Data Science Projects For Beginners

  • Siddhartha
  • May 10, 2020
Data Science Projects For Beginners

 

The Economist has claimed that the world’s most valuable resource is no longer oil but DATA. The amount of data generated and collected through sources such as sensors and user activities on the Internet has given rise to a new digital economy. As the world continues to become more data-centric with the advent of new technologies, the profession of data scientists continues to become more demanding. Termed as Harvard Business Review’s ‘sexiest job of the 21st century,’ data science has proven to be the most vital and sought after job role by most leading companies today. The combination of factors including the boom in data collection, development of algorithms to model this data and increasingly cheap computing power have enabled data scientists to become an integral part of organizations today.

This post provides a list of six data science projects that cover different aspects related to data science. Whether you have just completed a data science course or just getting started with data science, implementing such projects provides a vigorous understanding and experience with core concepts required in data science.

Project 1: Credit Card Fraud Detection

 

               Credit Card Fraud Detection data science projects

 

The role of credit cards as a method of transaction has gained a lot of popularity over the years as the world aims to be a cashless society. However, it is also important to consider that credit card fraud is ranked as the most common kind of identity theft fraud. One of the principal tasks that can be done by machine learning algorithms is classification. Every credit card transaction results in the generation of some data that can be used by machine learning algorithms to develop a classifier. Using such a classifier in real-time can help detect fraudulent transactions almost immediately resulting in not only time being saved but also money.


DatasetCredit Card Fraud Detection Data
Concept: Classification Algorithms

Project 2: House Price Prediction

 

               House Price Prediction using Regression Algorithms

 

Data science deals with two kinds of statistics – descriptive statistics and inferential statistics. Inferential statistics help predict results from unseen data using previously known data. The Boston housing dataset contains such data that can be used to predict the median value of owner-occupied homes by applying machine learning algorithms. Machine learning algorithms, specifically regression-based algorithms can extract patterns from the data and use these patterns to process new information and predict a real value. This dataset can help explore and understand different regression-based algorithms.


DatasetHouse Price Prediction Data
Concept: Regression Algorithms

Project 3: Customer Segmentation

 

               Customer Segmentation using Clustering Algorithms

 

Customer segmentation is the grouping of market customers that share similar characteristics into collections. Customer segmentation according to their characteristics can be a huge advantage to developing uniquely appealing products. Promoting products to a particular customer segment can be more advantageous than advertising to less interested customers. Predicting a customer’s spending patterns according to the cluster they are classified into can be of significant business value. Clustering algorithms in machine learning help with clustering i.e. grouping similar data points. The dataset for customer segmentation contains attributes such as gender, age, annual income, and spending score that can help grouping customers that share a common pattern. Using data science for clustering is very beneficial for predictive analysis.


Dataset: Customer Segmentation Data
Concept: Clustering Algorithms

Project 4: Gender Detection & Age Prediction

 

               Gender Detection using Convolutional Neural Networks

 

An important form of data that data scientists may have to work with is images, especially images of people. The rise of deep learning algorithms and computer vision algorithms have allowed data scientists to be able to detect and extract a person’s facial features from images. Deep learning models that involve neural networks contain several convolutional layers that make it easier to extract information from images. A combination of a convolutional neural network and a classifier can help extract facial data from images and predict their age and gender. Hence, developing this project can be a good introduction to CNNs and image processing methods.


Dataset: Gender and Age Classification Data
Concept: Convolutional Neural Networks

Project 5: Movie Recommendation Engine

 

               Movie Recommendation data science projects

Recommendations are another type of prediction that a data scientist is now capable to develop using data. Recommendation engines are most commonly used in e-commerce sites and proven to have immense business value. Content streaming sites like Netflix are able to suggest movies using previous customers watch history and patterns from other similar users. This corresponds to the two most common types of recommendation systems – content based filtering and collaborative filtering. Developing this project involves building a recommendation engine that recommends other movies based on a specific movie.


Dataset: Movie Recommendation Engine Data
Concept: Recommendation Systems

Project 6: Sarcastic News Detection

 

               Sarcastic News using Natural Language Processing

 

Data scientists use machine learning and deep learning tools such as natural language processing to enable machines to detect sentiment from text-based data. One such sentiment is sarcasm, which admittedly even humans find difficult to detect. Websites such as The Onion post satirical news articles that many people mistake to be real headlines, leading to misinformation. This project involves developing a classification model to classify a news headline as sarcastic or not sarcastic. Building such a model will introduce important NLP concepts such as word embeddings and LSTMs in neural networks.


Dataset: Sarcastic News Detection Data
Concept: Natural Language Processing

Hopefully, this post provides some helpful ideas to get started with data science projects. Coming shortly, we will be uploading detailed posts on the implementation of these six projects and hopefully clarify these core Data Science concepts. Do make sure to comment below and let us know which project you are looking forward to, along with your email ID so we can notify you right away. FavTutor is always here to provide you with help from expert tutors 24/7.

 

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