practice_models

A curated collection of machine learning practice projects covering classification, regression, time series, and exploratory modeling. Each folder contains a Jupyter notebook (and in one case a Python script) with datasets and analysis steps used to build, evaluate, and visualize models.

Repository Structure

• Football match predictor/

  • footbal-match-prediction-model.ipynb - Notebook for predicting football match outcomes.
  • footbal-match-prediction-model.py - Python script version of the model.
  • former_names.csv, goalscorers.csv, results.csv, shootouts.csv - Supporting football match datasets.

• Heart Desease/

  • heart_disease.ipynb - Notebook for heart disease prediction using medical dataset.
  • heart.csv - Heart disease dataset.

• House Price model/

  • house_price.ipynb - Notebook for house price prediction.
  • house_price_train.csv - Training data for the house price model.

• IPL/

  • IPL.ipynb - Notebook covering IPL match and player analytics.
  • Players.xlsx - Player details dataset.
  • deliveries.csv, matches.csv, most_runs_average_strikerate.csv, teams.csv, teamwise_home_and_away.csv - IPL datasets.

• Netflix Title/

  • netflix.ipynb - Notebook exploring Netflix titles and metadata.
  • netflix_titles.csv - Netflix dataset.

• Spam Message Detection/

  • spam_detection.ipynb - Notebook for spam detection model using text classification.
  • spam.csv - Spam message dataset.

Getting Started

Prerequisites

• Python 3.8+
• pip
• Jupyter Notebook or Jupyter Lab

Install Dependencies

cd /workspaces/practice_models
pip install -r requirements.txt

Launch Notebooks

jupyter notebook

Then open the notebook for the project you want to explore.

Project Highlights

• Football match predictor: Predicts football match outcomes from match and historical result data.
• Heart Desease: Builds a classification model to detect heart disease from medical features.
• House Price model: Trains a regression model to estimate house prices from real estate features.
• IPL: Performs exploratory data analysis on IPL matches, players, and team statistics.
• Netflix Title: Analyzes Netflix catalog data for trends in titles, genres, and release years.
• Spam Message Detection: Uses NLP and classification techniques to distinguish spam from non-spam messages.

Notes

• Folder names include spaces; use quotes when referencing them in terminal commands.
• The notebooks are self-contained and usually include dataset loading, preprocessing, modeling, and evaluation steps.
• In this repo I will be posting and updating my future models.

License

This repository is provided for practice and learning purposes. Feel free to explore, modify, and expand the notebooks.