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10 Easy Machine Learning Project For High School Student

This collection of machine learning project ideas for students is designed for novices and those just beginning out with Machine Learning or Data Science in general.

These machine learning project ideas will get you beginning with all the practicalities you need to succeed in your career as a Machine Learning specialist.

Further, if you’re seeking Machine Learning project ideas for the final year, this list should get you started.

So, without further ado, let’s move straight into some Machine Learning project ideas that can improve your basis and help you ascend the ladder.

1. Stock Prices Predictor

One of the most acceptable options to start exploring hands-on Machine Learning projects for kids is working on Stock Prices predictors.

Nowadays, business organizations and enterprises are on the hunt for software that can monitor and evaluate the company performance and anticipate future values of various stocks.

And with so much data available on the stock market, it is a hotspot of opportunity for data scientists with a penchant for finance.

2. Sports Predictor

In Michael Lewis’ Moneyball, the Oakland Athletics squad revolutionized the face of baseball combining scientific player scouting methods in their game plan.

And just like them, you too can change sports in the real world! This project is an excellent machine learning task for beginners.

Since there is no scarcity of data in the sports industry, you can employ this data to develop fun and innovative machine learning projects such as analyzing college sports analytics to predict which athlete will have the most incredible career in which specific sports.

You might also choose to strengthen team management studying the strengths and weaknesses of the members in a team and categorizing them appropriately.

With the number of sports analytics and data accessible, this is a fantastic area to practice your data research and visualization abilities. For anybody with a flair in Python, Scikit-Learn will be the right pick as it provides various essential tools for regression analysis, classifications, data intake, and so on.

Mentioning Machine Learning initiatives for the last year might assist your resume to appear much more fascinating than others.

3. Develop A Sentiment Analyzer Machine Learning Project

This is one of the exciting machine learning project ideas. Although most of us use social media platforms to share our personal feelings and ideas for the world to see, one of the enormous obstacles is identifying the ‘sentiments’ behind social media posts.

Machine Learning Sentiment Analyzer

And this is an excellent concept for your next machine learning project!

Social media is growing with loads of user-generated material. By constructing an ML system that could assess the sentiment behind texts, or a post, it would become so much easier for corporations to understand consumer behavior; this, in turn, would help them to improve their customer service, so offering the opportunity for ideal consumer pleasure.

You might try to mine the data from Twitter or Reddit to get started with your sentiment analyzing machine learning project; This might be one of those rare situations of deep learning projects that can benefit you in other aspects.

4. Machine Learning Project to Enhance Healthcare

AI and ML applications have already started to permeate the healthcare business and are also swiftly reshaping the face of global Healthcare. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered AI. They are not only assisting HCPs (Health Care Providers) in giving speedy and better healthcare services but are also lowering the dependency and workload of doctors to a substantial amount.

Health Care Machine Learning Image

So, why not use your expertise to design an excellent machine learning project based on Healthcare? To handle a project with Machine Learning algorithms for beginners might be helpful to create your profession with a strong start.

The healthcare business has tremendous volumes of data at its disposal. By utilizing this data, you can create:

Diagnostic care systems can automatically scan photographs, X-rays, etc., and deliver an accurate diagnosis of suspected disorders.

Preventative care apps can forecast the potential of epidemics such as flu, malaria, etc., both at the national and community level.

5. Iris Flowers Classification Machine Learning Project

One of the most acceptable options for exploring hands-on Machine Learning projects for kids is working on the Iris Flowers categorization ML project.

Iris flowers dataset is one of the most incredible datasets for classification problems. Since iris blooms are of diverse species, they may be recognized based on the length of sepals and petals. This ML research seeks to categorize the flowers into three species – Virginica, Setosa, or Versicolor.

This specific ML experiment is commonly referred to as the “Hello World” of Machine Learning.

The iris flowers dataset comprises numeric properties, and it is excellent for novices to learn about supervised ML methods, notably how to load and handle data. Also, since this is a tiny dataset, it can readily fit in memory without requiring extra transformations or scaling skills. And this is an excellent concept for your next machine learning project!

You may obtain the iris dataset here.

6. BigMart Sales Prediction Machine Learning Project

This is a good ML project idea for beginners. This ML project is excellent for understanding how unsupervised ML algorithms operate. The BigMart sales dataset includes 2013 sales data for 1559 goods across ten shops in different cities.

The purpose here is to utilize the BigMart sales information to construct a regression model that can forecast the sale of each of 1559 goods in the following year in the ten different BigMart shops. The BigMart sales dataset provides particular variables for each product and outlet, enabling you to understand the qualities of the many items and locations that impact the total sales of BigMart as a brand.

7. Recommendation Engines using MovieLens Dataset

Recommendation algorithms have grown tremendously popular in online buying and streaming sites. For instance, online content streaming providers like Netflix and Hulu employ recommendation algorithms to adapt their material according to individual client interests and browsing history. By adjusting the fabric to accommodate the watching demands and tastes of individual clients, these sites have enhanced the market for their streaming services.

As a newbie, you may try your hand at developing a recommendation system using one of the most popular datasets accessible on the web — the MovieLens dataset. This collection comprises over “25 million ratings and one million tag applications applied to 62,000 movies 162,000 users.” You may begin this project developing a world-cloud representation of movie titles to make a movie recommendation engine for MovieLens.

You may check out the MovieLens dataset here.

8. Predicting Wine Quality using Wine Quality Dataset

It’s a well-established truth that aging makes wine better — the older the wine, the better it will taste. However, age is not the only element that impacts a wine’s flavor. Numerous criteria impact the wine quality certification, including physiochemical tests such as alcohol amount, fixed acidity, volatile acidity, density, and pH level, to mention a few.

In this ML project, you need to construct an ML model to study a wine’s chemical attributes to forecast its quality. The wine quality dataset you’ll be utilizing for this project consists of around 4898 observations, comprising 11 independent variables and one dependent variable. Mentioning Machine Learning initiatives for the last year might assist your resume to appear much more fascinating than others.

9. MNIST Handwritten Digit Classification

This is one of the intriguing machine learning projects. Deep Learning and neural networks have found use cases in many real-world applications, including picture recognition, automatic text production, autonomous automobiles, and much more. However, before you go into these problematic aspects of Deep Learning, you should begin with a small dataset like the MNIST dataset.

So, why not leverage your expertise to construct a remarkable machine learning project based on MNIST?

The MNIST digit classification project is aimed to educate machines to identify handwritten digits. Since novices frequently find it hard to deal with picture data over flat relational data, the MNIST dataset is suitable for beginners.

This project will utilize the MNIST datasets to train your ML model using Convolutional Neural Networks (CNNs) (CNNs). Although the MNIST dataset may effortlessly fit in your PC RAM (it is incredibly tiny), the process of handwritten digit identification is relatively challenging.

You may obtain the MNIST dataset here.

This is one of the trendy machine learning project ideas; the smartphone dataset comprises the physical activity record and information of 30 participants. This data was taken with a smartphone outfitted with inertial sensors.

This ML research seeks to create a classification model that can recognize human fitness activities with high accuracy. By working on this ML project, you will master the foundations of classification and address multi-classification challenges.

10 Object Detection using Deep Learning

This is one of the intriguing machine learning projects to construct. When it comes to picture categorization, Deep Neural Networks (DNNs) should be your go-to solution. While DNNs are being employed in numerous real-world picture categorization applications, this ML effort intends to kick it up a notch.

In this ML project, you will address the challenge of object detection utilizing DNNs. You will have to design a model that can categorize things and properly locate objects of various classes. Here, you will handle the task of object detection as a regression issue to object bounding box masks. Also, you will design a multi-scale inference technique that can yield high-resolution object detections at a low cost.

Chitranshu Harbola

Self taught programmer, Web Developer and an aspiring Machine learning engineer cum Data Science student

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