PROJECT REPORT
DATA SET NAME: LOKSABHA ELECTION DATA ANALYSIS USING PYTHON
*PROJECT OBJECTIVE -THE OBJECTIVE OF THIS PROJECT IS TO PERFORM A COMPREHENSIVE EXPLORATORY DATA ANALYSIS ON LOKSABHA ELECTION DATA.
*ACCORING TO THIS ANALYSIS WE CAN FINDOUT THE VOTING TRENDS ACCROSS DIFFERENT LOKSABHA CONSTITUENCY.
CONCLUSION
*Based on this analysis, the party that received the most votes is IND. BSP is in second place and INC is in third place.
*Among the top 10 parties, IND received 61.0% of the votes, followed by BSP with 7.8% and INC with 7.7%.
*The party that received the most votes in 2009 was INC.
*The party that received the most votes in 2014 was BJP.
*Based on this data, many parties are changing. Even though it is common, first INC and BJP will change. Later after 2014, BJP will rule.
2.Tools Used Pandas – Data cleaning, preprocessing, and analysis, NumPy – Numerical operations, Matplotlib – Data visualization, Seaborn – Statistical and advanced visualizations, Google Colab – Project development environment,
- Steps Followed
*Data Loading and Initial Exploration, Imported the dataset using Pandas, *head(), *info(), *describe(), *Data type inspection,
- Data Cleaning and Preprocessing, *Checked for missing values, *Checked and removed duplicate records, *Cleaned percentage and numeric columns, *Corrected inconsistent data types, *Created derived columns for additional analysis,
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Exploratory Data Analysis (EDA) Performed: *Univariate analysis, *Bivariate analysis, *Multivariate analysis, *Statistical summaries, *Groupby analysis, *Pivot tables, *Correlation analysis,
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Data Visualization -Created multiple visualizations using Matplotlib and Seaborn, including:
*Line charts, *Bar charts, *Histograms, *Scatter plots, *Box plots, *Heatmaps,