From 0de509569f5b029d693756128d3716c2afcf19da Mon Sep 17 00:00:00 2001
From: Anand Raj <66896800+anandr07@users.noreply.github.com>
Date: Sun, 18 Feb 2024 20:35:42 +0530
Subject: [PATCH] Update index.html

---
 index.html | 43 +++++++++++++++++--------------------------
 1 file changed, 17 insertions(+), 26 deletions(-)

diff --git a/index.html b/index.html
index bc7257c..4bebbc9 100644
--- a/index.html
+++ b/index.html
@@ -368,6 +368,9 @@ <h3 class="page-title white-text teal">Experience</h3>
               <li>
                 Technical Writer for TowardsAI and Stackademic.
               </li>
+              <li>
+              Authored engaging technical blogs focused on Artificial Intelligence and Autonomous Cars.
+              </li>
             </ul>
           </div>
           <div class="card-action">
@@ -394,16 +397,13 @@ <h3 class="page-title white-text teal">Experience</h3>
             <div class="role brown-text text-darken-2">Software Engineer</div>
             <ul>
               <li>
-                Product Development - Advanced Driver Assistance Systems - Driving Functions - Safety Functions : EBA(Emergency Brake Assist, RPCP(Rear Pre-Crash Predict), BSW(Blind Spot Warning). Developed products for Volkswagen and Mercedes Benz  in agile methodology.
+                Worked on Advanced Driving Assistance Systems and developed products like Emergency Brake Assist, and Rear Pre-Crash Predict. Major products: Volkswagen ID Buzz and Mercedes Benz Sprinter Van. 
               </li>
               <li>
-                Algorithm Development in C and Testing using GTest at L3 Level for data coming from ARS (Advance Radar Sensor) - 5th Generation and SRR (Short Range Radar). 
+               Developed algorithm using C. Implemented automation using Python scripting.
               </li>
-              <li>Performed reverse engineering for fixing bugs using the C programming language and providing problem-solving solutions to customer-reported problems in the simulation environment.</li>
-              <li>Major Products Worked On: Volkswagen ID Buzz </li>
-              <li>Reduced lead time and increased productivity by Automating manual tasks </li>
-              <li>Achieved better KPI i.e. 2 False Positives for 10000 kms as per customer expectation. </li>
-          
+              <li>Provided problem-solving solutions to customer-reported problems in the simulation environment.</li>
+              <li>Delivered better performance with just 2 false positives per 10,000 kilometers, optimizing key performance indicators.</li>
               <li>
                 <b>Skills learnt:</b> Development in C, Python for scripting, Git, GTest
                 for Testing, QAC for Quality, JIRA for ticketing, Product
@@ -435,17 +435,11 @@ <h3 class="page-title white-text teal">Experience</h3>
             <div class="role brown-text text-darken-2">Data Science Intern</div>
             <ul>
               <li>
-                Collaborated with a dynamic team to conduct in-depth data analysis utilizing Python and Tableau, providing valuable insights into 
-                the client's Sales data.
-              </li>
-              <li>
-                Analyzed user behavior, temporal trends, and distinctions between Free and Paid users.
-              </li>
-              <li>
-                Formulated data-driven recommendations and compelling narratives, and communicated to our client.
+               Collaborated with a dynamic team to conduct in-depth data analysis utilizing Python and Tableau, providing valuable 
+               insights into client's sales data. Analyzed user behavior, temporal trends, and distinctions between free and paid users.
               </li>
               <li>
-                Increased sales by 14%.
+                Formulated data-driven recommendations and compelling narratives and communicated to our client
               </li>
               <li>
                 <b>Skills learnt:</b>Python, Data Analysis, ML Modelling, Flask.
@@ -475,7 +469,7 @@ <h3 class="page-title white-text teal">Experience</h3>
             <div class="role brown-text text-darken-2">Intern</div>
             <ul>
               <li>
-                Worked on Validation and Verification Process Standards in avionics hardware.
+                Worked on validation and verification process standards in avionics hardware.
               </li>
               <li>
                 Collaborating with different teams and Reviewing standards of all the Validation and verification processes.
@@ -521,8 +515,7 @@ <h3 class="page-title white-text teal">Projects</h3>
                 <ul>
                   <li><b>Tools:</b> Python </li>
                   <li> Applied Natural Language Processing techniques to determine if two questions have similar meaning. </li>
-                  <li>Conducted comprehensive data analysis, implemented feature engineering, and generated 33 new features.</li>
-                  <li> Featurized text data using Tf-Idf word2vec, compared multiple ML models and found XGBoost performed the best, minimizing log loss.</li>
+                  <li> Performed comprehensive data analysis, feature engineering, and text data featurization to develop a predictive model.</li>
 
                 </ul>
                 <div class="card-action">
@@ -556,10 +549,9 @@ <h3 class="page-title white-text teal">Projects</h3>
                     class="mdi-navigation-close right"></i></span>
                 <ul>
                   <li><b>Tools:</b> Python </li>
-                  <li>Aims to predict the trip duration time given pick up and drop off co-ordinates of New York City. (~1.5 million rows).</li>
-                  <li>Performed extensive cleaning and conducted comprehensive data analysis including time series and demand analysis. </li>
-                  <li>XGBoost performs the best with an RMSE of 224 seconds ~ 3.7 minutes. </li>
-                </ul>
+                  <li>Developed a model for predicting trip duration using New York City pick-up and drop-off coordinates, involving 
+                   extensive data cleaning, analysis, and training various ML models, achieving the lowest RMSE of 224 seconds.</li>
+                   </ul>
                 
                 <div class="card-action">
                  <!-- <a aria-label="Visit " href="https://galvanic-music.herokuapp.com/" target="_blank" data-position="top"
@@ -596,9 +588,8 @@ <h3 class="page-title white-text teal">Projects</h3>
                     class="mdi-navigation-close right"></i></span>
                 <ul>
                   <li><b>Tools:</b> Python, Flask, AWS-for deployment.</li>
-                  <li>To predict if a review of a product given by user is positive or negative.</li>
-                  <li>Performed extensive text cleaning and featurizing text data using Bag of Words, Tf-Idf and Tf-Idf word2vec comparing these
-features with different ML Models achieving an AUC score of 0.90 using SGD Classifier. </li>
+                  <li>Developed a model to predict if a text review of a product given by user is positive or negative</li>
+                  <li>Performed extensive text cleaning and featurizing text data, achieved an AUC score of 0.90 using SGD Classifier. </li>
                   <li>Deployed using Flask on AWS EC-2 virtual machine. </li>
 
                 </ul>