4 Datasets for Your Next Data Science Project
Top 4 Datasets for Data Science Project Based on Classification
As a newbie, it is very difficult to choose which projects to work on, and which ones would be simple, to begin with and would assist increase your resume? This article will assist you in this regard.
In this article, I’ll show you 4 datasets where you can use a classification algorithm (supervised learning). I’m looking at datasets that are mostly focused on classification algorithms to see where you can use this approach.
I’ve used Kaggle and filtered the datasets (classification). Each dataset listed has a link. You may also look at how to approach them if you need some direction or inspiration.
Prepare to begin working on some of the most exciting Python projects!
1. Credit Card Fraud Detection
The dataset includes credit card transactions performed by European cardholders in September 2013. This dataset contains 492 frauds out of 284,807 transactions that happened over the course of two days. Feature ‘Class’ is the answer variable, with a value of 1 indicating fraud and 0 otherwise.
Dataset: Link
2. Iris Species
This is a small and famous dataset for newbies. It comprises three iris species, each with 50 samples, as well as basic information about each flower. Classify base on the SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, and Species.
Dataset: Link
3. Heart Attack Analysis & Prediction Dataset
This dataset contains information about patients age, sex, chest pain type, cholestoral, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, and the target variable ( 0=less chance of heart attack and 1=more chance of heart attack).
Using a classification method, you can forecast the likelihood of a heart attack.
Dataset: Link
4. Customer Personality Analysis
This dataset is all about predicting whether a consumer would buy something or not based on their birth year, education, marital status, income, and previous expenditure on fruits, fish, meat, wine, sweets, gold, and so on.
There are 2240 rows and 29 features.
You may use this to determine which customer group is most likely to purchase the product.
Dataset: Link
This article will undoubtedly improve your grasp of how and when to apply classification algorithms.
I truly hope you enjoyed reading this article. Please follow me and leave a comment if you have any recommendations or criticism; this will help me grow so that I can serve you by improving my writing abilities and expertise.
