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Data Science & Data Analysis Projects

This repository is a comprehensive collection of Data Science and Data Analysis projects focused on exploring real-world datasets. It covers a variety of workflows, including data cleaning, exploratory data analysis (EDA), predictive modeling, and interactive dashboard creation.

🚀 Repository Structure

The project is entirely built using Jupyter Notebooks (.ipynb) and organized by specific analytical objectives:

  • Analyzing_Socioeconomic_dataset_with_SQLPython.ipynb

  • Focus: Investigating socioeconomic metrics using SQL queries within Python.

  • Key Workflows: Data extraction, relational querying, and correlation analysis.

  • House_Sales_in_King_Count_USA.ipynb

  • Focus: A deep dive into the real estate market of King County, USA.

  • Key Workflows: Feature engineering, handling missing values, and building predictive models for house pricing.

  • chicago_dataset_analysis.ipynb

  • Focus: Urban public dataset analysis centered on the city of Chicago.

  • Key Workflows: Geospatial patterns, city metrics evaluation, and filtering large public records.

  • Revenue Data and Building a Dashboard.ipynb

  • Focus: Financial/revenue analysis.

  • Key Workflows: Aggregating financial data, tracking KPIs, and rendering visual data dashboards.

  • Data Science Ecosystem.ipynb

  • Focus: An introductory breakdown of the data science lifecycle.

  • Key Workflows: Best practices, standard tooling setups, and ecosystem fundamentals.

🛠️ Tech Stack & Tools

  • Programming Language: Python
  • Database Querying: SQL
  • Core Libraries:
  • pandas (Data manipulation & structural analysis)
  • numpy (High-performance scientific and numerical computing)
  • matplotlib & seaborn (Statistical data visualization)
  • scikit-learn (Machine learning & regression modeling)

💻 Getting Started

To explore and run these notebooks locally, follow these steps:

1. Clone the Repository

git clone https://github.com/AnkitSharma-29/DataScience-Projects.git
cd DataScience-Projects

2. Set Up Your Environment

It is highly recommended to use a virtual environment:

# Create environment
python -m venv env

# Activate environment (Windows)
.\env\Scripts\activate

# Activate environment (Mac/Linux)
source env/bin/activate

3. Install Required Packages

pip install pandas numpy matplotlib seaborn scikit-learn notebook

4. Launch the Notebook Ecosystem

jupyter notebook

This will open your local browser where you can step through each notebook line by line.

By Ankit