The customers need to provide personal details such as their gender, marital status, educational qualifications, income, existing liabilities, number of dependents, loan amount, credit history, and so on. Various factors go into deciding whether the applicant gets the loan or not. Banks have a variety of loan products with different eligibility criteria. Insurance and banking companies make the maximum use of data analytics today. Let us now move one step ahead on the difficulty level and look at the Loan Prediction Data Set. Every problem in life would not be as simple. The Iris dataset is a straightforward data science project for beginners as it involves only 4 columns and 150 rows of data. The columns constitute the distinguishing features whereas the rows contain data from the 50 samples from each of the three species of Iris. It is simply because the project involves the study of only 150 rows and 4 columns. The Iris data set is probably the easiest and most versatile dataset in pattern recognition literature. Fisher used a combination of these four features to develop a linear discriminant analysis model for distinguishing one species from the other. The dataset involves measuring of four features from the sample, the sepal length, the sepal width, the petal length, and the petal width. The dataset comprises of 50 samples from each of these three species of Iris, the Iris Setosa, The Iris Virginica, and the Iris Versicolor. Edgar Anderson collected the data required for the quantification of the morphological variation of three related species of the Iris flower. It is a simple example of linear discriminant analysis. The credit for introducing this multivariate data set goes to a British biologist Ronald Fisher in 1936. Top 5 data science projects for beginners 1. We shall now discuss some of the simple but exciting data science projects for beginners. These data science project examples are creative and should form part of your CV when you graduate as a qualified data scientist. These projects include high dimensional data as well. It is best suited for people having adequate knowledge of data science aspects such as neural networks, recommender systems, and deep learning. Advanced level – As the name suggests, you need high levels of understanding to prepare such projects.Machine learning projects form a vital part of such intermediate-level data science projects. You need to have an engineering background to understand and take on such projects. They contain data sets that require serious pattern recognition skills. Intermediate level – Compared to the data science projects for beginners, these projects are more challenging.Students at the elementary level can solve those using simple methods like classification algorithms or basic regression. Beginner level – Naturally, these data science project ideas are reasonably easy to work with because you do not need to use any complex data science technique.We shall classify data science projects as belonging to three levels
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