Utilizing Logistic Regression to determine the likelihood of heart disease presence in an individual.
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Updated
Nov 1, 2022 - Jupyter Notebook
Utilizing Logistic Regression to determine the likelihood of heart disease presence in an individual.
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization
A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.
Multivariate Linear and Logistic Regression Using Gradient Descent Optimization.
I executed this assignment for a US-based housing company named Surprise Housing, wherein a regression model with regularisation was used to predict the actual value of the prospective properties and decide whether to invest in them or not
How much is the NBA dollar worth in terms of team success?
Creating Neural Net from scratch using python , Numpy.
Chapman University CS-510 Computing For Scientists Final Project
Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi
The aim was to create and implement a predictive model that can forecast the number of items sold for a period of 8 weeks ahead.
This repository is about machine learning algorithms
The dataset that I am performing this regression analysis on, comes from Kaggle, titled crimes In India. This dataset holds complete information about various aspects of crimes that have taken place in India in a 17 year span, from 2001 to 2018.
The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.
Comparision of Linear Regression, Ridge Regression, Lasso Regression
linear regression with different types and datasets. Understanding of linear regression with Boston dataset using numpy.
Identifying text in images in different fonts using deep neural network techniques.
Wrapper on top of liblinear-tools
Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.
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