Top 5 Machine Learning Projects for Beginners
Machine Learning (ML) from the name only we can say that it helps machines learn from input and perform repetitive tasks without being explicitly programming. A significant concept of artificial intelligence.
The use of ML technology is growing rapidly. Machine learning-based apps and devices are playing a vital role in our daily lives. For instance, Siri and Alexa are the two popular smart virtual assistants developed using machine learning capabilities to answer your questions and follow your instructions.
Herein, we are providing some interesting Machine Learning projects which help you to enhance your talents and explore more on this. From a carrier perspective also, it will help a lot. So. Let’s start.
Here are some guidelines you can follow before getting started:
- Get an easily analyzed data set
- Apply some ML algorithms to that data set and see how it reacts and the performance rate of each algorithm.
- Choose the best-performed algorithm and adjust the data sets accordingly
After knowing the guidelines, now you can start your Machine Learning project. Here, we are representing some examples, how to use the ML algorithms with the data sets, adjusts them, and how perfectly analyze the data.
#1 Supervised Machine Learning with Iris Flowers Classification
Iris Flowers dataset is one of the standard examples of classification. It is just like an introduction to the world of machine learning and helps you to discover and load the data. The main advantage of this dataset is that it takes a small amount of memory to load, having only 150 rows and it contains only four characteristics, which include the length of Petal, width of Petal, length of Sepal, and width of Sepal.
In this project, you have to identify four different species by using the four characteristics of Iris flowers. The data involved in this dataset is categorized. That’s why the dataset helps you to use a controlled machine learning algorithm whereas if the data is uncategorized, we have to find out the unseen arrangements of the algorithm.
Aim: According to the characteristics of the flowers, sizes of the petals and sepals, they can be classified in between three species of flowers.
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#2 Transactions Predictions
For many years, Machine learning has been one of the most popular subjects but because of its high cost, they are not accessible to most of the developers. ML platform with Blockchain to protect the user’s data from risks.
ML can predict all retail transactions and ensures high security and privacy to the user’s data. The in-built structure of Blockchain manages the data and makes us capable to control over the data.
Since the last block is in the active stage, the validation of the successive block can already be started and improves the performance. Only if the customer has enough balance, then validation starts checking. In case the expected transactions are wrong, then the complete process required to be reprocessed again.
Aim: Forecast the next transaction of the customers based upon their purchasing history.
#3 Sentiment Analysis
Sentiment analysis has become a vital innovation. As it helps to make better decisions on trading, many people have attempted to construct trading bots using sentiment analysis.
A lot of platforms are also available for sentiment analysis such as Facebook, LinkedIn, etc. But, because of the steady strategy of the data on Twitter, this sentiment analysis is the most preferable data for machine learning. It’s very easy to process as the tweets are majorly texts, hashtags, and URLs.
- Sentiment environment of any new released film can analyze it with IMDB reviews and other available rating websites.
- Expect the future direction of the top 50 cryptocurrency prices according to the sentiments of its related tweets.
Aim: Behind a content, a sentiment analyzer studies numerous sentiments. This analysis helps to investigate different designing models to label a tweet as a positive tweet or a negative tweet. Later, we can label the tweets in more different ways like ‘neutral’, ‘angry’, ‘optimistic’ etc.
#4 Recommender Systems
Among the applications of machine learning technology, Recommender systems are one of the most popular and accepted applications. In every system, recommender systems will be there. When we are watching any YouTube video, a recommender system is embedded in the YouTube algorithm which will suggest videos to us, according to our interests.
Generally, there are majorly two types of algorithms for recommender systems including-
- Content-based: Content similarity is checked
- Collaborative filtering methods: Identified similarities in interactions. For instance, these methods identified similar user ratings and compare it with other content to find out the behaviors.
MovieLens is one of the most famous datasets that provide movie ratings. It helps beginners do experiment with it.
Aim: According to their ratings, the user can predict which type of movies will attract users in the future.
#5 Stock Price Predictions
Stock price predictor studies the overall performance of an organization and forecasts its future stock values. The interesting thing about the stock price predictions is that in this system different types and data sources can be utilized such as:
- Volatility indices
- Past values
- Global macroeconomic indicators
- Fundamental analysis
- Technical analysis using indicators
The main advantage of predicting the stock market is that it contains small feedback cycles, and they help to verify your estimates.
Aim: Forecast the future price using the basic and technical indicators.
The above-mentioned machine learning projects have been designed for beginners to enhance their fundamental skills and provide them an opportunity to explore more in various industries like Finance, Insurance, Retail, Manufacturing, etc.