How to Start a Machine Learning Project

Machine Learning Project Ideas

As Artificial Intelligence (AI) continues to progress rapidly in 2022, achieving mastery over Machine Learning (ML) is becoming increasingly important for all the players in this field. This is because both AI and ML complement each other. So, if you are a beginner, the best thing you can do is work on some Machine Learning projects.

But first, let’s address the more pertinent question that must be lurking in your mind: why to build Machine Learning projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. The more you experiment with different Machine Learning projects , the more knowledge you gain.

While textbooks and study materials will give you all the knowledge you need to know about Machine Learning, you can never really master ML unless you invest your time in real-life practical experiments – projects on Machine Learning. As you start working on machine learning project ideas, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. In this tutorial, you will find 15 interesting machine learning project ideas for beginners to get hands-on experience on machine learning.

These courses will guide you to create the best ML projects .

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So, here are a few Machine Learning Projects which beginners can work on:

Here are some cool Machine Learning project ideas for beginners

This list of machine learning project ideas for students is suited for beginners, and those just starting out with Machine Learning or Data Science in general. These machine learning project ideas will get you going with all the practicalities you need to succeed in your career as a Machine Learning professional.

Further, if you’re looking for Machine Learning project ideas for final year , this list should get you going. So, without further ado, let’s jump straight into some Machine Learning project ideas that will strengthen your base and allow you to climb up the ladder.

1. Stock Prices Predictor

One of the best ideas to start experimenting you hands-on Machine Learning projects for students is working on Stock Prices Predictor. Business organizations and companies today are on the lookout for software that can monitor and analyze the company performance and predict future prices of various stocks. And with so much data available on the stock market, it is a hotbed of opportunities for data scientists with an inclination for finance.

However, before you start off, you must have a fair share of knowledge in the following areas:

Predictive Analysis: Leveraging various AI techniques for different data processes such as data mining, data exploration, etc. to ‘predict’ the behaviour of possible outcomes.
Regression Analysis: Regressive analysis is a kind of predictive technique based on the interaction between a dependent (target) and independent variable/s (predictor).
Action Analysis: In this method, all the actions carried out by the two techniques mentioned above are analyzed after which the outcome is fed into the machine learning memory.
Statistical Modeling: It involves building a mathematical description of a real-world process and elaborating the uncertainties, if any, within that process.

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2. SportsPredictor

In Michael Lewis’ Moneyball , the Oakland Athletics team transformed the face of baseball by incorporating analytical player scouting technique in their gameplan. And just like them, you too can revolutionize sports in the real world! This is an excellent machine learning projects for beginners.

Since there is no dearth of data in the sports world, you can utilize this data to build fun and creative machine learning projects such as using college sports stats to predict which player would have the best career in which particular sports (talent scouting). You could also opt for enhancing team management by analyzing the strengths and weaknesses of the players in a team and classifying them accordingly.

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With the amount of sports stats and data available, this is an excellent arena to hone your data exploration and visualization skills. For anyone with a flair in Python, Scikit-Learn will be the ideal choice as it includes an array of useful tools for regression analysis, classifications, data ingestion, and so on. Mentioning Machine Learning projects for the final year can help your resume look much more interesting than others.

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3. Develop A Sentiment Analyzer

This is one of the interesting machine learning project ideas. Although most of us use social media platforms to convey our personal feelings and opinions for the world to see, one of the biggest challenges lies in understanding the ‘sentiments’ behind social media posts.

And this is the perfect idea for your next machine learning project!

Social media is thriving with tons of user-generated content. By creating an ML system that could analyze the sentiment behind texts, or a post, it would become so much easier for organizations to understand consumer behaviour. This, in turn, would allow them to improve their customer service, thereby providing the scope for optimal consumer satisfaction.

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You can try to mine the data from Twitter or Reddit to get started off with your sentiment analyzing machine learning project. This might be one of those rare cases of deep learning projects which can help you in other aspects as well.

4. Enhance Healthcare

AI and ML applications have already started to penetrate the healthcare industry and are also rapidly transforming the face of global healthcare. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered by AI. They are not only helping HCPs (Health Care Providers) to deliver speedy and better healthcare services but are also reducing the dependency and workload of doctors to a significant extent.

So, why not use your skills to develop an impressive machine learning project based on healthcare? To handle a project with Machine Learning algorithms for beginners can be helpful to build your career with a good start.

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The healthcare industry has enormous amounts of data at their disposal. By harnessing this data, you can create:

Diagnostic care systems that can automatically scan images, X-rays, etc., and provide an accurate diagnosis of possible diseases.
Preventative care applications that can predict the possibilities of epidemics such as flu, malaria, etc., both at the national and community level.

Six Core Steps To Build Your First ML Project Are

Problem Definition And Data Collection

To define a machine learning problem, you need to know this three types of machine learning and the problem that comes under each type

Problem Definition

1st Type : Supervised learning

Supervised learning, is called supervised because you have data and labels. A machine learning algorithm tries to learn what patterns in the data lead to the labels. The supervised part happens during training. If the algorithm guesses a wrong label, it tries to correct itself.

For example, if you were trying to predict heart disease in a new patient. You may have the anonymized medical records of 100 patients as the data and whether or not they had heart disease as the label.

Problems with labels come under supervised learning

2nd Type : Unsupervised learning

Unsupervised learning is when you have data but no labels. The data could be the purchase history of your online video game store customers. Using this data, you may want to group similar customers together so you can offer them specialized deals. You could use a machine learning algorithm to group your customers by purchase history.

3rd Type : Transfer learning

Transfer learning is when you take the information an existing machine learning model has learned and adjust it to your own problem.

For example, in training a classifier to predict whether an image contains food, you could use the knowledge it gained during training to recognize drinks

Now, you can identify the problem. So, Let’s move towards data collection

Data Preparation

Their are three steps for Data Preparation

i. Exploratory data analysis (EDA) – Learning about the data you’re working with

ii. Data Preprocessing – Preparing data for model training

iii. Data Splitting – Splitting data into sets

Train Model On The Data You’ve Prepared

Core Steps of training a model:

i. Choose an algorithm, ii. Over fit the model, iii. Reduce overfitting with regularization

i. Choosing an algorithm

Many of the algorithms you see here are already implemented for you in libraries such as Scikit-Learn. You always look to use a pre-implemented version of a machine learning model before building your own. If in doubt, search something like “[ALGORITHM_NAME] scikit learn”. Every learning algorithm has: a loss function (how wrong the model is) an optimization criterion (a criteria for improving the loss, also called an optimizer, like stochastic gradient descent) an optimization routine which uses training data to find a solution for the optimization criterion (use a plethora of examples of what a model ‘should’ learn to improve what it ‘does’ learn)

Choose an algorithm as per your requirement and move towards the next phase i.e. fitting the model

ii. Overfitting and Underfitting the Model

There are two types of fitting in machine learning

a. Underfitting: Happens when your model doesn’t perform as well as you’d like on your data. Try training for longer or a more advanced model.

b. Overfitting: Happens when your validation loss (how your model is performing on the validation dataset, lower is better) starts to increase. Or if you don’t have a validation set, happens when the model performs far better on the training set than on the test set (e.g. 99% accuracy on training set, 67% accuracy on the test set). Fix through various regularization techniques.

Analysis/Evaluation

Evaluation metrics for Classification and Regression type of Problems

Classification Type Problems

Do you want to predict whether something is one thing or another? Such as whether a customer will churn or not churn? Or whether a patient has heart disease or not? Note, there can be more than two things. Two classes is called binary classification, more than two classes is called multi-class classification. Multi-label is when an item can belong to more than one class.

For Classification type problems, Things you should pay attention to

a. False negatives — Model predicts negative, actually positive. In some cases, like email spam prediction, false negatives aren’t too much to worry about. But if a self-driving cars computer vision system predicts no pedestrian when there was one, this is not good.

b. False positives — Model predicts positive, actually negative. Predicting someone has heart disease when they don’t, might seem okay. Better to be safe right? Not if it negatively affects the person’s lifestyle or sets them on a treatment plan they don’t need.

c. True negatives — Model predicts negative, actually negative. This is good.

d. True positives — Model predicts positive, actually positive. This is good.

e. Precision : What proportion of positive predictions were actually correct? A model that produces no false positives has a precision of 1.0.

f. Recall : What proportion of actual positives were predicted correctly? A model that produces no false negatives has a recall of 1.0.

g. F1 Score : A combination of precision and recall. The closer to 1.0, the better.

h. Receiver operating characteristic (ROC) curve & Area under the curve (AUC) : The ROC curve is a plot comparing true positive and false positive rate. The AUC metric is the area under the ROC curve. A model whose predictions are 100% wrong has an AUC of 0.0, one whose predictions are 100% right has an AUC of 1.0.