Awesome Data Science Project Portfolio

Working on some cool and creative projects shows a higher level of skills in technical skills. Convincing an employer to hire you can be quit challenging if your portfolio is below average. Data science field has a million of awesome projects. Displaying awesome line of codes and nice models keeps you on the safer side of getting that dream job. The best thing about Data science is that projects can never be exhausted, so basically you can use this sector to solve the very basic challenge you meet every day.
Some simple tricks to build your portfolio as a data science are checking job listings to determine what your future employer is going to expect from you, generating project ideas and this is basically answering that answered question posing a challenge in a real life situation, determining some very useful resources as a developer and keep going every day. Below are some top five projects that are cool and I recommend them for intermediate level data science developers.

1. Customer’s Loan Eligibity System.

This sounds really cool! Developing a model that checks if a client is eligible to get financial loan from a financial institution like a bank or microfinance is very possible using algorithms. Algorithms to be used here include Logistic Regression, Native Bayes and Random Forest. To implement a successful model you need Python and R languages. Steps followed by this system are;

 Client files the loan application form
 Submission at the institution
 Checking of his/her Credit level score
• A low score will lead to loan decline
• High score leads to loan Approval

1. Forest Fire Prediction.

Forest fire poses a great challenge to the global climate condition. In an event of such fires, millions of hectares are destroyed by fire and wildlife habitat is destroyed. Also these fires may kill wild animals as well as humans who depend on this forest for their daily living; these humans are game wardens, and other forest workers. As a data scientist it is our duty to keep our environment clean and healthy.
Implementing these projects needs Artificial Neural Networks and Support Vector Machine algorithms. Languages to help you achieve this success are Python and R. Some areas are more prone to forest fire outbreaks, developing a model that predicts the next location of a forest fire can be a millstone in the world anti-forest fire campaign. Globally forest fires area threat to every government especially during the sunny seasons. Stages for this project are;

 Dataset
 Data Exploration
 Training the model
 Model Evaluation
 Visualization predictions

1. Climate Change Visualization and Prediction.

Sometimes predicting the condition of the weather tricks forecasters and might lead to catastrophic events. Wrong weather forecasting leads to confusion; also sometimes the weather tricks us! A model that predicts the climatic conditions is a cool project to work on especially in 2021 when everyone has embraced online and working from home. Such a model tells you what time to go for a date and when to expect harsh low temperatures; this will make you buy some coffee. Algorithms to use in this model include Support Vector Machine and Decision tree. Actually data science will turn your career to a weather researcher! Having a proper background knowledge and understanding on python and R languages is all you need for this project. Steps followed for this success are;

 Dataset
 Visualize and find insights
 Predict the future climate data.

1. Driver Drowsiness Detection Being safe is a right of every data scientist, oh! I mean everyone. A system that detects if a driver is drowsy sounds very cautious. Using Python with Haar and Support Vector Machine Algorithms can make this very much possible. This is even easier compared to the top mentioned projects. PDC, security officers and other road monitors as well as users can use this idea to limit road accidents and traffic congestion to some point. Steps involved are;  Using Webcam  Detecting Driver’s Face  ROI extraction  Tracking the eyes(if eyes closed longer than threshold then)  Alert
2. Fake Job Listings Detections. The internet today has a million of fake job in advertisement. As a data science you should be on the frontline to recognize such jobs. Every day such jobs are posted and aimed for the target audience. Majority of them look so legitimate and they aim at getting applicants personal details and ask them to make payment also called the application fee. Millions of people lose a lot of money this way. We as data science can solve this problem by developing a model that figures out which internet job is legit and identifying a scam. Python and R languages can be used here. Algorithms you should apply for this project are Support Vector Machine and Decision Tree. To achieve this success you should follow the following steps;  Training and Testing Dataset  Pre-processing(tokenizing, stemming and removing stop words)  Feature extraction.  Classification of the Job(Fake or Real)