As a career path for this era, Data Science continues to grow in popularity. There are many exciting and exciting options available. There are several numbers of companies that are looking to hire Data Scientists and the number is growing nowadays.
In case you’re interested in Data Science and want to gain a deeper understanding of the technology, now is the perfect time to develop your skills to manage the upcoming issues.
Though the terminology and concepts used in the field can be overwhelming at first, you will soon become acquainted with them with regular practice. Consider becoming a Data Scientist if you are interested.
I’ve listed 15 open-source data science projects here that you may use to get started learning.
1. Detecting Fake News with Python
Yellow journalism, or fake news, is the spread of false information and hoaxes using social media and other online means to promote political objectives.
Using Python, we will build a model to detect whether a piece of news is real or fake in this data science project idea. To classify news into “Real” and “Fake”, we will build a TfidfVectorizer and employ a PassiveAggressiveClassifier. Jupyter Lab will be used to run everything we do with a dataset of shape 7796*4.
Source Code – https://github.com/nishitpatel01/Fake_News_Detection
2. Road Lane Line Detection with Python
Human drivers follow the lane lines on roads. In addition, they steer the vehicle in a specific direction. Developing driverless cars relies heavily on this application.
An application can be created that identifies track lines from continuous video or input images.
For self-driving cars to be developed, this Data Science Application is imperative.
Source Code – https://github.com/amusi/awesome-lane-detection
3. Sentiment Analysis
Sentiment analysis is the process of analyzing the words in a text to determine whether they reflect positive or negative sentiments and opinions.
In this data science project, we will implement a classification where the classes can be binary (positive and negative) or multiple (happy, angry, sad, disgusted, etc.).
R will be used to implement this data science project and we will be using the dataset provided by the package Jane Austen. AFINN, Bing, and Loughran will be used since they are general-purpose lexicons. After we perform an inner join, we will display the result in a word cloud.
Source Code – https://github.com/yashspr/sentiment_analysis_ml_part
4. Gender and Age Detection with Data Science
Python is used in this data science project. With just one picture, you can predict an individual’s age and gender. The purpose of this lecture is to introduce you to the fundamentals of computer vision. For the Adience dataset, we will build a Convolutional Neural Network using models trained by Tal Hassner and Gil Levi. In addition to .pb, .pbtxt, .prototxt, and .caffemodel, we will use some other files along the way.
Source Code – https://github.com/smahesh29/Gender-and-Age-Detection
5. Diabetic Retinopathy
The leading cause of blindness in diabetes is diabetic retinopathy. This condition can be detected automatically if a screening method is developed. Images of normal and affected retinas can be used to train neural networks.
Patients will be classified according to whether they have retinopathy or not.
Source Code – https://github.com/rsk97/Diabetic-Retinopathy-Detection
6. Detection of Drowsiness in Drivers
Driving while drowsy is extremely dangerous, and there are thousands of accidents caused by driver fatigue each year. We are going to construct a system that detects sleepy drivers in Python and alerts them through a beeping alarm.
Using Keras and OpenCV, this project has been implemented. By using OpenCV, we will be able to detect faces and eyes, and by using Keras, we will be able to classify the state of the eye (Open or Closed) by using deep neural network techniques.
Source Code – https://github.com/topics/driver-drowsiness-detection
7. Chatbot Project in Python
Businesses can’t function without chatbots. A lot of businesses require a lot of time, effort, and manpower to deal with customers.
By answering some of the frequently asked questions by customers, chatbots can automate most customer interactions.
Chatbots can be divided into two categories: domain-specific and open-domain. This type of chatbot is commonly used to solve a problem specific to a particular domain. For it to be effective, you need to customize it cleverly.
It requires a lot of data to train the Open-domain chatbots since they can answer any question.
8. Credit Card Fraud Detection Project
In the context of the project, we will be using algorithms such as Decision Trees, Logistic Regression, Artificial Neural Networks, Gradient Boosting Classifiers, and Logistic Regression. Our goal is to classify the transactions made with credit cards into fraudulent and genuine ones based on the Card Transactions dataset.
For each model, we will plot performance curves showing how the models perform.
9. Customer Segmentation
Among the most popular projects in Data Science is this one. Companies create multiple customer segments before running any marketing campaign.
Unsupervised learning is a popular method for creating customer segments. To target potential customers, companies identify segments of customers using clustering. Customers are divided into groups based on traits like gender, age, interests, and spending habits so that marketing can be targeted to each group effectively. K-means clustering will be used, as well as a visualization of age and gender distributions. Income and expenditure data will be analyzed following this.
Source Code – https://github.com/jalajthanaki/Customer_segmentation
10. Project on Breast Cancer Classification
We’ll also learn how to detect breast cancer using Python, a medical contribution of data science. IDC_regular will be used to test for invasive ductal carcinoma, the most common form of breast cancer.
Breast cancer occurs when an invading cell invades an external milk duct. As part of this data science project idea, we’ll use the Keras library and Deep Learning for classification.
11. Traffic Signs Recognition
Traffic signs and rules must be followed by every driver to avoid accidents. A driver needs to know how the sign looks to follow it. Before a person is allowed to drive, they must learn all the traffic signs.
The rise of autonomous vehicles will eliminate the need for human drivers shortly. By taking an image as input for the Traffic signs recognition project, you will learn how to make a program identify the type of traffic sign.
To recognize traffic signs by class, a Deep Neural Network is trained on the German Traffic signs recognition Benchmarked dataset (GTSRB). To facilitate interaction with the application, a simple GUI is built.
Source Code – https://github.com/topics/traffic-sign-recognition
12. Recommendation System for Films
This data science project uses the language R to generate recommendations based on machine learning. Based on the browsing history and interests of other users, recommendation systems send out suggestions to users. In this way, the platform can engage customers more effectively.
Source Code – https://github.com/topics/traffic-sign-recognition
13. Speech Recognition through the Emotions
We use speech as a fundamental strategy for communicating and it involves a variety of emotions, such as silence, anger, happiness, passion, etc.
A person’s emotional state can be used to reorganize his or her emotions, the service they provide, and the end products offered to create a personalized service by evaluating their emotions.
This project’s primary goal is to identify and get the emotions from multiple sound files containing human speech. The SoundFile, Librosa, NumPy, Scikit-learn, and PyAaudio Python packages are all capable of producing something similar. Additionally, RAVDESS, a Ryerson Audio-Visual Database of Emotional Speech and Song, contains over 7300 files.
Source Code – https://github.com/topics/speech-emotion-recognition
14. Resume Parser Using NLP
This data science project involves building an NLP algorithm that parses resumes looking for the words (skills) in the job description. To match the words and phrases in the resume documents, you will use the Phrase Matcher feature of the NLP library Spacy.
This helps recruiters screen ideal candidates for job openings by counting the occurrence of words (skills) for each resume under various categories.
15. Detecting Forest Fire
An alternatively good example of demonstrating one’s Data Science skills would be identifying the forest fire and wildfire system. Wildfires are fires that develop in forests that are uncontrollable. During weekends, all that forest fir will create havoc in animal habitats, surrounding environments, and human property.
By using k-means clustering, key hotspots during forest fires can be identified and reduced, regulated, or even predicted. This enables the necessary resources to be allocated. To improve the accuracy of the model, climatological data should be used to determine the common times and seasons for wildfires.
Source Code – https://github.com/Skar0/fire-detection
The above-mentioned Data Science project ideas may show to be most suitable for you to learn something new and exciting. Decide which project idea thrills you more and guides you further.
There are a few easy projects and a few tricky ones, but your job is to tinker with every project to discover your hidden skills and talents. They also help if you land your dream job.