<jupyter_start><jupyter_text>Demand ForecastThe **goal** of this exercise is to create a demand forecast model for different products (or clusters of products).The **evaluation** of the assignment will follow:* Design process and thinking as a data analyst.* Validation of knowledge on the different tools and steps throughout the process.* Storytelling and visualisation of the insights.Exercise **workflow**:* Import dependencies & data (`../data/Historical\ Product\ Demand.csv`).* Explore the dataset to assess its quality (nulls, skew, data types).* Explore the dataset to obtain insights related to the production (e.g., unvariate / bivariate analysis).* Explore the dataset as a time-series (min/max dates, seasonality, trends etc.).* Implement / apply a forecast model:    * Theory    * Parameters tuning    * Build/fit & Validation    * Evaluate performance (e.g., forecast accuracy)    Notes:Feel free to choose how to present the results throughout the exercise, what libraries (e.g., seaborn, bokeh, etc.) and tools (e.g., PowerBI or Tableau).      Preamble<jupyter_code>import pandas as pd
import numpy as np
import seaborn as sb

import matplotlib.pyplot as plt
%matplotlib inline

# not mandatory, but might come handy
from scipy import stats
import statsmodels.api as sm<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0797872340425532, 'fraction_numerical': 0.0, 'mean_word_length': 4.726086956521739, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '552810', 'license': 'Apache-2.0', 'n_tokens_mistral': 351, 'n_tokens_neox': 341, 'n_words': 166, 'path': 'data-analysis/demand-forecast.ipynb', 'repo_name': 'DrN0/recruitment', 'size': '1316'}