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Table of contents
1.
Introduction
2.
Breast Cancer Detection
3.
Titanic Dataset
4.
House Price Prediction
5.
MNIST Handwritten Digit Recognition
6.
Iris Dataset
7.
Sentiment Analysis
8.
Fake News Detection
9.
Data Science Project on Detecting Forest Fire
10.
YouTube Comments Analysis
11.
Dogecoin Cryptocurrency Prices Predictor with LSTM
12.
Frequently Asked Questions
12.1.
What are some data science projects?
12.2.
How do I start a data science project?
12.3.
What is considered a data science project?
12.4.
Where can I practice data science?
13.
Conclusion
Last Updated: Mar 27, 2024
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Top 10 Data Science Projects

Author Juhi Sinha
1 upvote
Crack Google SDE interview : Essential projects
Speaker
Saurav Prateek
SDE-2 @
20 Jun, 2024 @ 01:30 PM

Introduction

Data science is a vast subject of study and experiment. Recently, data science gained the tag of the sexiest job of the 21st century. The buzz around it and the curiosity to learn data science might have made you learn various prerequisites to learn data science such as algebra, machine learning, calculus, and statistics. With this much knowledge and information to consolidate, you can truly materialise your understanding of data science by building projects.

Data Science Project

Data Science is all about extracting pattern and information using various statistical models and machine learning practices. It is really useful to build projects to show your expertise in the subject of study. These data science projects will establish a solid base for your job applications as a data scientist.

Breast Cancer Detection

Breast cancer is one of the most common cancers among women around the globe. It is a topic of research to classify the tumours into malignant (cancerous) and benign(non-cancerous). The classification of the cells based on the complex features of the tumour can be done using machine learning methodology. The UCI Machine learning repository has a breast cancer dataset.

The dataset consists of attributes namely IDnumber, and Diagnosis(M=malignant, B=benign). The features that you will be using to train your model will be the cell nucleus features such as radius, texture, perimeter, area, smoothness, compactness, concavity, concave point, symmetry and fractal dimension.

You will have to select parameters that are most helpful in the classification of cancer cells. The various steps that will be involved will range from data exploration, data modification, data splitting, model selection, training and testing.

You can have various classification models to check the accuracy that each one of them gives. The models that you should consider are Logistic regression, nearest neighbour algorithm, support vector machine, kernel SVM, naive Bayes, random forest algorithm and decision tree.

Dataset: Link

Repository for reference: Link

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Titanic Dataset

We all have seen the movie titanic and know the story of a great tragedy. The ship didn’t have enough lifeboats for everyone resulting in the death of 1502 passengers out of 2224. It can be said that it was luck for survival but there is an observation of a certain set of people had a chance of survival more than others. This is what the titanic dataset is all about.

You have to build a predictive model to predict whether a person is likely to survive or not based on various features such as name, age, gender, socio-economic class and other features. It is a really interesting case study and a learning resource.

You can participate in this practice competition of Kaggle to get hands-on learning and a Kaggle environment experience.

Link to competition

House Price Prediction

House buying is a deal of money for your dream house. The price of a house depends on many factors. The area, the number of rooms, the furniture, street, location and many more possible features. This is a real-world utilisation of price prediction using machine learning.

The dataset you will be using for this project will be Ames Housing Dataset by Dean De Cock. There is a beginner competition on Kaggle for this dataset too. You can learn after-regression techniques using ti dataset along with learning feature engineering.

Link to Competition

MNIST Handwritten Digit Recognition

It is one of the most standard datasets to learn classification algorithm. It contains the image of handwritten digits 0-9. It is used in computer vision and deep learning basics. You can train a neural network to predict handwritten digits. The dataset contains 60,000 images to train and 10,000 images to test. This dataset will help you get started with TensorFlow.

Link

Iris Dataset

Iris is one of the most standard and basic datasets to step your feet into the world of data science. It is a small dataset of three varieties of flower namely- Iris Setosa, Iris Versicolour and Iris Virginica. Each flower has 50 instances with various features such as sepal length and width, petal length and width. It is a pretty straightforward dataset where you need to predict the variety of the flower out of the three.

Dataset: Link

Sentiment Analysis

Sentiments hold a greater value in today’s world of likes, reviews, tweets, and Reddit. Sentiment analysis can be used in a lot of domains to filter out abusive tweets, analyze the likeability of a product by the customers, and leverage a better understanding of text data. Some of the most common emotions that can be detected are excited, sad, angry, happy, etc. It can help you learn a different branch of  Data science which is NLP ie- Natural language processing. There are many popular datasets to practice sentiment analysis, such as Stanford sentiment treebank.

Fake News Detection

This project aims to make a classifier that can distinguish between real and fake news articles using natural language processing and machine learning. You can use a dataset of labeled news articles from various sources and apply techniques such as tokenization, stemming, lemmatization, vectorization, feature extraction, and model selection to train and evaluate your classifier. You can also explore other ways to improve the accuracy and performance of your model, such as using word embeddings, sentiment analysis, or deep learning. This project can help you develop text analysis, classification, and Python programming skills. 

Repository for reference: Link

Data Science Project on Detecting Forest Fire

This project uses satellite images to detect and monitor forest fires in real time. You can use a dataset of images from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, which provides daily global coverage of the Earth’s surface at 250 meters per pixel resolution. You can apply various image processing techniques such as segmentation, edge detection, thresholding, morphological operations, and contour detection to identify the fire regions in the images. We can also use machine learning algorithms such as neural networks, decision trees, random forests, or logistic regression to classify the images into fire or non-fire categories. This project can help you learn how to work with image data, computer vision, and machine learning. 

YouTube Comments Analysis

This project involves analyzing the comments on YouTube videos to understand the sentiment, emotion, topic, and opinion of the viewers. You can use Python to scrape the comments from YouTube using its API, perform text analysis using libraries like NLTK or spaCy, and visualize the results using libraries like Matplotlib or Seaborn.

Dogecoin Cryptocurrency Prices Predictor with LSTM

This project involves using time series analysis and deep learning to predict the future prices of the Dogecoin cryptocurrency. You can use Python to collect the historical data of Dogecoin prices from online sources, perform data preprocessing and feature engineering, and apply the LSTM (Long Short-Term Memory) neural network to train and test the prediction model.

Frequently Asked Questions

What are some data science projects?

Some of the most popular data science projects are plant disease detection, covid-19 data analysis, breast cancer detection, housing price prediction, fake news detection, movie recommendation and many more datasets available in the public domain, which can be utilised to make data science projects.

How do I start a data science project?

A data science project has various steps which start with data exploration. You try different visualisation and learn about the dataset. Data cleaning is yet another very important aspect of data science before training the model. Model selection is the next step. After this, you work on details by testing different algorithms and applying techniques such as hyperparameter optimisation and feature engineering.

What is considered a data science project?

A data science project is a task where data is collected and analyzed to find insights, patterns, or solutions to real-world problems, making informed decisions.

Where can I practice data science?

There are various platforms with active data science and machine learning community to help each other. The competition on these platforms can help you leverage your skills and enjoy the process of learning. Kaggle, dock ship and ods.ai are the popular ones. There are more which you can know from mlcontests.com.

Conclusion

In this blog, we have discussed the top six data science projects. Data science is an application of statistical methods and machine-learning practices to gain insights and useful information from raw data.

Also, refer to our Guided Path on Coding Ninjas Studio to upskill yourself in Data Structures and AlgorithmsCompetitive ProgrammingJavaScript, and many more! If you wish to test your competency in coding, you may check out the mock test series and participate in the contests hosted on Coding Ninjas Studio! 

You can also consider our online coding courses such as the Data Science Course to give your career an edge over others.

Happy Learning!

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