feel free to pull

.github/workflows/jekyll.yml

@@ -0,0 +1,64 @@
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# Sample workflow for building and deploying a Jekyll site to GitHub Pages
+name: Deploy Jekyll site to Pages
+
+on:
+  # Runs on pushes targeting the default branch
+  push:
+    branches: ["master"]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  # Build job
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Setup Ruby
+        uses: ruby/setup-ruby@8575951200e472d5f2d95c625da0c7bec8217c42 # v1.161.0
+        with:
+          ruby-version: '3.1' # Not needed with a .ruby-version file
+          bundler-cache: true # runs 'bundle install' and caches installed gems automatically
+          cache-version: 0 # Increment this number if you need to re-download cached gems
+      - name: Setup Pages
+        id: pages
+        uses: actions/configure-pages@v4
+      - name: Build with Jekyll
+        # Outputs to the './_site' directory by default
+        run: bundle exec jekyll build --baseurl "${{ steps.pages.outputs.base_path }}"
+        env:
+          JEKYLL_ENV: production
+      - name: Upload artifact
+        # Automatically uploads an artifact from the './_site' directory by default
+        uses: actions/upload-pages-artifact@v3
+
+  # Deployment job
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

_config.yml

@@ -1,6 +1,18 @@
-title: Minimal theme
-logo: /assets/img/logo.png
-description: Minimal is a theme for GitHub Pages.
-show_downloads: true
-google_analytics:
+title: <p align="center"> Subham Srivastava </p>
+logo: /assets/img/subham_profile_picture.jpg
+
+description:
+  <p align="center">
+  <a href="assets/img/subham_srivastava_resume.pdf">Resume</a> |
+  <a href="https://www.linkedin.com/in/subhamsrivastava/">LinkedIn</a> |
+  <a href="https://github.com/subham0206">GitHub</a>
+
+  <br>   
+  Expert in Data Science, Machine Learning, and AI with experience at BuzzClan, BD and TCS in advanced data solutions. 
+  Holding a degree from IIIT Bangalore in Data Science and AI.
+  <br>
+  </p>
+
+
+
 theme: jekyll-theme-minimal

index.md

@@ -1,123 +1,117 @@
+Data Science Portfolio
 ---
-layout: default
+## Machine learning
+
+### AI-Driven Meta-Human Interviews: Redefining the Future of Hiring
+
+Developed an AI-powered meta-human bot for candidate interviews, enhancing recruitment efficiency by reducing interview time by 70% for a UAE-based recruitment organization, while providing real-time candidate evaluation for faster, data-driven decision-making.
+
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/AI-Based-Meta-Human-Interviewer)
+
+<center><img src="assets/img/Meta_Human_Interviewer.jpeg"/></center>
+
+---
+
+### Revolutionizing Pharmaceutical Logistics: AI-Powered Invoice Processing
+
+Led development of end-to-end AI-powered invoice processing, leveraging NLP, OCR, and RPA technologies, resulting in 5000+ man-hours saved and a 90% improvement in efficiency.
+
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206)
+
+<center><img src="assets/img/Invoice Processing Snip.PNG"/></center>
+
+---
+### Time Series Demand Forecasting For BDX
+
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206)
+
+Collaboratively developed a highly accurate multivariate demand forecasting model with BCG, utilizing tree-based ensemble models, feature engineering, and hyperparameter tuning. Achieved 80-85% forecast accuracy, resulting in reduced inventory levels, minimized backorders, and enhanced production planning efficiency.
+
+<center><img src="assets/img/time-series-forecasting.PNG"/></center>
+
 ---
+### Next-Gen Neural Networks: Revolutionizing Smart Appliance Interaction with Gesture Recognition
+
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/Gesture-Recognition-Models-for-Smart-TV-Control.git)
 
-Text can be **bold**, _italic_, or ~~strikethrough~~.
+This project aimed to develop a smart TV feature for recognizing five user gestures: thumbs up, thumbs down, left swipe, right swipe, and stop. It involved testing various models using a dataset of short videos. The top performer was transfer learning with VGG16, achieving 99.5% training accuracy and 85% validation accuracy. CNN + RNN models also showed strong performance with 90% training accuracy and 75% validation accuracy. However, Conv3D models faced challenges with accuracy and overfitting.
 
-[Link to another page](./another-page.html).
+In conclusion, VGG16 emerged as the most effective model for gesture recognition, showcasing its ability to leverage pre-trained knowledge effectively. CNN + RNN architectures also demonstrated robust performance in capturing both spatial and temporal features from video data. These insights not only optimize gesture recognition systems for smart TV interfaces but also mark a significant stride toward smarter appliance interaction
 
-There should be whitespace between paragraphs.
+<center><img src="assets/img/Screenshot 2024-07-02 193025.png"/></center>
 
-There should be whitespace between paragraphs. We recommend including a README, or a file with information about your project.
+---
+### Detecting Effusions in Chest X-Ray Images Using Neural Networks
 
-# Header 1
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/Analysis-of-Chest-X-Ray-images.git)
 
-This is a normal paragraph following a header. GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.
+This project leverages neural networks to analyze Chest X-Ray (CXR) images for identifying "effusion" (fluid in the lungs) versus 'nofinding' (normal). Key steps included standardizing image resolution, applying morphological operations, normalization, and augmentation for data pre-processing. The model was built using AUC as the evaluation metric and incorporated weighted cross-entropy to handle class imbalance.
 
-## Header 2
+Thorough experiments were conducted, including ablation studies and hyperparameter tuning. The final model achieved 90% accuracy and 88% AUC, with significant improvements in the ROC curve due to the use of weighted cross-entropy. Technologies used include numpy, pathlib, ResNet, keras, and tensorflow.
 
-> This is a blockquote following a header.
->
-> When something is important enough, you do it even if the odds are not in your favor.
+<center><img src="assets/img/x-ray-classes.png"/></center>
 
-### Header 3
+---
+### Deep Learning for Dermatology: Melanoma Detection with CNNs
 
-```js
-// Javascript code with syntax highlighting.
-var fun = function lang(l) {
-  dateformat.i18n = require('./lang/' + l)
-  return true;
-}
-```
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/Melanoma-Detection-.git)
 
-```ruby
-# Ruby code with syntax highlighting
-GitHubPages::Dependencies.gems.each do |gem, version|
-  s.add_dependency(gem, "= #{version}")
-end
-```
+The CNN-based model I constructed accurately detects melanoma, a type of skin cancer responsible for 75% of skin cancer deaths, which can be deadly if not detected early. An automated solution that evaluates images and alerts dermatologists about the presence of melanoma has the potential to significantly reduce manual diagnostic efforts. The model achieved accuracy of 90% after addressing class imbalance through data augmentation. Technologies used include numpy, skimage, keras and tensorflow. The project steps involved data reading and understanding, dataset creation and visualization, model building and training, analyzing model overfit/underfit, applying data augmentation to resolve class imbalances, and training a final CNN model to accurately detect nine classes of melanoma.
 
-#### Header 4
+<center><img src="assets/img/melanoma.png"/></center>
 
-*   This is an unordered list following a header.
-*   This is an unordered list following a header.
-*   This is an unordered list following a header.
+---
+### Revolutionizing Customer Support: AI Model Uncovers True Drivers Behind Support Tickets
 
-##### Header 5
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/Customer-Support-Ticket-Classification.git)
 
-1.  This is an ordered list following a header.
-2.  This is an ordered list following a header.
-3.  This is an ordered list following a header.
+Addressing challenges with unreliable CRM fields, our project focused on accurately classifying customer support tickets using advanced machine learning techniques. Leveraging the Customer Support Ticket Dataset from Kaggle, we utilized Python to preprocess and analyze the data, achieving a notable 92% accuracy in ticket classification.
 
-###### Header 6
+By implementing both unsupervised topic modeling and supervised classification approaches across 50 predefined topics, we uncovered key insights into global customer support trends. These insights were visualized in Power BI, providing interactive dashboards for stakeholders to navigate and understand ticket drivers effectively.
 
-| head1        | head two          | three |
-|:-------------|:------------------|:------|
-| ok           | good swedish fish | nice  |
-| out of stock | good and plenty   | nice  |
-| ok           | good `oreos`      | hmm   |
-| ok           | good `zoute` drop | yumm  |
+This initiative not only enhances operational efficiency but also empowers global contact centers with actionable insights for informed decision-making and improved customer service delivery.
 
-### There's a horizontal rule below this.
+<center><img src="assets/img/Screenshot 2024-06-15 195115.png"/></center>
 
-* * *
+---
+### Sterilization Chamber Capacity Utilization Across BDX
 
-### Here is an unordered list:
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206)
 
-*   Item foo
-*   Item bar
-*   Item baz
-*   Item zip
+Designed and implemented a forecasting algorithm for sterilization chamber capacity across 30 manufacturing sites, utilizing time series analysis techniques, ARIMA, and SARIMA models, resulting in a significant 40% improvement in Overall Equipment Efficiency (OEE).
 
-### And an ordered list:
+<center><img src="assets/img/Sterilization Capacity Utilization.PNG"/></center>
 
-1.  Item one
-1.  Item two
-1.  Item three
-1.  Item four
+---
+### Bike Sharing Demand Problem
 
-### And a nested list:
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/Bike-Sharing-Assigment.git)
 
-- level 1 item
-  - level 2 item
-  - level 2 item
-    - level 3 item
-    - level 3 item
-- level 1 item
-  - level 2 item
-  - level 2 item
-  - level 2 item
-- level 1 item
-  - level 2 item
-  - level 2 item
-- level 1 item
+Build a Multiple linear regression model to solve bike sharing problem for a company. We are required to build a shared bikes demand model with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market.
 
-### Small image
+<center><img src="assets/img/demand_projection.JPG"/></center>
 
-![Octocat](https://github.githubassets.com/images/icons/emoji/octocat.png)
+---
+### Lending Club CaseStudy
 
-### Large image
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206/LendingClubCaseStudy.git)
 
-![Branching](https://guides.github.com/activities/hello-world/branching.png)
+Lending company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilize this knowledge for its portfolio and risk assessment. This is an assignment provided by IIT B Upgrad program, It will give an idea about how real business problems are solved using EDA. In this case study, apart from applying the techniques i have learnt in EDA, I have also developed a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.
+The dataset is obtained from lending club portal. It contains the complete loan data for all loans issued through the time period 2007 t0 2011.
 
+<center><img src="assets/img/lending club.JPG"/></center>
 
-### Definition lists can be used with HTML syntax.
+---
+
+### User's Account Provisioning/Deprovisioning
+
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/subham0206)
+
+Led a cross-functional team in implementing an end-to-end user account management automation solution, leveraging UiPath (RPA tool) and data analysis techniques to improve efficiency. Achieved a significant 60% increase in operational efficiency by eliminating manual tasks, reducing processing time, and optimizing user account management processes.
+
+<center><img src="assets/img/user_account_management.PNG"/></center>
+
+---
 
-<dl>
-<dt>Name</dt>
-<dd>Godzilla</dd>
-<dt>Born</dt>
-<dd>1952</dd>
-<dt>Birthplace</dt>
-<dd>Japan</dd>
-<dt>Color</dt>
-<dd>Green</dd>
-</dl>
 
-```
-Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
-```
 
-```
-The final element.
-```