i have task in data science i will send you part by part and when i ask give me suggestions 
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ChatGPT
Sounds great! Feel free to share the first part of your task, and I'll help you out. Let me know when you need suggestions or clarifications. 😊






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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load



# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
        paths.append(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

import py7zr
for filenames in paths:
    with py7zr.SevenZipFile(filenames, mode='r') as z_ref:
        z_ref.extractall(path='/kaggle/working')

Description
Brick-and-mortar grocery stores are always in a delicate dance with purchasing and sales forecasting. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leaving money on the table and customers fuming.

The problem becomes more complex as retailers add new locations with unique needs, new products, ever transitioning seasonal tastes, and unpredictable product marketing. Corporación Favorita, a large Ecuadorian-based grocery retailer, knows this all too well. They operate hundreds of supermarkets, with over 200,000 different products on their shelves.

Corporación Favorita has challenged the Kaggle community to build a model that more accurately forecasts product sales. They currently rely on subjective forecasting methods with very little data to back them up and very little automation to execute plans. They’re excited to see how machine learning could better ensure they please customers by having just enough of the right products at the right time.

Evaluation
Submissions are evaluated on the Normalized Weighted Root Mean Squared Logarithmic Error (NWRMSLE), calculated as follows:

$$ NWRMSLE = \sqrt{ \frac{\sum_{i=1}^n w_i \left( \ln(\hat{y}i + 1) - \ln(y_i +1)  \right)^2  }{\sum{i=1}^n w_i}} $$

where for row i, \hat{y}_i is the predicted unit_sales of an item and y_i is the actual unit_sales; n is the total number of rows in the test set.

The weights, w_i, can be found in the items.csv file (see the Data page). Perishable items are given a weight of 1.25 where all other items are given a weight of 1.00.

This metric is suitable when predicting values across a large range of orders of magnitudes. It avoids penalizing large differences in prediction when both the predicted and the true number are large: predicting 5 when the true value is 50 is penalized more than predicting 500 when the true value is 545.

Submission File
For eachid in the test set, you must predict theunit_sales. Because the metric uses ln(y+1), submissions are validated to ensure there are no negative predictions.

The file should contain a header and have the following format:

id,unit_sales
125497040,2.5
125497041,0.0
125497042,27.9
etc.

Splitting Data into Training and Test Sets
The data you are working with splited into training and test sets and stored in the following files:

/kaggle/working/train_data.csv – the training dataset used for model training.
/kaggle/working/test_data.csv – the test dataset used to evaluate the model's performance.

%%time
data = pd.read_csv("/kaggle/working/train.csv")
data["date"] =  pd.to_datetime(data["date"])

monthly_counts_data = data['date'].dt.to_period('M').value_counts().sort_index()

plt.figure(figsize=(12, 6))
monthly_counts_data.plot(kind='bar', color='skyblue')
plt.title('Count of Data for Each Month for All data', fontsize=14)
plt.xlabel('Month', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

%%time
THRESHOLD_TRAIN_DATE = pd.to_datetime("2017-07-01")
THRESHOLD_TEST_DATE = pd.to_datetime("2017-08-01")

data[data["date"] < THRESHOLD_TRAIN_DATE].to_csv("/kaggle/working/train_data.csv")
data[(data["date"] >= THRESHOLD_TRAIN_DATE)&(data["date"] < THRESHOLD_TEST_DATE)].to_csv("/kaggle/working/test_data.csv")

%xdel data

%%time
df = pd.read_csv("/kaggle/working/train_data.csv")
test_df = pd.read_csv("/kaggle/working/test_data.csv")

df["date"] = pd.to_datetime(df["date"])
test_df["date"] = pd.to_datetime(test_df["date"])

monthly_counts = df['date'].dt.to_period('M').value_counts().sort_index().reset_index()
monthly_counts_test = test_df['date'].dt.to_period('M').value_counts().sort_index().reset_index()

monthly_counts['type'] = 'Train'
monthly_counts_test['type'] = 'Test'
combined = pd.concat([monthly_counts, monthly_counts_test])

plt.figure(figsize=(12, 6))
sns.barplot(data=combined, x='date', y='count', hue='type', palette=['blue', 'red'])
plt.title('Count of Data for Each Month (Train vs Test)', fontsize=14)
plt.xlabel('Month', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xticks(rotation=45)
plt.legend(title='Dataset')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

it is not all wait
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ChatGPT
Got it! Feel free to continue sharing the next parts, and I'll assist with any issues or provide suggestions as needed. 😊






Вы сказали:
Additional Data
To make more effective use of the data, you also have access to several auxiliary datasets containing contextual information:

1. holidays_events.csv
Description: Contains information about holidays and events that may influence sales.
Useful Columns:
date: The date of the holiday or event.
type: The type of event (national, regional, local).
locale: The level of impact (national, regional, local).
description: A description of the event.
How to Use:
Add a flag indicating whether a date is a holiday to the training and test sets.
Categorize holidays by their type and consider their impact on sales.
Use the locale column to analyze regional effects.
add Codeadd Markdown

holiday_events = pd.read_csv("/kaggle/working/holidays_events.csv")
print(holiday_events.shape)
holiday_events.head()

items.csv
Description: Contains information about the items being sold.
Useful Columns:
item_nbr: Unique identifier for each item.
family: The category or family the item belongs to (e.g., "Beverages").
class: A finer categorization of items.
perishable: A binary indicator for whether the item is perishable (1 for perishable, 0 otherwise).
How to Use:
Use family and class to group items and analyze trends across categories.
Include the perishable indicator to weight predictions for perishable items.

items = pd.read_csv("/kaggle/working/items.csv")
print(items.shape)
items.head()

oil.csv
Description: Contains daily oil price data, which can serve as an external economic indicator.
Useful Columns:
date: The date corresponding to the oil price.
dcoilwtico: The daily oil price.
How to Use:
Join this dataset with the training and test sets on the date column to include oil price as a feature.
Analyze the correlation between oil price fluctuations and sales trends.

oil = pd.read_csv("/kaggle/working/oil.csv")
print(oil.shape)
oil.head()

stores.csv
Description: Contains information about the stores where items are sold.
Useful Columns:
store_nbr: Unique identifier for each store.
city: The city where the store is located.
state: The state where the store is located.
type: The store type.
cluster: A grouping of similar stores based on sales patterns.
How to Use:
Use city and state for regional analysis of sales trends.
Incorporate type and cluster to identify patterns and group stores with similar behavior.

stores = pd.read_csv("/kaggle/working/stores.csv")
print(stores.shape)
stores.head()

and its not all
ChatGPT сказал:
ChatGPT
You're building a robust pipeline by incorporating auxiliary datasets for richer feature engineering and analysis—great job so far! Feel free to continue sharing the remaining parts, and once you've provided the full context, I can help suggest enhancements, optimizations, or additional techniques. Keep going! 😊






Вы сказали:
now the task do it step by step in notebooks style Exploratory Data Analysis and Model Training
1. Baseline Model
Consider which model would be suitable as DummyModel for this task, then create, train, and use its predictions as a baseline.
2. Feature Engineering
Generate new features by incorporating data from additional datasets to enhance the model's predictive capabilities.
3. Model Training and Optimization
Utilize advanced regression models, such as LGBM Regression or CatBoost Regression, to achieve the best results. Focus on hyperparameter tuning and evaluation metrics to optimize performance. Use foremention Normalized Weighted Root Mean Squared Logarithmic Error.
Thats All :-)