Data Analytics Portfolio

Repository Structure

/
├── DataAnalysis/
│   ├── notebooks/              # Jupyter notebooks
│   │   ├── archives/           # Archived notebooks
│   │   ├── model/              # Trained models or model notebooks
│   │   ├── datasets/           # Raw or processed data used in notebooks
│   │   ├── report_html/        # Data profiling or HTML reports
│   │   └── exploratoryEDA/     # Exploratory data analysis notebooks
│   ├── scripts/                # Python analysis scripts
│   ├── assets/                 # Images, charts, or supporting files
│   ├── .gitignore
│   ├── cleanup.bat
│   ├── requirements.txt
│   └── README.md

Key Components

1. Data Analysis

Feature	Description
Machine Learning	Scikit-learn pipelines & model evaluation
Machine Learning Lifecycle	Model training, evaluation, and deployment
Visualization	Plotly/Matplotlib/Seaborn dashboards
EDA	Automated Pandas Profiling reports
SQL Integration	Querying structured data

import pandas as pd
from pandasql import sqldf

df = pd.read_csv("data.csv")
sqldf("SELECT * FROM df WHERE age > 30")

Installation

# Clone with large file support
git clone https://github.com/yourusername/DataPortfolio.git --config core.longpaths=true

# Install analysis dependencies
pip install -r requirements.txt \
  scikit-learn \
  plotly \
  pandasql \
  jupyterlab

Notebook Setup

# Start Jupyter Lab
jupyter lab --ip=0.0.0.0 --port=8888

Typical notebook structure:

# % Title
## 1. Business Objective
## 2. Data Loading
## 3. Exploratory Analysis
## 4. Feature Engineering
## 5. Model Development
## 6. Insights & Recommendations

Workflow Example

# 1. Explore data
jupyter lab DataAnalysis/notebooks/exploratory/data_profiling.ipynb

Maintenance

# Run cleanup script (Windows)
cleanup.bat
cleanup.sh