📚 UIU Data Mining Lab

This repository contains all the Data Mining lab exercises and examples for UIU Data Mining Course. It demonstrates fundamental data mining concepts, preprocessing techniques, visualization, and machine learning models with hands-on Python examples.

---

Prerequisites

Before running the code, ensure you have:

• Python 3.x installed

• Python libraries:

  • numpy → Numerical operations
  • pandas → Handling tabular data
  • matplotlib → Data visualization
  • scikit-learn → Machine learning models

Install missing libraries with:

pip install numpy pandas matplotlib scikit-learn

---

Repository Structure

UIU-DataMining-Lab/
│
├─ README.md
├─ Social_Network_Ads.csv
├─ social_network_ads.csv
├─ tennis.csv
├─ Data1.csv
├─ DM-Code.ipynb          # all code here in one file

---

Key Concepts in this Lab

• Data preprocessing: Handling data before applying ML

  • Selecting features
  • Scaling values for consistent range

• Slicing & indexing in pandas and numpy

• Train/Test split to avoid overfitting

• Random_state → Ensures reproducible splits

• Logistic Regression → Binary classification

• Model evaluation → Confusion matrix, classification metrics

---

Other Labs (Coming / Covered)

• Missing Values Handling → Fill with mean/median/mode
• Noise Removal → Detecting and cleaning noisy data
• Clustering & K-Means → Grouping data without labels
• Decision Trees & Regression → Predicting outcomes
• Ensemble Learning & Random Forest → Combining multiple models
• Cross-Validation → Stratified K-Fold to avoid bias

---

How to Run

1. Clone this repository:

git clone https://github.com/TashinParvez/UIU-DataMining-Lab.git
cd UIU-DataMining-Lab

2. Place datasets in the folder (if not already present).
3. Run the Python script:

python social_network_ads.py

4. Observe plots and console outputs for model evaluation.

---

Note: All original code and datasets were provided by the course faculty. The scripts in this repository include my modifications, experiments, and enhancements for learning purposes.

🌐 Contact

For any questions, suggestions, or feedback, please feel free to reach out to the repository maintainer.

Happy Learning! 🚀