Problem:

I keep running into errors and cant debug. Hoping I can get some help to better understand my problem

You are hired as a data analyst to try to identify whether a mushroom is poisonous based on several of its
characteristics. The dataset you are provided has the following variables:
• class
• cap-diameter
• cap-shape
• cap-surface
• cap-color
• does-bruise-or-bleed
• gill-attachment
• gill-spacing
• gill-color
• stem-height
• stem-width
• stem-root
• stem-surface
• stem-color
• veil-type
• veil-color
• has-ring
• ring-type
• spore-print-color
• habitat
• season
The class variable has the classification on whether a mushroom is poisonous (p) or edible (e). You can
read more about these variables here:
https://archive.ics.uci.edu/dataset/848/secondary+mushroom+dataset
The company would like to create an automated system for predicting whether a mushroom is poisonous
(p) or edible (e). Your task is:
1.) Create the machine learning model to predict the poisonousness with the help of the some or all
of the variables in the dataset.
A.) Create a blank file in Google Colab and name it: Project_1_my_name.
B.) Choose a data mining (machine learning) method to create the prediction model. We have
learned several methods for this kind of tasks during the semester. Try more than one
method and choose the one that you think works best.
C.) Split the data into training and testing datasets. Use the training dataset to train your model
and use your model to make predictions on the testing dataset. Evaluate the precision of
your model using the metrics we learnt during the semester.
D.) Save your code (just like for the HW).
2.) Create a written report
A.) Use your results to create an approximately 7-800 word report (excluding tables) to “hand
in” to the CEO. It should be a written report that contains the following parts:

• Goal of your work (e.g.: automatic predication system)
• Short description of the methods
• Data collection (you just need to describe the training dataset to show the CEO what
  you worked with)
• Results (show how precise you model is by evaluating your using your chosen
  metrics for the testing dataset).

Tried using Multiple Linear Regression but getting this value and sytax errors