Machine Learning involves the use of Artificial Intelligence to permit machines to automatically learn and improve a task from experience without programming them specifically about that task. This process starts with feeding them good quality data also called training data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data we have and what kind of task we are performing to make predictions or decisions.
As one knows no amount of theory can replace hands-on practice. Theories and lessons online can soothe you into a false belief of mastery because the code and solution are right there in front of you. But once you try to apply it, you might find that it’s harder than it looks. So here are the top 5 most important and interesting beginner projects in Machine Learning to help you improve your applied Machine Learning skills quickly, which you can also add to your portfolio or resume.
1. Stock Price Predictor
A stock price predictor is a machine learning system that learns about the current performance of a company and based on this, predicts future stock prices. To begin working with stock market data, you can predict and make a simple machine learning problem like predicting 6-month price movements based on fundamental indicators or building time series models, or even recurrent neural networks, on the delta between implied and actual volatility from an organizations’ quarterly report. One can also extend this project to find similar stocks based on their price movements and other factors and look for periods when their prices diverge.
2. Music Recommendation System
This is yet another and one of the most popular machine learning projects and can be used across different spheres. You might be very familiar with a music recommendation system if you’ve used apps like JioSaavn or Spotify. The system recommends some songs based on the songs you’ve liked or listened to. How does the system do this? This is a typical example where Machine Learning can be applied. This can further be extended for the recommendation system which many E-Commerce sites use to suggest some other products which you like to buy with the current one or can be extended for the recommendation system in apps like Netflix or Amazon Prime.
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3. Sales Forecasting
The goal of this another interesting yet beginner-friendly machine learning project is to forecast or predict sales for each department in each outlet. Prediction is to be done in such a way that it helps the company to make better data-driven decisions for channel optimization and inventory planning. For this, you can use Walmart datasets, that have sales data for 98 products across 45 outlets! The datasets contain sales per store, per department every week, and also contain selected markdown events that affect sales and should be taken into consideration.
4. Housing Price Prediction
The goal of this interesting but important machine learning project is to predict the selling price of a new home by applying basic machine learning concepts to the housing prices data using some of the well-known facts about the house like its size, location, facilities, etc. To begin with this project, you could use the Boston House Prices Dataset which consists of the prices of houses across different places in Boston. The dataset also consists of information on areas of non-retail business, the age of people who own a house, the crime rate in that locality, and several other attributes.
5. Sentiment Analyzing
A sentiment analyzer learns about various sentiments behind content through machine learning and predicts the same using AI. By creating an ML system that would analyze the sentiment behind texts, or a post, it might become so a lot easier for organizations to know and understand their consumer behavior better. Twitter data is taken into account as an ultimate entry point for beginners to practice sentiment analysis machine learning problems. Using Twitter datasets, one can get a charismatic combination of tweet contents and other related metadata such as hashtags, location, retweets, users, and many more which pave way for insightful analysis. The foremost problem that you can start working on as a beginner is to build a model to classify users’ profile photos as sad happy or neutral.