A cluster of Machine Learning algorithms
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Updated
Nov 18, 2021 - Jupyter Notebook
A cluster of Machine Learning algorithms
A Mathematical Intuition behind Linear Regression Algorithm
Models for Predicting Missing Stock Prices.
A demand forecasting model for an E-Commerce retailer, built using KPIs from Google Analytics & implemented in RStudio. Models: time-series, ARIMA, Regression (multivariate & dynamic). Open-source & contributions welcome.
A Collection of Utilities for Modeling Conditional Data Using Generalized Linear Models
Deep Neural Network for Weather Time Series Forecasting
Basic Feed Forward Neural Network Implementation with ELU activations and MSE loss/Cross entropy loss for uni-variate regression. Uses Optuna for Hyperparameter search
Using auto regressive time series models to fit univariate equations in data.
This is an implementation of univariate linear regression from scratch in Python
code will related to time series data and forecast
This project utilizes univariate and multivariate linear regression, to predict the temperature the next day, analyzing the provided dataset. The file contains a line by line walkthrough of the code, with an explanation of each step, along with ample visual and verbal analysis.
Univariate Linear Regression in Python
Problem to solve: How long do you have to study to get a specific score. We can try to see a pattern in that data and predict a score based on how many hours the subject studies.
Simple Linear Regression in Python using Scatter Plot. Update it with your dataset. This code will work for any dependency of form H:X->Y . I have attached a pdf document of my own notes for this model. Feel free to download. Note : The pdf is for help purpose. Any type of reuse or restructuring is subject to copyright.
Univariate Linear Regression using sklearn on Diabetes Dataset
This project examines how model complexity and training size impact prediction stability in polynomial regression. By analyzing models of degrees 1 to 4 with confidence intervals, we gain insights into balancing overfitting and underfitting for optimal model selection.
linear regression with gradient descent-univariate and multivariate on house pricing dataset
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