Welcome to my Portfolio

CC1: Hosting and Displaying

Taken charts from the chart library

CC2: Creating Your Own Visualisation

Graph made from EconomicsObservatory

CC3:Debating the Gender Pay Gap 1997-2023

*TODO: add discussion My graph supporting the gender pay gap discussion at the Festival of Economics: Dimensions of Inequalities. The graph looks at the gender pay gap between different types of employment. This graph supports the argument that the UK still has inequalities that are decreasing over time.

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CC3: Gender Pay Growth

My graph exploring the growth of annual income separated by gender. During the talk, Richard explored the narrative of annual income growth amongst men but neglected to make the comparison against women's annual income growth. Women's annual income on average out performed on growth. However if we were to look at this critically, the gender pay gap is still prevalent. In order to mitigate this women's annual income must grow substantially more.

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CC4: Replication of a Financial Times Graph

I have replicated a graph from an article from the Financial Times, Falling rates and growing confidence in US economy drive a bond revival. I manually traced the data using their graph. I added axis titles and tool tip to increase clarity to the data being presented.

FinancialTimes

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CC5: Scraping S&P 500 Returns from wikipedia

A graph scraped from wikipedia on the different returns on the S&P500

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CC6: Stock Price Charts

Using AlphaVantage API to build a loop dashboard to download time series (unadjusted) closing stock prices for 9 stocks in the S&P 500

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CC7: Maps

Map of thailand. Map 1 shocases all the provinces in Thailand. Map 2 showcases the population in each province in Thailand,

CC8: Advanced Analytics: Correlation and Histogram

I wanted to explore the relationship between VIX, Volatility Indicator Index (S&P500) with the Fear and Greed Index (CNN) which explores market sentiment.

A higher VIX (Volatility Index) means that the market is expected to be more volatile in the future.

Fear and Greed Assigning a reading to the market of 50 as neutral, 0 to 49 as fear, and 51 to 100 as greed.

FinancialTimes
FinancialTimes FinancialTimes

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CC9: Big Data

Left Chart: looked at what goods my friends would buy and wanted to explore how prices effected them over time.

Right Chart: Price of Takeaway Coffee by Reigon in the UK over time

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CC10: Machine Learning: Clusters and Random Forest

Hypothesis: "Market sentiment (Fear & Greed) and volatility (VIX) jointly predict returns, with distinct patterns emerging across bullish, bearish, and neutral market clusters."

Clustering Results:

Random Forest Results:

FinancialTimes FinancialTimes

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