NumPy Jobs

Python Data Analysis & Machine Learning Project Description: Hi, I need a freelancer to help with a data analysis and machine learning project. The project includes: Data Cleaning & Preprocessing: Handle missing values, remove duplicates, encode categorical data. Prepare the dataset for analysis and ML model training. Data Analysis & Visualization: Analyze the dataset to identify trends, patterns, and insights. Create visualizations using Python libraries (Pandas, Matplotlib, Seaborn). Machine Learning Model Development: Build and train a supervised learning model (KNN, classification, or regression). Evaluate the model performance using metrics like accuracy, precision, recall. Documentation & Report: Provide a brief report of the analysis, model results, and r...

I have a set of time-stamped observations and I need a robust machine-learning solution that can forecast future values with solid accuracy. The end goal is an automated prediction/forecasting model that I can retrain as new data arrives and easily integrate into a larger Python pipeline. Here’s what I need from you: • Exploratory analysis of the raw time-series data, spotting seasonality, trends, and anomalies. • Thoughtful feature engineering (lags, rolling stats, calendar effects, exogenous variables if useful). • Model selection and training – I’m open to traditional approaches such as ARIMA/Prophet as well as more advanced architectures like Gradient Boosting or LSTM; choose what performs best and explain why. • Rigorous evaluation on a ho...

I am looking for an experienced Python developer to build a trading analysis tool for synthetic indices (such as volatility indices). The tool will help analyze market data, test strategies, and provide a live dashboard for monitoring Requirements Data Collection Connect to a trading platform API (example: Deriv or similar). Collect and store historical price data. Ability to update data automatically. Market Analysis Engine The tool should: Analyze price movements. Detect patterns or indicators. Allow custom strategy rules. Backtesting System Test strategies using historical data. Show results such as: Win rate Profit/loss Drawdown Number of trades Live Analysis Run the strategy on live data. Generate signals or alerts when conditions are met. Dashboard A simple web da...

OpenCV Hill Climb Project – Description Project Title: Autonomous Hill Climbing Robot using OpenCV Project Description: Developed an AI-based computer vision system using OpenCV and Python that enables a robot/vehicle to automatically detect and climb slopes or hills. The system processes real-time camera input to analyze terrain conditions such as slope direction, obstacles, and surface edges. Implemented image processing techniques like edge detection, contour detection, and gradient analysis to determine the steepest safe path for climbing. Applied the Hill Climbing optimization algorithm to continuously choose the best next movement direction based on terrain analysis. Integrated real-time decision making so the robot adjusts movement dynamically while climbing uneven surf...

Project Title: MERN Stack Web Application Development Project Description: I am looking for a developer who can build a web application using the MERN Stack (MongoDB, , React.js, Node.js). This project is for academic purposes. Requirements: • Frontend: React.js with responsive UI • Backend: Node.js and • Database: MongoDB • Features: User authentication (login/signup), CRUD operations, basic dashboard • Clean and well-structured code Additional Requirements: • Source code must be provided • Basic documentation of the project • Proper comments in the code Budget: Open for discussion Deadline: 5–7 days If you have experience with MERN Stack development, please place your bid and share your previous work if available.

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I'm seeking a Python expert to develop a credit risk management model, focusing specifically on market data. Key Requirements: - Credit Risk Expertise: In-depth understanding of credit risk, particularly related to market data. - Python Proficiency: Advanced skills in Python for data analysis and modeling. - Data Handling: Experience in working with large datasets, especially market data. - Financial Acumen: Strong background in finance and risk management principles. Ideal Skills and Experience: - Proven experience in developing credit risk models. - Familiarity with relevant Python libraries (e.g., Pandas, NumPy, Scikit-learn). - Ability to provide clear documentation and insights on the model developed. Please share your relevant experience and approach to this project. I look ...

I’m working through an year long online course on Artificial Intelligence and Machine Learning and I’m running short on time for several graded assignments and mini-projects. Rather than general tutoring or technical platform troubleshooting, I specifically need someone who can step in, finish the required tasks, and submit clean, well-commented solutions on my behalf. This will be an ongoing project and will span an year. Typical exercises range from data preprocessing and feature engineering to training and evaluating models with libraries such as Python, NumPy, pandas, scikit-learn, TensorFlow or PyTorch. Code must run flawlessly in the course’s Jupyter-based environment and meet the rubric laid out in each brief (accuracy thresholds, narrative explanations, and any...

I have already trained and deployed a Logistic Regression model in Streamlit that classifies breast-tumour samples as malignant or benign. What I need now is a polished data-visualization layer so users can quickly grasp how each feature influences the prediction. My immediate focus is on bar-chart visualisations. I want clear, well-labelled bars that compare malignant vs. benign distributions, show feature importances, and surface any other insight you think adds value. The work should plug straight into my current Streamlit app and read from the same Pandas DataFrame I am already passing to the model. Although the main task is visualisation, I am also experimenting with feature selection, so if your code can be structured in a way that makes it easy to toggle feature subsets, that wi...

I need an 8- to 10-page conference paper that presents a hybrid machine-learning Security Information and Event Management (SIEM) framework combining Random Forest and Isolation Forest for network-threat detection. The manuscript must follow either Springer LNCS or Scopus proceedings guidelines, complete with the correct template, figure sizing, and reference style. Core structure • Introduction and literature review that positions the problem, surveys recent SIEM advances, and justifies the hybrid approach. • Methodology and data analysis describing data-preprocessing, feature engineering, model building in scikit-learn, and experimental evaluation on publicly available cybersecurity datasets (e.g., CIC-IDS 2017, UNSW-NB15, or similar). • Conclusion and future work...

I’m building a research-grade quantum simulator in Python and need a robust codebase that can accurately model multi-qubit circuits, apply standard gate operations, and return state-vector or density-matrix outputs. Whether you prefer to work directly with NumPy/SciPy or leverage existing open-source frameworks such as Qiskit, Cirq, or QuTiP is completely up to you; the key requirement is clean, well-documented code that runs reliably under Python 3.11. Please provide: • A modular simulation engine capable of handling at least 10-15 qubits, with optional noise or decoherence modelling • A clear, Pythonic API for defining circuits, executing simulations, and extracting results (probabilities, expectation values, etc.) • Unit tests plus a concise README that cover...

I have already deployed a full Streamlit application that predicts loan approvals in real time (live demo: , source: ). The pipeline currently includes Logistic Regression, K-Nearest Neighbors, and Naive Bayes models with standard scaling and the usual EDA-driven feature engineering. What I want now is a measurable lift in overall model performance, with the F1-score as the guiding metric. Feel free to explore more advanced algorithms (e.g., Gradient Boosting, XGBoost, LightGBM, calibrated ensembles, or even a tuned version of my existing classifiers) as long as they integrate cleanly with the existing Python | Pandas | NumPy | Scikit-learn stack and can be surfaced through the current Streamlit front-end. Key points you should address • Re-examine preprocessing and feature sele...