Top 10 Resume-Building Data Science Projects with Source Code

The top 10 data science projects with source code can help you to make your resume stronger.

Have you attempted to develop any data science projects to boost your CV but been overwhelmed by the complexity of the code and the number of ideas required? Is it too far away, and has it dashed your hopes of becoming data scientists? This article will discuss the top 10 data science projects with source code, allowing anyone to engage in real-time data science initiatives. These will improve your confidence while also demonstrating to the interviewer that you are serious about data science.

Detection of Fake News

False information and hoaxes are distributed through social media and other online media to achieve a political objective, making fake news the king of yellow journalism. Python is used to construct a model that can reliably determine whether a piece of news is authentic or false in this one of the best data science projects. To classify news into "Real" and "Fake," a TfidfVectorizer will be developed, and a PassiveAggressiveClassifier will be utilized. The dataset will be of shape 77964, and everything in Jupyter Lab will be run.

Language: Python

Dataset: news.csv

Sentiment Analysis:

The act of evaluating words to discover sentiments and views that may be positive or negative in polarity is known as sentiment analysis. This is a form of categorization in which the classifications are either binary (positive or negative) or multiple (happy, angry, sad, disgusted, and many more). The dataset from the 'janeaustenR' package will be utilized in this one of the top data science projects, which will be implemented in the language R. AFINN, bing, and Loughran will be utilized as general-purpose lexicons, and an inner join will be conducted, followed by the creation of a word cloud to illustrate the results.

Language: R

Dataset: janeaustenR

Parkinson's Disease Detection

Data science is being utilized to enhance healthcare and services so that diseases can be predicted early. It offers several benefits in terms of prognosis. As a result, you may learn how to diagnose Parkinson's Disease with Python in this one of the top data science projects ideas. This is a central nervous system neurodegenerative illness that impairs mobility and produces tremors and stiffness. This damages the brain's dopamine-producing neurons, and it affects more than 1 million people in India each year.

Language: Python

Dataset: UCI ML Parkinsons dataset

Color Detection with Python:

Among the data science projects ideas, this is one of them. Based on the varied RGB color values, there may be 16 million hues, but we only recall a handful. As a result, you may create an interactive app that will identify the selected color from any image in this project. To do so, you'll need labeled data for all of the known colors, and then you'll be able to figure out which color most closely resembles the specified color value.

Language: Python

Dataset: Codebrainz Color Names

Recognition of Emotions in Speech

Speech Emotion Recognition is performed using librosa in this one of the top data science projects. The practice of attempting to detect human emotion and emotional states from speech is known as speech recognition. SER is feasible because humans utilize tone and pitch to communicate emotion through speech, but it is difficult since emotions are subjective, and annotating audio is difficult. The RAVDESS dataset and the mfcc, chroma, and mel characteristics will be utilized to identify the emotion. For the model, an MLPClassifier will be created.

Language: Python

Dataset: RAVDESS dataset

Data Science for Gender and Age Detection

This is one of the intriguing Python data science projects. You'll learn to guess a person's gender and age range based on only one photograph. Computer Vision and its fundamentals will be covered in this course. A convolutional neural network will be created, and models trained on the Adience dataset by Tal Hassner and Gil Levi will be used. Along the process,. pb, .pbtxt ,.prototxt, and .caffemodel files will be utilized.

Language: Python

Dataset: Adience

Uber Data Analysis in R:

This is a ggplot2 data visualization project in which R and its libraries will be used to evaluate various factors such as travels by the hours of the day and trips by the months of the year. The Uber pickups in New York City dataset will be used to build visualizations for various periods throughout the year. This will show how the passage of time impacts customer journeys.

Language: R

Dataset: Uber Pickups in New York City dataset

Python Chatbot Project

Chatbots are a necessary component of every organization. Many organizations must provide services to their consumers, which necessitates a significant amount of labor, time, and effort. Chatbots can automate the majority of client interactions by addressing some of the most frequently requested queries. Domain-specific and open-domain chatbots are the two main types of chatbots. A domain-specific chatbot is frequently used to solve a specific issue. As a result, for it to operate successfully in your domain, you'll need to tweak it carefully. Because open-domain chatbots may be asked any sort of inquiry, training them necessitates a large quantity of data.

Language: Python

Dataset: Intents json file

Project to Recognize Handwritten Digits

Data scientists and machine learning aficionados are familiar with the MNIST collection of handwritten digits. It's a fantastic project for learning about data science and the processes involved in a project. The project will be developed using convolutional neural networks, and then a pleasant graphical user interface will be constructed to draw digits on a canvas, and the model will predict the digit in real-time.

Language: Python

Dataset: MNIST

Credit Card Fraud Detection Project

Decision trees, logistic regression, artificial neural networks, and gradient boosting classifiers are among the methods utilized in this one of the best data science projects. The card transactions dataset will be used to distinguish between fraudulent and legitimate credit card transactions.