Numpy jobs

...generate fills, P&L, margin use, and drawdowns bar-by-bar, as though it were a live account. Walk-forward testing and position-level tracking are essential. • Reporting & visualizations – after each run I should be able to view equity curves, heat-maps, trade logs, and summary stats, then export everything to CSV or Excel. Technical preferences • Python 3.x with well-supported libraries (pandas, NumPy, Plotly/Matplotlib, and a lightweight GUI toolkit such as PySide6 or Tkinter). • Optimised code: vectorised calculations first, multithreading or multiprocessing where bottlenecks appear. • Modular structure so I can plug in new data feeds, strategies, and commission models later. Deliverables 1. Fully-functional Windows executable or in...

Ich arbeite bereits mit einem vollständigen Python-Skript, das Aktien-Backtesting über CSV-Dateien in Google Colab durchführt und Handelssignale per Telegram versendet. Aufbau, Modellbeschreibung und die gesamte Codebasis sind vorhanden – jetzt möchte ich das System um zusätzliche Datenanalyse-Funktion...‑Notebooks. • README mit kurzer Anleitung, Abhängigkeiten und Startbefehlen. • Ein Testlauf, der Beispiel-CSV einliest und die erweiterten Ergebnisse wie besprochen nach Telegram pusht. Ich stelle dir das aktuelle Anthropics-Skript, Beispiel-Daten sowie den Bot-Token zur Verfügung. Teile mir bitte kurz mit, welche Bibliotheken (pandas, numpy, ta-lib, matplotlib o. Ä.) du einsetzt und welchen Zeitrahmen du für die Umse...

...approach for initial data exploration and feature engineering, so the workflow should accommodate both phases gracefully. Here’s what I expect: • A brief, plain-English plan outlining the supervised objective (classification or regression, as appropriate once we review the dataset) and how clustering will support data insights. • End-to-end code in Python, preferably using scikit-learn, pandas, NumPy, and (if deep learning proves beneficial) TensorFlow or PyTorch. • Reproducible training pipeline: data loading, preprocessing, feature engineering, model training, hyper-parameter tuning, evaluation, and final model export. • A concise report or notebook explaining metrics, feature importance, and any clustering findings. • Clear instructions...

...is already in .exe format, I did that myself. Do not attempt to charge me more than $250, it will be declined. dependancies as follows: pip install sounddevice pip install pyaudio pip install soundfile pip install pydub pip install playsound pip install webvtt-py pip install faster-whisper pip install openai-whisper pip install PyQt5 pip install llama-cpp-python pip install pillow pip install numpy pip install pyttsx3 pip install vosk pip install ffmpeg-python pip install scipy pip install cffi pip install pyppeteer pip install diskcache pip install typing-extensions pip install torch pip install PyQt5 pip install PyQtWebEngine pip install PyQt5-sip pip install PyQt5-tools within anaconda environment "final-evolution" there is also these things must be installed duri...

... 3. What’s involved • Programming assignments: implementing missing Python functions, cleaning up logic, ensuring correct output formatting. • Machine-learning assignments: completing model training/evaluation sections (likely scikit-learn, pandas, NumPy), fixing runtime or shape errors, and confirming metrics meet the notebook’s stated targets. Deliverables 1. Updated notebooks that execute without errors in Google Colab. 2. Inline comments where significant changes were made, so I can follow your reasoning. 3. A brief summary of fixes and steps needed to replicate results. Acceptance criteria The “Runtime → Run all” command must finish

I’m sitting on several datasets—a mix of tidy spreadsheets and raw text logs—and I need them transformed into business-ready insights with reliable predictive power. Your first task will be to clean and consolidate this structured and unstructured data in Python using Pandas / NumPy and, where helpful, SQL queries. Once the data pipelines are solid, I’d like an exploratory analysis that highlights key patterns or anomalies before we move into supervised models that forecast the metrics I care about most. For visual communication, every important finding should flow into an interactive Power BI dashboard. I lean on these dashboards to brief non-technical stakeholders, so clarity and drill-down capability matter as much as aesthetic polish. Deliverables &bu...

...uncertainty calculations are reproducible from the provided data. 2. Graphs include error bars or confidence bands where appropriate. 3. Narrative links numerical uncertainty back to possible sources (instrument precision, temperature drift, human timing, etc.). 4. Document is fully proofread, clear, and ready for submission. Typical tools that fit nicely here are Excel, MATLAB, Python (NumPy/Pandas), or Origin, but I’m flexible—use whatever lets you justify each step transparently. Turnaround within one week would be ideal; let me know if you need to see the raw files before confirming....

Headline: Computational Chemist & Molecular Modeller | Computer-Aided Drug Design (CADD) Expert I provide high-precision in-silico drug discovery services, leveraging a deep technical stack that ranges from rapid virtual screening to high-level qua...Quantum Chemistry (QM): Precision electronic structure calculations, geometry optimization, and transition-state analysis using ORCA. • Molecular Dynamics (MD) & Simulations: Analysis of protein-ligand stability, RMSD/RMSF, and binding free energy calculations (ΔG). • Custom Python Automation: I develop bespoke Python scripts to automate data extraction, perform statistical analysis (using Pandas/NumPy), and integrate bioinformatic pipelines. • Pharmacokinetics: Predictive ADMET profiling to ensure lead comp...

...actionable insights Goal is to support data-driven decision-making and improve business performance Scope of Work: Clean and preprocess raw data (Excel/CSV/Database) Perform exploratory data analysis (EDA) Identify trends, patterns, and key insights Create interactive dashboards and visualizations Generate reports with clear business recommendations Tools & Technologies Required: Python (Pandas, NumPy, Matplotlib, Seaborn) SQL for data extraction and querying Excel / Google Sheets Power BI or Tableau (for dashboards) Deliverables: Cleaned and structured dataset Insightful visualizations and dashboards Summary report with key findings and recommendations (Optional) Presentation of insights Preferred Qualifications: Experience in data analytics or business intelligence Stro...

...clearly, demonstrate practical applications, and mentor me in solving industry-relevant problems. Responsibilities: - Conduct interactive online training sessions. - Cover key topics: - SQL (queries, joins, optimization) - Excel (functions, pivot tables, data cleaning) - Power BI (dashboard creation, DAX, visualization best practices) - Python (data analysis with Pandas, NumPy, project workflows) - Provide real-world datasets and project case studies. - Offer guidance on interview preparation and recruiter-ready examples. Requirements: - Minimum 4–5 years of professional experience in Data Analytics. - Prior training/mentoring experience preferred. - Strong communication and problem-solving skills. - Ability to share practical, industry...

...complementary / Mahony / Madgwick / EKF, quaternion math, Euler angle conventions • Solid DSP background: FIR/IIR filter design, Allan variance, PSD analysis, group delay, fixed-point vs. floating-point trade-offs • Experience tuning fusion-filter gains for embedded targets (memory and CPU constrained) • Comfortable with raw int16 sensor data, scaling, and BLE packet parsing • Proficient in Python (NumPy, SciPy, Pandas, Matplotlib) for analysis, filter design, and plotting • Experience comparing IMU output against a reference (rate table, optical tracking, surveyed reference IMU, or calibrated turntable) • Bonus: prior experience with Bosch BHI260AP or BMI323, BLE sniffing tools (Wireshark + nRF Sniffer / TI sniffer), or embedded firmware debugging ...

...multiple sources Write optimized SQL queries to extract and manipulate data Perform data analysis using Python and libraries such as Pandas, NumPy, Matplotlib, and Seaborn Develop interactive dashboards and reports using Power BI and Tableau Automate repetitive data tasks and reporting processes Identify trends, patterns, and actionable insights from data Work closely with stakeholders to understand business requirements Ensure data accuracy, consistency, and integrity Perform exploratory data analysis (EDA) and statistical analysis Create and maintain documentation for data processes and reports Technical Skills Required Programming: Python (Pandas, NumPy, Matplotlib, Seaborn) Database: SQL (Joins, CTEs, Window Functions, Query Optimization) Visualization Tools: Power BI,...

...Prediction Output │ ▼ Dashboard Visualization Project Structure stock_ai_project │ ├── models │ ├── │ ├── │ ├── │ ├── │ └── │ ├── sentiment │ └── │ ├── data │ └── │ ├── └── Technologies Used Programming Python Libraries Streamlit Pandas NumPy Scikit-learn TensorFlow / Keras Statsmodels Matplotlib yfinance NLTK Tools VS Code GitHub Machine Learning Models Random Forest A supervised machine learning algorithm used for regression that predicts stock prices based on historical features such as: Open price High price Low price Volume ARIMA A time-series forecasting model used for analyzing and predicting future stock prices based on historical

I’m preparing a machine-learning and NLP pipeline and need a solid, reproducible cleaning and exploratory-analysis pass on a mixed dataset that combines structured tables with free-text fields. The work must be carried out in Python, using Pandas and NumPy as the core libraries; feel free to pull in complementary packages (e.g., scikit-learn, spaCy) where that will speed things up, but the final code has to run end-to-end in a clean environment that I can recreate with a single requirements file. Here’s what I’m aiming for: • A well-commented Jupyter notebook or .py script that ingests the raw data, handles missing values and outliers, normalises categorical variables, and applies common text-processing steps (tokenisation, stop-word removal, lower-casing, e...

...actionable insights that lift sales and revenue. I have three rich sources ready to work with—customer transaction history, direct feedback, and full website-usage logs—and I need them cleaned, explored, and visualised in a way that makes next-step decisions obvious. You will start by shaping reliable, analysis-ready tables from all three feeds, using the tooling you know best: Python with pandas and NumPy for wrangling, SQL for structured pulls or joins, and Excel where quick sanity checks help. Once the data is tidy, I expect a solid exploratory analysis that surfaces the patterns, correlations, and outliers that matter most to revenue growth. From there, translate those findings into an interactive Power BI (or comparable) dashboard that sales leaders can refresh ...

...FastAPI project. Problem: Project is breaking due to environment/dependency issues: ModuleNotFoundError: TensorFlow fails to load () NumPy 2.x incompatibility with TensorFlow _ARRAY_API not found error Uvicorn crashes while importing Project Details: Backend: FastAPI Model: TensorFlow (Keras) Files: (API) (model loading + prediction) dataset/model/ (, , ) dataset/PlantVillage/ (class folders) Requirements: Create a clean working Python environment Fix all dependency issues: TensorFlow compatibility NumPy version compatibility (<2) Keras / model loading issues Ensure model loads correctly using: Fix API endpoint: /disease-detection-file should accept image

I’m putting together a Python-based solution for an aviation use case and need an experienced developer who knows both the language and the industry. The exact feature set is still flexible—ranging from data-driven analytics and task automation to a lightweight application layer—but the end goal is the same: clean, reliable code that fits...refine the requirements • Unit tests and basic CI setup so the code can be trusted in production • Concise developer docs and a quick-start README to make future iterations painless If you’re comfortable brainstorming the final spec with me, then moving quickly into an MVP and iterating from there, I’d like to hear how you would approach the build, the libraries you’d lean on (pandas, FastAPI, NumPy...

I need an end-to-end Spotify analytics dashboard, ready for production use between March 2026 and April 2026. The data arrives as raw streaming logs; you will prepare it in Python (pandas, NumPy, Matplotlib/Seaborn where helpful) and feed a clean model into Power BI. The finished report must speak the language of Analysts, exposing deep drill-through while staying light and fast. Core visuals include KPI cards for Distinct Songs, Average Popularity, and Position-1 Hits, an area graph that tracks monthly growth, plus a mix of charts, tables, slicers and other interactive elements that let users pivot by artist, track, album type or time period. Consistent colour and typography should guide the eye and keep the music theme alive. Key objectives • Transform messy CSV/JSON export...

I already code in C++ and R, yet I have never had a structured journey through Python. I want to start with the absolute fundamentals—syntax, data types, functions—and, step by step, reach advanced topics such as object-oriented design, decorators, asynchronous programming and popular libraries like pandas, NumPy and Flask. Because I learn fastest by doing, I need live over recorded sessions paired with hands-on exercises and short projects that we review together. Each lesson should leave me with runnable code, clear explanations and a small assignment so I can practise before we meet again. By the end, I expect to feel confident building a complete, well-structured application on my own. If you are comfortable teaching from basics all the way to expert-level patter...

I have a set of customer survey responses and I need them transformed into clear, actionable product-feedback insights. The workflow I have in mind is straightforward: • Load and clean the raw survey data in python so it is well-structured for analysis. • Explore and model the data with Python (pandas, NumPy, matplotlib or seaborn are fine) to surface patterns in what shoppers like, dislike, and expect next from our product range. • Build an interactive Power BI dashboard that lets non-technical teammates drill down by questions, segments, or time periods and quickly spot the most common themes and emerging preferences. Please deliver the cleaned SQL tables, well-commented Python notebooks or scripts, a packaged .pbix file, and a concise slide or PDF summary o...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...guardrails) • Integrated backtesting module so I can validate ideas on historical data before going live • Clean, well-commented codebase that I can extend, plus simple configuration files for tweaking parameters without rewriting code I’m comfortable providing API keys and sample datasets; you just need to wire them into the solution. Preferred stack is Python 3.x with libraries such as pandas, NumPy, TA-Lib/ta, backtrader (or another reliable framework) and WebSocket handling for streaming quotes. Deliverables are the complete source code, setup instructions, and a short video or written walkthrough showing the algorithm running in both backtest and paper-trade modes. If anything is unclear, let’s clarify early so the first milestone already compiles...

...physical reference measurement) Stage 6-7: Manual and auto annotation tools (browser-based) Stage 8: Multiview triangulation of asset 3D positions Stage 9: Taper ordering and work zone measurements Stage 10: Ground truth validation Stage 11: Report generation (, , ) Tech Stack Python 3.10+ COLMAP (custom GPU build) RT-DETR object detection model (HuggingFace) NumPy, pandas, OpenCV, matplotlib ENU coordinate system, GPS/IMU sensor fusion What We Need Testing — run the full pipeline on multiple video segments and verify outputs are correct at each stage Validation — compare pipeline measurements (cone spacing, taper length) against known ground truth values, document accuracy Bug Fixes — identify and fix remaining issues including: Stage 9 taper/lane closure

I'm seeking an experienced Python developer to help with data analysis and automate data scraping tasks. Key Requirements: - Develop Python scripts for data analysis. - Automate data scraping from websites. - Deliver clean, structure...analysis and automate data scraping tasks. Key Requirements: - Develop Python scripts for data analysis. - Automate data scraping from websites. - Deliver clean, structured datasets for analysis. Ideal Skills: - Proficiency in Python, especially for data manipulation. - Experience with libraries like BeautifulSoup, Scrapy, or Selenium. - Strong background in data analysis, preferably with Pandas or NumPy. - Familiarity with data storage solutions (e.g., SQL, NoSQL). Looking for someone who can write efficient, reliable scripts and has a keen ...

...data-driven insight into which products are really driving revenue. The goal is simple: surface product-level sales trends so I can see which SKUs consistently outperform and which ones lag behind, all viewed through the lens of revenue generated rather than just unit counts. You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that: • Cleans and normalises the raw data • Calculates revenue by product over selectable time windows • Highlights statistically significant up- or down-trends • Presents the findi...

I’m a complete beginner who wants to move well beyond theory and into practical, project-based learning. The journey should start with data-science fundamentals—Python, NumPy, pandas, basic statistics, data cleaning and visualization—then progress step-by-step to machine-learning models, large-language-model workflows, retrieval-augmented generation (RAG), agent frameworks, and automation best practices. Hands-on projects are essential; each topic needs to come with a small, self-contained exercise or micro-app that I can build, run, and extend. Think Jupyter notebooks, lightweight Flask or FastAPI endpoints, and scripts that hit real APIs (OpenAI, Hugging Face, LangChain, etc.). Code must be well-commented so I can retrace the logic afterward. Ideal flow &...

I need an experienced machine learning expert to develop a clustering model. The project involves working with numerical data. Key requirements: - Expertise in clustering algorithms (e.g., K-means, hierarchical, DBSCAN) - Proficiency in Python and relevant libraries (e.g., scikit-learn, NumPy, pandas) - Strong background in data preprocessing and feature engineering - Ability to interpret and visualize clustering results - Experience with model evaluation and validation techniques Please provide examples of similar projects you've completed.

I need a Python coder to help with basic data analysis tasks. The data is structured, so experience with CSV and SQL is essential. Key tasks include: - Data cleaning and preprocessing - Data visualization - Statistical analysis The ideal freelancer should have: - Proficiency in Python - Experience with data analysis libraries (e.g., Pandas, NumPy, Matplotlib) - Ability to work with structured data formats like CSV and SQL Please provide examples of similar work.

...Use suitable techniques for categorical exploration—cross-tabulations, chi-square tests, association rules, clustering on encoded variables, or any other method you feel is insightful. • Present the findings in clear, non-technical language supported by concise visuals (bar charts, heat-maps, mosaic plots or similar). • Provide a short, well-commented notebook or script (Python with Pandas, NumPy, SciPy, scikit-learn or R equivalents) along with an executive summary slide or PDF. Acceptance criteria The deliverable should let me: 1. See the key relationships between categories at a glance. 2. Understand any notable trends or unexpected concentrations. 3. Walk away with two or three actionable insights I can share with stakeholders. If you’re ...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...standard deviation for every variable so the artists immediately see the “typical” trajectory as well as the natural spread. Other metrics such as percentiles or min/max can stay in the background; we will only display them later if the director asks. • A short Python/TensorFlow (or PyTorch) pipeline is preferred so we can regenerate results when the blast parameters change. Feel free to leverage NumPy, Pandas and Plotly or Matplotlib for plotting—whatever keeps the workflow light and editable. • Final delivery should include the source code, a reproducible notebook, the generated CSV/JSON of raw predictions and the rendered charts (PNG or interactive HTML). Acceptance A run of at least 500 simulated blasts must complete in under 10 minutes on...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...I WENT WITH IS NOT REAL (10-11) Project: **A Flask + MySQL web system with Machine learning module for preliminary classification and statistical analysis of suspicious death cases in Albania, with expert review and correction.** Use only: * Python Flask * MySQL * HTML/CSS/Bootstrap 5 * * SQLAlchemy * Flask-Login * Flask-WTF or equivalent * Werkzeug security * scikit-learn * pandas, numpy * joblib Important requirements: * Everything must be built **from scratch** * No public/premade dataset * No pretrained/premade model I need an **original synthetic dataset** created from scratch only for this project. It will be temporary and later replaceable with real data. Dataset requirements: * generated with custom Python code * realistic but simple forensic-style patterns * aro...

...suspicious death cases in Albania, with mandatory expert verification and correction.** Use this stack only: * Backend: Python Flask * Database: MySQL * Frontend: HTML, CSS, Bootstrap 5 * Charts: * ORM: SQLAlchemy * Auth: Flask-Login * Validation: Flask-WTF or equivalent * Password hashing: Werkzeug security * Machine Learning: scikit-learn * Model saving: joblib * Data processing: pandas, numpy Very important requirements: 1. **Everything must be built from scratch.** 2. **Do not use any premade dataset.** 3. **Do not use any downloaded/public dataset.** 4. **Do not use any pretrained or premade model.** 5. **Do not use any external AI/API for classification.** I need an **original improvised synthetic dataset** created by you from scratch specifically for this project. It...

...suspicious death cases in Albania, with mandatory expert verification and correction.** Use this stack only: * Backend: Python Flask * Database: MySQL * Frontend: HTML, CSS, Bootstrap 5 * Charts: * ORM: SQLAlchemy * Auth: Flask-Login * Validation: Flask-WTF or equivalent * Password hashing: Werkzeug security * Machine Learning: scikit-learn * Model saving: joblib * Data processing: pandas, numpy Very important requirements: 1. **Everything must be built from scratch.** 2. **Do not use any premade dataset.** 3. **Do not use any downloaded/public dataset.** 4. **Do not use any pretrained or premade model.** 5. **Do not use any external AI/API for classification.** I need an **original improvised synthetic dataset** created by you from scratch specifically for this project. It...

...correct type. Once the dataset is tidy, I want to explore it visually. I am especially interested in bar charts, line graphs and scatter plots that help surface the main trends and relationships hidden in the numbers and categories. Feel free to suggest any additional plots that would add real insight; however, the three mentioned above are the minimum I need delivered. Python with pandas, NumPy and either Matplotlib or Seaborn is perfectly fine, but I am open to R or another proven toolset if you prefer—it just has to be reproducible. Deliverables • Cleaned CSV ready for downstream work • The code or notebook used (well-commented so I can rerun it) • High-resolution bar charts, line graphs and scatter plots (PNG or PDF) with brief captions exp...

I have several months of raw sales data that I need turned into clear, ...those formats you prefer. Here is what I expect back: • A concise analytical report (PDF or slide deck) highlighting the key trends you discover, complete with charts or visualisations. • A clean, well-commented workbook or script so I can replicate or extend the analysis later. • A brief summary of recommendations drawn directly from the data. Your toolkit is up to you—Python (Pandas, NumPy, Matplotlib), R, Power BI, Tableau, Excel Power Query—whichever lets you dig deepest and present findings clearly. Timing is flexible but I would like to see an initial glimpse of your approach within a few days, with the full analysis soon after. If this first project goes well, there i...

...businesses reduce churn, improve customer retention, and increase profitability. Dataset Details Dataset includes customer demographic, financial, and behavioral features: RowNumber, CustomerId, Surname CreditScore, Geography, Gender Age, Tenure, Balance NumOfProducts, HasCrCard, IsActiveMember EstimatedSalary Target Variable: Exited (Churn Status) Tools & Technologies Python Libraries: pandas, numpy (data processing) matplotlib, seaborn (data visualization) scikit-learn (ML modeling) Advanced Techniques: SMOTE (handling class imbalance) Feature scaling & encoding Model evaluation metrics Exploratory Data Analysis (EDA) Performed deep EDA to uncover: Customer behavior trends Churn patterns across geography, age, and balance Correlation between features and churn Create...

I can assist with implementing ML models (e.g., regression, classification, clustering), data preprocessing, feature engineering, and performance evaluation using libraries like scikit-learn, pandas, and numpy. I ensure results are accurate, well-documented, and suitable for academic submission. Deliverables: • Clean, well-commented Python code (Jupyter Notebook) • Data preprocessing and feature engineering • ML model implementation and evaluation • Graphs, results, and interpretation • Concise explanation for report inclusion Ready to start immediately. Share the details and I’ll take it from there. Best regards, Hasan

We are seeking a skilled data analyst to assist with the following tasks: - Data cleaning and preprocessing, including handling missing values and duplicates. - Conducting Explorat...including handling missing values and duplicates. - Conducting Exploratory Data Analysis (EDA) to identify trends and actionable insights. - Building interactive dashboards and reports using Power BI. - Generating actionable business insights to support decision-making. Preferred Tools/Software: - Excel (advanced formulas, pivot tables) - SQL (data extraction and querying) - Python (Pandas, NumPy, Matplotlib) - Power BI The project is expected to be completed within 2 to 3 weeks, depending on complexity. The fixed budget for this project is ₹20,000, with slight flexibility based on scope and ...

...maintenance scheduling models: Baseline approach Greedy algorithm Integer Programming (PuLP or similar) Integrate machine learning predictions into scheduling decisions 5. Analysis and Results Compare AI-based maintenance with traditional methods Perform cost-benefit analysis (target improvement: 10–15%) Conduct sensitivity analysis Technical Requirements: Python (mandatory) scikit-learn, pandas, numpy Optimization libraries (PuLP or OR-Tools) Jupyter Notebook Experience in predictive maintenance or engineering datasets is preferred Deliverables: Clean, well-documented Python code Model outputs and evaluation metrics Simulation results (graphs and comparisons) Brief explanation of methodology for thesis use...

...g., KS test) 4. Optimization & Simulation Develop maintenance scheduling: Baseline model Greedy algorithm Integer Programming (PuLP or similar) Integrate ML outputs into scheduling decisions 5. Analysis & Results Compare: AI-based vs traditional maintenance Perform: Cost-benefit analysis (target ~10–15% improvement) Sensitivity analysis Tech Stack Required: Python (must) scikit-learn, pandas, numpy Optimization tools (PuLP / OR-Tools) Jupyter Notebook Experience with power systems / predictive maintenance (bonus) Deliverables: Clean, well-documented Python code Model outputs + evaluation metrics Simulation results (graphs, comparisons) Brief explanation of methodology (for thesis writing)...

... and lay down a robust preprocessing pipeline that feeds seamlessly into our future predictive models. The end-goal for this phase is clear: extract insights that help us validate product hypotheses and impress early pilot partners. We move fast—ASAP delivery is non-negotiable—so I’m looking for someone who can jump in, spin up notebooks, and iterate daily. Expect to work with Python (Pandas, NumPy, SciPy), SQL, and ideally Spark for the heavier ETL steps; experience deploying workflows on AWS or GCP is a plus because we’ll be productionising soon. Deliverables • Cleaned, well-documented datasets ready for downstream modelling • A reproducible preprocessing pipeline (scripts or notebooks + environment files) • An insights report high...

I need a Python-based trading algorithm that trades both the Nifty and Bank Nifty indices. The code should run locally on Python (feel free to lean on pandas, NumPy, TA-Lib, backtrader or similar libraries) and must be able to import and work with historical market data only—no live feed is required for this milestone. Here is what I expect: • A clean, well-commented Python script (or notebook) that ingests historical data, generates trade signals, executes the logic, and outputs detailed performance metrics and an equity curve. • Clear instructions on how to map the code to CSVs or API endpoints I already use for historical NSE data. • A short README explaining any configurable parameters so I can tweak settings for further experiments. Back-testing acc...

...Python expert to take several CSV and Excel files and turn them into solid, statistically sound insights. The work is focused entirely on data analysis and processing—no web scraping or app development—so your time goes straight into cleaning the raw tables, exploring patterns, and building the statistical models that answer my business questions. You should feel at home with pandas for wrangling, NumPy and SciPy for numerical work, and a modeling library such as scikit-learn or statsmodels to run regressions, clustering, or any technique you recommend. If you prefer working in a Jupyter notebook, that’s perfect; a well-commented .py script is also fine. I’ll supply the data files and a brief outlining the hypotheses I want tested the moment we start. De...

...involves straightforward data entry from spreadsheets, deeper analytical summaries drawn from a database, or shaping web-scraped records into a narrative that can power a report, a machine-learning pipeline, or an interactive dashboard. Here’s what I need from you: • A brief outline of how you would tackle the job once you see the raw files. • Your preferred tools—Excel, SQL, Python (Pandas, NumPy), or any other stack you’re comfortable with. • A sample of comparable work, if available, so I can gauge fit and style. • An estimated turnaround time for an initial milestone, plus how you’d verify accuracy and completeness. Deliverables will ultimately include the cleaned dataset in its native format, a clearly documented descript...

...choices Deliverables • Clean and processed dataset • Skill extraction pipeline (with comparison results) • Salary prediction models + validation analysis • Demand forecasting models + evaluation • Streamlit dashboard for visualization • Research report including: • Methodology • Experiments • Evaluation metrics • Results and insights • Limitations Requirements • Strong Python (pandas, numpy) • NLP experience (regex, spaCy, embeddings) • Machine learning experience • Understanding of evaluation metrics (precision, recall, F1, RMSE, etc.) • Experience with research-style work (preferred) • GitHub usage with regular commits • Must speak Hindi or Gujarati • Must attend ...