chetandudhane / ml-modelling-text-analytics

This project uses ML Classification models to predict Election Exit Polls and Text Mining with NLTK to analyse US Presidents' Speeches

ML Classification Modelling and Text Analytics

This project involves 2 cases - Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning and Text Analytics

This project is based on 2 case-studies: Vote Prediction and Text Analysis.

The first project is to predict which party a citizen is going to vote for on the basis of their age and according to the answers given by the citizens to the questions asked in a survey conducted. EDA is used to generate insights on voters. ML algorithms are used for classification. Classification Models used - Naive Bayes, Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour - Optimal K=15, Adaptive Boosting, Gradient Boosting, Bagging - Random Forest.

The second project is based on the analysis of the inaugural U.S.A. Presidential speeches. One has to draw inferences based on the analysis done on these speeches. Text mining with NLTK is used.

Skills and Tools

Python, EDA, Naive Bayes, Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour - Optimal K=15, Adaptive Boosting, Gradient Boosting, Bagging - Random Forest, Text Mining