Best Data Science Projects from 2021

Data science can help solve real-world problems by properly using relevant data. A data scientist can help businesses understand customer behaviour, forecast product performance based on data. That's why companies looking for data scientists prefer applicants who have earned a Data Science Certification from a reputed university.

If you want your resume to stand out from the crowd while looking for a job, it must include some fresh data science initiatives. Here are fifteen data science initiatives that might help you build a strong online presence.

1. Sentiment Analysis

What is this and how does it work?

Customer’s attitude toward a product or service is assessed via sentiment analysis. Businesses use it to gauge client satisfaction. 

Aim: To determine why sales are below expectations or why a product or service is not well received by the target market.

Project description

With the use of NLP, computational linguistics, text analysis, and biometrics, this data science research can uncover substantial insights. The basic goal of sentiment analysis is to categorise consumer opinions. Responses might range from positive to negative, happy to sad, neutral to thrilled. Popular data science project topic that you can customise.

It also helps identify necessary changes to make the brand's offering more appealing to its target market.

 

2. Detection of Fake News

What is Fake News?

It's difficult to tell the difference between real and fake news. How can we verify the accuracy of data spread across numerous platforms and channels? False news can lead to misinterpretation, which can inflict worldwide harm. Fake news has grown rapidly in recent years due to the constant influx of data. How can Big Data be used to spot fake news?

Project description

This data science project proposal can be implemented in Python. TfidfVectorizer and PassiveAggressiveClassifier are included. Their job is to sort news into true or false. JupyterLab is a web-based user interface for Jupyter notebooks, text editors, terminals, and custom components. In this scenario, 7796*4 data is quite valuable.

3. Prediction Of Next Word

What is PoNW?

We've all used Google Docs, WhatsApp, or Google search. You've probably noticed that when you type, you're given word recommendations. This is what the term "word prediction" means. There are many algorithms that can predict and recommend our next word.

Project description

One benefit of working on data science projects is building predictive models. Like Google Docs, WhatsApp, and even the Google search box, these services predict the next word by suggesting a new term.

Beginners to intermediates will enjoy this project. Find the next word using NLP or deep learning. This approach manages the entire memory using deep learning and a network of fake cells. So they can guess the next word better.

4. Movie Recommender 

How does Movie Recommender work?

The popularity of recommendation systems is growing. Using Netflix streaming? Should. The software learns your preferences and suggests related films or TV series.

This tool helps users find more engaging material. It makes a list for each user based on their choices. Some of these suggestions may be based on what other individuals with similar demographics or interests watch.

Project description

Undoubtedly, this is one of the most popular data science projects! In the end, who doesn't prefer getting free movies and TV shows on YouTube or Netflix? To finish this project, you must gather and categorise moviegoer feedback.

This movie recommendation algorithm is written in R. This study will use the MovieLens dataset. With 58k movies, reshape2, ggplot2, and data.table.

5. Customer Segmentation

What is the definition of Customer Segmentation?

Customer segmentation is the process of dividing a company's customers into groups based on similarity. To maximise consumer value, we must identify how to relate to each category. What we want.

Psychographics, demographics, geographic segmentation, and behavioural traits are used to segment customers. There are several ways to divide the client group. Companies separate customers because they recognise that each group has distinct needs. A corporation's services must be tailored to fulfil the distinct needs of various groups.

Project description

Businesses are always looking for innovative methods to segment customers and improve overall customer service. It allows the organisation to establish consumer-specific strategies and supply products or services tailored to each segmented group. This is a must-do before starting any web marketing strategy.

Unsupervised learning is frequently used in business to segment customers. The organisation uses clusters to define and categorise its clients into groups based on criteria such as region, gender, age, and interests. Customers' annual incomes and purchasing habits are also identified in this initiative.

6. Recommendation System Project

Recommended algorithm use in content-based applications such as blogs and streaming platforms is crucial. A recommendation system proposes fresh content from the site's content collection (database) based on previous viewings. A system's ability to categorise and recommend content based on a user's preferences requires data about both the person and the site. Recent research shows that recommendation systems are a popular Data Science project idea.

These systems can be developed in numerous ways, as shown below:

Collaborative filtering generates user suggestions based on what other users have liked in the past. Because comparable users may have changed their minds about a film they previously liked, the engine may suggest a film that you dislike. Inappropriate recommendations are sometimes skewed by users' regional and cultural roots.

The algorithm proposes content based on what users have already viewed and liked. That is because it is based on consumers' personal preferences for material as well as content attributes that do not alter over time.

This is a fascinating Data Science Project. For a beginner, these two talents are all you'll need to learn. You can teach it to recommend movies, blogs, and items.

Usages:

● Movie recommendation system

● Product recommendation system

● Blog post recommendation system

7. Data Analysis Project

Data analysis is one of the most important abilities a Data Scientist should have. Data analysis is the process of looking deeper into a set of data to make better conclusions. We can make the analysis easier by producing visualisations. Another great Data Science Project Idea.

In today's economy, data trumps oil. Every company keeps a database of customers and their purchases. A corporation can use this data to improve regulations and services that serve consumers and promote platform engagement.

A company's statistics may demonstrate that some countries' customers only buy specific items. To improve product suggestions and promote customer engagement, we need this data.

Uber, Amazon, Flipkart, and more companies employ data research to improve services and pricing. Many firms have embraced and customized it.

We can use data from e-commerce or ride-hailing applications like Uber, Ola, etc. to analyse data for Data Science Projects.

Usages:

● Analysis of cab and weather data

● Analysis of store sales data

● Generating offers using association rule mining

8. Fraud Detection Project

One of the most difficult Data Science Project Ideas for final-year students is fraud detection. A difficult Data Science Project for Seniors. As more people use online and digital transactions, the risk of fraud increases. Data regarding current and historical transactions, as well as consumer purchasing history, can be utilised to analyse transaction fraud.

Every digital action generates data. We can identify possibly fraudulent online payments using transaction data and our trained model. This is a crucial Data Science Project Idea for building models based on user behaviour data.

Every day, huge sums of money are exchanged digitally, therefore records should be verifiable. To do so, we use historical transactional data to develop models. These models look at variables such as transfer amount, origin, and destination. These factors are taken into account when initiating new transactions, which are either fraudulent or genuine.

Usages:

● Credit card fraud detection

● Transaction records fraud detection

9. Image Classification Project

Image classification employs content to categorise and classify photographs. An image's content can be used to categorise and tag it Image categorization is common in research and security. Image classification is another important Data Science application because standard application programming methodologies fail to classify photographs. Identifying photographs used to be difficult and error-prone. Large amounts of annotated pictures can be used to train data science models. Once this is done, we can keep feeding the models additional photos to classify.

Classes can be created using a variety of algorithms, and it is best to test many to see which works best on our dataset. For training and testing, we must employ a huge number of high-resolution photographs. Basic picture ideas and manipulation techniques include reshaping, resizing, edge detection, etc.

Usages:

● Digit recognition system

● Face detection system

● Gender and age detection system

10. Image Caption Generator Project in Python

Any social networking application that permits photo sharing also allows captioning. The captions provide further context for the photos. These captions help with SEO and content ranking. An image caption or detailed description of what an image displays can also be highly helpful to readers. Adding captions to photos makes them more accessible to screen reader software. Making subtitles is a tough Data Science Project Idea.

Working with large numbers of photographs makes creating captions a tiresome and time-consuming operation. We can fix this by creating captions from the image's content. A man surfing or a puppy smiling will be described in the subtitles.

To do so, we need to comprehend convolutional neural networks (CNNs) and long-term memory (LSTM). Many huge datasets, like the Flickr 8K dataset, are available. If we can't construct a new model on our present machine, we can use pre-trained models. For learning how to analyse photographs using neural networks, this is one of the greatest Data Science Project Ideas.

Usages:

● Twitter hashtag generator for images

● Facebook images caption generator

● Blog post image alt-text generator

11. Chatbot Project in Python

Chatbots are vital in today's digital world. They help track customer issues, handle them faster, and produce unambiguous directives. Several Slack and GitHub bots allow us to execute specific activities just by typing them in the chat box. Customers can also use chatbots to handle issues, eliminating human interaction. Zomato and Swiggy deploy chatbots to help consumers with refunds, missing food, and lost items.

Two types of chatbots:

• Domain-specific chatbot- A domain-specific chatbot may only answer queries related to a single area, such as healthcare, engineering, etc., thus it must be tailored to our needs.

• Open-domain chatbots- An open-domain chatbot, on the other hand, can ask questions about any domain, requiring no customisation. But it needs a lot of data to learn.

Data Science Task These concepts heavily rely on NLP. Creating a chatbot involves knowledge of Natural Language Processing (NLP) and access to a dataset containing patterns to look for and responses to give to the user.

Usages:

● Customer care using a chatbot

● Customer feedback using a chatbot

● Price quote generation using a chatbot

12. Brain Tumor Detection with Data Science

Also in healthcare is Data Science. One is detecting brain tumours. To train a model, this programme uses millions of labelled MRI scan images. After properly training the model, we can use it to detect brain tumours on MRI scans.

Here are some Data Science Project Ideas. Luckily, Kaggle offers datasets for you. Now we must utilise these photos to train our algorithm to distinguish between individuals with and without brain tumours. While such models do not totally eliminate the need for expert consultation, they do facilitate it.

Usages:

● Brain tumor detection using MRI images

● Brain tumor detection using vital information

● Brain tumor detection using patient history

13. Traffic Sign Recognition

Self-driving automobiles are currently a hot data science topic. Although working with a self-driving car is difficult and expensive, we can include a vital function: traffic sign recognition.

In this lesson, we'll categorise photographs of traffic signs to show their messages. The model gets more accurate with additional photos, but training takes longer. We first train the model using photos labelled with traffic signal information. Our model may learn from these photographs and captions. Input images are then categorised by the model using their properties.

Usages:

● Gesture recognition system

● Sign language translator

● Product quality checking system

14. Fake News Detection

Real news spreads six times quicker than fake news, MIT found. Many sectors of society are concerned about fake news. They contribute to political discord, bloodshed, misinformation, and religious and cultural strife. Also, social media is growing in relevance. Worse, these platforms lack tools to discriminate between phoney and real news.

To deal with situations like this on a smaller scale, we can use a text-based dataset that contains both fake and real news. NLP and technologies like TF-IDF Vectorizer can aid here (term frequency-inverse document frequency vectorizer). Enter words from a news storey and obtain a label indicating whether it's phoney or not. However, these labels are not always exact, but they can offer us a good sense.

Usages:

● Fake news checker

● Fact checker

● Information verification system

15. Detection of the Parkinson Disease

What is Parkinson's disease?

Parkinson's disease results from ageing. It progresses from hand tremors and torso rigidity to foot shuffling and stride shuffling. Stage 1 is the most unrestricted, while Stage 5 is the most confined. Late diagnosis causes most people extra pain.

Project description

Data science is here. You can use Python to diagnose Parkinson's in XGBoost. This data science research can predict Parkinson's illness. It is possible to warn patients at risk of Parkinson's disease or showing indicators of potential effects.

Conclusion

It is impossible to master data science if you learn the tools and methods. Diverse initiatives are the greatest way to evaluate a technology's utility. Enough exposure while improving problem-solving skills.

Top Data Science Projects for 2021

Program participants can expect to complete eight or more industry projects, real-world projects, industry expert mentorship, and Program Manager support.