XGBoost Research Articles

A study of different machine learning algorithms for state of charge estimation in lithium‐ion battery pack

AbstractForecasting the state of charge (SOC) using battery control systems is laborious because of their longevity and reliability. Since battery degradation is typically nonlinear, predicting SOC estimation with significantly less degradation is laborious. So, the estimation of SOC is an increasingly major problem in ensuring the effectiveness and safety of the battery. To overcome these issues in SOC estimation, we found many methods in the scientific literature, with differing degrees of precision and intricacy. The SOC of lithium‐ion batteries can now be precisely predicted using supervised learning approaches. Reliable assessment of the SOC of a battery ensures safe operation, extends battery lifespan, and optimizes system performance. This work compares and studies the performance, benefits, and drawbacks of five supervised learning techniques for SOC estimates. Different SOC estimate methods are discussed, including both conventional and contemporary methods. These consist of techniques using voltage and current measurements and more complex algorithms using electrochemical models, impedance spectroscopy, and machine learning methods, incorporating the use of artificial intelligence and machine learning for flexible SOC estimation. In the future, SOC estimates will be a crucial component of a larger ecosystem for energy management, allowing for the seamless integration of energy storage into smart grids and adopting more environmentally friendly energy habits. The five methods we compare are random forest RF, gradient boosting machines, extra tree regressor, XG Boost, and DT. In these five methods, we are going to investigate, review, and discuss the current algorithms and overcome them to select one of the most precise and accurate algorithms to predict the accurate estimation of lithium‐ion battery SOC.

Designing IDS to Analyze Malicious Attacks using Machine Learning

Abstract: This study presents a novel approach to enhance network security through the design and implementation of an Intrusion Detection System (IDS) employing machine learning (ML) techniques. The primary aim is to address current challenges in real-time threat detection by seamlessly integrating ML models, including Logistic Regression, Random Forest, and XGBoost. The research tackles drawbacks in existing systems, emphasizing the struggle with real-time threat detection, the complexity of ML integration. The working model classifies incoming requests as normal or intrusions, categorizing the latter into DDoS, R2L, U2R, or Probing attacks. Leveraging insights from four referenced papers, this study builds on efficient activation functions, optimized feature selection, and empirical analyses of ML models for adaptive network intrusion detection. Results showcase promising comparisons between ML algorithms which include Logistic Regression, Random Forest, XG Boost

Data & Semantic Analysis Fake News Detection Based Domain-Specific Auto Encoder Learning

Abstract: Efforts to create technology for automatically detecting fake news are actively underway as the spread of misinformation on social media continues to escalate. However, most of these approaches primarily concentrate on the linguistic and structural aspects of fake news, such as identifying sources or authors, message length, and the frequency of negative language. In contrast, our study introduces a phony news detection model leveraging machine learning that incorporates user behaviors, news, and social capital-based social network dynamics. We used the XG Boost model to evaluate each feature's significance and identify the critical elements affecting the identification of fake news to capture the variables related to the spread of phony news entirely. We implemented SVM, RF, LR, CART, and NNET, well-known machine learning classification models, using these identified factors and assessed how well they detected bogus news. This work used a cross-validation process to prevent overfitting and to generalize the established models. Additionally, the predicted accuracy of the established models was compared. The RF model had the best forecast accuracy, about 94%, while the NNET had the worst performance rate, about 92.1%. As disinformation is created and disseminated with growing sophistication, the results of this study should improve the effectiveness of false news detection systems.

Development of a Machine Learning Algorithm to Forecast the Likelihood of Postoperative Neurological Complications in Patients With Parotid Tumors.

Objective: The objective of this study was to create and verify a machine learning-driven predictive model to forecast the likelihood of facial nerve impairment in patients with parotid tumors following surgery. Methods: We retrospectively collected data from patients with parotid tumors between 2013 and 2023 to develop a prediction model for postoperative facial nerve dysfunction using 5 ML techniques: Logistic Regression (Logit), Random Forest (RF), XGBoost (XGB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Predictor variables were screened using binomial-LASSO regression. Results: The study had a total of 403 participants, out of which 56 individuals encountered facial nerve damage after the surgery. By employing binomial-LASSO regression, we have successfully identified 8 crucial predictive variables: tumor kind, tumor pain, surgeon's experience, tumor volume, basophil percentage, red blood cell count, partial thromboplastin time, and prothrombin time. The models utilizing ANN and Logit achieved higher area under the curve (AUC) values, namely 0.829, which was significantly better than the SVM model that had an AUC of 0.724. There were no noticeable disparities in the AUC values between the ANN and Logit models, as well as between these models and other techniques like RF and XGB. Conclusion: Using machine learning, our prediction model accurately predicts the likelihood that patients with parotid tumors may experience facial nerve damage following surgery. By using this model, doctors can assess patients' risks more accurately before to surgery, and it may also help optimize postoperative treatment techniques. It is anticipated that this tool would enhance patients' quality of life and therapeutic outcomes.

Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann–Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios.

Heart rate is an important predictor of pacing induced cardiomyopathy in a machine learning analysis

Abstract Background 10-20% of patients who have right ventricular pacing develop pacing induced cardiomyopathy (PICM). Home monitoring (HM) could identify patients developing PICM in real-time and facilitate echocardiographic confirmation, medication optimisation and if necessary cardiac resynchronisation therapy (CRT). Purpose To identify predictors of PICM using machine learning models. Methods Consecutive cases of patients who developed PICM (12) after dual chamber pacing were identified from our implant database and matched 4:1 with propensity matched controls who underwent pacing but did not develop PICM (48) (Table 1.) HM data from the 6 months prior to echocardiographic diagnosis of PICM was analysed. PICM was defined as (1) a deterioration in left ventricular (LV) ejection fraction (EF) of >10%, (2) to an EF of <45%, (3) with right ventricular (RV) pacing >20% and (4) the exclusion of alternative causes. Three types of ML models were generated (1) random forest, (2) extreme gradient boosted (XGBoost) (3) K-nearest neighbours algorithm (KNN), with 10-fold cross-validation. Results HM data were available for 12 PICM cases (75 ± 8 years, 72% male). PICM occurred at 2.0 ± 1.2 years. There was no difference between cases and controls in atrial pacing (8±15% vs 18±22% p=0.14), ventricular pacing (81±29% vs 85±24% p=0.62) or base rate (53±5bpm vs 53±5bpm p=0.80). Of the 34 RM features analysed, mean ventricular heart rate was identified as the most important predictor of PICM; area under curve with XG Boost 0.95, KNN 0.95, and random forest 0.86. Other RM features failed to add any more predictive power to the models. Mean ventricular rate was significantly higher in cases than controls (80±6 bpm vs 69±9 bpm p<0.001). Sensitivity analysis was performed to assess whether episodes of atrial fibrillation (AF) were responsible for the higher ventricular rates. After days of AF days were excluded, mean ventricular rate was still significantly higher in cases than controls (79± 6vs 69±9bpm p<0.001) (Figure 1). 10/12 patients had a subsequent CRT upgrade. No consistent pattern emerged in heart rate changes in the first 3 months after CRT upgrade. Conclusion In a group of patients with normal left ventricular function at implant, remotely monitored heart rate can predict early-onset PICM. Further work is required to understand when these heart rate differences emerge, and whether they improve with positive LV remodelling.Table 1.Figure 1.

#460 Digital twin in hemodialysis model, with machine learning algorithms. Data from the Andalusian Hemodialysis Registry. SICATA

Abstract Background and Aims Orthodoxly, a digital twin is a computer simulation of an individual, “in silico”, and in real time. A digital twin can also be created with BIG DATA. The Objective It is the development of a survival digital twin model using Machine Learning algorithms using data from the Andalusian SICATA registry. Method The database was composed of 29,189 records at the time of analysis and contains variables in the areas of comorbidity, anemia, efficacy of renal replacement therapy (RRT), and renal bone disease. The algorithms used were: - XG Boost, AdaBoost, Cat boost. KNN, Random Forest, Decision Trees, Logistic Regression. A survival model was developed with each Machine Learning (ML) algorithm . The metrics of the models were taken into account: precision, calibration, accuracy. The software used was Orange based on Python and Rattle based on R. An explanation of the “black box” type models was carried out using SHAP (Shapley values) based on game theory. Results The characteristics that the digital twin that we have modeled with the greatest survival would have are: 1. Age equal to or less than 45 years, 2. Being included in the kidney transplant waiting list. 3. Longer duration of the hemodialysis session. 4. Low doses of Erythropoietin. 5. Normal or high serum albumin levels. 6. BMI > 21.The sample was stratified according to the Ultrafiltration (UF) ratio during the Hemodialysis session in ml/kg/hour. Group1: UF <4 ml/kg/hour. Group2: UF 5-9 ml/kg/hour. Group3: UF>9 ml/kg/hour. And a mortality study was carried out using multivariate Logistic Regression. We found that the OR for mortality in groups 2 and 3 was 17% and 24% in relation to group 1, which had the lowest mortality. Conclusion ML has revealed that the UF ratio (ml/kg/hour) is an important predictor variable of mortality. In our studio a UF ratio<4 ml/kg/hour is safe ML has found an answer to a question that we had not asked ourselves when beginning the study: What is the optimal UF rate in the hemodialysis session?

Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room.

Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.

Prediction of adolescent weight status by machine learning: a population-based study

BackgroundAdolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have been developed. This study aimed to predict the short- and long-term weight status of Hong Kong adolescents and assess the importance of predictors.MethodsA population-based retrospective cohort study of adolescents was conducted using data from a territory-wide voluntary annual health assessment service provided by the Department of Health in Hong Kong. Using diet habits, physical activity, psychological well-being, and demographics, we generated six prediction models for successive weight status (normal, overweight, obese and underweight) using multiclass Decision Tree, Random Forest, k-Nearest Neighbor, eXtreme gradient boosting, support vector machine, logistic regression. Model performance was evaluated by multiple standard classifier metrics and the overall accuracy. Predictors’ importance was assessed using Shapley values.Results442,898 Primary 4 (P4, Grade 4 in the US) and 344,186 in Primary 6 (P6, Grade 6 in the US) students, with followed up until their Secondary 6 (Grade 12 in the US) during the academic years 1995/96 to 2014/15 were included. The XG Boosts model consistently outperformed all other model in predicting the long-term weight status at S6 from P4 or P6. It achieved an overall accuracy of 0.72 or 0.74, a micro-averaging AUC of 0.92 or 0.93, and a macro-averaging AUC of 0.83 or 0.86, respectively. XG Boost also demonstrated accurate predictions for each predicted weight status, surpassing the AUC values obtained by other models. Weight, height, sex, age, frequency and hours of aerobic exercise were consistently the most important predictors for both cohorts.ConclusionsThe machine learning approaches accurately predict adolescent weight status in both short- and long-term. The developed multiclass model that utilizing easy-assessed variables enables accurate long-term prediction on weight status, which can be used by adolescents and parents for self-prediction when applied in health care system. The interpretable models may help to provide the early and individualized interventions suggestions for adolescents with weight problems particularly.

Machine Learning-Based Research for Predicting Shale Gas Well Production

Machine Learning-Based Research for Predicting Shale Gas Well Production

Drug Burden Index Is a Modifiable Predictor of 30-Day Hospitalization in Community-Dwelling Older Adults With Complex Care Needs: Machine Learning Analysis of InterRAI Data.

Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimize ML models further. Accurately predicting hospitalization may tremendously affect the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient. In this retrospective cohort study, a data set of 14198 community-dwelling older adults (≥65 years) with complex care needs from the International Resident Assessment Instrument-Home Care database was used to develop and optimize 3 ML models to predict 30-day hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all 3 models to identify key predictors of 30-day hospitalization. The area under the receiver-operating characteristics curve for the RF, XGB, and LR models were 0.97, 0.90, and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day hospitalization. Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.

Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods

Monitoring tools are needed to maximise living systems' ability to mitigate emissions and adapt to changing environmental conditions. Therefore, it is important to be able to predict the fundamental fluxes in crops, in this case vineyards, such as sensible heat flux (H), latent heat flux (LE) and carbon dioxide flux (CO2), in order to know their capacity to adapt to the environmental effects of climate change. In this study, Linear Regression (LR), Elastic Net (EN) regression, K-Nearest Neighbours (KNN), Gaussian-Process (GP), Decision Tree (TREE) Regression, Random Forest (RF) Regression, XGBoost (XGB) Regression, Support Vector Regression (SVR) and Multi-layer Perceptron (MLP) models have been applied to predict fundamental fluxes of an eddy-covariance station from conventional meteorological parameters. These models reproduced well the estimations of three output parameters from the eddy-covariance station. The performance of each predictive model was evaluated using Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE) and the coefficient of determination (R2). The findings indicate that for the variable H, the GP model outperformed the SVR and all other models, achieving an R2 value of 0.99. Conversely, the SVR demonstrated superior performance for the variables LE and CO2, with R2 values of 0.96 for both. In summary, these findings suggest that the three models proposed show a robust performance in the prediction of the studied fluxes, underlining their versatility and adaptability to the various environmental conditions of the vineyard.

Pharmaceutical Supply Chain Management

Supply chain management (SCM) refers to the coordination and oversight of all activities involved in the production, procurement, transformation, and delivery of goods or services from the point of origin to the point of consumption. It encompasses the planning, execution, and control of various processes involved in the flow of materials, information, and finances across the entire supply chain. In this paper, the authors have done a detailed analysis of the pharmaceutical dataset and predicted useful metrics that can be used in the shipping industry. This paper aims to demonstrate the application of machine learning models on the prediction of shipping mode. The model is trained on various features of the pharmaceutical supply chain dataset such as unit price, first-line designation, and delivery dates. Data cleaning and feature engineering were done and multiple models were trained. Out of the models tested, XGboost was found to give the best results for the prediction of weight, shipment modes and delivery date. Key Words: Supply chain management, Machine Learning models, Shipping Industry, XG Boost

Detection of Malicious URLs using Machine Learning based on Lexical Features

As the digital world evolves, the risk of valuable information being exposed to unauthorized parties is increasing. One common vulnerability is the use of malicious Uniform Resource Locator (URL), which are fraudulent links spread across various platforms such as social media and emails. Traditional methods of identifying these URLs, such as blacklisting and heuristic search, rely heavily on syntax or keyword matching, but struggle to keep up with the evolving tactics of cyber attackers. Hence, this paper proposes a solution for detecting malicious URLs and their types based on lexical features. Lexical features in a URL refer to the components that convey semantic and lexical meaning. These can include domain names, path lengths, special characters, and other elements that can be analyzed for patterns or anomalies. In our proposed method, we use 23 different lexical features that focus on the semantic and lexical meaning of the URLs. An Exploratory Data Analysis (EDA) is used to filter the most important lexical features that effectively contribute to predictions. With these carefully curated features, we address the problem as a multi-classification task, aiming to assess the performance of three distinct classifiers: Random Forest, which currently stands as the domain's best solution and a pure bagging technique, as well as XG Boost and Light GBM, both of which utilize boosting techniques. With the proposed method, we could achieve over 93\% accuracy for all the three classifiers while Random Forest achieving the best performance.

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.

Impact of downstream obstructions on ogee weir efficiency: a regression analysis

ABSTRACT This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments, the influence of various obstruction angles on Cd was scrutinized applying a suite of regression analysis to develop predictive models. The analysis was enriched by considering hydraulic parameters such as flow rate, water level, and weir geometry. Despite the established importance of Cd in hydraulic designs the nuanced effects of downstream obstructions have received limited attention, highlighting a critical research gap. The findings highlight a strong correlation between obstruction angles and Cd, with developed regression models demonstrating notable predictive strength. Remarkably the models exhibited varying levels of accuracy, with the Random Forest regressor achieving an exceptionally low root mean square error (RMSE) of 0.005, indicating superior predictive performance. Conversely, traditional models like Decision tree and XG BOOST reflected higher RMSE values of 0.60, suggesting less predictive accuracy in this context. LASSO, Bayesian Ridge, and OMP regressors stood out with an RMSE of zero, denoting perfect predictions under the study's specific conditions.

Contextually Propagated Term Weight based Approach for Author Profiling: Gender, Age and Language Variety Prediction

Author profiling creates characterization of an author based on attributes such as age, gender, language, dialect region variety, personality and so on. In recent years it has garnered significant attention for its varies applications across forensic linguistics, marketing, cybersecurity and social media analytics. Most of the research focused on stylistic, content based and term weight measures based feature representations. We observed that context semantics is not considered in the feature representation. In this propose contextually propagated term weight measures for feature representation. We implemented SVM, Random Forest and XG Boost machine learning algorithms on those feature representations. The results demonstrated that the proposed contextually propagated term weight with inverse category frequency outperformed the existing methods.

The ZTF Source Classification Project. III. A Catalog of Variable Sources

The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open source and adaptable to future time-domain surveys.

Invest in Idea: Crowd Funding with Predicting Startup Success

Abstract: Crowd funding enables individuals or businesses to raise funds for their projects, ventures, or caused by tapping into a large pool of contributors. It democratizes access to capital, allowing creators to bypass traditional financial institutions and engage directly with their community or target audience. This method fosters innovation, empowers grassroots movements, and facilitates the realization of diverse ideas that might otherwise struggle to secure funding through conventional channels. Utilizing a rich dataset encompassing funding details, milestones, relationships, and geographical data of various startups, we embark on an in- depth analysis involving data preprocessing, feature engineering, and exploratory data analysis to uncover key success determinants. Employing advanced classifiers like LGBM, XG Boost and gradient Boosting, our model undergoes rigorous training and evaluation, with LGBM emerging as the top performer, achieving an accuracy of 90.48%. The analysis underscores the critical role of funding, and investor presence in forecasting startup success. This research not only equips stakeholders with a powerful predictive tool but also highlights significant features influencing startup outcomes, offering a novel perspective on strategic investment planning. Additionally, we developed an interactive frontend platform to complement our predictive model. Utilizing AngularJS, the platform serves as a gateway for startups to register their ventures, providing realtime access to predictive insights. Through intuitive forms, startups enter essential information such as geographical location, funding received, and sector of operation. Upon submission, the data is processed by our backend, which evaluates it against our machine learning model to predict success rates. This integration empowers startups with immediate insights and facilitates data-driven decision-making among investors, thereby fostering a more dynamic and informed startup ecosystem.