Machine Learning with Python | Multiple Linear Regression with Solved Examples - P10

Machine Learning with Python | Multiple Linear Regression with Solved Examples - P10
#MultipleLinearRegression #MachineLearning

In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
In linear regression, the relationships are modelled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.

Github link - https://github.com/technologycult/PythonForMachineLearning/tree/master/Part62

All the playlist link of this youtube channel
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1. Data Preprocessing in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPuOjFcbnXLFvSQaHFK3ymUW

2. Confusion Matrix in Machine Learning, ML, AI
https://www.youtube.com/playlist?list=PLE-8p-CwnFPvXzvsEcgb0IZtNsw_0vUzr

3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz
https://www.youtube.com/playlist?list=PLE-8p-CwnFPsBCsWwz_BvbZZHIVQ6wSZK

4. Cross Validation, Sampling, train test split in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPsHtol5WXHhq_B3kQPggHH2

5. Drop and Delete Operations in Python Pandas
https://www.youtube.com/playlist?list=PLE-8p-CwnFPtvqVVK7QVFsMvDvp2YgCnR

6. Matrices and Vectors with python
https://www.youtube.com/playlist?list=PLE-8p-CwnFPsndwnZnL7nXW5mIrdRmgdg

7. Detect Outliers in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPvyCX35yES5D9W7vThiUzwk

8. Time Series preprocessing in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPv10bru3719xzDNIgbO6hXA

9. Handling Missing Values in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPvOec0LZ40Bt8OQcbLFa236

10. Dummy Encoding in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPvu7YriqMZsL9UDbqUUk90x

11. Data Visualisation with Python, Seaborn, Matplotlib
https://www.youtube.com/playlist?list=PLE-8p-CwnFPuYBYsmbfMjROOCzKjCwyMH

12. Feature Scaling in Machine Learning
https://www.youtube.com/playlist?list=PLE-8p-CwnFPtwpVV3FwzwYZYR5hT3i52G

13. Jupyter Notebook Operations
https://www.youtube.com/playlist?list=PLE-8p-CwnFPtqkFd67OZcoSv4BAI7ez5_


Code Starts Here
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Topic to be Covered - MultipleLinear Regression

Step 1 - Import the necessary libraries and the dataset

Step 2 - Plot the Seaborn Pairplot

Step 3 - Plot the Seaborn Heatmap

Step 4 - Extract the Features and Labels

Step 5 - Cross Validation (train_test_split)

Step 6 - Create the Linear Model (LinearRegression)

Step 7 - Interpreting the Coefficient and the Intercept

Step 8 - Predict the output

Step 9 - Predict the Score (% Accuracy)

Step 10- Verification of the Predicted Value

Step 11- Calculate the MSE and RMSE

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Step 1 - Import the necessary libraries and the dataset
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv('taxi.csv')

Step 2 - Plot the Seaborn Pairplot
sns.pairplot(df)

Step 3 - Plot the Seaborn Heatmap
sns.heatmap(df.corr(),linewidth = 0.2, vmax=1.0, square=True, linecolor='red',annot=True)

Step 4 - Extract the Features and Labels

features = df.iloc[:,0:-1].values
labels = df.iloc[:,-1].values

Step 5 - Cross Validation (train_test_split)

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features,labels,test_size=0.3,random_state=0)

Step 6 - Create the Linear Model (LinearRegression)
from sklearn.linear_model import LinearRegression

regressor = LinearRegression()
regressor.fit(X_train,y_train)

Step 7 - Interpreting the Coefficient and the Intercept
y_pred = regressor.predict(X_test)