Data Analysis and Modeling Repository

Overview

• This repository showcases data analysis and machine learning (ML) projects.
• Includes Exploratory Data Analysis (EDA) notebooks, ML Models, and Evaluation Metrics.
• Built using Python libraries: NumPy, Pandas, Scikit-learn, and Statsmodels.
• Demonstrates end-to-end processes from data exploration to model evaluation.

📊 Projects

1. EDA and Predictive Analysis: Coffee Shop Sales

• 📊 EDA Notebook: Data cleaning, exploration, and visualization.
• 🔮 Predictive Analysis Notebook: Time Series Analysis for sales forecasting.
• 📝 Executive Summary: Key findings and recommendations.
• 📂 Dataset: Coffee Shop Sales.xlsx.

2. Cost and Profit Analysis: Food Delivery Platform

• 📊 EDA Notebook: Cost, Revenue and Profitability analysis.
• 📝 Executive Summary: Key insights on cost, revenue and profitability and recommendations.
• 📂 Dataset: food_orders_new_delhi.csv.

3. EDA and Machine Learning: Global Road Accidents

• EDA notebook on GitHub: Comprehensive Exploration of the data on Github.
• EDA notebook on Kaggle: Comprehensive Exploration of the data on Kaggle.
• Machine Learning notebook on GitHub: Classification of Accident Severity by Logistic Regression, Random Forest Classifier and XGBoost.
• 📂 Dataset: road_accident_dataset

🚀 How to Use

• Clone the repository:

  https://github.com/mayur-de/Data_Analysis_and_Modeling.git

• Explore:
  • Notebooks: Jupyter notebooks for EDA and model building.
  • Executive Summary: Executive Summary of the Analyses.
  • Models: Stored scripts for ML models.
  • Metrics: Evaluation results and performance metrics.

💡 How to Contribute

• Fork the repo and submit a pull request for improvements or new models.

🎉 Acknowledgments

• Libraries: NumPy, Pandas, Scikit-learn, Statsmodels for data analysis and machine learning.
• Datasets and resources from the open-source community.