Newcomers spend a lot of time learning the theoretical concepts of data science, which, though necessary, cannot substitute for the data science projects. Data Science Projects are crucial for the learners as they not only help in getting the learners a hands-on experience but also train them in the practical application of all the theories that they might have learned.
To make real progress along the path of becoming a great data scientist, it is important to start working on data science projects as soon as possible.
If you’re thinking of making your own data science projects but aren’t getting any ideas, you can seek inspiration from others. Here are some of the data science project ideas that are worth trying!
Analyzing the words used in the sentence to determine sentiments that may be either negative or positive is called sentiment analysis. In this type of classification, the classes can be binary where the sentiment is either positive or negative; or the classes may be multiple (happy, sad, angry, confident..). This data science project can be implemented in the language R using the dataset by ‘janeaustenR’package. General-purpose lexicons like Bing, AFINN, and Loughran may be used. And in the end, build a word cloud for displaying the result.
It goes without saying that customer reviews play an important role in influencing prospective buyers. Hence, spam reviews whether positive or negative can influence the potential buyer in either a positive or negative direction. Any of these can lead to a wrong decision by the buyer which in turn may affect the general opinion on online buying. In this data science project, you can investigate the opinion of spam in reviews. It should be noted that this project is different from email spam classification which is an unsolicited commercial advertisement that is done to promote their product or services. They generally have certain characteristic features.
But the problem that this data science project is tackling is more challenging as untruthful opinions are much harder to deal with. It is because these spam reviews are very carefully crafted and made indistinguishable. The tools that this project will employ include Python [Packages: NLTK, sklearn] and the techniques used will be Shingle Method, n-grams, Feature Extraction.
This data science project will use Librosa to recognize the emotion of speech. Speech Emotion Recognition is the process of recognizing human emotion and affective states from speech. Owing to the fact that humans use tone and pitch to convey emotion while speaking, Speech Emotion Recognition is possible. But what makes it a bit difficult is the fact that emotions are not objective, they are very subjective in nature which makes annotating audio challenge. The language used for the project will be Python. Further, the project will employ mfcc, mel features, and chroma. Use the RAVDESS dataset to recognize the emotion. An MLPClassifier can be built for the model.
Assuming that you have an advanced-level knowledge in data science, let’s move on to trying a more challenging data science project! In this project, use R with algorithms like Logistic Regression, Decision Trees, Artificial Neural Networks, and Gradient Boosting Classifier. Use the Card Transactions dataset to classify credit card transactions into fraudulent and genuine. Different models and plot performance curves may be used for this data science project.
How many times has it occurred to you that you stream something online and sleep in the middle of it? And then when you wake up the next day, you couldn’t realize how far have you watched! It has happened even to the best of us. Hence, this could be one of the most challenging yet most fun data science projects!
During this project, focus on developing an application that may be able to detect if you’re asleep and if you indeed are, then pause the video for you. The system will wait for 30 mins to see if you wake up. In case you don’t wake up, it will save a screenshot of the screen and close all the windows thereby shutting down your computer.
The tools used for this project may be Python, Open CV, Tensorflow, Kera and the techniques used may be Viola-Jones algorithm on Rapid Object Detection using a Boosted Cascade of Simple Features, Inception V3, LSTM!
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