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tugce ozcelik's Projects

ai-credit-modelling icon ai-credit-modelling

As a team of 4, we intend to use machine learning and artificial intelligence (with the context of statistical modelling) for social good, to evaluate credit risk and improve the access and fairness of credit to reach underserved communities.

awesome-r icon awesome-r

A curated list of awesome R packages, frameworks and software.

ense-480-project icon ense-480-project

Project for ENSE 480, the Artificial Intelligence class. This project will be an app that will detect fraud in credit card statements.

ml-fraud-detection icon ml-fraud-detection

Credit card fraud detection through logistic regression, k-means, and deep learning.

plotly icon plotly

An interactive graphing library for R

python-credit-card-case-study-segmentation icon python-credit-card-case-study-segmentation

The case requires developing a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behaviour of 9000 active credit card holder during the last 6 months.

rsmtool icon rsmtool

RSMTool is a python package for facilitating research on building and evaluating automated scoring models.

shiny icon shiny

Easy interactive web applications with R

siraj_course_how-to-do-linear-regression-using-gradient-descent icon siraj_course_how-to-do-linear-regression-using-gradient-descent

# linear_regression_live This is the code for the "How to Do Linear Regression the Right Way" live session by Siraj Raval on Youtube ## Overview This is the code for [this](https://youtu.be/uwwWVAgJBcM) video on Youtube by Siraj Raval. I'm using a small dataset of student test scores and the amount of hours they studied. Intuitively, there must be a relationship right? The more you study, the better your test scores should be. We're going to use [linear regression](https://onlinecourses.science.psu.edu/stat501/node/250) to prove this relationship. Here are some helpful links: #### Gradient descent visualization https://raw.githubusercontent.com/mattnedrich/GradientDescentExample/master/gradient_descent_example.gif #### Sum of squared distances formula (to calculate our error) https://spin.atomicobject.com/wp-content/uploads/linear_regression_error1.png #### Partial derivative with respect to b and m (to perform gradient descent) https://spin.atomicobject.com/wp-content/uploads/linear_regression_gradient1.png ## Dependencies * numpy Python 2 and 3 both work for this. Use [pip](https://pip.pypa.io/en/stable/) to install any dependencies. ## Usage Just run ``python3 demo.py`` to see the results: ``` Starting gradient descent at b = 0, m = 0, error = 5565.107834483211 Running... After 1000 iterations b = 0.08893651993741346, m = 1.4777440851894448, error = 112.61481011613473 ``` ## Credits Credits for this code go to [mattnedrich](https://github.com/mattnedrich). I've merely created a wrapper to get people started.

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