5 Best Data Science Projects for Beginners

1. Gradient Booster Loan Default Prediction Project

Since a large portion of a bank's earnings is directly derived from the interest on these loans, loans are banks' main source of income. However, there is a lengthy validation and verification process for loan acceptance based on numerous variables. Furthermore, despite extensive verification, banks are still unsure whether a borrower could return the loan without incident. Today, almost all banks utilize machine learning to automate the loan eligibility process in real-time based on several variables, including credit score, marital status, employment status, gender, existing loans, the total number of dependents, income, and expenses, among others.

Building a prediction model to target the proper loan applicants automatically is the goal of this fascinating data science project in the financial industry. You can utilize the data about a loan application to determine whether or not they will be able to repay the loan in this data science classification challenge. Exploratory data analysis will be the first step, then pre-processing, and finally, testing the created model. After finishing this assignment, you'll have a firm grasp on applying machine learning to solve categorization problems. For further details on categorization and other ML techniques, refer to the Data science certification course in Delhi.  

2. Severity Modeling for Insurance Claims

No one wants to spend their time and energy on filing insurance claims and managing the paperwork with an insurance broker or agent. Insurance providers worldwide are utilizing data science and machine learning to streamline the claims servicing process and make it hassle-free. Insurance companies are using predictive machine learning models to improve customer service and speed up the claims processing process in this introductory data science project.

When someone files an insurance claim, an insurance agent carefully examines all the supporting documentation before determining the claim amount that will be approved. 

This project will use the Allstate Claims dataset, which has approximately 300,000 rows of masked and anonymous data, each row representing an insurance claim, 116 categorical variables, and 14 continuous features.

3. Python-based market basket analysis using the Apriori algorithm

You will always notice that baby wipes and diapers, bread and butter, pizza base and cheese, alcohol, and chips are all grouped together in the store for sale whenever you go to a retail supermarket. Market basket analysis examines the relationships between the products customers purchase in groups. Market basket analysis is a flexible use case in the retail sector that aids in product cross-selling in a physical store and assists e-commerce companies in making product recommendations to clients based on product associations. The most often used machine learning algorithms for association learning to carry out market basket analysis are apriori and FP growth.

You will do Market Basket Analysis in Python using the Apriori and FP Growth Algorithm based on association rules in this entry-level data science assignment to uncover hidden insights on enhancing product recommendations for clients. You'll learn how to assess the association rules using a variety of metrics, including Support, Lift, and Confident.

4. Insurance Claim Severity Modeling 

Nobody likes to spend their time and energy dealing with insurance claims and the associated paperwork with an insurance broker or agent. Insurance businesses all over the world are utilizing data science and machine learning to streamline the claims servicing process and make it hassle-free. This entry-level data science project explores the use of predictive machine learning models by insurance companies to improve customer service and speed up the claims processing process.

When someone files an insurance claim, an insurance agent carefully examines all the supporting documentation before deciding on the sanctioned claim amount. It takes time to complete all of the information necessary to estimate the cost and severity of the claim. You will create a machine learning model for this project to forecast the claim severity based on the input data.

5. Python Chatbot Development

Do you recall the last time you called or chatted with a customer service representative about an incorrect item delivered to you by Amazon, Flipkart, or Walmart? Instead of a customer service agent, you would have most likely spoken with a chatbot. According to Gartner, chatbots are expected to handle 85% of customer interactions by 2023.

• Pattern Matching - Uses pattern matches to group text and generates a response.

• Natural Language Understanding (NLU) - The process of converting textual information into a machine-readable structured data format.

• Natural Language Generation (NLG) refers to the process of converting structured data into text.

So these were the popular projects for your data science career. If you’re still not sure how to get started, register in a Data science course in Delhi. Get a chance to work on multiple data science projects under the supervision of industry experts.