Using Machine Learning to explain and predict the life expectancy of different countries

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The project tries to create a model based on data provided by the World Health Organization (WHO) to evaluate the life expectancy for different countries in years. The data offers a timeframe from 2000 to 2015. The data originates from here: The output algorithms have been used to test if they can maintain their accuracy in predicting the life expectancy for data they haven’t been trained. Four algorithms have been used:

Linear Regression
Ridge Regression
Lasso Regression
ElasticNet Regression
Linear Regression with Polynomic features
Decision Tree Regression
Random Forest Regression

A venture in crypto-currency trading

Posted 5 CommentsPosted in Datathons Solutions, Learn, Team solutions

In [315]: import numpy as np import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.arima_model import ARIMA from import plot_acf, plot_pacf import time Enthusiast Team: Datathon Case¶Predicting Cryptocurrency prices¶Reading the Currencies for the First Problem¶ In [88]: currency_info = pd.read_csv(“currencies.txt”, sep = “\t”) currency_info Out[88]: Currency ticker CoinID 0 Bitcoin BTC […]