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Name: Priyanka Adiga
Type: User
Bio: Aspiring Data Scientist| Graduate Student at Northeastern
Location: Cary,NC
Name: Priyanka Adiga
Type: User
Bio: Aspiring Data Scientist| Graduate Student at Northeastern
Location: Cary,NC
In this Project, I am exploring the Capital Bike sharing dataset, made available to us by the UC Irvine Machine learning repository [1]. I start by taking a look at the dataset, the variables involved and then clean the dataset for model building. The Capital bikeshare system collected the historical data logs in Washington DC over a period of two years, i.e. 2012 & 2012. With analysis in this paper, I plan to determine the correlation of environmental factors like the weather conditions, season, time of the day, day of the week, etc. with the bike rental behavior of the public and then use the results from this analysis to come up with a strategy to promote the usage of bike sharing in both the casual and registered users, whilst also aim at converting the casual users into registered ones.
Persona Panels is a market research company in combination with AI has formulated machine learning bots which helps customers to understand and determine the target consumers. The product, Millennial Animated Persona, is an artificial bot created by AI technology and machine learning. The project aims at determining and analyzing the patterns available in the dataset obtained by Persona Panels our sponsors. This company is committed to creating a tool for intelligent market research, which is implemented using AI technology and machine learning. In this project we will be analyzing the keywords recorded by the six millennial animated personas in order to understand the similarities and differences between them and also gain insights on some of the key features of the millennial animated personas. Since these personas are formulated mirroring the characteristics of plausible target consumers this analysis will help us understand the characteristic features of the target audience and identify their behavior.
In this project we have used the acquired skills of Intermediate Analytics in order to uncover useful aspects of the data under analysis. We have used the methods of Hypothesis Testing to test an hypothesis related to the heart disease like whether higher age contributes in a higher probability of heart disease or not. We have used the methods of logistic regression and classification using decision trees to build a model using our training dataset and predict on the test dataset and observe the accuracy rate of our built model.
Formulated a model to predict the cancellation of the hotel bookings. Analyzed the various factors responsible for booking cancellation and improved the model accuracy using XG-Boost algorithm and performed hyperparameter tuning using RandomsearchCV and achieved an accuracy rate of 86%
An attempt summarizing my education, work experience and skills.
Formulated three models using spaCy library for advanced NLP that extracted phrases responsible for a tweet being categorized as positive, negative, or neutral and achieved an average Jaccard score of 74%.
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