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Posted: **Tue Jul 12, 2022 8:28 am**

: Jupiter The Algorithm Predicts The Prices That Could Be Gained From A Avg Area Income 85 Avg Area Number Of Bedrooms 1 (48.3 KiB) Viewed 30 times

: Jupiter The Algorithm Predicts The Prices That Could Be Gained From A Avg Area Income 85 Avg Area Number Of Bedrooms 2 (26.33 KiB) Viewed 30 times

: Jupiter The Algorithm Predicts The Prices That Could Be Gained From A Avg Area Income 85 Avg Area Number Of Bedrooms 3 (28.77 KiB) Viewed 30 times

Jupiter The algorithm predicts the prices that could be gained from a Avg. Area Income 85 Avg. Area Number of Bedrooms. (Take X= 'Avg. Area Income','Avg. Area Number of Bedrooms' and Y= 'Price') You can Download the dataset from https://www.kaggle.com/code/gopalchettr ... n/notebook NOTE: Implement Multivariate Linear Regression without using any built-in library. You can only use Pandas, Numpy and Matplotlib. Only implement the Multivariate Linear Regression by following the below steps. Only Correct work will be appreciated.
- Load the dataset - Analyze the dataset - Split the dataset into testing and training - Create function for Multivariate hθ(x)=θ0+θ1x1…θnxn - Create Cost Function that is also known as MSE (Mean Square Error) J(θ0,θ1…θn)=2m1∑i(hθ(xi)−yi)2R2=1−SSMean SSres - Create Function of Linear Regression > Initialize Weights > Call Multivariate Function
Update the weights → (Updating of theta's through Gradient Descent Algorithm θj=θj−α∂θj∂J(θ0,θ1…θn) (Derivation w.r.t. θ ) θ0=θ0−α∂θj∂J(θ0,θ1…θn) θn=θn−αm1i∑m(hθ(xi)−yi)∗xni - Call Linear regression function w.r.t to train data - Call the Multivariate function w.r.t to test data - Find the Mean Square error and R2 of test data - Plot the predicted Output