Scikit-learn (Sklearn) is Python's most useful and robust machine learning package. It offers a set of fast tools for machine learning and statistical modeling, such as classification, regression, clustering, and dimensionality reduction, via a Python interface. This mostly Python-written package is based on NumPy, SciPy, and Matplotlib. In this article you’ll understand more about sklearn linear regression.
What is SKlearn Linear Regression?
Scikit-learn is a Python package that makes it easier to apply a variety of Machine Learning (ML) algorithms for predictive data analysis, such as linear regression.
Linear regression is defined as the process of determining the straight line that best fits a set of dispersed data points:
The line can then be projected to forecast fresh data points. Because of its simplicity and essential features, linear regression is a fundamental Machine Learning method.
Sklearn Linear Regression Concepts
When working with scikit-linear learn's regression approach, you will encounter the following fundamental concepts:
- Best Fit - The straight line in a plot that minimizes the divergence between related dispersed data points
- Coefficient - Also known as a parameter, is the factor that is multiplied by a variable. A coefficient in linear regression represents changes in a Response Variable
- Coefficient of Determination - It is the correlation coefficient. In a regression, this term is used to define the precision or degree of fit
- Correlation - the measurable intensity and degree of association between two variables, often known as the 'degree of correlation.' The values range from -1.0 to 1.0
- Dependent Feature - A variable represented as y in the slope equation y=ax+b. Also referred to as an Output or a Response
- Estimated Regression Line - the straight line that best fits a set of randomly distributed data points
- Independent Feature - a variable represented by the letter x in the slope equation y=ax+b. Also referred to as an Input or a predictor
- Intercept - It is the point at where the slope intersects the Y-axis, indicated by the letter b in the slope equation y=ax+b
- Least Squares - a method for calculating the best fit to data by minimizing the sum of the squares of the discrepancies between observed and estimated values
- Mean - an average of a group of numbers; nevertheless, in linear regression, Mean is represented by a linear function
- OLS (Ordinary Least Squares Regression) - sometimes known as Linear Regression.
- Residual - the vertical distance between a data point and the regression line
- Regression - is an assessment of a variable's predicted change in relation to changes in other variables
- Regression Model - The optimum formula for approximating a regression
- Response Variables - This category covers both the Predicted Response (the value predicted by the regression) and the Actual Response (the actual value of the data point)
- Slope - the steepness of a regression line. The linear relationship between two variables may be defined using slope and intercept: y=ax+b
- Simple linear regression - A linear regression with a single independent variable
How to Create a Sklearn Linear Regression Model
Step 1: Importing All the Required Libraries
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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
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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
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sns.lmplot(x ="Sal", y ="Temp", data = df_binary, order = 2, ci = None) # Plotting the data scatter |
Step 4: Data Cleaning
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# Eliminating NaN or missing input numbers df_binary.fillna(method ='ffill', inplace = True) |
Step 5: Training Our Model
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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
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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 |
Our model's poor accuracy score indicates that our regressive model did not match the current data very well. This implies that our data is ineligible for linear regression. However, a dataset may accept a linear regressor if only a portion of it is considered. Let us investigate that option.
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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 observe that the first 500 rows adhere to a linear model. Continuing in the same manner as previously.
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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Conclusion
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