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![]() ![]() Adam as the optimizer which is one of the widely used methods.pile(loss='mse', optimizer=optimizer, metrics=) The compiling phase is as below: # Adam optimizer The linear relationship between two variables of (, ) is estimated by designing an appropriate optimization problem which its requirement is a proper loss function. The criteria for optimization is called loss function which supervises the training. Model Compiling schema: In this step, the job is to define (1) how the model is going to behave in terms of optimizing and (2) what criteria it should use for optimization.Optimizer: We use stochastic gradient descent optimization.To do the model compiling, we should set the following items: To train our model, we have the general following schema: įor our dataset, we have as pairs of data where and are input and target values, respectively. In our implementation, we desire to obtain an estimate of this linear model as. We assume we have the linear model in which and are two unknown parameters that represent the intercept and slope of the line. # does not exist in the trainDataset frame anymore! # After using trainDataset.pop('RM'), the 'RM' column We use approach (2) as below: # Pop command return item and drop it from frame. After using trainDataset.pop(‘RM’), the ‘RM’ column does not exist in the trainDataset frame anymore! Pop command: It returns an item and drops it from the frame.We can use two approaches to access the data columns: pop() command, the associated columns are extracted.įrom the train-test data, we should extract the data and labels associated with the Linear Regression for one variable experiment. Let’s plot the MEDV against RM, i.e, visualize how MEDV is changed by changing RM. Basically we have and we desire to estimate the function using Linear Regression. In a simple word, we want to predict the Median value of owner-occupied homes (in $1000’s) based on the average number of rooms per dwelling (RM). In the Linear Regression with one variable, we only have one independent and one dependent variable: Here, we desire to model the relationship between the dependent variable and the independent variable. TestDataset = dataset.drop(trainDataset.index)Ībove, we took a portion of the data ( ) for training (line 4) and the remaining samples for testing (line 5). TrainDataset = dataset.sample(frac=p,random_state=0) We should now split data into train/test splits. By demonstrating the last 10 rows of the data (line 11), you should get the following output: Data Processing Using the Pandas library, we created the data frame by assigning columns’ names with attributes (line 1) and created the data object by reading the downloaded dataset (line 3). # This function returns last n rows from the object ![]() Raw_dataset = pd.read_csv(dataset_path, names=column_names, The first step is to show some of the data samples: column_names = ['CRIM','ZN','INDUS','CHAS','NOX', MEDV: Median value of owner-occupied homes in $1000’s.LSTAT: % lower status of the population.B: where is the proportion of blacks by town.TAX: full-value property-tax rate per $10,000.RAD: index of accessibility to radial highways.DIS: weighted distances to five Boston employment centers.AGE: the proportion of owner-occupied units built prior to 1940.RM: average number of rooms per dwelling.NOX: nitric oxides concentration (parts per 10 million).CHAS: Charles River dummy variable (= 1 if tract bounds river 0 otherwise).INDUS: the proportion of non-retail business acres per town.ZN: the proportion of residential land zoned for lots over 25,000 sq.ft.The last one (attribute 14): Median Value is the target variable.The first 13 features are numeric/categorical predictive features.The characteristics and attributes of the dataset are as below: Characteristics The goal of our Linear Regression model is to predict the median value of owner-occupied homes. We can download the data as below: # Download the daset with _fileĭataset_path = _file("housing.data", "") Becoming Familiar with Data We conduct our experiments using the Boston house prices dataset as a small suitable dataset which facilitates the experimental settings. ![]()
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