Projects for Data Science Beginners
Projects for Data Science Beginners, Are you looking for beginner data science projects? If so, this article contains a list of 10 beginner-friendly data science projects.
These projects can help you hone your data science skills while also enhancing your résumé.
Projects for Data Science Beginners
Let’s look into 10 Projects for Data Science Beginners.
1. Recommendation System
If you’re new to machine learning, a Recommendation system is a good place to start. You must create a system that will recommend things based on the user’s previous purchases.
Something along the lines of Amazon or Netflix.
You can create a music or movie recommendation system, for example.
You may find the datasets for the recommender system on the UCSD portal. This portal contains several extensive datasets that were used in UCSD lab research projects.
This gateway has a variety of datasets for recommender systems, including Goodreads book reviews, Amazon product reviews, bartender data, and more.
You can also take a look at this R project on Movie Recommendation System.
2. Driver Drowsiness Detection
Road accidents are a big concern, and one of the main causes is drowsy drivers. However, you may avoid this issue by implementing a driver drowsiness detection system.
The Driver Tiredness Detection system detects the driver’s drowsiness by continuously analyzing his eyes and notifying him with alarms.
A webcam is required for this project to monitor the driver’s eyes. The driver is alerted when he feels tired using Python, OpenCV, and Keras.
Driver Drowsiness Detection System with OpenCV & Keras is a complete project tutorial that you can get here.
3. Sentiment Analysis
Sentiment analysis is used in natural language processing to interpret sentiments and categorize them as positive, negative, or neutral.
Sentiment analysis is utilized in a variety of fields, including business. Businesses are employing sentiment analysis to learn about their customers’ thoughts and enhance their services by leveraging client feedback.
This tutorial for the Sentiment Analysis Project in R is also worth a look.
4. Fake News Detection
Fake news is widely disseminated around the globe. So, how can we tell the difference between accurate and false news?
Python is used to provide the solution. In this assignment, you must use the Python programming language to create a model that can determine whether the news is true or false.
To complete this project, use a TfidfVectorizer and utilize a PassiveAggressiveClassifier to divide news into “Real” and “Fake” categories.
5. Build a Chatbots
When you have a problem with a product, you should contact customer service. So, if you send a message with your question, you’ll get a response in a matter of seconds.
So, this is a Customer Support Bot that processes your language and then responds to your query.
Build Your First Python Chatbot Project is a guide for creating your first chatbot from start.
6. Stock Price Predictor
Another excellent machine learning experiment for beginners is this one. Various corporations and industries are looking for software that can track and assess their performance as well as forecast future stock prices.
You can create a machine learning project that forecasts the stock price for the next few months as a novice.
This tutorial on Stock Price Prediction in Python may be found here. In this lesson, you’ll learn how to use the LSTM neural network to predict stock values.
And how to use Plotly dash to create a stock research dashboard.
7. Forest Fire Prediction
Forest fires are a typical occurrence in today’s society. Our environment is harmed by forest fires. Forest fires are also a serious threat to wildlife.
Using k-means clustering, you may create a Forest fire prediction system. Major fire hotspots and their intensity are identified by a forest fire forecast system.
To improve the accuracy of your model, you might use meteorological data to determine the most prevalent seasons for wildfires and various weather conditions.
This tutorial on forest fire prediction can be found here.
8. Credit Card Fraud Detection Project
In this project, you will use R programming and algorithms such as Decision Trees, Logistic Regression, Artificial Neural Networks, and Gradient Boosting classifiers to detect credit cards.
You’ll use the Card Transactions dataset to distinguish between fraudulent and legitimate credit card transactions. You’ll also use various machine learning techniques and plot performance curves to assess accuracy.
This Project Tutorial is available at DataFlair.
9. Road Lane line detection
Another interesting project option for data science beginners is this one. This project will use lines put on the road to provide instruction to human drivers on lane recognition.
The OpenCV library is used to implement computer vision ideas in this project. The white markers on both sides of the lane must be detected in order to detect the lane. Frame masking is used for this.
The project’s source code is available for download here.
10. Color Detection with Python
This is an entry-level project in which you must create an interactive app. This program will recognize the chosen hue in any photograph. Based on the varied RGB color values, there are 16 million colors, but we only know a handful.
To carry out this project, you’ll need a labeled dataset of all the colors we know, and then you’ll need to figure out which color most closely resembles the selected color value.
You need to be familiar with the OpenCV and Pandas Python libraries for Computer Vision in order to complete this project.
You can find all of the information on this project here.