ML_Projects

This repository contains various data science and AI projects, including machine learning models, deep learning examples, exploratory data analysis, and domain-specific case studies. Below is a summary of each project.

1. Flight Delay Prediction

Directory: ABI

A flight delay prediction project built around airline and airport data, with notebooks exploring delay factors and predictive modeling.

• Goal: Analyze flight delay patterns and build models to predict delay likelihood.
• Model: Exploratory data analysis, classification modeling, and performance comparison.
• Key Topics: Data ingestion, exploratory data analysis, feature engineering, model training, evaluation, and visualization.
• Dataset: Flight delay-related tables and a dataset data dictionary (DATA DICTIONARY for Flight Dataset.docx).
• Files: FLIGHT DELAY PREDICTIONS.ipynb, internship flight delay predictions.ipynb, machine learning of flight delay predictions.ipynb.

2. BCG

Directory: BCG

A business case project focused on customer and pricing data exploration, feature engineering, and modeling.

• Goal: Perform EDA and prepare data for modeling business outcomes.
• Model: Data cleaning and feature engineering workflows.
• Key Topics: Data exploration, data cleaning, feature engineering, visualization, and business insights.
• Datasets: Client and pricing datasets with cleaned output (client_data.csv, price_data.csv, clean_data_after_eda.csv).
• Files: EDA_Starter.ipynb, Feature_Engineering.ipynb, Model_Answer_EDA_Starter.ipynb, Data Description.pdf.

3. Customer Churn Prediction

Directory: Capstone_Project

A capstone project for predicting customer churn from a customer dataset.

• Goal: Build and evaluate churn prediction models using customer data.
• Model: Classification modeling and evaluation.
• Key Topics: Data preprocessing, feature selection, model training, validation, and case study reporting.
• Dataset: Customer churn dataset (Customer-Churn.csv).
• Files: CustomerChurnPrediction.ipynb, CUSTOMER_CHURN_PREDICTION_CASESTUDY.pdf.

4. Computer Vision

Directory: Computer_Vision

A computer vision project containing face, hand, and pose detection experiments using image and webcam data.

• Goal: Demonstrate visual detection techniques with Python and computer vision libraries.
• Model: Image processing and detection algorithms.
• Key Topics: Image analysis, object detection, pose estimation, and visualization.
• Dataset: Sample image assets and visualization outputs (Pose_Detection.ipynb, palm.jpg, face_eyes_smile_detection.png, HandDetection.png, Hand_Detection.png, PoseDetection.png, Pose_Detection.png).
• Files: Computer_Vision.ipynb, Hand_Detection.ipynb.

5. Customer Segmentation

Directory: Customer_Segmentation

A customer segmentation project that segments customer data into groups for analysis.

• Goal: Identify meaningful customer segments using clustering and profiling.
• Model: Segmentation and clustering analysis.
• Key Topics: Data exploration, clustering, segmentation, and customer profiling.
• Dataset: Customer dataset (Customers.csv).
• Files: Customer_Segmentation.ipynb.

6. Customer Segmentation (Alternate)

Directory: Customer_Segmentation_

An alternate customer segmentation project using Acme customer data and project documentation.

• Goal: Analyze customer behavior and segment the dataset to derive business insights.
• Model: Segmentation analysis with supporting documentation.
• Key Topics: Data exploration, segmentation, and business reporting.
• Dataset: Acme customer dataset (Acme.csv).
• Files: Customer_Segmentation.ipynb, Acme's Store.pdf.

7. Exploratory Data Analysis

Directory: EDA

A data exploration project featuring sales and automotive datasets.

• Goal: Perform exploratory data analysis and visualize trends in sales and automotive data.
• Model: Data analysis and visual storytelling.
• Key Topics: Data cleaning, visualization, insights discovery, and comparison.
• Datasets: Sales and automobile datasets (Dune_Sales_Data.csv, SoranAutosData.csv).
• Files: Dune_Sales_Analysis.ipynb, SoranAutomobiles.ipynb.

8. Gamma Telescope

Directory: Gamma_telescope

A classification project using gamma telescope data to analyze event detection and model performance.

• Goal: Explore gamma-ray event data and build predictive models.
• Model: Classification analysis on telescope data.
• Key Topics: Data exploration, model evaluation, and feature interpretation.
• Datasets: Gamma telescope measurements (magic04.data, magic04.names).
• Files: Magic.ipynb.

9. Heart Disease Prediction

Directory: Heart_Disease

A health data project for predicting heart disease using clinical features.

• Goal: Build models that predict heart disease risk from patient data.
• Model: Classification modeling and medical data analysis.
• Key Topics: Data preprocessing, model training, evaluation, and interpretation.
• Dataset: Heart disease dataset (Heart.csv).
• Files: Peterside_Hospital_Heart_Disease.ipynb.

10. Hires Analytics

Directory: Hires

An analysis project based on historical hiring data.

• Goal: Analyze hiring patterns and derive insights from past hires.
• Model: Data exploration and analytics.
• Key Topics: Hiring data analysis, trend identification, and summary reporting.
• Dataset: Past hires dataset (PastHires.csv).
• Files: HR.ipynb.

11. HR Employee Churn Prediction

Directory: HR_Employee_Churn_Prediction

A dedicated HR churn project that models employee turnover risk.

• Goal: Predict employee churn using HR dataset features.
• Model: Classification modeling for churn prediction.
• Key Topics: Feature engineering, model evaluation, and HR analytics.
• Dataset: Employee churn dataset (HR_Dataset.csv).
• Files: HR_Employee_Churn_Prediction.ipynb.

12. Internship Projects

Directory: Internship

A container for internship project work across flight delay prediction, job analysis, and Sterling e-commerce/customer segmentation.

• Goal: Present multiple internship-focused data science projects and findings.
• Model: Multiple analyses spanning prediction, classification, and segmentation.
• Key Topics: Practical internship work, domain-specific modeling, and business case studies.
• Dataset: Flight, jobs, and e-commerce datasets (Sterling_E-Commerce_Data.xlsx).
• Folders: Flight_Delay_Prediction/, Jobs/, Sterling/.

13. NumPy and pandas Tutorials

Directory: Numpy_and_Pandas

A collection of tutorials and exercises covering NumPy and pandas fundamentals.

• Goal: Learn data manipulation and analysis techniques with Python libraries.
• Model: Educational notebooks for array and dataframe operations.
• Key Topics: NumPy arrays, pandas dataframes, data cleaning, and analysis.
• Dataset: Health data sample (Health_Data.csv).
• Files: Numpy.ipynb, Pandas.ipynb, Copy_of_CONTROL.ipynb, Numpy and Pandas C23-08.ipynb.

14. Python Application

Directory: Python

A simple Python project containing an application script.

• Goal: Demonstrate a Python application structure.
• Model: Python script execution.
• Key Topics: Python scripting and application logic.
• Dataset: N/A.
• Files: app.py.

15. Titanic Survival Prediction

Directory: Titanic

A classic Titanic survival prediction project using passenger data.

• Goal: Predict passenger survival using feature engineering and classification.
• Model: Titanic survival analysis.
• Key Topics: Data preprocessing, model building, evaluation, and feature interpretation.
• Dataset: Titanic passenger dataset (titanic.csv).
• Files: Titanic.ipynb.

16. Waze Churn and Regression Analysis

Directory: Waze

A project focused on churn prediction and regression analysis using Waze dataset.

• Goal: Build models to predict churn and analyze regression relationships.
• Model: Classification and regression modeling.
• Key Topics: Data exploration, predictive modeling, model comparison, and case study reporting.
• Dataset: Waze churn dataset (waze_dataset.csv).
• Files: waze_churn_prediction.ipynb, waze_Regression.ipynb, dec_model.pkl, waze churn case study.pdf.