Python代写代考

Tutorial_07_new Predictive Analytics (QBUS2820) Tutorial 7: Variable Selection In this tutorial we will study how to implement the variable selection in Python. Data Standardising the predictors Variable selection in OLS Regularisation methods This notebook relies on the following imports and settings. In [5]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import

Tutorial_01_Part_B (1) QBUS2820 – Predictive Analytics Tutorial 1 – Part B¶ Download the DirectMarketing.xlsx file from Blackboard and place it in the same folder as your Python file or Jupyter Notebook. In [3]: import pandas as pd marketing = pd.read_excel(‘DirectMarketing.xlsx’) marketing.head() Out[3]: Age Gender OwnHome Married Location Salary Children History Catalogs AmountSpent Gender_b Married_b Location_b Ownhome_b

Tutorial_06_new QBUS2820 – Predictive Analytics Tutorial 6 – Model Selection and Estimation¶ Recall that, in Tut 04, we focused on demonstrating the bias-variance decomposition, and in Tut 05 we practiced model selection for kNN. In Tut 06, we 1) Continue model selection for kNN using alternative built-in CV methods 2) Model selection in linear regression

Tutorial_04_task_solution_updated Tutorial 4 Tasks Solution¶¶ Generating Synthetic Data¶ Lets create a synthetic dataset as below $f(x) = \beta_0 + \beta_1 x + \beta_2 x^2 $ In [13]: import numpy as np import matplotlib.pyplot as plt # Initialise RNG, so we get same result everytime np.random.seed(0) # Number of training points m = 50 x = np.linspace(0.0,

Tutorial_04-6 QBUS2820 – Predictive Analytics Tutorial 4- Model Selection¶ KNN model selection¶ Let’s start the tutorial with the solution of the tasks last week¶ In [1]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_context(‘notebook’) %matplotlib inline In [2]: data=pd.read_csv(‘credit.csv’, index_col=’Obs’) train = data.sample(frac=0.7, random_state=1) test = data[data.index.isin(train.index)==False] In [3]: