A collection of real-world Python projects focused on data cleaning, exploratory data analysis, SQL–Python integration, and machine learning. These projects demonstrate practical analytical workflows, business-driven insights, and industry-relevant data analysis skills.
This repository showcases end-to-end data analytics projects built using Python.
Each project addresses a realistic problem statement and follows a structured workflow commonly used by data analysts and data scientists in professional environments.
The projects emphasize:
- Clean data handling
- Analytical thinking
- Tool integration
- Clear insights and outcomes
- Python
- pandas
- NumPy
- matplotlib
- seaborn
- SQL Server
- pyodbc
- Jupyter Notebook
python-data-analytics-projects/ │ ├── Project_1_Zomato_Data_Cleaning/ │ ├── zomato_data_cleaning.ipynb │ └── README.md │ ├── Project_2_COVID19_Trend_Analysis/ │ ├── covid19_trend_analysis.ipynb │ └── README.md │ ├── Project_3_SQL_Python_Analytics_Pipeline/ │ ├── sql_python_analytics_pipeline.ipynb │ └── README.md │ ├── Project_4_Customer_Churn_ML/ │ ├── customer_churn_analysis.ipynb │ └── README.md │ └── README.md
- Cleaned and prepared raw restaurant data
- Handled missing values, duplicates, and inconsistent formats
- Performed initial exploratory analysis to validate data quality
Skills: Data cleaning, preprocessing, pandas
- Analyzed COVID-19 trends across time
- Explored confirmed cases, recoveries, and fatalities
- Visualized patterns to understand spread and impact
Skills: Exploratory Data Analysis (EDA), visualization, trend analysis
- Integrated SQL Server with Python using pyodbc
- Executed SQL queries directly from Python
- Performed operational analysis and KPI evaluation
- Visualized and exported analytical results
Skills: SQL–Python integration, business analytics, pandas, visualization
- Built predictive models to identify customer churn
- Implemented Logistic Regression, Decision Tree, and Random Forest models
- Evaluated and compared model performance
Skills: Machine learning fundamentals, model evaluation, predictive analytics
- Raw datasets, database files, and backups are intentionally not included
- Projects focus on analytics logic and workflows rather than data distribution
- Dataset sources and assumptions are documented within individual notebooks
This repository demonstrates the ability to:
- Work across multiple stages of the data lifecycle
- Combine SQL and Python effectively
- Translate raw data into meaningful insights
- Build clean, interpretable, and reproducible analytics projects
For feedback, collaboration, or opportunities, feel free to connect via GitHub.
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