Whether you’re an experienced data scientist or just beginning your journey, there are two primary ways to initiate a new data science project. You can either develop a project by implementing an idea from scratch or by discovering an engaging dataset that sparks a project concept.
Both methods have their merits, and you’ll likely alternate between them throughout your data science journey. As a beginner in data science, I preferred searching for intriguing datasets to inspire my projects. I often found captivating datasets that enabled me to apply and implement various data science algorithms.
A simple yet powerful domain in data science involves regression algorithms. Numerous types of regression algorithms exist in data science, such as linear, logistic, lasso), polynomial, and more. Machine learning applications employ these algorithms to build predictive models that analyze relationships between dependent and independent variables in datasets.
This article delves into various project ideas employing different regression analyses and the datasets you can use for implementation. These projects are suitable for those who are studying machine learning or preparing for job interviews, as well as for experts looking to challenge themselves.
Fraud can take numerous forms, whether it’s a single stolen credit card or credit card details getting compromised by a merchant using tools like credit card skimming devices.
This take-home project takes 1-2 hours to complete and asks you to create a model to determine if a credit card transaction is fraudulent.
You are also required to document your solution by providing a clear and concise explanation of the methods you used, the assumptions you made about the data, and any other methods you considered.
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 data science 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 projects 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.
Iris flower classification is a classic machine-learning problem that is perfect for beginners. The objective is to classify iris flowers into three species (setosa, versicolor, and virginica) based on four features: sepal length, sepal width, petal length, and petal width. This is a simple supervised learning problem, and you can experiment with various classification algorithms such as k-Nearest Neighbors, Decision Trees, and Support Vector Machines.
You can start by using the famous Iris Flower Dataset from the UCI Machine Learning Repository.
Your client is a movie studio, and they need to be able to predict movie revenue in order to greenlight the project and assign a budget to it. Most of the data is comprised of categorical variables. While the budget for the movie is known in the dataset, it is often an unknown variable during the greenlighting process.
How to do the Project: Prepare a 20 to 30-minute presentation on a specific topic. The purpose of this exercise is to demonstrate your ability to draw insights from data, put insights in a business-friendly format and confirm coding knowledge.
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.
Air pollution has become a growing concern for people all around the world. Machine learning models can be used to predict the Air Quality Index (AQI) based on various factors like weather conditions, traffic data, and industrial activities. A time series model such as ARIMA or LSTM can be utilized for this purpose.
To create your own AQI prediction model, you can use the Air Quality in Madrid (2001-2018) dataset, which contains hourly data of air quality in Madrid, Spain.
In the age of e-commerce, understanding customer behavior and preferences has become increasingly important. Machine learning models can be employed to predict whether a customer will make a purchase based on their online activity. Common models used for this task include decision trees and logistic regression.
You can create your own predictive model using the Online Shoppers Purchasing Intention Dataset, which contains information about user behavior on an e-commerce platform. This dataset includes features like pages visited, time spent on the website, and the month and type of device used for browsing.
Insurance safeguards our health, possessions, and future. Being able to predict the insurance charges based on various parameters is crucial for both insurance providers and policyholders.
What are the actual factors that influence insurance premiums? With this dataset, we can predict the insurance charges and discern the key drivers behind the cost
With the recorded vertical acceleration of different cars on a given road, we can accurately cancel it using feedforward control.
For that purpose, we can map the road data in terms of road velocity and store the data on the server. However, instead of actually driving different cars on the same road and recording their vertical accelerations, it is more efficient to train a neural network model for each car to predict the acceleration whenever some new road data is available through crowd-sourcing.
In this assignment, you are asked to train such a model with given data. This Machine learning takehome asks you to train a neural network that takes road velocity (m/s2)(m/s2) as output.
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.
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.
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.
Human Activity Recognition (HAR) is an advanced machine-learning problem that involves predicting human activities (walking, standing, sitting, etc.) based on data from wearable sensors, like accelerometers and gyroscopes. This is a challenging problem because it involves working with time-series data and requires advanced feature engineering or deep learning techniques such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs).
To build your own HAR model, you can use the UCI Human Activity Recognition Using Smartphones Dataset, which contains accelerometer and gyroscope data collected from smartphones worn by 30 subjects performing various activities.
Life expectancy doesn’t just indicate the average duration of life. It tells stories of healthcare, socio-economic dynamics, public policies, and regional challenges. Through this, countries and regions can evaluate their success and areas in need of attention.
What drives one nation to boast a higher life expectancy than another? This dataset lets you dig deep into the variables that play a pivotal role in shaping life expectancy.
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 backward 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.
This course is designed to help you learn everything you need to know about working with data, from basic concepts to more advanced techniques.