Top 9 Data Science Projects – Data Science Projects for Practice

Data Scientists are one of the most coveted IT professionals performing some of the most crucial activities that have direct impact on the business. Therefore, companies look for professionals who have rich experience in handling large datasets, complex coding, and lengthy calculations. Most importantly, professionals, who have experience in working in real-world business scenarios and critical business problems, are in-demand.

There are many Professionals with relevant skills knowledge; however, they still find it difficult to get their first job. The main differentiator is the kind of experience required and this is where working on relevant Data Science Projects becomes very crucial.

Therefore, it is always recommended that aspirants should get a hands-on experience of working on various projects as a freelancer. It creates a positive impression on the interviewer.

Hence, we are presenting a list of Data Science projects which can set you on the course of success.

Getting a better hold of Data Science Projects will be even easier, if you have a solid understanding of the fundamentals. Read the blog:

What is Data Science | Start Your Career in Data Science Today!

Data Science Projects for Beginners

This blog will highlight 9 major Data Science Projects that an aspirant should work on! Keep scrolling down!


Credit Card Fraud Detection

Detecting the fraudulent activities is one of the major risk assessments done by banking and financial institutions. Credit Card Fraud Detection is a primary activity that is conducted by the team of Data Scientists to investigate and find out the suspicious transactions with the help of Machine Learning.


Some of the metrics that can be used to determine the unusual behavior are User Zone, Product Category, Client’s Behavioral Pattern, etc. The data set for this project can be easily found on Kaggle. All you have to do it run the information through a trained model and classify the outcome to analyze whether a transaction is fraudulent or not.

Algorithms to be Used

Some of the algorithms used in this project are Decision Trees, Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier.

Language(s) to be Used

This project can be easily implemented using Python, but you can also use R.

Customer Segmentation

Customer Segmentation is one of the most popular practices that every business adopts in order to understand the taste and references of each segment clearly. It helps companies customize the strategies as per the target audience.

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Though the segmentation differs based on the purpose that is being tried to solve. However, the most common bases if segmentation are Age, Gender, geography, interests, etc., so that targeted promotions can be done.

Some of the environments and tools used in developing this project are:

    1. Scikit-learn
    2. Seaborn
    3. Numpy
    4. Pandas
    5. Matplotlib

Algorithms to be Used

K-means Clustering Algorithm is applied to group data points into distinct subgroups. Customer Segmentation is one of the major applications of this algorithm.

Language(s) to be Used

This project can be developed using both Python and R.


Sentiment Analysis

Any business especially ecommerce companies show a keen interest in knowing the sentiment of customers after purchasing products online. Similar interest has been trending in hospitality industry, travel and tourism, and human resource also.


Not only this, social media platforms like Facebook, LinkedIn, and Twitter have augmented their codes with Machine Learning to analyze the sentiments of common people for each post. Therefore, any aspirant who wishes to master Data Science should definitely go for Sentiment Analysis.

Algorithms to be Used

You can develop this project using following three algorithms:

    1. Naive Bayes
    2. Decision trees
    3. Package Tidytext

Language(s) to be Used

Sentiment Analysis can be developed using both R and Python.

Speech Emotion Recognition

Speech Recognition is a new trend in searches. Google, Amazon, and Apple all the top firms are adding voice search options in the applications. Whether it is email, or search, or music applications, all have voice search options enabled in them.


Nowadays speech recognition can also be embedded with sentiment analysis. Speech Emotion Analysis is a latest addition in Data Science, which seeks to analyze the emotions in human voice. SER does it through extracting the features from the audio recordings. In this various sound files are used as a data set.

Algorithms to be Used

Some of the algorithms to be used in this project are:

    1. Convolutional Neural Network (CNN)
    2. Recurrent neural networks (RNN)
    3. Neural Network (NN)
    4. Gaussian mixture model (GMM)
    5. Support Vector Machine (SVM)

Language(s) to be Used

This project can be developed in Python.

Predictive Analytics

Predictive Analytics is statistical analysis which takes historical and current data to predict the unknown future events. Predictive Analytics is used in a plenty of different fields like equipment management, customer service and support, retail, quality assurance, customer support and employee retention and many more to analyze a lot of aspects.

Algorithms to be Used

The most prominently used algorithm for Predictive Analytics are Logistic Regression and Neural Network. Some of the other algorithms are Linear Models, Decision Trees, Support Vector Machines, and Naïve Bayes.

Language(s) to be Used

This project can be developed in both R and Python languages.

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Time series Analysis and Modeling

Time Series Analysis is a widely used Data Science technique that is used in a plethora of applications such as weather forecasting, predicting sales, analyzing year trends, predicting tractions, website traffic, competition position, and so much more.

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Time Series is a statistical calculation that records the occurrence of a particular phenomenon or event over a certain period of time so that future trends and patterns can be identified.

Algorithms to be Used

Some of the models used in Time Series Analysis are Autoregressive Integrated Moving Average (ARIMA), K-Means, Support Vector Machine, Neural Nets, etc.

Language(s) to be Used

You can develop this project using both R and Python.

Regression Analysis

Regression Analysis is a technique that measures the closeness of an independent variable to a dependent variable. It is conducted to predict the value of an independent variable based on historical data.

It is one of the strongest techniques that are the examination of the various variables to understand the relationship. The motive behind carrying out this analysis is to predict the outcomes on the basis of historical data.

Algorithms to be Used

Some of the algorithms used in Time Series are-

    1. CART — Factor target
    2. Decision Trees — Factor target
    3. Linear Regression — Numeric target
    4. Logistic Regression — Factor target

Language(s) to be Used

Some of the language for this program!

Movie Recommendation System

Recommendation System is a technique that suggests its users with various content based on their taste and preferences. It typically analyzes the selection history of a user and recommends various related content to him.


You might have encountered it on ecommerce platforms. When you add a particular product in the cart, it suggests product that users also buy along with that, and products that are similar in nature. Similarly, on video streaming platforms, when a user watches a particular content, the platform starts suggesting related content to the viewer.

Algorithms to be Used

Some of the algorithms used in this project are Collaborative filtering, Matrix decomposition for recommendations, Clustering, and Deep Neural Networks. 

Language(s) to be Used

This project can be built using R programming language.

Chatbot

Chatbots are the smart technology that automates the process of answering the most frequently asked questions asked by the customers of a business. The Chatbots are developed using Artificial Intelligence which needs to be customized as per the domain to use it effectively and integrate into the business. 

Most of the companies deploy Chatbots to address the questions of their customers and track the leads from the interactions. This turns out to be specifically useful for the sales operations.

Some of the libraries used in developing chatbots are-

  • train_chatbot.py
  • chatgui.py
  • classes.pkl
  • words.pkl
  • intents.json
  • chatbot_model.h5

Language(s) to be Used

This project is to be developed in Python.

Conclusion

With quarantine seem to be like going on forever, this is the right time aspiring Data Scientists can indulge themselves into projects that can help them enhance their skills and make them well-acquainted to the real-world scenarios. These projects will not only help the candidate upskill but will also provide them a competitive advantage.

Hence, get started without further delay!

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