Freelancers using Python in BangladeshFreelancers using Python in Bangladesh
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Istiak Ahmed Khan's avatar
Istiak Ahmed Khan
Dhaka, Bangladesh
Power BI Data Analyst + ML AI Automation Expert
5.0
Rating
101
Followers
Power BI Data Analyst + ML AI Automation Expert
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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5
688
Cover image for Saving Lives through Early Clinical
Saving Lives through Early Clinical Risk Detection Application is live at: https://495a-35-237-151-197.ngrok-free.app The Problem: Heart failure is a leading cause of global mortality; the difference between survival and fatality often depends on how early a high-risk patient is identified. The Solution: I developed a clinical diagnostic tool that analyzes patient health markers to forecast mortality risk, assisting medical professionals in prioritizing life-saving care. Key Impacts: Early Intervention: The model identified that follow-up time is the single most critical factor in reducing fatalities, emphasizing the need for early diagnosis and consistent monitoring. Precision Diagnostics: By analyzing heart efficiency (ejection fraction) and chemical markers like serum creatinine, the tool provides a high-accuracy (84.49%) risk score for every patient. Clinical Support: The system helps doctors look past "statistical flukes" by accounting for outliers in medical data, ensuring that extreme clinical cases are caught rather than ignored. Actionable Health Insights: Demonstrated a clear link between age, heart efficiency, and chemical abundance, giving providers a data-driven framework to improve long-term patient outcomes
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3
688
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.
2
5
726
Python(7)
MD Robin Islam's avatar
MD Robin Islam
proDhaka, Bangladesh
Full-Stack Developer · Framer, Webflow & WordPress Expert
17
Followers
Full-Stack Developer · Framer, Webflow & WordPress Expert
Cover image for Building a White-Label Platform That Scaled to Toyota & BMW
Building a White-Label Platform That Scaled to Toyota & BMW
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6
Cover image for A Modular Booking SaaS That Replaced 100% of Manual Work
A Modular Booking SaaS That Replaced 100% of Manual Work
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3
Cover image for An EdTech Platform Running 10x Faster
An EdTech Platform Running 10x Faster
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1
Cover image for Building a Solana DeFi Platform That Handles $1B+ in Assets
Building a Solana DeFi Platform That Handles $1B+ in Assets
0
11
Python(3)
MD. Tofael Ahmed's avatar
MD. Tofael Ahmed
Dhaka, Bangladesh
UI/UX | Full Stack Web Developer | WordPress,Webflow Expert
UI/UX | Full Stack Web Developer | WordPress,Webflow Expert
Cover image for 🚀 Figma to Live Website
🚀 Figma to Live Website conversion: EIC Limited Website Figma Design Implementation: Faithfully converted multi-page Figma mockups into structured, production-ready code/components, maintaining pixel-perfect fidelity across typography scales, card layouts, and intricate spacing matrices. Performance Optimization: Optimized modern web assets, minified payloads, and engineered clean structures to guarantee ultra-fast page speeds—crucial for compliance clients visiting from mobile data networks across the APAC region. Complex Data & UI Architecture: Effectively structured dense compliance service grids, documentation timelines, and heavy tabular comparison lists (e.g., EIC vs. Global vs. Local firms) into responsive, scannable, and clean interfaces. Interactive UI Elements: Built interactive lead pathways, including the "Request a Scoping Estimate" forms and custom dynamic accordion structures for their multi-category technical FAQs.
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212
Cover image for Custom Web Design & Frontend
Custom Web Design & Frontend Layout for SerialBox Plus (Requirement-Based) Requirement-Driven Grid Layouts: Instead of relying on a generic template, I built a high-density, card-based responsive grid tailored precisely to the client's complex media categories. This layout cleanly structures large volumes of television networks (like Star Jalsha and Zee Bangla) without overwhelming the viewer. Media UI/UX Design: Focused on establishing an intuitive visual hierarchy for regional audiences. By implementing clean card components, high-contrast thumbnail frames, and legible typography scaling, I made it effortless for users to scan, browse, and jump to their favorite entertainment channels. Frontend Optimization & Performance: Built the interface with a mobile-first philosophy. I optimized core frontend code to minimize asset weight, ensuring incredibly fast page load speeds and smooth scrolling performance across both desktop screens and standard mobile data networks. Frictionless Content Navigation: Executed a clean, structured component mapping system for the main navigation. This ensures users can transition seamlessly between sections like Home, TV, Paper, and Radio based entirely on the predetermined user flow requirements.
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182
Cover image for Requirement-Based Design & Development for
Requirement-Based Design & Development for SelfPublicationBD I developed this platform based strictly on a set of defined client requirements, focusing on turning specific business logic into a functional, clean user experience. The goal was to build a structured web presence for an educational publishing hub in Bangladesh that balances test preparation resources with book showcases. What I Focused On: Requirement-Driven Architecture: Instead of building a generic layout, I mapped every section from the targeted mathematics preparation matrices to the dedicated e-book collections directly to the client's feature checklist. Localized UI/UX Execution: Implemented a clean, high-contrast user interface tailored specifically for Bengali typography. I ensured that dense educational materials and text-heavy layouts remain scannable and easy on the eyes for students. Performance & Core Layout: Focused heavily on optimizing front-end assets so the platform loads quickly across standard mobile networks, keeping resource accessibility smooth for users with varying internet speeds. Structured Resource Filtering: Built out the clean frontend structure needed to separate academic materials, notice boards, and book categories, making it intuitive for visitors to find exactly what they need in a few clicks.
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232
Cover image for I built Pngpoint (https://pngpoint.com/) to
I built Pngpoint (https://pngpoint.com/) to solve a common designer headache: finding high-quality, transparent PNGs without the clutter or "fake" backgrounds. The goal was to create a clean, minimalist resource hub that stays out of the way and lets the assets shine. What I Focused On: Search & Speed: Optimized the image delivery and search functionality so users can find and download exactly what they need in seconds. Clean UI/UX: Kept the interface distraction free. No aggressive ads or complicated navigation just a smooth, visual first experience. Asset Management: Developed a robust system for categorising and serving transparent images, ensuring high resolution and true transparency for every file.
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192
Python(4)
Prottoy Dev's avatar
Prottoy Dev
Bangladesh
Data Scraper, ML Engineer, Email Marketer
Data Scraper, ML Engineer, Email Marketer
Cover image for Zillow Scraper and Form Automation
Zillow Scraper and Form Automation
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25
Cover image for Flight Deal Notifier
Flight Deal Notifier
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4
Cover image for Spotify Playlist Generator
Spotify Playlist Generator
0
17
Cover image for Password Manager Tool
Password Manager Tool
0
2
Python(5)
suhal samad's avatar
suhal samad
Chattogram, Bangladesh
AI & ML Engineer|Real-Time Computer Vision & Edge AI Expert
New to Contra
AI & ML Engineer|Real-Time Computer Vision & Edge AI Expert
Cover image for AI Shoplifting Detection System: Intelligent
AI Shoplifting Detection System: Intelligent Video Analytics for Retail Loss Prevention Protect your storefront with an automated AI security solution that never sleeps. This project implements a full-stack Computer Vision pipeline capable of monitoring 16+ simultaneous RTSP streams. By utilizing ByteTrack for stable person re-identification and Deep Learning action classifiers, the system detects unauthorized entry into staff zones and alerts management to shoplifting incidents as they happen. A robust, scalable solution for grocery stores and retail outlets looking to modernize their security infrastructure.
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31
Cover image for AI-Powered PPE Compliance & Safety
AI-Powered PPE Compliance & Safety Monitoring
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42
Cover image for multi camera ai tracking for
multi camera ai tracking for mask detection and motion analytics Stop monitoring manually. Start automating safety. In high-stakes environmentshospitals, construction sites, and manufacturing plantscompliance isn't optional. I build Industrial-Grade AI Video Analytics that combine real-time Face Mask Detection with advanced Person Movement Tracking to ensure 24/7 safety oversight without human error. The Synergy: Why Both Matter Most developers offer one or the other. I integrate them into a single, high-performance pipeline: Compliance Monitoring: Instant detection of PPE/Face Mask violations with timestamped logging. Behavioral Tracking: Beyond simple detection, I track individual movement paths to identify "High-Risk" behaviors or unauthorized entry into restricted zones. RTSP Scalability: My systems don't just work on one webcam; they are optimized to handle multi-camera RTSP feeds (16+) with zero lag. Key Features of the System: Dual-Stream Intelligence: Real-time Mask/No-Mask classification paired with unique Person IDs (Re-ID). Zone-Aware Analytics: Define specific "Mask-Mandatory Zones" vs. "Common Areas" to reduce false alerts. Motion & Velocity Insights: Track if a person is running, loitering, or entering a hazardous area without prop
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Cover image for yolov11 alpr anpr system with
yolov11 alpr anpr system with rtsp stream and sql database You will get a production-ready AI-powered vehicle monitoring and license plate recognition system designed for real-world environments such as apartments, parking garages, and secure facilities. This solution combines advanced computer vision (YOLO + OCR + tracking) with a modern React dashboard to deliver accurate, real-time vehicle identification and tracking. What sets my work apart is the focus on reliability, privacy, and scalability. The system is optimized to reduce OCR errors using intelligent plate stabilization, ensuring consistent and accurate results even in challenging conditions. All data is processed and stored locally, making it ideal for privacy-sensitive deployments. The dashboard provides live vehicle cards with owner details, parking status, and access control (authorized, visitor, blocked), giving you full visibility and control. The system is modular and can be extended with features like automated gate control, alerts, analytics, and multi-location support. With a strong background in AI and computer vision systems, I build solutions that are not just demos—but ready for real-world production use.
0
54
Python(4)
Shamim Ferdous's avatar
Shamim Ferdous
Dhaka, Bangladesh
Expert Fullstack Engineer. 8+ yrs exp.
Expert Fullstack Engineer. 8+ yrs exp.
Cover image for FoodEx - Food Delivery Platform
FoodEx - Food Delivery Platform
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14
Cover image for Nexa Store | App & Website
Nexa Store | App & Website
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10
Cover image for Biz Solution - POS & Business Manager
Biz Solution - POS & Business Manager
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5
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Python(3)