Prophet Forecasting And Rolling Mean

Predicting Stock Using Prophet.

Let’s start coding. 

%matplotlib inline

import numpy as np
import pandas as pd
import glob
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.notebook import tqdm
from itertools import combinations, product
from functools import partial
from multiprocessing import Pool, Manager, cpu_count
from IPython.display import display_html
sns.set_context("notebook")

import warnings
warnings.filterwarnings("ignore")

This code is used to import necessary libraries and set up the environment for data analysis and visualization. The code starts by importing the required libraries: numpy, pandas, glob, matplotlib.pyplot, seaborn, tqdm, itertools, functools, multiprocessing, and IPython.display. The `%matplotlib inline` line enables inline plotting in Jupyter Notebook, allowing for the automatic display of plots in the output cells. Next, the code sets the appropriate visualization style using `sns.set_context(“notebook”)`, which is provided by the seaborn library. This will ensure that the visualizations produced later in the code have a consistent and aesthetically pleasing appearance in a notebook context. Lastly, the code uses `warnings.filterwarnings(“ignore”)` to suppress any warning messages that may be generated during the execution of the code. This can be useful when dealing with large datasets or more complex operations. Overall, this code sets up the necessary environment and configurations for data analysis and visualization, ensuring that the necessary libraries are imported, relevant settings are adjusted, and warning messages are ignored.

If you want to download source then check the link in the comment.