Top 20 Data Science Projects For Beginners and Experts

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Data Science has been flourishing for the past few years, and the focus in the Artificial Intelligence domain due to several innovations will take it to heights. As industries have started realizing the significance of Data Science, several opportunities can be tapped from the market. If you are into Data Science and eager to achieve a robust grip on the technology, now is the perfect time as ever to sharpen your skills to know and execute the forthcoming hurdles in Data Science. So, this article is mainly for sharing practical and current ideas for your upcoming data science project, which will help you boost your confidence level but also play a hefty role in enhancing your skills.

Top 20 Magnetic Data Science Projects That You Should Not Miss

Knowing Data Science from its core can be a little daunting job initially, however, with continuous practice and efforts, you can easily commence to learn several notions and terms in the niche. There’s a particular route you can get access to Data Science apart from going through the literature is to have some valuable objects which will not only hone your entire skillset but will also build your resume more robust.

Let’s dive in to learn the top 20 Data Science Projects 

1. Building Chatbots

Building chatbots is a part of data science projects which helps with building more interactions.

Chatbots play a crucial role for businesses due to their effortlessly handling of a plethora of customer queries and messages without any issue. They simply lessen the customer service workload for every one of us by on a hand by automating the hefty part of the process. However, they execute this by using their best techniques backed with Machine Learning, Artificial Intelligence, and Data Science. Besides, Chatbots work well by in-depth analysis of the input from the customer and then replying with a proper mapped response. If you wish to properly train the chatbot, you can employ Recurrent Neural Networks with the intents JSON dataset within the app can be swiftly handled greatly using Python. It doesn’t matter whether it be domain-specific or open-domain as it totally depends on its goal. The intelligence and accuracy of chatbots increase with the chatbots processing more interactions.

2. Credit Card Fraud Detection

Credit Card Fraud detection

Credit Card frauds are highly common these days and on average, we are on the way to cross a billion credit card users by the end of 2022. All thanks to the creativity in technologies such as Data Science, Machine Learning, and Artificial Intelligence, credit card companies are now allowed to successfully recognize and intercept these frauds with enough accuracy.

The basic idea behind this is to interpret and analyze the usual behavior of the customer involving mapping the location of those spendings to find the fraud transactions from the non-fraud ones. So, for this particular project, you can employ either R or Python with the customer’s transaction history as the dataset and ingest it into Artificial Neural Networks, decision trees, and Logistic Regression. Your overall accuracy can be enhanced if you feed more data to your system.

Also Read | Machine Learning Engineer v/s Data Scientist

3. Fake News Detection

There is no requirement for us to introduce you all to what fake news exactly is. In today’s scenario, it has become absolutely easy to share fake news over the web. You all must have seen false information being spread over the web from unauthorized sources that not only makes you face issues but also has the great potential to cause a huge level of panic and in some cases, violence. To stop this spread, it seems daunting but you need to recognize the authenticity of the information, which can be easily done utilizing this Data Science Project. For this, you can choose Python and develop a model with PassiveAggressiveClassifier and TfidVectorizer to divide the real news from the fake one. There are some Python Libraries that are well-suited for this data science project such as NumPy, Pandas, and Scikit-Learn, and for the Dataset, you can use News.csv.

4. Forest Fire Prediction

Developing a forest fire and wildfire prediction system will be another great utilization of the capabilities provided by Data Science. A forest fire or a wildfire is vitally an uncontrolled fire in a forest. Every single incident there has certainly caused a hefty amount of damage to not only nature however the animal habitat and human property as well. To control the chaotic nature of wildfires and even predicting them, you can utilize k-means clustering to recognize big fire hotspots and their intensity. This could be valuable in properly allocating resources. Moreover, you can also make good use of the meteorological data to know common periods, seasons for wildfires to improve your model’s accuracy.

5. Driver Drowsiness Detection

We all are aware of the amounts of road accidents occurring every year and their cause has been mostly the sleepy drivers. It is looked like a potential cause for accidents on the road, one of the finest ways to be safe is to apply a drowsiness detection system. Building a driver drowsiness detection system like this is yet another data science project that has the great potential to save a plethora of lives by constantly detecting the driver’s eyes and alerting him with alarms in case the system finds often closing of driver’s eyes. We require a webcam for this project specifically to permit the system to continuously monitoring the driver’s eyes. If we want this to happen in real, this Python project will demand a deep learning model and libraries like TensorFlow, OpenCV, Keras, and Pygame.

6. Gender Detection & Age Prediction

Now is the perfect chance to check your Computer Vision Skills and Machine Learning skills. This Gender Detection and Age Prediction project will develop a system that capture’s a person’s image and attempts to recognize their gender and age. You can apply Convolutional Neural Networks for this project and use Python along with the Open CV package. Besides, you can hold the audience dataset for this project. There are certain factors like lighting, makeup, facial expressions, that will make this a daunting job, and try to throw your model off, so keep these things in mind.

7. Sentiment Analysis

Find more details on data science tools.

Sentiment Analysis is a fine tool also known as opinion mining fully backed by Artificial Intelligence. It assists you to recognize, collect, and analyze people’s opinions about a certain subject or a thing. However, all these opinions could be from a bunch of different sources involving survey responses, online reviews, and could comprise a range of emotions like angry, happiness, positive, negative, love, excitement, and more. Sentiment Analysis is truly a thing for modern data-driven companies to benefit from as it provides a crucial insight about the people’s reaction over certain things supposes the dry run of a fresh product launch or a slight change in the business strategy. So, to develop this system, you can go for R with janeaustenR’s dataset along with the tidytext package.

Also Read | Best Examples Of Data Science In Finance

8. Customer Segmentation

We have seen modern businesses attempting by giving some great personalized services to their beloved customers, which eventually would not have been possible without some sort of customer categorization or call it segmentation. With this, companies have a chance to structure their services and products well around their customers while targeting them to push more revenue. You will need to use unsupervised learning for this project to arrange your customers into clusters based on person’s aspects like gender, age, religion, interests, etc. K-means clustering or hierarchical clustering will suit you here however, you also have a way to experiment with Fuzzy clustering or Density-based clustering methods. Furthermore, you can go for the Mall_Customers dataset as sample data.

9. Recognizing the Speech Emotions

Speech has been looked at as the most foundational way of expressing ourselves, and it certainly shields several emotions inside it, like joy, anger, calmness, and excitement, etc. By interpreting these emotions behind the speech, it is likely to use this information to reform our services and actions, and products to deliver a more personalized service to particular people. However, this Speech Recognition project puts you to make an effort to identify and pluck emotions from various sound files including human speech. For this, you need to use SoundFile, Librosa, NumPy, Scikit-learn, and PyAudio packages. Furthermore, for the dataset, go for Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS), which has around 7300 files to use.

10. Recommender Systems(Movie/Web Show Recommendation)

Have you ever thought of the way media platforms like Netflix, Amazon Prive Video, YouTube, etc. suggest to us what to binge next? For this, they employ a tool called the recommender/recommendation system. They consider various metrics into consideration for this like previously watched shows, age, most-watched genre, sees frequency, and puts them into a Machine Learning Model which then forms what the user might like to binge next. So, it all depends on your input data and preference and you can also develop either a content-based recommendation system or a collaborative filtering recommendation system. For this specific project, you can choose R with the MovieLens Dataset that embraces ratings for around 58,000 movies and you can use recommenderlabggplot2reshap2, and data.table for the packages.

11. Market Basket Analysis in Python using Apriori Algorithm

Whenever you head towards a retail supermarket, you will see a pizza base, beer, baby wipes, bread and butter, cheese, and chips are positioned collectively in the store for sales. This is what exactly market analysis is all about – knowing and analyzing the association among various products purchased together by customers. Market Basket analysis is a handy use case in the retail industry now that assists cross-sell products in a tangible outlet and also enables e-commerce businesses to suggest products to customers completely based on product associations. FP growth and Apriori are the most famous machine learning algorithms that are certainly used for association learning to execute market basket analysis.

This is a beginner-level project and here you have to execute Market Basket analysis in Python employing FP growth and Apriori algorithm based on rules to find some hidden insights on how to enhance product suggestions for customers. Here, along with all this, you will be able to implement metrics such as Lift, Support, and Confident to calculate the association rules.

12. Loan Default Prediction Project using Gradient Booster 

Loans are the basic source of revenue for banks as a hefty part of their profit comes in the form of interest on these loans. But, the loan approving procedure is quite accelerated with lots of validation, and verification is completely based on various factors. Also, after multiple verifications, banks are still not assured if an individual will be able to repay his loan without any hurdles. Now, almost every bank has employed machine learning to automate the loan eligibility procedure in real-time based on multiple factors like Marital and Job Status, Credit Score, Existing loans, Gender, Income, Total number of dependents, and expenses.

This specific data science project in the financial domain where you will create a predictive model to start the process of hitting the accurate applicants for loans. Besides, this issue is just the classification issue where you can use the information regarding a loan applicant to predict if they can really repay the loan or not. You will start with exploratory data analysis, along with pre-processing and finally testing the model you have built. When you reach the end of this project, you will build a robust understanding of solving classification problems with the help of machine learning.

Also Read | Open Source Projects For Beginners

13. Diabetic Retinopathy

Diabetic retinopathy happens by damage to the blood vessels in the tissue at the eye’s backside. The risk factor is uncontrolled blood sugar levels in your body. Some of its early symptoms are dark areas of vision, floaters, toughness in viewing colors, blurriness, etc. However, you can create an automatic procedure for diabetic retinopathy screening. Moreover, you can train a neural network on retina images of normal and affected individuals. This entire project will divide whether the patient has some symptoms of diabetic retinopathy or not.

Dataset: Diabetic Retinopathy Dataset

14. Handwritten Digit Recognition Project

Handwritten digit recognition is the working of computers to see identify human handwritten digits. It is mainly the answer to this specific problem that utilizes the image of a digit and identifies the digit already in the image. The MNIST dataset of these handwritten digits is broadly scattered among machine learning and data scientists enthusiasts. However, this project is an amazing thing to commence with data science and know well the complete processes included in the project. Furthermore, this data science project is applied using the Convolutional Neural Networks, and then for some real-time prediction that we create a great graphical user interface to draw digits on a canvas, and later the model will certainly predict the digit.

Language: Python

Dataset: MNIST

15. Image Caption Generator Project

Image Caption Generator Project is one of the best data science projects as telling what is there in an image is an easy job for humans. But, for computers, describing an image is just like a bunch of numbers that display the color value of every pixel. So, its a daunting task for computers to know what exactly is in the image and then creating the description in Natural language such as English is another tough job. Furthermore, this data science project employs in-depth learning techniques where we apply a Convolutional neural network (CNN) with Recurrent Neural Network (LSTM) to create the image caption generator.

Dataset: Flickr 8K

Language: Python

Framework: Keras

16. Breast Cancer Classification

If we look towards the medical contributions made by Data science, it’s overt that detecting breast cancer with Python is a thing. For this, we will use IDC_regular dataset to find the presence of Invasive Ductal Carcinoma, which is the most common form of breast cancer. However, it builds in a milk duct attacking the fibrous or some fatty breast tissue that is outside the duct. For classification, we have used Deep Learning and the Keras library for classification purposes.

Language: Python

Dataset/Package: IDC_regular

17. Uber Data Analysis in R

Uber Data Analysis is a data visualization data science project with ggplot2 where we will employ R and its various libraries and examine several other parameters such as trips by the hours in a day and trips within months in a year. However, we will employ the Uber Pickups in New York City dataset and make some visualizations for varied time-frames of the year. Moreover, this specifies how time affects customer trips.

Language: R

Dataset/Package: Uber Pickups in New York City dataset

Also Read | Beginner’s Guide To Python

18. Color Detection with Python

This happens more often with all of us that even after viewing, we face difficulty in recognizing the name of the color? You will see there are around 16 million colors entirely based on the various RGB color values however we only learn a few of them. So, here you learn how to develop an interactive and innovative application that will easily detect the chosen color from any image. Moreover, to apply this all, we require a labeled data of all the known colors then we will measure which color exactly matches the most with the chosen color value.

Language: Python

Dataset: Codebrainz Color Names

19. Detecting Parkinson’s Disease

With time, the application of data science is constantly occurring to enhance services in healthcare. Also, if we can determine a disease a bit early, it has many benefits on its prognosis. However, in this data science project idea, you will learn properly to find out Parkinson’s disease with the help of Python. Moreover, this is a neurodegenerative and a little progressive disorder of the main nervous system that affects every little movement and causes stiffness and tremors. All this deeply affects dopamine-generating neurons in your brain and every year, it tends to affect more than 1 million people across India.

Language: Python

Dataset/Package: UCI ML Parkinsons dataset

20. Road Lane Line Detection

We are aware that lines that are drawn on the road basically are for guiding the human drivers where exactly the lanes are. However, it shows you the direction the steer the vehicle. All this implementation is cardinal for building driverless cars. Furthermore, you can create an application that can recognize track lines from input images or consecutive video games.

To Sum Up

So, these are top 20 Data Science projects for beginners or experts that you should have knowledge about before choosing one for your company. Begin with building a data science project for your business by wise selection. We at Codersera, will help you in getting the source code of these data science projects.

FAQs

  1. What data science projects can you execute using R?

    Sentiment Analysis.
    Uber Data Analysis.
    Movie Recommendation System.
    Customer Segmentation.
    Credit Card Fraud Detection.
    Wine Preference Prediction.

  2. Which is better Python or R?

    Python is a good choice for machine learning and hefty-scale applications, mainly for analysing data within web applications. R programming is most suitable for statistical learning with unmatched libraries for experimentation and data exploration.

  3. What are the steps involved in building data science projects?

    Step 1: Define Problem Statement
    Step 2: Data Collection
    Step 3: Data Cleaning
    Step 4: Data Analysis and Exploration
    Step 5: Data Modelling
    Step 6: Optimization and Deployment

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