This project involves the analysis of COVID-19 data to uncover trends and insights regarding the spread and impact of the virus globally. The goal is to explore and visualize patterns in the data and perform predictive analysis using various data science techniques.
In this project, I have used publicly available datasets related to the COVID-19 pandemic. The analysis includes:
Exploratory Data Analysis (EDA): Cleaning and pre-processing raw data. Data Visualization: Visualizing infection rates, recovery trends, and death rates across different regions using libraries like Matplotlib and Seaborn. Statistical Analysis: Identifying patterns and correlations within the data. Predictive Modeling:Forecasting future trends based on historical data using time series analysis and regression models.
Python (Pandas, NumPy) Data Visualization: Matplotlib, Seaborn Jupyter Notebook Machine Learning: Scikit-learn for predictive modeling COVID-19 Dataset: Publicly available datasets from sources such as Johns Hopkins University.
Analysis of infection and recovery rates across different countries. Visualization of the impact of government interventions like lockdowns. Predictive models to forecast future infection rates.