https://github.com/devoloper-1/los-angeles-crimes-analysis
EDA for crimes trends in LOS ANGELES from 2019 to 2024
https://github.com/devoloper-1/los-angeles-crimes-analysis
jupyter-notebook kaggle-dataset streamlit-dashboard
Last synced: 9 days ago
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EDA for crimes trends in LOS ANGELES from 2019 to 2024
- Host: GitHub
- URL: https://github.com/devoloper-1/los-angeles-crimes-analysis
- Owner: DEVOLOPER-1
- License: mit
- Created: 2024-07-01T15:42:21.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-22T11:48:55.000Z (almost 2 years ago)
- Last Synced: 2025-02-23T17:17:18.944Z (over 1 year ago)
- Topics: jupyter-notebook, kaggle-dataset, streamlit-dashboard
- Language: Jupyter Notebook
- Homepage:
- Size: 18.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://www.kaggle.com/datasets/sudhanvahg/crimes-in-las-angeles/code). 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**:
```bash
git clone https://github.com/DEVOLOPER-1/Los-Angeles-Crimes-Analysis.git
```
2. **Install the required packages (Easy copy and paste into ur cmd)**:
```bash
pip install -r requirements.txt
```
3. **Run the Streamlit dashboard**:
```bash
streamlit run main.py
```
4. **A Quick Overview ON the DashBoard**:
https://github.com/DEVOLOPER-1/Los-Angeles-Crimes-Analysis/assets/153318445/8e87321e-9829-4108-87cc-e27deed4bc02
## 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.
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## 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