Manan1811/STOCK--Price-Prediction

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Manan1811/STOCK--Price-Prediction

Manan1811

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Prediction of Stock Price using LSTM

Recommendation-Engine-with-KNN-algorithm

Collaborative Filtering , Fuzzy Logic

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Weather-App

Node.js Project

CNN-Classification

CNN Architecture- using Data Augmentation & Classify Images

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Tensorboard-Tutorial

It gives the Visualization of whole model- Graphs, Computational graphs, Histograms , Distributions and Metric Evaluation too.

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Neural-Style-Transfer

Amazing Loss Function Intuition

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Convolutional-Autoencoder

Fashion MNIST Dataset

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CLAHE

Contrast limited adaptive histogram equalization (CLAHE)

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Movie-Recommendation-System

Movie Recommendation System using Top 10 recommended movies (Content based Filtering) using NLP

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Image-Denoising-Autoencoder

Keras Denosing Autoencoder on MNIST Dataset using Conv2DTranspose (additional-tool)

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COMPUTER-VISION--Practice

All practice modules

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Image-Operations-and-Edge-Filters

Channelising and basic conversions between the 3 (RGB) channels

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SVM

Support Vector Machines

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Movie-Recommender-System--LightFM

Movie Recommender system using LightFM library

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Time-Series-Forecasting-

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HAR

Human Activity Recognition

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COVID-19-Project

Advanced Visualizations of World Wide spread of Novel Corona Virus using World Map tracking for various countries - India, Italy , South Korea and China(Wuhan)

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Tuner-with-Fashion-MNIST

Using Tuner Search for Hyperparameter Optimisation

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Stock-Sentiment-Analysis

NLP based analysis of Kaggle dataset: Stock-Headlines

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Natural-Language-Processing

All the features of NLP

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LSTM--1

Kaggle Dataset Practice

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Digital-Image-Processing-

Doing Various Techniques.

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SMS-Spam-Classifier-

Stack: NLP, Naive Bayer Classifier | Accuracy : 98.6%

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Spam-Classifier-APP

ML app with Flask

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FaceNet-Model

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Tweet-Sentiment-Analysis

Tweet Sentiment Analysis

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Churn-Model

Accuracy of 86% predicting whether the customer continues with the bank or not based on various factors and transactions using XGBOOST (RandomisedSearchCV)

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UNet-Autoencoder

U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany. The network is based on the fully convolutional network[2] and its architecture was modified and extended to work with fewer training images and to yield more precise segmentations

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Variational-Autoencoder

Great Article for Reference: https://www.machinecurve.com/index.php/2019/12/30/how-to-create-a-variational-autoencoder-with-keras/

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Devanagari-Character-Recognition

The DHCD (Devnagari Character Dataset) of handwritten digits. It consists 46 characters from क to ज्ञ and ० to ९.

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