Top 10 Real-Time Data Science Projects You Need to Get Your Hands-on

Whether we’re aware or not, almost every online activity we undertake leaves digital footprints. The online trail we leave behind has the potential to unearth meaningful insights about consumer behavior and the world around us in general. From online shopping and browsing movies on OTT platforms to booking a cab, every online action of users is like a goldmine of information that data scientists can analyze to understand trends and patterns. So, when real-time data is available at our fingertips, why not use it to design some exciting and engaging data science projects?

The Best 10 Data Science Project Ideas

Data science has undoubtedly become one of the most sought-after skills in the world. But merely learning the theory of it is no use unless you put your skills to practice. If you’ve been looking for some inspirational data science project ideas, here’s a list of the top 10 data science projects for beginners.

1. Fake news detection

In a world where information is just a phone tap away, immunity from fake news is a luxury that almost none of us can afford. Fake news is false and misleading information that is usually spread through social media and other online platforms to achieve, in most cases, a political agenda. What’s worse, these spread much faster than authentic news. Hence, this project aims to get a grip on false journalism and detect the authenticity of social media news. It can be done using Python, where you have to build a TfidfVectorizer and use a PassiveAggressiveClassifier to categorize news into “Fake” and “Real.” All of this will be executed in the JupyterLab using a 7796×4 shaped dataset.

2. Visualizing climate change and the impact on global food supply

An integral part of data science is visualizing and presenting data insights to a larger audience. As part of this project, the primary goal of the researcher will be to visualize changes in the global mean temperatures and the rise of carbon dioxide concentrations in the atmosphere. Furthermore, this data science project also focuses on how the changing (and worsening) global climatic conditions affect food production worldwide. Hence, the project will aim to study the implications of changing temperature and precipitation patterns and how it impacts staple crop production and compare the output in different time zones.

3. Sentiment analysis

Many data-driven companies today leverage the sentiment analysis model to assess consumer behavior towards their products and services. It refers to the process of analyzing and categorizing views expressed in feedback or review to determine if a customer’s impression of the product/service is positive, negative, or neutral. It is a type of classification where the classes could be binary (positive and negative) or multiple (happy, sad, angry, disgusted, etc.). You can implement this data science project in R and use the janeaustenR or Tidytext package dataset.

4. Road lane line detection

Self-driving cars may still seem like something from a science fiction novel, but now, they are here! One of the key technologies instrumental in developing driverless cars is the live lane-line detection system, where lines are drawn on the roads to guide the vehicle where the lanes are. It also comes in handy for human drivers and shows the direction in which to steer the car. The live road lane line detection project can be done in Python. The goal will be to develop an application to identify a road lane line through the input images or a continuous video frame.

5. Chatbots

Chatbots have become an indispensable communication tool for businesses that want to offer a top-notch customer experience. Besides providing personalized customer service, chatbots have become commonplace across organizations due to the sheer amount of time and money they save. No wonder their widespread use makes them one of the most in-demand data science projects worth trying. Chatbots use deep learning techniques to interact with consumers and are primarily trained using RNNs (recurrent neural networks). The chatbot project can be done using the Intents JSON file dataset of Python.

6. Driver drowsiness detection

Another interesting data science project idea is building a Keras and OpenCV drowsiness detection system using Python. Accidents are occurring due to drivers falling asleep while driving is commonplace, and this project is a great way to try and mitigate the problem. The goal is to build a model to detect the sleepy driver’s behavior on time and raise an alert through a buzzing alarm. It makes use of a deep learning model where images are classified based on whether the human eyes are open or close. While OpenCV detects face and eye movements, Keras uses deep neural networks to determine if the driver’s eyes are closed or open. 

7. Gender and age detection

The gender and age detection project with OpenCV is one of beginners’ most exciting data science projects. It is based on computer visioning, and through this project, you’ll be able to learn the practical utilities of CNNs (convolutional neural networks). This real-time project aims to develop a model that can recognize a person’s age and gender through his/her/their facial image. Since various factors like facial expressions, makeup, and lighting can make determining a person’s actual age difficult, this project uses a classification model instead of a regression model. Thus, it makes for an impressive data science project with ample scope to upscale your coding skills.

8. Handwritten digit recognition

The MNIST handwritten digit dataset is an excellent resource for budding data scientists and machine learning enthusiasts to get their hands on. The project is implemented through CNNs, and it aims to empower a computer system to recognize characters and digits in handwritten formats. For the real-time prediction, you will build a graphical user interface to draw numbers on a canvas and build a model to predict the digits. The project involves the practical applications of Keras and Tkinter libraries and is a great way to sharpen your data science skills.

9. Image caption generator

Image caption generation involves natural language processing and computer vision to recognize the context of images and describe them in a language like English. Although describing the image content accurately using well-formed sentences is challenging, it has an immense impact on users, particularly the visually impaired. With the availability of massive datasets and the advancement of deep learning techniques, it is possible to build models that can generate captions for images. The goal of this project is to create an image caption generator using CNN and RNN. Flickr8k is an excellent dataset to get started with image captioning. 

10. Speech emotion recognition

Speech emotion recognition is a popular data science project where human emotions are interpreted through their voice. The dataset comprises various sound files to monitor human emotions. Furthermore, the project entails using an MLPClassifier that can sense emotions from an individual’s voice. The Python package Librosa for music and audio analysis is used here, along with NumPy, Soundfile, Pysudio, and Sklearn. Speech emotion recognition finds applications in several fields such as in call centres to detect the customer’s reaction about a product, in IVR systems to improve the speech interaction, in the development of computer systems adapted to the emotions and mood of an individual, etc.

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How do you start a data science project?

Starting a data science project only requires the following three steps:

1. Identifying a real-world problem to solve.
2. Choosing the datasets you want to work with.
3. Deep diving into the data, performing analysis, and modeling.

What makes data science projects successful?

Any successful data science project is an amalgamation of the following factors:

1. A skillful and competent team.
2. Understanding the problem at hand and framing an optimum solution.
3. Following short, iterative cycles of data gathering, analysis, development, integration, testing, and visualization.
4. Integration of the business and technical teams

Which programming language is best for data science?

The top programming languages used in data science are Python, R, Java, SQL, Julia, Scala, Javascript, MATLAB, and C/C++. While Python and R are the foundational programming languages in data science, the choice of language also depends on your experience level and the goal of your project.

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