell its credit cards to its existing customers. The bank has identified a set of customers that are eligible for taking these credit cards.



Now, the bank is looking for your help in identifying customers that could show higher intent towards a recommended credit card, given:



- Customer details (gender, age, region etc.)

- Details of his/her relationship with the bank (Channel_Code,Vintage, 'Avg_Asset_Value etc.) 



Solution steps I followed:



1. Loaded the data set in Jupyter note book and did data cleaning as follows:


    a) Imputed missing values in "Credit_Product" with "Not Known", which indicates that there's no information w.r.t. a customer having any credit product or no.


    b) Ignored "ID" column as it is not useful for our analysis.


    c) Visualized our target column "Is_Lead" and found that there was a class imbalance. To resolve this issue, I used "resample" module from sklearn.utils and performed                  resampling operation thus making our data unbiased.


    d) Next, I converted all the 6 categorical columns to numerical columns using "LabelEncoder" module from sklearn.preprocessing.


    e) Then, 70% of data set is split into training set while the remaining 30% into testing set.


    f) Next, except the columns having binary values, scaled all the other columns using a "MinMaxScaler" from sklearn.preprocessing, making our data ready for model building.



2. Experimented with various Machine learning algorithms (all with default parameters) :


   a) Logistic regression - training accuracy: 0.7107 & validation accuracy: 0.7103


   b) Random Forest Classifier - training accuracy: 0.7810 & validation accuracy: 0.7785


   c) AdaBoost Classifier - training accuracy: 0.7967 & validation accuracy: 0.7954


   d) Gradient Boosting Classifier - training accuracy: 0.8050 & validation accuracy: 0.8036


   e) XGBoost Classifier - training accuracy: 0.8211 & validation accuracy: 0.8117.


   

   From all the above 5 algorithms, I chose Gradient boosting classifier algorithm for building the application because the XGBoost classifer caused a trouble in my PyCharm        resulting in hanging 😂, otherwise it is a best one to go. 



3. Next, I created a flask application and integrated my front-end html file and the model file which was stored in my local disk using pickle module.



4. Then deployed in Heroku and ran using gunicorn module. link to the web application:- https://credit-card-lead-prediction.herokuapp.com/