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Sarthak Niwate's Projects

boston-data-analysis-using-tensorflow-keras icon 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.

cnn-fashion-mnist-data-analysis icon 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.

hackerrank-python-3-solutions icon 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.

machine-learning-concepts icon machine-learning-concepts

The essential algorithms of Supervised Learning and Unsupervised Learning will be explained in this repository. I have taken the guidance of YouTube Channel "Code Basics" for the data sets and references.

python-master-coding icon python-master-coding

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

regression-project-bangalore-house-price-prediction icon 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!

sarthakniwate_week_1 icon sarthakniwate_week_1

These are tasks completed during Python Data Engineer Internship at Chistats Labs. Guided by Girish Bamane, Umaima Surti.

sarthakniwate_week_2 icon sarthakniwate_week_2

These are tasks completed during Python Data Engineer Internship at Chistats Labs. Guided by Girish Bamane, Umaima Surti.

sarthakniwate_week_3 icon sarthakniwate_week_3

These are tasks completed during Python Data Engineer Internship at Chistats Labs. Guided by Girish Bamane, Umaima Surti.

temperature-humidity-regression-analysis icon 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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