sample-quarto.qmd

gistfile1.txt

---
title: "Diamonds Dataset Analysis"
format:
  revealjs:
    embed-resources: true
---

```{python}
#| echo: false

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
url = "https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv"
df = pd.read_csv(url)
```


## Outline
- Introduction
- Exploratory Data Analysis
  - Price Distribution
  - Carat vs Price
  - Cut Distribution
- Detailed Analysis
  - Color vs Price
  - Clarity vs Price
- Conclusion

## Price Distribution
```{python}
plt.figure(figsize=(10, 6))
sns.histplot(df['price'], bins=30, kde=True)
plt.title('Price Distribution')
plt.xlabel('Price')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()
```


## Carat vs Price
```{python}
plt.figure(figsize=(10, 6))
sns.scatterplot(x='carat', y='price', data=df, alpha=0.5)
plt.title('Carat vs Price')
plt.xlabel('Carat')
plt.ylabel('Price')
plt.grid(True)
plt.show()
```

## Cut Distribution
```{python}
plt.figure(figsize=(10, 6))
sns.countplot(x='cut', data=df, order=df['cut'].value_counts().index)
plt.title('Cut Distribution')
plt.xlabel('Cut')
plt.ylabel('Count')
plt.grid(True)
plt.show()
```

## Color vs Price {.smaller}
:::{columns}
::: {.column width="20%"}
```{python}
#| echo: false

plt.figure(figsize=(2,2))
sns.boxplot(x='color', y='price', data=df, order=df['color'].value_counts().index)
plt.title('Color vs Price')
plt.xlabel('Color')
plt.ylabel('Price')
plt.grid(True)
plt.show()
```
:::
::: {.column width="80%"}

### Discussion
- **Color Impact**: The plot shows how the color grade affects the price of diamonds.
- **Trend**: Generally, there is a variation in price across different color grades, but the trend is not strictly linear.
- **Outliers**: Noticeable outliers for certain colors, indicating high-value diamonds in those categories.

:::
:::

## Clarity vs Price
```{python}
plt.figure(figsize=(10, 6))
sns.boxplot(x='clarity', y='price', data=df, order=df['clarity'].value_counts().index)
plt.title('Clarity vs Price')
plt.xlabel('Clarity')
plt.ylabel('Price')
plt.grid(True)
plt.show()
```

# Conclusion
- The dataset reveals significant insights into the diamond market.
- Price distribution is right-skewed, indicating a majority of diamonds fall within the lower price range.
- Carat weight has a positive correlation with price.
- Different diamond cuts have varying distributions and can influence the price.
- Both color and clarity have noticeable impacts on the price, though with distinct patterns.