Q$\mathrm{.}4$: Build a linear regression model to predict the price of a house based on its features.

Here's a sample of the dataset:

$\backslash$table$[[$Area$,\backslash$table$[[$Bed$-],[$rooms$]],\backslash$table$[[$Bath$-],[$rooms$]],$Age,Location,Garage,$\backslash$table$[[$Yard$],[$Size$]],$Stories,Quality,Price$],[2000,3,2,20,$Downtown,$1,500,2,7,30000],[2500,4,3,15,$Suburbs,$1,1000,1,8,400000],[1800,3,2,10,$Downtown,$0,400,1,6,25000],[3000,5,4,5,$Suburbs,$1,1200,2,9,50000],[2200,4,3,8,$Downtown,$1,600,1,7,350000]]$

Perform exploratory data analysis $($EDA$)$ on the dataset to understand the distribution of features, identify outliers, and check for correlations between features and the target variable $($Price$).$

Preprocess the dataset by handling missing values, encoding categorical variables, and scaling numerical features if necessary.

Split the dataset into training and testing sets $($e$.$g$.,80\%$ training, $20\%$ testing$).$

Build a linear regression model to predict house prices using the features provided.

Evaluate the performance of the model using appropriate metrics such as mean squared error $($MSE$)$ or R$-$squared.

Interpret the coefficients of the model and discuss the significance of each feature in predicting house prices.

Use the trained model to predict the price of a new house with the following features:

Area: $2800$ sq$.$ ft$.$

Bedrooms: $4$

Bathrooms: $3$

Age: $12$ years

Location: Suburbs

Garage: Yes

YardSize: $800$ sq$.$ ft$.$

Stories: $2$

Quality: $8$