jupyter

jupyter notebooks scratch

Note to self:

Run locally

0. Make sure your Anaconda environment is started conda activate .
1. Navigate to the project directory cd ~/jupyter .
2. Load your dependencies pip install -r requirements.txt .
3. Start jupyter notebook jupyter notebook .

To install packages from inside jupyter simply run the following command:
! pip install --user <package-of-choice> To set the theme copy and paste the following

from jupyterthemes import get_themes
import jupyterthemes as jt
from jupyterthemes.stylefx import set_nb_theme
set_nb_theme('onedork')

Automate backward elimination

import statsmodels.formula.api as sm
def backwardElimination(x, sl):
    numVars = len(x[0])
    for i in range(0, numVars):
        regressor_OLS = sm.OLS(y, x).fit()
        maxVar = max(regressor_OLS.pvalues).astype(float)
        if maxVar > sl:
            for j in range(0, numVars - i):
                if (regressor_OLS.pvalues[j].astype(float) == maxVar):
                    x = np.delete(x, j, 1)
    regressor_OLS.summary()
    return x
 
SL = 0.05
X_opt = X[:, [0, 1, 2, 3, 4, 5]]
X_Modeled = backwardElimination(X_opt, SL)

How to encode categorical data the new way

from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.compose import ColumnTransformer

// [3] is the row that contains the categorical data
ct = ColumnTransformer([('encoder', OneHotEncoder(), [3])], remainder='passthrough')
X = np.array(ct.fit_transform(X), dtype=np.float)