Regression Analysis Jobs

research paper on economic diversification in the UAE, focusing on the role of non-oil sector growth in GDP performance (2000–2023). Key Requirements: • Clear introduction presenting the economic context and research question • Well-structured literature review relevant to economic diversification and growth • Identification of the research gap and positioning of the study • Development of clear and logical hypotheses • Presentation of an econometric model (e.g. GDP Growth = f(Non-oil GDP Growth, Oil Price, Investment)) • Clear definition and justification of all variables • Collection and presentation of data (2000–2023) • Descriptive data analysis (tables, statistics, graphs) • Regression analysis (OLS or appropriate meth...

I need a freelancer to perform statically analysis. For my DNP project outcomes, comparing pre and post implementation surveys. Requirements: - Expertise in predictive statistics - Proficiency with numerical data - Experience with regression analysis, time series analysis, or machine learning algorithms Ideal Skills and experience Working with DNP students Please provide examples of previous work and relevant qualifications.

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...

I need a skilled data professional to turn my historical sales records into reliable projections for the months ahead. The raw files are already exported from our POS and e-commerce platforms; they cover daily transactions, product categories, promotions, and regional outlets. Your first task will be to explore and clean this sales data, handle any missing values or outliers, and engineer features that capture seasonality, campaigns, and other business drivers. My primary goal is an accurate sales forecast and projection roadmap. I am particularly interested in machine-learning approaches—think gradient-boosted trees, LSTM, Prophet, or any other model you feel best suits the data’s structure. Classical time-series or regression techniques are fine as benchmarks, but the core d...

? HAGO Contest #2 – April ? For data scientists and analytics enthusiasts, we present a new challenge with Amazon sales dataset. ? Tasks: Explore the dataset in depth Analyze patterns and trends Build a predictive model using Python Submit results in a PDF file within our private community ? Prize: The winner receives $15 USD for the best analysis and prediction ? Don’t forget to join the IFAI Contest for future forecasting, where the winner earns $10 USD. ✨ Tips to increase your chances of winning: Make your analysis comprehensive yet easy to understand Add clear and insightful visualizations Focus on prediction accuracy and creative ideas

Please complete the following problems from Chapter 8 as listed below: Chapter 8 (Use Excel, ignore other software POM): Problem 3 (just run a regression analysis using the data analysis tool, then you can answer) Problem 5 (age is the independent variable, maintenance is the dependent variable). Please ignore least squares; just run a regression analysis using the data analysis tool. Problem 7 Problem 9 Please show all your calculations (not only answers). More help file in Excel Please show all your calculations (not only answers). Highlight answer please The Excel file below has data for your problems (go to the question chapter tab, copy and paste to your own Excel sheet or Word document). Do not solve in the given Excel sheet. Data from Excel for homework, The Excel file...

我手上有一份来自模拟的曲线数据，需要你用神经网络把它拟合成一条可直接调用的经验公式，并确保任意数据点的误差率都控制在 5% 以内。网络架构我还没有想法，期待你根据数据特点给出专业建议。 核心工作流程 1. 数据理解与预处理 • 读取并检视我提供的专用数据集（文件将随项目开始共享）。 • 做必要的归一化、划分训练/验证/测试集，并说明理由。 2. 网络设计与训练 • 结合曲线特征提出1–2套候选架构（如全连接网络、CNN、Transformer 等），解释选择依据。 • 用常见框架（Python + PyTorch 或 TensorFlow 均可）实现并训练；包含超参数调优与早停策略。 3. 误差评估与公式提取 • 给出完整的误差分析，逐点列出相对误差，确保全部 ≤5%。 • 将最佳模型转化为可复现的数学/代码形式： - 明确公式、系数与激活函数。 - 提供 Python 函数示例，方便直接调用。 4. 交付物 • 可执行源码与依赖清单。 • 训练日志、可视化结果（loss 曲线、散点图对比）。 • 技术报告：数据处理、模型架构、实验参数、误差验证、最终经验公式。 • 一键复现实验脚本或 Jupyter Notebook。 验收标准 - 全数据集点对点误差 ≤5%。 - 代码在常规 GPU/CPU 环境下即可运行，无缺失依赖。 - 报告内容完整、条理清晰，步骤可追溯。 如果你对曲线拟合、深度学习或函数提取有成熟经验，请展示相关案例或 GitHub 链接，让我更快锁定合适的人选。