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aorellanat committed Dec 13, 2024
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219 changes: 11 additions & 208 deletions docs/_includes/plotly/rq1_metrics.html
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2 changes: 2 additions & 0 deletions docs/_includes/plotly/tropes_boxplot.html
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2 changes: 1 addition & 1 deletion docs/index.md
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Expand Up @@ -112,7 +112,7 @@ This project is part of the Applied Data Analysis course at EPFL. We've analyzed
*Interactive visualization of trope combinations and their impact on ratings*

<div class="plotly-visualization">
{% include plotly/rq6_tropes.html %}
{% include plotly/tropes_boxplot.html %}
</div>

## Want to Know More?
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32 changes: 24 additions & 8 deletions src/utils/get_metrics.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -142,23 +142,39 @@
"# Compute ROI\n",
"df['ROI'] = df['profit'] / df['budget']\n",
"\n",
"# Optional: Handle extreme ROI values if necessary\n",
"# For example, limit ROI to a reasonable range\n",
"# df = df[df['ROI'].between(-1, 5)]\n",
"# ------------------------ #\n",
"# 5. Remove Outliers\n",
"# ------------------------ #\n",
"\n",
"# Define a function to remove outliers using IQR method\n",
"def remove_outliers_iqr(data, col):\n",
" Q1 = data[col].quantile(0.25) # First quartile\n",
" Q3 = data[col].quantile(0.75) # Third quartile\n",
" IQR = Q3 - Q1 # Interquartile range\n",
" lower_bound = Q1 - 1.5 * IQR\n",
" upper_bound = Q3 + 1.5 * IQR\n",
" return data[(data[col] >= lower_bound) & (data[col] <= upper_bound)]\n",
"\n",
"# Remove outliers for relevant columns\n",
"columns_to_check = ['vote_average', 'revenue', 'ROI']\n",
"for col in columns_to_check:\n",
" before = df.shape[0]\n",
" df = remove_outliers_iqr(df, col)\n",
" after = df.shape[0]\n",
" print(f\"\\nAfter removing outliers in '{col}': {before - after} rows removed, remaining {after} rows\")\n",
"\n",
"# ------------------------ #\n",
"# 5. Save Processed Data as JSON\n",
"# 6. Save Processed Data as JSON\n",
"# ------------------------ #\n",
"\n",
"# Convert DataFrame to dictionary\n",
"processed_data = df.to_dict(orient='records')\n",
"\n",
"# Save to 'metrics.json'\n",
"with open('metrics.json', 'w') as f:\n",
"with open('../../docs/assets/data/metrics.json', 'w') as f:\n",
" json.dump(processed_data, f, indent=2)\n",
"\n",
"print(\"\\nProcessing complete. 'metrics.json' has been created.\")\n",
"\n"
"print(\"\\nProcessing complete. 'metrics.json' has been created.\")"
]
}
],
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