2019 Indian General Elections Data Analysis
This project is to 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 - Source
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) Explore here
- Seaborn (used alongside Matplotlib for visualization) Explore here
- Numpy (used for operations on numeric data) Explore here
- Pandas (used for utilising DataFrames and organising the data) Explore here
- Jovian (used for downloading dataset and to run, save and upload the Notebook) Explore here
To install all required libraries, run the following Command:
pip install matplotlib seaborn numpy pandas jovian --upgrade
The following Tasks are implemented in the Project:
- Data Preparation and Cleaning
- Exploratory Analysis and Visualization
- Asking and Answering Questions
- Inferences and Conclusion
- References and Future Work
LET'S DIVE INTO THE PROJECT !!!
Downloading the Dataset
The dataset is unpacked and opened using the opendatasets package from the jovian library.
!pip install jovian opendatasets --upgrade --quietdataset_url = 'https://www.kaggle.com/prakrutchauhan/indian-candidates-for-general-election-2019' Let's begin by downloading the data, and listing the files within the dataset.