Hey there, stock lovers! 📊 Ever puzzled how one can predict stock prices using some fancy math? 🤓 Successfully, proper now, we’re diving into the world of kernel regression with a contact of interactivity! 🎉 Let’s embark on this journey to predict Google stock prices using Python, statsmodels, and some cool widgets… 🌟
🧩 Setting Up Our Devices…
First points first, we’ve got to gather our devices for this magical journey. 🛠️ We’ll be using a mix of extremely efficient libraries like numpy, pandas, matplotlib, yfinance, statsmodels, and ipywidgets. That is the lineup:
# Arrange important libraries
!pip arrange yfinance statsmodels matplotlib ipywidgets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
import statsmodels.api as sm
import ipywidgets as widgets
from ipywidgets import interactive
With these libraries, we’re capable of fetch stock info, perform kernel regression, and make our visualization interactive! 🌈
💾 Fetching Google Stock Data…
To start, we’d like some historic stock prices for Google (ticker picture: GOOGL). Our magic carry out fetch_data will simply try this. 🪄
def…
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