Journal of Science Engineering Technology and Management Science

The Hospital Admission Analysis and Prediction system is designed to analyze hospital admission data and predict the likelihood of patient admission based on historical healthcare information. Hospitals generate large amounts of patient data related to demographics, medical conditions, symptoms, previous visits, test results, and treatment information. Analyzing this data effectively can help healthcare organizations understand admission patterns and improve resource planning. The proposed system uses historical hospital records to identify important factors associated with patient admissions. The collected data is first preprocessed by handling missing values, duplicate records, inconsistent entries, and categorical attributes. Exploratory data analysis is performed to understand admission trends based on patient characteristics, medical conditions, and other relevant factors. Data visualization techniques are used to present admission statistics and patterns through charts, graphs, and dashboards. Machine-learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be applied to build a prediction model. The trained model analyzes relevant patient information and predicts the possible admission outcome for new cases. The performance of the prediction models can be evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The system can help hospitals estimate potential admission demand and support better allocation of beds, staff, and other resources. It can also assist healthcare administrators in identifying admission patterns and planning hospital operations more efficiently. The system is intended as a decision-support tool and not as a replacement for clinical judgment or medical diagnosis. Overall, the Hospital Admission Analysis and Prediction system demonstrates how data analytics and machine learning can be used to analyze healthcare admission patterns, generate predictions, and support efficient hospital resource planning and decision-making.

Keywords :

Author : A Rajashekar, C Shailaja, G Shiva Kumar Reddy, G Abhinav, G Tharun

Title : HOSPITAL ADMISSION ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 376-384

The Patient Billing Tracker and Prediction system is designed to manage, analyze, and predict patient billing information in a structured and efficient manner. Hospitals and healthcare organizations generate a large volume of billing records related to consultations, diagnostic tests, medicines, treatments, procedures, room charges, and other medical services. Managing these records manually can be time-consuming and may result in calculation errors, missing information, and difficulty in tracking payment status. The proposed system provides a centralized platform for recording patient billing details, monitoring expenses, tracking paid and pending amounts, and analyzing historical billing patterns. The collected billing data is first preprocessed to handle missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory data analysis is performed to identify relationships between patient characteristics, medical services, treatment types, and total billing amounts. Data visualization techniques can be used to display billing trends, payment status, department-wise expenses, and monthly revenue. Machine-learning algorithms such as Linear Regression, Decision Tree, Random Forest, and Gradient Boosting can be applied to analyze historical billing data and predict the possible billing amount for new or ongoing patients. The system can also classify billing records based on payment status and identify patients with pending payments. Model performance can be evaluated using suitable metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score for billing amount prediction. The system provides useful dashboards and reports that help hospital administrators and authorized staff monitor financial information efficiently. Overall, the Patient Billing Tracker and Prediction system combines billing management, data analysis, visualization, and machine learning to improve billing transparency, support financial planning, and provide data-driven insights into patient healthcare expenses.

Keywords :

Author : Dr. M Dileep Kumar, R Kavya, K Palvasha, S Harshith Raj , A Shashank

Title : PATIENT BILLING TRACKER AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 385-393

The Students Results Tracker and Prediction system is designed to efficiently manage, analyze, and predict students' academic performance using historical academic data. Educational institutions generate a large amount of student information related to marks, attendance, assignments, internal assessments, examinations, subjects, and overall performance. Managing and analyzing this information manually can be timeconsuming and may make it difficult to identify students who require additional academic support. The proposed system provides a centralized platform for recording student results, tracking academic progress, analyzing subject-wise performance, and predicting future results. The collected student data is first preprocessed by handling missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory Data Analysis is performed to identify relationships between attendance, previous marks, study patterns, subject performance, and overall results. Data visualization techniques are used to display student performance through charts, graphs, and dashboards. Machine-learning algorithms such as Linear Regression, Decision Tree, Random Forest, and Gradient Boosting can be applied to predict students' expected marks or performance levels based on historical academic information. The trained models are evaluated using suitable metrics such as MAE, MSE, RMSE, and R² score for marks prediction, or accuracy, precision, recall, and F1-score for performance classification. The system can identify students who may be at academic risk and provide useful insights to teachers and administrators. It also helps students understand their academic progress and areas that may require improvement. Overall, the Students Results Tracker and Prediction system combines result management, data analysis, visualization, and machine learning to support academic monitoring, performance prediction, and data-driven educational decisionmaking.

Keywords :

Author : S Sudarshan, V Sindhuja, E Kavitha, B Dharma Rao, V Soma jaya Raju

Title : STUDENT RESULTS TRACKER AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 394-402

The Fee Payment Tracker Analysis and Prediction system is designed to efficiently manage, analyze, and predict student fee payment information using historical payment data. Educational institutions handle a large number of fee records related to tuition fees, examination fees, hostel fees, transportation fees, scholarships, installments, and pending payments. Managing these records manually can be timeconsuming and may lead to errors in payment tracking, record maintenance, and identification of outstanding fees. The proposed system provides a centralized platform for recording student fee details, tracking payment status, monitoring pending amounts, and analyzing payment patterns. The collected data is first preprocessed to handle missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory Data Analysis is performed to identify payment trends based on courses, academic years, fee categories, payment methods, and student information. Data visualization techniques are used to display total collections, paid and pending amounts, monthly payment trends, and fee-category distributions through charts and dashboards. Machine-learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting can be used to predict payment status or identify students who may have a higher possibility of delayed payment. Regression techniques can also be applied when the objective is to predict future fee amounts or expected collections. The trained models are evaluated using suitable metrics such as accuracy, precision, recall, F1-score, ROCAUC, MAE, RMSE, and R², depending on the prediction task. The system provides useful reports and dashboards that help administrators monitor fee collections and outstanding payments efficiently. It can also assist institutions in planning future collections and taking timely action regarding pending payments. Overall, the Fee Payment Tracker Analysis and Prediction system combines fee management, data analysis, visualization, and machine learning to improve payment tracking, identify payment patterns, and support effective financial planning and decision-making in educational institutions.

Keywords :

Author : Vijayata Ramteke, G Rishitha, B Rajender, N Mahesh, K Sai Kumar

Title : FEE PAYMENT TRACKER ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 403-411

The Machine Downtime Analysis and Prediction system is designed to analyze industrial machine downtime data and predict possible future downtime events using data analytics and machine-learning techniques. In manufacturing industries, unexpected machine failures and downtime can reduce production efficiency, increase maintenance costs, delay production schedules, and affect overall productivity. Traditional maintenance approaches often depend on fixed schedules or manual monitoring, which may not effectively identify early signs of machine failure. The proposed system collects historical machine information such as operating hours, temperature, vibration, pressure, machine load, maintenance history, failure type, and downtime duration. The collected data is preprocessed to handle missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory Data Analysis is performed to identify downtime patterns, machine failure trends, maintenance requirements, and factors associated with production interruptions. Data visualization techniques are used to display machine utilization, downtime frequency, downtime duration, failure categories, and maintenance trends through charts and dashboards. Relevant machine and operational features are selected for developing predictive models. Machine-learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting can be used to predict whether a machine is likely to experience downtime. Regression techniques can also be applied to estimate the expected duration of downtime when sufficient historical data is available. The trained models are evaluated using suitable metrics such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, RMSE, and R², depending on the prediction objective. The system provides analytical reports and prediction results that can help maintenance teams take preventive actions before serious failures occur. Overall, the Machine Downtime Analysis and Prediction system combines data analysis, visualization, and machine learning to reduce unexpected downtime, improve machine reliability, support preventive maintenance, and increase overall industrial productivity.

Keywords :

Author : Bharath Kumar, S Pavani, T Kamal Tej, Ujwala, G Vamshi

Title : MACHINE DOWNTIME ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 412-420

The Raw Material Usage Tracker and Prediction system is designed to monitor, analyze, and predict the consumption of raw materials in an organization. The system maintains detailed records of material purchases, available stock, daily usage, production requirements, and remaining inventory. It helps organizations track raw material usage efficiently and identify consumption patterns over time. Historical usage data is analyzed using data analytics and machine learning techniques to predict future raw material requirements. The prediction feature helps estimate upcoming demand and supports timely material procurement. The system can also identify materials with high consumption rates and provide useful insights for inventory management. By continuously tracking stock levels, it can help reduce the chances of material shortages and unnecessary overstocking. Data visualization through charts and dashboards makes it easier for users to understand consumption trends and inventory status. The system improves planning by providing data-driven estimates instead of relying only on manual calculations. It can support better purchasing decisions, reduce material wastage, and improve production efficiency. Overall, the proposed system provides a centralized and intelligent solution for raw material tracking, consumption analysis, inventory monitoring, and future usage prediction.

Keywords :

Author : N Ramya Krishna, Y Priyanka, S Sai manikanta Reddy, K Dilip, P Madhu

Title : RAW MATERIAL USAGE TRACKER AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 421-427

The Supply Chain Delivery Analysis and Prediction system is designed to analyze supply chain delivery data and predict delivery performance using data analytics and machine-learning techniques. In modern supply chains, timely delivery is essential for maintaining customer satisfaction, reducing operational costs, and ensuring smooth business operations. Delays can occur due to factors such as transportation problems, warehouse processing time, inventory availability, weather conditions, route distance, supplier performance, and order volume. The proposed system collects historical supply chain information such as order details, product category, supplier, warehouse, order date, shipping date, delivery date, transportation mode, distance, shipping cost, delivery status, and delay information. The collected data is preprocessed to handle missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory Data Analysis is performed to identify delivery patterns, delay trends, supplier performance, transportation efficiency, and warehouse-related issues. Data visualization techniques are used to represent delivery times, delayed orders, supplier performance, transportation modes, and monthly delivery trends through charts and dashboards. Relevant features are selected and transformed into a suitable format for machine-learning models. Classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting can be used to predict whether an order is likely to be delivered on time or delayed. Regression algorithms can also be applied to estimate the expected delivery duration when sufficient historical data is available. The trained models are evaluated using suitable metrics such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, RMSE, and R², depending on the prediction objective. The system provides analytical reports and prediction results that can help supply chain managers identify potential delivery delays and take preventive actions. Overall, the Supply Chain Delivery Analysis and Prediction system combines data analysis, visualization, and machine learning to improve delivery planning, reduce delays, optimize logistics operations, and support data-driven decision-making in supply chain management.

Keywords :

Author : K Srikanth, D Venkata Akash Babu, A Srijan, M Charvini

Title : SUPPLY CHAIN DELIVERY ANALYSIS AND PREDICTION

Volume/Issue : 2026;03(09)

Page No : 428-436

The Retail Store Performance Comparison Dashboard is a data analytics and visualization system designed to help retail businesses monitor and compare the performance of multiple stores. Retail organizations often operate several branches across different locations, and each store may generate different levels of sales, revenue, customer traffic, and profit. An effective dashboard can help managers understand these differences and make better business decisions. The proposed system collects retail information such as sales revenue, number of transactions, products sold, customer visits, profit, discounts, and inventory levels. The collected data is processed and transformed into meaningful performance indicators. The system organizes the information by store, product category, location, time period, and other business dimensions. This allows managers to compare stores using consistent performance metrics. The dashboard provides interactive charts, graphs, tables, and key performance indicators. Users can compare stores based on total sales, average transaction value, profit margin, customer traffic, product performance, and inventory turnover. Filters can be applied based on date, location, store, product category, and sales channel. This makes it easier to identify high-performing and underperforming stores. The system can also identify performance trends and highlight important differences between stores. Managers can determine which stores are achieving their targets and which locations may require additional attention. Historical comparisons can be used to understand monthly, quarterly, or yearly performance. The dashboard can also provide insights into products and categories contributing most significantly to store performance. Overall, the Retail Store Performance Comparison Dashboard provides a centralized and user-friendly platform for retail performance analysis. It reduces dependence on manually prepared reports and helps managers make faster, datadriven decisions. Future enhancements can include AI-based sales forecasting, anomaly detection, automated recommendations, customer segmentation, real-time data integration, and predictive inventory management.

Keywords : Retail businesses generate large amounts of data through daily transactions, customer interactions, inventory operations, and sales activities.

Author : 1 DR.M.Dileep, 2 D Akhila, 3 A Ashritha, 4 G Neeraj Kumar, 5 J Gunguli

Title : RETAIL STORE PERFORMANCE COMPARISON DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 437-443

The E-commerce Customer Analytics Dashboard is a data analytics and visualization system designed to help online businesses understand customer behavior, purchasing patterns, and overall sales performance. E-commerce platforms generate large amounts of data from customer registrations, product searches, orders, payments, reviews, and website interactions. Analyzing this information can help businesses understand their customers and improve their marketing and sales strategies. The proposed system collects customer-related information such as customer ID, order history, purchase frequency, order value, product categories, location, browsing activity, and transaction dates. The system processes this information and calculates important customer metrics such as total customers, active customers, average order value, purchase frequency, customer retention, and customer lifetime value. The dashboard presents customer analytics through interactive charts, graphs, tables, and Key Performance Indicators (KPIs). Business users can analyze customer segments based on purchase behavior, spending level, location, product preference, and engagement. Filters allow users to select specific time periods, product categories, customer groups, or geographic regions. The system can also help identify important customer behavior patterns. Businesses can observe high-value customers, frequently purchased products, inactive customers, repeat-purchase trends, and changes in customer engagement. Customer segmentation can help organizations design more targeted marketing campaigns and improve customer retention strategies. Overall, the E-commerce Customer Analytics Dashboard provides a centralized platform for transforming raw customer and transaction data into meaningful business insights. It reduces manual analysis and supports data-driven decision-making. Future enhancements can include AI-based customer segmentation, churn prediction, personalized product recommendations, sales forecasting, sentiment analysis, and real-time customer analytics.

Keywords : E-commerce has transformed the way customers purchase products and interact with businesses.

Author : 1 DR.B.Anil, 2 K Tulasi, 3 A Shravan Kumar, 4 M Manasa, 5 Y Sunil

Title : E-COMMERCE CUSTOMER ANALYTICS DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 444-450

The Stock Portfolio Performance Tracker is a software-based financial analytics system designed to help users monitor and analyze the performance of their stock investments. Investors often hold multiple stocks purchased at different prices and times, making it difficult to manually calculate overall portfolio performance. The proposed system provides a centralized platform where users can record their investments and monitor important portfolio metrics. The system allows users to enter information such as stock name, quantity, purchase price, purchase date, and transaction details. The system organizes these investments into a structured portfolio and calculates important metrics such as total investment value, current portfolio value, profit or loss, percentage return, and individual stock performance. Users can view their holdings and understand how each investment contributes to the overall portfolio. A major feature of the system is interactive performance visualization. The dashboard can display portfolio value through charts and graphs, allowing users to observe changes over different periods. It can also compare individual stocks, sectors, and investment categories. Performance indicators help users identify which holdings are contributing positively or negatively to the portfolio. The system can also provide portfolio analytics such as asset allocation, sector distribution, gain/loss analysis, and investment concentration. Historical transaction information can be maintained to calculate returns more accurately. Optional marketdata integration can be used to update stock prices and automatically recalculate portfolio values when new market information is available. Overall, the Stock Portfolio Performance Tracker provides an organized and userfriendly approach to portfolio monitoring and financial analysis. It can help investors understand their investment performance without relying heavily on manual calculations. The system is intended as an information and analytics tool, not financial advice, and investment decisions should be made after considering reliable market information and appropriate professional guidance.

Keywords : Investing in stocks has become an important method of participating in financial markets and building long-term wealth.

Author : 1 M Shirisha, 2 R Ramya, 3 D Shiva, 4 D Yash raj, 5 M Karunakar

Title : STOCK PORTFOLIO PERFOMANCE TRACKER

Volume/Issue : 2026;03(09)

Page No : 451-457

The Employee Attrition Trend Dashboard is a data analytics and visualization system designed to help organizations understand employee attrition patterns and workforce trends. Employee attrition refers to employees leaving an organization over a period of time. High attrition can affect productivity, recruitment costs, employee workload, and organizational performance. Therefore, understanding historical attrition patterns can help organizations plan workforce strategies more effectively. The proposed system collects employee-related organizational data such as department, job role, tenure, salary range, employment status, joining date, exit date, and other relevant workforce attributes. The system processes this information and calculates important metrics such as attrition rate, employee count, average tenure, department-wise attrition, and monthly or yearly trends. The dashboard presents the analyzed information through interactive charts, graphs, tables, and Key Performance Indicators (KPIs). Managers and HR teams can compare attrition across departments, job roles, experience groups, and time periods. Filters can be used to focus on specific organizational units or reporting periods, making the analysis easier and more flexible. The system can also identify recurring workforce patterns and highlight areas where attrition levels have changed significantly. Historical trend analysis can help organizations understand whether attrition is increasing, decreasing, or remaining relatively stable. Optional analytics can provide aggregate-level insights that support workforce planning without making automated decisions about individual employees. Overall, the Employee Attrition Trend Dashboard provides a centralized platform for understanding workforce attrition through data visualization and analytics. It can reduce manual reporting efforts and help HR teams make better-informed organizational decisions. The system is intended for aggregate workforce analysis and planning, and it should not be used to automatically label, rank, or make employment decisions about individual employees.

Keywords : Employee retention is an important concern for organizations across different industries.

Author : 1 M Bharath Kumar, 2 K Akhila, 3 Premraj, 4 K Pavani, 5 K Uday Kumar

Title : EMPLOYEE ATTRITION TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 458-464

The Customer Support Ticket Volume & Resolution Time Dashboard is a data analytics and visualization system designed to help organizations monitor customer support activities and evaluate the efficiency of their support teams. Customer support departments receive a large number of tickets through email, chat, websites, phone systems, and other communication channels. Managing and analyzing these tickets manually can make it difficult to understand workload, response performance, and resolution efficiency. The proposed system collects support ticket information such as ticket ID, creation date, category, priority, status, assigned agent, first response time, resolution time, and customer-related information. The system processes this information and calculates important performance indicators such as total ticket volume, open tickets, resolved tickets, average response time, average resolution time, and resolution rate. The dashboard presents the analyzed information through interactive charts, graphs, tables, and Key Performance Indicators (KPIs). Support managers can compare ticket volumes across different days, weeks, months, categories, priorities, and support agents. Filters allow users to focus on specific time periods, ticket categories, teams, or priority levels. The system also helps identify trends and operational bottlenecks. Managers can determine when ticket volume is increasing, which categories generate the most requests, and which types of tickets take longer to resolve. Resolution-time analysis can help support teams identify areas where processes, staffing, or knowledge resources may need improvement. Overall, the Customer Support Ticket Volume & Resolution Time Dashboard provides a centralized and user-friendly platform for monitoring customer support performance. It reduces dependence on manually prepared reports and helps organizations make data-driven decisions to improve customer service. Future enhancements can include AI-based ticket categorization, workload forecasting, SLA breach prediction, sentiment analysis, automated alerts, and intelligent support recommendations.

Keywords : Customer support is an important part of maintaining customer satisfaction and building long-term relationships with users.

Author : 1 A Veerender, 2 M Shreeya, 3 S Ruthwik, 4 M Manisha, 5 A Kannaiah

Title : CUSTOMER SUPPORT TICKET VOLUME & RESOLUTION TIME DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 465-471

The Hospital Readmission Trend Dashboard is a healthcare data analytics and visualization system designed to monitor and analyze patterns in hospital readmissions. Hospital readmission refers to a patient being admitted to a hospital again within a defined period after a previous discharge. Monitoring readmission trends can help healthcare organizations understand changes in patient outcomes, resource utilization, and healthcare service performance. The proposed system collects relevant hospital information such as admission date, discharge date, department, diagnosis category, patient age group, length of stay, readmission status, and other appropriate de-identified clinical or administrative information. The system processes this data and calculates important indicators such as readmission count, readmission rate, average length of stay, and department-wise readmission trends. The dashboard presents the analyzed information through interactive charts, graphs, tables, and Key Performance Indicators (KPIs). Healthcare administrators can compare readmission patterns across departments, diagnosis categories, age groups, and time periods. Filters can be applied to examine specific reporting periods and organizational areas, making the analysis easier and more flexible. The system can also identify changes and trends in readmission rates over time. Managers can observe whether readmissions are increasing or decreasing and identify departments or categories that may require further investigation. Historical comparisons can support quality-improvement activities, resource planning, discharge-process evaluation, and hospital management. Overall, the Hospital Readmission Trend Dashboard provides a centralized platform for understanding hospital readmission patterns through data analytics and visualization. It is intended primarily for aggregate-level reporting, quality improvement, and operational planning, rather than diagnosing patients or predicting individual medical outcomes. Future enhancements can include risk-factor analysis, predictive modeling using validated clinical data, real-time hospital integration, anomaly detection, and automated quality reports.

Keywords : Healthcare organizations generate large amounts of information through patient admissions, diagnoses, treatments, discharges, laboratory services, and hospital operations.

Author : 1 D Saritha, 2 M Amulya, 3 M Sathwik, 4 R Sruthi, 5 B Karan

Title : HOSPITAL READMISSION TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 472-478

The Clinical Trial Enrollment Funnel Dashboard is a healthcare analytics and visualization system designed to monitor and analyze the participant enrollment process in clinical trials. Clinical trials require eligible participants to move through several stages, including initial outreach, screening, eligibility assessment, consent, enrollment, and study participation. Monitoring these stages is important for understanding recruitment progress and identifying delays or drop-offs. The proposed system collects trial-enrollment information such as participant screening status, eligibility results, consent status, enrollment date, study site, recruitment source, trial phase, and other appropriate administrative information. The system processes this information and calculates important indicators such as total candidates, screened participants, eligible participants, consented participants, enrolled participants, enrollment rate, and stage-wise conversion rates. The dashboard represents the enrollment process as a visual funnel, allowing clinical research teams to understand how participants move from one stage to another. Interactive charts, graphs, KPI cards, and tables provide detailed information about enrollment performance. Users can compare recruitment progress across study sites, time periods, recruitment channels, and trial phases where appropriate. The system can also identify stages where participant drop-off is high or enrollment is slower than expected. Historical trend analysis can help research teams understand changes in recruitment performance over time. These insights can support operational planning, site management, recruitment strategy evaluation, and resource allocation without making decisions about individual participants. Overall, the Clinical Trial Enrollment Funnel Dashboard provides a centralized and user-friendly platform for monitoring clinical trial recruitment and enrollment performance. It reduces manual reporting efforts and helps research teams understand enrollment patterns more efficiently. Future enhancements can include enrollment forecasting, recruitment-source analytics, automated alerts, site-performance benchmarking, and carefully validated predictive analytics.

Keywords : Clinical trials are an important part of developing and evaluating new medicines, treatments, medical devices, and healthcare interventions.

Author : 1 M Jyothi, 2 K Bharath, 3 U Naveen, 4 S Praneeth, 5 R Raghevendra

Title : CLINICAL TRIAL ENROLLMENT FUNNEL DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 479-485

The Air Quality vs Respiratory Illness Trend Comparison system is a data analytics and visualization platform designed to study the relationship between airquality conditions and aggregate respiratory illness trends. Air pollution is influenced by pollutants such as particulate matter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide. Monitoring these environmental indicators alongside respiratoryhealth data can help researchers and public-health teams understand patterns that may require further investigation. The proposed system collects air-quality information such as PM2.5, PM10, NO₂, SO₂, CO, ozone, Air Quality Index (AQI), and measurement date and location. It also uses appropriately aggregated and de-identified respiratory-health information such as reported respiratory cases, hospital visits, or outpatient counts. The system processes these datasets and aligns them by location and time period for comparison. The dashboard presents the analyzed information through interactive charts, graphs, maps, tables, and Key Performance Indicators (KPIs). Users can compare air-quality measurements with respiratory illness trends across different months, seasons, locations, or other approved categories. Filters allow researchers and administrators to select specific pollutants, regions, time periods, and health indicators. The system can identify correlations, trends, and changes between environmental conditions and respiratory-health indicators. For example, users may observe whether periods with higher particulate pollution coincide with increased aggregate respiratory visits. However, observed relationships are presented as analytical associations rather than proof that air pollution directly caused a specific illness or individual health outcome. Overall, the Air Quality vs Respiratory Illness Trend Comparison system provides a centralized platform for combining environmental and public-health data. It can support research, environmental monitoring, public-health planning, and awareness activities. Future enhancements can include real-time air-quality feeds, advanced statistical analysis, predictive modeling, geographic risk visualization, anomaly detection, and integration with additional environmental and health datasets.

Keywords : Air quality is an important environmental factor that can affect population health.

Author : 1 K Srikanth, 2 S Sai divya, 3 M Manikanta, 4 Archana Yadav, 5 Afroz

Title : AIR QUALITY VS RESPIRATORY ILLNESS TREND COMPARISON

Volume/Issue : 2026;03(09)

Page No : 486-492

The E-commerce Cart Abandonment Rate Analysis system is a data analytics and visualization platform designed to monitor and analyze customer behavior during the online shopping process. Cart abandonment occurs when customers add products to their shopping cart but leave the website or application without completing the purchase. A high abandonment rate can result in lost sales and may indicate problems in pricing, checkout experience, shipping costs, payment methods, or customer engagement. The proposed system collects e-commerce activity data such as customer sessions, product views, cart additions, cart removals, checkout attempts, completed orders, product categories, device types, traffic sources, and timestamps. The system processes this information to calculate important metrics such as cart abandonment rate, checkout completion rate, conversion rate, average cart value, and abandoned cart value. The dashboard presents the analyzed information using interactive charts, graphs, tables, funnel diagrams, and Key Performance Indicators (KPIs). Business users can observe how customers move from product browsing to cart creation, checkout, and final purchase. Filters allow users to analyze abandonment patterns based on product category, device, traffic source, customer segment, and time period. The system can identify stages in the shopping journey where customers frequently leave without purchasing. It can also highlight changes in abandonment rates over time and compare customer behavior across different categories and devices. These insights can help businesses investigate potential checkout issues and improve the overall shopping experience. Overall, the E-commerce Cart Abandonment Rate Analysis system provides a centralized platform for understanding shopping-cart behavior and lost-conversion patterns. It reduces manual analysis and supports data-driven e-commerce optimization. Future enhancements can include AI-based abandonment prediction, personalized recovery campaigns, checkout optimization recommendations, customer segmentation, real-time alerts, and intelligent product or offer recommendations.

Keywords : E-commerce platforms provide customers with convenient ways to browse products, add items to carts, and complete purchases online.

Author : 1 Malleswar Rao, 2 Ch Madhumathi, 3 K Nagakruthika, 4 Ch Devaji, 5 G Gowtham Raj

Title : E-COMMERCE CART ABANDONMENT RATE ANALYSIS

Volume/Issue : 2026;03(09)

Page No : 493-499

The Insurance Claims Trend Analysis system is a data analytics and visualization platform designed to help insurance organizations monitor, analyze, and understand patterns in insurance claims. Insurance companies process a large number of claims related to health, vehicles, property, travel, and other insurance products. Analyzing historical claims data can help organizations understand claim volumes, costs, processing times, and changing trends. The proposed system collects insurance claim information such as claim ID, policy type, claim date, claim amount, claim status, location, claim category, processing time, and other relevant business information. The system processes the collected data and calculates important indicators such as total claims, approved claims, rejected claims, total claim amount, average claim amount, and claim settlement rate. The dashboard presents the analyzed information using interactive charts, graphs, tables, and Key Performance Indicators (KPIs). Insurance managers can compare claims across different policy types, regions, claim categories, and time periods. Filters allow users to select specific dates, claim types, locations, or statuses for detailed analysis. The system can identify changes in claim volume, claim amounts, rejection rates, and settlement times. Trend analysis can help insurance organizations identify periods with unusually high claim activity and understand how claim patterns change over time. The system can also support operational planning by highlighting areas that may require additional investigation or resources. Overall, the Insurance Claims Trend Analysis system provides a centralized and user-friendly platform for transforming raw claims data into meaningful business insights. It reduces manual reporting efforts and supports data-driven insurance operations. Future enhancements can include fraud-pattern detection, claim-cost forecasting, anomaly detection, automated alerts, AI-assisted document analysis, and predictive analytics, subject to appropriate validation and human review.

Keywords : Insurance companies manage large volumes of claims every day across different insurance products and geographic regions.

Author : 1 Sachin Chawahan, 2 M Mahishree, 3 M Pal Dinakar, 4 E Rishwan, 5 A Venkatesh

Title : INSURANCE CLAIMS TREND ANALYSIS

Volume/Issue : 2026;03(09)

Page No : 500-506

The App Onboarding Funnel & User Drop-off Analysis Dashboard is a data analytics and visualization system designed to understand how users interact with a mobile or web application during the onboarding process. Onboarding is the initial journey a user follows after installing or opening an application, such as registration, profile setup, permissions, feature introduction, and first successful use. Analyzing this journey helps organizations understand where users stop progressing. The proposed system collects user-event information such as app installation, app launch, registration, login, profile completion, permission acceptance, tutorial completion, feature interaction, and successful onboarding. The system processes these events and calculates important metrics such as total users, onboarding completion rate, step-wise conversion rate, drop-off rate, average completion time, and returning-user percentage. The dashboard represents the onboarding process as an interactive funnel. Business and product teams can observe the number of users progressing through each onboarding stage and identify stages with significant user drop-offs. Filters can be applied based on device type, operating system, application version, acquisition source, user segment, and time period. The system also provides trend and comparison analysis to understand whether onboarding performance changes after application updates or across different user groups. Charts, graphs, KPI cards, and tables make complex user-event data easier to understand. These insights can help product teams investigate usability problems and improve the onboarding experience. Overall, the App Onboarding Funnel & User Drop-off Analysis Dashboard provides a centralized platform for analyzing user onboarding behavior. It reduces manual analytics efforts and supports data-driven product optimization. Future enhancements can include AI-based drop-off prediction, user journey clustering, personalized onboarding recommendations, automated anomaly detection, real-time alerts, and experiment-performance analysis.

Keywords : Mobile and web applications compete heavily for user attention, and the first few interactions can influence whether users continue using an application.

Author : 1 A Rajashekar, 2 Samyuktha, 3 A Renu Sri, 4 Rohith, 5 Sudheer

Title : APP ONBOARDING FUNNEL & USER DROP-OFF ANALYSIS DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 507-513

The Telemedicine Consultation Volume Dashboard is a healthcare analytics and visualization system designed to monitor and analyze the volume of telemedicine consultations. Telemedicine enables patients to interact with healthcare professionals through digital platforms without requiring a physical hospital visit. As the use of virtual healthcare services increases, healthcare organizations need effective tools to understand consultation demand and service utilization. The proposed system collects appropriate administrative and aggregate consultation information such as consultation date, specialty, consultation type, patient age group, geographic region, appointment status, consultation duration, and service channel. The system processes this information and calculates important indicators such as total consultations, completed consultations, cancelled appointments, average consultation duration, specialty-wise volume, and consultation growth rate. The dashboard presents the analyzed information through interactive charts, graphs, tables, maps, and Key Performance Indicators (KPIs). Healthcare administrators can compare consultation volumes across specialties, regions, time periods, and appointment types. Interactive filters allow authorized users to focus on specific departments, reporting periods, locations, or consultation categories. The system can also identify changes in consultation demand and service utilization over time. Healthcare managers can observe periods of high or low consultation volume and compare demand across specialties. These insights can support workforce planning, scheduling, resource allocation, and operational improvements in telemedicine services. Overall, the Telemedicine Consultation Volume Dashboard provides a centralized platform for transforming consultation data into meaningful operational insights. It reduces manual reporting efforts and improves visibility into virtual-care utilization. Future enhancements can include demand forecasting, appointment-volume prediction, automated alerts, capacity planning, patient-flow analysis, and real-time integration with telemedicine platforms.

Keywords : Telemedicine has become an important component of modern healthcare by allowing patients to consult healthcare professionals through digital communication technologies.

Author : 1 B Vijay Kumar, 2 A Nandini, 3 B Keerthana, 4 Ch Goutham Ram, 5 Romen Ghosh

Title : TELEMEDICINE CONSULTATION VOLUME DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 514-520

The Bank Branch Performance Comparison Dashboard is a data analytics and visualization system designed to monitor and compare the operational and business performance of different bank branches. Banks operate through multiple branches that may differ in customer volume, deposits, loans, transactions, revenue, and service performance. Analyzing these indicators helps management understand branch-level performance and identify areas that require improvement. The proposed system collects appropriate branch-level information such as branch ID, location, customer count, deposits, loans, transactions, account openings, revenue, operating expenses, and service metrics. The system processes this information and calculates important Key Performance Indicators (KPIs) such as total customers, deposit growth, loan volume, transaction volume, revenue, profit contribution, and customer-service performance. The dashboard presents branch performance through interactive charts, graphs, tables, maps, and KPI cards. Bank managers can compare branches based on deposits, loans, customer acquisition, transactions, revenue, and other approved business indicators. Filters allow users to analyze performance according to region, branch type, reporting period, and business category. The system can identify high-performing and underperforming branches by comparing their KPIs and historical trends. Managers can observe changes in branch performance over time and identify differences between regions or branch categories. These insights can support resource allocation, branch planning, customer-service improvement, and business strategy. Overall, the Bank Branch Performance Comparison Dashboard provides a centralized platform for transforming branch-level banking data into meaningful management insights. It reduces manual reporting efforts and supports data-driven decision-making. Future enhancements can include AI-based performance forecasting, branch benchmarking, anomaly detection, customer segmentation, profitability prediction, and intelligent resource-allocation recommendations.

Keywords : Banks operate through a network of branches that provide services such as account opening, deposits, withdrawals, loans, payments, and customer support.

Author : 1 S Sudharshan, 2 G Raghavi, 3 Mamatha, 4 P Abhishek, 5 Sk Mufazal

Title : BANK BRANCH PERFORMANCE COMPARISON DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 521-527

The Workforce Productivity Trend Dashboard is a data analytics and visualization system designed to monitor and analyze workforce productivity trends within an organization. Employee productivity can be influenced by workload, completed tasks, working hours, project assignments, attendance, deadlines, and team collaboration. Monitoring these factors can help organizations understand overall work patterns and improve operational planning. The proposed system collects appropriate workforce and operational information such as department, team, completed tasks, assigned tasks, working hours, project progress, attendance, deadlines, and productivity indicators. The system processes this information and calculates useful Key Performance Indicators (KPIs) such as task completion rate, average tasks completed, project completion rate, workload distribution, productivity trend, and deadline adherence. The dashboard presents the analyzed information using interactive charts, graphs, tables, and KPI cards. Managers can compare productivity across departments, teams, projects, and time periods. Filters allow authorized users to analyze selected departments, teams, projects, reporting periods, or productivity indicators. The system can identify changes in productivity over time and highlight differences in workload and task completion. Trend analysis can help managers understand whether productivity is improving, remaining stable, or declining. The dashboard is intended to support workforce planning and process improvement rather than making unsupported judgments about individual employees. Overall, the Workforce Productivity Trend Dashboard provides a centralized platform for transforming workforce data into meaningful operational insights. It reduces manual reporting effort and supports data-driven workforce management. Future enhancements can include workload forecasting, capacity planning, anomaly detection, team-level recommendations, project-risk analysis, and AI-assisted productivity insights with appropriate privacy and fairness safeguards.

Keywords : Workforce productivity is an important factor in the success of an organization.

Author : 1 Md Riaz, 2 V Ruthvik, 3 G Uday Kiran, 4 B Shivakumar, 5 S Akhila

Title : WORKFORCE PRODUCTIVITY TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 528-534

The Credit Card Spending Pattern & Customer Transaction Analytics Dashboard is a data analytics and visualization system designed to analyze credit card transaction patterns and customer spending behavior. Credit card transactions generate large amounts of information related to purchase amounts, transaction categories, locations, payment channels, and transaction dates. Analyzing these patterns can help financial organizations understand spending trends and improve business planning. The proposed system collects appropriate transaction information such as transaction date, transaction amount, merchant category, transaction type, payment channel, geographic region, customer segment, and transaction status. The system processes this information and calculates important Key Performance Indicators (KPIs) such as total transaction value, transaction count, average transaction amount, spending by category, monthly spending growth, and transaction success rate. The dashboard presents the analyzed information using interactive charts, graphs, tables, maps, and KPI cards. Authorized analysts and managers can compare spending patterns across different merchant categories, customer segments, regions, payment channels, and time periods. Filters allow users to focus on specific transaction categories or reporting periods. The system can identify changes in spending behavior, high-volume transaction categories, seasonal spending patterns, and differences between customer segments. It can also help identify unusual aggregate transaction patterns that may require further investigation. These insights can support customer-service planning, product strategy, financial reporting, and transaction monitoring. Overall, the Credit Card Spending Pattern & Customer Transaction Analytics Dashboard provides a centralized platform for transforming transaction data into meaningful business insights. It reduces manual reporting efforts and supports datadriven financial analysis. Future enhancements can include anomaly detection, spending forecasting, customer segmentation, personalized financial insights, realtime transaction monitoring, and AI-assisted trend analysis.

Keywords : Credit cards are widely used for purchasing products and services, making payments, and managing short-term financial transactions.

Author : 1 N Ramya Krishna, 2 P Supritha, 3 V Nithin Raj, 4 S Vijay

Title : CREDIT CARD SPENDING PATTERN & CUSTOMER TRANSACTION ANALYTICS DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 535-541

The Supply Chain Delay Pattern Analysis system is a data analytics and visualization platform designed to identify, analyze, and monitor delays that occur across different stages of the supply chain. Modern supply chains involve suppliers, manufacturers, warehouses, transportation providers, and customers, making timely delivery an important factor in operational efficiency. Delays in any stage can affect inventory levels, production schedules, customer satisfaction, and overall business performance. The proposed system collects appropriate supply-chain information such as order dates, supplier details, shipment dates, expected delivery dates, actual delivery dates, transportation modes, warehouse locations, product categories, and delivery status. The system processes this information to calculate important Key Performance Indicators (KPIs) such as total shipments, delayed shipments, average delay duration, on-time delivery rate, supplier performance, and transportation delay rate. The dashboard presents analyzed information through interactive charts, graphs, tables, maps, and KPI cards. Supply-chain managers can compare delays across suppliers, transportation modes, warehouses, product categories, and geographic regions. Time-based filters allow users to analyze daily, weekly, monthly, quarterly, or yearly delay patterns. The system can identify recurring delay patterns and highlight areas where delivery performance is below expectations. Trend analysis can help managers understand whether delays are increasing or decreasing over time. The system can also identify relationships between delays and factors such as transportation mode, supplier, warehouse, route, or product category, while avoiding unsupported assumptions about the cause of a specific delay. Overall, the Supply Chain Delay Pattern Analysis system provides a centralized platform for transforming supply-chain data into meaningful operational insights. It reduces manual reporting efforts and supports better logistics planning, supplier evaluation, inventory management, and delivery optimization. Future enhancements can include AI-based delay prediction, route-risk analysis, demand forecasting, realtime shipment tracking, automated alerts, and predictive supply-chain planning.

Keywords : Supply chains connect suppliers, manufacturers, warehouses, transportation networks, retailers, and customers.

Author : 1 Vijayata Ramteke, 2 B Akhil Paul, 3 P Bhuvanesh, 4 Saniya Firdous

Title : SUPPLY CHAIN DELAY PATTERN ANALYSIS

Volume/Issue : 2026;03(09)

Page No : 542-548