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Abdul R.

@MiniDeveloper21

monthly-level-two.svg
5.0
79
5.2
5.2
99%

Data Scientist | Python | Chatbots | Web Scraper

$10 USD / Hour
・
Flag of
Pakistan (12:16 PM)
・
Joined on January 18, 2020
$10 USD / Hour
・
Hey there! I'm a seasoned Data Scientist with 3+ years of experience in various AI fields, including Computer Vision, NLP, Deep Learning, and Machine Learning. I'm dedicated to creating impactful solutions that drive transformative results. I'm an active contributor on popular freelancing platforms, where I take on diverse projects and deliver innovative AI solutions. My expertise includes: ➤ Web Scraping ➤ Chatbots and Voice Agents ➤ Object Detection ➤ Object Tracking / Counting ➤ LLM (Language Modeling and Learning) Fine tuning ➤ Machine learning Model Development ➤ API Creation (Flask API /Fast API) ➤ OCR Invoice Parsing ➤ Model Deployment ➤ GANS I'm passionate about the entire AI development lifecycle, from model development and deployment to API creation. I take pride in my commitment to excellence and attention to detail, ensuring that every project meets the highest standards. With experience in various AI tools and technologies, I'm able to tackle complex problems and deliver solutions that exceed expectations. Whether it's building custom chatbots or developing sophisticated object detection models, I'm committed to driving value and delivering results.
Verifications
On time
98%
On budget
96%
Accept rate
76%
Repeat hire rate
16%
Freelancer Java Developers Pakistan
See all Java Developers in Pakistan

Portfolio

Portfolio

8568057
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
11840712
The provided repository includes PyTorch code for detecting cracks on concrete surfaces. The implementation is based on the DeepCrack model, which utilizes Convolutional Neural Networks (CNNs) for crack damage detection. DeepCrack employs a deep hierarchical feature learning architecture specifically designed for crack segmentation
Crack Detection & Segmentation
11840701
Professional editors manually crops the interesting events that occurs in a match and joins in sequence to make cricket highlights. Each year many cricket matches are recorded and the viewers wants quick updates of the match by watching highlights. Simple Genetic Algorithm Approach: 1. Generating Initial Population 2. Setting Up Fitness Function 3. Making Probability List 4. Random Selection (Roulette Wheel Selection) 5. Performing Crossover 6. Performing Mutation
Cricket Highlights Extraction
11840708
In this project, we aim to develop a robust system for person detection, tracking, and counting using state-of-the-art computer vision algorithms. Leveraging the power of YOLO (You Only Look Once) for accurate object detection and DeepSORT (Deep Simple Online and Realtime Tracking) for person tracking, our system will analyze video footage to identify and monitor individuals throughout the sequence.
Person Detection & Tracking
11840420
In this innovative project, I leverage the power of computer vision to precisely measure the angles of the elbow joints in human arms. The foundation of our solution lies in the integration of the OpenPose model, a cutting-edge technology capable of accurately detecting and tracking key body joints from images or video streams.
Elbow-Angles-Detection-Using-Pose-Estimation
6120193
Scraping the records of football stats and safe it in excel files......
Web Scraping(Text Extraction and Excel File Savage)
8568057
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
* Techniques for filling missing data: + Mean/Median/Mode Imputation + Regression Imputation + K-Nearest Neighbors (KNN) Imputation + Multiple Imputation by Chained Equations (MICE) * Features Scaling: + Min-Max Scaling + Standardization * Calculate correlation between different attributes: + Pearson Correlation Coefficient + Spearman Rank Correlation * Applying PCA (Principal Component Analysis) to convert it into 2 dimensions: + Data Normalization + Covariance Matrix Computation * Training of Algorithms include: + Clustering: K-Means Clustering + Decision Trees: ID3 Algorithm + Regression Models: Linear Regression + Evaluation Metrics: Mean Absolute Error (MAE)
Data Preprocessing & Visualization
11840712
The provided repository includes PyTorch code for detecting cracks on concrete surfaces. The implementation is based on the DeepCrack model, which utilizes Convolutional Neural Networks (CNNs) for crack damage detection. DeepCrack employs a deep hierarchical feature learning architecture specifically designed for crack segmentation
Crack Detection & Segmentation
11840701
Professional editors manually crops the interesting events that occurs in a match and joins in sequence to make cricket highlights. Each year many cricket matches are recorded and the viewers wants quick updates of the match by watching highlights. Simple Genetic Algorithm Approach: 1. Generating Initial Population 2. Setting Up Fitness Function 3. Making Probability List 4. Random Selection (Roulette Wheel Selection) 5. Performing Crossover 6. Performing Mutation
Cricket Highlights Extraction
11840708
In this project, we aim to develop a robust system for person detection, tracking, and counting using state-of-the-art computer vision algorithms. Leveraging the power of YOLO (You Only Look Once) for accurate object detection and DeepSORT (Deep Simple Online and Realtime Tracking) for person tracking, our system will analyze video footage to identify and monitor individuals throughout the sequence.
Person Detection & Tracking
11840420
In this innovative project, I leverage the power of computer vision to precisely measure the angles of the elbow joints in human arms. The foundation of our solution lies in the integration of the OpenPose model, a cutting-edge technology capable of accurately detecting and tracking key body joints from images or video streams.
Elbow-Angles-Detection-Using-Pose-Estimation
6120193
Scraping the records of football stats and safe it in excel files......
Web Scraping(Text Extraction and Excel File Savage)
Changes saved
5.0 · 79 Reviews
Reviews
User Avatar
AI Chatbot Setup An Existing Bot & Test • $20 USD
Great to work with. He has managed to cover all the delivarables and provide support.
Arvin J.
@AGJ2020
•
Flag of
shah alam, Malaysia
•
23 days ago
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User Avatar
AI Chatbot Setup & Testing • $20 USD
It was great working with Abdul on the project. He was skilled, and proficient at giving appropriate advice for this project.
Arvin J.
@AGJ2020
•
Flag of
shah alam, Malaysia
•
2 months ago
Share
User Avatar
AI Chatbot Setup & Testing for Existing Chatbot (1 Phone Number) • $20 USD
Great working with him, a professional in his field. He provides excellent support when required.
Arvin J.
@AGJ2020
•
Flag of
shah alam, Malaysia
•
3 months ago
Share
User Avatar
Phase 2: Improving Chatbot Architecture & adding E-commerce Shop • $300 USD
Abdul has taken a skilled approach in completing this project. He has continued to demonstrate good critical thinking and problem solving abilities within his domain of expertise. He is well adapted to working on projects, as well as maintaining a professional relationship.
Arvin J.
@AGJ2020
•
Flag of
shah alam, Malaysia
•
3 months ago
Share
User Avatar
AI Chatbot (GPT & Whatsapp) Enhancement with Payment Integration • $60 USD
Abdul effectively identified and solved the problems outlined in my project posting. He provided valuable recommendations, changing the architecture and components to make my chatbot more reliable and cost-efficient to run. His communication was clear and professional throughout the project. I highly recommend Abdul to anyone looking for a skilled and reliable freelancer. Thank you, Abdul, for your excellent work.
Arvin J.
@AGJ2020
•
Flag of
shah alam, Malaysia
•
6 months ago
Share
Experience
Data Scientist
Feb, 2023 - Present
•
1 year, 10 months
Sparkix Technologies
Feb, 2023 - Present
•
1 year, 10 months
* Develop and implement data models to extract insights from large and complex datasets * Collaborate with cross-functional teams to identify business problems and determine how data can be used to solve them * Clean, preprocess, and wrangle data to prepare it for analysis and modeling * Communicate findings and recommendations to stakeholders through visualizations, reports, and presentations * Stay up-to-date with the latest industry trends and advancements in data science techniques and tools
Feb, 2023 - Present
•
1 year, 10 months
Data Scientist
Oct, 2021 - Feb, 2023
•
1 year, 4 months
Machine Learning 1
Oct, 2021 - Feb, 2023
•
1 year, 4 months
➤ Analyzing large datasets to extract meaningful insights and trends ➤ Developing and implementing machine learning models for predictive analytics ➤ Creating and validating hypotheses to guide data-driven decision-making ➤ Visualizing data and results to communicate findings to non-technical stakeholders ➤ Implementing data science solutions in a scalable and reproducible manner ➤ Developing and deploying machine learning models into production environments
Oct, 2021 - Feb, 2023
•
1 year, 4 months
Python Web Scraper
May, 2021 - Aug, 2021
•
3 months
Eziline
May, 2021 - Aug, 2021
•
3 months
➤ Developing web scraping scripts or tools to extract data from websites. ➤ Implementing proxy rotation and anti-bot measures to avoid detection. ➤ Handling and troubleshooting issues related to website access and data extraction. ➤ Managing and optimizing the storage and organization of scraped data. ➤ Documenting the scraping process, methodologies, and any challenges faced.
May, 2021 - Aug, 2021
•
3 months
Education
COMSATS Institute of Information Technology
2016 - 2020
•
4 years
Bachelors in Software Engineering
Flag of
Pakistan
2016 - 2020
•
4 years
Qualifications
Full Stack Data Scientist
2021
iNeuron
➤ Data Collection and Cleaning ➤ Database Management ➤ Data Analysis and Modeling ➤ Data Visualization ➤ Web Development ➤ APIs and Microservices ➤ Cloud Computing ➤ Machine Learning and Predictive Analytics ➤ Statistical Analysis ➤ Programming languages (e.g., Python) ➤ Version Control (e.g., Git) ➤ Software Development Life Cycle (SDLC) ➤ Deployment and Integration of Models ➤ Data Security and Privacy Measures ➤ Continuous Learning and Keeping Up with Industry Trends
2021
Certifications
P
Preferred Freelancer Program SLA
AI Training - Freelancer Global Fleet
AI Training - Freelancer Global Fleet
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