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saundaryasharma28's Projects

ebay-auctions icon ebay-auctions

Competitive Auctions on eBay.com. The file eBay-Auctions.xlsx contains information on 1972 auctions that transacted on eBay.com during May–June 2004. The goal is to use these data to build a model that will classify auctions as competitive or non-competitive. A competitive auction is defined as an auction with at least two bids placed on the item auctioned. The data include variables that describe the item (auction category), the seller (their eBay rating), and the auction terms that the seller selected (auction duration, opening price, currency, day-of-week of auction close). In addition, we have the price at which the auction closed. The task is to predict whether or not the auction will be competitive.

marketingcampaign-universalbank icon marketingcampaign-universalbank

Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business. In particular, it wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise smarter campaigns with better target marketing. The goal is to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign. The file UniversalBank.xls contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign. I have used KNN to predict whether a new customer will accept a loan offer or not. The model showed an accuracy of 91% on validation dataset.

pagerank icon pagerank

Write a function called pagerank to calculate the PageRank of all nodes of a graph. Input: G - Networkx Directed Graph Object max_iter - Number of Iterations (should have some default value) d - damping parameter (should have some default value) has_weight - boolean to be set if using a weighted graph (should have some default value) Output: A dict where key is a node and values is the pagerank value Note: The function should Create Transiston Matrix, A Create initial vector, v0 Write update equation, Av0 = v1 Write convergence condition, number of iterations or no change in page rank values Iteratively update the PageRank values until convergence condition has been reached Make sure you add checks for things that may not make your algorithm to run (e.g., Graph is empty). If for some reason PageRank cannot be executed, it should fail elegantly

sentiment-analyzer-yelp icon sentiment-analyzer-yelp

Built a Naive Bayes Sentiment Analyzer by not only extracting over 5000 Toronto based restaurant's ratings and review via Yelp API but also by using the JSON file format to read and clean the data. The analyzer accurately classifies the customer sentiments based on their reviews (using Natural Language Tool Kit (NLTK) package) thereby helping respective hotels to either offer new or improve existing services.

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