https://github.com/ALAM-1005/NM2023TMID03157-Quantitative-Analysis-Of-Candidates-In-2019-Lok-Sabha-Elections
The Lok Sabha, or House of the People, is the lower house of India’s bicameral Parliament, with the upper house being the Rajya Sabha. Members of the Lok Sabha are elected by an adult universal suffrage and a first-past-the-post system to represent their respective constituencies, and they hold their seats for five years or until the body is dissolved by the President on the advice of the council of ministers. The house meets in the Lok Sabha Chambers of the Sansad Bhavan, New Delhi.
General elections were held in India in seven phases from 11 April to 19 May 2019 to elect the members of the 17th Lok Sabha. Votes were counted and the result was declared on 23 May. Around 912 million people were eligible to vote, and voter turnout was over 67 percent — the highest ever and the highest ever participation by women voters.
The Bharatiya Janata Party received 37.36% of the vote and won 303 seats, further increasing its substantial majority and the BJP-led National Democratic Alliance (NDA) won 353 seats. The Indian National Congress won 52 seats, failing to get 10% of the seats needed to claim the post of Leader of Opposition, and the Congress-led United Progressive Alliance won 91. Other parties and their alliances won 98 seats.
One of the most critical ways that individuals can influence governmental decision-making is through voting. We know that everyone has the right to vote in our country. But many people are not aware of politics. So by this project, we can learn about the different political parties, their background history, and their recent success and failure in the Lok Sabha election 2019 in India. Unfortunately, we have found few candidates with criminal history also. So through this data analysis, we can be aware of the candidate’s history and the nature of the political party. We can learn about the winning party and their success in 2019. In this work, we will mainly focus on STATE, WINNER, PARTY, GENDER, CRIMINAL CASES, AGE, CATEGORY, EDUCATION.