Here's the formatted version of the project descriptions:

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**1. E-Commerce Sales Analysis**

- **Dataset**: Online Retail II Dataset (UK-based online retailer)
- **Project Description**: 
  Analyze sales data to uncover patterns in customer behavior, product performance, and sales trends.

- **Guiding Questions**:
  - **Total Sales Revenue**: What is the total sales revenue over the entire dataset period?
  - **Top-Selling Products**: Which products have the highest and lowest sales volumes?
  - **Monthly Sales Trends**: How do sales numbers trend each month? Are there seasonal peaks?
  - **Customer Segmentation**: Who are the top 10 customers based on total spending?
  - **Average Order Value**: What is the average order value per customer segment?
  - **Sales Channels Performance**: Which sales channels (e.g., online, mobile) generate the most revenue?
  - **Return Rates**: What is the return rate, and which products have the highest return rates?
  - **Geographical Insights**: How does sales performance vary across different geographical regions?

- **PySpark Focus**: 
  Utilize transformations like `filter`, `groupBy`, `agg`, and actions like `count`, `sum`, `collect` to process and analyze sales data.

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**2. Movie Ratings and Recommendations**

- **Dataset**: MovieLens 20M Dataset (20 million ratings for 27,000 movies by 138,000 users)
- **Project Description**: 
  Examine movie ratings data to identify trends, user preferences, and potential recommendations.

- **Guiding Questions**:
  - **Highest-Rated Movies**: What are the highest-rated movies overall and per genre?
  - **Active Users**: Which users have submitted the most ratings?
  - **Genre Popularity**: What is the average rating per genre, and which genres are most popular?
  - **Temporal Analysis**: How do movie ratings change over time? Are newer movies rated differently?
  - **User Preferences**: What are the top genres preferred by different user demographics?
  - **Rating Distribution**: What is the distribution of ratings across all movies?
  - **Correlation Analysis**: Is there a correlation between the number of ratings a movie receives and its average rating?
  - **Recommendation Insights**: Based on rating patterns, which movies could be recommended to specific user groups?

- **PySpark Focus**: 
  Employ `join`, `groupBy`, `window functions`, and actions like `collect`, `take` to analyze and derive insights from user-movie interactions.

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**3. Flight Delay and Cancellation Analysis**

- **Dataset**: Flight Delays and Cancellations (US commercial flights data)
- **Project Description**: 
  Investigate factors contributing to flight delays and cancellations to improve airline performance and customer satisfaction.

- **Guiding Questions**:
  - **Overall Delay Metrics**: What is the average delay time across all flights?
  - **Airline Performance**: Which airlines have the highest and lowest average delays?
  - **Temporal Patterns**: Are there specific months or seasons with more delays?
  - **Airport Analysis**: Which airports experience the most delays and cancellations?
  - **Delay Causes**: What are the main reasons for flight delays (weather, technical issues, etc.)?
  - **Time of Day Impact**: How do delays vary by departure time (morning vs. evening flights)?
  - **Cancellation Rates**: What is the cancellation rate per airline and per airport?
  - **Trend Over Years**: How have delay and cancellation patterns changed over the years?

- **PySpark Focus**: 
  Utilize `filter`, `groupBy`, `aggregate`, and actions like `mean`, `count` to dissect flight performance data.

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**4. COVID-19 Case and Vaccination Analysis**

- **Dataset**: COVID-19 Data Repository by the New York Times (US daily counts of cases and deaths)
- **Project Description**: 
  Analyze COVID-19 case trends and vaccination data to understand the pandemic's impact and the effectiveness of interventions.

- **Guiding Questions**:
  - **Case Trends**: What are the global trends in COVID-19 cases and deaths over time?
  - **Top Affected Regions**: Which countries or states have the highest number of cases and fatalities?
  - **Impact of Interventions**: How did the implementation of lockdowns and social distancing measures affect case numbers?
  - **Vaccination Rates**: What is the correlation between vaccination rates and case decline?
  - **Demographic Insights**: How do case and death rates vary across different age groups and demographics?
  - **Recovery Rates**: What are the trends in recovery rates over the course of the pandemic?
  - **Geographical Distribution**: How are cases and vaccinations distributed geographically within a country?
  - **Healthcare Capacity**: Is there a relationship between healthcare capacity (e.g., hospital beds) and COVID-19 outcomes?

- **PySpark Focus**: 
  Implement `window functions`, `groupBy`, `join datasets` (cases and vaccinations), and use actions like `describe`, `collect` for comprehensive analysis.

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This formatting breaks down each project clearly by dataset, description, guiding questions, and the PySpark tools you’ll use.
Q-Top-Selling Products: Which products have the highest and lowest sales volumes?

Oky the second questions is about finding a pattern in the data set , where we want to know the products with the highest and lowest sales volumes ? 

We we need learned from the previous example 
We need first need to deal with the concealed translation we do not include them in the calculation 
Then we need to group the products by their volum of sales (price * quantity) 
The sort them in decreasing order 
Get the first items : that represent the highest product sales by product 
get the last items : that represent the lowest product sales by product