Freelancers using Python in Dhaka DivisionFreelancers using Python in Dhaka Division
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Istiak Ahmed Khan's avatar
Istiak Ahmed Khan
Dhaka, Bangladesh
ML AI Automation Expert + Data Analyst
5.0
Rating
60
Followers
ML AI Automation Expert + Data Analyst
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 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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600
Cover image for End-to-End Machine Learning Pipeline for
End-to-End Machine Learning Pipeline for Telecom Customer Churn 1. The Business Problem Customer churn is a major challenge for telecommunications companies, driven by competition, service issues, and changing consumer preferences. This project was designed to transition the company from reactive support to proactive retention using data-driven strategies such as customer segmentation, personalized offers, and loyalty programs,. 2. Data Exploration & Insights (EDA) I performed a comprehensive descriptive analysis on a database of 7,043 customers with 21 distinct variables,. Key findings included: Contractual Risk: Customers on month-to-month contracts showed significantly higher churn compared to those on one- or two-year commitments,. Service Preference: While Fiber Optic plans were the most popular, they also represented a critical segment for monitoring due to their higher price points,. Financial Indicators: Churned customers had a higher average monthly charge of $74.44, compared to $61.27 for retained customers. Payment Behavior: The "Electronic Check" payment method was most strongly associated with service cancellation,. 3. Engineering & Preprocessing Pipeline To prepare the data for high-performance modeling, I implemented a rigorous preprocessing workflow: Data Cleaning: Removed irrelevant identifiers like customerID and addressed potential data quality issues. The dataset was verified to have zero missing or NaN values,. Feature Engineering: Applied Label Encoding to transform categorical text variables into a numerical format suitable for machine learning algorithms,. Data Splitting: Adopted a standard 80/20 train-test split to ensure the model could generalize effectively to unseen data,. 4. Model Development & Benchmarking I developed and benchmarked eight distinct machine learning algorithms to identify the most effective solution for this specific application: Linear & Probabilistic: Logistic Regression, Naive Bayes. Tree-Based: Decision Tree, Random Forest. Boosting Frameworks: AdaBoost, Gradient Boosting, XGBoost, and LightGBM,. 5. Performance Evaluation & Results Models were evaluated using ROC curves, confusion matrices, and detailed classification reports,. Winner: Logistic Regression achieved the highest accuracy at 81.83%,. Secondary Performers: Gradient Boosting (81.05%) and AdaBoost (80.98%) also showed strong predictive power. 6. Technical Conclusion This data-driven approach proves that proactive churn prediction is essential for business sustainability. By identifying that customers prioritize high-speed fiber optic services but are sensitive to pricing and contract terms, the company can now optimize its pricing and retention strategies to maximize user satisfaction and revenue.
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Python(7)
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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13
Cover image for Nexa Store | App & Website
Nexa Store | App & Website
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7
Cover image for Biz Solution - POS & Business Manager
Biz Solution - POS & Business Manager
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5
Placeholder project card media
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Python(3)
Shahriar Jubayer Eshat's avatar
Shahriar Jubayer Eshat
Dhaka, Bangladesh
Fullstack Developer: Quality & Efficiency Delivered
Fullstack Developer: Quality & Efficiency Delivered
Cover image for Blog Website | Custom Blog Development
Blog Website | Custom Blog Development
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8
Cover image for Project Overview – ZIALED Energy
Project Overview – ZIALED Energy Pouch Website I designed and developed a high-converting ecommerce website for a performance-focused brand selling nicotine-free energy pouches. The goal of the project was to create a premium, modern shopping experience that clearly communicates the product’s value—clean energy, focus, and convenience—while driving conversions. What I did: Transformed the brand vision into a clean, high-impact UI Built a conversion-focused landing experience with strong hero messaging and clear CTAs Designed product sections to highlight flavors, benefits, and quick purchase options Structured the site to guide users from awareness → trust → purchase Integrated lifestyle visuals to align the product with performance-driven users (fitness, outdoor, productivity) Key Features: Fully responsive, mobile-first design Optimized product display with “Quick Add” functionality Clear benefit-driven sections (zero sugar, nicotine-free, minimal ingredients) Social proof integration (reviews + Instagram content) Smooth user flow to reduce friction and increase conversions Result: The final website delivers a premium brand feel while maintaining a strong focus on usability and conversion, making it easy for users to understand the product and purchase quickly. This project reflects my ability to combine UI/UX design with real business goals—especially for modern DTC and Shopify brands.
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Cover image for Custom Inventory Management | Tailored CRM for Efficiency
Custom Inventory Management | Tailored CRM for Efficiency
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Cover image for Social Network Project
Social Network Project
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7
Python(1)
Nadir Ul Kaysar's avatar
Nadir Ul Kaysar
Dhaka, Bangladesh
7 yrs of xp in Full Stack Business, Marketing & AI Services
New to Contra
7 yrs of xp in Full Stack Business, Marketing & AI Services
Cover image for Engineered with VS Code, PHP, Angular, Custom Framework, SQL...
Engineered with VS Code, PHP, Angular, Custom Framework, SQL, Python, and elite LLMs (Gemini, Claude), this isn't a generic AI wrapper. Guided by a six figures marketer, my team and I built a compliant B2B growth engine that replaces expensive middleware (n8n/Make), saving over $12k/mo. It scrapes leads, sends spam-proof emails in your exact brand voice, and auto-generates blogs with JSON-LD Schema for GEO ranking. The Flex: Custom Python logic turns 7 hours of marketing into 15 minutes, handling strict compliance rules that no prompt or drag-and-drop Ai app builder can touch. 🚀 Hit me up on Contra to build the complex apps that AI app builders can't. Or, I and my team can pick up exactly where your AI builder or 'vibe coding' got stuck.
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Cover image for Generic AI builders like Bolt, Lovable, or Anti-Gravity are ...
Generic AI builders like Bolt, Lovable, or Anti-Gravity are impressive for demos, but they choke on real-world complexity. The Challenge: Build a fully automated, AI-powered Business Directory & Listing platform capable of rivaling giants like Thumbtack. The Result: In just under 6 months, I and my team engineered CT Home Proz is the 2nd true pioneer of AI-powered contractor marketplaces after Thumbtack. Why this breaks the mold: a) 100% Automated: The system connects Homeowners to Contractors without manual intervention. b) Complex Logic, Simple UI: While the backend handles thousands of logic gates which AI auto-coders fail to process, the frontend is effortless for users. If you want a landing page, use an AI builder. If you want a Scalable Platform with complex business logic that actually works, you need an Engineer. 🏗️
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Cover image for I created a 4k box
I created a 4k box office and blockbuster level realistic action movie trailer in 2 hours using AI. Many people White Lies, such as created by using one prompt or in a minute but I did end to end myself to make this trailer below. It took 2 hours including rendering and AI. How did you like the movie trailer? #aidesignflow #whennotai
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Cover image for I usually craft traditional action
I usually craft traditional action trailers, but I challenged myself to build a cinematic, high-octane YouTube Shorts trailer (9:16) using advanced AI generation, dynamic editing, and sound design. Do you preferer editing and generating movie's official trailer or the Full AI Action Movie 🍿 (https://higgsfield.ai/contests/make-your-action-scene/submissions/40f65184-dd97-4846-b560-4f763324bc14?utm_source=contest_submission_page_copy_link&utm_medium=share&utm_content=contest_submission)? (P.S. I’m currently taking on new projects! If you need cutting-edge AI filmmaking, official movie trailer editing, 3D animation, or video editing, let's collaborate.)
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204
Python(2)
Subrin Al Azad's avatar
Subrin Al Azad
Dhaka, Bangladesh
Global Product Sourcing, Conversion Optimization, BI Report
5.0
Rating
Global Product Sourcing, Conversion Optimization, BI Report
Cover image for ETL process with Google Cloud Platform
ETL process with Google Cloud Platform
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23
Cover image for Super Store Data analysis with Python
Super Store Data analysis with Python
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Cover image for Conversion Rate Optimization
Conversion Rate Optimization
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9
Cover image for BigQuery Data Management
BigQuery Data Management
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5
Python(2)