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.
The dataset was retrieved using the Kaggle API token, ensuring an automated and up-to-date data extraction process.
- Pandas: Used for initial data manipulation and cleaning.
- PyJanitor: Assisted in further cleaning tasks, such as removing null values, renaming columns, and filtering data.
Attempted to use the LangChain API to perform initial EDA, but it did not yield satisfactory results.
Leveraged Pandas Sketch to analyze the dataframe and receive recommendations based on specific queries, leading to more insightful EDA.
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.
The refined data was visualized using a Streamlit dashboard, providing an interactive and user-friendly interface to explore the analysis results.
The EDA revealed significant insights into crime patterns in Los Angeles, such as:
- Trends over time ⏳
- Geographic hotspots 🗺️
- Crime types and frequencies 📝
- 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.
- Clone the repository:
git clone https://github.com/DEVOLOPER-1/Los-Angeles-Crimes-Analysis.git
- Install the required packages (Easy copy and paste into ur cmd):
pip install -r requirements.txt
- Run the Streamlit dashboard:
streamlit run main.py
- A Quick Overview ON the DashBoard:
10.MB.mp4
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.
This project is licensed under the MIT License.
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