Python-Project

🌍 Landslide Data Analysis & Visualization

This project performs a detailed exploratory data analysis (EDA) on a landslide dataset using Python, pandas, matplotlib, and seaborn. The goal is to uncover meaningful patterns, trends, and outliers related to landslide occurrences, helping researchers or decision-makers understand environmental and human factors.

📊 Key Features

• Correlation heatmap of all numerical features
• Monthly frequency of landslides
• Environmental condition distributions:
  • Precipitation
  • Temperature
  • Slope Angle
• Population density vs. Landslide category
• Types of landslides
• Top 10 locations with most landslides
• Landslides by day of the week
• Landslides by hour of the day
• Outlier detection using IQR for:
  • Fatalities
  • Injuries
  • Economic loss

✅ All visualizations display automatically in sequence with fallback (dummy) data generated for missing columns.

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🧠 Technologies Used

• Python 3.x
• pandas
• seaborn
• matplotlib
• numpy

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🗂 Dataset

The dataset used is expected to be a CSV file with a minimum column named:

• event_date (used to extract month, hour, day)

Optional (but auto-filled if missing):

• precipitation
• temperature
• slope_angle
• population_density
• landslide_category
• landslide_type
• location
• fatalities, injuries, economic_loss

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/landslide-eda.git
   cd landslide-eda