Priyankadiddi

EDA-of-Real-or-Not-NLP-with-Disaster-Tweets

Here challenge is to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t.

EDA-of-Titanic-Survival-Prediction

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

Prediction-using-Unsupervised-ML-Iris-Flower-Classification

This problem statement and data given by The Sparks Foundation. Here objective is to predict class of iris flower by posing this problem as unsupervised Learning Problem.

Priyankadiddi/EDA-of-Real-or-Not-NLP-with-Disaster-Tweets

Here challenge is to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t.

Priyankadiddi/EDA-of-Titanic-Survival-Prediction

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

Priyankadiddi/Prediction-using-Unsupervised-ML-Iris-Flower-Classification

This problem statement and data given by The Sparks Foundation. Here objective is to predict class of iris flower by posing this problem as unsupervised Learning Problem.

Priyankadiddi/AutoViz

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Priyankadiddi/bank-management-sourcecode

Priyankadiddi/billing-desktop-application

Priyankadiddi/Covid-19-Detection

Detecting Covid-19 from X-ray

Priyankadiddi/datasist

A Python library for easy data analysis, visualization, exploration and modeling

Priyankadiddi/demo_chinese_text_classification_bert_fastai

Priyankadiddi/educate-resource-for-machine-learning

Priyankadiddi/gensim

Priyankadiddi/Heroku-Demo

Priyankadiddi/Kaggle-Kernels

I will set upload some of my work on Kaggle as backup

Priyankadiddi/keras-bert

A simple technique to integrate BERT from tf hub to keras

Priyankadiddi/ktrain

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

Priyankadiddi/library-management-source-code

Priyankadiddi/myProjects

Repository about projects or things I do with data :)

Priyankadiddi/Pandas

Priyankadiddi/Prediction-using-Supervised-ML-Students-Performance-Prediction

The data and problem statement given by "The Sparks Foundation". Nowadays, people are using machine learning in education field also to automate most of the things in return it reduces man power , saves time. In classroom,there are many students and mostly one teacher has to work on students progress so its not always possible to interact with each student and know their progress. Before the final exam,every teacher would like to know their students progress, percentage of marks will secure by his/her students in upcoming exams so that they can work more on weaker students.Here challenge is to predict the percentage of marks of an student based on the number of study hours.

Priyankadiddi/simple-search-engine

Priyankadiddi/Time-series-data-analysis

Priyankadiddi/Titanic-Machine-Learning-from-Disaster

Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics

Priyankadiddi/voice-recognition-desktop

AI based voice recognition desktop application

Priyankadiddi/web-app-for-interior-designer

This is dynamic web application for interior designers .

Priyankadiddi/your-first-kaggle-submission

How to perform an exploratory data analysis on the Kaggle Titanic dataset and make a submission to the leaderboard.