WHO categorized the COVID-19 coronavirus outbreak as a pandemic by march 2020.
This project gives a brief idea about how pandemic spread and spreading till now.

Visualization is a very important phase of the project since it unlocks various insights into the data.
Python's libraries like Pandas, Matplotlib, Seaborn, Plotly for world maps are used for it.

Showed confirmed, recovered, death and active cases on bar charts concerning the country, state, and date.
Bar charts give a clear number of victims, recovery, and fatalities with active cases.
And the line graph shows the growth curve of the COVID-19 pandemic.

Visualize cases using world maps according to countries for a better understanding of spread worldwide.
One can detect sensitive areas using world maps.

The predictive model shows that the curve is exponentially increasing and there are fewer chances that curve will flatten to zero.

WHO categorized the COVID-19 coronavirus outbreak as a pandemic by March 2020.This project gives a brief idea about how pandemic spread and spreading till now.Visualization is a very important phase of the project since it unlocks various insightsinto the data. Pythons libraries like Pandas, Matplotlib, Seaborn, and Plotly for worldmaps are used for it.Showed confirmed, recovered, death and active cases on bar charts with respect tocountry, state, and date. Bar charts give a clear number of victims, recovery, andfatalities with active cases. And the line graph shows the growth curve of the COVID-19 pandemic.Visualize cases using world maps according to countries for a better understanding ofspread worldwide. One can detect sensitive areas using world maps.The predictive model shows that the curve is exponentially increasing and there arefewer chances that curve will flatten to zero.

Commercial banks receive a lot of applications for credit cards. Many of them getrejected for many reasons. Manually analysing these applications is mundane, error-prone, time-consuming.Then performed EDA on given dataset like extracting summary statistic, treatingmissing values for features columns of both numeric and object data type.Using Label Encoder convert all non-numeric values to numeric one.Build an automatic credit card approval predictor using machine learning techniques,just like the real banks do.Used Logistic Regression model and achieve accuracy 83.5 %