📊 Analyzing Data with Pandas and Visualizing Results with Matplotlib

📝 Description

This project demonstrates how to load, explore, analyze, and visualize data using Python, specifically with the pandas and matplotlib libraries.
It covers basic data exploration, statistical analysis, and the creation of simple yet meaningful plots to extract insights from a dataset.

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🎯 Objective

• Load and analyze a dataset using the pandas library in Python.
• Create simple plots and charts with the matplotlib (and optionally seaborn) library for visualizing the data.

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📂 Submission Requirements

Your submission should include a Jupyter notebook (.ipynb) or a Python script (.py) containing:

1. Data loading and exploration steps.
2. Basic data analysis results.
3. Visualizations.
4. Findings or observations.

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🛠️ Tasks

Task 1: Load and Explore the Dataset

• Choose a dataset in CSV format (e.g., Iris dataset, sales dataset, or any dataset of your choice).
• Load the dataset using pandas.
• Display the first few rows using .head() to inspect the data.
• Explore the structure: check data types and missing values.
• Clean the dataset (fill or drop missing values).

Task 2: Basic Data Analysis

• Compute basic statistics of numerical columns (mean, median, standard deviation) using .describe().
• Perform groupings on a categorical column and compute aggregate values (e.g., mean).
• Identify patterns or interesting findings from your analysis.

Task 3: Data Visualization

Create at least four plots:

1. Line Chart – showing trends over time (e.g., sales data).
2. Bar Chart – comparing numerical values across categories (e.g., average petal length per species).
3. Histogram – distribution of a numerical column.
4. Scatter Plot – relationship between two numerical columns (e.g., sepal length vs. petal length).

Customize plots with titles, axis labels, and legends. Optionally, use seaborn for better visuals.

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📊 Dataset Suggestions

• Kaggle Datasets
• UCI Machine Learning Repository
• Iris dataset via sklearn.datasets.load_iris()

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⚡ Error Handling

• Handle errors such as file not found, missing data, or incorrect data types using try/except.

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✅ Submission Notes

• Ensure all code runs without errors.
• Include explanations for each analysis step.
• Make plots clear, labeled, and insightful.

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🖥️ Example Files in This Repo

• data_analysis_assignment.ipynb → Jupyter Notebook version
• data_analysis_assignment.py → Python script version
• README.md → Project overview (this file)

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