Linear Regression
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Linear Regression is one of the most fundamental algorithms in Machine Learning. It is a type of supervised learning used to predict a continuous numerical value based on one or more input features.
1. Concept
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Establishes a linear relationship between dependent variable (Y) and independent variable(s) (X).
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Represented by the equation: Y=mX+c
Where: Y = predicted output
X = input feature
m = slope of the line
= intercept
For multiple features, it becomes Multiple Linear Regression
Y=b0+b1X1+b2X2+...+bnXn
2. Types of Linear Regression
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Simple Linear Regression – One independent variable
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Multiple Linear Regression – Two or more independent variables
3. How Linear Regression Works
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Collect and preprocess data
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Identify input features (X) and target variable (Y)
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Fit a line that minimizes the difference between predicted and actual values (using Least Squares Method)
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Evaluate the model using metrics like Mean Squared Error (MSE), R² score
4. Applications
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Predicting house prices
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Stock market forecasting
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Sales and revenue prediction
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Weather prediction
5. Advantages
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Simple to implement and interpret
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Works well with linearly correlated data
6. Limitations
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Cannot model non-linear relationships
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Sensitive to outliers
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