BEGINNER Data Science (Machine Learning) Projects #1
Data science (Machine Learning) projects offer you a promising way to kick-start your career in this field. Not only do you get to learn data science by applying it but you also get projects to showcase on your CV! Nowadays, recruiters evaluate a candidate’s potential by his/her work and don’t put a lot of emphasis on certifications. It wouldn’t matter if you just tell them how much you know if you have nothing to show them! That’s where most people struggle and miss out.
You might have worked on several problems before, but if you can’t make it presentable & easy-to-explain, how on earth would someone know what you are capable of? That’s where these projects will help you. Think of the time you’ll spend on these projects like your training sessions. The more time you spend practicing, the better you’ll become!
We’ve made sure to provide you with a taste of a variety of problems from different domains. We believe everyone must learn to smartly work with huge amounts of data, hence large datasets are included. Also, we’ve made sure all the datasets are open and free to access.
Beginner Level: This level comprises of data sets that are fairly easy to work with, and don’t require complex data science techniques. You can solve them using basic regression or classification algorithms. Also, these data sets have enough open tutorials to get you going. In this list, we have also provided tutorials to help you get started. You can also check out AV’s ‘Introduction to Data Science‘ course along with this.
Table of Contents
- Iris Data
- Loan Prediction Data
- Bigmart Sales Data
- Boston Housing Data
- Time Series Analysis Data
- Wine Quality Data
- Turkiye Student Evaluation Data
- Heights and Weights Data
1. Iris Data Set
This is probably the most versatile, easy and resourceful dataset in pattern recognition literature. Nothing could be simpler than the Iris dataset to learn classification techniques. If you are totally new to data science, this is your start line. The data has only 150 rows & 4 columns.
Problem: Predict the class of the flower based on available attributes.
2. Loan Prediction Dataset
Among all industries, the insurance domain has one of the largest uses of analytics & data science methods. This dataset provides you a taste of working on data sets from insurance companies – what challenges are faced there, what strategies are used, which variables influence the outcome, etc. This is a classification problem. The data has 615 rows and 13 columns.
Problem: Predict if a loan will get approved or not.
3. Bigmart Sales Data Set
Retail is another industry that extensively uses analytics to optimize business processes. Tasks like product placement, inventory management, customized offers, product bundling, etc. are being smartly handled using data science techniques. As the name suggests, this data comprises of transaction records of a sales store. This is a regression problem. The data has 8523 rows of 12 variables.
Problem: Predict the sales of a store.
4. Boston Housing Data Set
This is another popular dataset used in pattern recognition literature. The data set comes from the real estate industry in Boston (US). This is a regression problem. The data has 506 rows and 14 columns. Thus, it’s a fairly small data set where you can attempt any technique without worrying about your laptop’s memory being overused.
Problem: Predict the median value of owner-occupied homes.
5. Time Series Analysis Dataset
Time Series is one of the most commonly used techniques in data science. It has wide-ranging applications – weather forecasting, predicting sales, analyzing year on year trends, etc. This dataset is specific to time series and the challenge here is to forecast traffic on a mode of transportation. The data has ** rows and ** columns.
Problem: Predict the traffic on a new mode of transport.
6. Wine Quality Dataset
This is one of the most popular datasets along with data science beginners. It is divided into 2 datasets. You can perform both regression and classification tasks on this data. It will test your understanding in different fields – outlier detection, feature selection, and unbalanced data. There are 4898 rows and 12 columns in this dataset.
Problem: Predict the quality of the wine.
7. Turkiye Student Evaluation Dataset
This dataset is based on an evaluation form filled out by students for different courses. It has different attributes including attendance, difficulty, the score for each evaluation question, among others. This is an unsupervised learning problem. The dataset has 5820 rows and 33 columns.
Problem: Use classification and clustering techniques to deal with the data.
8. Heights and Weights Dataset
This is a fairly straightforward problem and is ideal for people starting off with data science. It is a regression problem. The dataset has 25,000 rows and 3 columns (index, height, and weight).
Problem: Predict the height or weight of a person.
