we will predict the results of election before few weeks of the elections through a machine learning model .
Election Results Prediction - US
This project uses Natural Language Processing (NLP) and machine learning techniques to predict election results in the United States by analyzing sentiment in tweets related to two prominent candidates: Joe Biden and Donald Trump.
The project utilizes two CSV files for analysis:
Prerequisites Before running the code, make sure you have the following libraries installed:
pandas numpy seaborn matplotlib textblob plotly.graph_objects plotly.express You can install them using pip:
Run the Jupyter Notebook or Python script to perform sentiment analysis on the tweets and predict election results.
bash Copy code pip install pandas numpy seaborn matplotlib textblob plotly Usage Clone the repository to your local machine. bash Copy code git clone cd ELECTION_RESULTS_PREDICTION-US Ensure you have the required CSV files (Trumpall2.csv and Bidenall2.csv) in the project directory.
Run the Jupyter Notebook or Python script to perform sentiment analysis on the tweets and predict election results.
bash Copy code python election_prediction.py Sentiment Analysis Sentiment polarity is used to determine whether a tweet expresses a positive, negative, or neutral sentiment. Tweets are classified into "positive," "negative," or "neutral" categories based on their sentiment polarity. Data Cleaning Neutral tweets are removed from the dataset to balance both datasets. Prediction The project predicts election results based on the sentiment analysis of tweets. It analyzes the number of positive and negative sentiments in both candidates' accounts and presents the results in a graphical format.
The project's predictions are displayed in a bar chart, comparing the percentage of positive and negative sentiments for both Joe Biden and Donald Trump.
Contributors RADHE KRISHNA License This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments