khairy84 Goto Github PK

4-databricks-exams-projects-using-apache-spark-sql

4 Databricks Exams projects using Apache-spark-sql

a-visual-history-of-nobel-prize-winners.io

py

analyzing-tv-data

Py

case-study

Job application case study

classification-for-abu-dhabi-traffic-accident-data

Cleaning the Data, translate from Arabic to English, feature engineering and get the data ready for applying Logistic Regression with-penalty-l1-l2-and-KNN, to predict the injuries and find the effective feature which can lead to accident with injuries, ## Dataset is linear dataset: Hense linear algorithms like LinearSVC and Logistic Regression will give us better results, however i will apply linear and nonlinear methods to see the difference

credit-card-defaulted-dashboard

Extract, Transform and Load raw data with Power BI

deep-neural-network-predicting-revenue-

Deep Neural Network predicting hotels revenue in Abu Dhabi Zones

dr.-semmelweis-and-the-discovery-of-handwashing

py

hospitality-revenue-predictor

To predict which area is the best to invest in hospitality business

investigating-netflix-movies-and-guest-stars-in-the-office

py_DS

job-application-case-study

Job application case study

keywords-and-faces-recognition

Final project in python 3 programming specialization by university of michigan

khairy84

Config files for my GitHub profile.

pdfs

Technically-oriented PDF Collection (Papers, Specs, Decks, Manuals, etc)

plotting-weather-patterns

Assignment 2Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to Preview the Grading for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment. An NOAA dataset has been stored in the file data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv. This is the dataset to use for this assignment. Note: The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) Daily Global Historical Climatology Network (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe. Each row in the assignment datafile corresponds to a single observation. The following variables are provided to you: id : station identification code date : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012) element : indicator of element type TMAX : Maximum temperature (tenths of degrees C) TMIN : Minimum temperature (tenths of degrees C) value : data value for element (tenths of degrees C) For this assignment, you must: Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk. The data you have been given is near Ann Arbor, Michigan, United States, and the stations the data comes from are shown on the map below.

practical-data-science-on-the-aws-cloud

10 learning projects using AWS SageMaker.

predicting-credit-card-approvals

price-analysis

Finding the reason of loss in some sales account and product cost, extracting the data from Azure sql server using T-SQL, transfrom it using T-SQL & Power BI, Create dashboard using Power BI showng the findings

recommender-system-in-postgresql-using-python

Using python to build a movie recommendation engine inside postgresql database

sales-analysis-and-forecasting-using-dax

Cleaning data , Pivot and forecasting sales by category and market segment, Pivot and forecast revenue variance by time series, YTD revenue analysis, Logistics analysis

sales-dashboard-by-area

1. Extracted from my own AZURE SQL Server, using Microsoft SQL Server Management Studio. 2. Transform using T-SQL. 3. Transform using Power BI.

sales-lstm-time-series

daily historical sales data for group of shops

text-mining-project-for-e-mail-spam

the-github-history-of-the-scala-language

Py_DS

ud120-projects

Starter project code for students taking Udacity ud120

world-population-dashboard

Extract data from Wikipadia.org than transformed and create a dashboard by Power BI