President Election

Overview

In 2024, more than 60 nations, including the United States, will hold national elections. This creates an extraordinary opportunity to showcase your data skills. An election-related project on your portfolio can make recruiters take a closer look at your CV. This project guides you through the steps to predict election trends using real-world data and advanced techniques.

Step 1: Finding the Dataset

• Recommendation: Scrape election-related tweets using the Twitter API.

  • Learning Opportunity: Enhance your scraping skills by exploring Twitter’s API.

• Alternative: Use pre-collected datasets available on Kaggle.

  • Example Dataset: Includes 970K tweets about Trump and Biden.

Step 2: Data Exploration

The dataset includes:

• Tweet Data: Content, likes, retweets.
• Geographical Data: User locations, enabling regional sentiment analysis.

Insights: Explore public opinion trends and variations across regions.

Step 3: Data Preparation and Sentiment Analysis

Prepare your dataset by:

1. Preprocessing:

   • Clean tweets.
   • Remove unwanted characters using regex.
   • Standardize data types.

2. Sentiment Analysis:

   • Classify tweets as positive, negative, or neutral.

Goal: Infer public sentiment towards each candidate.

Step 4: Data Visualization

Visualizations are key to making your project impactful.

• Tools to Use: Folium or GeoPandas for mapping state-wise data.
• Focus Areas:
  • Visualize geographical sentiment trends.
  • Represent public opinion dynamically.

Pro Tip: Even if predicting the 2024 election is uncertain, strong visuals demonstrate your analytical and technical skills in job interviews.