Rith's Portfolio

K Nearest Neighbors Project I've been given a classified data set from a company(fake data again..). It has hidden the feature column names but has given the data and the target classes. I'll try to use KNN to create a model that directly predicts a class for a new data point based off of the features. Import Libraries I'll import libraries that I need to use as always Get the Data Exploratory Data Analysis ¶ Since this data is artificial, I'll just do a large pairplot with seaborn. Standardize the Variables ¶ Time to standardize the variables. Train Test Split Using KNN Predictions and Evaluations Choosing a K Value after I saw accuracy is quite low, so I will pick a new K value. Retrain with new K Value   after I saw K value around 25-28 are quite okay, so I will pick K value at 27

Logistic Regression Project ¶ In this project I will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. I 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 Import Libraries       First of all, import the lib

E commerce 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.(it's fake data. it's not real credit card numbers or emails) -------------------------------------------------------------------------------------------------------------------------- GET THE DATA  First of all I will import libraries that I need to use in this project, then I will import data set to my jupyter notebook. Next, I will Check the head of customers, and check out its info() and describe() methods Exploratory Data Analysis I will use seaborn to create a jointplot to compare the Time on W