ratnasankeerthanreddy/Ratna-Professional-Seminar-Project

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ratnasankeerthanreddy/Ratna-Professional-Seminar-Project

ratnasankeerthanreddy

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Python-advanced

python mandatory assignments

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monkai-cars-model-classification

st car classification

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ratnasankeerthanreddy

randomsearchCV

Implementing Custom RandomSearchCV. Here i am using the KNN classifier and custom implementing the randomsearchCV

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I-m-Something-of-a-Painter-Myself

hello-github-actions

recommendation-system

documentation of recommendation system

Self-Driving-car

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Resume

Thresholding-Operations-using-inRange

Human-Activity-Recoginisation

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python-random-quote

A file-based quote bot written in Python

Quora-Insincere-Question-classification-using-fastai

Quora Insincere Question classificationusing fastai

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image-classification-with-fast.ai

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Chatbot-for-Personal-assisatance

Pdf-to-Text-

This notebook contains stuff related to how to convert pdf to text format using pdfminer open source tool

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TensorFlow-Course

Python-basics-

Practice Questions On Functions

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Performance-Metrics

Performance-metrics

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Face-detection

Basics-of-pandas

Pandas is an opensource library that allows to you perform data manipulation in Python. Pandas library is built on top of Numpy, meaning Pandas needs Numpy to operate. Pandas provide an easy way to create, manipulate and wrangle the data. Pandas is also an elegant solution for time series data.

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Iris-Dataset

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.[1] It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species.[2] Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus".

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EDA-on-titanic-data-set

The data has been split into two groups: training set (train.csv) test set (test.csv) The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features. The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic. We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like. Data Dictionary

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california-housing-price

This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome.

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GridSearchCV

Implementing Custom GridSearchCV.# it will take classifier and set of values for hyper prameter in dict type dict({hyper parmeter: [list of values]}) # we are implementing this only for KNN, the hyper parameter should n_neighbors

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MNIST-Logistic-regression

The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive. The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image. Each pixel column in the training set has a name like pixelx, where x is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero). For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below. Visually, if we omit the "pixel" prefix, the pixels make up the image like this:000 001 002 003 ... 026 027 028 029 030 031 ... 054 055 056 057 058 059 ... 082 083 | | | | ... | | 728 729 730 731 ... 754 755 756 757 758 759 ... 782 783 The test data set, (test.csv), is the same as the training set, except that it does not contain the "label" column. Your submission file should be in the following format: For each of the 28000 images in the test set, output a single line containing the ImageId and the digit you predict. For example, if you predict that the first image is of a 3, the second image is of a 7, and the third image is of a 8, then your submission file would look like: ImageId,Label 1,3 2,7 3,8 (27997 more lines) The evaluation metric for this contest is the categorization accuracy, or the proportion of test images that are correctly classified. For example, a categorization accuracy of 0.97 indicates that you have correctly classified all but 3% of the images.

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Exploratory-Data-Analysis-on-Haberman-Dataset

The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.

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MNIST-Dataset-using-KNN

The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive. The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image. Each pixel column in the training set has a name like pixelx, where x is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero). For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below. Visually, if we omit the "pixel" prefix, the pixels make up the image like this:000 001 002 003 ... 026 027 028 029 030 031 ... 054 055 056 057 058 059 ... 082 083 | | | | ... | | 728 729 730 731 ... 754 755 756 757 758 759 ... 782 783 The test data set, (test.csv), is the same as the training set, except that it does not contain the "label" column. Your submission file should be in the following format: For each of the 28000 images in the test set, output a single line containing the ImageId and the digit you predict. For example, if you predict that the first image is of a 3, the second image is of a 7, and the third image is of a 8, then your submission file would look like: ImageId,Label 1,3 2,7 3,8 (27997 more lines) The evaluation metric for this contest is the categorization accuracy, or the proportion of test images that are correctly classified. For example, a categorization accuracy of 0.97 indicates that you have correctly classified all but 3% of the images.

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Naive-Bayes-on-Donors-Choose-dataset

Founded in 2000 by a Bronx history teacher, DonorsChoose.org has raised $685 million for America's classrooms. Teachers at three-quarters of all the public schools in the U.S. have come to DonorsChoose.org to request what their students need, making DonorsChoose.org the leading platform for supporting public education. To date, 3 million people and partners have funded 1.1 million DonorsChoose.org projects. But teachers still spend more than a billion dollars of their own money on classroom materials. To get students what they need to learn, the team at DonorsChoose.org needs to be able to connect donors with the projects that most inspire them. In the second Kaggle Data Science for Good challenge, DonorsChoose.org, in partnership with Google.org, is inviting the community to help them pair up donors to the classroom requests that will most motivate them to make an additional gift. To support this challenge, DonorsChoose.org has supplied anonymized data on donor giving from the past five years. The winning methods will be implemented in DonorsChoose.org email marketing campaigns.

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Mercedes-Benz-Greener-Manufacturing

Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include, for example, the passenger safety cell with crumple zone, the airbag and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium car makers. Daimler’s Mercedes-Benz cars are leaders in the premium car industry. With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams. . To ensure the safety and reliability of each and every unique car configuration before they hit the road, Daimler’s engineers have developed a robust testing system. But, optimizing the speed of their testing system for so many possible feature combinations is complex and time-consuming without a powerful algorithmic approach. As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Daimler’s production lines.