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Supervised-Learning-Social-Network-Ads
March 13, 2020 – March 13, 2020
Supervised-Learning-Social-Network-Ads — GitHub repository
View ProjectNatural-Language-Processing-Restaurant-Reviews
March 12, 2020 – March 12, 2020
The purpose of this analysis is to build a prediction model to predict whether a review on the restaurant is positive or negative. To do so, we will work on Restaurant Review dataset, we will load it into predictive algorithms Multinomial Naive Bayes.
View ProjectLogistic-Regression-Advertising-Data
March 9, 2020 – March 9, 2020
In this project we will be working with an advertising data set, indicating whether or not a particular internet user clicked on an Advertisement on a company website. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. This data set contains the following features: * 'Daily Time Spent on Site': consumer time on site in minutes * 'Age': cutomer age in years * 'Area Income': Avg. Income of geographical area of consumer * 'Daily Internet Usage': Avg. minutes a day consumer is on the internet * 'Ad Topic Line': Headline of the advertisement * 'City': City of consumer * 'Male': Whether or not consumer was male * 'Country': Country of consumer * 'Timestamp': Time at which consumer clicked on Ad or closed window * 'Clicked on Ad': 0 or 1 indicated clicking on Ad
View ProjectLinear-Regression-Predicting-Housing-Prices
March 8, 2020 – March 8, 2020
Your neighbor is a real estate agent and wants some help predicting housing prices for regions in the USA. It would be great if you could somehow create a model for her that allows her to put in a few features of a house and returns back an estimate of what the house would sell for. She has asked you if you could help her out with your new data science skills. You say yes, and decide that Linear Regression might be a good path to solve this problem! Your neighbor then gives you some information about a bunch of houses in regions of the United States,it is all in the data set: USA_Housing.csv. The data contains the following columns: * 'Avg. Area Income': Avg. Income of residents of the city house is located in. * 'Avg. Area House Age': Avg Age of Houses in same city * 'Avg. Area Number of Rooms': Avg Number of Rooms for Houses in same city * 'Avg. Area Number of Bedrooms': Avg Number of Bedrooms for Houses in same city * 'Area Population': Population of city house is located in * 'Pric
View ProjectLinear-Regression-Ecommerce-Data
March 7, 2020 – March 11, 2020
An Ecommerce company based in New York City that sells clothing online but they also have in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want. The company is trying to decide whether to focus their efforts on their mobile app experience or their website.
View ProjectLinear-Regression-PREDICTING-CAR-PRICE
March 6, 2020 – March 6, 2020
PREDICTING PRICE OF PRE-OWNED CARS using Linear Regression
View ProjectBank-Account-Manager---Project-in-Python
March 5, 2020 – March 5, 2020
Project Scope To tackle this project, first consider what has to happen. There will be three different types of bank account (Checking, Savings, Business) Each account will accept deposits and withdrawals, and will need to report balances Project Wishlist We might consider additional features, like: impose a monthly maintenance fee waive fees for minimum combined deposit balances each account may have additional properties unique to that account: Checking allows unlimited transactions, and may keep track of printed checks Savings limits the number of withdrawals per period, and may earn interest Business may impose transaction fees automatically transfer the "change" for debit card purchases from Checking to Savings, where "change" is the amount needed to raise a debit to the nearest whole dollar permit savings autodraft overdraft protection
View ProjectBookStore--GUI-Using-Python
March 4, 2020 – March 4, 2020
A simple python project to display the content of the database on the GUI. Library used -Tkinter -sqlite3
View ProjectPersonal-Income-Case-Study
February 29, 2020 – February 29, 2020
Classifying Personal Income Case Study using Logistic Regression and KNN- K Nearest Neighbors classifier
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic in nature, focusing on fundamental data science concepts. While this demonstrates initiative, there is no evidence of collaborative projects, open-source contributions, or diverse industry applications that would strongly indicate cultural fit for a senior role. The projects are well-aligned with the 'Data Scientist' target role, but lack the breadth and complexity expected at a senior level.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate an ability to work independently on defined problems.