Bag of Colors implementation in notebook form

Extensive Collection of Jupyter Notebooks focused on Machine Learning covering different techniques includes Feature Engineering, Feature Selection, Feature Extraction, Model Training & Testing.

Computer Vision Course Notebooks, 2022

Notebook for NLP feature engineering

An image processing repository with Jupyter Notebooks

This notebook involves sentiment analysis on US airline tweets dataset.

A Jupyter notebook documentation of an ETL (extract -> transform -> load) data pipeline

This notebook demonstrates visualization and analysis of music and audio files using the Librosa python library.

This Jupyter notebook serves as a machine learning template to quickly make predictions and analyse feature importance in a dataset.

CNN Based Approach for Audio File Classification. Contains Notebooks Illustrating Data Preprocessing, Feature Extraction, Model Training, & Model Inference Workflows & Overall Pipeline

This notebook illustrates feature selection reverting many of the selection method results into pandas dataframes so that you get the appropriate column headings.

It is end-to-end CNN Image Classification Model notebook which identifies the food in your image. In this project I was working with pre-trained classification model EfficientNetB1 and Food101 Dataset.

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 .

The project is mostly concerned with feature engineering. To help the model grasp the data better, created additional features based on the disaster tweets. In the included notebook, each and every step is described in depth. In the supplied data set, I also dealt with the class imbalance. Final results: F1 Score 0.7031431897555296 Precision Sco…

Interactive ML Toolset

The notebook break down a problem of classification based on a weather in australia's data set. The idea of this work is to show different aproaches in how to visualize a data set, besides the idea is to develop different kind of Machine Learning Algorithms as Random Forest, SVM and Neural Networks.

Computer Vision: Vehicle Detection

Iconographic Visualization Inside Computational Notebooks

SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time