Welcome to my collection of data science projects! In this repository, you'll find a curated selection of projects that demonstrate my expertise and skills in data analysis, machine learning, and data visualization. Each project is designed to solve real-world problems using cutting-edge techniques and methodologies.
- Automobile Fuel Efficiency Predictions: This project reflects the culmination of deploying machine learning models using Flask and the cloud service Heroku. It showcases the deployment of various models, including Linear Regression, Decision Tree, Random Forest, and Support Vector Machines, providing a comprehensive demonstration of model deployment techniques.
- Discovery of Handwashing: The project is designed to facilitate hands-on exploration and experimentation with data analysis methodologies, offering a practical learning experience for individuals interested in statistics, data science, or historical medical research.
- Maximizing Sales with Customer Segmentation using EDA, RFM, and Case Study Analysis: In this project, sales data analysis was conducted to understand the target market. The project encompassed Exploratory Data Analysis (EDA), Sales per country, a Case study on the UK using the Apriopri algorithm, and Customer Segmentation using RFM.
- Data-Science-Challenge: The "Data-Science-Challenge" project bridges Python-based analysis in the first part with SQL-based analysis in the second part, offering a comprehensive approach to understanding the dataset and deriving insights from multiple perspectives.
- Yaw Gear Failures: The project named "Yaw Gear Failures Analysis" utilizes data science techniques such as time series forecasting to study factors contributing to failures, aiming to prevent future instances. As well as making predictions for future failure rates based on previous time data.
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- SQL
- Heroku
- MlXtend