This project involves downloading and analyzing a dataset from Kaggle using NumPy and Pandas, creating insightful visualizations with Matplotlib and Seaborn, and developing a Flask web application to showcase key data insights and conclusions.
- Data Preprocessing: Cleaning and transforming raw data for meaningful analysis.
- Exploratory Data Analysis (EDA): Extracting insights and patterns using statistical techniques.
- Data Visualization: Creating impactful visualizations with Seaborn and Matplotlib.
- Web Dashboard: Interactive web application using Flask to present insights in a user-friendly manner.
- Python 🐍 – Core programming language for data analysis and web development.
- NumPy 📊 – Efficient numerical computations and array manipulations.
- Pandas 🗄️ – Data manipulation and preprocessing.
- Matplotlib 📈 – Customizable static visualizations.
- Seaborn 🎨 – High-level statistical visualizations.
- Flask 🌐 – Web framework for building interactive dashboards.
- HTML, CSS 🎨 – Frontend UI for the web application.
- Dataset Acquisition: Downloading data from Kaggle.
- Data Cleaning & Transformation: Handling missing values, formatting, and preparing for analysis.
- Exploratory Data Analysis (EDA): Understanding data distribution, trends, and correlations.
- Data Visualization: Graphical representation of key insights.
- Web Deployment: Showcasing insights via a Flask-powered web application.
🔹 Heatmaps, bar charts, histograms, and scatter plots to visualize trends and correlations.
- Clone this repository:
git clone https://github.com/your-username/your-repo-name.git
- Install dependencies:
pip install -r requirements.txt
- Run the Flask application:
python app.py
- Open the browser and navigate to
http://127.0.0.1:5000/
✅ Add interactive visualizations using Plotly or Dash. ✅ Implement machine learning models for predictive insights. ✅ Deploy on cloud platforms like AWS/GCP for broader accessibility.
🔹 Star this repo ⭐ if you find it helpful!
Let me know if you’d like any modifications! 🚀