Udacity Data Analyst Nanodegree - Project III
A trading algorithm that identifies stocks with the largest potential for growth while heavily considering its volatility using quantopian
Modelo de pricing actuarial frecuencia-severidad (GLM Poisson ×ばつ GLM Gamma) con scoring comercial XGBoost sobre el dataset freMTPL2 — 678.000 pólizas de seguro automotor francés.
The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.
A regression based modeling project to forecast the sales of Walmart
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.
Linear Regression model for a US bike-sharing provider BoomBikes
in this repository i will upload all the projects which i would make regarding deep learing. i will upload as much neural networks as possible.
My notes in Jupyter Notebooks for statistics, probability, and plotting applied with major python libraries as an introduction to machine learning.
Random Good Data Science Stuff
A time series forecasting project comparing SARIMA and Holt-Winters models to analyze temperature trends in Kenya using real-world climate data.
An implementation of an ARIMA time series forecast using Python statsmodels and scipy.
Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?
A predictive machine learning model to forecast the Algerian Forest Fire FWI using Python, Scikit-learn, and Statsmodels. Includes complete data cleaning and EDA.
ARIMA baseline forecasting on AMZN daily log returns with a reproducible pipeline (local mode + BigQuery feature store + SQL window functions) and an auto-generated report.
End-to-end pairs-trading research framework: data acquisition, cointegration-based pair selection, vectorized backtests, analytics, and publication-ready figures (Python 3.10+).
A data analytics project that utilizes PANDAS, Numpy, Matplotlib and statsmodel to analyze the results of hypothesis testing and regression modeling in determining whether a website update should be launched.
Time Series Analysis
Model to identify the potential lead by assigning a score for their rate of conversion. Therefore, reaching out to potential is no more a brainstorming task.
I perform a retrospective analysis on the linear regression analysis that I previously performed on the NYC Bike Counts dataset. Specifically, I analyze my linear regression analysis to identify anything that I could have done differently.
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