Exploratory data analysis & regression model in python by 20/08

Part1 : Provided a dataset of volume sales of products from 2019 to 2022 run an extensive exploratory data analysis including the following: .
1. Data Quality & Structure Checks
Missing values, duplicates, negative sales, outliers
Date consistency (no gaps, proper frequency, handling holidays/weekends)
2. Descriptive Statistics
Overall distribution of daily sales (mean, median, std, skewness, kurtosis)
By dimension: product, customer
Identify top products per customer by volume
3. Time Series Exploration
Trend: long-term upward or downward movement
Seasonality: daily/weekly patterns (weekdays vs weekends), monthly, quarterly, yearly cycles
Rolling averages (7-day, 30-day) to smooth patterns
4. Visualization Layer
Time series plots: raw daily sales, moving averages
Boxplots: distribution of sales by weekday or month
Histograms/density plots: sales distribution
5. Anomaly & Outlier Detection
Unusual spikes/drops
Use Z-scores or interquartile ranges to flag anomalies
6. Correlation & Drivers of Sales
Correlation if needed
7. Performance Metrics (Baseline)
Set benchmarks to prepare for forecasting models:
Average daily sales per SKU/store
Volatility (Coefficient of Variation)
Baseline forecast error (e.g., naïve forecast MAPE)

EDA Deliverables :
By the end of an extensive EDA, I should have:
Clear understanding of demand patterns, seasonality, and anomalies
Insights into drivers of sales (internal like price/promo, external like weather/events)
Segmentation of products into high/medium/low performers
A baseline performance snapshot to compare forecasting models against.


Part 2 :
After cleaning the data based on the above analysis, run a linear regression-based model to prepare a sales volume forecast at product & customer level for 2022 in python or/and pyspark. Measure the accuracy by introducing quality measures and explain why have you introduced these measures.