Finding data science projects for your data analytics portfolio can be tricky, especially when you’re new to the field. You might also think that your data projects need to be especially complex or showy, but that’s not the case. The most important thing is to demonstrate your skills, ideally using a dataset that interests you. And the good news? Data is everywhere—you just need to know where to find it and what to do with it.
Components of a Good Data Analytics Project that can Impress Anyone
To understand this one and only data analytics project idea, let’s break down the components of exactly what an interviewer is looking for in a data science project and why they’re looking for it.
What an interviewer looks for is a data scientist with real-world skills — both in analytics/coding and in using modern technologies. This helps you get closer to becoming a full-stack (or fully independent) data scientist.
ONLEI Technologies is the best training company to learn Data Science. Data science is a multidisciplinary blend of data inference, algorithms development, and technology in order to solve analytically complex problems . At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it.
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data . In turn, these systems generate insights which analysts and business users can translate into tangible business value.
Exciting Data Science Project Ideas
- Fake News Detection
Fake news is false information. In this data science project, we can use Python to build a model that can classify whether a piece of news is real or fake. To implement this project, you should be very well aware of the terms like Fake News, TfidfVectorizer, PassiveAggressiveClassifier, and Python libraries pandas, numpy, and sklearn.
Language: Python
Datasets / Packages : news.csv
- Chat bot
A chatbot is one of the most famous projects among aspiring data scientists and plays an important role in business. Chatbots are used to provide better services to customers with less manpower. It uses deep learning techniques to interact with customers, and you can easily implement this project with Python. There are two types of chatbot: the first one is domain-specific which can solve a particular problem and the second one is an open-domain chatbot that can be asked any type of question, so it requires huge amounts of data to train.
Language : Python
Datasets : Intents JSON file
- Credit Card Fraud Detection
Credit card fraud has skyrocketed. The objective of this project is to build a classifier. This classifier will detect whether the card transaction is true or not. In this project, various machine learning algorithms are used which will differentiate between a non-fraudulent transaction and fraudulent one. Moreover, by working on this project, you will procure knowledge in how to make machine learning algorithms for classification.
Language : R or Python
Datasets : Data on the transaction of credit cards is used here as a dataset.
- Driver Drowsiness Detection
We have seen many accidents that occur due to driver’s drowsiness. A dazed driver is very dangerous for himself and for others as well. That’s why this Python project has been introduced. This project will detect the dazed drivers and will also flag them by beeping alarms. This Python project is based on a deep learning model. This model will assess whether the driver’s eyes are closed or open. Moreover, for working on this project, a webcam is required.
Language : Python
Datasets : OpenCV, Tensorflow, Pygane, Keras
- Speech Emotion Recognition
SER which is an acronym for speech emotion recognition and is a very compelling Python project. This project attempts to perceive human emotions from the speech. In the project, you’ll learn how to build an MLP classifier. This classifier will be capable of sighting emotions from a human’s voice. Moreover, for sighting human emotion, different sound files are used as the dataset. Along with this, by working on the project you’ll rack up knowledge in the Librosa package which is used for analyzing music and audio.
Language: Python
Dataset: RAVDESS
Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
- Breast Cancer Classification
If you want to gain proficiency in machine learning as well as in deep learning, then go for this Python project. You’ll become experienced in terms like deep neural networks, convolutional neural networks, recurrent neural networks, deep belief networks, etc. Along with this, you’ll also get familiar with the Keras library. In the project, a classifier will be made. This classifier will be 80% trained with the image dataset and the rest is for validation.
Languages : Python
Dataset : IDC (Invasive Ductal Carcinoma)
Packages : NumPy, OpenCV, Pillow, Tensorflow, Keras, Imutils, Scikit, Matplotlib
- Movie Recommend System
The movie recommendation system is an R project which will make you grow your skills in machine learning. Basically, it is a recommendation system that suggests users different suggestions based on their browsing history and preferences. Recommendation systems are of two types- collaborative filtering recommendation and content-based recommendation system. This project is on a collaborative filtering recommendation system. This type of recommendation system will suggest movies based on the browsing history of other people who might see movies of the same preferences.
Language : R
Dataset : Movie Lens
Packages : recommenderlab, ggplot2, data.table, reshape2
- Sentiment Analysis Project
Almost every data-driven organization is using the sentiment analysis model to determine the attitude of its customers toward the company products. If you are engrossed with machine learning and want to elevate your skills in the same then, this project would be perfect for you. This R project is based on the classification.
The sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the consumer’s attitude towards a particular product or topic is positive, negative, or neutral.
Language : R
Dataset : janeaustenR
Package : Tidytext
- Customer Segmentation
Customer segmentation is a basic project and one of the most vital exercises of unsupervised learning. Companies use the clustering process for sighting the segments of people with similar behavior. They do so for targeting the potential user base. By working on the project you’ll become a buddy-buddy to the K-means clustering. K-means clustering is a top method for clustering unlabelled dataset. With the help of customer segmentation, companies get to know their customers and their requirements better. In this, data correlated with demographics, economic status, geography, and behavioral patterns are very important.
Language : R
- Gender and Age Detection
For upgrading your skills in computer vision, you can pin down the gender and age detection python project. A model will be built in the project which will recognize the age and gender of a person through his/her single image of the face. Though, age and gender could not be detected exactly because of many factors like makeup, facial expressions, lighting, etc. That’s why this detection is disposed of as classification instead of a regression problem.
Language : Python
Dataset : Audience
Package : Open CV
If you have good knowledge of Python and R then doing a Data Science project is not a hard cookie to crack. “You Don’t have to be Great to Start, But you have to Start to be a Great”
Finally, now you know about some exciting and data science projects for college. Projects help in increasing the knowledge and help to know the real-time application in data science . It is always good practice to start building projects for whatever you learned as it helps to make your core strong and get a good command of language. Also, these projects give light to your resume and help to get good opportunities in the future.
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