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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Code

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]]

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Quotation

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

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  1. Footnote can have markup

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  2. Footnote text. ↩︎ ↩︎

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