Getting into the dynamic field of data science requires you to catch up and build on the trends of the industry. Building your portfolio is the right direction for it and solving the existing problems that can orchestrate breakthroughs in the industry is the perfect path to take. Finding the right project that fits your knowledge, matches with requirements of the industry, and gives you real world practical experience is a decision-heavy task. 

We have compiled a  list of trending data science projects that you can explore to help refine your resume and land a job of your choice in 2023!

Sentiment Analysis

For natural language processing, this data science project involves determining whether the data inferred is positive, negative, or neutral. This can help social media platforms analyse posts and the emotions behind them, which can then be insightful for review information on public sites.

AutoML

Machine learning involves a lot of processes that, if automated, can increase the efficiency of researchers and scientists. Scaling time-consuming tasks to run automatically can limit the time spent on machine learning tasks that are rather redundant.

Detection of Fake News

Identification and classification of fake news is the need of the hour. Using Python, developers can build a machine learning model that judges and predicts misleading journalism on digital platforms. Using classifiers like ‘PassiveAggressive’ or ‘Inverse Document Frequency’, this data science project can move ahead in the right direction.

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Movie Recommender

The recommendation systems of OTT platforms work decently well even in their current state. It works on two different systems—one of collaborative filtering and another, content based filtering. The collaboration of both these into one single recommendation based on browsing habits of others with the similar taste in movies is an ideal project to take on.

Automated Data Cleaning

The accuracy and efficiency of a machine learning model is dependent on the data that it is trained on. An algorithm that can detect and correct flaws in the data without the need for manual intensive labour can help scientists and researchers focus on the higher impact of machine learning models.

Interactive Data Visualisation

Graphs and charts are the best way to display information about a topic. Creating interactive elements in data visualisation can attract more attention to the topic and result in effective interpretation of the data. Businesses are actively regarding interactive data visualisation as critical for decision making.

Recognition of Speech Emotion

Similar to sentiment analysis in text, identifying emotion in speech can help in customisation of the needs of individuals. An intermediate level project, it leverages several algorithms into a single project and can solve a lot of marketing and research problems in speech recognition.

Customer Segmentation

The most popular and trendy data science projects related to digital marketing, customer segmentation deals with clustering methods to identify the customer choices and delivering products based on the habits, interests areas, and more—including the data of annual income of the customers.

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Forest Fire Prediction

Predicting forest fires beforehand can help tackle disasters and prevent significant damage to the ecosystem. Similar to customer segmentation, this project can also leverage k-means clustering to identify hotspots for fire using the meteorological data such as the seasons when fires are more prone and frequent to occur.

Credit Card Fraud Detection Project

An advanced level project, detecting credit card fraud using datasets of card transactions and implementing them on algorithms like decision tree, logistic regression, artificial neural networks, and gradient boosting classifier will help you fit different algorithms in a single model and upskill for better opportunities in the industry.

Stock Market Prediction

Though stock prices are extremely volatile and difficult to predict, there are various organisations and researchers actively trying to build a model that can predict the rise and fall of stocks in the market. A machine learning model based on the stock market data along with natural language processing can be an excellent, albeit risky, project to build.

Sound Classification

Speech separation has always been a difficult problem to solve in machine learning. Improving and building on speech recognition systems using natural language processing is the need of the hour in the AI industry and efforts in this direction can propel your professional career towards great success.

Road Traffic Prediction

Along with detecting road lanes and lines, predicting the traffic-clustered areas of a city is a major task for furthering research in automation of vehicles. Similar to classification and detection of hotspots of fire prone areas, using the datasets of streets, accidents, and traffic signals, a machine learning model can definitely map areas chronically plagued with heavy traffic.

Crime Analysis

There are several failed machine learning models that were used either to predict crimes or within the criminal justice system. Building a reliable model that can deliver accurate crime predictions and analysis can help the government, police, and judicial system in their operations, and make your resume stand out among industry peers.

Store Sales Prediction

Based on the past trends of stores and the interested customers in the area, predicting the future sales of the store can help in action plans for the right products to be sold to the right consumers. This project can be used globally for better management and overall planning of the business.

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