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Istiak Ahmed Khan's avatar
Istiak Ahmed Khan
Dhaka, Bangladesh
Power BI Data Analyst + ML AI Automation Expert
5.0
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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 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 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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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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