• a. Lesson Notes
• b. Exercises

Acquiring and importing the data we will be using

• aquire.py file

Preparing and cleaning our imported data

• prepare.py file

• data should be tabular (made up of rows and columns)
• there should only be one value per cell
• each variable should be one column
• each observation shpould be one row Melt required when one variable is spread across multiple columns Pivot required when one column contains multiple variables

EDA | In this step we determine which features to feed into our model

• initial investigations
• discover patterns
• spot anomolies
• formulate and test hypothesis
• check assumptions
  • summary statistics
  • graphical representations
    

X_train: Feature variable columns, drop target variable column
y_train: Series with our target variable column

How we evaluate our classification model's performance

Visualize (Decision Tree) Feature Importance (Random Forest)

******ignore warnings
import warnings
warnings.filterwarnings("ignore")

import numpy as np
import pandas as pd
from scipy import stats
import os

******files/data
from pydataset import data
import env
import acquire
import prepare

******visualizations
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

******sklearn
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

np.random.seed(123)