Kalkidanalemaye

AlphabetSoup

Created a binary classifier that is capable of predicting whether or not an applicant will be successful if funded by Alphabet Soup using the features collected in the provided dataset.

Bikesharing_

Used a visualization software called Tableau to present a business proposal to investors who are potentially interested in citi bike sharing program.

Credit_risk_resampling

To build and evaluate several machine learning models to assess credit risk, using data from LendingClub; a peer-to-peer lending services company.

Cryptocurrencies

Used unsupervised learning to analyze data on the cryptocurrencies traded on the market.

Linear_regression_salary

Used Python to build and evaluate several machine learning models to predict credit risk to help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

Mapping_Earthquakes

Developed visuals to show the differences between the magnitudes of earthquakes all over the world for the last seven days.

Modeling-Probability-Of-Delinquency

Analysis of USA mortgage market to see if a common lifestyle or pattern exists to mortgages which ended up being put in default or bankrupt status.

plotly_deployment

Created charts to visualize data using Plotly. The data is stored in a json file and the code was executed using javascript.

Portfolio

Portfolio website created to display experience and projects that I participated in with their corresponding links to the codes and deployed web-pages.

Pyber_Analysis

Analyze all the rideshare data from January to early May of 2019 and create a compelling visualization for the CEO, V. Isualize. Created an overall snapshot of the ride-sharing data. Presented data on a summary table of key metrics of the ride-sharing data by city type, and a multiple-line graph that shows the average fare for each week by each city type.

Kalkidanalemaye/Credit_risk_resampling

To build and evaluate several machine learning models to assess credit risk, using data from LendingClub; a peer-to-peer lending services company.

Kalkidanalemaye/Linear_regression_salary

Used Python to build and evaluate several machine learning models to predict credit risk to help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

Kalkidanalemaye/AlphabetSoup

Created a binary classifier that is capable of predicting whether or not an applicant will be successful if funded by Alphabet Soup using the features collected in the provided dataset.

Kalkidanalemaye/Bikesharing_

Used a visualization software called Tableau to present a business proposal to investors who are potentially interested in citi bike sharing program.

Kalkidanalemaye/Cryptocurrencies

Used unsupervised learning to analyze data on the cryptocurrencies traded on the market.

Kalkidanalemaye/Mapping_Earthquakes

Developed visuals to show the differences between the magnitudes of earthquakes all over the world for the last seven days.

Kalkidanalemaye/Modeling-Probability-Of-Delinquency

Analysis of USA mortgage market to see if a common lifestyle or pattern exists to mortgages which ended up being put in default or bankrupt status.

Kalkidanalemaye/plotly_deployment

Created charts to visualize data using Plotly. The data is stored in a json file and the code was executed using javascript.

Kalkidanalemaye/Portfolio

Portfolio website created to display experience and projects that I participated in with their corresponding links to the codes and deployed web-pages.

Kalkidanalemaye/Pyber_Analysis

Analyze all the rideshare data from January to early May of 2019 and create a compelling visualization for the CEO, V. Isualize. Created an overall snapshot of the ride-sharing data. Presented data on a summary table of key metrics of the ride-sharing data by city type, and a multiple-line graph that shows the average fare for each week by each city type.

Kalkidanalemaye/dash

Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.

Kalkidanalemaye/Election_Analysis-1

Based on the election commission's request, I confirmed the voter turnout for each county that voted in this congressional district. Used for loops and conditional statements to calculate the voter turnout for each county as well as the percentage of votes each county contributed to the election.

Kalkidanalemaye/Greek_Gods

Kalkidanalemaye/Kickstarter-analysis

Conducted a data analysis to answer how many kick-starter campaigns came close to their fundraising goal in a short period of time and determine whether the length of a campaign contributes to its ultimate success or failure.

Kalkidanalemaye/Mission-to-Mars

Using a specific method of gathering the latest data: web scraping data was pulled from multiple websites, stored in a database, then presented in a central location: a web-page.

Kalkidanalemaye/Neural-Network-Model

Explore and implement neural networks using the TensorFlow platform in Python.

Kalkidanalemaye/Pewlett-Hackard-Analysis

Pewlett Hackard has fallen a bit behind in the database department, so it will be a huge achievement to get their employees' status organized for the company.

Kalkidanalemaye/R_Analysis

Performed a series of statistical tests and created a technical report that provides my interpretation of the findings to help analyze the production data in order to justify some last-minute design decisions of a new prototype by AutosRUs.

Kalkidanalemaye/School_District_Analysis

The grades of the ninth graders at Thomas High School have been changed. While administrators do not know the full extent of this academic dishonesty, they want to uphold the standards of state testing. In order to analyze the data correctly, the math and reading scores for that high school will be replaced without removing those ninth-grade students from the analysis.

Kalkidanalemaye/stock-analysis

Built code in VBA for the analysis of stocks in 2017 and 2018 to find stocks that are performing well. Refactored original code for Steve to loop through the data only once and collect all of the information it needs in a single pass.

Kalkidanalemaye/surfs-up

Gathered data on the seasons of Oahu and determine whether the seasons could affect the surf and ice cream shop business. Specifically, certain times of the year when business might be slower.

Kalkidanalemaye/UFOs

Create an interactive web-page that allows readers to parse the data around UFO sightings.

Kalkidanalemaye/World_Weather_Analysis

Collected and analyzed weather data across cities worldwide. PlanMyTrip will use the data to recommend ideal hotels based on clients’ weather preferences. Created a Pandas DataFrame with 500 or more of the world’s unique cities and their weather data in real time.