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Sarthak Niwate (@Sarthak-GitHub)

Sarthak-GitHub

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Hackerrank-Python-3-Solutions

Hackerrank being the top platform to test your coding skills and optimize the way of writing code as you practice more. I have shared the solutions of questions which I solved and the test cases were passed and solution is accepted.

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Hackerrank-SQL-Solutions

Every problem from hackerrank SQL will be uploaded with solution and problem url.

Appstore-Games-Data-Analysis-in-Python-

Breadth-First-Traversal-in-Python

Python-Master-Coding

I have clubbed the programming problems, concepts, small basic algorithms like the bubble sort, etc., complexity problems related to order(N).

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CNN-Fashion-MNIST-Data-Analysis

Fashion MNIST dataset is famous dataset of 60000 raw images of fashion clothes. We have to categories and identify the type of clothes.

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Hackerrank-Data-Structures-Solutions

Every problem from hackerrank Data Structures will be uploaded with solution and problem url.

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Temperature-Humidity-Regression-Analysis

This simple small project is all about analyzing 72 readings of temperature and humidity. Using Linear Regression, the analysis of past readings and forecast is shown in the two different graphs of temperature and humidity. The project is simply coded and anyone who knows the basics can easily understand the logic and implementation. The most valuable features like reshape, fit_transform, polynomial feature with degree n, applying model, predicting and forecasting with the graphical representation in the end.

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Exploratory-Data-Anlysis-Basics-with-Referee-Dataset

Creating a small subgroup of data set values from a huge data. The tables created in the file are players and clubs. You can try out more because the helping function is already created in the file.

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Exploratory-Data-Analysis-on-Different-Datasets

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Loan-Status-Prediction-RegEx-Softwares-Project

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Regression-Project-Bangalore-House-Price-Prediction

The Bangalore House Price Dataset is taken from kaggle source. The data set is all about flat housing property and its features. The most tedious task in this dataset was data cleaning. It took lot of time and good logics to be resolved and get the optimized dataset. Else the another tasks of One Hot Encoding, Model Building are more of self-explanatory, if your basics are good and you know something about it before reading this project. In the end using GridSearchCV, I have tested the accuracy of model with comparing with one another model and then predicted the prices of properties/houses on the basis of some features. I hope you will enjoy it! Happy Learning!

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Boston-Data-Analysis-using-TensorFlow-Keras

the Housing dataset which contains information about different houses in Boston. This data was originally a part of UCI Machine Learning Repository and has been removed now. We can also access this data from the scikit-learn library. There are 506 samples and 13 feature variables in this dataset. The objective is to predict the value of prices of the house using the given features.

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