justStarted.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


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


# In[2]:


# 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")


# In[3]:


# 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"]]


# In[4]:


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


# In[5]:


# Select a linear model
model = sklearn.linear_model.LinearRegression()
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = [[22587]] # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.96242338]]


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