Ever felt lost in an Ice Cream shop while choosing your favourite flavour OR ever imagined the plight of the Football Club owners while choosing the International players.
Well, today I would like to share my experience on a similar situation -
How to select the best features ( Or Flavours ) from many available features for your machine learning model — With only one GOAL : To improve the prediction accuracy.
I was always fascinated with the idea of ,how a machine can read Reviews given by the Customers and classify them as Positive or Negative and had many other questions like -
How to remove punctuation marks & HTML tags. ?
Will a mixture of lowercase & Upper case letters will confuse the machine learning algorithm?
How to remove repetitive words like ‘a, an, and, the’ which do not add any value.?
How to remove emoticons 😅?
All of the above questions were answered when I solved a ‘Amazon Alexa Sentiment Analysis problem’ to predict whether the sentiment is Positive…
Here is the story of my first experience with Machine Learning Hackathon on “Loan Prediction Practice Problem” (Click Here for problem details) hosted by AnalyticsVidhya.
Complete Python code can be found on my GitHub repository.
Final submission earned me 80.55% Accuracy & a Rank of 122 (Top 2%) out of 5250 participants as of 20th Feb’21
Feature engineering helped in increasing the accuracy from 77% to 80.55% with the help of additional features
Sometimes adding new features may take down the accuracy , hence need to choose the new features carefully.
So lets straight dive into the Hackathon Arena 😃
I am passionate about new technologies, especially Data Science, AI and Machine Learning. Interested in developing a software that solves real-world problems.