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 the 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
Python3
import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as pltfrom sklearn import preprocessing, svmfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegression |
Step 2: Reading the dataset You can download the dataset
Python3
df = pd.read_csv('bottle.csv')df_binary = df[['Salnty', 'T_degC']]# Taking only the selected two attributes from the datasetdf_binary.columns = ['Sal', 'Temp']#display the first 5 rowsdf_binary.head() |
Output:
Step 3: Exploring the data scatter
Python3
#plotting the Scatter plot to check relationship between Sal and Tempsns.lmplot(x ="Sal", y ="Temp", data = df_binary, order = 2, ci = None)plt.show() |
Output:
Step 4: Data cleaning
Python3
# Eliminating NaN or missing input numbersdf_binary.fillna(method ='ffill', inplace = True) |
Step 5: Training our model
Python3
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 columndf_binary.dropna(inplace = True)# Dropping any rows with Nan valuesX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)# Splitting the data into training and testing dataregr = LinearRegression()regr.fit(X_train, y_train)print(regr.score(X_test, y_test)) |
Output:
Step 6: Exploring our results
Python3
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 |
Output:
The low accuracy score of our model suggests that our regressive model has not fit very well with 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
Python3
df_binary500 = df_binary[:][:500] # Selecting the 1st 500 rows of the datasns.lmplot(x ="Sal", y ="Temp", data = df_binary500, order = 2, ci = None) |
Output:
We can already see that the first 500 rows follow a linear model. Continuing with the same steps as before.
Python3
df_binary500.fillna(method ='fill', 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)) |
Output:
Python3
y_pred = regr.predict(X_test)plt.scatter(X_test, y_test, color ='b')plt.plot(X_test, y_pred, color ='k')plt.show() |
Output:
Step 8: Evaluation Metrics For Regression
At last, we check the performance of the Linear Regression model with help of evaluation metrics. For Regression algorithms we widely use mean_absolute_error, and mean_squared_error metrics to check the model performance.
Python3
from sklearn.metrics import mean_absolute_error,mean_squared_errormae = mean_absolute_error(y_true=y_test,y_pred=y_pred)#squared True returns MSE value, False returns RMSE value.mse = mean_squared_error(y_true=y_test,y_pred=y_pred) #default=Truermse = mean_squared_error(y_true=y_test,y_pred=y_pred,squared=False)print("MAE:",mae)print("MSE:",mse)print("RMSE:",rmse) |
Output:
MAE: 0.7927322046360309 MSE: 1.0251137190180517 RMSE: 1.0124789968281078
