Solved end-to-end Data Science Projects in Banking and Finance

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Data Science Projects in Banking and Finance

Isolation Forest Model and LOF for Anomaly Detection in Python

Credit Card Fraud Detection Project - Build an Isolation Forest Model and Local Outlier Factor (LOF) in Python to identify fraudulent credit card transactions.

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AWS MLOps Project to Deploy a Classification Model [Banking]

In this AWS MLOps project, you will learn how to deploy a classification model using Flask on AWS.

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Loan Eligibility Prediction in Python using H2O.ai

In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.

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Insurance Pricing Forecast Using XGBoost Regressor

In this project, we are going to talk about insurance forecast by using linear and xgboost regression techniques.

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Build Classification Algorithms for Digital Transformation[Banking]

Implement a machine learning approach using various classification techniques in Python to examine the digitalisation process of bank customers.

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Credit Card Fraud Detection as a Classification Problem

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

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Census Income Data Set Project-Predict Adult Census Income

Use the Adult Income dataset to predict whether income exceeds 50K yr based oncensus data.

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Credit Card Default Prediction using Machine learning techniques

In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

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Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction

In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

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Loan Eligibility Prediction using Gradient Boosting Classifier

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

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Build an optimal End-to-End MLOps Pipeline and Deploy on GCP

Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP

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Loan Eligibility Prediction Project using Machine learning on GCP

Loan Eligibility Prediction Project - Use SQL and Python to build a predictive model on GCP to determine whether an application requesting loan is eligible or not.

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Build Portfolio Optimization Machine Learning Models in R

Machine Learning Project for Financial Risk Modelling and Portfolio Optimization with R- Build a machine learning model in R to develop a strategy for building a portfolio for maximized returns.

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Build Deep Autoencoders Model for Anomaly Detection in Python

In this deep learning project , you will build and deploy a deep autoencoders model using Flask.

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Predictive Analytics Project for Working Capital Optimization

In this Predictive Analytics Project, you will build a model to accurately forecast the timing of customer and supplier payments for optimizing working capital.

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Expedia Hotel Recommendations Data Science Project

In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

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German Credit Dataset Analysis to Classify Loan Applications

In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

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Time Series Analysis Project in R on Stock Market forecasting

In this time series project, you will build a model to predict the stock prices and identify the best time series forecasting model that gives reliable and authentic results for decision making.

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Credit Card Anomaly Detection using Autoencoders

In this Deep Learning Project, you will use the credit card fraud detection dataset to apply Anomaly Detection with Autoencoders to detect fraud.

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Customer Love

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and that's where ProjectPro helped me. I also got a chance to talk to experts who have worked on these domains - they helped me by walking through the project. Kudos to the ProjectPro team!

Gautam Vermani

Data Consultant at Confidential

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain hands-on experience and prepare for job interviews. I would highly recommend this platform to anyone looking to upskill and stay updated with the latest projects and solutions. Overall this platform is awesome and worth the money spent as we get a lot of value out of it and helps soar our career to greater heights.

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills in Data Engineering/Science and hoping to find real-world projects fortunately, I came across Project Pro. Project Pro helped me by providing an in-depth explanation of the end-to-end real-world data engineering projects. From data extraction, transformation, and storage up to data visualization. I learned more about Kafka, AWS, NI-FI, and Spark. Thru the help of the knowledge I gained from Project Pro, I was able to do well in the coding exams, interview and helped me land a job at EY. I will recommend every aspiring data professional as well as existing data science/engineer expert to try Project Pro to enhance their knowledge.

Ed Godalle

Director Data Analytics at EY / EY Tech

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Savvy Sahai

Data Science Intern, Capgemini

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good theoretical knowledge, the practical approach, real word application, and deployment knowledge were missing. ProjectPro helped me bridge that gap. ProjectPro has real-time projects that helped me improve my skills. What I liked most is that I get exposure to so many projects, given the work nature I wouldn't have gotten exposure to such a variety of projects and their approaches. It is helping me apply knowledge to other projects too. I highly recommend ProjectPro to everyone who wants to excel in their DataScience career.

Ameeruddin Mohammed

ETL (Abintio) developer at IBM

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Abhinav Agarwal

Graduate Student at Northwestern University

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop Admin, Hadoop projects. I have been happy with every project. They have really brought me into the forefront of Data Science and Big data. I would recommend this to everyone. It is more than worth the price. After working with them I feel so much more employable for current projects.

Ray han

Tech Leader | Stanford / Yale University

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data. In each learning path, there are many customized projects with all the details from the beginner to the expert. As a new data science learner, you can just follow these projects to master the important techniques quickly. It is really helpful for both my research and job searching. Hope you can come and join ProjectPro to win a great future for yourself.

Jingwei Li

Graduate Research assistance at Stony Brook University

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