Pinned Repositories
Heart-Disease
The dataset is available on Kaggle. The prediction of having heart diseases is done using Logistic Regression, I also used SMOTE and REF for enhancing the prediction model.
Web-Scraping
The internet has massive amount of data, to access the data web scraping is used. Web Scraping : It is extraction of data from the websites. The information is collected and then exported into the format useful to the user. There are various ways by which we can scrap the data: Auto Scraper 2. Selenium 3. Beautiful Soup
Boston-House-Prices---EDA
You are a Data Scientist with a housing agency in Boston MA, you have been given access to a previous dataset on housing prices derived from the U.S. Census Service to present insights to higher management. Based on your experience in Statistics, what information can you provide them to help with making an informed decision?
Provide-Insights-to-Executive-team-in-Telecom-Domainain
AtliQo is one of the leading telecom providers in India and launched it’s 5G plans in May 2022 along with other telecom providers. Create the comparison report based on the mock-up provided.
Salary-Prediction-Regression
This report is about prediction of developers salary. There are various Linear Regression algorithms used to predict the salary of the developers. The report will entail the brief overview about the dataset, exploratory analysis such as identifying outliers, null values, duplicates, feature engineering etc.
Wine-Quality-Prediction
This project is about the various classification algorithms. The wine quality dataset uses various classification algorithms to predict the wine quality and choose best algorithm for the prediction.
Heart
This is web deployment of https://github.com/Sood-Akriti/Heart-Disease
SVHN
For the capstone project, I used the SVHN dataset. This is an image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. "Reading Digits in Natural Images with Unsupervised Feature Learning". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. The goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes.
Big-Mart
Sales Prediction for Big Mart Outlets The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities.
Boston-House
IBM - Statistics for Data Science with Python
Sood-Akriti's Repositories
Sood-Akriti/Time-series-forecasting
Sood-Akriti/Taiyo.ai---Web-Scraping
Sood-Akriti/Big-Mart
Sales Prediction for Big Mart Outlets The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities.
Sood-Akriti/Wine-Quality-Prediction
This project is about the various classification algorithms. The wine quality dataset uses various classification algorithms to predict the wine quality and choose best algorithm for the prediction.
Sood-Akriti/Salary-Prediction-Regression
This report is about prediction of developers salary. There are various Linear Regression algorithms used to predict the salary of the developers. The report will entail the brief overview about the dataset, exploratory analysis such as identifying outliers, null values, duplicates, feature engineering etc.
Sood-Akriti/Boston-House-Prices---EDA
You are a Data Scientist with a housing agency in Boston MA, you have been given access to a previous dataset on housing prices derived from the U.S. Census Service to present insights to higher management. Based on your experience in Statistics, what information can you provide them to help with making an informed decision?
Sood-Akriti/Provide-Insights-to-Executive-team-in-Telecom-Domainain
AtliQo is one of the leading telecom providers in India and launched it’s 5G plans in May 2022 along with other telecom providers. Create the comparison report based on the mock-up provided.
Sood-Akriti/Boston-House
IBM - Statistics for Data Science with Python
Sood-Akriti/Heart
This is web deployment of https://github.com/Sood-Akriti/Heart-Disease
Sood-Akriti/SVHN
For the capstone project, I used the SVHN dataset. This is an image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. "Reading Digits in Natural Images with Unsupervised Feature Learning". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. The goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes.
Sood-Akriti/MNIST
It consists of a training set of 60,000 handwritten digits with corresponding labels, and a test set of 10,000 images. The images have been normalised and centred. The dataset is frequently used in machine learning research, and has become a standard benchmark for image classification models. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. Your goal is to construct a neural network that classifies images of handwritten digits into one of 10 classes.
Sood-Akriti/EuroSat
It consists of 27000 labelled Sentinel-2 satellite images of different land uses: residential, industrial, highway, river, forest, pasture, herbaceous vegetation, annual crop, permanent crop and sea/lake. For a reference, see the following papers: Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification. Patrick Helber, Benjamin Bischke, Andreas Dengel. 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018. The goal is to construct a neural network that classifies a satellite image into one of these 10 classes, as well as applying some of the saving and loading techniques you have learned in the previous sessions.
Sood-Akriti/Iris
It consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. For a reference, see the following papers: R. A. Fisher. "The use of multiple measurements in taxonomic problems". Annals of Eugenics. 7 (2): 179–188, 1936. Your goal is to construct a neural network that classifies each sample into the correct class, as well as applying validation and regularisation techniques.
Sood-Akriti/Web-Scraping
The internet has massive amount of data, to access the data web scraping is used. Web Scraping : It is extraction of data from the websites. The information is collected and then exported into the format useful to the user. There are various ways by which we can scrap the data: Auto Scraper 2. Selenium 3. Beautiful Soup
Sood-Akriti/Heart-Disease
The dataset is available on Kaggle. The prediction of having heart diseases is done using Logistic Regression, I also used SMOTE and REF for enhancing the prediction model.