"BTVN"
"1"
# Read the following strategy:
# K-folds
# Stratified k-fold
# Leave one-out
# Leave P-out
# Applied above strategies with “advertising.csv”. And choose the best strategy

# Fix bug in Logistic Regression in Python.ipynb
# Apply feature selection from sklearn
# Apply oversampling method: SMOTE
# Fix bug of drawing AUC
# Try to do the same with full data: bank-full.csv

"2"
# Apply SVM for sentiment classification:
# Dataset: tweet1000	csv (1000 documents)
# Algorithm: Compare between SVM and Linear Model (accuracy, precision, recall, training time)
# Feature extraction : Try both Bag-of-words and TF-IDF

"3"

# Build a Gender Recognition classifier using the Random Forest algorithm from the voice dataset.
# The idea is to identify a voice as male or female, based upon the acoustic properties of the voice and speech.
# The dataset consists of 3,168 recorded voice samples, collected from male and female speakers.

# Practice with Titanic dataset in kaggle and submit the result to the kaggle and give me the ranking result
