Complete EDA on IRIS dataset
Given various features like id, Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel and Vintage we had to predict if the person would be intereseted in taking an insurance. This was represented by Response where 0 meant no and 1 meant yes. This challenge was organised by Learn ML on Dockship.
Accuracy - 87.65973%
Certificate - https://dockship.io/certificates/6048a679c2c7fa59d94edd2f
The aim of this task is to predict the stock market prices for 5 stocks.
Certificate - https://dockship.io/certificates/6005b5f2aaf1695ff537ad36
The aim of this task was to predict if a person would survive on Titanic or not. The attributes are PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin and Embarked. The feature among these to be predicted is Survived. Algorithms used - K-NN, Logistic Regression, SVM Accuracy - 77.5%
It is a subset of a large 142.8 million Amazon review dataset that was made available by Stanford professor, Julian McAuley. This sentiment analysis dataset contains reviews from May 1996 to July 2014. The dataset reviews include ratings, text, helpful votes, product description, category information, price, brand, and image features. The complete dataset is available here: http://jmcauley.ucsd.edu/data/amazon/. worked on the smaller dataset of “SPORTS AND OUTDOORS” where we had to classify the reviews as positive, negative and neutral.
The aim was to classify the tweets as positive or negative. Accuracy Score - 0.7804147727272728