At first, data science and its subfields might seem discouraging. This is because transitions in statistics, programming abilities, and algorithms (supervised or unsupervised) are challenging to learn and implement. Are you preparing to abandon this war without fighting because you believe you are simply a beginner? This will complicate matters, and to save yourself, you need to obtain hands-on experience by working on projects and addressing real-time difficulties quickly and profitably.
Let's look at a few project ideas focused on data science concepts that can not only sharpen your skills but also leave an indelible impact on the minds of recruiters.
Detecting Credit Card Fraud Using Python
Scammers commit the majority of credit card crimes in today's epidemic era. Such people are smart enough to steal your credit card details like CVV and card numbers and use them to access your account without your knowledge. Because there are so many digital ways to access someone's account, the odds of catching such fraudulent crooks are nearly nil. Consider ways to maximize the likelihood of capturing such con artists! With this CC Fraud Detection project, which includes hidden capabilities of Machine Learning, ANN, i.e., Artificial Neural Network, and decision trees, insights into the customers' data will be labeled with proper modeling of their spending behavior.] Such scammers will follow those who spend more to take their victims' financial freedom. With such tracking, the odds of preventing fraudsters from accomplishing what they genuinely desire increase, therefore protecting information privacy and general accuracy.
Customer Group Segmentation Using Machine Learning
ML algorithms necessitate originality and excellent study in order to be applied in real-time in the most basic and clear manner. Unsupervised learning algorithms are among the most complex to implement, yet they accurately reflect the needs of consumers. For client segmentation, we will use the K-means unsupervised learning technique (which is easier than others). Such segmentation is influenced by characteristics such as yearly income, purchasing and selling habits, age, gender, and interests. The language would be R, and the dataset would be Mall Customers. If you're wondering what it's good for, the answer is running an internet marketing campaign for a company's needs. In a word, you or the newbies are well-prepared to assist the ventures in successfully structuring their products and services around their targeted clients and enthralling the customers by offering what they truly desire.
Refer to the data science course to build projects with the help of industry experts.
Putting in place a Driver Fatigue Detection System
Driver fatigue or drowsiness is a significant contributor to traffic accidents. According to an IEEE survey, more than 30 percent of accidents occur throughout the day or night as a result of drivers' falling asleep while traveling longer or shorter distances. What if we discovered a mechanism that could identify such weariness at any time? This is achievable thanks to the real-time implementation of a driver drowsiness project that requires a camera and certain Python programming tools (those libraries would be Keras and OpenCV). They are as follows: Keras will check whether the driver's eye is closed or open (you will notice the use of Deep Neural Network techniques while using Keras); OpenCV will scan the driver's eye and face. As the driver falls asleep, these libraries and webcams come into action and force the alarm's trigger to alert the driver. Such a project can help minimize the frequency of traffic accidents while ensuring public safety throughout the day.
Sentiment Analysis Supported by R Dataset
Sentiment analysis is very beneficial since it extracts subjective data from accessible sources that companies may use to analyze societal attitudes. These opinions offer businesses a summary of what consumers say about a brand or other related services. I am figuring out how to start such an analysis immediately! We'll identify the negative and positive emotions of the large number of individuals remarked or mentioned with the help of general-purpose LEXICONS and the computational capability of R datasets (like Jane Austen). This sentiment analysis platform has given businesses meaningful insights by analyzing all of the social media comments with a deeper meaning related to a brand or service. Scores ranging from 0 to 9 will later be assigned to those sentiments, enabling businesses to make valuable decisions or re-create their pre-decided strategies. Beginners may thus begin working on this project to assess how one should derive significant, game-changing insights from the study carried out for a particular brand or service.
Creating your first Python chatbot
Creating it By tracking and effectively resolving all of the real-time client complaints, Python chatbots are a means for businesses to become more customer-centric. I am considering how to accomplish this now! These chatbots have specific conversational NLP scripts running that allow them to comprehend the inquiries and then respond with the answers in the form of customer-focused feedback. For this project, the Python language accesses an immense volume of data via an Intents JSON file. These patterns will be helpful in delivering the right answers that users want to get to solve their problems. Such answers can be synced with the appropriate adjustments to handle open-domain or domain-specific issues effectively.
I hope this list of data science project ideas will help you develop your portfolio as a beginner. For more information, feel free to check out the IBM-accredited Data science course in Delhi and become a competent data scientist.