Python | Linear Regression using sklearn

• Difficulty Level : Easy
• Last Updated : 28 Nov, 2019

Prerequisite: Linear Regression

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. You can learn about it here.

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:\Users\Dev\Desktop\Kaggle\Salinity

# 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()

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