Scikit-Learn Practice Projects

This repository contains machine learning practice notebooks, datasets, saved models, and a small Flask-based house price prediction project.

Folder Structure

1. Bagging - Bagging classifier examples with the diabetes dataset.
2. Decision Tree - Decision tree examples using salary and Titanic datasets.
3. One Hot Encoding - One-hot encoding and dummy variable examples.
4. Hyperparameter Tuning - Model tuning examples.
5. K-Means Clustering - K-Means clustering examples for income and Iris datasets.
6. K-Fold Cross Validation - K-Fold and cross-validation examples.
7. K-Nearest Neighbor Classification - KNN classification notebook.
8. Linear Regression - Linear regression notebooks, sample CSV files, and saved models.
9. Logistic Regression - Binary and multiclass logistic regression notebooks.
10. Multivariate Regression - Multivariate regression examples.
11. Naive Bayes - Naive Bayes examples for Titanic, wine, and spam classification.
12. Principal Component Analysis - PCA example notebook.
13. Projects - Bangalore house price prediction project with notebook, model, client, and Flask server.
14. Random Forest - Random forest notebook.
15. Regularization - Regularization notebook and housing dataset.
16. Support Vector Machine - SVM notebook.
17. Train And Test Split - Train/test split notebook and data.
18. Miscellaneous Files - Additional compiled class files.

Requirements

The notebooks use common Python data science packages:

• Python
• Jupyter Notebook
• numpy
• pandas
• matplotlib
• scikit-learn
• flask and flask-cors for the house price prediction server

Install the main dependencies with:

pip install numpy pandas matplotlib scikit-learn flask flask-cors notebook

Running Notebooks

Start Jupyter from the repository root:

jupyter notebook

Then open any notebook from the numbered topic folders.

Study Tracker

Open study-tracker.html in any browser to track completed topics, study hours, target dates, and notes. Progress is saved in the browser with localStorage.

House Price Prediction Project

The project is under 13. Projects.

To run the Flask server:

cd "13. Projects/server"
python server.py

Then open 13. Projects/client/app.html in a browser. The client expects the API at http://127.0.0.1:5000.

Notes

• Folder names have been numbered and capitalized for a cleaner learning sequence.
• Generated Python cache files and Jupyter checkpoint folders are ignored by git.