Project summary

Dharitrikumari Rathod

COVID-19 Vaccination Visualization

2020 U.S.

Presidential

Election Analysis

Introduction:

Analysis of 2020 Presidential debates and townhall meetings
Comparison of 2020 Presidential debates and 2016 presidential debates

Research questions:	Method
1. What were the most prominent words said by each candidate during the different stages of debate during the 2020 and 2016 presidential debates?	Word cloud Bigram
2. Which candidate was most talkative during the debate?	Statistics
3. How many times did they get interrupted by others?	Heatmap
4. What were the positive and negative sentiments of the 2020 debate?	Sentiment analysis
5. Which topics were discussed during the 2020 Presidential debates?	TF-IDF, Cosine similarity
6. How does the similarity of the candidate’s speech compare to the other candidate on the same topic?	TF-IDF, LDA model
7. How does the similarity between President Trump's speech during 2020 and 2016?	TF-IDF, LDA model

Most prominent words: 2020

Biden 2020

Trump 2020

Most Prominant topics: 2020

Bigram: Joe Biden

Topics
Green infrastructure
Social security
Mail in ballot
Health insurance/ Affordable care act
First responders
Tax plans

Extracted Topics
New york
Individual mandate
Law enforcement
Stock market
Justice reform
Economy
Small businesses
Oil industry
Forest Management
Obamacare/Health Insuran

#2 Most talkative in 2020:

Debate_1

Debate_2

Crosstalk/Heat moment

Sentiment Analysis:

1st_debate

Overall 2020

2nd_debate

TF-IDF vectorizer

TF-IDF 1st debate

Biden

Trump

TF-IDF 2nd debate

TF-IDF Topics

TFIDF and Cosine similarities

Built the TF-IDF Model to check if the given topic was discussed in the 2020 election or not. Also, compared the topic using cosine similarity at each stage of the Presidential election 2020.

Extracted Topics TFIDF

Bag of words

TF-IDF

Topic extract

What topics?

BoW	TF-IDF
Second, insurance, healthcare, obamacare	Industry, child, website, reform, school, business
Industry, business, website, plague, enormous, nuclear, opportunity,	Talk, totally, month, well, police, discredit
Family, Clean, filthy, emission, police, scientist, pollutant, environmental, carbon	Family, immigration, political, protest, tremendous, democratic
Ballot, election, inaugural, racist, chance	People, million, evidence, healthcare, opposite, condition, fantastic, nuclear
Create, building, question, economic, energy, company	Election, chance, separate
Excuse, energy, subsidy, dangerous, federal, border, highway, ecnomically	Ballot, number, answer
Statement, plant, pollute, chemical, refinery, superpredator	Crosstalk, economy, segment, disaster, vaccine
Crosstalk, Hispanic, environment, global warming, gasoline, unemployment	Second, dollar, radical, deserve
Dollar, fracke, billion, emission, climate, market	Support, energy, federal,

HEART DISEASE PREDICTION

Goal: For the Heart Disease Dataset, we want to classify the presence of heart disease using the best combinations of the dataset files (Cleveland, Hungarian, Switzerland, VA) based on the most relevant patient attributes.

Procedure:

Decided to test different combination of databases rather than only using the Cleveland dataset.
Imputing missing values.
Results would show if we can achieve better results for classification.