Maternal Mental Health Data Analysis - 01/07/2026 13:08 EDT

@neerajdhameliya

Hello, To better understand how different social and demographic factors affect maternal mental health, I will use two methods. First, I'll apply logistic regression to identify which factors have the strongest impact. Then, I'll use an XGBoost model to improve prediction accuracy and help identify mothers who may be at risk, so they can receive support as early as possible. For the past 8 years, I've specialized in public health data analysis. I'll clean and prepare your data using R or Python, build reliable predictive models, and deliver well-documented, reproducible code along with an easy-to-understand report that turns your findings into practical insights for better healthcare decisions. Looking forward to hearing from you. Best, Niral

@devendrathakur12

Hello, I can help analyze maternal mental health data and identify the key factors that affect mothers' well-being. I have experience with data cleaning, statistical analysis, predictive modeling, and data visualization using Python and R. I can perform regression analysis, build machine learning models if needed, and explain the results in a clear public health context. You will receive a cleaned dataset, detailed analysis report, visualizations, and well-documented code and methodology. I focus on accurate results, clear communication, and timely delivery. I am ready to discuss your dataset and research goals and start the project as soon as possible. Best regards, Dev S.

@corpmember29

Hello, I have carefully reviewed your project requirements. With 10+ years of experience in Data Science, Statistics, Machine Learning, and Python/R-based analytical research, I can conduct a comprehensive analysis of maternal mental health datasets, including data cleaning, exploratory analysis, regression modeling, risk factor identification, predictive modeling (Random Forest/XGBoost where appropriate), and meaningful visualization of findings. I have experience delivering reproducible research with well-documented methodologies and interpreting statistical results in an evidence-based manner to support healthcare and public health decision-making. **** You can track the project’s progress using the tracker. I’m available to work 40-45 hours per week **** I am available to start immediately and eagerly await your positive response. Thanks.

@quan9824

As a seasoned data scientist with remarkable experience in public health and mental health research, I believe I am an excellent fit for your maternal mental health data analysis project. I have successfully conducted numerous data-driven studies and statistical analyses on complex health-related issues, including maternal health. This has provided me with extensive knowledge of regression analysis and machine learning - key skills needed for this job. By utilizing my expertise, we can ensure meticulous data cleaning, preprocessing, and comprehensive descriptive statistical analyses of the mental health indicators among mothers. Additionally, my experience working with socio-demographic, economic, and health-related variables will be invaluable in identifying key risk factors affecting mothers' psychological well-being. One of my core strengths as a professional is my ability to make data accessible to non-technical audiences. My strong background in statistics complements my proficiency in Python and R Programming Language, ensuring cogent interpretation of results in a context that public health professionals can readily understand. Allow me the honor of providing you with a 360-degree solution - from well-documented methodology and results to compelling predictive models- all aiding evidence-based interventions for maternal mental health improvement. Together we will make an impactful difference!

@sajedali784

Dear Researcher I am experienced academic writer and expert in Power BI, EXCEL, SMARTPLS, NVIVO, SPSS and AMOS for data analysis (Regression, ANOVA, Correlation, t-test, Chi Square test, Block Plot, SEM, Mediation Analysis, CFA and EFA etc.). I always provide results along with descriptive statistics. Looking forward for more discussion please. You can see my related reviews: https://www.freelancer.com/projects/spss-statistics/Help-with-PhD-data-analysis/reviews https://www.freelancer.com/projects/spss-statistics/Data-analysis-SPSS-Discussion-conclusion/reviews https://www.freelancer.com/projects/statistical-analysis/Data-Analysis-for-Research-Article/reviews https://www.freelancer.com/projects/spss-statistics/Statistics-SPSS-29759215/reviews https://www.freelancer.com/projects/statistical-analysis/SPSS-Analysis-25195398/reviews https://www.freelancer.com/projects/spss-statistics/SPSS-assignment-24766032/reviews https://www.freelancer.com/projects/r-programming-language/Statistical-Project/reviews https://www.freelancer.com/projects/electrical-engineering/Engineering-statistics/reviews Regards! Dr Sajed Ali, PhD Note: Bid is just to connect. The actual time and budget may be negotiated as per project details.

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@Aaleen110

I understand you need a data science expert to analyze maternal mental health conditions and identify key risk factors. My experience includes successfully identifying actionable insights from complex health datasets to inform intervention strategies, similar to your goal of supporting evidence-based interventions. I will perform thorough data cleaning and preprocessing of the maternal mental health data. Using Python with libraries like Pandas for manipulation and Scikit-learn for statistical analysis and model building, I will develop predictive models for identifying mothers at risk of depression and anxiety. The output will include clear visualizations of risk factor prevalence and model performance metrics. What is the primary data source for this analysis, and what is its approximate volume? Ready to start as soon as you confirm scope.

@mykhaylob4

✋ Hi there. I can analyze your maternal mental health dataset to identify key risk factors and develop predictive models for depression and anxiety. ✔️ I have rich experience in public health data analysis and recently delivered a similar project identifying socio-economic risk factors for postpartum depression using logistic regression and machine learning. I will develop this by performing data cleaning and preprocessing, conducting descriptive statistical analysis, applying regression models and machine learning algorithms like Random Forest to identify risk factors, and delivering a comprehensive report with visualizations and well-documented methodology. Please click the 'Chat' button to start our valuable conversation. Looking forward to collaborating with you! Best regards, Mykhaylo

@karaki93

Hello, I am a statistician and data scientist with experience analyzing clinical, epidemiological, and public-health datasets using R, Python, SPSS, and Stata. For this study, I can manage the complete analytical workflow: Clean and validate the maternal mental-health dataset Assess missing data, outliers, variable coding, and scale reliability Summarize depression, anxiety, and psychological well-being indicators Identify socioeconomic, demographic, obstetric, and health-related risk factors Fit logistic, ordinal, or multilevel regression models as appropriate Report adjusted odds ratios, confidence intervals, predicted probabilities, and model diagnostics Develop and validate Random Forest or XGBoost risk-prediction models where the sample size supports them Produce clear visualizations, a reproducible methodology, and a public-health interpretation of the findings My relevant work includes analysis of longitudinal HIV cohort data containing physical and mental-health indicators, clinical statistical analysis plans, logistic regression, subgroup analyses, missing-data treatment, odds-ratio interpretation, and predictive modeling. I have also worked with questionnaire reliability, factor analysis, clinical outcomes, and demographic risk-factor assessment.

@MZITSERVICES

Hi there, I am A.R.M. MASUD with a strong background in data science. I propose to conduct an econometric analysis aimed at Econometrics, Finance, Accounting Analysis "examining the impact of education on income levels using cross-sectional data. This project will involve data cleaning and preparation, Model specification and estimation using appropriate Econometrics, Finance, and accounting techniques (such as OLS, panel data, probit/logit, or time series methods). Provide visualizations (regression plots, residual plots) and interpret the results using diagnostic tests to ensure robustness. The analysis will be performed using statistical software such as SPSS, STATA, R, or Python, and findings will be summarized in a clear, concise report with actionable insights or policy recommendations. The project will be completed within your timeframe and will include all relevant datasets, code, and documentation. https://www.freelancer.com.bd/projects/excel/Finance-Excel-Sheet-Population/reviews https://www.freelancer.com.bd/projects/python/Python-Financial-Modeling-Aid-39392137 Don't hesitate to get in touch with us for any further clarifications or modifications to the proposal. Thanks A.R.M MASUD

@demivision

Hi there, Thank you for sharing the details of your project on maternal mental health data analysis. We at DemiVision, LLC are excited about the opportunity to collaborate with you on this important initiative. Our team has extensive experience in biostatistics, public health research, and advanced data science, with proficiency in Python, R, and statistical modeling techniques. We understand the importance of rigorously analyzing maternal mental health conditions to identify key socio-demographic and health-related risk factors. Our previous work includes analyzing perinatal mental health datasets, developing predictive models for postpartum depression, and providing actionable insights for maternal and child health interventions. For your project, we propose a structured approach: - Comprehensive data cleaning and preprocessing to ensure the integrity and usability of your datasets. - Descriptive statistical analysis to map the prevalence and distribution of mental health conditions among mothers. - Identification of significant risk factors using regression analysis and, if suitable, multilevel modeling to account for clustered data. - Application of machine learning models—such as Random Forest or XGBoost—for risk prediction, enabling targeted intervention strategies. - Clear and concise interpretation of findings within a public health context, supported by impactful visualizations. Our deliverables will include a cleaned and annotated dataset, a detailed statistical analysis report, a predictive modeling component (if required), and well-documented methodology to ensure transparency and reproducibility. We look forward to leveraging our expertise to help you generate evidence-based recommendations that can meaningfully inform maternal and child health programs. If you would like, we can share examples of similar projects we have completed in the fields of maternal and mental health research. Best regards, DemiVision, LLC

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe)& I can develop a reproducible maternal mental-health analysis workflow covering data cleaning,descriptive statistics,risk-factor modelling,optional ML prediction,visualisation My approach: -Review outcome definitions for depression/anxiety,questionnaire scores,missingness,sampling structure,& subgroup variables -Clean & code socio-demographic,economic,obstetric,behavioural,& clinical factors -Produce prevalence estimates,subgroup comparisons,confidence intervals,& clear risk-factor visuals -Build interpretable logistic regression first,adding multilevel modelling if needed -Compare optional Random Forest/XGBoost models using ROC-AUC,sensitivity,specificity,precision,recall,& calibration -Report adjusted odds ratios,confidence intervals,model limitations,& intervention-focused findings Relevant experience: -Built healthcare analytics pipelines using patient-level clinical & laboratory data,including missing-value handling,abnormality flags,cohort summaries,risk-factor analysis,& interpretable model outputs -Developed anomaly-aware clinical modelling workflows using z-scores,IQR rules,Isolation Forest,LOF,Logistic Regression,& tree models to identify high-risk patient groups -Worked on fetal & maternal imaging/biometry projects with clinical measurements,confidence indicators,quality checks -Supported thesis & technical-report preparation,including data description,regression methodology,results,policy implications

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings

@SkillCrafts

I understand you're looking to analyze maternal mental health data to identify risk factors and potentially build predictive models, similar to how I've previously developed risk stratification tools for chronic disease management using complex healthcare datasets. My experience in identifying subtle patterns within patient data will be directly applicable here. My approach will involve a robust data cleaning and preprocessing pipeline using Python libraries like Pandas and NumPy to handle missing values, outliers, and inconsistencies. For risk factor identification, I'll employ statistical methods such as regression analysis (logistic and linear) and correlation analysis. If predictive modeling is pursued, I will explore machine learning algorithms like Random Forests or Gradient Boosting, focusing on interpretability and performance metrics relevant to healthcare outcomes. To ensure alignment and maximize impact, could you clarify the primary data sources you'll be utilizing and any specific demographic groups you're most interested in stratifying? I'm eager to discuss how my analytical capabilities can contribute to your vital maternal and child health initiative.

@codeandcommand

For maternal mental health data, I would use Python with pandas and statsmodels to clean, analyze, and visualize your dataset, producing a clear report with key findings. Regression or logistic modeling is straightforward to add depending on your research questions. I can start today and have initial results within 2 days. These numbers are based on the post as written. We will refine them after a quick scope conversation. Want to jump on a quick call?

@Georgeoustar

Hi, This project requires more than just data analysis. The key is identifying meaningful maternal mental health risk factors and translating them into actionable public health insights. I can support the full workflow from data cleaning and preprocessing to statistical modeling, predictive analysis, and interpretation. I have experience in health related analytics, predictive modeling, and risk factor analysis using Python, R, and machine learning frameworks. I’ve worked on similar projects involving behavioral trends, health outcomes, segmentation, and forecasting, using logistic regression, Random Forest, and XGBoost to generate research driven insights and visual reports. I can deliver a structured analysis with clear methodology, findings, and practical recommendations. Best regards, George

@bizvivo

Greetings, With a robust background in statistics and data science, complemented by a prolific academic writing portfolio, I am well-equipped to tackle complex data-driven challenges. My expertise is rooted in the successful completion of numerous PhD-level thesis projects, where I employed advanced statistical methodologies to extract meaningful insights from diverse datasets. My professional journey has been marked by collaborations with various companies, leading to projects that demanded high-level quantitative analysis and data interpretation. These projects enabled me to delve into trend analysis, temporal behaviour studies, and comparative assessments of data variables. I possess proficiency in a suite of analytical tools, including SPSS, R, Python, OpenCV, WEKA, Tableau, Power BI, and Excel. My skill set extends to sophisticated techniques such as image processing, machine learning, deep learning, artificial intelligence, natural language processing, hypothesis testing, forecasting, T-tests, and ANOVA, among others. I am eager to engage in discussions that leverage my comprehensive skill set to provide innovative solutions in AI and ML domains. Warm regards, Radhika

@alyn48

Dear Sir, I can support your maternal mental health research project with a rigorous data science and biostatistical approach, focusing on both statistical inference and predictive modeling to identify key risk factors affecting maternal psychological well-being. My approach will begin with structured data cleaning and preprocessing, ensuring dataset quality and consistency. I will then conduct descriptive statistical analysis to explore maternal mental health indicators, followed by inferential modeling to identify socio-demographic, economic, and clinical risk factors using logistic regression and/or multilevel models where appropriate. If suitable for your dataset, I can also develop machine learning models (Random Forest, XGBoost) to enhance predictive capability for identifying at-risk mothers. All findings will be translated into a clear public health interpretation, emphasizing actionable insights for intervention design. Deliverables will include a cleaned dataset, fully documented analysis, statistical report, predictive models (if applicable), and publication-ready visualizations. Kind regards, Aly Nurdin

@romany55

Hi, I have substantial experience in leading projects focused on maternal mental health analysis and can execute this study successfully. The project is well-defined, but it would be beneficial to clarify the specific datasets available and any existing frameworks for the predictive models. Once I have that information, I will ensure a rigorous data cleaning process, followed by comprehensive statistical analysis to uncover key risk factors. I will utilize advanced modeling techniques, including logistic regression and machine learning, to deliver actionable insights and visualizations that support your public health objectives. What specific outcomes are you hoping to achieve with the predictive models? Roman