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proLahore, Pakistan

AI Integration & Automation Engineer | Full-Stack Web Apps

$50k+: Earned

65x: Hired

4.9: Rating

131: Followers

Expert

Expert

+2

AI Integration & Automation Engineer | Full-Stack Web Apps

Cover image for Autonomous Multi-Agent Market Research System Development

Autonomous Multi-Agent Market Research System Development

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14

Cover image for Magnai | UK Public Affairs

Magnai | UK Public Affairs

5

73

Cover image for Humoni - secure housing in under 72 hours

Humoni - secure housing in under 72 hours

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128

Cover image for Wellbeing Wizard AI

Wellbeing Wizard AI

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185

Dhaka, Bangladesh

Power BI Data Analyst + ML AI Automation Expert

5.0: Rating

108: Followers

Power BI Data Analyst + ML AI Automation Expert

Cover image for Education Program Analytics Dashboard –

Education Program Analytics Dashboard – Data Analytics Solution Handling large-scale program data without clear visibility can make impact measurement difficult. This dashboard is designed to give organizations a complete, real-time view of program performance, reach, and funding — all in one place. What You Get : A powerful, interactive dashboard that helps track beneficiaries, program outputs, regional performance, and donor contributions with clarity and precision. Key Capabilities: Monitor total beneficiaries and gender distribution Track program reach across provinces and sectors Analyze quarterly trends and growth patterns Evaluate donor funding allocation and impact Identify top-performing program categories and outputs Explore education level distribution and engagement For NGOs and large programs, data is critical for decision-making and reporting. This dashboard helps you measure impact, improve transparency, and optimize resource allocation — making your data meaningful and actionable. Perfect For : NGOs and non-profit organizations Government programs International development agencies Research and policy teams If you want a professional, insight-driven dashboard that clearly communicates impact and performance, I can create a customized solution tailored to your organization.

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Cover image for 3D Molecular Intelligence: Accelerating Drug

3D Molecular Intelligence: Accelerating Drug Discovery through Predictive Analytics The Impact: This project addresses a critical bottleneck in pharmaceutical research: determining molecular solubility (logS). By replacing slow, expensive lab tests with high-precision machine learning, this system enables scientists to screen thousands of compounds in seconds, significantly reducing the cost and time required to bring new life-saving drugs to market. Drastic Cost Reduction: The predictive pipeline reduces early-stage experimental screening costs by 70–90%, allowing research teams to focus resources on the most promising drug candidates. High-Precision Forecasting: Utilizing a hybrid 3D feature engineering approach, the system achieves a remarkable 91.3% accuracy (R² score) in predicting solubility, providing a highly reliable alternative to physical testing. Accelerated R&D Cycles: By automating the identification of viable molecules, the tool dramatically shortens the "hit-to-lead" time in pharma and materials science, getting products to market faster. Empowering Researchers: I deployed a professional Streamlit dashboard featuring an interactive 3D molecular viewer. This allows non-technical chemists to visualize complex structures and make data-driven decisions without needing to write a single line of code

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Cover image for Predictive Marketing Analytics: Optimizing Advertising

Predictive Marketing Analytics: Optimizing Advertising ROI 1. The Business Problem Companies often struggle to determine which marketing channels actually drive revenue. Without a data-driven approach, advertising budgets are often misallocated across platforms like TV, Radio, and Newspapers, leading to inefficient spending and missed sales targets. This project aimed to build a predictive model to quantify the relationship between multi-channel marketing spend and total sales. 2. Strategic Insights & Market Analysis Through a rigorous analysis of historical advertising data, I identified the specific drivers of revenue growth: Dominant Revenue Driver: TV advertising emerged as the most critical factor, showing a massive 0.9 correlation with sales. Efficiency Analysis: While Radio and Newspaper spending contributed to the marketing mix, their direct impact on sales was significantly lower (0.35 and 0.16 correlation, respectively), suggesting a need for budget reallocation. Predictive Power: My analysis revealed that 81.6% of the variance in sales can be explained by TV advertising spend alone, providing a highly reliable foundation for future budget forecasting. 3. Data-Driven Solution I developed a Linear Regression model to provide leadership with a mathematical framework for sales forecasting. Reliability: The model was validated using a 70/30 train-test split, ensuring it performs accurately on new, unseen market data. Accuracy: The system achieved a strong R-squared value of 0.79 on the test set, meaning it can accurately predict nearly 80% of sales fluctuations based on planned marketing spend. Error Management: I performed a detailed residual analysis to confirm that the model’s error terms were normally distributed, ensuring the reliability of the forecasted figures. 4. Business Impact Budget Optimization: Provided a clear mathematical equation (Sales=6.948+0.054×TV) that allows the marketing team to calculate the expected return on every dollar spent on TV advertising. Strategic Planning: Enabled the transition from "gut-feeling" marketing to precision budgeting, allowing the company to maximize ROI by prioritizing high-impact channels. Risk Mitigation: By identifying the variance that the model couldn't explain, I helped the business identify where external market factors might still influence sales, allowing for more conservative and realistic financial planning. Technical Stack Modeling: Simple Linear Regression, Statsmodels (OLS), Scikit-learn. Analytics: Python, Pandas, NumPy. Visualization: Seaborn, Matplotlib, 3D Scatter Plots

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Cover image for Sentiment-Driven E-commerce Optimization: Amazon Review

Sentiment-Driven E-commerce Optimization: Amazon Review Analysis & Rating Prediction. Project Overview ✅ This project serves as a machine learning proof of concept designed to transform raw Amazon product reviews into actionable business insights. By automating the prediction of review ratings and analyzing customer sentiment, the system enables brands to optimize product listings, proactively address customer pain points, and drive higher conversion rates. Process ✅ I developed an end-to-end pipeline covering data acquisition, complex text processing, and model deployment. Automated Data Scraping, Integrated the Apify API to extract real-time customer feedback directly from Amazon product URLs. I configured the scraper to handle up to 100 reviews per run, capturing critical metadata including rating scores, review descriptions, and verified purchase status. Data Refinement & Feature Engineering: Cleaned a dataset of approximately 1,944 reviews by removing noise (punctuation/symbols) and stop words using NLTK. I implemented TF-IDF Vectorization to convert text into numerical features and applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, ensuring the model could accurately predict rare negative reviews. Model Benchmarking ✅ Developed and compared three distinct architectures to identify the most robust predictor: Naive Bayes: High-speed probabilistic classification. Support Vector Classifier (SVC): Optimized for high-dimensional text data. Neural Network (MLPClassifier): To capture complex semantic patterns. Web App Deployment: Built a dedicated Streamlit dashboard that allows non-technical stakeholders to input raw review text and receive instant rating predictions with confidence scores. Technical Stack✅ Languages & Tools: Python, Apify Client. ML & NLP Libraries: Scikit-learn (SVC, Naive Bayes, MLP), NLTK (Tokenization, Stopwords), Imbalanced-learn (SMOTE). Deployment: Streamlit, Joblib (Model Serialization). Visualization: Plotly, WordCloud, Matplotlib. Key Results ✅ Achieved a peak accuracy of 95.27% using the Neural Network model, with the SVC model following closely at 94.46%. Developed sentiment-based feedback loops within the app: high ratings (4-5 stars) trigger positive marketing recommendations, while low ratings (1-2 stars) alert teams to address product issues like battery life or build quality. Enabled real-time competitive analysis by providing a user-friendly interface for cross-functional marketing and product development teams to audit customer sentiment at scale.

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737

Abu Aasif Ansari

Bhiwandi, India

I build AI agents & internal tools that act on data

12: Followers

I build AI agents & internal tools that act on data

Cover image for AI-Powered Data Cleaning Tool Development

AI-Powered Data Cleaning Tool Development

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Cover image for AI-Powered Data Cleaning Tool

AI-Powered Data Cleaning Tool

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Cover image for Smart Data Analyst — AI-Powered Data Analysis App

Smart Data Analyst — AI-Powered Data Analysis App

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7

Cover image for PersonaSkill AI — Career Assessment Tool

PersonaSkill AI — Career Assessment Tool

1

12

Antalya, Turkey

PYTHON DEVELOPER | DATA & AUTOMATION SOLUTIONS

New to Contra

PYTHON DEVELOPER | DATA & AUTOMATION SOLUTIONS

Cover image for Global Job Intelligence — Updated

Global Job Intelligence — Updated Version I designed and built an automated job-market intelligence platform that collects, cleans, normalizes, deduplicates, and analyzes listings from six global sources through daily data pipelines. The latest version includes: • Approximately 20,000 job records • 93 passing automated tests • 16 standardized job categories • Improved parent/child category hierarchy • More accurate normalization and deduplication • Expanded filters for company, category, seniority, work mode, salary availability, source, and publication date • Interactive market intelligence and pipeline-health monitoring • Downloadable CSV and Excel reports Built with Python, PostgreSQL, Pandas, Streamlit, Plotly, APIs, Playwright, and GitHub Actions. This project demonstrates how I transform fragmented web data into reliable, searchable, and decision-ready business intelligence. Live Dashboard: https://global-job-intelligence.streamlit.app/ GitHub: https://github.com/niisa0/global-job-intelligence Portfolio: https://niisa0.github.io/

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Cover image for Built a full Business Analytics

Built a full Business Analytics & Reporting Platform designed to transform raw sales data into decision-ready insights. The platform covers the complete analytics workflow — from data cleaning and KPI calculation to interactive filtering, profitability analysis, automated business insights, and downloadable CSV/Excel reporting. The project is deployed as a live Streamlit web application, allowing users to interact with filters, explore business performance, and generate reports directly in the browser. Key features include dynamic KPI tracking, revenue and profit trend analysis, product/category/regional performance breakdowns, data quality monitoring, and multi-sheet Excel reports with Executive Summary, KPIs, Product Performance, Regional Analysis, and Filtered Data. Built with Python, Pandas, Streamlit, Plotly, and OpenPyXL using a modular project structure focused on maintainability and real-world reporting workflows. Live Demo: https://business-analytics-platform.streamlit.app/ (https://business-analytics-platform.streamlit.app/)GitHub: https://github.com/niisa0/business-analytics-platform

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Cover image for Customer Retention Intelligence is a

Customer Retention Intelligence is a deployed interactive analytics application built to turn 7,043 telecom customer records into actionable retention insights. I designed the project as a modular data product rather than a static dashboard. It combines customer data preparation with dynamic segmentation, a tenure × contract churn risk matrix, churn-driver exploration, priority segment analysis, reported churn reasons, financial exposure metrics, and filtered CSV/Excel exports. The analysis surfaced a clear contract pattern: observed churn was 45.8% among Month-to-Month customers, compared with 10.7% for One Year and 2.5% for Two Year contracts. These results are presented as observed associations rather than causal claims. The codebase separates data processing, analytics, visualization, and export logic for maintainability. The application was built with Python, Pandas, Streamlit, Plotly, and openpyxl and deployed as a live Streamlit application. Live Demo: https://nisa-retention-intelligence.streamlit.app/ Source Code: https://github.com/niisa0/customer-retention-intelligence

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Cover image for I’ve expanded my Web Change

I’ve expanded my Web Change Monitor project with Playwright support, enabling it to monitor selected content on both static and JavaScript-rendered websites. The application uses Requests and Beautiful Soup for static pages, while Playwright handles content that appears only after JavaScript runs. Users can provide a page URL and CSS selector to monitor specific information such as prices, stock availability, product details, or announcements. Key capabilities: • URL and CSS selector validation • Static and JavaScript-rendered page support • SQLite storage for monitored pages and latest values • Comparison of current and previous results • Timestamped change history with old and new values • Error handling and activity logging The system can be adapted for price tracking, stock monitoring, competitor research, and other website-monitoring workflows. Built with Python, Requests, Beautiful Soup, Playwright, SQLite, and Logging. GitHub: https://github.com/niisa0/web-change-monitor (https://github.com/niisa0/web-change-monitor)Portfolio: https://niisa0.github.io/ #Python #Playwright #WebAutomation #WebScraping #SQLite

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Emmanuel Ezeokeke

AI Expert |AI Agent| AI RAG | LangChain | LangGraph | CrewAI

AI Expert |AI Agent| AI RAG | LangChain | LangGraph | CrewAI

Cover image for All My Reviews on Upwork

All My Reviews on Upwork

1

6

Cover image for Local AI Agent Troubleshooter

Local AI Agent Troubleshooter

1

6

Cover image for AI System for Doctor Appointment Management

AI System for Doctor Appointment Management

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11

Cover image for I made a tutorial for an AI agent where I built an AI-powere...

I made a tutorial for an AI agent where I built an AI-powered blockchain analytics platform (https://www.youtube.com/watch?v=ceRZWLkxEbU) built with CrewAI that analyzes crypto wallets across Ethereum, Polygon, BSC, Arbitrum, and Base. Uses 4 specialized agents - Portfolio Analyst, Transaction Specialist, Investment Strategist, and Intelligence Synthesizer - working sequentially to process on-chain data via Zapper API. Delivers good reports with portfolio composition, risk assessment, behavioral patterns, and investment recommendations through CLI and Streamlit interfaces with real-time tracking and downloadable outputs. Try it out: https://onchain-ai-agent.onrender.com/ Github code: https://github.com/Emarhnuel/Onchain-AI-agent

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Amsterdam, Netherlands

Marketing Data Specialist bridging marketing & data science

Marketing Data Specialist bridging marketing & data science

Cover image for Apple Sales Dashboard

Apple Sales Dashboard

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Cover image for Global Tourism Interactive Dashboard

Global Tourism Interactive Dashboard

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Cover image for WeWorkRemotely Remote Jobs Dashboard

WeWorkRemotely Remote Jobs Dashboard

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2

Cover image for Global Energy Transition Dashboard

Global Energy Transition Dashboard

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5

Freelance Data Scientist | Python & ML Expert

10: Followers

Freelance Data Scientist | Python & ML Expert

Cover image for Fraud Transaction Detection System

Fraud Transaction Detection System

1

10

Cover image for Hybrid AI Movie Recommendation System for Pre-2015 Films

Hybrid AI Movie Recommendation System for Pre-2015 Films

1

5

Cover image for Lung Cancer Survival Prediction Model Development

Lung Cancer Survival Prediction Model Development

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Senior FullStack & AI Engineer | Product-Driven Solutions

$25k+: Earned

7x: Hired

5.0: Rating

26: Followers

Senior FullStack & AI Engineer | Product-Driven Solutions

Cover image for Regulated Builds

Regulated Builds

0

18

Cover image for Chronicler | An a16z funded Startup

Chronicler | An a16z funded Startup

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Cover image for AuditionAid - AI Career Platform for Actors (Web & Mobile)

AuditionAid - AI Career Platform for Actors (Web & Mobile)

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Cover image for Development of Daemora Autonomous AI Agent

Development of Daemora Autonomous AI Agent

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