Exercícios do curso "Profissao: Cientista de Dados", sendo realizado pela EBAC - Escola Britânica de Artes Criativas e Tecnologia.

Stock price prediction models for alpaca.markets

Exploring simple linear regression models to analyze the relationship between advertising expenditure and sales.

manylinux2014 Python pkg builds

Time Series Analysis of Airline Passenger Data, In this time series forecasting, taking data from kaggle site and applying ARIMA and SARIMAX model to evaluate seasional trends of passenger travelling via airlines.

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

Repositório que reuni os módulos 7 ao 13 da trilha Desenvolvimento IA 2023-2024, desenvolvido pela Rocketseat Education.

Python Scripts and Jupyter Notebooks for Prof. Robert Wrobel's Applied Business Statistics course at Webster University and anybody else who wants to learn!

Data Science DRY OOP Umbrella Library

📜 🎉 Automated reporting of objects in R

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t

Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.p

Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

A neural network model for predicting cryptocurrency prices using machine learning and time series analysis techniques.

Apply machine learning techniques in Python to forecast wind power production.

modelling with SciViews::R

gradient-boosted regression and decision tree models on behavioural animal data

Analyzing O3 Air Quality Index trends (2000-2023) in the U.S., this project identifies regions with rising pollution. Utilizing exploratory data analysis and time-series modeling, it offers actionable insights for informed policy decisions on urgent O3 pollution issues.

Project Explores Toronto Neighborhoods and Housing using a variety of data science and machine learning techniques.

Improved the accuracy of Bitcoin stock price predictions on ARIMA model by reducing the seasonality factor. Achieved RMSE value of 68.99 after implementation of SARIMAX model to reduce seasonality.