To be, or not to be

It is a Data Classifier Project.

What it Does?

• It determines the player based on the features.
• To get the accuracy rate for prediction

Datset Used

• Shakespeare Plays : https://www.kaggle.com/kingburrito666/shakespeare-plays -- It contains data from Shakespeare's plays such as Play, player, Player Line and Act

Data Classification

• We are classifying Player Line using 4 different classification models.

• Models

    1) Random Forest
    2) Support Vector Machine
    3) Naive Byes
    4) Logistics Regression
  

• To classify text, we need to convert into numerical forms. So, the player line has been converted to a tfidf vector
• This vector is then used to train the model.

Analysis

• Data has been split into two sets - Train and Test. Using default methodology.
• Below graph shows the Play vs No of Players

• Accuracy

  --Accuracy for each model is evaluated. -- Logistic Regression accuracy rate was higher than the other models.

Models	Accuracy
SVM	0.043564
Logistic Regression	0.046845
Naive Byes	0.032881
Random Forest Classifier	0.021660

• Accuracy Comparison of all the models
• 

Project Struture

Src

• Shakespeare.py - python file exported from Jupyter

Notebooks

• Shakespeare.ipynb - Jupyter notebook

Data

• External - Shakespeare Data

Reports

• Plot - Plays vs Player Count and Accuracy Comparison