For this assignment, you need to build a linear regression model from scratch and optimize the linear regression model using the gradient descent algorithm. Below is a detailed instruction on what you may need to do. Dataset Preparation You need to load the dataset using sklearn.datasets.load_diabetes After loading the dataset, randomly shuffle the dataset to split the dataset to train/dev/test sets.Use the 70% of data for the train set, 15% for the dev set, and 15% for the test setMake sure that the labels and featuresare still matching after shuffling the data.You may want to use the random shuffle function provided by NumPy.Linear Regression DevelopmentoImplement a single-variable linear regression model from scratch.oUsea gradient descent algorithm to solve the optimal parameters for the linear regression model.The gradient descentalgorithm must beimplementedfrom scratch.No high-level libraries can be used. Using online referenceand tutorials are allowed.oThe dataset contains 10 features; however,for a single-variablelinear regression model, you may only use one feature. Thus, to build the linear regression model, you need to decide which feature to you. There are three approaches you may use:You may train 10 single-variablelinear regression models, one model for each feature, and select the one with the best performance on the dev set.You may run a feature selection algorithm to select a feature. There are plenty of feature selection algorithms available. Feel free to search on the internet and use a method atyour choice.You may use PCA for dimension reduction to reduce the number of features to 1. There are many python libraries that provide PCA algorithms you may use. In fact, scikit-learnalso has a PCA function. Feel free to search on the internet and choose one atyour choice.Test the model using the test set

NEED CODE IN PYTHON USING GOOGLE COLAB OR JUPYTER OR PYCHARM.