So you have decided to make a career in business analytics and Data Science. You have your graduate degree, the necessary skills, and languages and are on the next step in your professional journey. Whether as a fresher or an experienced IT professional, you want to land a lucrative job at a top-notch enterprise. You have registered for a Business Analytics with Excel program that will make you career-ready. At the same time, you want to stand out from the other applicant when you apply for a job and appear for an interview.
The best way is to work on your portfolio. Build Data Science projects that add value to your resume. Data Science projects are practical applications of your skills and showcase your knowledge of tools, language proficiency, and ability to apply your learning to real-world scenarios for solving business problems. A Data Science project demonstrates your skills in data collection, cleaning, analysis, and machine learning, to would-be employers.
Begin with basic Data Science projects and move on to more challenging ones. From importing a dataset to creating a complex speech recognition system, Data Science projects are also a fine way to apply your learning and skillsets.
Top 10 Data Science Project ideas
The best way to prepare for a Data Science interview is by having a portfolio with projects that showcase your skills in handling data, using various tools, and working on real-world projects.
Whether you are a fresher or an IT practitioner, working on beginner and intermediary Data Science projects can add value to your resume and strengthen your portfolio.
Here are a few Data Science projects ideas to get started:
1. Customer Segmentation
Customer segmentation is critical for e-commerce and service apps like ride-sharing and food delivery.
Businesses categorize customers across demographic, behavioral, and preference data to create user profiles. They deliver products and services to customers based on these user profiles or customer segments.
Apply your knowledge of techniques, such as supervised learning, unsupervised learning, R, principal component analysis (PCA), K-means clustering, hierarchical clustering, or density-based clustering. Identify the target customer base and apply clustering algorithms to classify based on purchase history, cart abandonment, interests, etc., for targeted ads, cross-sells, and upsells.
2. Building Chatbots
Chatbots are the automated customer service component of businesses. They handle customer queries and messages without any silos.
Building chatbots is a great Data Science project for beginners. You can use Python language for the implementation, Natural Language Processing (NLP), Recurrent Neural Networks (RNN), and Machine Learning techniques for the build. The basic chatbot build can further experiment with the detection of user sentiment. You can choose from the two types of chatbots: domain-specific and open-domain chatbots.
Chatbots analyze the customer inputs and reply with a mapped response from a data set of ready customized answers.
3. Recommendation systems
With the proliferation of OTT streaming services that deliver content like movies and web series over the internet, companies want to ensure an optimal customer experience. To enhance the user experience, you can design recommendation systems for suggesting movies and other content to the end customer. Suggestions are based on behavior, time spent, browsing history, age, favorites, preferences, genre, feeds, and other metrics. These metrics are fed into a machine learning model to generate a recommendation system with collaborative filtering. You can build this using the R language.
4. Road Lane Line Detection
Traffic management is better with road lane lines detection, where self-driving cars run. We know that lane lines indicate the vehicle’s steering direction. Herein computer vision techniques can help identify lane lines with uninterrupted video feeds. A road line detection system can be built in Python, with the application using OpenCV library, NumPy, Spatial Convolutional Neural Networks (CNN), Hough Transform, and other machine learning techniques.
.5. Predictive Policing
Crime enforcement agencies want to forecast and prevent crimes. They are increasingly using data for pattern detection and hot spots. Create a project on predictive policing for predicting crime in a given area and time. You can use linear regression, K-nearest neighbors, random forest regressor, XGBoost, and deep learning model multilayer perceptron to predict the location where crime is likely to occur. This makes it possible for police teams to be dispatched there for crime prevention and law and order enforcement.
6. Fake News Detection
Fake news turns viral at a faster rate than the truth. In turbulent times, fake news and videos can create panic, riots, and social unrest. So there is a need to detect and filter false news to call them out. Classifiers are a way to build your project on fake news detection. Use Python and NLP for content-based classification by training models with key phrases or words, and develop the classifier model with the Passive-Aggressive Classifier ML algorithm.
7. Credit Card Fraud Detection
Credit card transactions are on the rise globally. With more and more digital transactions conducted 24/7, the chances of fraud have also risen. Banks, Fintech apps, and financial institutions are leveraging ML techniques to identify and avert fraudulent transactions. Transaction data of customers, including the location of card use, usual spending behavior, transaction value, etc., can train the algorithm for detecting unusual activity and create alerts for timely action. Use R or Python on the customer transaction data and build a classification engine for credit card fraud detection. Use decision trees, Artificial Neural Networks, K-nearest neighbor, logistic regression, support vector machine, logistic regression, random forest, and XGBoost.
8. Implementing a Driver Fatigue Detection System
Driver Fatigue is a cause of road accidents. The sleepiness of drivers traveling long routes or driving continuously through the night often leads to errors and road accidents. You can build a system to detect such driver fatigue to prevent road accidents. Use real-time data of driver drowsiness with a webcam and Python libraries. While the webcam uses face recognition to watch the driver, Keras examines the driver’s eye for whether it is closed or open, while Open CV scans the eye and face. The moment the driver shuts his eyes or displays visible signs of drowsiness, these libraries and webcams trigger an alarm to wake up the driver. Such a Data Science project can help cut down the number of road accidents.
9. Speech recognition through emotions
Speech is a form of communication that includes various emotions like pause, silence, hesitation, anger, happiness, excitement, etc. Design a project to use the emotions behind speech and leverage the insights for delivering customized service and the end products that result in customer satisfaction. The purpose is to identify the emotions from multiple audio files. Build this model with Python’s SoundFile, NumPy, Scikit-learn, and PyAaudio packages.
10. Sentiment Analysis Backed by R Dataset
Sentiment Analysis is useful for businesses with an online and social media presence. It helps to understand the success of a product or service rollout and understand user sentiments for feedback. Online platforms like social media behavior and interactions are analyzed in real-time to understand what their target customers talk about, what they want, and how they feel about the company’s brand.
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