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  <title>Data Exploration & Visualization Project</title>
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  <header>
    <h1>Data Exploration & Visualization</h1>
    <p>By Britta Anderson</p>
  </header>
  <main>
    <section>
      <h2>Project Overview</h2>
      <p>This project focused on exploring and visualizing a real-world dataset using Python. Inspired by a lab on the Titanic dataset, I selected a new dataset of interest, performed exploratory data analysis (EDA), and created visualizations to uncover insights.</p>
    </section>

    <section>
      <h2>Technologies Used</h2>
      <ul>
        <li>Python</li>
        <li>Pandas</li>
        <li>Matplotlib</li>
        <li>Google Colab</li>
      </ul>
    </section>

    <section>
      <h2>1. Dataset Loading and Inspection</h2>
      <p>The dataset was loaded using pandas and the initial structure was inspected with <code>head()</code>:</p>
      <pre><code>import pandas as pd

df = pd.read_csv("data.csv")
df.head()</code></pre>
    </section>

    <section>
      <h2>2. Data Cleaning</h2>
      <p>Missing values were identified and cleaned to ensure accurate analysis:</p>
      <pre><code>df.isnull().sum()
df.dropna(inplace=True)</code></pre>
    </section>

    <section>
      <h2>3. Exploratory Data Analysis</h2>
      <p>Basic statistics and distributions were explored:</p>
      <pre><code>df.describe()
df["Category"].value_counts()</code></pre>
    </section>

    <section>
      <h2>4. Data Visualizations</h2>
      <p>Histograms and bar charts were created using Matplotlib:</p>
      <pre><code>import matplotlib.pyplot as plt

plt.hist(df['Age'])
plt.title("Age Distribution")
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.show()</code></pre>

      <pre><code>df['Gender'].value_counts().plot(kind='bar')
plt.title("Gender Breakdown")
plt.show()</code></pre>
    </section>

    <section>
      <h2>5. Conclusions</h2>
      <p>This project improved my data analysis skills, deepened my understanding of pandas and matplotlib, and showed how data can be used to support meaningful insights. I learned how to ask the right questions, clean messy data, and create visual narratives from datasets.</p>
    </section>
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    <p>&copy; 2025 Britta Anderson. All rights reserved.</p>
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