Python AI & ML Works

This repository contains projects, exercises, and experiments in Python related to Machine Learning, AI, and Data Analysis. It serves as a personal space to practice skills, gain experience, and explore different ML techniques and libraries.

Repository Structure

E-Commerce Sales Prediction

• dataset.csv: Dataset for predicting e-commerce sales.
• E-Commerce-Sales-Prediction.ipynb: Jupyter notebook implementing predictive models for e-commerce sales.

Linear Regression Tests

• bostonhouses.csv, HousingData.csv, turboaz.csv: Example datasets for regression tasks.
• BostonHouses-LinearRegression.ipynb: Linear regression example on Boston housing data.
• PandasTest1.ipynb, PandasTest2.ipynb: Testing data manipulation and analysis with pandas.

Logistic Regression Tests

• exams.csv, titanic.csv: Datasets for classification tasks.
• LogisticRegression-Exams.ipynb, LogisticRegression-Titanic.ipynb, LogisticRegressionExercise.ipynb, LogisticRegressionOnIrisDataset.ipynb, LogisticRegressionYTest.ipynb: Jupyter notebooks experimenting with logistic regression on various datasets.

Python Scripts & Other Tests

• BasicBankSystem.py: Python script demonstrating a basic banking system.
• ClassTest.ipynb: Notebook for practicing Python classes and OOP concepts.
• DropDelivery/Code.ipynb: Project-related notebook (details not specified).

Purpose

• Test and practice Python programming skills.
• Gain hands-on experience with machine learning models, regression, and classification.
• Explore different datasets and problem-solving approaches.
• Experiment with libraries like pandas, scikit-learn, and others.

How to Use

1. Clone the repository:

   git clone <repository-url>