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Muhammad

@bilal6901

monthly-level-two.svg
5.0
1

0.0

0.0

100%

AI & Cloud Engineer | ML | Linux | Virtualization

$10 USD / Hour

・

Flag of PAKISTAN

Pakistan (6:24 PM)

・

Joined on June 28, 2024

$10 USD / Hour

・

Hi, I’m Bilal 👋 I’m an AI Engineer specializing in Machine Learning, Data Science, and Python. I help clients clean, analyze, visualize, and understand their data in a simple and meaningful way. I can help you with: — Data cleaning and preprocessing — Exploratory Data Analysis (EDA) — Data visualization and reporting — Machine learning model development — Classification and regression tasks — Forecasting and prediction projects — Python scripts for data-related work — Jupyter Notebook projects with clear explanations — CSV, Excel, and basic SQL data handling Tools and skills I use: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook, Excel, CSV files, and SQL. Why work with me? — Clear and honest communication — Clean and understandable code — Well-organized work — Easy-to-understand results — Proper documentation — Quality-focused and on-time delivery Whether you need data analysis, visual reports, machine learning models, or Python-based data solutions, I can help you turn your data into clear insights.

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100%

On budget

100%

Accept rate

100%

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Portfolio

Portfolio

13970170
Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

13956185
Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

13956101
Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

13956054
Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

13970170
Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

Created a realistic customer/order records Excel workbook for data entry and cleanup. Entered and organized 400+ messy customer/order records into structured columns. Cleaned inconsistent names, extra spaces, capitalization errors, and mixed date formats. Removed duplicate records using Order ID based checking. Fixed missing phone/email values and corrected invalid contact formats. Standardized phone numbers, email addresses, cities, product names, and order dates. Applied clean Excel formatting, filters, readable headers, and client-ready layout. Prepared a Quality Check sheet showing duplicates, missing values, invalid contacts, fixed records, and final status. Created a Summary sheet with total raw records, duplicates removed, missing values fixed, and final clean records. Added a copy typing / PDF-to-Excel style sample to show typing accuracy and document formatting

Excel Data Entry & Cleanup QC

13956185
Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

Developed a self-built data cleaning and analytics platform using Python, Pandas, Plotly and Streamlit. The app profiles messy CSV/Excel datasets, detects missing values, duplicate rows and outliers, calculates a data quality score, applies automated cleaning rules, compares before/after quality, and exports cleaned datasets plus Excel quality reports. The platform includes an executive data health dashboard, interactive data profiler, auto-cleaning studio, before/after quality comparison, AI-style insight generator, and downloadable client-ready reports. This project demonstrates practical data preparation, business analytics, dashboard development and client-ready reporting for messy real-world datasets. Key Features: * CSV/Excel upload * Data quality scoring * Missing value detection * Duplicate row detection * Outlier detection using IQR * Automated cleaning rules * Before/after data quality comparison * Column profiling and EDA visuals * Cleaned dataset export * Excel data qua

Data Cleaning AI

13956101
Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

Developed a self-built Computer Vision object detection platform using Python, YOLOv8, OpenCV, Plotly and Streamlit. The system detects objects in images and batch uploads, generates annotated detection outputs, analyzes object counts, calculates confidence metrics, and provides scene-level attention insights. The app includes a premium dashboard, single image detection, batch object detection, detection confidence analytics, object count charts, downloadable CSV reports, and a model intelligence section explaining the YOLOv8 detection pipeline. This project demonstrates practical AI/Computer Vision implementation for traffic monitoring, retail analytics, safety review, logistics monitoring and visual intelligence workflows. Key Features: * YOLOv8 object detection * Single image detection * Batch image detection * Annotated detection output * Object count analytics * Confidence score analysis * Scene attention scoring * CSV detection report export * Model intelligence and detection

SmartVision AI-Object Detection App

13956054
Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

Developed a self-built Machine Learning dashboard that predicts customer churn risk and helps businesses prioritize retention decisions. The system analyzes customer behavior, identifies high-risk users, estimates revenue at risk, and provides actionable retention recommendations. The dashboard includes an executive overview, customer risk segmentation, individual churn-risk prediction, revenue-at-risk indicators, model validation comparison, confusion matrix, and a retention playbook for high-risk customers. Built using Python, Pandas, Scikit-learn, Plotly and Streamlit. Key Features: - Customer churn prediction - High-risk customer identification - Revenue-at-risk analysis - Interactive ML dashboard - Individual customer risk scoring - Retention recommendation playbook - Model validation metrics

ChurnGuard AI - Churn Dashboard

5.0 · 1 Review
1 Review
R
Flag of SAUDI ARABIA

Rayan

5.0

$15 USD

48 minutes ago

Visio WAN Architecture Diagram

Thanks for working you are professional

Graphic Design
Project Management
Microsoft Visio
Data Visualization
Technical Documentation

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Flag of PAKISTAN

Muhammad

5.0

$5 USD

12 days ago

Need Ml engineer for one task

I had a great experience working with Bilal on my AI/ML project. He was highly professional, technically skilled, and understood the requirements very well. The work was completed with excellent quality, attention to detail, and within the agreed timeline. I recommend him for future work.

Python
Matlab and Mathematica
Software Architecture
Machine Learning (ML)
Data Science

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Education

COMSATS Institute of Information Technology

2023 - 2026

•

3 years

Bs(computer science)

Flag of PAKISTAN

Pakistan

2023 - 2026

•

3 years

Qualifications

Google Advanced Data Analytics Capstone

2026

Google via Coursera

Completed a capstone project applying data analytics skills including data preparation, analysis, visualization, and business insight generation.

2026

Regression Analysis: Simplify Complex Data Relationships

2026

Google via Coursera

Completed a regression analysis course focused on understanding relationships in data and building interpretable predictive models.

2026

The Nuts and Bolts of Machine Learning

2026

Google via Coursera

Completed a machine learning course focused on core ML workflow, model building, evaluation, and applied predictive analytics.

2026

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