import pandas as pd import numpy as np import joblib from sklearn.model_selection import train_test_split from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.pipeline import make_pipeline, Pipeline from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import ColumnTransformer d1 = {‘Social_media_followers’:[1000000, np.nan, 2000000, 1310000, 1700000, np.nan, 4100000, 1600000, 2200000, 1000000], ‘Sold_out’:[1,0,0,1,0,0,0,1,0,1]} df1 = […]
Train Test Split
Train Test Split is an important concept that future Data Scientists or Machine Learning Engineers need to pick up early on. When building models, you’ll want to split your data into two different sets. One for training a model, and one for testing a model. This article is based on the popular YouTube video on […]
PACF Partial Autocorrelation Function
In this Data Science article, we are going to take a look at the Partial Autocorrelation Function (PACF). We will go over the background and then look at plotting both non stationary and stationary data. If you want to watch a video based around this tutorial, it is embedded below. https://youtu.be/XstPVx78yi8 PACF Background The PACF […]
ACF Autocorrelation Function
In this Data Science lesson we are going to take a look the Autocorrelation Function. Often abbreviated as ACF it can let us know if our data is stationary or not. We will go over some of the background behind it and plot it with the help of Python. If you want to network with […]