Examples of ML using the Scikit-Learn Library using Python in Jupyter Notebooks

a maching learning model to automating the detection of spam in emails

This Repository provides NoteBook for Regression Problem to predict Houses Prices

My Machine Learning and Data Science projects created using Jupyter Notebooks and Google Colab Notebooks.

Implements a genetic algorithm to select the most impactful features in a dataset to improve classifier performance. Written in Jupyter Notebook using pandas, numpy, scikit-learn. Results displayed with accuracy, precision, recall, F1 score comparison to using all features.

PSO feature selection improves classifier performance. Implemented in Jupyter Notebook with pandas, numpy, scikit-learn. PSO done from scratch. Results compared using accuracy, precision, recall, F1 score. Improves results compared to using all features. Can be applied to various classification problems.

Detailed Data Science using Python-Jupyter Notebook ( Data Analysis using Pandas and NumPy, Visualization using plotly express, Exploratory Data Analysis, Supervised ML models: Linear Regression, KNN, Logistic Regression, Support Vector Machine, Decision Trees Ensemble Models: Voting Bootstrap/ Bagging Aggregation, Unsupervised: K-Means

This repository contains code for analyzing and predicting outcomes in the Indian Premier League (IPL) cricket matches from 2008 to 2022. It includes data analysis notebooks, a prediction model, and a Flask-based web application for interactive predictions. Explore historical match data, gain insights, and make predictions on upcoming matches .