DATA-SCIENCE-capstone-projects

In this data science capstone project, we will analyze a dataset of 911 emergency calls to gain insights into the nature and distribution of emergencies in a given region. The dataset, provided in Excel format, contains information such as timestamps, call types, locations, and other relevant details. Our goal is to identify patterns and trends that can help improve emergency response times and resource allocation.

We will utilize Python libraries such as pandas, numpy, matplotlib, and seaborn to perform data cleaning, manipulation, and visualization. The project will involve the following steps:

Data Import and Exploration: Load the 911 calls dataset into a pandas DataFrame and perform an initial exploration to understand the structure and content of the data.

Data Cleaning: Clean and preprocess the data by handling missing values, converting data types, and creating new features (e.g., extracting hour, day, and month from timestamps).

Data Analysis: Perform various statistical analyses to identify trends and patterns in the data, such as the most common types of emergencies, peak hours for calls, and the distribution of calls across different days of the week and months of the year.

Data Visualization: Create a series of plots to visualize the findings from the data analysis, including:

Bar plots to display the frequency of different call types.

Count plots to show the distribution of calls across hours, days, and months.

Heatmaps to visualize the relationship between call volume and time (hour, day, and month).

Conclusion: Summarize the key insights gained from the project and discuss potential applications for improving emergency response strategies.

By the end of this capstone project, you will have demonstrated your proficiency in using Python libraries for data science tasks and showcased your ability to analyze real-world datasets to derive meaningful insights.