Given the rising need for automated machine solutions along with AI and data science, ML (also known as Machine Learning) is one of the technologies that is quickly expanding. It is now one of the most well-liked developing technologies! And doing tasks is the greatest way to understand this technology. More experts are considering employment as machine learning engineers as the field grows. This article lists the best 7 machine learning projects for BTech and MTech students, which they may use to advance their knowledge in the field.

The top 7 Machine Learning projects for BTech and MTech students to learn from and put their talents to the test are as follows:

1. Comparative Analysis of ML Algorithms for Drought for Prediction

Forecasting droughts is exceedingly challenging due to the complexity of Earth's climate. Monitoring or predicting the severity of the drought on specific lands is urgently required to solve these serious issues. Drought forecasting keeps getting better as more data is gathered and more accurate models are created, yet certain droughts are easier to predict than others and some still catch us off guard.

Artificial intelligence is now becoming more entrenched in every industry. In order to anticipate the drought, we will use the subset machine learning from artificial intelligence.

In this project, conventional time series analysis techniques like Auto Regressive Integrated Moving Averages (i.e., ARIMA) and Vector Auto Regression (i.e., VAR) were used. Additionally, we put into practise the most potent and entirely automated algorithm prophet created by the Facebook data science team.

The fundamentals of machine learning help to understand what really it is and how does it vary from that of AI. It must be properly understood so that it can be implemented in projects for solving the real-world problems.

2. Machine Learning Algorithm For Brain Stroke Detection

In order to categorise the research works into 2 groups based on their functionalities, this study set out to thoroughly examine the state of the art in ML approaches for brain stroke.

Moreover, this study provides predictions by employing seven different machine learning algorithms: SVM, Decision Tree, Logistic Regression, Naive Bayes, Random Forest, KNN, and Multi-layer Perceptron (MLP-NN).

Must Read: Innovative Deep Learning Projects List

3. Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model

In this work, an XGBoost (i.e., Extreme Gradient Boosting) model was put out to offload or transfer storage, forecast power prices, and so lower data centre energy expenditures.

In order to efficiently reduce energy usage and offload data storage in data centres, the performance of this strategy is assessed using a real-world dataset given by the IESO (i.e., Independent Electricity System Operator) in Ontario, Canada. 30 percent is used for testing, while 70 percent is for training.

4. Analysis for Disease Gene Association Using Machine Learnin g

In this effort, novel computational techniques for identifying disease-related genes were developed and examined. For the purpose of selecting candidate genes, certain cutting-edge topological and biological factors that are currently disregarded were included.

Based on TP rate, FP rate, precision, recall, F-measure, and ROC curve assessment parameters, the various computational approaches were assessed using disease-gene association data from DisGeNET in a 10-fold cross-validation mode.

5. Predicting Flight Delays with Error Calculation using Machine Learned Classifiers

Airline, airport, and traveller losses result from flight delays. For all parties involved in commercial aviation, their prognosis is important when making decisions. The aviation industry has experienced enormous expansion in recent decades, which is clogging up the roads. Aside from unsuitable weather, delays might also be the result of divine intervention.

The effort will evaluate the flight delay data and using machine learning methods to forecast it. The methods for Gradient boosting, Bayesian ridge, Random forest, and Decision Tree will be compared.

Also Try: Top 7 Artificial Intelligence Project Ideas

6. An Experimental Study for Software Quality Prediction with Machine Learning Methods

By utilising pertinent elements of a sizable data collection, the study seeks to increase estimation accuracy. Higher accuracy was achieved using a feature selection technique and correlation matrix. Additionally, the study has tested even more modern techniques that have been effective for various prediction tasks.

To forecast software quality and identify the relationship between quality and development features, data is subjected to machine learning techniques like Xgboost, Random Forest, and Decision Tree. The outcomes of the experiment demonstrate that machine learning algorithms are capable of making accurate estimates of software quality levels.

7. Machine Learning based Rainfall Prediction

One of the key methods for predicting the weather in every country is rainfall forecasting. With the use of MLR (i.e., Multiple Linear Regression), this application suggests a rainfall prediction model for an Indian dataset. To more accurately anticipate the amount of rainfall, the input data includes a variety of meteorological characteristics.

The metrics utilised to validate the suggested model are accuracy, correlation, and MSE (i.e., Mean Square Error). The results show that the suggested machine learning model produces superior outcomes compared to the various methods that have been published.

Know more: Machine Learning based Rainfall Prediction