Machine Learning Mastery: Linear Regression using sklearn

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Linear Regression using sklearn

 

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables, they are considering and the number of independent variables being used.

This article is going to demonstrate how to use the various Python libraries to implement linear regression on a given dataset. We will demonstrate a binary linear model as this will be easier to visualize.

In this demonstration, the model will use Gradient Descent to learn.

 

Step 1: Importing all the required libraries

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

 

Step 2: Reading the dataset

You can download the dataset here.

cd C:UsersDevDesktopKaggleSalinity
 
# Changing the file read location to the location of the dataset
df = pd.read_csv('bottle.csv')
df_binary = df[['Salnty', 'T_degC']]
 
# Taking only the selected two attributes from the dataset
df_binary.columns = ['Sal', 'Temp']
 
# Renaming the columns for easier writing of the code
df_binary.head()
 
# Displaying only the 1st  rows along with the column names

Step 3: Exploring the data scatter

sns.lmplot(x ="Sal", y ="Temp", data = df_binary, order = 2, ci = None)
 
# Plotting the data scatter

Step 4: Data cleaning

# Eliminating NaN or missing input numbers
df_binary.fillna(method ='ffill', inplace = True)

Step 5: Training our model

X = np.array(df_binary['Sal']).reshape(-1, 1)
y = np.array(df_binary['Temp']).reshape(-1, 1)
 
# Separating the data into independent and dependent variables
# Converting each dataframe into a numpy array 
# since each dataframe contains only one column
df_binary.dropna(inplace = True)
 
# Dropping any rows with Nan values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
 
# Splitting the data into training and testing data
regr = LinearRegression()
 
regr.fit(X_train, y_train)
print(regr.score(X_test, y_test))

Step 6: Exploring our results

y_pred = regr.predict(X_test)
plt.scatter(X_test, y_test, color ='b')
plt.plot(X_test, y_pred, color ='k')
 
plt.show()
# Data scatter of predicted values

The low accuracy score of our model suggests that our regressive model has not fitted very well to the existing data. This suggests that our data is not suitable for linear regression. But sometimes, a dataset may accept a linear regressor if we consider only a part of it. Let us check for that possibility.

Step 7: Working with a smaller dataset

df_binary500 = df_binary[:][:500]
 
# Selecting the 1st 500 rows of the data
sns.lmplot(x ="Sal", y ="Temp", data = df_binary500,
                               order = 2, ci = None)

We can already see that the first 500 rows follow a linear model. Continuing with the same steps as before.

df_binary500.fillna(method ='ffill', inplace = True)
 
X = np.array(df_binary500['Sal']).reshape(-1, 1)
y = np.array(df_binary500['Temp']).reshape(-1, 1)
 
df_binary500.dropna(inplace = True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
 
regr = LinearRegression()
regr.fit(X_train, y_train)
print(regr.score(X_test, y_test))

 

y_pred = regr.predict(X_test)
plt.scatter(X_test, y_test, color ='b')
plt.plot(X_test, y_pred, color ='k')
plt.show()

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

 

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