Aspiring Data Scientists? You don’t want to miss these projects!

Aspiring Data Scientists? You don’t want to miss these projects!
Aspiring Data Scientists? You don’t want to miss these projects!


From predicting winners in an election such as exit polls to building recommendation systems, Data Science plays a vital role in every industry.

We know that there is a vast advancement in technology these days with the growth of Artificial Intelligence and Machine Learning but the thing underpinning all of this is data. Nowadays, more than gold and land, information is considered to be a wealth of a particular organisation or even a country.

So before jumping into this fascinating stuff, you need to produce some projects that you will improve your chances of getting into a Data Science career and taking you a step closer to your dream.

Perks of doing a Data Science project

  • They help you realise that you can use your Data Science techniques and solve real problems
  • Practical application is one of the most idealistic ways to acquire Data Science knowledge and learn new skills

Basic things a Data Science project should always have;

  • Data Collection
  • Feature engineering and high-level analysis

Did you know?
Tracking down a perfect idea for your Data Science project is more complicated than implementing the project itself because of the number of projects available. Therefore without further ado, let’s get right into the most exciting Data Science projects we have for you to try out.

Fake News Detection – Do not false for a hoax again

Everywhere around the world, fake news is considered to be the talk of the town, and ordinary people are misled by the fake news floating around that it has become hard to differentiate the real story from fake news. So this project is built to check if the report given by mainstream media and social media are fake or not. This project can be created using the Tfidf vectorise and the Passive-aggressive classifier.

Tfidf vectoriser (Term frequency-inverse document frequency vectoriser) where term frequency means the no. of times a word appears in a document and inverse document frequency means the words which occur many times in a document. IDF shows the significance of a term in the entire corpus. Passive Aggressive algorithms remain passive for a correct classification outcome which turns aggressive during a miscalculation and adjusting. It does not converge. So, using this, we can differentiate real news from fake news.

Human Activity Recognition – Adds more taste to your workout routine

Should we always be dependent on large datasets for building projects using Data Science? The answer to that would be no. Small data implementation is an efficient way of learning methodologies. HMR (Human Movement Recognition) is a time series classification problem, where you can use long short term memory (LSTM) and (RNN) recurrent neural networks to analyse the activities of humans like walking, jumping etc. The project first transforms the accelerometer data into time-sliced representation. The aim is to classify activities into five activities performed.
LSTM is a recurrent neural network capable of learning order dependence, and it helps in remembering values over arbitrary intervals. Some of the activities in the data set include:

  • Jogging
  • Walking
  • Standing
  • Walking upstairs
  • Walking downstairs

Loan Prediction – Makes life easy for loan seekers

In India, from a common man to millionaire Vijay Mallaya, everyone has loan troubles. With this, let’s get insight into what loan prediction is. Most of the financial establishments pass through the difficulty of finding if the person is eligible for a loan. This can be built by using classification techniques like logistic regression, and random forest technique. You can proceed by doing EDA (Exploratory Data Analysis) through Python or any programming language of your own choice. In most cases, we can move to a conclusion by just doing EDA as it analyses the data of the particular loan applicant thoroughly. The objective of the project is to classify if the loan status is yes or no.

Breast Cancer Prediction – Prevention is better than cure

Breast cancer is one of the most delicate and endogenous diseases in the medical field. This is one of the crucial reasons for the death of women around the world as 1 in 10 women die because of this. “Early detection means there is a good chance of survival” as the statement conveys breast cancer prediction is one of the tools to detect breast cancer at an early stage.

Using the supervised learning algorithm of Support Vector Machine (SVM), experimental results show that the model has achieved a remarkable performance with 96.09% classification accuracy on the testing subset. Artificial Intelligence (AI) can be used for better and accurate detection and diagnosis of breast cancer, as well as to prevent overtreatment. For example, doctors decide on the biopsy output for detecting breast cancer if the patient needs surgery or not.

Hate speech prevention – Sentiment Analysis on the impudent

In recent days there is a lot of news about hate speeches not being screened without bias. So, sentiment analysis is one of the automated processes of analysing tweets and separating it into sentiments such as positive, neutral and negative. In online sentiment analysis tools, emotions can be analysed without any code. Sentiment analysis is also known as Opinion Mining.

Steps included in performing sentiment analysis are:

  • Section A: Composing the Test Set
  • Section B: Composing the Training Set
  • Section C: Pre-processing Tweets in the Data Sets
  • Section D: Naive Bayes Classifier and finally testing the model

Predicting wine quality – Make sure your wine is wine, not a grape juice

To apply Machine Learning models to figure out the quality of something you love is fascinating to the core, which is WINE. For this project, you can use Kaggle’s red wine data set to build various models to classify, where each is marked a score of 0 to 10. You can find whether each wine is of good quality by converting the output to a binary outcome. You can also see which feature of the wine indicates good quality using a different model.

Advertisement classification – Way to save yourself from fraudulent companies

Have you ever got yourself into trouble after ordering things using a new website or have you been in a situation where you were created by a company when you went looking for a job? Many innocent people fall into this trap and lose everything. EMS CAD (Employment Scam Aegean Dataset) is a data set used to identify fake advertisements. This dataset contains 17880 real jobs postings in which 17015 are genuine, and 865 are fake. It is available in Kaggle. You can visualise the insights from the advertisements, and then you can use a support vector classifier in this task which will predict real and fraudulent jobs.

Weight prediction – Knowing your weight keeps you from gaining more

Weight is predicted based on your height and gender. You need to build a model and train it on this Kaggle dataset such that their size and gender can predict the weight. This is a linear regression problem. This dataset on Kaggle has 10000 rows which give the height, weight and gender of the person. The steps involved in this are analysing data, converting gender to number, splitting the dataset into a training set and test set, finally fitting regression models.

Colour detection – Not all colours are needed to be remembered

Today, we have more than 10million different colours existing, and you cannot recognise every colour. The project of detecting colours uses Python to carry a set of colours listed and programmed. The time when you select a colour from an image or video, the data set will provide you with the name of the colour. The RGB set has pixel limits ranging for R from (100 to 200 ), G ( 15 to 56 ), B ( 17 to 50 ). Once you have listed all the limits, you have to provide an upper limit and lower limit for your pixel values. The upper and lower limits will decide whether the pixel falls under the range and applies it to the image. Using Python, you can use the data set of colours and need not remember it all.

Real-time image animation – Bring out your inner child

The expression and features of the person performing will be monitored and recorded. This is one of the best Data Science projects existing. It requires a single image from the user with no separate hardware needed and runs in the usual 23 frames-per-second. A non-rigid tracking system is used to recognise facial expressions, and it uses Pytorch. Data science and computer vision are significant aspects of this project. Animated avatars are more interesting than real ones.

Related Articles
DBSCAN Clustering In Machine Learning


The listed 10 data science projects are specially for beginners to start their careers with impressive data science techniques. We’re ending this article with a note that there is no best or second best when it comes to projects. You need to decide the best one for you and start working on it for better advancements in your Data Science career.