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Project 3: Criminal Cases Against Indian Politicians


 The Indian Lok Sabha elections of 2019 saw intense political competition among various political parties and their candidates. However, it is also essential to evaluate the possibility of criminal charges against the candidates. In this regard, the data for criminal cases registered against Lok Sabha MPs, who contested in the 2019 elections, has been extracted from myneta.info. This data has been analyzed using Python libraries like pandas, NumPy, and other machine learning algorithms to predict criminal cases against the candidates.

The Python script starts by scraping data from a specific URL using the requests library and parses the data using BeautifulSoup. The script then imports various libraries such as re, sqlite3, pandas, and numpy, and creates two tables (candidates and winners) using SQL queries in an SQLite database. It then inserts data into these tables using the executemany() method of the cursor object and saves the changes to the database using the commit() method.

After data cleaning, fundamental data analysis has been performed using pandas, NumPy, and Matplotlib libraries. The word cloud generated displays the political parties with a higher number of candidates contesting in the 2019 Lok Sabha Elections.

The analysis also used various machine learning algorithms like logistic regression, K Neighbors, SVC Linear, SVC rbf, Gaussian NB, Decision Tree, and Random Forest to predict criminal cases against the candidates. The models produced impressive training accuracies ranging from 80.8% to 99.5%.

Logistic regression, K Neighbors, SVC Linear, SVC rbf, Gaussian NB, and Random Forest produced training accuracies of 81.05%, 81.71%, 81.06%, 81.2%, 80.8%, and 96.4%, respectively. These accuracies suggest that these models are effective in predicting criminal cases of Lok Sabha candidates. Decision Tree produced a training accuracy of 99.5%, indicating that it is a highly reliable method for predicting criminal cases of Lok Sabha candidates.

In conclusion, this project demonstrates that machine learning algorithms, along with Python libraries like pandas, NumPy, and others, can be used to predict criminal cases against Lok Sabha candidates with impressive accuracies. Such predictions can provide valuable insights into the nature of Indian politics and the role of criminal charges in shaping electoral outcomes. These insights can help political parties and voters to make informed decisions during elections.

The complete project code can be found at the following link: navyasrivattikuti/LokSabha2019 (github.com)
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Comments

  1. AnonymousAugust 26, 2026 at 7:16 PM

    nice

    ReplyDelete
  2. AnonymousOctober 17, 2026 at 10:41 PM

    great

    ReplyDelete
  3. AnonymousOctober 17, 2026 at 10:43 PM

    The analysis of criminal cases involving Indian politicians is an interesting application of data science to real-world public information. Combining web scraping, data cleaning, visualization, and machine learning can help researchers explore patterns in the available data while highlighting the importance of data quality and responsible interpretation.

    Classification algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, and Random Forest can provide different perspectives when building predictive models. Understanding how to select, train, and evaluate these algorithms is an important part of learning through a Machine Learning Classification Course.

    ReplyDelete
  4. AnonymousOctober 17, 2026 at 10:43 PM

    Projects like this also demonstrate how fundamental machine learning concepts can be applied to practical datasets. A strong foundation in preprocessing, feature selection, model evaluation, and performance comparison helps learners make informed decisions when developing data-driven solutions through a Machine Learning Course.

    ReplyDelete

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