How to create a Data Science Project on GitHub?
Data Science being the most demanding career fields today with millions of job opportunities flooding in the market. in order to ensure that you have a great career in Data Science, one of the major requirements is to create and have a Github Data Science project.
Github is basically a distributed version control system that helps you in tracking changes within a source code during software development. Data Science projects require you perform different projects and track changes in your project using a version code. It helps you in having a systematic project and lists the modifications you make.
If you want to become an efficient Data Scientist and grab that job role you’ve been looking for, you need to work on Github for Data Science projects. Some of the Data Science Projects on Github that you work upon have been listed in this blog. Let’s take a look!
Top 10 Best Data Science Project on Github
1. Face Recognition
One of the most effective Github Projects on Data Science is a Face Recognition project that makes use of Deep Learning and Histogram of Oriented Gradients (HOG) algorithm. The system is designed specifically to find the faces in an image, align transformations using an ensemble of regression trees, face encoding and make predictions. You can make use of HOG algorithm for orientation gradients and use Python library for creating and viewing HOG representations.
2. Kaggle Bike Sharing
Bike-sharing systems is one of the best Data Science project on Github that allows you to book and rent motorbikes/bicycles and return them. The entire system is automated and is more like a Kaggle competition. It requires you to combine historical usage patterns with weather data for predicting the demand of rental services.
The primary goal of the Kaggle competition is creating an ML Model that can predict the total number of bikes rented. The first part requires you to focus on understanding, analysing and processing datasets; the second part is about designing the model using ML Library.
3. Identifying fraudulent Credit Card Transactions
Fraud Detection in credit card transactions is one of the best Data Science projects Github for beginners. The project will make you highly proficient in identification of the data patterns and anomalies. Within this project you can work with any dataset relevant to credit card transaction that contains fraudulent transactions as many as 500 for instance from a 300,000 of total transaction.
You start with data exploration for understanding the dataset structure and checking the missing values in a dataset using Pandas Library. It can be followed by data pre-processing handling the missing values, removing unnecessary variables and creating new features using feature engineering. The next step is to train ML models considering different ML algorithms and can be followed by evaluating the performance using different metrics like recall, precision, etc.
4. Sentiment Analysis on Twitter Data
The field of Twitter is famous for different kinds if data that makes it a good source for participation in tasks of learning and Data Science tasks. Accordingly, the project aims to analyse the sentiments behind the most popular channel, Twitter, using NLP.
The Data Science projects in Github will help you in gathering Twitter data using Streaming Twitter, API, MySQL, Python and Tweepy. You can then perform setiment analysis for identifying specific emotions and opinions. Monitoring these sentiment can help individuals or organisation to make better decisions and improve customer experiences.
Read Blog: How to Scrape Twitter Data using Python?
5. Analysing Netflix Movies and TV Shows
One of the most enticing real-world Data Science projects Github can include the project focusing to analyse Netflix movies and TV shows. Using Netflix user data, you need to undertake Data Analysis for running workflows like EDA, Data Visualization and interpretation.
The Data Science projects on Github aims to improve your skills and use libraries like Matpotlib, Seaborn and world cloud for interpreting Netflix data. For the project, you can also make use of Netflix Original Films and dataset scores from IMDb dataset available on Kaggle.
6. Customer Segmentation using K-Means Clustering
One of the most crucial uses of data science is customer segmentation. You will need to use the K-clustering method for this GitHub data mining project. This renowned unsupervised machine learning approach splits data into K clusters based on similarities. The purpose of the undertaking is to use the K-means clustering method to categorise clients visiting a mall depending on different factors such as their yearly earnings, spending habits, and so on.
To segment clients, you are going to have to collect data, do preparatory study and information pre-processing, and train and test a K-means clustering model. You can use a dataset on Mall customer segmentation that contains five characteristics and information on 200 customers.
7. Medical Diagnosis with Deep Learning
Deep learning is a recent branch of Machine Learning which consists of numerous layers of artificial neural networks. Due to its tremendous analysing abilities, it is frequently used for complicated applications. As a consequence, participating in a Github data science project, incorporating deep learning, will be extremely helpful for your Github data analyst portfolio.
Using deep-learning convolution models, this GitHub data science effort tries to identify multiple conditions in chest X-rays. After finishing, you should have a good understanding of how deep learning/machine learning is utilised in radiography.
8. Predicting Housing Prices with Machine Learning
One of the most popular data analyst projects on GitHub is house price prediction. The purpose of this project is to forecast house values based on a variety of parameters and investigate the relationships between them. After finishing this course, you will be able to interpret how each of these factors influences house prices.
You will use a dataset with more than 13 elements, such as ID (to count the records), zones, area (lot size in square feet), build type (kind of housing), year of construction, year of remodelling (if valid), and sale price (to be projected).
9. DeepCTR
DeepCTR promotes it as a “easy-to-use, modular, and extendible package of Deep Learning-based CTR models.” It additionally provides an array of additional helpful functions and layers to generate customised models. TensorFlow was employed to create the DeepCTR project.
While TensorFlow is an excellent tool, it is not for everyone. As a consequence, the DeepCTR-Torch library came into existence. The most recent version includes the entire DeepCTR code for PyTorch.
10. StringSifter
If you are interested in cybersecurity, then you are going to enjoy being involved with this project! StringSifter, a machine learning tool developed by FireEye, can intelligently rank strings based on their analysis of malware significance.
Strings are usually present in ordinary computer programmes to carry out certain activities such as generating a registry key, copying information from one spot to another, and so on. StringSifter is an excellent tool for preventing cyber threats. StringSifter, on the contrary hand, requires Python 3.6 or greater for operations and download.
Conclusion
In conclusion, the above blog has been able to present you with top best Data Science projects on Github that you can do as part of developing your Github profile. These projects are excellent and can help you with acquiring jobs effectively. Moreover, you can also opt for Data Science courses from Pickl.AI like the Advanced Data Science course with Capstone project. Through the course you can be part of a Capstone Project that can help you develop your Github profile, thus, opening up job opportunities for you.
FAQs
Is GitHub good for Data Science?
Github is one of the most integral tools for Data Science practitioners which can help you create Data Science projects. You can make modifications in the codes and further improvise your Data Science project project on Github using Linear Model.
Is GitHub projects free?
Github can offer you to create projects which are both free and paid products for software development is a great fit for open-source projects.
Should I put beginner projects in GitHub?
Yes, even as a beginner, you can upload your Data Science projects on Github and have your work backed-up thus, keeping track of all your work and make changes whenever necessary.
Should I put my GitHub on my resume?
While putting up your Github is not a requirement for your resume, you can always include it to make your resume impressive as it helps in demonstrating your skills efficiently.
