Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm


Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning

Machine Learning Projects for the Final Year Students

We live in a technologically advanced age where machines and various technologies are all around us. Machine learning is a method for teaching computers to think and learn. In the present era, machine learning can greatly benefit humans. We analyse a set of data in machine learning to provide predictions. Machine learning is applicable to a wide range of industries, including engineering, medicine, agriculture, and many more. In this post, we'll examine a few excellent and cutting-edge project concepts in the subject of machine learning.

1. Stock Price Predictor

If you wish to advance your career in finance, this project can be a suitable choice. For a machine learning developer who is interested in data science, it is a great project concept. Everyone is interested in the stock market these days. You may have noticed that there are numerous stock price prediction applications. Although it is challenging, machine learning can resolve this issue. You may encounter some challenges in this assignment because you will be given a variety of stock-related variables, including volatility indices, prices, macroeconomic indicators at the international level, fundamental indicators, and others. For this project, time series can be used.

2. Social Media Sentiments Analysis

These days, social media platforms are used by every one of us. Through social media platforms, we communicate with the public and share our ideas. It follows that it is evident that social media sites like Facebook, Twitter, Instagram, and others have a large amount of data that represents our attitudes, feelings, and views regarding various topics. This may be an election, a movie release, the cricket world cup, a crime, some images of famous people, some literature, or many other things. This large collection of data can be used by machine learning developers to analyse how a community feels about a certain topic. To analyse this dataset, apply machine learning or deep learning techniques.

3. Fake News Detection

This is a fantastic concept as well. News and media have a significant role in protecting democracy for all of humanity. Consider the past, when it might take several days to deliver a letter. But it is now simple. We must accept that it is a lot simpler to receive news now than it was before the invention of the internet. On the internet, we may access news from a variety of sources, including social media. But occasionally, it causes us great harm. When someone spreads risky bogus news, society is harmed. Because internet access is so inexpensive, it is quite simple to disseminate this kind of bogus news. It is crucial to stop this from happening. However, it is difficult to scrutinise each text and video. So, machine learning is required in this situation. You may create a model in this project that will stop fake news from propagating.

4. Machine Learning Project to Detect Loan Eligibility

It may be a significant undertaking that gives your resume some unique value. The main goal of this research is to assess if a loan request application is legitimate or not. You must be familiar with the bank's loan-granting policies. Determining whether a person or organisation can repay a loan is absolutely essential. Truth be told, it is not their fault when banks wait too long to do this kind of detection. Before granting the loan, they must verify all of the customer's information, which adds time to the process. However, a consumer who needs a loan quickly should do so. This is where machine learning can be useful. Your current task is to make the bank's work of determining loan eligibility simpler. You'll be working with a dataset. The customer's age, sex, marital status, wealth, income, qualifications, guarantor, past loan information, credit card information, and many other variables are all included in this dataset. You must use machine learning methods to identify eligibility in this data. You will undoubtedly develop your machine learning concepts through this project.

5. Inventory Demand Forecasting

Just consider Zomato, Swiggy, and other online food delivery services. Customers of this business are connected to local good food stores. These businesses frequently offer specials and discounts on holidays. Customers really appreciate this item. However, because they are unsure of how client demand will develop, restaurants find it challenging to manage their inventory. It frequently happens that a lot of their food is wasted. This phenomenon is not exclusive to food applications or restaurants; it also occurs in large corporations or industries. Demand from inventors is crucial for your company. However, humans find it extremely challenging to notice this object. So, we must enlist the aid of machines to tackle this issue. We will forecast inventory demand using machine learning. It would help if you created a single model that will forecast inventory demand based on several factors pertaining to that business and its product. For this situation, you must employ pertinent machine learning techniques. In this project, we'll employ algorithms like state variable machines (SVM), XGBoost, Gradient Boosting Machine (GBM), and bagging. Therefore, you should choose this assignment if you want to add some worth to your resume.

6. Plant Species Identification by Machine Learning

You should choose this project if you are interested in botany and machine learning or data science, or vice versa. You will learn a lot about botany and machine learning through this project. The application of botanical concepts in machine learning is rather perplexing. However, it is not impossible. Just consider how machine learning enables people to work efficiently. In this situation, the machine is also assisting us. In this study, we want to identify leaves based on their structures and shapes. The project itself is extremely intriguing. You must utilise machine learning and deep learning methods in this project to determine the precise name of the leaf matching.

7. Driver Demand Prediction

You should include this project in your resume. You've probably heard of the applications that allow us to reserve cars and order food. In these two situations, the organisation requires car drivers. These drivers may be on bikes or four wheels. If a firm decides to launch its operations in a new location, it will need to determine whether or not it can find enough drivers in the region. In a different scenario, it's critical to comprehend how many drivers are needed throughout the day to keep everything running well. In this project, we'll use data on drivers, numbers, local businesses, if the day is unusual or not, how long customers have previously waited at the same pick-up location, and much more. To forecast the need for drivers, a model will be developed. Throughout this project, you will undoubtedly acquire more crucial machine learning concepts, and this project will undoubtedly be very beneficial to you in the future.

8. Sales Forecasting

Forecasting the degree of demand for each item in their inventory is important for large B2C businesses and markets. With the use of sales forecasting, business owners can plainly identify which products are in high demand. The amount of waste will be reduced and the additional impact on future budgets will be calculated with accurate sales forecasting. Retailers like Walmart, IKEA, Big Basket, and Big Bazaar employ sales forecasting to anticipate future product demand. You need to be knowledgeable with a number of techniques for cleaning raw data in order to construct such ML projects. Additionally, extensive understanding of regression analysis, particularly simple linear regression, is required. To create these kinds of programmes, you must need libraries like Dora, Scrubadub, Pandas, NumPy, etc. Such machine learning initiatives can be modelled using dummy datasets such as time-series datasets, datasets for shampoo sales, etc.

9. Patient’s Sickness Prediction System

Machine learning's efficacy has also been demonstrated in the field of healthcare. Meeting the needs of millions of patients becomes increasingly challenging for conventional healthcare institutions. However, as machine learning (ML) technology advanced, the paradigm changed in favour of value-based care. There are apps that can save patient data inside every piece of modern medical technology, including gadgets and equipment. These data can be utilised to create a system that can foresee the patient's state and the admission at the same time. KenSci is an AI-based tool that analyses clinical data, predicts sickness, and helps allocate resources more effectively. Use open-source medical datasets like CHDS (Child Health and Development Studies), HCUP, and Medicare to test your machine learning approach. These initiatives can be used to telemedicine, remote monitoring, wearable medical devices, etc. To build such algorithms, you need to grasp the following concepts thoroughly:

  1. Classification and clustering model is used when the given data does not clearly indicate a result. Machine learning (ML) clustering looks for recognisable patterns in the data. Data classification is determined via classification.
  2.  Regression model: The primary objective of regression analysis is value discovery. The prediction strategies your system will employ while interacting with the target data and independent factors serving as dependent variables are covered in this method (predictor data).

Use libraries like NumPy, Pandas, Matplotlib, Theano, and others, as well as frameworks like Keras and Hugging Face, to put this machine learning project into practise.

10. Digit Classification Project using MNIST Dataset

An impressive machine learning project that uses neural networks and machine learning principles is the digit classification project. Working with unstructured data (such as the image dataset) and turning it into structured data was difficult from the beginning of machine learning (texts). Convolutional neural networks (CNNs) will be used in this research to train machine learning algorithms. Your system will be able to recognise handwritten digits with ease, thanks to the smooth training of ML models made possible by the MNIST dataset. Computer vision is the field that this kind of project falls under. It would be best if you used libraries like NumPy, PIL, Pillow, Scikit-image, Tkinter, etc., to develop this project. Tkinter will support you in creating a GUI for your application. Keras will oversee all neural network algorithms. Frameworks like TensorFlow and Keras must be used. The MNIST dataset, which contains approximately ten thousand images for testing and sixty thousand photos with handwritten digits from zero to nine, can be used to train your model.