Whether you’re a professional data scientist or just starting your learning journey, there are two main ways to start a new project – either from scratch with an idea that you want to implement, or from coming across an exciting dataset that triggers a project idea.
Both approaches are great, and throughout your data science journey, you’ll likely find yourself alternating between both of these methods. When I first started out in data science, I preferred to look for datasets that interested me to begin developing a project. I always found one or more datasets that were so interesting and that could be used to apply and implement various data science algorithms.
One straightforward yet powerful area of data science is regression algorithms. In data science, there are various types of regression algorithms– linear, logistic, lasso, polynomial, and so on. These algorithms are used in machine learning applications to create predictive models that analyze the relationship between dependent and independent variables in a dataset.
This article will explore a variety of project ideas that use different regression analyses and the datasets you can use to implement these projects - no matter what stage you’re at in your data science journey.
Disease diagnostics is a crucial aspect of how data science is involved in many aspects of our lives. PCOS is one of the conditions that machine learning models have proven efficient in decreasing the chances of misdiagnosis due to human error. Diagnostic models for PCOS are often built using logistic regression.
Using Polycystic Ovary Syndrome (PCOS) dataset, you can create your own.
One fun project for movie lovers is to create a machine learning model to predict a particular movie’s revenue and rating based on historical data of the genre. This information can help movie production companies decide what movies to invest in based on how well they draw in an audience.
You can design and implement this project using the prebuilt TMDB 5000 Movie Dataset, or you could build your custom dataset with The Movie Database API. Any multivariate regression model would be great for this project.
Everything today is online – including some of our most critical and private information. Machine learning algorithms, especially logistic regression mixed with decision trees, can be used to keep track and analyze credit card transactions, predicting fraud when it occurs.
You can build your own predictive model to classify credit card transactions and detect fraudulent ones using a Card Transactions dataset.
When it comes to the finance world, stock prices are important to both companies and individuals, thereby rendering the ability to predict prices accurately very valuable.
You can use machine learning algorithms with multiple linear regressions to develop a stock prices predictor. This can be taken even further by using Lasso and Ridge regression models, and tested on the Tesla stock from the 2010 to 2020 dataset from Kaggle.
Last, but not least, for all the wine-loving data scientists out there, Kaggle has a red wine dataset that can be used to build a classification algorithm to predict whether a particular wine is good or bad based on 11 different variables. You can use linear or logistic regression to score wines and rank their overall quality.
One of the most essential things for any business is knowing how much stock they need in order to meet consumer demand in their area. Accurate demand forecasting can save companies a lot of money and help reduce losses due to waste, perishable products, or inability to meet demand.
Again, data science– particularly machine learning-based demand forecasting models– comes to the rescue. Although there are various algorithms you can use in this project, linear regression is one of the simplest and most powerful ones.
To implement a demand forecasting project, you can use the Forecasts for Product Demand dataset, which contains historical product demand for a manufacturing company with four central warehouses.
We all have to deal with ads online – you’ve probably seen a few just in getting to this article. When it comes to ads, customer engagement is the top priority. The more clicks an ad gets, the higher the possibility that a customer will make a purchase.
Because of that, many companies focus on creating predictive models, often using logistic regression to analyze patterns and optimize ad locations and timing.
You can try this logistic regression project out by using the Predicting Customer Ad Clicks dataset, or design and build a Bayesian Logistic Regression mode more suited to incorporate the real-time probability of ad clicks data.
Pollution and its impact on the environment are among the most significant concerns globally. Data science and machine learning can help us better understand how to tackle and solve that problem.
You can use multiple datasets to analyze the change in temperature, air pollution, and overall climate throughout the years with linear and other forms of regression. You can find multiple datasets to work with in this GitHub repository.
Although many of the projects mentioned in this article are beneficial for different reasons, sometimes we want to build a project just for fun and hone our skills.
One such project is predicting who would have survived the Titanic.
You can create a machine learning algorithm using the Kaggle Titanic dataset, which contains information about the names, ages, and sexes of around 891 passengers in the training set and 418 passengers in the testing set with a linear regression model.
Video games are one of the biggest markets out there, with a lot of time, money, and effort going into designing, developing, and distributing new video games. For video game companies, gamers, or anyone else working in that particular industry, having access to sales data can make a tremendous difference.
Using the Video Game Sales dataset with a neural network regression model can help you create a games sales predictor that will give you helpful information about what games to invest in, or if you’re a gamer, to buy and play.
Music is an essential part of everyone’s life. We use it to destress, express ourselves, or spend time with others. Getting a song on the top 10 or top 20 lists is a challenging yet desirable goal for artists.
This raises an important question – what makes a song reach top status? If you’re a music fan, you can use the Spotify dataset with a regression model, like decision tree, to predict which song will reach the top 10 list and the commonalities between the songs.
When it comes to the field of data science, the more projects you create, the more fluent in data science you get, and the better and more appealing your profile becomes.
When I first started, I couldn’t decide on a project simply because I didn’t have enough knowledge to choose a project and a dataset. One of the things that helped me was browsing datasets websites (e.g., Kaggle) and reading about different datasets and how they can be used. That gave me the inspiration I needed to kick start new projects, and also the ability to work backwards starting with an algorithm in mind, and then deciding on a particular project and dataset.
As your knowledge base and experiences grow with implementing more projects, you’ll soon develop an eye for suitable datasets and how to see their potential.