This repository contains a collection of Jupyter Notebooks that demonstrate my skills and experience in handling, analyzing, and visualizing data using Python.

• Python: Primary programming language.
• pandas & geoPandas: For data manipulation and analysis.
• matplotlib & seaborn: For creating visualizations.
• scikit-learn: For building machine learning models.

This project involves analyzing crime data to identify patterns and trends. Through the use of Python libraries such as pandas and matplotlib, this notebook provides a thorough exploration of crime statistics, offering visualizations to highlight areas of interest and concern.

Key Concepts:

• Data Cleaning
• Data Visualization
• Statistical Analysis

A predictive model using decision tree algorithms to forecast flooding events based on meteorological data. This notebook not only builds a robust model but also discusses the accuracy and efficiency of the prediction, aiding in disaster management and preparedness strategies.

Key Concepts:

• Predictive Modeling
• Decision Trees
• Model Evaluation

Focused on the manipulation and analysis of extensive climate datasets, this project demonstrates my ability to handle large volumes of data and use it to generate actionable insights. The use of geoPandas alongside traditional data science tools helps in understanding the spatial distribution of climatic changes over time.

Key Concepts:

• Geospatial Data Handling
• Data Aggregation
• Trend Analysis

To get a local copy up and running, follow these simple steps:

1. Clone the repo

   git clone https://github.com/yourusername/your-repository-name.git