Machine Learning Projects

Diabetes Prediction  Support Vector Machine Fig: Support Vector Machine Workflow Predicting Diabetes Using Support Vector Machine: A Focus on Accuracy Evaluation Abstract The early detection of diabetes is crucial for effective management and treatment. This study investigates the use of the Support Vector Machine (SVM) algorithm to predict diabetes based on a dataset containing various health indicators. The SVM model was evaluated based solely on accuracy and achieved an accuracy of 78%. The findings highlight the potential of SVM as a reliable tool for aiding early diabetes diagnosis. 1. Introduction Diabetes is a chronic condition that affects millions of people globally. Early diagnosis and management are essential to prevent severe complications associated with the disease. Traditional diagnostic methods can be resource-intensive, relying on extensive medical testing. Machine learning, particularly Support Vector Machines (SVMs), offers a more efficient alternative by...

 Sonar Rock Vs Mine Prediction Fig: Rock Vs Mine Workflow: Fig: Workflow with Logistic Regression Rock vs Mine Classification Using Logistic Regression: A Sonar Data Analysis Abstract The classification of underwater objects, such as distinguishing between rocks and mines, is a crucial task in various fields, including defense and resource exploration. This paper presents a machine learning approach using Logistic Regression to predict whether an object is a rock or a mine based on sonar signal features. The sonar dataset used contains 60 features representing sonar energy readings, with the goal of accurately classifying these readings into the respective categories. The Logistic Regression model, a linear classifier, was chosen for its simplicity and interpretability. The model was trained and tested on the dataset, achieving an accuracy of 85%. The results demonstrate that Logistic Regression is a viable method for this classification task, providing a strong baseline for fu...

  Comparative Analysis of Advanced Clustering Algorithms for Market Segmentation - A Case Study on Mall Customer Data Abstract This study conducts a comparative analysis of advanced clustering algorithms for market segmentation using Mall Customer Data. The algorithms evaluated include K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), Agglomerative Clustering, BIRCH, Spectral Clustering, OPTICS, and Affinity Propagation. Evaluation metrics such as Silhouette Score, Davies-Bouldin Score, and Calinski-Harabasz Score are employed to assess the clustering performance and determine the most suitable algorithm for segmenting mall customers based on their spending habits. Methodology The methodology involves several key steps: 1.      Data Collection: Mall Customer Data is obtained, comprising various demographic and spending attributes. 2.      Data Preprocessing: Data is cleaned, normalized, and prepared for cl...