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Crimes in Los Angeles - Exploratory Data Analysis (EDA) 🕵️‍♂️

Overview 📊

Welcome to the repository for the exploratory data analysis (EDA) of the Crimes in Los Angeles dataset, available on Kaggle. This project involves data scraping, cleaning, and visualization to uncover insights about crime patterns in Los Angeles.

Project Steps 🚀

1. Data Scraping 📥

The dataset was retrieved using the Kaggle API token, ensuring an automated and up-to-date data extraction process.

2. Data Cleaning 🧼

  • Pandas: Used for initial data manipulation and cleaning.
  • PyJanitor: Assisted in further cleaning tasks, such as removing null values, renaming columns, and filtering data.

3. Initial EDA with LangChain API 🧠

Attempted to use the LangChain API to perform initial EDA, but it did not yield satisfactory results.

4. Advanced Analysis with Pandas Sketch 🔍

Leveraged Pandas Sketch to analyze the dataframe and receive recommendations based on specific queries, leading to more insightful EDA.

5. Data Refinement ✨

The dataset was large and required additional cleaning. New dataframes were generated with specific re-cleansed columns, applying custom conditionals to ensure data accuracy and relevance.

6. Visualization with Streamlit 📈

The refined data was visualized using a Streamlit dashboard, providing an interactive and user-friendly interface to explore the analysis results.

Results 🏆

The EDA revealed significant insights into crime patterns in Los Angeles, such as:

  • Trends over time ⏳
  • Geographic hotspots 🗺️
  • Crime types and frequencies 📝

Technologies Used 💻

  • Kaggle API: For data scraping.
  • Pandas: Data manipulation and cleaning.
  • PyJanitor: Advanced data cleaning.
  • LangChain API: Initial EDA attempts.
  • Pandas Sketch: Dataframe analysis and recommendations.
  • Streamlit: Dashboard creation and data visualization.

How to Run 🏃‍♀️

  1. Clone the repository:
    git clone https://github.com/DEVOLOPER-1/Los-Angeles-Crimes-Analysis.git
  2. Install the required packages (Easy copy and paste into ur cmd):
    pip install -r requirements.txt
  3. Run the Streamlit dashboard:
    streamlit run main.py
  4. A Quick Overview ON the DashBoard:
10.MB.mp4

Conclusion 🎯

This project showcases a comprehensive approach to EDA, from data scraping to visualization. The use of various tools and libraries ensures thorough data cleaning and insightful analysis, making it a valuable resource for understanding crime dynamics in Los Angeles.

License 📜

This project is licensed under the MIT License.


Stay in Touch 📬

Thank you for using NeuroImg2PNG! If you have any questions or need any more help, please feel free to reach out.

https://shorturl.at/nQqEd