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Repository Details

Tthis repo contains my all of deep learning related projects ,works

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1

Time-series-Analysis-Using-Facebook-Prophet-Algorithm-Alg

In this notebook, i am going to show how to predict future stock prices of Google using a time series forcasting model "Facebook Prophet"
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sarcasm-detector-on-news-headline-with-EDA-and-RNN

I used publicly available "sarcasm detection on news headlines" dataset where i peformed data preprocessing like cleaning, word embedding , then created a bidirectional LSTM model to predict whether a news is sarcastic or not!
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BreastCancer Classificication using RessNet50
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kubeflow-restoration-test

test kubeflow restoration on new cluster
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6

kubeflow-practices

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7

leisure-time-projects

These are the Datascience and Deep Learning projects I did as my part of datascience , machine learning, deep learning, A.I learning!
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8

kubernetes

I use this for k8s dep test
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9

webscrapping

All of my webscraping codes are stored here. Ilove to scrap data.
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10

Data-Wrangling-and-Analysis-of-WeRateDogs

I have analysed WE RATE DOGS twitter data analysis and tried to understand the visualization insights.
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11

machinelearning

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12

Machine-Learning-with-Scratch

In this repo, you will find raw codes of building deep learning models to learn insights from scratch.
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13

Uunderstanding_Seaborn-Data-Visualization-

Seaborn is a great lib for python data visualization. In this repo, i have tried most useful plots with description using california housing database to understand the data with various plots like scatterplot, countplot, boxplot, swarmplots etc.
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14

Massive-Data-Analysis-and-Visualization

This repo contains various data extraction, analysis and visualization using python and javascript.
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15

Data-Visualization-on-FM-radio

In this repo i have analyzed and visualized various FM radio listening activities like top tracks, listeners popularity, trending etc.
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16

Data-Preprocessing-from-Scratch

In this project, i have taken 2 datasets and applied various data preprocessing techniques like min-max, z score etc normalization, linear- non linear dimension reduction techinues, naive bayes classification techniques.
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