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$13 USD / time
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$13 USD / time
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Tilmeldt juli 25, 2022
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Rasha A.

@rashaaburkab2

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Logical Data Scientist ( Python )

I am a Rising Talent Data Scientist, Studying Data Science and Artificial intelligence You will be happy with me because of my skills such as Efficient time management, Teamwork, flexibility, and Working under pressure. I strive to make the customer happy and to carry out the work with the best quality and accuracy, I work hard and know the value of time because Time is the most valuable thing a person can spend, and I serve full time. I have many skills that enable me to do the following: ✔️ Writing code in Python ✔️ Data Analysis ✔️ Microsoft office ✔️ dealing with environment Google Colab and Jupyter Notebook. ✔️ Data cleaning and arrangement ✔️ Dealing with many Format like Excel files(CSV, Xls, xlsx, ...etc) ✔️ Dealing with Pandas library (reading files and manipulating data) ✔️ Dealing with Numpy library (to deal with data and do arithmetic operations) ✔️ Dealing with Seaborn library (creating graphical visualizations) ✔️ Dealing with Matplotlib library (create graphs) ✔️ Dealing with Sklearn library (Machine learning model building) ✔️ Data Visualization with Matplotlib, Seaborn ✔️ Create a Supervised Machine Learning Model ✔️ Create an Unsupervised Machine Learning Model ✔️ Make a Regression, Classification, Clustering I will be happy to communicate with you and hear your ideas, I can discuss your project and present my ideas to you, Your works will stand out to me, contact me to start now.
Freelancer Python Developers Palestinian Territory

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Portefølje indlæg

This project aims to anticipate the percentage of customer dropout from the bank, the fact that it is expensive to acquire new customers, but losing existing customers will cost more to the company or organization.
In this project, a machine learning model was built from the data, where the data was read using the Pandas library then the data was cleaned and arranged through the Numby library.
Then the model was built using the sklearn library.
the model was validated using many methods, including:
Linear Regression , KNN Regression , Random Forest Regressor, SVM
Predicting the Customer churn
Twitter has emerged as a fundamentally new tool for social metrics. For example, researchers have shown that the "mood" of communicating on Twitter can be used to predict the stock market.
This project was accomplished in Python by downloading and preparing a dataset from Twitter for analysis.
The text of the tweet contains a lot of information that is not necessary for the task of sentiment analysis. For example, it contains a comma, a reference to a Twitter user, and a link to an external website that we cleaned up through the Re library using Regular Expression.
The clean Tweet's sentiments are then calculated based on the sentiment scores for the terms in the Tweet. Sentiments for a Tweet are the sum of the Sentiment Score for each term in a clean Tweet.
According to the score, the user's mood is judged.
Sentiment analysis of Twitter data
This prediction was generated for a ready-made database, the tips dataset.
This database contains restaurant visitor data, and based on this data, a prediction was generated for the amount of tip collected during the meal, by which the restaurant's customer satisfaction rate is known, and whether there are negative factors that the restaurant management fixes.
The data was read through the pandas library and the data was dealt with using the numpy library.
Classification was created using the sklearn library.
The machine learning model in the project was validated using:
LOGISTIC REGRESSION , KNN CLASSIFIER , RANDOM FOREST CLASSIFIER , SUPPORT VECTOR CLASSIFICATION
The model was also tested using K-FOLD CROSS-VALIDATION .
Tips Prediction
The supermarket management system is a code written in Python and it is an integrated system that contains many options through which it can:
1. Print items information
The following information is displayed for all items in the supermarket:
Serial number, item name, price and total number of items (available in supermarket + sold)
2. Find an item
The user can choose to search for an item by entering either its name (or part of it), or by entering the item's serial number. You must display all information for all matching items, otherwise an appropriate message is displayed indicating no matching items User can search using lowercase or uppercase.
3. Add a new item
To add a new item, the user must enter the required information except for the number of items sold.
The serial number must be unique for each item in the supermarket.
The information entered must be validated as follows:
Item serial number must consist of 4 digits and must not be equal to any other used serial number,
supermarket management system
The project is designed to calculate the cost of medical insurance based on the person's data (age, gender, smoking, number of children, residential area and body mass index (BMI)).
Health insurance, is a contract between you and a health insurance company that requires the health insurance company to pay some or all of your health care costs
The differences in health insurance depend on your age, work and any health problems you currently have
Project Idea:
In the project, I analyzed health insurance data based on a database to determine the cost of medical insurance.
The database contains a number of data on which the cost of medical insurance depends, namely (age, gender, smoking, number of Children, residential area, and body mass index (BMI) were read using the Pandas library.
The importance of predicting the cost of medical insurance enables insurance companies to help insurance companies by collecting the necessary funds and then distributing them to individuals according to the
Predict the cost of medical insurance

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Data science and artificial intelligence

Palestinian Territory 2018 - 2022
(4 år)

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