Statsmodels: statistical modeling and econometrics in Python
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Updated
May 30, 2024 - Python
Statsmodels: statistical modeling and econometrics in Python
A Trading Model Utilizing a Dynamic Weighting and Aggregate Scoring System with LSTM Networks
Projects from the Learn SQL certification from Codecademy.
This project is a Jupyter Notebook that analyzes how a regression model can be tuned to predict the stock market prices of Tesla (TSLA). The objective was to create a prediction algorithm to forecast the closing price of Tesla stock on a specific date.
Student maths score prediction project with its webapp, All the codes are in modular format. Python flask is used as web framework.
🌌 The R easystats-project
An End-to-end Project To Predict the Price Of Diamond 💎
The repository presents a comprehensive project that leverages relevant features to accurately predict the price of Houses.
This repository implements a custom artificial neural network in PyTorch, achieving 87% accuracy on a regression dataset (outperforming Keras). It provides a hands-on approach to building neural networks from scratch while leveraging PyTorch for efficient training.
Implementação do Trabalho de conclusão de curso, com o tema definido "Aproximação da cinemática inversa de um robô manipulador didático através de algoritmos de aprendizado de máquina"
Presentation-Ready Data Summary and Analytic Result Tables
A collection of 8 Applied Data Science projects.
Personal notes during reading "An R Companion to Applied Regression"
A list of MI-AI Projects and Model builds
Modelling and Inference of MICrobiomes Project (MIMIC) is a Python package dedicated to simulate, model, and predict microbial communities interactions
Deterministic algorithms for objective Bayesian inference and hyperparameter optimization
Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.
In this project I built machine learning models using Multiple Linear Regression, Ridge regression, Lasso regression, Elasticnet regression and then created a pickle file of the regression model which gave best accuracy
Regression model building and forecasting in R
How weather directly impacts renewable energy generation.
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