Covid-19 Analysis in Python using Holt Model

By Rajasree Unnithan R

Analysis of the Covid-19 data to check the spread of the pandemic and growth in the number of cases reported. Also this analysis help to forecast the spread of the disease.

1. Data Preprocessing

In the packet, initially the data is loaded into the notebook and before starting the analysis, the data is preprocessed. The null values, unused columns, etc. are deleted from the dataset.

2.Analysis

In the analysis section, the data is analyzed and visualized. Visualizations helps to easily understand the trend in the data. This also helps to compare various values.

i.Worldwide Analysis

  Covid-19 data allover the world is considered and check the status of confirmed, recovered and deceased cases. This help to analyse which countries have more Covid-19 cases and where more focus should be given.

ii.Countrywise Covid-19 status analysis (India)

  Whole data is grouped based on the country, and analysis of countrywise data is conducted. Countrywise analysis facilitates us to understand the status of Covid-19 spread in the country. Also comparison of Covid-19 data for different countries are made.

3. Data Modelling

Before performing Time Series Analysis, Linear Regression and Support Vector Regression are used to model the data. This models help to predict the values for future. This can also be used to compare with the Time Series Forecasting.

4.Time Series Analysis

As the data is a time series data, Time Series Analysis is the best method to analyze this data. Among the Time Series models, Holt model is used in this analysis. Forecasting using in Holt helps to interpret the future values. This also have better accuracy in prediction, when compared to other models.