Exploratory Data Analysis of the 2019 Indian General Elections using a dataset from Kaggle.

This project is a part of the Course - Data Analysis using Python: Zero to Pandas offered by Jovian.ai.

We perform Exploratory Data Analyis on the 2019 Indian General Elections dataset. Here we use various Python libraries to perform Data Cleaning and Visualization. The Dataset which is used in this project is from Kaggle, authored by the user Prakrut Chauhan.

Link to the Dataset used - https://www.kaggle.com/prakrutchauhan/indian-candidates-for-general-election-2019

The dataset contains information of all the candidates who contested the elections from various Constituencies. Data includes personal information like Assets, Education, Criminal Record, etc. as well as electoral information such as Contesting Constituency, Political Party, Total Votes received, etc.

The Libraries used in the Project are:

• Matplotlib (for visualization of data),

• Seaborn (used alongside Matplotlib for visualization),

• Numpy (used for operations on numeric data),

• Pandas (used for utilising DataFrames and organising the data),

• Jovian (used for downloading dataset and to run, save and upload the Notebook).

Apart from the above mentioned libraries, we use the opendatasets package to directly download the files from Kaggle and parse the data. Link to the package - https://github.com/JovianML/opendatasets

To view the Jupyter Notebook containing the EDA, click on the .ipynb file to open it. Scroll down to see the analysis. Some contents might not be visible in Dark Theme, so I recommend viewing the notebook in Light Theme.

The Notebook can also be viewed in Google Colab and Binder or can be downloaded and viewed locally.

Link to a Blog Post will be added soon.

Hope you like my work !!!