1. Iris categorization using machine learning
It is one of the most common and easy projects for beginners. It is said that no scientist has learned the clustering without going through the Iris data set. The required data here are the attributes of iris flower-like size of a sepal and petal dimension etc. The data set is small and does not require much cleaning, to begin with, the clustering of sepal and petal. Here the aim of the project is to classify Iris flowers into their three variants – Virginia, setosa and Versicolor. For this, clustering is used.
2. Boston Housing Price detection
The Boston Housing data is another famous dataset used by beginners in machine learning. The main aim of Boston housing is to predict the housing prices in different areas of Boston. It will be having some essential information like age, the property tax rate, crime rate, and even proximity to employment centers that can come to the factor into the housing rates.
The dataset is clean and small and it is easy to play around with for beginners. Regression algorithms are mostly used on the different attributes to find out what contributes to the housing price in Boston. This is one of the excellent resources to practice regression techniques and to improve their performance.
3. Titanic Survivors data set
This project is regarded as one of the most fun challenges to drive into the world of ML. The Titanic challenge is not only a popular machine learning project but also helps to be familiarized with the Kaggle data science platform. The Titanic dataset comprises actual data from the incident which is not famous. It consists of attributes like age, socio-economic class, gender, cabin number, departure port, and most importantly, whether the person survived or not.
Here the decision tree classifier and K-Nearest Neighbor approach have given the best results for this project
4. Predicting Wine Quality using Machine Learning
“Wine tastes better with age” is a very popular word. With this beginner-friendly machine learning project, you can determine the quality of the wine by using Machine Learning. It is a fairly larger dataset with about 5k rows. It contains results of physiochemical tests such as alcohol quantity, acidity, density, pH measure, sugar content, and more.
5. Stock Market Prediction
Whether you are in the financial domain or not, but the stock market prediction project is one of the interesting ones. Stock market data is analyzed for academics, business and even it act as a secondary source of income for many people. Studying and exploring the time series data is also an essential skill for a data scientist. Stock market data is an ideal place to start your project. As a project, the main aim is to predict the future value of a stock. This is done on the basis of current market performance and previous year’s data.
6. Movie recommendation system
I bet everyone knows the feeling after watching a really good movie. We know that OTT platforms like Netflix, Amazon have really improved their recommendation systems. Machine learning students must learn how such systems work to target customers based on their needs and ratings.
The IMDB data set available on Kaggle is perhaps one of the most comprehensive ones on which recommendation models based on the movie title, customer rating, etc. can be implied. It is also a good way to learn about Feature Engineering and Content-Based Filtering.
7. Social Media Sentiment Analysis with the use of Twitter data
Opinions and trends have become comparatively easier to extrapolate thanks to social media platforms like Twitter, Facebook. This data is used to filter out views and opinions about events, personalities, of life from political campaigns to product reviews.
If you have completed some basic-level projects and are familiar with Python, this project will be a great addition to your profile. One can practice approaches like Support vector machines, regression and classification techniques for emotion detection, and aspect-based analysis.
8. Loan Prediction using Machine learning
This is a very popular classification-based Machine Learning project for Beginners, the loan data set consists the attributes like gender, marital status, employment education, income, and the amount of loan provided to the applicant. Supervised machine learning models are deployed to understand whether a loan applicant should be given a loan or reject the application.
Due to many characteristics to be accounted for, models like logistic regression, feature engineering, random forest classifiers, etc are more ideal for this project. So if you’re looking to work on some complex machine learning exercises then try this one on Kaggle.
9. Grocery Item recommendation system
Clustering, regression, and classification methods are not the only ones vital to learn for a beginner in Machine Learning. Collaborative filtering is also a great method where automatic predictions are made based on the interests of a user by collecting preferences or of previous customers who have similar tastes.
The InstaCart dataset is of a great way to sharpen your skills in collaborative filtering. This data set is extensive and contains data of over 3 million grocery orders stored across multiple tables – aisles, products, orders, and departments.
10. Fake News Detection
Social media platforms like WhatsApp Facebook and Twitter are being overwhelmed by unreliable sources of information. Such news make more harm than good. This type of news is capable of inciting unnecessary fear in people. With our increase in technology, it has now become important to filter out such fake news.
Natural Language Processing (NLP) techniques are used for this purpose. They filter out news that is misleading and untrustworthy. The following dataset contains language, headlines, source, country, news text, and spam score features.