Imports¶

In [2]:

import pandas as pd
import os
import sklearn
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split
%matplotlib inline

print sklearn.__version__
print pd.__version__

0.19.1
0.20.3

Loading data¶

In [3]:

# read data with read_table
# put sep=',' as data is ',' seperated
# renames the 0th row which is header with easy to read names
# display top 5
DATA_DIR = '../data'

df = pd.read_table(
                    os.path.abspath(os.path.join(DATA_DIR,'day1/regression.csv')), 
                    sep=',', 
                    header=0, 
                    names=['independent', 'dependent']
                  )
df.head(5)

Out[3]:

	independent	dependent
0	24	21.549452
1	50	47.464463
2	15	17.218656
3	38	36.586398
4	87	87.288984

In [4]:

# checking for any NaN value in the dataset
print df['dependent'].map(lambda x: np.isnan(x)).unique()
print df['independent'].map(lambda x: np.isnan(x)).unique()

[False]
[False]

In [5]:

X = df.iloc[:,:1].values
Y = df.iloc[:,1].values

Data Split¶

In [6]:

# ideal practice is to use test as 20% - 30% of training data
# defined by test_size in train_test_split()
# random_state is required to avoid sequential biasness in the data distribution
def data_split(X, Y):
    X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)
    return X_train, X_test, Y_train, Y_test

X_train, X_test, Y_train, Y_test = data_split(X, Y)

In [7]:

print X_train.shape, X_test.shape

(799, 1) (200, 1)

Regression Functionality¶

In [8]:

class Regression:
    
    def __init__(self):
        self.regressor = LinearRegression()
    
    def train(self, X_train, Y_train):
        model = self.regressor.fit(X_train, Y_train)
        return model

    def predict(self, model, X_test):
        return model.predict(X_test)

In [9]:

regress = Regression()
model = regress.train(X_train, Y_train)
predictions = regress.predict(model, X_test)

Visualize¶

In [10]:

plt.scatter(X_train , Y_train, color = 'red')
plt.scatter(X_train , model.predict(X_train), color ='blue')

Out[10]:

<matplotlib.collections.PathCollection at 0x7f23a6aea550>

In [12]:

plt.scatter(X_test , Y_test, color = 'red')
plt.scatter(X_test , predictions, color ='blue')

Out[12]:

<matplotlib.collections.PathCollection at 0x7f23a6a320d0>