Praful John
Praful John
  • Видео 31
  • Просмотров 2 070
256 Project Demo video
Waste management is a pressing global issue, and manual sorting often leads to inefficiency, errors, and health risks for workers. To address this, we developed Trash Classifier, an AI-powered solution that automates waste classification into three categories: Recyclable, Compostable, and Trash.
Using a ResNet-50 Convolutional Neural Network (CNN) fine-tuned on a curated dataset, the classifier achieves an impressive 97.16% accuracy. The model processes images uploaded via a user-friendly Gradio interface and provides real-time predictions. Key features include data preprocessing (normalization, resizing, and augmentation) and Grad-CAM visualizations, which enhance model interpretability b...
Просмотров: 6

Видео

Clustering walkthroughClustering walkthrough
Clustering walkthrough
Просмотров 41Месяц назад
Advanced Dimensionality Reduction | Image Data | Tabular Data | DatabricksAdvanced Dimensionality Reduction | Image Data | Tabular Data | Databricks
Advanced Dimensionality Reduction | Image Data | Tabular Data | Databricks
Просмотров 48Месяц назад
Exploring TimeGPT | Tabula 8b | RDLExploring TimeGPT | Tabula 8b | RDL
Exploring TimeGPT | Tabula 8b | RDL
Просмотров 77Месяц назад
Apache Beam Data Engineering | Auto EDA | EDA with D3.jsApache Beam Data Engineering | Auto EDA | EDA with D3.js
Apache Beam Data Engineering | Auto EDA | EDA with D3.js
Просмотров 12Месяц назад
Learning-Augmented K-Means: PCA Integration - A Survey Paper OverviewLearning-Augmented K-Means: PCA Integration - A Survey Paper Overview
Learning-Augmented K-Means: PCA Integration - A Survey Paper Overview
Просмотров 17Месяц назад
In this video, I provide a detailed overview of the survey paper "Learning-Augmented K-Means Clustering Using Dimensional Reduction" by Jabari et al. (2024). The paper explores the integration of Principal Component Analysis (PCA) with the k-means algorithm, supported by machine learning predictors, to address the challenges of clustering high-dimensional datasets. Key topics covered include: C...
Data preparation EDA and visualization using AutoVIML on Time series dataData preparation EDA and visualization using AutoVIML on Time series data
Data preparation EDA and visualization using AutoVIML on Time series data
Просмотров 462 месяца назад
Time Series Dataset: Retail Sales Data The second dataset comprises time series data related to retail sales, enriched with additional features like holidays, oil prices, and transactions. This dataset enables exploration of sales patterns, holiday effects, and other influencing factors. Attributes: Date, Store Number, Sales, Family (Product Categories), On Promotion Supplementary Data: Oil pri...
Data preparation EDA and visualization using AutoVIML on tabular dataData preparation EDA and visualization using AutoVIML on tabular data
Data preparation EDA and visualization using AutoVIML on tabular data
Просмотров 332 месяца назад
Tabular Dataset: Musical Track Features and Genres The first dataset includes various numerical and categorical attributes that describe the characteristics of musical tracks and their genres. This dataset is perfect for genre classification and analyzing musical patterns. Attributes: Danceability, Energy, Loudness, Speechiness, Acousticness, Instrumentalness, Tempo, Valence Genres: Categorical...
How i used ChatGPT to do Data Science on Heart UCI Dataset | Data ScienceHow i used ChatGPT to do Data Science on Heart UCI Dataset | Data Science
How i used ChatGPT to do Data Science on Heart UCI Dataset | Data Science
Просмотров 332 месяца назад
Dataset Selection: A popular Heart Disease dataset from Kaggle was chosen for analysis. ChatGPT as Data Science Assistant: I used ChatGPT's code interpreter to guide me through various stages of data science, from data preprocessing to model building and evaluation. Data Science Workflow The workflow included: Data Collection: Uploading the dataset and understanding its structure. Exploratory D...
Learning AutoML with Pycaret video 9 | using Gradio part 2Learning AutoML with Pycaret video 9 | using Gradio part 2
Learning AutoML with Pycaret video 9 | using Gradio part 2
Просмотров 93 месяца назад
Learning AutoML with Pycaret video 8 | using Gradio part 1Learning AutoML with Pycaret video 8 | using Gradio part 1
Learning AutoML with Pycaret video 8 | using Gradio part 1
Просмотров 243 месяца назад
Learning AutoML with Pycaret video 7 | Time series forecasting with Exogenous variableLearning AutoML with Pycaret video 7 | Time series forecasting with Exogenous variable
Learning AutoML with Pycaret video 7 | Time series forecasting with Exogenous variable
Просмотров 133 месяца назад
Learning AutoML with Pycaret video 6 | Associative Rule miningLearning AutoML with Pycaret video 6 | Associative Rule mining
Learning AutoML with Pycaret video 6 | Associative Rule mining
Просмотров 223 месяца назад
Learning AutoML with Pycaret video 5 | Anomaly DetectionLearning AutoML with Pycaret video 5 | Anomaly Detection
Learning AutoML with Pycaret video 5 | Anomaly Detection
Просмотров 183 месяца назад
Learning AutoML with Pycaret video 4 | ClusteringLearning AutoML with Pycaret video 4 | Clustering
Learning AutoML with Pycaret video 4 | Clustering
Просмотров 333 месяца назад

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