Dalya Lami's repositories
Mars_News_Scraping
Web-scrape and data analyse using both automated browsing with Splinter and HTML parsing with Beautiful Soup.
City_School_District_Analysis
Analyzing the district wide standardized test results data by aggregate it to showcase obvious trends in school performance.
credit-risk-classification
Use various techniques to train and evaluate a model based on loan risk. I’ll use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
Crowdfunding_ETL
Build an ETL pipeline using Python and Pandas to extract and transform the data then create CSV files and use them to create an ERD and a table schema in order to use Postgres to create tables and explore the data.
CryptoClustering
Using Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.
Evaluating_Food_Hygiene_Rating
Evaluating some of the food hygiene ratings data in order to help the UK Food Standards Agency journalists and food critics decide where to focus future articles.
Exploring_Suicide_Rates_and_Factors
This research project seeks to comprehensively investigate suicide rates and their underlying factors over the period from 1985 to 2020.
Exploring_the_Spectrum_of_Suicide_Rates
Explore the various factors that influence suicide rates, such as the impact of suicide prevention and support organizations, geographical variations, the interplay of age groups and generational factors, and make insightful comparisons with other leading causes of death.
Home_Sales
Determine key metrics about home sales data using SparkSQL and then use Spark to create temporary views, partition the data, cache and uncache a temporary table, and verify that the table has been uncached.
Movie_Recomendation
We are focused on developing a movie recommendation model that incorporates user ratings to provide personalized recommendations.
Nonprofit_Foundation_Alphabet_Soup
Using machine learning and neural networks, use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
Home_Credit_Risk_Model_Stability
Predicting default of clients based on internal and external information that are available for each client.
Module-1-Challenge
Excel
VBA_Challenge_Module_2
Multiple_Year_Stock_Data