Enhancement of general election by using different ML method

[PRIYANSHU2026]

1. Data Collection

Gather historical data on:

Indian General Election dates and outcomes. Historical stock market data (e.g., BSE Sensex and NSE Nifty). Macroeconomic indicators (e.g., inflation, interest rates, GDP growth). 2. Data Preprocessing

Clean the data to handle missing values and outliers. Normalize the stock market data for consistency. Label the data to identify pre-election and post-election periods. 3. Feature Engineering

Create features representing election periods, such as days before and after the election. Incorporate macroeconomic indicators as additional features. 4. Exploratory Data Analysis (EDA)

Visualize the historical trends using plots. Analyze the volatility and market sentiment before and after elections. 5. Model Selection

Choose suitable ML models to predict stock market reactions:

Time Series Analysis: ARIMA, SARIMA. Regression Models: Linear Regression, LSTM for sequence prediction. 6. Model Training

Split the data into training and testing sets. Train the chosen models on historical data. Evaluate model performance using metrics like RMSE