Twitter-gender-text-Analysis

(Deployed using Flask)
This notebook follows this procedure for end to end analysis :-

1. The dataset is initially analysed to identify trends, patterns, outliers etc
2. Preliminary data cleaning is futher proceeded for filtering out required parameters.
3. Data exploration and feature engineering are done for fine tuning of the reduced dataset.
4. ML modelling and accuracy checking to find the optimal algorithm for the dataset.

For a little extra, the notebook also covers analysis of color attributes corresponding to each Gender available
To be deployed using Flask