5 Machine Learning Projects to Implement as a Beginner

Artificial intelligence (AI) and machine learning (ML) are impacting our everyday lives in ways hereto unimaginable. From intelligent games and apps to autonomous cars and healthcare, machine learning has brought about incredible transformation in several industries.

Particularly, IT engineering and development has witnessed some amazing transformations over the years—from generating data to harnessing it, the industry has come a long way. Incorporating some kind of intelligence into apps has become almost an essential aspect of development, be it a regular form-based application or an advanced application capable of aiding decision making.

As such, ML and AI have been generating renewed interest among academia and novices alike. If you’re new to machine learning and are looking for projects to complement your learning, your search ends right here. In this article, we’ll discuss 5 fun and insightful ML projects, which will give you a glimpse of various challenges that you may come across as an ML engineer. Let’s jump right in!

1. Iris Flowers Classification –
   Considered to be one of the best datasets in classification literature, the Iris flowers dataset is the first thing that a beginner must consider to get started with supervised machine learning. Often referred to as the “Hello World” of machine learning, the Iris dataset comprises several numerical attributes that beginners need to handle and classify accordingly. Being small and compact, the Iris dataset easily fits into memory and doesn’t require any scaling/transformations as such.
   You can download the Iris dataset from the UCI ML repository here.
2. BigMart Sales Prediction –
   The BigMart sales dataset features 2013 sales data of 1559 products across 10 different outlets in various cities, along with certain product and store attributes. The BigMart sales prediction project aims to predict the upcoming year’s sales performance of each of these 1559 products in every store. You need to employ unsupervised learning techniques to compute the predictions, helping BigMart identify the unique qualities across products and outlets that help increase their sales.
3. Sentiment Analysis using the Twitter Dataset –
   Social media platforms like Twitter, Facebook, and YouTube are the breeding grounds of massive amounts of data. The main goal sentiment analysis is to mine this data to learn and analyze consumer behavior, which in turn aids branding, marketing, and even product design.
   Twitter is the perfect place to start with for beginners wanting to practice sentiment analysis problems. The Twitter dataset comprises a comprehensive blend of various tweets and metadata (hashtags, retweets, etc.), what with a galaxy of user opinions across issues and topics, which aid data analysis and inference, thereby helping generate relevant insights. As a beginner project, you can start off with identifying and classifying tweets as positive or negative.
4. Sales Forecasting using the Walmart Dataset –
   With sales data presenting the weekly sales per store, per department for over 98 products across 45 outlets, the Walmart dataset gives a pretty comprehensive sales picture if inferred properly. The main goal of the sales forecasting project is to forecast the sales for each department in every outlet to aid effective, data-driven decisions that can help optimize channels and inventory. The challenge with the dataset lies, however, in selected markdown events, which may have a negative impact on the sales and should thereby be taken into consideration.
5. Movie Recommender System with the Movielens Dataset –
   The world is witnessing an incredible surge in the digital movie streaming, what with the emergence of various streaming platforms like Netflix, Hulu, Prime Video and others. Consumers can now access their favorite movies and genres on their fingertips. This has led to an exponential increase in the demand for a streamlined and efficient movie recommender system.
   The Movielens dataset is probably the most popular and comprehensive movie dataset available on the web, with over 1 million movie ratings of around 4, 000 global movies made by over 6, 000 users. This is why the Movielens dataset is ideal for machine learning beginners to learn how to build a movie recommender system.

Final Thoughts –
Machine learning is here to stay and play a pivotal role in the evolution of the IT industry as a whole. Gone are the days when intelligent machines used to be a part of sci-fi and folklore. We’re already interacting with AI and ML-based applications on a regular basis and across several platforms. Consequently, the domain of machine learning is sure to attract many enthusiasts and professionals; however, therein lay the problem. There’s a huge skill gap in the industry right now, as the demand for more intelligent ‘bots’ seems to be ever on the rise. Nevertheless, the future is already here and the ball is literally in your court.