March 2024

Chronic Kidney Disease Prediction Using Machine Learning The healthcare industry is one of the most important domains for data mining. Every day, the healthcare business creates extended data regarding patients, diseases, hospitals, medical equipment, treatment costs, etc. Data mining assists clinicians in making proper treatment decisions and disease prediction in the early stages, which helps avoid or lessen the effects of diseases like heart disease, cancer, and chronic kidney disease, among others. Chronic Kidney Disease (CKD) is characterized by kidney damage or decreased function, as indicated by a glomerular filtration rate (GFR) of less than 60 ml/min per 1.73 m2 or both for at least three months. The kidneys are unable to filter blood properly. Approximately 80 million Americans are at risk of CKD. So, predicting chronic kidney disease is critical for clinicians to make an informed judgment about whether the patient is infected and to give treatment in the early stages to prevent the patient from developing the disease.In 2016, CKD was the tenth most significant cause of death in the US. Over 500000 individuals received dialysis, and 200000 underwent kidney transplants. It affects an estimated 37 million people in the United States, or around 15% of adults. It is more common in women (15%) than in males (12%). Approximately 80 million Americans are at risk of CKD. And almost 90% of those people are unaware they have CKD. The dataset collects, analyzes, and distributes information regarding chronic kidney and stage renal disease in the United States. CKD is a condition in which kidneys are damaged and cannot filter blood as well as they should. Because of this, additional fluid and wastes accumulate from the blood remains in the body and may cause other health problems such as heart disease and stroke. So, this dataset will help to predict the machine learning techniques. SVM in classificaction problems SVM classifies the output into Two classes with CKD and without C KD main objective of this study is to predict the patient with CKD using a smaller number of attributes while maintaining the access accuracy Our main parameter will be the GLOMERULAR FILTRATION RATE called vital parameters. Another parameter will be blood circulation rate, age, gender, and other characteristics can be used to calculate this. Recommended Reading AI Music Composer using Machine Learning Real-Time Object Detection Using Machine Learning 30 Creative Final Year Projects with Source Code Background Study The majority of CKD is increasing worldwide. In the United States, over 37 million people have CKD, with the majority of cases going untreated. CKD is also a prominent cause of death, especially among elderly persons. Diabetes, high blood pressure, obesity, and smoking are all risk factors for chronic kidney disease (CKD). Despite efforts to improve the early detection and management of CKD, many patients do not receive adequate care, and better techniques for diagnosing and managing CKD are required. Objective of Study The primary goal of this study is to create and test machine learning models for predicting the risk of CKD using patient data. We want to develop models that accurately predict the existence of CKD using demographic, clinical, and laboratory data. ➢ Evaluate the effectiveness of various feature extraction strategies for finding key predictors of CKD. ➢ Evaluate the effectiveness of several machine learning methods for predicting CKD risk. ➢ Evaluate the effect of sample size and data imbalance on model performance. ➢ Identify critical factors linked with CKD risk and improve diagnosis and management. Classification Algorithms The classification techniques used in this research:   Logistic Regression: a statistical model used to predict a dependent variable based on a given set of independent variables; it uses a logistic function to build a model to predict binary values. Naive Bayes: a classifier calculated the probability of a given dataset to perform classification. Each attribute in data is independent of others. The highest likelihood of class is the output class.   Decision Tree: This technique is one of the decision support techniques that apply a graph model and its likely values; it consists of nodes, branches and leaves; each node represents a test of variables, branches represent the test results, and the leaves represent the class label. Also, it is a way of presenting a conditional algorithm.   K-Nearest Neighbor (KNN) is among the simplest machine learning algorithms. It is the non-parametric method used for classification and prediction. It can be used to give weight to the contributions of the neighbours, so the nearer neighbours contribute more to the average than more distant ones. Support Vector Machine (SVM): is machine learning algorithm that is very useful i n solving classification problems. It is used to classify data in an imaginary line, providing that a barrier separates the points from each other. Data mining Tools Python WEKA tool Orange tool Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning Data mining technique in chronic kidney disease ▪ Many researchers use data mining techniques to predict kidney disease (Kunwar et al., 2016). The authors used the classification techniques like Naive Bayes and Artificia l Neural Network (ANN); their experiment was in the Rapid Miner tool; the tool showed that Naive Bayes is more accurate. It obtained 100% accuracy compared to ANN, which has 72.73% accuracy (Vijayarani & Dhayanand, 2015). They used the Naive Bayes and Support Vector Machine (SVM) to predict four types of kidney disease, and the result showed that the SVM it the best performance and accuracy. It was 76.32 when compared to Naive Bayes, which has 70.96%. ▪ They used Probabilistic Neural Networks (PNN), Multilayers Perceptron (MLP), Support Vector Machine (SVM), and Radial Basis Function (RBF) techniques for the prediction n stages of kidney disease; the result showed that the PNN is the highest accuracy 96 .7%, comparing with others, the SVM 60.7%, RBF 87%, MlP 51.5%, (Subas et al., 20 17) they used ANN, SVM, C4.5 decision tree, KNN, and Random Forest, the result showed that the KNN 95.75%, C4.5 decision tree 99%,

Diabetes prediction using machine learning The diabetes dataset is widely used in machine learning and data analysis. The dataset comprises medical information about patients with diabetes and is frequently used to predict whether or not a patient has diabetes based on clinical criteria.The dataset contains information on 768 patients, 8 medical predictors, and an outcome variable that indicates whether or not the patient has diabetes. The predictors are:Pregnancies: How many times have you been pregnantGlucose: Plasma glucose levels after 2 hours of an oral glucose tolerance test.Blood pressure: Diastolic blood pressure (mm Hg).Skin Thickness: Triceps skin fold thickness (mm)Insulin: 2-hour serum insulin (mU/mL)BMI is calculated as weight in kilograms divided by height in meters squared.Diabetes-Pedigree-Function: Diabetes Pedigree FunctionAge: in years. The outcome variable is binary, with 1 indicating that the patient has diabetes and 0 indicating that the patient does not have diabetes. The dataset is often used in machine learning projects for predicting diabetes and can also be used for data analysis and visualization. It is a widely used dataset in the field of healthcare and is an important resource for researchers and data scientists working on diabetes-related projects. Background of the Study Diabetes is a chronic medical disorder signified by high blood sugar levels. Diabetes has a long history, and our understanding and treatment of disease have changed dramatically over time. Here is a brief history of diabetes. Ancient Egypt: Diabetes symptoms were initially described in ancient Egypt around 1550 BCE. It represented a condition known as “too much emptying of the urine.” Ancient India: Diabetes was first discovered and classified by Indian physicians as madhumeha (meaning “honey urine”) and vridhameha (meaning “large urine”). They also noticed that diabetics had a sweet taste in their urine. Ancient Greece and Rome: In the 2nd century AD, Greek physician Aretaeus of Cappadocia described the disease as “diabetes”, which means “to pass through” in Greek, referring to the excessive urine output. In the 1st century AD, Roman physician Celsus recommended a diet low in carbohydrates and fibre for treating diabetes. Middle Ages: During this time, there was little progress in understanding diabetes. The sweet taste in the urine still identified it, which was thought to be caused by too much food and drink. 18th and 19th centuries: In 1776, English physician Matthew Dobson discovered that sugar was present in the urine of people with diabetes. In 1889, German physician Oskar Minkowski found that removing the pancreas from a dog caused the dog to develop diabetes. In 1869, Paul Langerhans identified the clusters of cells within the pancreas that produce insulin, which was later discovered to be the hormone responsible for regulating blood sugar. 20th century: In 1921, Canadian scientists Frederick Banting and Charles Best discovered insulin, which became the first effective treatment for diabetes. In 1959, the oral medication tolbutamide was approved for diabetes treatment. In 1982, the first biosynthetic human insulin was produced. In the late 1990s, the first oral medication for type 2 diabetes, metformin, was introduced. 21st century: Diabetes continues to be a significant health problem worldwide, and research in genetics, cell biology, and technology are advancing the understanding and treatment of diabetes. In 2020, the first “artificial pancreas” system was approved for use in people with type 1 diabetes. Diabetes is now recognized as a complex condition that requires regular treatment to avoid long-term problems. There are two forms of diabetes: type 1 and type 2.  Type 1 diabetes, which usually appears in childhood or adolescence, is an autoimmune illness in which the immune system targets and destroys insulin-producing cells in the pancreas.  Type 2 diabetes, which is more frequent and usually appears in adulthood, happens when the body becomes insulin resistant or does not create enough insulin to maintain normal blood sugar levels. Other types of diabetes include gestational diabetes, which develops during pregnancy, and uncommon genetic variants of diabetes. Diabetes can cause a variety of health consequences, including heart disease, renal disease, nerve damage, and blindness. Treatment typically involves managing blood sugar levels through diet, exercise, medication, and insulin therapy. Prevention efforts for type 2 diabetes include maintaining a healthy weight, exercising regularly, and following a healthy diet. Early diagnosis and effective management are critical for preventing long-term complications and improving outcomes for people with diabetes.   Diagrams explain the Objectives Here are some diagrams and tables to explain the area/domain of the diabetes dataset. Scatterplot matrix A scatterplot matrix can be utilized to show the relationships between the variables in the diabetes dataset. The diagonal represents the distribution of each variable, but the off-diagonal parts define the pairwise relationships between them. Correlation matrix A correlation matrix shows the pairwise correlations among the variables in the dataset. The correlation coefficient goes from -1 to 1: -1 means a perfect negative correlation. 0 shows no association. 1 indicates a perfect positive correlation.  The scatterplot matrix and correlation matrix reveal that there are some correlations between the variables. For example, there is a positive relationship between glucose levels and the outcome variable, meaning that greater glucose levels are associated with an increased risk of diabetes.  Histograms Histograms can be used to visualize the distribution of each variable in the dataset.  From the histograms, we can see that some of the variables, such as blood pressure and BMI, are normally distributed, while others, such as insulin and skin thickness, have skewed distributions. Summary statistics Summary statistics can be used to summarize the central tendency, variability, and distribution of each variable in the dataset. Variable Mean Standard deviation Minimum Maximum Pregnancies 3.85 3.37 0 17 Glucose 120.89 32.00 0 199 Blood Pressure 69.10 19.36 0 122 Skin Thickness 20.54 15.95 0 99 Insulin 79.80 115.21 0 846 BMI 31.99 7.88 0 67.1 Diabetes Pedigree Function 0.47 0.33 0.08 2.42 Age 33.24 11.76 21 81 From the summary statistics, we can see that the range of values for each variable varies widely. For example, the range of insulin values is from 0 to 846, while the range of age values is from 21 to 81. This

Car Price Prediction Using Machine Learning Machine learning models are becoming more common across a wide range of businesses as society becomes more dependent on technology. Machine learning is improving the automotive industry. Machine-learning algorithms, in particular, have been proven to be quite effective in assisting individuals and businesses in determining the value of their cars in the area of old car price prediction. Old car price forecasting is an important component of the automotive industry since it assists individuals and corporations in determining the worth of their automobiles. This information is required for various purposes, including vehicle sales, insurance claims, and financial planning. However, evaluating the value of a used car can be difficult because it depends on various factors, including the make, model, year, mileage, and state of the car. Machine learning models are useful in this situation. They may learn to identify patterns and trends in vast datasets of previous auto sales, which can then be used to forecast the value of a specific vehicle. These models can take into account the make, model, year, mileage, condition, and even the location of the car. This means that the estimated worth of a car can be very precise, giving people and companies important information they can use to make wise decisions. There are numerous different types of machine learning models that can be utilized to predict the price of used cars. The linear regression model is one of the most well-liked ones. To predict the value of a certain car, this model first finds the line of best fit through a dataset. Another well-liked paradigm is the decision tree model, which divides a dataset into progressively smaller subgroups in accordance with predetermined criteria. This procedure keeps going until the model makes a choice or a prediction. In addition to these models, there are other more refined machine learning models, like neural networks and support vector machines. These algorithms can be very good at estimating the value of used cars because they can identify complex patterns and connections in the data. In general, the automotive sector is using machine-learning models more frequently to anticipate the price of used cars. These algorithms may learn to identify patterns and trends in the data by examining vast datasets of previous automobile sales, which can then be used to forecast the value of a specific vehicle. For both individuals and companies, this information may be extremely helpful in guiding their decisions about the purchase, insurance, and financial planning of their automobiles. Machine learning models for predicting the price of used cars are going to get much more complex as technology develops. Background Several economies throughout the world depend heavily on the automotive sector, and the purchasing and selling of cars is a multi-billion dollar industry. Yet figuring out a used car’s worth can be difficult because it depends on so many different things, including the make, model, year, mileage, and condition of the car. Machine learning models have recently become a highly successful tool for estimating the worth of used automobiles, giving people and businesses useful information they can use to make wise decisions. In order to help people and businesses assess the value of their vehicles, old car price forecast is a crucial component of the automotive industry. This data is essential for various purposes, such as car sales, insurance claims, and financial planning. Yet estimating the value of a used car may be challenging because it depends on several variables, including the make, model, year, mileage, and state of the car. In the past, figuring out the worth of a used car required performing a complicated series of calculations depending on the age, mileage, make, and model of the car. These computations, however, were frequently time-consuming and prone to mistakes, resulting in unreliable projections and possibly causing people and organisations to make bad judgements. Machine learning algorithms have been a highly successful technique in recent years for estimating the value of used cars. These models can learn to identify patterns and trends in the data by analysing enormous datasets of historical automobile sales using cutting-edge algorithms. These algorithms may produce extremely precise projections of the value of a given vehicle by taking into account a wide range of criteria, including the make, model, year, mileage, condition, and even the location of the vehicle. The capacity of machine learning models to account for a variety of parameters is one of the main benefits of using them to anticipate used car prices. This covers not only the fundamental details of the vehicle’s make, model, and year but also more intricate details like its condition, the location of the transaction, and even its particular features and choices. This level of specificity enables incredibly precise valuation estimates for a specific vehicle, giving people and organisations useful data they may use to make wise decisions. Another benefit of employing machine learning algorithms to anticipate used car prices is their ability to learn and adapt over time. These models can keep making predictions as new data becomes available, increasing the precision and dependability of their predictions over time. This indicates that people and companies can trust the forecasts made by these models because they are founded on the most recent and reliable data available. In general, the automotive sector is using machine learning models more frequently to anticipate the price of used cars. These algorithms are quite good at estimating the worth of used cars, giving people and companies useful information they can use to make wise decisions. As technology develops, even more complex machine learning models will be created in the future for the prediction of used car prices, enhancing the precision and dependability of these predictions. Diagram Correlations Output Diagram Explanation of Dataset We picked the dataset “CarPrice” online. This dataset has the record of old cars to predict the price.   No of rows: 206  No of Columns:26   This dataset covers all the required information of the car to predict the car price. car_ID 

Maize Leaf Disease Detection Corn is one of the most important cereal crops globally, providing essential nutrients and calories to millions of people. However, corn plants are highly susceptible to various diseases, which can cause significant yield losses. Crop diseases are responsible for over 10% of global crop losses, and timely detection and management of these diseases are crucial to minimize these losses and ensure food security. Early detection and management of crop diseases require constant monitoring and identification of various pathogens, which can be labor-intensive and time-consuming. Advancements in machine learning and artificial intelligence have opened up new possibilities for the early detection of crop diseases, offering an alternative to manual inspection. Machine learning models can process large amounts of data and identify patterns that are not visible to the human eye, making them effective tools for crop disease detection. In this study, we propose a machine learning approach using a maize dataset to detect corn diseases. The maize dataset comprises images of corn leaves affected by various diseases such as gray leaf spot, common rust, and northern corn leaf blight. Our proposed approach uses convolutional neural networks (CNNs) to classify images into different disease categories, enabling accurate and timely detection of corn diseases. The CNNs are trained on a large dataset of corn leaf images, enabling them to identify patterns and features that are unique to different disease categories. Our study aims to provide a reliable and efficient approach to detecting corn diseases, which can assist farmers in making informed decisions regarding crop management. Early detection of diseases can lead to the timely implementation of management strategies, such as the use of fungicides or cultural practices, reducing the spread and severity of diseases. Furthermore, the proposed machine learning approach can reduce the dependency on manual inspection, which can be costly and often prone to errors. In conclusion, our study presents a novel approach to early detection of corn diseases using a maize dataset and convolutional neural networks. The proposed approach can assist in the development of sustainable agriculture practices by enabling timely and accurate disease detection, leading to improved crop yields and food security. We hope that this study will inspire further research in this field, ultimately leading to the development of more effective and efficient approaches to crop disease management. Background of the Study Corn is a staple food crop globally, and its cultivation and production are critical to food security. However, corn plants are susceptible to various diseases, such as gray leaf spot, common rust, and northern corn leaf blight, which can cause significant yield losses. Early detection and management of these diseases are crucial to minimize crop losses and ensure food security. Traditional methods of detecting and managing these diseases include visual inspection of crops, which can be time-consuming and prone to errors. Recent advancements in machine learning and computer vision techniques have opened up new opportunities for early detection of crop diseases. Machine learning models can process large amounts of data and identify patterns that are not visible to the human eye, making them effective tools for crop disease detection. These models have been applied to various crop diseases, including corn diseases, with promising results. In this study, we propose a machine learning approach to detect three common corn diseases, gray leaf spot, common rust, and northern corn leaf blight, using a maize dataset. The maize dataset comprises of images of corn leaves affected by the three diseases, enabling the development of a machine learning model that can identify and classify these diseases. Objectives of the Study The objective of this study is to develop a machine learning model that can detect and classify gray leaf spot, common rust, and northern corn leaf blight in corn leaves using a maize dataset. Developing a machine learning model using convolutional neural networks (CNNs) to identify and classify the three common corn diseases. Evaluating the performance of the developed model by measuring its accuracy, precision, and recall. Methodology The proposed methodology involves the following steps: Data Collection: We collected a maize dataset comprising of images of corn leaves affected by gray leaf spot, common rust, and northern corn leaf blight. The dataset will be curated to ensure that it is balanced, and each disease class has an adequate number of samples. Data Preprocessing: We preprocessed the data by resizing the images, removing noise, and augmenting the data to create a larger dataset for training the model. Model Development: We developed machine learning models Decision tree, Random Forest, Nave Baysien, Support Vector Machine, Support Vector Machine and CNN to identify and classify gray leaf spot, common rust, and northern corn leaf blight in corn leaves. The CNN model will be trained using the preprocessed dataset. Model Evaluation: We will evaluate the performance of the developed model by measuring its accuracy, precision, and recall. We will also compare the performance of the developed model with existing stateof-the-art methods for detecting corn diseases. Expected Outcome We expect that the developed machine learning model will achieve a high level of accuracy in detecting and classifying gray leaf spot, common rust, and northern corn leaf blight in corn leaves. The developed model can be used by farmers to make informed decisions regarding crop management, leading to improved crop yields and food security. Additionally, we expect to provide insights into the unique features and patterns associated with the three common corn diseases. This can assist in the development of more effective and efficient approaches to crop disease management, leading to sustainable agriculture practices. In conclusion, this study presents a novel approach to detecting common corn diseases using a maize dataset. Explanation of Dataset The maize dataset is a collection of images of corn leaves affected by three common corn diseases, gray leaf spot, common rust, and northern corn leaf blight. The dataset was collected from various farms and research institutions and curated to ensure that it is balanced, with each disease class having an adequate number of samples. The dataset comprises of

Titanic survival prediction using machine learning Technology’s impossible advancement has both facilitated and complicated our lives. One of the advantages of technology is that an extensive range of data can be retrieved quickly when needed. However, it can be challenging to obtain accurate information. Raw data that can be easily acquired from online sources does not make sense; it must be processed to serve as an information retrieval system. In this context, feature engineering techniques and machine learning algorithms are essential. This study aims to extract as many accurate findings as possible from raw and missing data using machine learning and feature engineering methods. Therefore, one of the most popular datasets in data science, Titanic, is used.  The science of machine learning has enabled analysts to gain insights from historical data and occurrences. The Titanic accident is one of the most famous shipwrecks in world history. The Titanic was a British cruise ship that sank in the North Atlantic Ocean a few hours after hitting an iceberg. While there are facts to back up the cause of the tragedy, there are numerous theories on how many passengers survived the Titanic disaster. Over the years, data on both survivors and dead passengers has been gathered. The dataset is publicly available on the website Kaggle.com.   The Kaggle Titanic dataset is one of the most widely used in machine learning. It is a dataset containing information about the passengers on the Titanic when it sank during its maiden voyage in 1912. The dataset is commonly used in predictive modeling and machine learning contests. The dataset has 891 rows, each representing a passenger, and 12 columns with information about each passenger, including their name, age, gender, cabin, and ticket number. The purpose of evaluating this dataset is to create a model that can correctly predict whether or not a passenger survived. Beginners and specialists commonly use the dataset for data cleaning, feature engineering, and model construction. It provides the ability to learn and use various machine-learning techniques, including logistic regression, decision trees, random forests, and neural networks, to mention a few.   The Kaggle Titanic dataset has become a benchmark dataset in the machine learning community, with numerous tutorials, blog posts, and courses developed around it to help beginners get started with machine learning. Using machine learning algorithms with a dataset of 891 rows in the train set and 418 rows in the test set, the article aims to discover the relationship between factors such as age, gender, fare, and the likelihood of passenger survival. These factors may have had an impact on the passengers’ survival rates. In this article work, multiple machine-learning techniques are used to predict passenger survivability. In particular, this article compares the algorithm based on the accuracy percentage on a test dataset. Background The R.M.S. Titanic is undoubtedly the most famous shipwreck in modern popular culture. Titanic was a British-registered ship in the White Star line controlled by a U.S. firm in which famous American financier John Pierpont “JP” Morgan held an important share. Harland & Wolff built the Titanic in Belfast, Northern Ireland, for the transatlantic passage from Southampton, England, to New York City. It was the largest and richest passenger ship of its time, and it was thought to be unsinkable. Titanic was launched on May 31, 1911, and set ship on its first trip from Southampton on April 10, 1912, carrying 2,240 passengers and crew. On April 15, 1912, after striking an iceberg, Titanic broke apart and sank to the bottom of the ocean, taking with it the lives of more than 1,500 passengers and crew. The sinking of the RMS Titanic in 1912 is one of history’s most horrific ocean tragedies, killing more than 1,500 people. The ship, which was on its first trip from Southampton to New York, collided with an iceberg in the North Atlantic and sank, prompting a worldwide flood of sadness and shock. The Titanic has remained a popular topic since its sinking, with countless books, films, and documentaries addressing the disaster and its aftereffects. The Titanic narrative became more than simply popular culture. The Titanic also caught the interest of the data science community, with the Kaggle Titanic dataset emerging as a classic example of machine learning. The Kaggle Titanic dataset was built to provide a real-world dataset for data scientists to practice their abilities on a relevant subject. The dataset includes information about the Titanic’s passengers, such as their age, gender, class, and whether they survived the catastrophe. The dataset has 891 passengers, which is enough for beginners. The dataset has become a standard for machine learning algorithms, creating a model that can reliably predict which passengers would most likely survive the disaster. The dataset is frequently used to teach data cleaning, feature engineering, and model-building approaches, making it an invaluable resource for those interested in data science and machine learning. Beyond its usage as a teaching tool, the Kaggle Titanic dataset has been the subject of numerous scholarly investigations. Researchers analyzed the dataset to look into the demographics of the Titanic’s passengers and the elements that may have influenced their chances of survival. According to studies, women and children are more likely to survive than men, and passengers in first class had a greater survival probability than those in third class. Overall, the Kaggle Titanic dataset provides fascinating insights into one of history’s most terrible tragedies and is an excellent resource for anyone interested in data science and machine learning. Explanation of dataset The Kaggle Titanic dataset is a popular dataset for machine learning and data analysis contests hosted on the Kaggle website. The collection includes information about the passengers on the RMS Titanic, which sank on its maiden journey in 1912. The dataset consists of 1309 rows, each representing a Titanic passenger, and 12 columns containing various pieces of passenger information. The dataset contains numerical and category information, with the goal variable representing whether or not the passenger survived the sinking. The columns in the dataset are: Passenger Id: A

Wine quality prediction using machine learning The quality of wine is crucial to both consumers and the wine business. The traditional (professional) method of determining wine quality is very complex. Nowadays, machine learning models are essential instruments for replacing human labor. In this scenario, various features can be used to predict wine quality, but not all will be significant for accurate prediction. As a result, our article focuses on what wine characteristics are critical for achieving a promising outcome. We employed three algorithms (SVM, NB, and ANN) to create a classification model and evaluate relevant features. This work examined two wine-quality datasets: red and white. We used the Pearson coefficient correlation and performance measurement matrices such as accuracy, recall, precision, and f1 score to compare the machine learning algorithms to determine feature importance. A grid search strategy was used to improve model accuracy.For this project, I used the Red Wine Quality dataset to create multiple classification models that predict whether a given red wine is “good quality” or not. Each wine in this dataset receives a “quality” score between 0 and 10. For this project, I changed the result to a binary output where each wine is either “good quality” (a score of 7 or more) or not (a score of less than 7). 11 input variables determine the quality of wine: Fixed acidity Volatile acidity Citric acid Residual sugar Chlorides Free sulfur dioxide Total sulfur dioxide Density pH Sulfates Alcohol Attributes Description fixed acidity Fixed acids, numeric from 3.8 to 15.9 volatile acidity Volatile acids, numeric from 0.1 to 1.6 citric acid Citric acids, numeric from 0.0 to 1.7 residual sugar residual sugar, numeric from 0.6 to 65.8 chlorides Chloride, numeric from 0.01 to 0.61 free sulfur dioxide Free sulfur dioxide, numeric: from 1 to 289 total sulfur dioxide Total sulfur dioxide, numeric: from 6 to 440 density Density, numeric: from 0.987 to 1.039 pH pH, numeric: from 2.7 to 4.0 sulfates Sulfates, numeric: from 0.2 to 2.0 alcohol Alcohol, numeric: from 8.0 to 14.9 quality Quality, numeric: from 0 to 10, the output target Background A variety of machine learning algorithms are available for the learning process. This section discusses classification algorithms used in wine quality prediction and related research. Classification algorithm Naive Bayesian The naive Bayesian is a simple supervised machine learning classification technique based on Bayes’ theorem. The algorithm assumes that the feature criteria are independent of the class. The naive Bayes algorithm contributes to developing fast machine-learning models capable of making quick predictions. The algorithm uses the likelihood probability to determine whether a specific section has a spot in a particular class. Support Vector Machine The most common machine learning algorithm is the support vector machine (SVM). It is a supervised learning model that performs classification and regression tasks. However, it is mainly employed to solve classification problems in machine learning. The SVM method seeks to find the best line or decision boundary to divide an n-dimensional space into classes. So we can quickly place the new data points in the appropriate groupings. The optimal choice boundary is known as a hyperplane. The support vector machine selects the extreme data points that contribute to the formation of the hyperplane. In the diagram above, two distinct groups are classified using the decision boundary or hyperplane. The SVM model applies to both nonlinear and linear data. It uses a nonlinear mapping to turn the primary preparation information into a larger measurement. The model searches for the linearly optimal splitting hyperplane in this new measurement. A hyperplane can divide the data into two classes using proper nonlinear mapping to achieve sufficiently high measurements, and this hyperplane SVM employs support vectors and edges to discover the solution. The SVM model represents the models as a point in space, with the distinct classes separated by a gap to be mapped to ensure that instances are as wide as possible. The model can do nonlinear classification. Artificial Neural Network An artificial neural network is a collection of neurons capable of processing information. It has been successfully applied to categorization tasks in various commercial, industrial, and scientific domains. The algorithm model is a connection between neurons linked to the input, hidden, and output layers. The neural network is constant because, even if one of its components fails, it can function in parallel without difficulty.The implementation of the artificial neural network consists of three layers: input, hidden, and output. The input layer’s function is mapped to the input attribute, which sends feedback to the hidden layer. Objectives The project’s objectives are as follows: Explaining data sets using Python code. To apply various machine learning techniques. Experiment with multiple ways to determine which produces the most accuracy. To establish which characteristics are most suggestive of high-quality wine. Wine quality prediction using machine learning with source code Step 1: Import Libraries Pyton import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.datasets import load_wine from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, classification_report,accuracy_score import warnings warnings.filterwarnings("ignore") import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.datasets import load_wine from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, classification_report,accuracy_score import warnings warnings.filterwarnings("ignore") Step 2: Reading Data Pyton import wine dataset wine = datasets.load_wine() # np.c_ is the numpy concatenate function wine_df = pd.DataFrame(data= np.c_[wine['data'], wine['target']], columns= wine['feature_names'] + ['target']) wine_df.head() import wine dataset wine = datasets.load_wine() # np.c_ is the numpy concatenate function wine_df = pd.DataFrame(data= np.c_[wine['data'], wine['target']], columns= wine['feature_names'] + ['target']) wine_df.head() There are 1599 rows and 12 columns. The data was clean in the first five rows, but I wanted to double-check that there were no missing values. Step 3:

10 exciting C++ projects with source code Are you looking for interesting C++ projects to work on? Look no further! This article will look at the top 10 C++ projects with source code. These projects are not only fun to work on, but they also offer excellent learning opportunities. Whether you’re a newbie or an experienced coder, there’s something here for you. Source Code is also available to help you in making these exciting projects. 1. Library management system The Library Management System aims to improve the library’s organization and retrieval of books. This C++ software lets librarians maintain book records, track borrowing and returning operations, and process user registrations. Users may use a user-friendly interface to browse for books, view availability, and check their borrowing history. Administrators can also generate reports, manage fines, and keep the library running smoothly. C++ #include <iostream> #include <vector> #include <string> using namespace std; // Class to represent a book class Book { public: string title; string author; int id; bool available; Book(string t, string a, int i) : title(t), author(a), id(i), available(true) {} }; // Class to represent a user class User { public: string name; int userId; User(string n, int id) : name(n), userId(id) {} }; // Class to manage the library class Library { private: vector<Book> books; vector<User> users; public: void addBook(string title, string author, int id) { Book book(title, author, id); books.push_back(book); } void addUser(string name, int userId) { User user(name, userId); users.push_back(user); } void displayBooks() { cout << "Library Books:" << endl; for (const Book& book : books) { cout << "ID: " << book.id << "tTitle: " << book.title << "tAuthor: " << book.author; if (book.available) { cout << "tStatus: Available" << endl; } else { cout << "tStatus: Checked Out" << endl; } } } void displayUsers() { cout << "Library Users:" << endl; for (const User& user : users) { cout << "ID: " << user.userId << "tName: " << user.name << endl; } } void borrowBook(int userId, int bookId) { for (Book& book : books) { if (book.id == bookId && book.available) { book.available = false; cout << "Book successfully borrowed by user ID " << userId << "." << endl; return; } } cout << "Book not available or invalid ID." << endl; } void returnBook(int bookId) { for (Book& book : books) { if (book.id == bookId && !book.available) { book.available = true; cout << "Book successfully returned." << endl; return; } } cout << "Invalid book ID or book already available." << endl; } }; int main() { Library library; // Adding some books and users for testing library.addBook("The Catcher in the Rye", "J.D. Salinger", 1); library.addBook("To Kill a Mockingbird", "Harper Lee", 2); library.addBook("1984", "George Orwell", 3); library.addUser("Alice", 101); library.addUser("Bob", 102); // Displaying the initial state of the library library.displayBooks(); library.displayUsers(); // Simulating book borrowing and returning library.borrowBook(101, 1); library.borrowBook(102, 2); library.returnBook(1); // Displaying the updated state of the library library.displayBooks(); library.displayUsers(); return 0; } #include <iostream> #include <vector> #include <string> using namespace std; // Class to represent a book class Book { public: string title; string author; int id; bool available; Book(string t, string a, int i) : title(t), author(a), id(i), available(true) {} }; // Class to represent a user class User { public: string name; int userId; User(string n, int id) : name(n), userId(id) {} }; // Class to manage the library class Library { private: vector<Book> books; vector<User> users; public: void addBook(string title, string author, int id) { Book book(title, author, id); books.push_back(book); } void addUser(string name, int userId) { User user(name, userId); users.push_back(user); } void displayBooks() { cout << "Library Books:" << endl; for (const Book& book : books) { cout << "ID: " << book.id << "tTitle: " << book.title << "tAuthor: " << book.author; if (book.available) { cout << "tStatus: Available" << endl; } else { cout << "tStatus: Checked Out" << endl; } } } void displayUsers() { cout << "Library Users:" << endl; for (const User& user : users) { cout << "ID: " << user.userId << "tName: " << user.name << endl; } } void borrowBook(int userId, int bookId) { for (Book& book : books) { if (book.id == bookId && book.available) { book.available = false; cout << "Book successfully borrowed by user ID " << userId << "." << endl; return; } } cout << "Book not available or invalid ID." << endl; } void returnBook(int bookId) { for (Book& book : books) { if (book.id == bookId && !book.available) { book.available = true; cout << "Book successfully returned." << endl; return; } } cout << "Invalid book ID or book already available." << endl; } }; int main() { Library library; // Adding some books and users for testing library.addBook("The Catcher in the Rye", "J.D. Salinger", 1); library.addBook("To Kill a Mockingbird", "Harper Lee", 2); library.addBook("1984", "George Orwell", 3); library.addUser("Alice", 101); library.addUser("Bob", 102); // Displaying the initial state of the library library.displayBooks(); library.displayUsers(); // Simulating book borrowing and returning library.borrowBook(101, 1); library.borrowBook(102, 2); library.returnBook(1); // Displaying the updated state of the library library.displayBooks(); library.displayUsers(); return 0; } 2. Online Exam System The Online Exam System provides a complete solution for administering exams digitally. Instead of traditional pen-and-paper tests, this system lets students take them on a computer or digital device. The goal is to make the test process more efficient for students and educators. C++ #include <iostream> #include <iomanip> #include <vector> #include <ctime> using namespace std; class Question { public: string question; vector<string> options; int correctOption; Question(string q, vector<string> opts, int correct) { question = q; options = opts; correctOption = correct; } }; class Exam { public: vector<Question> questions; int totalQuestions; Exam() { totalQuestions = 0; } void addQuestion(Question q) { questions.push_back(q); totalQuestions++; } void displayQuestion(int index) { cout << "Q" << index + 1 << ": " << questions[index].question << endl; for (size_t i = 0; i < questions[index].options.size(); i++) { cout << " " << char('A' + i) << ". " << questions[index].options[i] << endl; } } int

Top 13 IOT projects with source code The Internet of Things (IoT) is changing our lives with rapid technological advancements. It goes beyond just connecting devices; it transforms our daily lives. If you want to explore IoT, we have 13 project ideas with source code to help you learn and be creative. Get ready to code and join this exciting journey! 1. Wrong Posture Muscle Strain Detector A person’s posture is how they position their body to prevent excessive use their muscles when they move. Bad posture can lead to a number of health issues. Pains in the muscles might be caused by severe exhaustion, fractured bones, or any other type of damage. Two signs of bad posture that might occur and interfere with our daily activities are fatigue and back pain. The necessity for a device is growing since most individuals have back pain, injuries, neck pain, shoulder issues, etc., these days. Say goodbye to sitting over with an IoT-based wrong posture muscle strain detector. Integrate sensors that monitor body posture and provide real-time feedback to prevent muscle strain. This project is not only innovative but also promotes a healthier lifestyle. Source Code 2. Safety Monitoring System for Manual Wheelchairs Create a safety system for manual wheelchairs using sensors to detect obstacles and monitor speed. This project shows how IoT can improve people’s lives with diverse needs. Manual wheelchair users often encounter safety concerns, including accidents, falls, or difficulties navigating specific terrains. Traditional monitoring systems cannot provide timely assistance or alert caregivers in emergencies. The Safety Monitoring System aims to address these challenges by utilizing IoT technology to create a proactive and responsive solution for ensuring the safety of wheelchair users. Source Code 3. Remote Plant Monitor – IoT Home Automation Nowadays, it’s fashionable to decorate homes with lovely plants, and more and more people are buying indoor plants every day. Even though everyone has a hectic schedule these days, many people find that having indoor plants at home is a passion. We also know that having indoor plants at home is healthy, but taking care of the plants requires a lot of effort. Because indoor plants are difficult to care for and can die for unexpected reasons, growing them may often be quite challenging for people. So, It is a bright and innovative system designed to enhance plant care by integrating Internet of Things (IoT) technology into home gardening. The main purpose of this system is to address the challenges of monitoring and maintaining indoor plants, providing users with real-time insights and automated solutions for optimal plant health. Source Code 4. Tank Water Monitoring System A reliable source of water is essential to farm and agricultural productivity as well as our standard of living. In agriculture, keeping an eye on the water level in a source of water, like a borewell or water tank, is crucial. For instance, dry running of the pump motor may result in damage if the water level in a borewell falls below the level required for pumping. In this situation, keeping an eye on the water level and adjusting the water pump as needed become important responsibilities. Water level monitoring is a crucial responsibility in many other scenarios. It can be applied to research how much water a source uses or to preserve water. In response to worries about a lack of water, develop an IoT-based tank water monitoring system. Integrate sensors to track water levels and quality in tanks, providing valuable data for efficient water management. The Tank Water Monitoring System provides a modern alternative to traditional water level checks in tanks. This automated system provides real-time monitoring, which is more efficient and less prone to errors. Source Code 5. Crypto Alert System Using Bolt IoT Cryptocurrency markets operate 24/7, and sudden price fluctuations or significant events can significantly impact investment decisions. It becomes challenging for crypto enthusiasts and investors to stay updated on market changes continuously. The Crypto Alert System addresses this challenge by delivering timely alerts, ensuring that users are informed about critical market movements and can make informed decisions promptly. The system achieves real-time monitoring and alerting capabilities by integrating Bolt IoT, enhancing the overall user experience. This final year project idea is unique. If you select this definitely, you will definitely get good grades in the final year. Source Code 6. Mining Worker Safety Helmet- IoT-Based Project Mining is one of the riskiest occupations. In many countries, underground miners are not guaranteed social or safety, and in the event of an injury, they may be responsible for caring for themselves. Two of the negative societal outcomes include livelihood destruction and displacement. Among all industries combined, the mining sector has the highest rate of fatal workplace accidents. The Mining Worker Safety Helmet project showcases how IoT can be used to improve safety in mining. The project improves safety for mining workers by using innovative technology and real-time monitoring, reducing accidents and improving worker well-being. Mining operations involve inherent risks, with worker safety being a top priority. Accidents, such as falling objects or collisions, pose severe threats to the well-being of mining personnel. Source Code 7. IoT- Based Smoke Detector system Safeguard homes and businesses with an IoT-based smoke detector system. Create an intelligent system that detects smoke and triggers alarms or alerts. This project exemplifies how IoT can play a crucial role in emergencies, adding an extra layer of protection. Traditional smoke detectors only use sound alarms to alert people of possible fire risks. These alarms might not work well if people can’t hear them or are unable to move. The IoT-based smoke Detection system was developed to be a more robust and reliable smoke detection system. It can provide timely alerts and minimize false alarms. Source Code 8. IoT-Based Crop Monitoring System Since agriculture is such an important field, every technical improvement should be done in this domain. The demand for agriculture has grown significantly due to global population growth, and sadly, farmers are unable to meet this endless

Heart Disease Prediction Using Machine Learning Heart disease is a significant cause of death worldwide and requires creative solutions. Early heart disease detection and prediction are crucial for effective prevention and timely intervention. Technology and medicine can change how we predict heart disease in healthcare. With its ability to analyze large datasets and identify complex patterns, machine learning has emerged as a promising tool for predicting heart disease. In this article, we explore the application of machine learning in heart disease prediction, focusing on the best algorithms and discussing a sample project. This article explores heart disease prediction using machine learning, uncovering the reasons behind this exciting technological advance. We will also learn how to make heart disease predictions using machine learning. Source code is also given for your help. Understanding Heart Disease Prediction Heart disease prediction uses machine learning algorithms to analyze medical data and detect patterns that could suggest potential heart problems. This approach enables early detection and timely intervention, ultimately saving lives. Problem Statement Traditional methods to predict heart disease are unreliable because they require manual analysis and only consider a few pieces of information. This heart disease prediction project can cause delays in diagnosing and treating the disease. Also, these methods don’t provide real-time monitoring or personalized risk assessment, which is a big problem. Critical factors associated with heart disease Understanding and dealing with these factors through lifestyle changes, regular check-ups, and early treatment are vital to preventing and managing heart disease. Machine learning models can use these factors to predict a person’s risk and provide personalized precautions. Age: The risk of heart disease increases with age. Older individuals are more likely to develop cardiovascular conditions. Gender: Men tend to have a higher risk of heart disease than premenopausal women. However, after menopause, women’s risk increases and approaches that of men. Genetics and Family History: A family history of heart disease can significantly elevate an individual’s risk. Genetic factors can contribute to high blood pressure and high cholesterol. High Blood Pressure (Hypertension): High blood pressure strains the heart and blood vessels, increasing the risk of heart disease, stroke, and other cardiovascular conditions. High Cholesterol Levels: Increased levels of low-density lipoprotein (LDL or “bad” cholesterol) and low levels of high-density lipoprotein (HDL or “good” cholesterol) can contribute to the buildup of plaques in the arteries, leading to atherosclerosis. Smoking: Tobacco smoke contains chemicals that can damage blood vessels and heart tissue, leading to the development of atherosclerosis and other heart-related issues. Obesity and Overweight: Excess body weight, especially around the abdomen, is associated with an increased risk of heart disease. Obesity contributes to conditions such as diabetes and hypertension. Diabetes: Individuals with diabetes have a higher risk of heart disease. Diabetes can damage blood vessels and contribute to atherosclerosis. Physical Inactivity: A life of inactivity is a significant risk factor for heart disease. Regular physical activity helps maintain a healthy weight, lower blood pressure, and improve cardiovascular health. Unhealthy Diet: Diets high in saturated and trans fats, cholesterol, sodium, and added sugars contribute to elevated blood cholesterol levels, hypertension, and obesity, increasing the risk of heart disease. Excessive Alcohol Consumption: Heavy and chronic alcohol consumption can lead to high blood pressure, cardiomyopathy, and other heart-related issues. Stress: Chronic stress may contribute to heart disease through various mechanisms, including elevated blood pressure and unhealthy coping behaviors like overeating or smoking. Benefits of Machine Learning in Heart Disease Prediction Early Detection: Machine learning algorithms can find small patterns in health data to detect potential heart issues before symptoms appear. Personalized Risk Assessment: Customizing predictions based on a person’s health profile improves accuracy, enabling personalized preventive measures. Real-Time Monitoring: Continuous monitoring of health parameters in real time enables quick action in case of abnormalities, reducing response time and improving patient outcomes. Data analysis Perspectives: Machine learning analyzes large data sets to find patterns and trends, helping healthcare professionals make better decisions. Machine Learning Algorithms for Heart Disease Prediction Several machine learning algorithms have been successfully applied to predict heart disease. The choice of algorithm depends on the dataset characteristics and the specific goals of the prediction model. Some widely used algorithms include Logistic Regression: Logistic Regression is a commonly used algorithm for binary classification tasks, making it suitable for predicting whether an individual is at risk of heart disease. Decision Trees: Decision Trees are versatile and understandable, making them helpful in identifying patterns in heart disease risk factors. They can handle both numerical and categorical data. Random Forest: Random Forest is an ensemble learning technique that combines multiple decision trees to improve predictive accuracy and reduce overfitting. Support Vector Machines (SVM): SVM effectively separates data into classes and is particularly useful when dealing with complex datasets with non-linear relationships. Neural Networks: Deep learning models like Neural Networks can capture intricate patterns in large datasets, making them suitable for complex heart disease prediction tasks. Best Practices for Heart Disease Prediction Projects When doing a heart disease prediction project, it’s crucial to follow certain best practices: Data Preprocessing: Clean and preprocess the dataset to handle missing values, normalize features, and convert categorical variables into a suitable format for machine learning models. Feature Selection: Identify and select the most relevant features for the prediction model to improve accuracy and reduce computational complexity.  Model Evaluation: Employ appropriate evaluation metrics such as accuracy, precision, recall, and F1-score to assess the machine learning model’s performance.  Hyperparameter Tuning: Fine-tune the parameters of the chosen algorithm to optimize the model’s performance.  Validation and Testing: Split the dataset into training, validation, and testing sets to ensure the model generalizes well to new, unseen data. Challenges in Implementing Machine Learning for Heart Disease Prediction Data Quality: In healthcare, ensuring that the data used for training machine learning models is reliable and accurate is difficult. There are often issues with the quality and consistency of data sources. When health records are flawed or incomplete, it can introduce biases that make predictive models less effective. It is crucial to address these

Phishing website detection using Machine Learning What is Phishing? Phishing is a type of cyberattack in which hackers use fraudulent methods to deceive people to get sensitive information like passwords, credit card numbers, or personal details. This is often conducted through fake emails, websites, or other kinds of electronic communication that appear to originate from legitimate sources. Phishing aims to get personal or financial information that can then be utilized for identity theft, fraud, or other illegal activity. Phishing attacks usually involve the creation of fake websites or emails that seem like those of legitimate businesses, such as banks, social networking platforms, or online stores. These fraudulent websites or emails may include links or attachments that, when clicked or opened, push the victim to provide personal or financial information. Understanding Phishing Websites Before diving into the technical aspects of detecting phishing websites using machine learning, it’s essential to understand what phishing websites are and how they operate. Phishing websites are fraudulent websites that imitate legitimate ones, aiming to deceive users into disclosing sensitive information. These websites often have URLs that closely resemble those of reputable websites, making it challenging for users to distinguish between them. The Importance of Detecting Phishing Websites Detecting phishing websites is essential for several reasons. Most importantly, it helps customers avoid falling prey to phishing scams. Users can protect critical information from thieves by recognizing and blocking fake websites. Furthermore, detecting phishing websites helps businesses retain their reputation and integrity. If users link a brand with phishing attempts, they may lose trust in the brand, resulting in financial losses and reputational harm. Traditional Methods vs. Machine Learning Traditionally, phishing websites could be identified using rule-based systems that depended on established rules to identify phishing sites. While these procedures were beneficial in some cases, they had limits. For example, rule-based systems needed help to keep up with cybercriminals’ shifting strategies, causing them to be ineffective over time.  Machine learning, on the other hand, provides a more dynamic and flexible method for detecting phishing sites. ML algorithms can analyze vast volumes of data and uncover patterns that humans may miss. This enables ML models to detect phishing websites with greater accuracy and efficiency.  Recommended Reading Stock Price Prediction system using Machine Learning Real-Time Object Detection Using Machine Learning Ecommerce Sales Prediction using Machine Learning Key Features for Detecting Phishing Websites Many features can be used to detect phishing websites efficiently. Here are some key features which can be used to see phishing website   URL Analysis: Examining the URL of a website can reveal vital information about its validity. For example, phishing websites frequently utilize URLs similar to legal websites but have minor differences, such as misspellings or additional letters. Content Analysis: Analysing a website’s content can help detect phishing websites. Phishing sites, for example, frequently include generic or poorly written material since they are designed to deceive consumers quickly. SSL Certificate Analysis: Checking the SSL certificate of a website can help determine its legitimacy. Phishing websites often use self-signed or expired SSL certificates, which can be a red flag. Website Reputation: Analyzing a website’s reputation can also help detect phishing websites. For example, if a website has a history of hosting phishing attacks, it may be more likely to be a phishing website.   These are only few features there are many other feature which can be used to detect phishing website. Machine Learning Algorithms for Phishing Website Detection Several machine learning methods may be utilized to detect phishing websites efficiently. Some of the most widely used algorithms are: Random Forest: Random Forest is a group learning system that makes predictions based on several decision trees. It is ideal for detecting phishing websites since it can handle big datasets and is not prone to overfitting. Support vector machines (SVMs): SVM is a supervised learning technique that can be applied to classification tasks. It operates by determining which hyperplane best splits the data into multiple classes. SVM is good at detecting phishing websites because it can handle high-dimensional data and is resistant to noise. Logistic regression: Logistic regression is a statistical model used to perform binary classification tasks. It estimates the likelihood of a specific outcome based on the input features. Logistic regression helps detect phishing websites due to its simplicity and interpretability.But we will use random forest to create machine learning model for phishing detection Challenges and Limitations While machine learning is a promising way to detect phishing websites, it has drawbacks and limits. Some of the significant challenges are: Data Imbalance: Because phishing websites are uncommon compared to reputable websites, data imbalance concerns may arise. This can make it difficult for machine learning algorithms to learn from the data correctly. Feature Engineering: Identifying the appropriate elements for identifying phishing websites can be difficult. Phishing websites frequently employ advanced strategies to avoid detection, making it challenging to discover pertinent aspects. Model Interpretability: Certain machine learning algorithms, such as deep learning models, are challenging to interpret. This can make it difficult to comprehend why a specific website was flagged as phishing. Recommended Reading Hand Gesture Recognition Using Machine Learning 10 Advance Final Year Projects with source code Ecommerce Sales Prediction using Machine Learning Phishing website detection using Machine Learning Source Code Download Source Code Machine learning is a way to detect phishing websites accurately. Machine learning algorithms can find patterns in a website’s elements that humans may not see. However, it is critical to understand the problems and limitations of employing machine learning for phishing website identification. With additional study and development, machine learning has the potential to become a vital tool for countering phishing attempts. To Learn More: Phishing website detection using Machine Learning Research Paper Detecting phishing websites using machine learning Research Paper Detecting phishing websites Research Paper How do machine learning algorithms detect phishing websites? Machine learning algorithms detect phishing websites by investigating many aspects of the site, including its URL, content, SSL certificate, and reputation. Machine learning algorithms can detect phishing websites by recognizing trends in their properties. What are some