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12 Cool Data Science Projects Ideas for Beginners and Experts
“How many data science projects have you completed so far?”The domain of Data Science brings with itself a variety of scientific tools, processes, algorithms, and knowledge extraction systems from structured and unstructured data alike, for identifying meaningful patterns in it.Data Science has been on a boom for the last couple of years, and the push in the domain of Artificial Intelligence due to the various innovations is only going to take it further on to the next level. As more industries begin to realize the power of Data Science, more opportunities surface in the market.If you fancy Data Science and are eager to get a solid grip on the technology, now is as a good time as ever to hone your skills to comprehend and manage the upcoming challenges in Data Science. The purpose behind penning this article is to share some practicable ideas for your next project, which will not only boost your confidence in Data Science but also play a critical part in enhancing your skills.Data really powers everything that we do.— Jeff WeinerTop Interesting Data Science ProjectsUnderstanding Data Science can be quite confusing at first, but with constant practice, you can soon begin to grasp the various notions and terminologies in the subject. The best way to gain more exposure to Data Science apart from going through the literature is to take on some helpful projects which will not only upskill you but will also make your resume more impressive.In this section, we will share a handful of fun and interesting project ideas with you, which are spread across all skill levels, ranging from beginners, intermediate, and veterans.But before diving into this you can also check out some cool Python Project Ideas for Python Developers here —1. Building ChatbotsLanguage: PythonDataset: Intents JSON fileSource Code: Build Your First Python Chatbot ProjectChatbots play a pivotal role for businesses as they can effortlessly handle a barrage of customer queries and messages without any slowdown. They have single-handedly reduced the customer service workload for us by automating a majority of the process. They do this by utilizing techniques backed with Artificial Intelligence, Machine Learning, and Data Science.Chatbots work by analyzing the input from the customer and replying with an appropriate mapped response. To train the chatbot, you can use Recurrent Neural Networks with the intents JSON dataset while the implementation can be handled using Python. Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase.2. Credit Card Fraud DetectionPhoto by Avery Evans on UnsplashLanguage: R or PythonDataset: Data on the transaction of credit cards is used here as a dataset.Source Code: Credit Card Fraud Detection using PythonCredit card frauds are more common than you think, and lately, they’ve been on the higher side. Figuratively speaking, we’re on the path to cross a billion credit card users by the end of 2022. But thanks to the innovations in technologies like Artificial Intelligence, Machine Learning, and Data Science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.Simply put, the idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the dataset and ingest it into decision trees, Artificial Neural Networks, and Logistic Regression. As you feed more data to your system, you should be able to increase its overall accuracy.3. Fake News DetectionLanguage: PythonDataset/Packages: news.csvSource Code: Detecting Fake NewsWe’re sure fake news needs no introduction. In today’s all connected world, it has become ridiculously easy to share fake news over the internet. Every once in a while, you can see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.To curb the spread of fake news, it is crucial to identify the authenticity of the information, which can be done using this Data Science project. For this, you can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some of thePython libraries suited for this project arepandas,NumPy, and scikit-learn, and for the dataset, you can use News.csv.4. Forest Fire PredictionPhoto by Pixabay from PexelsBuilding a forest fire and wildfire prediction system will be another good use of the capabilities offered by Data Science. A wildfire or forest fire is essentially an uncontrolled fire in a forest. Every incident of a forest wildfire has caused an immense amount of damage to not only nature but the animal habitat and human property as well.To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of the meteorological data to find common periods, seasons for wildfires to increase your model’s accuracy.5. Classifying Breast CancerPhoto by Anna Shvets from PexelsLanguage: PythonDataset: IDC (Invasive Ductal Carcinoma)Source Code: Breast Cancer Classification with Deep LearningIn case you want to add a project related to the healthcare industry to your portfolio, you can try building a breast cancer detection system using Python. Breast cancer cases have been on the rise lately, and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.To build such a system with Python, you can use the IDC(Invasive Ductal Carcinoma) dataset, which contains histology images for cancer-inducing malignant cells, and you can train your model on this dataset. For this project, you’ll find Convolutional Neural Networks better suited for the task, and as for the Python libraries, you can use NumPy, OpenCV, TensorFlow, Keras,scikit-learn, and Matplotlib.6. Driver Drowsiness DetectionLanguage: PythonSource Code: Driver Drowsiness Detection System with OpenCV & KerasRoad accidents take many lives every year, and one of the causes of road accidents is sleepy drivers. Being a potential cause for danger on the road, one of the best ways to prevent this is to implement a drowsiness detection system.A driver drowsiness detection system such as this is yet another project that has the potential to save many lives by constantly assessing the driver’s eyes and alerting him with alarms in case the system detects frequent closing of eyes.A webcam is a must for this project to allow the system to periodically monitor the driver’s eyes. To make this happen, this Python project will require a deep learning model and libraries such as OpenCV, TensorFlow, Pygame, and Keras.7. Recommender Systems(Movie/Web Show Recommendation)Photo by Pixabay from PexelsLanguage: RDataset: MovieLensPackages: recommenderlab, ggplot2, data.table, reshape2Source Code: Movie Recommendation System Project in RHave you ever wondered how media platforms like YouTube, NetFlix, and others recommend you what to watch next? To do so, they use a tool called the recommender/recommendation system. It takes several metrics into consideration, such as age, previously watched shows, most-watched genre, watch frequency, and feeds them into a Machine Learning model which then generates what the user might like to watch next.Based on your preference and input data, you can try to build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can pick R with the MovieLens dataset that covers ratings for over 58,000 movies, and as for the packages, you can use recommenderlab, ggplot2, reshap2, and data.table.8. Sentiment AnalysisLanguage: RDataset: janeaustenRSource Code: Sentiment Analysis Project in RAlso known as opinion mining, sentiment analysis is a tool backed by Artificial Intelligence, which essentially lets you identify, gather, and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews, survey responses, and could involve a range of emotions such as happy, angry, positive, love, negative, excitement, and more.Modern data-driven companies are the ones that benefit the most from a sentiment analysis tool as it gives them the critical insight about the people’s reaction to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with janeaustenR’s dataset along with the tidytext package.9. Exploratory Data AnalysisPhoto by Lukas from PexelsLanguage: PythonPackages: pandas, NumPy, seaborn, and matplotlibSource Code — Exploratory data analysis in PythonData Analysis starts with EDA. The Exploratory Data Analysis plays a key role in the data analysis process as this step helps you make sense of your data and often involves visualizing them for better exploration. For visualization, you can pick from a range of options, such as histograms, scatterplots, or heat maps. EDA can also expose unexpected results and outliers in your data. Once you have identified the patterns and derived the necessary insights from your data, you are good to go.A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy, seaborn, and matplotlib.A great source for EDA datasets is the IBM Analytics Community.10. Gender Detection & Age PredictionLanguage: PythonDataset: AdiencePackages: OpenCVSource Code: OpenCV Age Detection with Deep LearningIdentified as a classification problem, this gender detection and age prediction project will put both your Machine Learning and Computer Vision skills to test. The goal here is to build a system that takes a person’s image and tries to identify their age and gender.For this fun project, you can implement Convolutional Neural Networks and use Python with the OpenCV package. You can grab the Adience dataset for this project. Factors such as makeup, lighting, facial expressions will make this challenging and try to throw your model off, so keep that in mind.11. Recognizing the Speech EmotionsLanguage: PythonDataset: RAVDESSPackages: Librosa, Soundfile, NumPy, Sklearn, PyaudioSource Code: Speech Emotion Recognition with librosaSpeech is one of the most fundamental ways of expressing ourselves, and it hides various emotions inside it, such as calmness, anger, joy, and excitement, to name a few. By analyzing the emotions behind the speech, it is possible to use this information to restructure our actions and services, and even products, to offer a more personalized service to specific individuals.This Speech Emotion Recognition project tries to identify and extract emotions from multiple sound files containing human speech. To make something like this in Python, you can use the Librosa, SoundFile, NumPy, Scikit-learn, and PyAaudio packages. For the dataset, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS), which has over 7300 files for you to use.12. Customer SegmentationPhoto by You X Ventures on UnsplashLanguage: RSource Code: Customer Segmentation using Machine LearningModern businesses strive by delivering highly personalized services to their customers, which would not have been possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.For this project, you will be going to use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region, interests, and so on. K-means clustering or hierarchical clustering will be suitable here, but you can also experiment with Fuzzy clustering or Density-based clustering methods. You can use the Mall_Customers dataset as sample data.More Data Science Project Ideas to Build —Coronavirus visualizationsVisualising climate changeUber’s pickup analysisWeb traffic forecasting using time seriesImpact Of Climate Change On Global Food SupplyDetecting Parkinson’s DiseasePokemon Data ExplorationEarth Surface Temperature VisualizationBrain Tumor Detection with Data SciencePredictive policingYou can also read data science posts in Spanish here.ConclusionThrough this article, we tried to cover more than 10 fun and handy Data Science project ideas for you, which will help you understand the ABCs of the technology. Being one of the hottest in-demand domains in the industry, the future of Data Science holds many promises, but to make the most out of the upcoming opportunities, you need to be prepared to take on the challenges it brings. Good luck!Note: To eliminate problems of different kinds, I want to alert you to the fact this article represent just my personal opinion I want to share, and you possess every right to disagree with it.If you have more suggestions or ideas, we’d love to hear about them.
