Beginner Task: Environment Check + Saayam Practice Dataset Exploration (Local Only)

Description:

Welcome to Saayam’s ML/Tech projects! Before you start contributing to issues in this repository, please complete this beginner-friendly local exercise.

It has two parts:

1. Check your environment (make sure Python and key ML libraries work).
2. Explore a sample dataset (practice analyzing team workload data with pandas).

Important:

1. This is a local-only exercise.
2. Do NOT push your script, notebook, or output to this repository.
3. The dataset is synthetic (randomly generated) and does not contain real Saayam timesheet data.
4. Do NOT close the issue.
5. Once you finish, you are ready to pick any open issue in this repository that interests you.

Part 1: Environment Check Script:

Create a Python script check_env.py on your computer that:

• Prints Python version.
• Prints installed versions of: numpy, pandas, scikit-learn, matplotlib. If any are missing, install them using: pip install numpy pandas scikit-learn matplotlib
• Prints GPU availability (if torch is installed).

Expected Output Example:

Python version: 3.10.12 numpy version: 1.24.3 pandas version: 2.0.1 scikit-learn version: 1.3.0 matplotlib version: 3.7.1 GPU available: False

Note: GPU available can be either True or False.

Part 2: Explore Saayam Practice Dataset

We’ve provided a synthetic dataset (team_hours.csv) that mimics team workload tracking (no real data). team_hours.csv

Your Tasks:

In a Jupyter Notebook on your local machine:

1. Load the dataset using pandas.
2. Show the first 5 rows.
3. Count the number of contributors.
4. Find the total hours worked.
5. Group by Team: Calculate total hours per team. Identify the team with the most hours.
6. Sort contributors by hours worked and show the top 3 contributors.
7. Create a bar chart of total hours per team.

Expected Output Example:

Contributor count: 40 Total hours worked: 202 Team with most hours: AI/ML (35 hours) Top 3 contributors: Priya Kapoor (35), Rohan Singh (30), Maria Lopez (28) A bar chart showing hours worked by each team.

Acceptance Criteria:

• Both parts run successfully on your local machine.
• Outputs are visible in your terminal (for Part 1) and Jupyter Notebook (for Part 2).
• No uploads to GitHub — this task is just for your onboarding.

Next Steps:

Once you finish this beginner task, you are free to pick any open issue in this repository that interests you and start working on it!