abdelrhman2022

analyze-maven-movies-using-sql-queries

SQL Movie Database The Maven Movies' company insurance policy is up for renewal and the insurance providing company’s underwriters need some updated information from it before they will issue a new insurance policy.

analyze_a-b_test

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. We will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. Your goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.

api

Build a python flask REST API then integrate it with Deep Learning Model

api-scraping-web-scraping-real-estate-data

Scrape data from a Real Estate website, using the hidden API, and store data in Excel Files and in the PostgreSQL database.

automation-extract-imdb-movie-ratings-and-their-details-using-selenium-beautifulsoup

I automated scrape the IMDb movie ratings and their details with the help of the BeautifulSoup and Selenium libraries of Python, then store data in csv file

bmi-calculator

# BMI-CALCULATOR-IN-FLUTTER fully BMI calculator consist of two screens 1. the first screen to get input from the user gender by selecting its icon height by slider age and weight by floating action button 2. the second screen to show the result to the user

car_price_prediction

Car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable numbers of distinct attributes are examined for reliable and accurate prediction. The data used for the prediction was collected from the kaggle.com

color-detection-using-opencv

In this color detection Python project, we are going to build an application through which you can automatically get the name of the color by clicking on them. So for this, we will have a data file that contains the color name and its values. Then we will calculate the distance from each color and find the shortest one

colour-detection

Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into color. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the color. Since childhood, we have mapped certain lights with their color names. We will be using the somewhat same strategy to detect color names.

converting-nested-json-structures-to-pandas-dataframes

I convert nested JSON structures to Pandas DataFrames. JSON with multiple levels In this case, the nested JSON data contains another JSON object as the value for some of its attributes. This makes the data multi-level and we need to flatten it as per the project requirements for better readability

data-engineering-capstone-project

This project is end to end which I developed a data pipeline that creates an analytics database for querying information about immigration into the U.S on a monthly basis. The analytics tables are hosted in a Redshift Database and the pipeline implementation was done using Apache Airflow.

data-modeling-with-postgres

Udacity Data Engineering Nanodegree - Project 1 - Data modeling with Postges

data-modeling-with-postgres-sparkify-etl

Build data modeling with Postgres , an ETL pipeline using Python. define fact and dimension tables for a star schema for a particular analytic focus, and write an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.

data-warehouse-project-fall-semester-2021-2022

SSIS Project and Create a Dashboard using Power bi

diabetic-retinopathy-detection

Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people. retina The US Center for Disease Control and Prevention estimates that 29.1 million people in the US have diabetes and the World Health Organization estimates that 347 million people have the disease worldwide. Diabetic Retinopathy (DR) is an eye disease associated with long-standing diabetes. Around 40% to 45% of Americans with diabetes have some stage of the disease. Progression to vision impairment can be slowed or averted if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment. Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital color fundus photographs of the retina. By the time human readers submit their reviews, often a day or two later, the delayed results lead to lost follow up, miscommunication, and delayed treatment. Clinicians can identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. While this approach is effective, its resource demands are high. The expertise and equipment required are often lacking in areas where the rate of diabetes in local populations is high and DR detection is most needed. As the number of individuals with diabetes continues to grow, the infrastructure needed to prevent blindness due to DR will become even more insufficient. The need for a comprehensive and automated method of DR screening has long been recognized, and previous efforts have made good progress using image classification, pattern recognition, and machine learning. With color fundus photography as input, the goal of this competition is to push an automated detection system to the limit of what is possible – ideally resulting in models with realistic clinical potential. The winning models will be open sourced to maximize the impact such a model can have on improving DR detection.

emotional-analysis

Multi-class sentiment analysis problem to classify texts into five emotion categories: joy, sadness, anger, fear, love , surprise. A fun weekend project to go through different text classification techniques. This includes dataset preparation, traditional machine learning with scikit-learn, LSTM neural networks and transfer learning using BERT (tensorflow keras).

etl-telecom-using-ssis

This project uses fake data related to the field of communication (such as IMSI / IMEI / TAC / SNR), but I would like to highlight this project that reviews concepts and topics that can be applied in any other project, and the part of the field of Tele-communication in this series is extremely limited.

expert-system-in-it-jobs-and-technologies

I Develop a data-driven solution for our students to answer and understand the relationships between the jobs and the technologies. . IT jobs and technologies keep evolving quickly. This makes our field to be one of the most interesting out there. But on the other hand, such fast development confuses our students. They do not know which skills they need to learn for which job.

exploratory-data-analysis-indian-premier-league

Perform 'Exploratory Data Analysis' on dataset 'Indian Premier League' As a sports analyst, find out the most successful teams, players, and factors contributing win or loss of a team.

exploratory-data-analysis-terrorism-using-power-bi

Exploratory Data Analysis Terrorism using Power BI Perform 'Exploratory Data Analysis' on dataset 'Global Terrorism' As a security/defense analysis trying to find out the hot zone of terrorism. What security issues and insights can derive from EDA?

explore-us-bikeshare-data

In this project, you will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. Answer interesting questions about it by computing descriptive statistics. Also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics.

extract-image-metadata-in-python

here are many reasons why you want to include the metadata of a video or any media file in your Python application. Video metadata is all available information about a video file, such as width, height, codec type, fps, duration, and many more.

face-mask-detection

This project uses a Deep Neural Network, more specifically a Convolutional Neural Network, to differentiate between images of people with and without masks. The CNN manages to get an accuracy of 99.38% on the training set and 99.44% on the test set. Then the stored weights of this CNN are used to classify as mask or no mask, in real time, using OpenCV. With the webcam capturing the video, the frames are preprocessed and and fed to the model to accomplish this task. The model works efficiently with no apparent lag time between wearing/removing mask and display of prediction.

finding-donors-for-charityml

CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Your goal will be evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

finding-donors-for-charityml-1

Udacity - Machine learning nanodegree - Project Finding Donors for CharityML

google-play-store-apps-and-reviews

Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. We'll look for insights in the data to devise strategies to drive growth and retention.

graph-coloring

Graph Coloring is an assignment any unique marks to the vertices of a graph called "colors" to elements of a graph object to certain constraints. Strictly speaking, a coloring is appropriate coloring if no two adjacent vertices have the same color. Graph coloring is still received a lot of attention of research

graphics

hand-detection-using-python

This project is a hand detection program written in Python. It uses computer vision techniques to identify and track the location of hands in real-time using a webcam.The program will open your default webcam and display the output on the screen. It will track the location of your hand in real-time and draw a bounding box around it.