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Somia A.

@somiaahmed8

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Data Scientist | Data analyst

$10 USD / Hour
・
Flag of
Egypt (12:00 PM)
・
Joined on October 12, 2024
$10 USD / Hour
・
I am a Data scientist Engineer with one year of experience. I have strong skills in data analysis with pandas, matplotlib and seaborn. you can check my repos on GitHub and see my projects. Also, I have skills in machine learning, (supervised learning & Unsupervised), deep learning. you can hire me when you need help in: ✅Machine Learning ✅deep Learning ✅Sentiment analysis ✅Excel ✅Power BI ✅Data analysis ✅Data Visualization
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Freelancer Python Developers Egypt
See all Python Developers in Egypt

Portfolio

Portfolio

12310490
It is a power BI dashboard to analyze and visualize the sales in united state provinces during the last ten years.
Item Sales Report
12278159
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
12278155
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
predict Age of a Fossil Analysis & Machine Learning.
predict Age of a Fossil Analysis & Machine Learning.
12278149
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
12310490
It is a power BI dashboard to analyze and visualize the sales in united state provinces during the last ten years.
Item Sales Report
12278159
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
Classified the profitability of menu items to (Low – Medium - High), Used Data Visualization to Know the relations among the features, Applied Data Preprocessing to handle categorical data, Built a machine learning model to classify the profitability by using Random Forest algorithm.
Restaurant Menu Items Profitability Classification.
12278155
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
Developed by using python programming language. Applied exploratory data analysis to understand the dataset well, used data preprocessing to handle outliers, categorical data and missing values, then built a machine learning model to predict the age of a fossil by using Gradient Boosting algorithm
predict Age of a Fossil Analysis & Machine Learning.
predict Age of a Fossil Analysis & Machine Learning.
predict Age of a Fossil Analysis & Machine Learning.
12278149
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
Developed By using Python Programming language to Know why 30% of patients missed their scheduled appointments. Used Data cleansing and Exploratory Data Analysis to get insights and the relationships among features and each other.
Data Analysis, No-show- appointment analysis
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Education
Zagazig University
2019 - 2024
•
5 years
Faculty of Engineering / Computer and Systems department
Flag of
Egypt
2019 - 2024
•
5 years
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