Top 5 Real-World Data Science Projects

The field of data science has gained much popularity over the past few years due to the fact that it helps organizations solve real-life problems by utilizing the real-world data also leading to an exponential growth in demand for data scientists.

Most of the tech-savvy people prefer a certification in data science for the mentioned reasons but theoretical knowledge is never considered enough. One’s in-depth understanding of the real-world problems and their ability to solve them is what makes a difference and thus, it is always an advantage to add some real-world data science projects on your resume.

In order to make things easy for you, we have added top 5 real-world data science projects for you to practice and eventually enhance your profile. They are discussed in detail further.

1. Fake News Detection

We live in a world where information flows to us from all the different kinds of sources constantly but all the information that flows to us is neither relevant nor genuine. Hence, it becomes important for us to filter what we read, in other words, detecting whether the news is fake or not is of utmost importance.

Data Scientist John Wales figured out a solution to this problem by building a model using Python and using various algorithms like linear regression, decision tree etc. to figure out if the news is genuine.

Click here for the dataset used in this project and here for the github repository of this project to practice and see for yourself how it works.

Click here to view the final product created by John Wales.


2. Uber’s Data Analysis

FiveThirtyEight, also rendered as 538, now owned by ABC news is a website that was launched on 7th March 2008 focusing mainly on opinion polls analysis, politics, sports, economics and blogging and they conducted an analysis on Uber by obtaining its data.

They analysed this data to give an insight on how Uber affects taxis and the public transport and is considered as one of the top projects in data science.

Click here to visit the original github repository of FiveThirtyEight and here and here for the articles that they published related to their analysis to understand this project in depth.


3. Movie Recommendation

Recommendation engines have been proved to be the most useful tools when it comes to providing customised experience to users and thus are one of the top 5 data science projects.

The Movie Lens recommendation system can be built using the Movie Lens 1M (comprising of nearly 1 million rows) dataset which was released by By analysing this data, new movies can be recommended to the users based on their interests and tastes after understanding the trends and patterns through analysis.

You can click here for the link to the dataset and code for the same and try it yourself to get a better understanding of this project.


4. Customer Segmentation

In today’s world, customer is considered as the king of marketing and thus analysing the customer for providing them customised experience and offers becomes important and this helps the organizations to ultimately expand their customer base too.

Customer Segmentation is a way of analysing the customers by segmenting them into various classes based on their purchase patterns, interests, age, preferences, demographics and other innumerable factors as required and almost all companies today look forward to analysing the customers by segmenting them into various groups or classes thereby making customer segmentation another hot topic of data science.

Click here for the link to the github repository containing the codes and dataset for Customer segmentation. Retentioneering tool, which is a python framework has been used in this project to segment the customers. A detailed information about this tool is also given in the repository.


5. Amazon V/S Flipkart: Book Price Comparison

Visiting different websites to compare the prices of product would be considered as one of the most tedious tasks and fortunately, this problem of price comparison can also be solved using data science. A dataset was thus uploaded by Kaggle for the data scientists to compare the prices of books on Amazon and Flipkart and analyse that which of the two is cheaper.

Click here for the link to the dataset as well as the code for the same for your practice and understanding.

You can also expand the scope of this project by later adding in more products for comparison.  

We hope that you liked these projects and will add few to your curriculum for analysis as practical knowledge is what makes a difference and helps you outshine the others. We wish you a Happy Learning!!

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