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Robert

@robertb297

monthly-level-four.svg
5.0
5

3.6

3.6

100%

AI Data & Automation Specialist | Python

$10 USD / Hour

・

Flag of NETHERLANDS

Netherlands (1:23 PM)

・

Joined on September 29, 2025

・

2 Recommendations

$10 USD / Hour

・

I specialize in building AI-powered data solutions and automation systems that help businesses streamline operations, extract insights, and scale efficiently. With a strong foundation in Python, data processing, and automation, I design and implement intelligent workflows that bridge traditional data systems with modern AI capabilities. I also leverage foundational knowledge in economics, econometrics, and operations research to enhance data-driven decision-making, improve predictive modeling, and design more efficient analytical solutions in real-world business contexts. Additionally, I have experience working with distributed data processing technologies and modern big data ecosystems to build scalable data workflows and analytics solutions. My experience also includes forecasting, quantitative analysis, risk-informed decision support, business performance analytics, and executive reporting through data-driven dashboards and visualization systems. 🔹 Core Expertise: ✔ Data Analysis, Processing & Web Scraping ✔ Automation & API Integration ✔ AI-powered Data Pipelines ✔ Applied Machine Learning (practical business use-cases) ✔ NLP & Document Processing (basic to intermediate) ✔ Intelligent Reporting & Dashboards ✔ RAG Workflows & AI Knowledge Systems ✔ Business Intelligence & Data-Driven Decision Support ✔ Big Data Processing & Distributed Data Analytics ✔ Data Lake & Large-scale Data Processing Workflows ✔ Project Analytics & Performance Reporting ✔ Forecasting & Predictive Analytics ✔ Quantitative Risk Analysis & Decision Support ✔ Executive Reporting & KPI Dashboards ✔ Cost & Business Performance Analytics 🔹 Growing Expertise (Applied AI): ✔ Large Language Models (LLMs) – practical usage ✔ Prompt Engineering ✔ Introductory RAG-based workflows ✔ AI-assisted automation & decision systems ✔ AI-Driven Forecasting & Predictive Decision Systems 🔹 Tools & Technologies: Python, Pandas, Matplotlib, Power BI, APIs, Web Scraping Tools, Scikit-learn, Basic NLP libraries, FastAPI (basic), Automation tools (n8n / scripting) Apache Spark (PySpark), Trino, Apache Hadoop, Apache HBase, Apache Hive, Apache Kafka Power BI, forecasting models, KPI reporting systems, quantitative analytics, business intelligence dashboards, and data-driven decision support frameworks. 🔹 Approach: I focus on practical, results-driven solutions — not just theory. My goal is to deliver systems that are reliable, efficient, scalable, and aligned with real business needs. I enjoy transforming complex operational and business data into actionable insights that support planning, forecasting, performance monitoring, and strategic decision-making.

Verifications

On time

100%

On budget

100%

Accept rate

100%

Repeat hire rate

17%

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Portfolio

Portfolio

14061548
An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

14061537
A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

14061506
An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

14059119
Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

14057903
Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

14056263
An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

14061548
An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

An exploratory data analysis of Formula 1 Grand Prix races from 1950 to 2025, examining decades of racing history to identify the sport’s most successful drivers, teams, and circuits. The analysis explores performance trends, championship dominance, and changes in competitive balance over more than seven decades of Formula 1.

Formula 1 Historical Data Analysis

14061537
A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

A comparative machine learning study using 731 days of bike-sharing data from the Capital Bikeshare system (2011–2012). The project evaluated simple, multiple, and polynomial regression models alongside a logistic classifier to identify the key factors influencing daily ride demand and compare model performance.

Bike Sharing Demand Prediction

14061506
An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

An end-to-end machine learning pipeline developed on a nationwide real estate dataset containing 1,048,575 listings. The project covered data cleaning and preprocessing, unsupervised customer segmentation, predictive classification, and automated feature selection using a Genetic Algorithm. The workflow was designed to transform large-scale raw property data into structured insights and reliable predictive models.

Real Estate ML & Predictive Analytic

14059119
Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

Developed an end-to-end retail sales analytics project using SQL and Python to transform raw transactional data into actionable business insights. The project covers data extraction, cleaning, preprocessing, feature engineering, exploratory data analysis (EDA), and data visualization. SQL was used to analyze sales performance, customer purchasing behavior, product performance, revenue distribution, and geographic sales patterns, while Python was used for data processing, analysis, and visualization. The analysis identified top-performing products, high-value customers, purchasing patterns, sales trends, revenue distribution, and time-based performance indicators. Interactive and analytical visualizations were created to communicate key findings and support data-driven decision-making across sales, inventory management, and customer engagement. This project demonstrates practical experience in combining SQL and Python for business analytics, performance analysis, and data-driven deci

Online Retail Sales Analysis

14057903
Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

Designed and developed an end-to-end customer churn prediction project using Python and machine learning techniques. The project covers the complete data science workflow, including data preprocessing, feature engineering, exploratory data analysis (EDA), data visualization, predictive modeling, and model evaluation. Customer behavior was analyzed to identify key factors associated with churn. Multiple classification models were trained and evaluated using industry-standard metrics, including Accuracy, Precision, Recall, F1-score, and Confusion Matrix. Actionable business insights were extracted from the analysis to support customer retention strategies and improve data-driven decision-making. The project demonstrates practical experience in developing reproducible machine learning workflows using open-source tools and techniques.

Customer Churn Prediction using AI

14056263
An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

An interactive Power BI dashboard developed to analyze and visualize pizza sales performance across revenue, order volume, product categories, pizza sizes, weekdays, months, and peak operating hours. Key features: Executive sales performance overview Order volume and revenue analysis Product and category performance Top-selling pizza rankings Monthly and weekday sales trends Pizza size performance analysis Peak hours and time-based insights Interactive filters and page navigation

Pizza Sales Analysis Dashboard

5.0 · 5 Reviews
5 Reviews
K
Flag of UNITED KINGDOM

Kate

5.0

£25 GBP

2 days ago

Find Top Tutor Marketing Platforms

Robert was great to work with, very responsive to communications. Will work with again if I need to.

SEO
Facebook Marketing
Market Research
Social Media Marketing
Digital Marketing

+5 more

Share

P

Former Client (inactive)

5.0

$180 CAD

7 months ago

E-commerce Data Analysis for SME (archived)

Everything went smoothly. Fast delivery and high-quality results. Highly recommended.

Share

M

Former Client (inactive)

5.0

$260 USD

9 months ago

Production Pipeline with CLV and Recommendation Engine (archived)

He’s very skilled and knows exactly what he’s doing. He completes everything quickly, has a professional attitude, and is reliable!!!

Share

M

Former Client (inactive)

5.0

$120 USD

9 months ago

Customer Churn Prediction Prototype (archived)

He delivered what I needed, stayed responsive, handled the tasks without issues, and had a very nice attitude!!!

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J
Flag of POLAND

Jakub

5.0

$75 USD

9 months ago

Predicting Real Estate Prices

Robert was extremely efficient and easy to work with. He finished everything much faster than expected and delivered solid, high-quality work. I’m very satisfied with the outcome and would happily work with him again in the future. Highly recommended!! :)

Python
Machine Learning (ML)
Data Science
Data Analytics
Data Visualization

+4 more

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