Freelancers using KerasFreelancers using Keras
ProjectsPeople
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 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.
4
709
Cover image for The E-Commerce Orders Dashboard provides
The E-Commerce Orders Dashboard provides a comprehensive overview of order performance, revenue trends, and customer purchasing behavior. Designed for online businesses, this dashboard transforms transactional order data into actionable insights that support growth, operational efficiency, and strategic decision-making.
2
10
1.3K
Cover image for Email Marketing Analytics Dashboard –
Email Marketing Analytics Dashboard – UI/UX Design Struggling to track campaign performance across multiple channels? This dashboard is designed to give you a complete, real-time view of your marketing efforts in one clean and intuitive interface. A powerful, easy-to-use dashboard that helps you monitor email, SMS, social media, and push campaigns without the confusion of scattered data. Every key metric is presented clearly so you can make faster, smarter decisions. Key Capabilities: Track open rates, click rates, conversions, and revenue in real time, Compare performance across multiple marketing channels, Identify your top-performing campaigns instantly, Understand audience engagement with clear visual breakdowns, Spot trends and optimize campaigns quickly. Most businesses run campaigns but struggle to understand what’s actually working. This dashboard eliminates guesswork by turning your data into clear, actionable insights — helping you improve ROI and scale winning strategies. Perfect For: Digital marketers, E-commerce brands, Agencies managing multiple campaigns, Startups looking to optimize growth. If you want a high-converting, professional dashboard that not only looks great but drives real business decisions — I can help you build it.
8
18
904
Cover image for Comprehensive Financial Performance Dashboard in Power BI
The Financial Performance Dashboard provides a comprehensive overview of an organization’s financial health by tracking revenue, expenses, profitability, and key financial indicators. Built using Power BI, this dashboard enables finance teams and decision-makers to monitor performance, identify trends, and make data-driven strategic decisions.
6
963
Keras(1)
Manideep racharla's avatar
Manideep racharla
Overland, USA
Data Analyst turning Data insights into business impact.
5.0
Rating
2
Followers
Data Analyst turning Data insights into business impact.
Cover image for Optimizing Home Delivery in Small Grocery Stores
Optimizing Home Delivery in Small Grocery Stores
0
4
Cover image for Parking Lot Utilization Analysis Dashboard
Parking Lot Utilization Analysis Dashboard
2
35
Cover image for Predictive Analysis of Airline Delays Using Machine Learning
Predictive Analysis of Airline Delays Using Machine Learning
0
5
Cover image for Stock Price and Trading Indicators Analysis
Stock Price and Trading Indicators Analysis
0
2
Keras(1)
Dylan Guidry's avatar
Dylan Guidry
Canada
Senior Software Engineer | 10+ Yrs Across Industries
7
Followers
Senior Software Engineer | 10+ Yrs Across Industries
Cover image for AI-Powered COVID-19 Detection via Chest X-Rays
AI-Powered COVID-19 Detection via Chest X-Rays
1
5
Cover image for AI-Powered Produce Inspection System
AI-Powered Produce Inspection System
1
3
Cover image for Forge SoftwareHub - Applied AI Development for Web, Mobile & Bl…
Forge SoftwareHub - Applied AI Development for Web, Mobile & Bl…
1
5
Cover image for AI-Powered Deposition Summaries for Legal Cases
AI-Powered Deposition Summaries for Legal Cases
1
22
Keras(2)
Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
I’m an AI & Machine Learning engineer with expertise in deve
Cover image for I recently built an AI-powered Mental Health Screening & Cli...
I recently built an AI-powered Mental Health Screening & Clinical Support System designed to assist both individuals and healthcare professionals in early detection and intervention. The system combines patient-reported questionnaires (PHQ-9 & GAD-7) with advanced NLP-based text analysis to assess depression, anxiety, and crisis risk levels. Based on the results, it generates personalized recommendations, safety plans, and professional referral letters, ensuring timely access to the right resources. This solution addresses a critical real-world problem: the growing gap in mental health care access. Many individuals experience symptoms but delay seeking professional help. Ultimately, it helps reduce the risk of untreated mental health crises and improves overall care outcomes.
0
61
Cover image for Everyone's talking about AI in
Everyone's talking about AI in healthcare. Nobody's building low-cost tools for the people who actually need early answers. Neurologists are overbooked. Clinical tremor assessments require in-person visits, specialist equipment, and months of waiting. The 10 million people living with Parkinson's globally and the millions more who don't yet know have no accessible way to flag early symptoms from home. So I built TremorLens a real-time hand tremor detection tool that runs entirely on a standard webcam. Here's what makes it more than just a webcam project: Instead of simple motion detection, I built a full signal processing pipeline on top of computer vision. MediaPipe tracks 21 hand landmarks per frame. The index fingertip's x/y displacement is buffered across a 3-second rolling window. scipy FFT then decomposes that signal into its frequency components and flags dominant activity in the 4–6 Hz range clinically associated with Parkinson's resting tremors. The live overlay shows you everything: → Real-time FFT power spectrum with the tremor zone highlighted → Dominant frequency readout in Hz with a 10-frame rolling average for stability → Color-coded STABLE / TREMOR DETECTED indicator → Fingertip displacement graph and movement trail → Auto-saved CSV session log timestamp, frequency, amplitude, tremor flag every session
0
11
Cover image for Everyone's talking about quantum computing.
Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.
1
0
39
Cover image for Most AI research tools are
Most AI research tools are just a chatbot with a search button. I built something different. Every time you ask an AI to research something, you're getting one model, one pass, no quality check. It writes confidently, cites poorly, and you have no idea if what it produced is actually accurate. For anyone making real decisions from AI-generated research, that's a silent risk most people ignore. The problem gets worse at scale the longer and more complex the question, the more a single model hallucinates, misses sources, and loses structure. There's no one checking its work. So I built ResearchOS a 5-agent pipeline where each agent has one job. A Supervisor breaks down your question. A Researcher runs parallel searches across 22+ sources. An Analyst extracts data and auto-generates charts. A Writer synthesises a cited report. A Critic fact-checks it and sends it back for revision if anything is wrong. The loop runs up to 3 times before the report is approved. One question in. A full cited report with charts and PDF export in under 10 minutes. I tested it live by watching the Critic catch a missing citation mid-run and send the Writer back to fix it before approval. That's the part that makes this actually usable for real work. Built on LangGraph, Groq, Tavily, ChromaDB and runs entirely on free tiers.
1
67
Keras(1)
MoonTech OneSixEight's avatar
MoonTech OneSixEight
Almelo, Netherlands
Tech Renaissance Leader: AI, FinTech, Web
Tech Renaissance Leader: AI, FinTech, Web
Cover image for Multi-MT5 Integrated, AI-Infused Financial Sentiment Engine
Multi-MT5 Integrated, AI-Infused Financial Sentiment Engine
0
18
Cover image for QuantumTrade ML Suite
QuantumTrade ML Suite
0
44
Cover image for AI MCQ | FIFA
AI MCQ | FIFA
0
11
Placeholder project card media
View more →
Keras(1)