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

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#Import the libraries and packages we need
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt


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#we are going to create two arrays that have our data. These should be the same length

x = np.array([10,40,25,30,55,18,7]).reshape(-1,1) #we have to use reshape because we are required to pass in x 
                                                  #as a 2-dimensional array. One column and as many rows as necessary
y = np.array([5,18,22,45,60,34,19])


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print(x)
print(y)


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print(x.shape)
print(y.shape)


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#we now create our model instance. We are just going to use the default parameters
#we also are going to fit the model
#this calculates the values for the slope and intercept in our regression equation
model = LinearRegression().fit(x,y) 


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#now we can see the results
#first our r-squared
r_sq = model.score(x,y)
print("r-squared: ",r_sq)


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#we can also find our intercept and slope
intercept = model.intercept_
slope = model.coef_
print("intercept: ",intercept)
print("slope: ",slope)


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#now we can find our predicted values
y_predicted = model.predict(x)


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y_predicted


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#we can now create our regression equation
y_predicted = intercept + slope*x


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#now we can plot the 
fig,ax = plt.subplots(figsize=(10,10))
plt.scatter(x,y)
plt.plot(x,y_predicted)


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