If you are a beginner and confused in choosing the best data science project to implement and add to your resume, you are at the right place. This article aims to make you familiar with some top data science project ideas. Data scientists are responsible for analyzing large sets of structured and unstructured data to uncover patterns and trends and extract valuable insights.
With the high availability of data and the rising demand for data-driven development, organizations are hiring skilled data science professionals who can make the most out of data generated every day. Therefore, one can have a promising and lucrative career in the data science domain. The initial step to becoming a data scientist is to have a data science certification that adds value to your resume.
Along with certification, adding one or more data science projects to your resume can definitely impress an interviewer. Hiring managers focus on the practical knowledge of applicants rather than only focusing on theoretical knowledge. Therefore, having data science projects on your resume can increase the chances of getting hired.
Before diving deep into the data science project ideas, let us first briefly understand what data science actually is. So, let us begin with a brief introduction to data science!
What is Data Science?
Data science is a multidisciplinary field that leverages scientific methods, algorithms, processes, and systems to extract valuable insights from large sets of unstructured and noisy data. In addition, organizations use these actionable insights to make informed decisions.
Alternatively, we can define data science as a process of unifying data analytics, informatics, statistics, and their related methods to understand and analyze large volumes of data, uncover hidden patterns and trends, and derive actionable insights.
Moreover, data science is an umbrella term that involves many other subfields, such as data analytics, machine learning , computer science, and statistics. The data science process has several phases. Each phase prepares the data for the subsequent phase. These phases include data preparation, parameter tuning, and model evaluation.
Top 10 Top Data Science Projects
Here is a curated list of top data science projects that you can add to your portfolio and get better job opportunities. Also, we have mentioned the link for the source code of each project. So, let's start with the fake news detection project.
1. Fake News Detection Using Python
We can refer fake news to false or misleading content or content whose source cannot be verified. Fake news can be generated to gain attention or damage the reputation of the targeted person. The popularity of the term fake news increased during the 2016 US President Elections. It was reported that fake news might have influenced the results of the election.
With the help of data science, it has become possible to classify fake news or identify the authenticity of the information. To develop a fake news detection system, we can leverage machine learning algorithms, natural language processing techniques, and Python libraries, such as Numpy, Panda, and sci-kit.
You can refer to the source code here .
2. Detection of Forest Fire Using CNN
One can showcase data science expertise by developing an intelligent system that can detect forest fires or wildfires. A forest fire or wildfire is an unwanted, uncontrollable, and unexpected fire that spreads over an area of woodland or forest. It damages trees, harms wild animals, and impacts the surrounding environment.
We can build a forest fire detection system using convolutional neural networks (CNNs) that can detect the start of a forest fire or the presence of the forest fire in an image. The primary concept of building such a data science project is to detect the forest fire from the aerial footage of the forest.
When there is a forest fire, the system provides an alert. When we get an alert about the presence or start of a forest fire, it becomes easier to allocate the required resources to control the fire. To take the forest fire detection system to the next level, we can use climatological data to predict the common seasons or periods for wildfire.
You can refer to the source code here .
3. Breast Cancer Classification
Breast cancer is a life-threatening disease, and breast cancer cases are increasing extensively day by day. The only way to combat it is to get it diagnosed at early stages and get proper treatment. We can leverage data science to develop a model that can detect breast cancer.
We can build a breast cancer detection system using Python. To build this data science project, we need to use the Invasive Ductal Carcinoma (IDC) dataset. This dataset provides histology images for cancer-inducing malignant cells. Moreover, CNNs are best suited to develop the breast cancer detection system.
Some P opular Python libraries required to develop this system are Numpy, TensorFlow, scikit-learn, Matplotlib, Keras, and OpenCV.
You can refer to the source code here .
4. Sentiment Analysis
Sentiment analysis is also known as emotion AI or opinion mining. It involves the use of natural language processing (NLP), computational linguistics, and text analysis to evaluate words to determine sentiments and opinions that may be positive or negative in polarity. It is a sort of classification that can be either binary, such as optimistic or pessimistic, or multiple, such as happy, sad, angry, excited, and no response.
Nowadays, companies use sentiment analysis to determine the likeability of their products in the market. Therefore, we can define sentiment analysis as the approach to analyzing the opinions of people on a specific product or service.
We can develop the sentiment analysis project using the R programming language and the data set provided by the Janeausten R package. In addition, we can use general-purpose lexicons in sentiment analysis, like Bing, Loughran, and AFINN.
You can refer to the source code here .
5. Speech Emotion Recognition
Oral communication is the most effective way to communicate since it enables us to express different feelings, such as anger, happiness, passion, silence, and so on. The speech emotion recognition (SER) system recognizes emotions and affective states from speech. The best application of an SER system is at call centers.
A call center can employ an SER system to recognize the emotions of customers to provide better customer support. We can build the speech emotion recognition system using MLPClassifier and Python packages, such as Librosa, Numpy, Scikit-learn, SoundFile, and PyAudio.
In order to implement this system, we first need to load the data, i.e., voices that reflect various emotions, extract features from data, and divide the dataset into training and testing sets.
Finally, we will initialize an MLPClassifier, train the model with the training dataset, and test it for accuracy with the testing dataset. For a dataset containing more than 7,300 files, we can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS).
You can refer to the source code here .
6. Gender Detection and Age Detection
Choosing the data science project of detecting gender and age can help you showcase your computer vision and machine learning expertise. Gender and age detection is a type of classification problem that falls under the supervised machine learning category. The primary purpose of this data science project is to identify the gender and age of a person by analyzing a single image of that person.
We can develop the gender and age detection system by implementing CNNs using Python and the OpenCV library. Also, we need to download the Adience dataset. It is essential to consider factors like cosmetics, lighting, and facial expressions, which can affect image analysis and make it difficult to identify the age and gender of a person.
You can refer to the source code here .
7. Chatbots
Chatbots are used extensively across various industries because they can provide 24*7 customer support by responding to customer queries. The use of chatbots has completely reduced the work pressure on humans since they can handle customer queries responsibly. A chatbot operates by accessing the user’s input and responding to the user with a mapped response.
We can build a chatbot using machine learning, AI, and various data science techniques. Recurrent Neural Networks (RNNs) are ideal for creating chatbots. In addition, we need to use the JSON dataset to train the chatbot and Python to implement it.
You can refer to the source code here .
8. Detection of Drowsiness in Drivers
Another interesting data science project is detecting drowsiness in drivers. Each year witnesses hundreds and thousands of road accidents. One of the major reasons for road accidents is drowsy driving. Sleepiness or fatigue causes drowsy driving. To avoid road accidents due to drowsy driving, we can install a system to detect drowsiness in a driver.
The driver drowsiness detection system alerts the driver as soon as it detects that the driver is becoming drowsy. This system continuously monitors the eyes of the driver and alerts in case the driver closes their eyes very often.
We require a webcam that can keep track of the eyes of a driver. The deep learning model and packages, such as Keras, Pygame, OpenCV, and TensorFlow, are required for implementing the driver drowsiness detection system.
You can refer to the source code here .
9. Detection of Credit Card Fraud
The rate of credit card fraud has increased considerably in recent years. A credit card fraud is a kind of identity theft where a scammer or a criminal uses someone else’s credit card to obtain cash or make purchases. But the evolution in technologies, such as artificial intelligence, data science, and machine learning, has made it possible to detect these frauds with impressive accuracy.
We can build a credit card fraud detection system using AI, ML, and data science. The primary aim of this system is to examine a customer’s regular spending pattern by involving geographic locations of such spendings to distinguish between fraudulent and non-fraudulent transactions.
For building a credit card fraud detection system, we can use Python and R programming languages to track the transaction history of a customer and use it as a dataset. We can then input the dataset in Artificial Neural Networks (ANNs), decision trees, and logistic regression.
You can refer to the source code here .
10. Movie Recommendation System
We'll wind up our list of the best data science project ideas with the movie recommendation system. Many individuals today use Netflix, Amazon, Prime, and other online streaming services to watch their favorite shows, movies, and web series. These online streaming services use recommendation systems to provide a user with movie recommendations based on their watch history and movie ratings.
We can consider building a movie recommendation system as a data science project. To develop such a system, we need to use the R programming language and the MovieLens dataset. This dataset consists of ratings for more than 58,000 movies. Also, we will require packages, such as reshap2, ggplot2, and data table.
In addition, we need to feed a machine learning model with information, such as the age of users, formerly watched movies or shows, watch frequency, and most-watched genre.
Based on this data, the movie recommendation system produces what a particular user may like to watch next.
You can refer to the source code here .
Conclusion
Working on various data science projects can help you boost your technical expertise. This means that you can enhance your knowledge of artificial intelligence, R and Python languages, neural networks, and machine learning.
This article provides a brief insight into data science and lists the top data science project ideas for beginners and professionals. The source code of all the above-listed data science projects is available on GitHub. You can refer to the source code and start implementing any of the above-mentioned data science project ideas.
Wish you luck!
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