<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ML_DL | Sahil Sangani</title><link>https://sahilsangani.netlify.app/tag/ml_dl/</link><atom:link href="https://sahilsangani.netlify.app/tag/ml_dl/index.xml" rel="self" type="application/rss+xml"/><description>ML_DL</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Sat, 01 Jul 2023 00:00:00 +0000</lastBuildDate><image><url>https://sahilsangani.netlify.app/images/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_2.png</url><title>ML_DL</title><link>https://sahilsangani.netlify.app/tag/ml_dl/</link></image><item><title>Uber Data Analytics</title><link>https://sahilsangani.netlify.app/project/uber-data-anlytics/</link><pubDate>Sat, 01 Jul 2023 00:00:00 +0000</pubDate><guid>https://sahilsangani.netlify.app/project/uber-data-anlytics/</guid><description>&lt;p>Harnessing the power of Python, Google Cloud Storage, Mage ETL, BigQuery, and Looker, our project delves into Uber cab data analytics. By extracting, transforming, and loading data with Mage, we employ Google&amp;rsquo;s ecosystem for storage and computation. Our BigQuery-driven data model comprehensively analyzes NYC&amp;rsquo;s TLC trip records, revealing trends in pick-up/drop-off times, locations, distances, fares, payment types, and more. The Looker Studio dashboard visualizes these insights, providing an interactive exploration of the dataset&amp;rsquo;s nuances.&lt;/p></description></item><item><title>Anomaly Detection Time Series Data</title><link>https://sahilsangani.netlify.app/project/anomaly-detection-time-series-data/</link><pubDate>Sun, 01 Nov 2020 00:00:00 +0000</pubDate><guid>https://sahilsangani.netlify.app/project/anomaly-detection-time-series-data/</guid><description>&lt;p>Anomalies detection using keras and tensorflaw in S&amp;amp;P 500 dataset.&lt;/p></description></item><item><title>IPL Winner Prediction</title><link>https://sahilsangani.netlify.app/project/ipl-winner-prediction/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://sahilsangani.netlify.app/project/ipl-winner-prediction/</guid><description>&lt;p>Data Analysis for IPL matches from 2008 - 2019 and predicting winner using SVM classifier.&lt;/p></description></item><item><title>Cataract Detection</title><link>https://sahilsangani.netlify.app/project/cataract-detection/</link><pubDate>Sun, 01 Sep 2019 00:00:00 +0000</pubDate><guid>https://sahilsangani.netlify.app/project/cataract-detection/</guid><description>&lt;p>Cataract is the most widespread causes of blindness. Early detection or precautions could reduce the suffering from cataract to the patients and mitigate the visual disability from turning into total blindness. But the cost may cause difficulties to everybody’s early interventions, because the expertise trained eye specialists cannot be afforded by everyone. Based on the data provided on Kaggle.com, we are trying to build a model that predict whether the patient is suffering from cataract or not.&lt;/p></description></item><item><title>Cheque Clearance System</title><link>https://sahilsangani.netlify.app/project/example/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://sahilsangani.netlify.app/project/example/</guid><description>&lt;p>The extensive use of cheques in daily life make the advancement of the conventional cheque clearing method. &amp;ldquo;Cheque Clearing System&amp;rdquo; is a portal which renders smooth, effective and convenient clearing of cheques. It enables to deal with different banks transaction. Receiver cashier can upload the scanned copy of cheque issued by another bank or same bank further clearance done by the admins with the verification of the sender bank. This system is developed using Python Flask Framework, HTML, CSS, Bootstarp, Google Vision API, MySQL, and Machine Learning algorithm SVM.&lt;/p></description></item></channel></rss>