Objective

Build a Sentiment Classifier



In [2]:

    
from __future__ import print_function  # Python 2/3 compatibility
import numpy as np
import pandas as pd

from IPython.display import Image

Load Data

Load Training Data
Split into Train and Validation



In [6]:

    
## Your Turn

Vectorize

Vectorize using Scikit-Learn

Hints: Checkout CountVectorizer, TFIDFVectorizer and others in sklearn.feature_extraction.text



In [7]:

    
## Your Turn

Model

Build Models
Evaluate them on Validation data



In [5]:

    
## Choosing an Estimator
# http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
    
Image("http://scikit-learn.org/stable/_static/ml_map.png")









    Out[5]:



In [8]:

    
## Your Turn

Model Tuning

Tune the Model if it's overfitting



In [10]:

    
## Your Turn

Feeling Good? - Let's Update Kaggle Submission

Steps:

Load Test Dataset
Vectorize the Features (Review)
Predict the sentiment
Create the CSV file and update the submission



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## Your Turn

Congratulations

Now you are a Kaggle Pro. Try your hand at other Competitions - https://www.kaggle.com/competitions