Q1 A. B.

Q2 A.

B.

Q3 A

B

C

D

Consider the following figure that shows the number of regions based on the average income Consider the following figure that shows the number of regions based on the average income mesos MONO 1600 1400 mesos income 1600 1200 1400 1000 1200 1000 600 600 400 400 200 Briefly describe the values of the Y-Axis Briefly describe the values of the X-Axis What is the value in X(3) before capping? What is the value in X[6] before capping? Consider the following code that obtains the information of the features for the housing dataset: In [6]: housing.info() Range Index: 20640 entries, 0 to 20639 Data columns (total 10 columns): longitude 20640 non-null float64 latitude 20640 non-null float64 housing_median_age 20640 non-null float64 total_rooms 20640 non-null float64 total_bedrooms 20433 non-null float64 population 20640 non-null float64 households 20640 non-null float64 median_income 20640 non-null float64 median_house_value 20640 non-null float64 ocean_proximity 20640 non-null object dtypes: float64(9), object(1) memory usage: 1.6+ MB Briefly Explain the method used to convert categories of ocean_proximity to numeric values. What is the difference between Def and Lambda functions? Consider the following code that obtains the information of the features for the housing dataset: In [6]: housing.info() Range Index: 20640 entries, 0 to 20639 Data columns (total 10 columns): longitude 20640 non-null float64 latitude 20640 non-null float64 housing_median_age 20640 non-null float64 total_rooms 20640 non-null float64 total_bedrooms 20433 non-null float64 population 20640 non-null float 64 households 20640 non-null float64 median_income 20640 non-null float64 median_house_value 20640 non-null float64 ocean_proximity 20640 non-null object dtypes: float64(9), object(1) memory usage: 1.6+ MB Explain the problem with total_bedrooms What is lambda? Consider the following code that develops the model to show the correlation between the body and brain in the animals' big dataset: In [41]: from sklearn.linear_model import LinearRegression from sklearn import model_selection X_train, x_test,Y_train, y_test = model_selection train_test_split(body_data, brain_data, test_size=0.3, random_state=0) regressor = LinearRegression() regressor.fit(X_train, Y_train) y_pred = regressor.predict(x_test) Explain the function and the output of the train_test_split. What is y_pred[5:)? Consider the following code for train and test variables, developed via jupyter notebook: In [42]: import pandas as pd df = pd. DataFrame(x_test) df['Y_test'] = Y_test df['Linear'] = y_pred print(df) = Explain your understanding about df and y_pred. How Y_train differs from y_pred? Consider the following code: housing "income_cat") = pd.cut(housing["median_incone" ], bins=[0., 1.5, 3.0, 4.5, 6., np. inf). labels=(1, 2, 3, 4, 5]) Briefly explain the bins and labels. What is the numerical range of the third bin (Use python range) Consider the following code developed via jupyter notebook: df = pd. DataFrame (X_test) Explain your understanding about DataFrame and pd