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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Objective
Build a Sentiment Classifier
Step1:  Load Data
Load Training Data
Split into Train and Validation
Step2:  Vectorize
Vectorize using Scikit-Learn
Hints
Step3:  Model
Build Models
Evaluate them on Validation data
Step4:  Model Tuning
Tune the Model if it's overfitting
Step5:  Feeling Good? - Let's Update Kaggle Submission
Steps
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Python Code:
from __future__ import print_function  # Python 2/3 compatibility
import numpy as np
import pandas as pd
from IPython.display import Image
Explanation: Objective
Build a Sentiment Classifier
End of explanation
## Your Turn
Explanation: Load Data
Load Training Data
Split into Train and Validation
End of explanation
## Your Turn
Explanation: Vectorize
Vectorize using Scikit-Learn
Hints: Checkout CountVectorizer, TFIDFVectorizer and others in sklearn.feature_extraction.text
End of explanation
## Choosing an Estimator
# http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
    
Image("http://scikit-learn.org/stable/_static/ml_map.png")
## Your Turn
Explanation: Model
Build Models
Evaluate them on Validation data
End of explanation
## Your Turn
Explanation: Model Tuning
Tune the Model if it's overfitting
End of explanation
## Your Turn
Explanation: 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
End of explanation
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