Hany Hassan Ahmed
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Дахаб,
Egypt
8 months 29 days back
available for hire
on the service 9 months 1 day
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I am Hany Hassan Ahmed, specialized in data analysis and creating data-driven reports to help businesses and entrepreneurs make the right decisions.
I have completed several real-world projects using Python, SQL, and Power BI, where I analyzed and cleaned data, and extracted actionable insights that helped improve productivity by up to 30% for some companies.
I always strive to transform raw data into clear, actionable insights, and I believe that data is the foundation of every successful decision and sustainable business.
If you are a business owner or entrepreneur and want to improve performance and compete with the big players, I am here to help you grow and develop.
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Programming
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Portfolio
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30 USD Cyber Security Threats Dataset
AI & Machine Learning
This dataset provides a detailed view of global cyber security threats and incidents. It includes various attributes such as attack sources, types, countries involved, financial impact, and defense mechanisms. The data has been enhanced with additional features to support advanced analytical and machine learning tasks.
1-File Name: cyber_security_threats.csv
…
2-Source: Kaggle
3-Rows: (3000)
4-Columns: 13
##Tools Used: 1-Python (Pandas, Matplotlib, Seaborn), Power BI
##Status: Cleaned, analyzed, and visualized
##Features
Feature Description
1-attack_source Origin or initiator of the attack (e.g., botnet, malware, insider). 2-attack_type Type of cyberattack (e.g., DDoS, phishing, ransomware). 3-country Country where the attack was initiated or reported. 4-defence_mechanism Method used to mitigate or respond to the attack. 5-financial_loss Estimated monetary loss due to the attack (USD). 6-incident_resolution_time Time taken to resolve the incident (in hours or days). 7-number_of_affected_user Number of users impacted by the attack. 8-security_vulnerability_type Vulnerability exploited (e.g., SQL injection, zero-day, misconfiguration). 9-target_industry Industry affected (e.g., finance, healthcare, education). 10-year Year of occurrence. 11-threats_level (Added) Custom-calculated level of threat (e.g., Low, Medium, High). 12-attacks_per_country (Added) Aggregated number of attacks per country.
##Use Cases
1-Exploratory Data Analysis (EDA)
2-Trend analysis of cybersecurity threats over the years
3-Predictive modeling of financial loss or resolution time
4-Clustering based on threat levels or regions
5-Visualization dashboards (Power BI, Tableau, etc.)
6-Cybersecurity risk analysis
7-Data storytelling dashboards
8-Machine learning experiments (e.g., predicting financial loss)
9-Corporate threat profiling
##The dataset was cleaned and processed using Python, including:
1-Handling missing values
2-Feature engineering (threats_level, attacks_per_country)
3-Data type conversions
4-Exploratory Data Analysis using Matplotlib & Seaborn
##Visualization
The processed dataset was imported into Power BI for interactive dashboard creation. Key visuals included:
1-Threat distribution by country and industry
2-Yearly trends of attack types
3-Financial loss analysis per threat level
4-Top 10 vulnerable countries and industries
##Example Insights
1-Top 5 countries with the highest number of cyberattacks
2-Most common types of attacks in the finance industry
3-Correlation between threat level and financial loss
4-Year-over-year growth of ransomware attacks
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30 USD Global Cancer Data Analysis
AI & Machine Learning
In this project, I analyzed global cybersecurity data to understand global trends in AI, and their insights help identify and address the most significant vulnerabilities in AI.
The analysis included:
… Categorization of new types (malware, phishing, ransomware, DDoS attacks, etc.)
Analysis of the most targeted countries
Reading for a year or more
Identification of Victorian sources (by country or type of label, if available)
Graphic presentation showing the geographic distribution of threats
Comparison of the number and response of countries
Tools used:
Python (Panda, Matplotlib, Seaborn)
Excel for data cleaning and processing
Tableau/Power BI for building an interactive dashboard
Jupyter Notebook for presenting the analysis and steps
Project outcomes:
Unveiling the new Cyber New York chief, identifying the most vulnerable countries, and helping build a clear understanding of the importance of strengthening digital defense and forensic threat awareness.
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30 USD Super Store Sales Data Analysis
AI & Machine Learning
I performed a comprehensive analysis of a Superstore dataset to uncover sales trends, customer behavior, and product performance insights. The project included data cleaning, exploratory data analysis (EDA), and interactive visualizations to support data-driven decision-making.
Key tasks completed:
… Cleaned and prepared raw sales data for analysis using Excel and Python (Pandas).
Conducted exploratory data analysis (EDA) to identify key patterns in sales, profit, and customer segments.
Built interactive dashboards using Power BI to visualize:
Sales by region, category, and sub-category
Profit trends over time
Top-performing products and loss-generating items
Generated actionable insights to optimize inventory and marketing strategies.
Tools used: Excel, Python (Pandas, Matplotlib, Seaborn), Power BI