A hands-on repository for learning and practicing Artificial Intelligence (AI), Machine Learning (ML), and Data Science concepts using Python. The repo covers both regression and classification algorithms using real datasets and well-commented code.
Assignments
- All assignment-related scripts for regression, including linear regression analysis with plotting, RMSE/R² calculation, and interpretation, can be found in the relevant assignment folders. (e.g.,
Assignment 4/Regression model.py)
Practice/Classwork
Regression/scikit-linear-reg.py: Linear regression using scikit-learn on provided datasets, including metrics and plots.Regression/ridge.py: Demonstrates Ridge regression with alpha (regularization) parameter tuning.Regression/lasso.py: Lasso regression for feature selection or regularization (example: diamond price prediction).
Datasets
AI with Python/linreg_data.csv,quadreg_data.csv,ridgereg_data.csv: Data for regression tasks.AI with Python/diamonds.csv: Used for price regression/classwork.AI with Python/Auto.csv: Vehicle dataset for regression or classification.
Assignments
- All assignment-related scripts for classification are placed in relevant assignment folders (add specifics if/when present).
Practice/Classwork
Classification/k-near.py: K-Nearest Neighbors (KNN) applied to the Iris dataset, plotting, confusion matrix, classification reports, and k error curves.
Datasets
AI with Python/iris.csv: Used for classification/KNN tasks.AI with Python/data_banknote_authentication.csv: Binary classification practice.
- Clone the repository:
git clone https://github.com/IrumShehryar/AI-with-Python.git
- Install dependencies (numpy, pandas, matplotlib, scikit-learn).
- Open and run scripts located in Regression or Classification sections, or in assignment folders.
- Scripts are ready for demonstration and hands-on learning.
- Assignments and practice/classwork are grouped under each topic for easy navigation.
- All datasets are provided for immediate use.
Last updated: 2026-04-10