15 Data Science Projects That Will Land You a Job in 2023

Entering the dynamic field of data science requires you to catch up and build on industry trends. Building your portfolio is the right direction for this and solving existing problems that can orchestrate breakthroughs in the industry is the ideal way to go. Finding the right project that matches your knowledge, meets industry requirements, and gives you real-world hands-on experience is a heavy decision-making task.

We’ve compiled a list of trending data science projects you can explore to sharpen your resume and land a job of your choice in 2023!

Sentiment analysis

For natural language processing, this data science project involves determining whether inferred data is positive, negative, or neutral. This can help social media platforms analyze messages and the emotions behind them, which can then be useful in reviewing information on public sites.

AutoML

Machine learning involves many 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 quite redundant.

Fake news detection

Identifying and classifying fake news is the need of the hour. Using Python, developers can create a machine learning model that judges and predicts misleading journalism on digital platforms. By using classifiers such as “PassiveAggressive” or “Inverse Document Frequency”, this data science project can move 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 the other of content-based filtering. The collaboration of the two into a single recommendation based on the browsing habits of other people with the same taste in movies is an ideal project to undertake.

Automated data cleaning

The accuracy and efficiency of a machine learning model depends on the data it is trained on. An algorithm that can detect and fix flaws in data without requiring intensive manual labor can help scientists and researchers focus on the higher impact of machine learning models.

Interactive data visualization

Graphs and charts are the best way to display information about a topic. Creating interactive elements in the data visualization can draw more attention to the topic and result in effective data interpretation. Businesses actively view interactive data visualization as critical to decision making.

Speech emotion recognition

Similar to sentiment analysis in text, identifying emotions in speech can help personalize the needs of individuals. A mid-level project, it leverages multiple algorithms in a single project and can solve many speech recognition marketing and research problems.

Customer segmentation

The most popular and trending data science projects related to digital marketing, customer segmentation deals with grouping methods to identify customer choices and deliver products based on habits, areas of interest , etc., including data on the annual income of customers.

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Forest fire forecast

Predicting forest fires in advance can help deal with disasters and prevent major damage to the ecosystem. Similar to customer segmentation, this project can also leverage k-means clustering to identify fire hotspots using weather data such as which seasons fires are most likely and frequent to occur.

Credit Card Fraud Detection Project

An advanced level project, detecting credit card fraud using card transaction datasets and implementing them on algorithms such as decision tree, logistic regression, artificial neural networks and the gradient boost classifier will help you integrate different algorithms into one model and improve your skills for better opportunities in the industry.

Stock market prediction

Although stock prices are extremely volatile and difficult to predict, various organizations and researchers are actively trying to build a model that can predict the rise and fall of stocks in the market. A machine learning model based on stock market data and natural language processing can be a great, if risky, project to build.

Sound classification

Speech separation has always been a difficult problem to solve in machine learning. Improving and developing voice 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 to great success.

Road traffic prediction

Along with detecting road lanes and lines, predicting traffic areas of a city is a major task to advance vehicle automation research. Similar to hotspot classification and detection of fire-prone areas, using datasets of streets, accidents, and traffic lights, a machine learning model can certainly map areas chronically plagued by a heavy traffic.

Crime analysis

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

Store sales prediction

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

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