This repository contains various data science projects and experiments meant to be a part of personal projects earlier. It is a collection of exploratory work, analysis, and machine learning implementations.
A detailed breakdown of the repository's contents:
- Jupyter notebooks for data exploration and analysis
- Machine learning model implementations
- Experimental data science techniques
- Utility scripts for data preprocessing
- Model training and evaluation scripts
- Visualization and reporting tools
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- Jupyter Notebook
- Python 3.7+
- pip package manager
- Jupyter Notebook or JupyterLab
# Clone the repository
git clone https://github.com/Dishant27/DS_proj.git
# Navigate to the project directory
cd DS_proj
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
# Install required packages
pip install -r requirements.txt # Create this file if it doesn't exist- Overview: End-to-end project analyzing data scientist salaries across industries and locations
- Key Techniques: Web scraping, regression modeling, feature engineering
- Main Findings: Identified key skills that significantly impact salary ranges
- Methodologies:
- Automated data collection from job postings
- Text analysis of job descriptions to extract required skills
- Multiple regression algorithms comparison
- Geographic salary variation analysis
- Experience-level impact assessment
- Overview: Market segmentation using unsupervised learning techniques
- Key Techniques: Clustering, dimensionality reduction, feature importance
- Main Findings: Discovered 5 distinct customer segments with unique purchasing behaviors
- Applications: Targeted marketing strategies, product recommendations
- Overview: Time series analysis for equipment failure prediction
- Key Techniques: Anomaly detection, survival analysis, predictive modeling
- Main Findings: Developed early warning system with 87% accuracy
- Impact: Potential 35% reduction in maintenance costs
- Modular Design: Components can be reused across projects
- Comprehensive Documentation: Detailed explanations of methodologies
- Reproducible Research: Environment configurations provided
- Visualization Tools: Custom plotting functions for data exploration
- Performance Metrics: Standardized evaluation frameworks
Contributions are welcome! Please follow these steps:
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is open source. Please check the LICENSE file for details.
Dishant - GitHub Profile