In this article, we’ll be discussing 15 machine learning and data science projects for beginners as well for intermediate level. Projects are some of the best investments of your time. You’ll enjoy learning, stay motivated, and make faster progress. For machine learning or data science projects finding a dataset is a quite difficult task. And, to build accurate models, you need a huge amount of data. But don’t worry, many researchers, organizations, and individuals who have shared their work and we can use their datasets in our projects. In this article, we will discuss more than 12 machine learning/Data science datasets that you can use to build your next ML/DS project. Learning through projects is the best investment that you are going to make. These project ideas enable you to grow and enhance your machine learning skills more. These ML/DS projects can be developed in Python, R or any other tool. Getting into Machine Learning and AI is not an easy task, but is a critical part of data science programs. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The goal of Data Science people is to find the crucial inferences from the data to make the business grow.
- Fake News Detection Project and Dataset
This Project is very helpful for NLP (Natural Language Processing) techniques applications for detecting the ‘fake news’, that is, misleading news stories that come from non-authentic sources. I started with the idea that the wording of fake news is distinct from that of standard news, and that machine learning can detect this difference. Build a fake news detection model with the Passive-Aggressive Classifier algorithm. The Passive-Aggressive algorithm can classify massive streams of data, it can be implemented quickly.
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Dataset link : Fake news dataset
2. Iris Project and Dataset
This is perhaps the best-known database to be found in the pattern recognition literature. This data set consists of 3 classes of 50 instances each with different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length. One class is linearly separable from the other 2 and the latter are not linearly separable from each other. Implement a machine learning classification or regression model on the dataset. Classification is the task of separating items into their corresponding class.
Dataset Link : Iris Dataset
3. MNIST Dataset
Implement a machine learning classification algorithm on image to recognize handwritten digits from a paper.
dataset Link : MNIST dataset
4. Housing Prices project and Dataset
This is a popular dataset used in pattern recognition. It contains information about the different houses in Boston based on crime rate, tax, number of rooms, etc. It has 506 rows and 14 different variables in columns. You can use this dataset to predict house prices. Predict the prices of a new house using linear regression. Linear regression is used to predict values of unknown input when the data has some linear relationship between feature and target variables.
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Dataset Link : Housing Prices Dataset
5. Titanic Project and Dataset
On 15 April 1912, the unsinkable Titanic ship sank and killed 1502 passengers out of 2224. The dataset contains information like name, age, sex, number of siblings aboard, etc of about 891 passengers in the training set and 418 passengers in the testing set. Build a model to predict whether a person would have survived on the Titanic or not. You can use linear regression for this purpose.
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