Chapter 11, Problem 16DP

this module is machine learning 700, please answer all correctly.   Question One       Predicting House Prices using Real Estate Data (25 Marks) 25 Marks A real estate company, UrbanNest Realty, wants to improve its property pricing strategy. Traditional valuation methods often lead to mispricing, affecting revenue and customer satisfaction. The company aims to use machine learning to predict house prices more accurately. Dataset: California Housing Prices from sklearn.datasets (a) Load the dataset and perform basic exploratory data analysis (EDA) by displaying summary statistics, handling missing values, and visualizing key features. (6 Marks) (b) Train a Linear Regression and a Decision Tree Regressor to predict house prices. Evaluate the models using Mean Absolute Error (MAE) and R² Score. (12 Marks) (c) Interpret the model performances and discuss how feature selection or additional preprocessing could improve accuracy. (7 Marks) Question Two       Customer Segmentation using Mall…

Determine the global stiffness matrix of the beam shown in Fig. 3. Assume supports at 1 and 3 are rollers and the support at 2 is a pinned support.  Indicate the degrees of freedom in all the stiffness matrices. EI is constant, w=60kN/m, L1=1.25m and L2=3.75m   please explain how the code numbers for global matrix are determined in detail

12.12 In the circuit of Fig. P12.12(a), is(t) is given by the waveform shown in Fig. P12.12(b). Determine iL (t) for t≥ 0, given that R₁ = R₂ = 2 2 and L = 4 H. is() R₁ R2: (a) Circuit is(t) 8A- 8e-21 elle (b) is(t) Figure P12.12 Circuit and waveform for Problem 12.12. iL