Application Of Data Analytics, Mining, Machine Learning & Network Science To Election Campaign Strategy

Application Of Data Analytics, Data Mining, Machine Learning & Network Science To Election Campaign Strategy

Analysis Of Political Survey Data

• Rows contain data of each participant in the survey (Age, M/F, Area, Profession, Which candidate are you going to vote for, Why, Which party did you vote for in 2008 Election, Why did you vote for that candidate, Which party did you vote for in 2001 Election). 
• Columns are features.
• The goal of Data Analysis is to group voters together to determine strategies. 
• Our candidate is weak in that particular area of his constituency.
• How do we win votes using our network map?
• Our candidate is weak among that particular age group of his constituency.
• Our candidate is weak among people belonging to that particular profession of his constituency.
• What social initiatives can we take for people belonging to that particular profession? 
• Swing voters - x% of total voters. 
• People who voted for candidates from different parties in 2001 and 2008 Elections.
• For people belonging to that profession, the reason behind candidate preference is “X”.
• From answers to our survey question - "Why did you vote for that candidate?" 
• For people belonging to that age group (say, young generation), the reason behind candidate preference is “Y”.
• What can we do to win the votes of this age group? Look at the reason. 
• Usage of Machine Learning Algorithms for extraction of patterns from Data.
• Decision Tree Learning can be utilized for predicting candidate preference of a particular voter from the voter's features. 
• A Decision Tree might learn, for example, if a voter 
• 18 < age < 35
• area = “X”
• is a Male
• Then, he will vote for “Nagorik Shakti”. 

Usage Of Network Map