Machine Learning mini-projects for students
Machine learning, a field experiencing rapid growth, possesses the potential to revolutionize our lifestyles and professional landscapes. As a computer science engineering (CSE) student, you possess the unique opportunity to delve into this dynamic realm through engaging in mini-projects. Such endeavours not only provide practical experience but also facilitate skill development. Presented below are some of the finest machine learning mini-project ideas tailored for CSE students.
The following are exemplary mini-projects that illuminate diverse facets of machine learning:
In this project, the goal is to construct a model proficient in categorizing images into distinct classes, such as distinguishing between cats and dogs. This undertaking offers invaluable insights into image processing techniques and the application of various machine learning algorithms.
Get a crash course on convolutional neural networks, and then build your own image classifier to distinguish cat photos from dog photos
Machine Learning Crash Course or equivalent experience with ML fundamentals
Proficiency in programming basics, and some experience coding in Python
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Early computer vision models relied on raw pixel data as the input to the model. However, as shown in Figure 2, raw pixel data alone doesn't provide a sufficiently stable representation to encompass the myriad variations of an object as captured in an image. The position of the object, background behind the object, ambient lighting, camera angle, and camera focus all can produce fluctuation in raw pixel data; these differences are significant enough that they cannot be corrected for by taking weighted averages of pixel RGB values.
Figure 2. Left: Cats can be captured in a photo in a variety of poses, with different backdrops and lighting conditions. Right: averaging pixel data to account for this variety does not produce any meaningful information.
To model objects more flexibly, classic computer vision models added new features derived from pixel data, such as color histograms, textures, and shapes. The downside of this approach was that feature engineering became a real burden, as there were so many inputs to tweak. For a cat classifier, which colors were most relevant? How flexible should the shape definitions be? Because features needed to be tuned so precisely, building robust models was quite challenging, and accuracy suffered.
Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.
Business: In marketing field companies use it to develop their strategies, to understand customers’ feelings towards products or brand, how people respond to their campaigns or product launches and why consumers don’t buy some products.
Politics: In political field, it is used to keep track of political view, to detect consistency and inconsistency between statements and actions at the government level. It can be used to predict election results as well!
Public Actions: Sentiment analysis also is used to monitor and analyse social phenomena, for the spotting of potentially dangerous situations and determining the general mood of the blogosphere.
Predictive sales analytics uses sales data to identify patterns in customers’ and leads’ behaviors to make future sales predictions. Hence, you can better understand your business’s financial health for the months or years to come.
A typical example of predictive sales analytics is lead scoring. In most sales situations, there is a long list of leads in the databases. Sales reps’ day-to-day job is to plan follow-ups on those leads, make phone calls, send emails, use their own subjective judgments to qualify leads, and so on.
All of those tasks will take a significant amount of time, making it hard for sales reps to prioritize resources and focus on closing deals. Whenever you spend too much time on a lead that has a low probability of becoming your customer, you may lose the opportunity to convert a “better” lead who is more interested in your products.
But that’s not the case if you use a predictive sales analytic tool. A predictive analytic tool for sales uses predictive modeling to predict future behavior based on past behaviors. It combines historical and current data to help you understand why a lead takes too long to convert or who you should focus your efforts on.
Because a predictive analytics tool applies data science and machine learning, it’ll help reduce errors in analysis and increase the accuracy of identifying quality leads.
Predictive analytics isn’t just beneficial for retail and ecommerce. Any industry can use predictive analytics to forecast sales, detect risks, and optimize sales operations.
For example, if you’re running a financial institution, you can use predictive analytics to detect fraud, measure credit risk, or identify new investment opportunities. For manufacturers, predictive analytics help discover factors leading to reduced quality, production failures, and distribution risks.
To understand the importance of predictive sales analytics, let’s take a look at its benefits for your business.
A comprehensive sales forecast considers competitors, historical sales, industry trends, economic shifts, and other data sources to project accurate future sales. Predictive analytics provide you with all of these data, plus actionable insights to create S.M.A.R.T (i.e., specific, measurable, attainable, relevant, and time-bound) goals for your sales team.
By anticipating sales using predictive analytics, you can better prepare for demand, inventory, and production. You’ll also know when to launch promotions and how to do that if sales are expected to be too weak. The more accurate the sales forecast, the more effective your sales planning, demand planning, inventory controls, and financial planning.
As said earlier, predictive analytics helps you quickly score leads and identify the right customers at the right time. With rich data sets, you can develop a detailed view of your prospects, improve outreach strategies, and build an effective funnel to turn those prospects into customers.
But the value of predictive analytics goes well beyond lead targeting. Some predictive analytics tools allow you to automate sales processes, helping your sales reps reduce repetitive tasks and have more time to talk with leads. These tools can even give your sales reps the most probable questions that prospects may ask and pinpoint the most opportune time to target them.
Predictive sales analytics enable you to understand your customers better. You’ll learn what makes them tick, what drives loyalty, and why they have certain behaviors. These insights are invaluable when developing your customer loyalty programs.
Predictive sales analytics help you create more targeted upselling and cross-selling strategies. For example, if you know that 30% of customers who buy product A come back to buy product B within one month, you can then create a bundle of products A and B to speed up the buying process and increase the average order value for each transaction.
The process of identifying human emotion
If someone showed you a picture of a person and asked you to guess what they’re feeling, chances are you’d have a pretty good idea about it. What if your computer could do the same? What if it could become even better than you are? It seems like an absurd thought, right?
Image Preprocessing
Feature Extraction
Feature Classification