Bigger data and more intelligent algorithms are being processed and analyzed faster in an API-enabled, open source environment. J.P. Morgan is committed to understanding how this technology-driven landscape could differentiate your stock, sector, portfolio, and asset class strategies.Here, J.P. Morgan summarizes key research in machine learning, big data and artificial intelligence, highlighting exciting trends that impact the financial community.

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像链接一样,图像也有脚注样式的语法。

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稍后在定义URL位置的文档中引用:

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn

# Load the data
oecd_bli = pd.read_csv("oecd_bli_2015.csv", thousands=',')
gdp_per_capita = pd.read_csv("gdp_per_capita.csv",thousands=',',delimiter='\t',
                            encoding='latin1', na_values="n/a")

# Prepare the data
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]

# Visualize the data
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()

# Select a linear model
lin_reg_model = sklearn.linear_model.LinearRegression()

# Train the model
lin_reg_model.fit(X, y)

# Make a prediction for Cyprus
X_new = [[22587]]  # Cyprus' GDP per capita
print(lin_reg_model.predict(X_new)) # outputs [[ 5.96242338]]






We are all in the gutter, but some of us are looking at the stars.

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面对人生的深渊,支撑我走下去的,其实是嫉妒、虚荣、不甘和报复。