ML Case Studies Repository

Machine Learning & Data Science Case Studies showcasing end-to-end workflows — from data preprocessing to model building, evaluation, and visualization. This repository contains multiple Jupyter Notebooks that demonstrate practical implementations of various machine learning algorithms and data science techniques.

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Project Structure

ML_Case_Studies/

├── Airbnb_listing.ipynb            # Airbnb price prediction
├── House_price_prediction.ipynb    # Predict house prices using regression models
├── Loan_tap.ipynb                  # Loan approval prediction
├── Porter_Delivery_Time_Estimation.ipynb # Delivery time prediction
├── Segmenting_Patients.ipynb       # Patient segmentation using clustering
├── Superstore_case_study.ipynb     # Retail store analytics & sales forecasting
├── numpy_case_study.ipynb          # Numpy-based data analysis
└── README.md

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Case Studies Covered

Project	Objective	Techniques Used
Airbnb Listing	Predict Airbnb listing prices	Linear Regression, EDA
House Price Prediction	Estimate house prices	Multiple Regression, Feature Engineering
Loan Predict	loan approvals	Logistic Regression, Classification
Porter Delivery Time Estimation	Estimate delivery times	Regression, Model Evaluation
Segmenting Patients	Cluster patients based on health data	K-Means, Clustering, PCA
Superstore Case Study	Analyze sales & predict future revenue	Regression, Forecasting
Numpy Case Study	Perform data analysis using Numpy	Data Manipulation, Statistics

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Technologies Used

• Languages: Python
• Libraries:
  • Pandas, NumPy, Matplotlib, Seaborn
  • Scikit-learn, XGBoost, LightGBM
  • Statsmodels, Plotly
• Tools: Jupyter Notebook, Google Colab
• Version Control: Git & GitHub

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Key Features

• End-to-End Machine Learning Pipelines
• Hands-on Exploratory Data Analysis (EDA)
• Feature Engineering & Model Building
• Model Evaluation & Performance Metrics
• Real-world, industry-relevant datasets

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How to Use

• Clone the repository

git clone https://github.com/UzmaKhatun/ML_Case_Studies.git
cd ML_Case_Studies

• Install dependencies

pip install -r requirements.txt

• Open Jupyter Notebook

jupyter notebook

• Explore the case studies
  • Open any .ipynb file and start experimenting

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Contact

• Author: Uzma Khatun
• 📧 Email: email
• 🔗 LinkedIn: LinkedIn
• 🌐 GitHub: UzmaKhatun