jkuruzovich

Prompt Driven Process for Python-based Data Science using CRISP-DM Framework

RPI - 2024

Framework: https://app.myeducator.com/reader/web/1421a/2/qk5s5/

Resources: https://colab.research.google.com

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def generate_lag(dframe, by, target, periods):
    #print("Generating laged values for", target)
    for num in range(1, periods+1):
        dframe[target+'_lag'+str(num)]=dframe.groupby(by)[target].shift(num)
        dframe[target+'_lag'+str(num)].fillna(0, inplace=True)
    return dframe
    

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# This will loop through a set of data files, creating samples of each file. 


import os, sys, importlib, glob
import numpy as np
from pathlib import Path
import pandas as pd 
sys.path.append(os.path.join(Path.cwd().parent, 'modules'))
import mongoparser as mp
importlib.reload(mp) 

jkuruzovich

Final Exam - Intoduction to Machine Learning Applications.

You are not allowed to collaborate with anyone on part 1 or 2. Other than that it is "open internet".

Part 1.

Part 1 consists of 20 multiple choice questions taken on the LMS. Once you begin you will have 1 hour to finish the exam.

Part 2

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jkuruzovich

Final Exam - Intoduction to Machine Learning Applications.

Part 2.

You are not allowed to collaborate with anyone on part 2.

This is the starter notebook.

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It is important for you to spend time understanding the mistakes from the homework.

For the midterm, please add the following code to a cell to run and debug any errors:

import os
files = "https://www.dropbox.com/s/fa7sp3r1enbk8xy/midterm.zip"
!pip install git+https://github.com/data-8/Gofer-Grader && wget $files && unzip -o midterm.zip
from client.api.notebook import Notebook

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jkuruzovich

Topic	Link
Revisiting Titanic with Ludwig
Revisiting Titanic with FastAI
FastAI - Pets
AutoML with TPOT	[](https://cola