a-memme Goto Github PK

a-memme

amazon_vine_analysis

arlington_crime_density

Exploration, analysis and forecasting of crime density in Arlington VA from 2016-2024

competitor_analytics_scrape

ETL pipeline creation, utilizing selenium web browser for scraping snapchat desktop UI, pushing data into a g-sheet database and loading into reporting

credit_risk_analysis

Comparing sampling techniques and classification algorithms to predict credit risk

cryptocurrencies

customer_churn

customer_churn_and_cltv

BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business

election_analysis

Analyzing sample election results

kickstarter_analysis

Analyzing theatre campaign data and providing insights on financial performance using Microsoft Excel

kiva_exploratory_analysis

Exploratory analysis of a non-profit crowdfunding organization using Python and Tableau

mapping_earthquakes

Building an interactive earthquake map with Leaflet

mapping_earthquakes_original

mechacar_analysis

Performing multiple regression to predict fuel efficiency (MPG) on an energy-efficient car model

mission-to-mars

movie_data_etl

Creating a data pipeline with movie-rating data - loading into a postgreSQL database.

nyc_bikesharing_analysis

Exploratory analysis of NYC bike sharing data with Tableau

pewlett-hackard-analysis

Leveraging postgreSQL to organize data and determine which employees from a large company's database are up for, or close to, retirement based on their age and tenure.

plotly_bacteria

plotly_bacterial_beef

predicting_crime_pca

Comparing the use of original data vs PCA in multiple regression. Analysis also showcases how principal components can be transformed back to their original vector space after model fitting for descriptive purposes

pyber_analysis

revenue_prediction_imputation

Random forest regression + ARIMA timeseries modeling to impute metric values and forecast revenue for reporting purposes.

school_district_analysis

Using Python3, Pandas, and Jupyter Notebook

snap_dynamic_scheduling

Auto-ARIMA timeseries forecasting in combination with PELT changepoint detection to predict social media viewership performance and identify major changes in performance trends. Models are deployed into a streamlit webapp for analytical functionality.

snapchat_correlation_analysis

Utilizing Python and R to perform statistical analyses on real-world snapchat performance data to discover how various metrics relate to viewership

stock_analysis

Analyzing annual stock trends of Daqo New Energy Corp (NYSE :DQ) in 2017/2018 using VBA

surf_shop_analysis

text_analysis_reddit

Tokenization/Lemmatization vs keyBERT algorithms to analyze keywords and infer top performing topics on a subreddit of choice. Data is accessed via Python Reddit API Wrapper (PRAW).

world_weather_analysis