Projects using Python in PunjabProjects using Python in PunjabCTRBoss, Programmatic CTR Optimisation & Local SEO GeoGrid Rank Tracking SaaS
Built an enterprise-grade CTR optimisation and local SEO rank tracking platform for digital marketing agencies combining stealth browser automation, geo-targeted proxy routing, smart traffic scheduling, and geographic GeoGrid rank visualisation into a single fully automated system.
Running CTR campaigns at scale is one of the most technically demanding challenges in SEO. Every bot session has to appear indistinguishable from a real human user across browser fingerprint, user agent, proxy IP, search behaviour, and on-site activity. Off-the-shelf automation tools get detected and blocked immediately. Agencies needed a platform that could run hundreds of realistic sessions per day across multiple client campaigns, target specific geographies, and visualise local SEO performance all without manual intervention.
CTRBoss is built on Django, Flask microservices, React, Next.js, and PostgreSQL. The core bot engine runs Undetected ChromeDriver with selenium-stealth, removes webdriver flags via Chrome DevTools Protocol, sets device-specific user agents, and routes every session through geo-targeted residential proxies from Geonode and Proxy Empire with country, state, and city-level granularity. For organic search campaigns, the bot types the keyword into Google, locates the client's website across organic results, Knowledge Panel, and paginated SERPs, clicks through, and spends session time on-site with multi-page browsing and gradual scrolling to mimic real human behaviour. Google Maps campaigns use a three-tier detection system covering the Knowledge Panel, the local 3-pack, and full Maps results with pagination.
The Django smart scheduler runs every minute implementing time-of-day traffic ramping so sessions are light overnight and peak during business hours, enforcing per-keyword interval rules and daily search limits, pre-verifying proxies before each run, and auto-restarting the Flask bot server on consecutive errors. Up to 12 parallel keyword campaigns run simultaneously across multiple clients. The GeoGrid rank tracker queries DataForSEO's Google Maps SERP API across a coordinate grid producing a visual map showing exactly where a Google Business Profile ranks across its target area and where competitors dominate. Three payment gateways Stripe, PayPal, and Braintree with a credit-based billing system made the platform commercially ready at launch.
Tech Stack: Django ¡ Flask ¡ React ¡ Next.js ¡ PostgreSQL ¡ Celery ¡ Redis ¡ Selenium ¡ Undetected ChromeDriver ¡ Playwright ¡ Geonode ¡ Proxy Empire ¡ DataForSEO ¡ Stripe ¡ PayPal ¡ Braintree ¡ ApexCharts Predictive Analytics Dashboard
Dataset: DataCo Smart Supply Chain Dataset
Size: ~180,519 Rows | 53 Columns
đ ď¸ Phase 1: Tech Stack & Tools Used
This project was completed using industry-standard tools:
Data Manipulation: Pandas, NumPy
Visualization: Matplotlib, Seaborn (for static EDA) and Plotly Express (for interactive dashboards)
Machine Learning: Scikit-learn (for preprocessing and metrics), XGBoost (for advanced regression)
Forecasting: Facebook Prophet (for seasonality) and XGBoost (for demand volume prediction)
Deployment: Streamlit (to build the live BI dashboard)
𧚠Phase 2: Major Hurdles & Data Cleaning
The dataset was heavily corrupted, presenting several challenges:
Misplaced Data: City names like âSĂŁo Paulo,â âRio de Janeiro,â and âGrande del Norteâ were incorrectly placed in the Order Status column.
Missing Statuses: Many rows had empty Order Status fields, while the actual status was found in Order State.
Solution: I developed a custom Restoration Engine that cleaned columns and relocated misplaced data to their correct fields (Order Region).
đ° Phase 3: Profit & Strategy Analysis
Beyond visualization, the project supported business decision-making:
Profit Analysis: Calculated profit margins for each product.
Price Optimization: Suggested a 5% price increase for high-selling products with margins below 10% to improve profitability.
đ Phase 4: Modeling & Predictions (Next 5 Months)
Advanced XGBoost models were used instead of basic regressions:
Demand Forecast: Predicted order volumes for the next five months to enhance inventory management.
Sales Trends: Optimized models to capture seasonality and trend effects on future sales.
đ Phase 5: The Final BI Dashboard
Built a complete interactive system using Streamlit:
11 Industry-Level Visualizations: Demand trends, top-selling products, regional sales, and late delivery root causes.
Interactive System: Management can use live filters to extract insights from over 100,000 rows of data.
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Iâll help you extract business insights through data analysis, dashboards, and forecasting.
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Built using React, React Native, TypeScript, Tailwind CSS, Ant Design, Material UI, Redux Toolkit, Socket.IO (http://Socket.IO), Docker, Kubernetes, and modern CI/CD infrastructure.
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