Exploratory Data analysis of Social Unrest in India

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA), is a process of examining and analyzing data sets to summarize their main characteristics. It is often used to gain insights into data and understand its underlying structure, patterns, and relationships. EDA typically involves using statistical and visualization techniques to explore the data, such as histograms, scatter plots, and box plots.

In this EDA project, we will use data analysis and visualization techniques to explore the patterns and trends for social unrest in India.

• We will collect and compile data from kaggle for the year 2016 - 2022.
• We will then preprocess and clean the data to ensure its accuracy and consistency, and to make it suitable for analysis.
• Next, we will use descriptive statistics and data visualization techniques to explore the data and identify patterns and trends for social unrest across different regions, time periods, and demographic groups.
• We will also examine the nature and causes of the events, such as the type of
  events, the types of actors involved and much more.
• The analysis will be understandable through the graphs and maps using Matplotlib, seaborn, plotly express, etc. These tools will be helpfull to make various graphs such as bar graph, line graph, histogram, sunburst, pie charts and various other graphs to give a visual representation effectively.
• The project will be followed up with a QNA section where the question related to the project could be answered precisely.
• The findings of this EDA project may be useful for policymakers, activists, and other stakeholders who seek to address the root causes of riots and promote social harmony in India.

Step 1 : Collecting the Data

The dataset is taken from Kaggle. It contains over 100,000+ rows of data and 31 columns. The dataset includes the information about the type of event, sub event, the actors involved, assosicated actors involved, date and time of the event, location, and much more.
The detailed description about each column is listed in the Kaggle Dataset.