<jupyter_start><jupyter_text><!--Author: <NAME>License: BSD-3-Clause-->U2S1M2 WARM UP=====================Welcome to Unit 2 Predictive modeling, partner up in your TL groups. When you've assembled in your zoom calls be sure to pull the changes to the `Daily-Warm-Ups` repo to your fork. Work together to complete the warm up assignment.  Define the following terms: ---  Supervised Learning machine learning where you have both the input and output and are trying to figure out the how  Regression statistical measurement that attempts to determine strength of relationship between a dependent variable and a series of changing variables  Classification task of approximating a mapping function (f) from input variables (X) to discrete output variables (y)---  Heuristic using available data to solve machine and human problems.  Descriptive Statistics Summarizing and organizing your data so that it can be easily understood. ----  What should you always begin with when constructing a predictive model? A line of best fit using your descriptive statistics.  How should one interpret the coefficients in a linear regression model? it is the difference in the predicted value of the response variable for each one unit change in the predictor variable assuming all other predictor variables are held constant. ----  Read [this article](https://towardsdatascience.com/scikit-learn-design-principles-d1371958059b) which gives a brief overview of the design principles employed in sklearn.  Share in slack something your group found interesting in the article ----  CodeUse the rest of the time before lecture to explore and familiarize yourself with the following dataset using the techniques you learned during Unit 1<jupyter_code>import sys

# If you're on Colab:
if 'google.colab' in sys.modules:
    DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Applied-Modeling/master/data/'

# If you're working locally:
else:
    DATA_PATH = '../data/'
    
# Ignore this Numpy warning when using Plotly Express:
# FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.
import warnings
warnings.filterwarnings(action='ignore', category=FutureWarning, module='numpy')
import pandas as pd
df = pd.read_csv(DATA_PATH+'elections/bread_peace_voting.csv')
df
# Your code here
df.describe()
print(df.shape)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07107023411371237, 'fraction_numerical': 0.007107023411371237, 'mean_word_length': 4.865196078431373, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '663142', 'license': 'MIT', 'n_tokens_mistral': 617, 'n_tokens_neox': 579, 'n_words': 299, 'path': 'Copy_of_u2s1m2_regression2.ipynb', 'repo_name': 'AndrewArnett/Daily-Warm-Ups', 'size': '2403'}