In the domain of artificial intelligence, data science has always been a resonance for the last few years. All the industries and the sectors have also been realizing the need for data science, and also more opportunities are finding their ways. For this generation data science is providing the best career option for everyone. The demand for data scientists is continuously increasing in the market. Following are 10 interesting data science projects for all the beginners as well as for the experts, those who are interested in data science:
Chatbots
Chatbots are one of the applications in data science, which can seamlessly manage customer queries and also messages in real-time without any break. Chatbots help us in reducing the work pressure of humans by responsibly handling customer questions. This is done by utilizing techniques supported with AI, ML, and data science. To train the chatbot, we can use recurrent neural networks with the intents JSON dataset and also, we can manage the application using Python.
There are two types of chatbots:
- Domain-specific chatbots: This domain-specific chatbot is a chatbot that can be used for answering questions that are based on a particular domain only, such as healthcare, engineering, etc., so it needs to be customized quite effectively to suit our human needs.
- Open-domain chatbots: This open-domain chatbot, on the other hand, can be used for asking questions about any domain, which means that it does not require careful customizations. However, this method does need a large volume of data to learn from.
Credit Card Fraud Detection
We can see credit card fraud everywhere happening nowadays; these frauds have become very common especially in the era of digital transformation. But some innovations in technologies like artificial intelligence, machine learning, and also data science, have led credit card companies to successfully recognize and also catch these frauds with adequate accuracy. For this project, we can use either R or Python to track the customer’s transaction history as the dataset that we have prepared or collected and also take it into decision trees, artificial neural networks, and finally the logistic regression.
Fake News Detection
Fake news, this word needs no introduction. With the advent of the internet and also social media, fake news is growing, indeed to a large extent. The spread of fake news is affecting almost the lifestyles of all. Therefore, Now and then we can also witness fake information that is being spread from unauthorized sources creating widespread panic. By using the data science projects, it is possible for us to identify the authenticity of any information whether it is fake or real. Using Python will help to segregate real news from fake ones. Some of the Python libraries suited for this project may be pandas, NumPy, and sci-kit-learn.
Forest Fire Prediction
Data science offers us numerous capabilities and also building a forest fire prediction, that can be one such use done with the help of those other capabilities. Forest fire or wildfire is something that cannot be controlled and that causes a huge amount of damage. For managing and also even assuming the disrupted nature of wildfires, we can use k-means clustering for spotting major fire hotspots and also their acuteness. This can also be useful in properly dispensing resources. We can also make use of the meteorological data for searching for specific seasons for the wildfires to increase the model’s accuracy.
Classifying Breast Cancer
Breast cancer is one of the deadliest diseases. Cases of breast cancers are rising every day, and the best possible way to fight breast cancer is to detect it at an early stage and also take suitable protective measures. We can create a breast cancer detection system using Python. To create such a system with Python, we should use the IDC (Invasive Ductal Carcinoma) dataset, which carries histology images for cancer-inducing malignant cells, and we can train the model on this dataset. For the Python libraries, we can use NumPy, OpenCV, TensorFlow, Kera’s, sci-kit-learn, and Matplotlib.
Also Read: A Complete Beginner’s Guide to Python for Data Science
Sentiment Analysis
Sentiment analysis is an act of exploring words to stimulate sentiments and also opinions that may be positive or negative in polarity. This is a type of codification where the codes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted, etc.). We can also apply this data science project in the language R and use the datasets by the ‘janeaustenR’ package. We will have to use general-purpose lexicons like AFINN, Bing, and Loughran, to display the result.
Color Detection
There are almost 16 million colors that are based on the different RGB color values but we only recall a few. So, in this project, we can also design an interactive app that will identify the selected color from any image. To execute this, we will need the classified data of all the known colors then we can also determine which colour favours the most with the selected colour value.
Driver Somnolence Detection
Sleepy drivers are highly prone to road accidents. One of the best ways for preventing this is to execute a fatigue detection system. This system can constantly evaluate the driver’s eyes and also can alert him with the alarms when the system detects the closing of eyes. A webcam is important for this project for enabling the system to occasionally monitor the driver’s eyes. This Python project will also require a deep learning model and also libraries such as OpenCV, TensorFlow, Pygame, and Keras.
Recommender Systems (Movie/Web Show Recommendation)
Just like how Netflix, Amazon Prime are using a recommendation system, we can make one for our project. We have to consider various metrics like age, formerly watched shows, most-watched genre, watch frequency, and inject them into the machine learning model which then produces what the user might like to watch next. For doing this project, we can select R with the Movie Lens dataset that also includes ratings for over 58,000 movies, and as for the packages, we can use ggplot2, reshape2, and data table.
Exploratory Data Analysis
For the data science project, the data analysis can be the best one for you. For data, analysis and visualization are important before exploration. For visualization, we can also pick histograms, scatterplots, or queries and heat maps. Once we have identified the structures and also obtained the necessary insights from the data, we are ready to go.
