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<!doctype html>
<!--
Copyright (c) 2025 Dhanesh B.B. All Rights Reserved.
Portfolio Website - Projects Page
Unauthorized copying or distribution is prohibited.
https://dhaneshbb.github.io
-->
<html lang="en">
<head>
<link rel="icon" type="image/svg+xml" href="media/favicon.svg" />
<meta charset="UTF-8" />
<meta name="viewport" content="width=1024" />
<title>Projects - Dhanesh B.B. | Data Science Portfolio</title>
<meta
name="description"
content="Explore innovative data science projects by Dhanesh B.B. including machine learning models, Python libraries, and analytics solutions."
/>
<meta name="author" content="Dhanesh B.B." />
<meta name="copyright" content="Copyright 2025 Dhanesh B.B. All rights reserved." />
<meta name="robots" content="index, follow" />
<!-- Bootstrap CSS -->
<link
href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css"
rel="stylesheet"
/>
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rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css"
/>
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href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&family=JetBrains+Mono:wght@400;500;600&display=swap"
rel="stylesheet"
/>
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<script src="scripts/core/theme-init.js"></script>
</head>
<body>
<!-- Navigation -->
<div id="nav-placeholder"></div>
<!-- Page Header -->
<section class="page-header">
<div class="container">
<h1 class="page-title fade-in">Project Portfolio</h1>
<p class="page-subtitle fade-in fade-in-delay-1">
Innovative Data Science Solutions & Machine Learning Applications
</p>
</div>
</section>
<!-- Filter Section -->
<section class="stats-badge-section">
<div class="container">
<div class="stats-badge-container fade-in fade-in-delay-2">
<!-- Filter Tabs -->
<div class="filter-tabs-wrapper">
<button class="filter-tab active" data-filter="all">
<i class="fas fa-grid-2"></i>
<span class="tab-text">All Projects</span>
<span class="tab-count">15</span>
</button>
<button class="filter-tab" data-filter="ml">
<i class="fas fa-brain"></i>
<span class="tab-text">Machine Learning</span>
<span class="tab-count">6</span>
</button>
<button class="filter-tab" data-filter="tools">
<i class="fas fa-tools"></i>
<span class="tab-text">Dev Tools</span>
<span class="tab-count">7</span>
</button>
<button class="filter-tab" data-filter="packages">
<i class="fab fa-python"></i>
<span class="tab-text">PyPI Packages</span>
<span class="tab-count">2</span>
</button>
<button class="filter-tab" data-filter="web">
<i class="fas fa-globe"></i>
<span class="tab-text">Web Apps</span>
<span class="tab-count">2</span>
</button>
</div>
</div>
</div>
</section>
<!-- Projects Grid (New Design) -->
<section class="projects-showcase">
<div class="container">
<!-- Featured Projects (Large Cards) -->
<div class="section-header">
<h2 class="section-title fade-in">Featured Projects</h2>
</div>
<div class="featured-grid">
<!-- 1. InsightfulPy - PyPI Package -->
<div
class="project-card-new featured"
data-category="packages tools"
data-topics="data-analysis, exploratory-data-analysis, statistics, visualization, pandas, data-science, eda, statistical-analysis, data-visualization, outlier-detection"
>
<div class="card-ribbon"><i class="fab fa-python"></i> PyPI Package</div>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-chart-line"></i>
</div>
<div class="project-meta-header">
<h3 class="project-title-new">
InsightfulPy
<a
href="https://pepy.tech/projects/insightfulpy"
target="_blank"
rel="noopener"
class="downloads-inline-badge"
>
<img
src="https://static.pepy.tech/personalized-badge/insightfulpy?period=total&units=NONE&left_color=blue&right_color=brightgreen&left_text=downloads"
alt="InsightfulPy Downloads"
loading="lazy"
/>
</a>
</h3>
<div class="project-subtitle">
Python Toolkit for Exploratory Data Analysis
</div>
</div>
<div class="project-status">
<span class="status-badge published">v0.2.0</span>
</div>
</div>
<div class="card-body-new">
<p class="project-description-new">
Python toolkit for exploratory data analysis with <strong>30+ functions</strong>
across <strong>11 files</strong> for statistical summaries, data quality checks,
and batch visualizations. Built with <strong>modular architecture</strong>
(10 specialized modules) and constants-driven design. Works in Jupyter, IPython,
and terminal. Features environment detection, built-in help system
(help(), quick_start(), examples(), list_all()), and multi-dataset comparison.
Published on PyPI.
</p>
<div class="project-highlights">
<div class="highlight-item">
<i class="fas fa-download"></i>
<span>9 Releases</span>
</div>
<div class="highlight-item">
<i class="fas fa-code"></i>
<span>Beta Status</span>
</div>
<div class="highlight-item">
<i class="fas fa-users"></i>
<span>Open Source</span>
</div>
</div>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python 3.8+
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/pandas/pandas-original.svg"
alt="Pandas"
class="tech-logo"
/>
Pandas
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/numpy/numpy-original.svg"
alt="NumPy"
class="tech-logo"
/>
NumPy
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/matplotlib/matplotlib-original.svg"
alt="Matplotlib"
class="tech-logo"
/>
Matplotlib
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/0/01/Created_with_Matplotlib-logo.svg"
alt="Seaborn"
class="tech-logo"
/>
Seaborn
</span>
<span class="tech-badge">
<img src="https://scipy.org/images/logo.svg" alt="SciPy" class="tech-logo" />
SciPy
</span>
<span class="tech-badge">
<i class="fas fa-chart-bar"></i>
Missingno
</span>
<span class="tech-badge">
<i class="fas fa-table"></i>
TableOne
</span>
<span class="tech-badge">
<i class="fas fa-flask"></i>
ResearchPy
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a href="https://github.com/dhaneshbb/insightfulpy" target="_blank" rel="noopener">
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/insightfulpy"
alt="InsightfulPy Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<div class="project-links-new">
<a
href="https://github.com/dhaneshbb/insightfulpy"
target="_blank"
rel="noopener"
class="btn-link github"
title="View source code on GitHub"
>
<i class="fab fa-github"></i> GitHub
</a>
<a
href="https://pypi.org/project/insightfulpy/"
target="_blank"
rel="noopener"
class="btn-link pypi"
title="Install from PyPI"
>
<i class="fab fa-python"></i> PyPI
</a>
</div>
<div class="project-date">Dec 2024 - Present</div>
</div>
</div>
<!-- 2. AutoCSV Profiler Suite - DevOps Tool -->
<div
class="project-card-new featured"
data-category="tools devops"
data-topics="python, data-science, automation, statistics, eda, pandas, data-visualization, statistical-analysis, multi-environment, data-analysis, profiling, dataprep, conda-environment, cli-tool, dependency-management, sweetviz, csv-analysis, ydata-profiling, subprocess-isolation"
>
<div class="card-ribbon"><i class="fas fa-layer-group"></i> Automation</div>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-layer-group"></i>
</div>
<div class="project-meta-header">
<h3 class="project-title-new">AutoCSV Profiler Suite</h3>
<div class="project-subtitle">Multi-Environment CSV Analysis Orchestrator</div>
</div>
<div class="project-status">
<span class="status-badge success">v2.0.0</span>
</div>
</div>
<div class="card-body-new">
<p class="project-description-new">
Multi-environment CSV orchestrator resolving <strong>numpy/pandas/scipy conflicts</strong>
through isolated conda environments. Unified interface for <strong>YData Profiling,
SweetViz, DataPrep</strong> engines with subprocess isolation. Features
<strong>Rich CLI</strong>, memory chunking (1GB default), parallel environment setup,
lazy loading, and graceful degradation. 28 tests, cross-platform support.
</p>
<div class="project-highlights">
<div class="highlight-item">
<i class="fas fa-layer-group"></i>
<span>4 Engines</span>
</div>
<div class="highlight-item">
<i class="fas fa-cogs"></i>
<span>Isolated Envs</span>
</div>
<div class="highlight-item">
<i class="fas fa-rocket"></i>
<span>Cross-platform</span>
</div>
</div>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/anaconda/anaconda-original.svg"
alt="Conda"
class="tech-logo"
/>
Conda
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/yaml/yaml-original.svg"
alt="YAML"
class="tech-logo"
/>
YAML
</span>
<span class="tech-badge">
<i class="fas fa-tools"></i>
EDA Tools
</span>
<span class="tech-badge">
<i class="fas fa-cogs"></i>
Automation
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a
href="https://github.com/dhaneshbb/autocsv-profiler-suite"
target="_blank"
rel="noopener"
>
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/autocsv-profiler-suite"
alt="AutoCSV Profiler Suite Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<div class="project-links-new">
<a
href="https://github.com/dhaneshbb/autocsv-profiler-suite"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> View Project
</a>
</div>
<div class="project-date">Mar 2025 - Present</div>
</div>
</div>
<!-- 3. Multi-AI Chat Manager - Desktop Application -->
<div
class="project-card-new featured"
data-category="tools desktop"
data-topics="electron, desktop-app, python, windows, productivity, window-management, ai-tools, chat-automation, multi-ai"
>
<div class="card-ribbon"><i class="fas fa-robot"></i> AI Tools</div>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-robot"></i>
</div>
<div class="project-meta-header">
<h3 class="project-title-new">Multi-AI Chat Manager</h3>
<div class="project-subtitle">AI Services Orchestration Platform</div>
</div>
<div class="project-status">
<span class="status-badge success">Demo</span>
</div>
</div>
<div class="card-body-new">
<p class="project-description-new">
Hybrid Python + Electron app for <strong>15+ AI platforms</strong> (ChatGPT, Claude,
Gemini, Perplexity, Grok, DeepSeek). Features <strong>browser extension</strong> for
simultaneous prompt distribution, grid/side-by-side layouts, multi-display support,
<strong>Groq API prompt enhancement</strong>, profile switching, and HTTP polling
server with JSON-RPC IPC communication.
</p>
<div class="project-highlights">
<div class="highlight-item">
<i class="fas fa-robot"></i>
<span>15+ Platforms</span>
</div>
<div class="highlight-item">
<i class="fas fa-puzzle-piece"></i>
<span>Extension</span>
</div>
<div class="highlight-item">
<i class="fas fa-code"></i>
<span>IPC Ready</span>
</div>
</div>
<div class="tech-stack-new">
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/electron/electron-original.svg"
alt="Electron"
class="tech-logo"
/>
Electron
</span>
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/windows8/windows8-original.svg"
alt="Windows"
class="tech-logo"
/>
Windows
</span>
<span class="tech-badge">
<i class="fas fa-rocket"></i>
Productivity
</span>
<span class="tech-badge">
<i class="fas fa-cogs"></i>
Automation
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a
href="https://github.com/dhaneshbb/Multi-AI-Chat-Manager-demo"
target="_blank"
rel="noopener"
>
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/Multi-AI-Chat-Manager-demo"
alt="Multi-AI Chat Manager Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<div class="project-links-new">
<a
href="https://github.com/dhaneshbb/Multi-AI-Chat-Manager-demo"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> View Demo
</a>
</div>
<div class="project-date">Oct 2025 - Present</div>
</div>
</div>
</div>
<!-- Regular Projects Grid -->
<div class="section-header mt-5">
<h2 class="section-title">Machine Learning Projects</h2>
<p class="section-subtitle">Classification, regression, and predictive analytics</p>
</div>
<div class="projects-grid-new">
<!-- 4. Home Loan Default Prediction -->
<div
class="project-card-new compact"
data-category="ml"
data-topics="python, machine-learning, scikit-learn, sklearn, jupyter-notebook, pandas, statistical-analysis, xgboost, lightgbm, data-analysis, feature-engineering, predictive-modeling, risk-assessment, hyperparameter-tuning, binary-classification, imbalanced-data, catboost, credit-risk, financial-modeling, loan-prediction"
>
<div class="card-header-new">
<div class="project-icon-new ml">
<i class="fas fa-home"></i>
</div>
<h3 class="project-title-new">Home Loan Default Predictor</h3>
</div>
<div class="card-body-new">
<p class="project-description-new">
Home loan default prediction analyzing <strong>58.44M records</strong> across 7
datasets. XGBoost classifier with 143 engineered features achieves
<strong>83% accuracy</strong>, <strong>52.8% recall</strong>, and
<strong>0.785 ROC AUC</strong> at threshold 0.60. Addresses class imbalance (8%
default rate), memory optimization (<strong>68.5% reduction</strong>),
multicollinearity, and saves <strong>1200+ hours</strong> of manual review through
intelligent risk assessment. <strong>Grade A</strong> evaluation.
</p>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/6/69/XGBoost_logo.png"
alt="XGBoost"
class="tech-logo"
/>
XGBoost
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg"
alt="Scikit-learn"
class="tech-logo"
/>
Scikit-learn
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/jupyter/jupyter-original.svg"
alt="Jupyter"
class="tech-logo"
/>
Jupyter
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a href="https://github.com/dhaneshbb/HomeLoanDef" target="_blank" rel="noopener">
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/HomeLoanDef"
alt="Home Loan Default Predictor Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<a
href="https://github.com/dhaneshbb/HomeLoanDef"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> Code
</a>
<a
href="https://www.kaggle.com/code/dhaneshbb/prcp-1006-homeloandef"
target="_blank"
rel="noopener"
class="btn-link kaggle"
>
<i class="fab fa-kaggle"></i> Kaggle
</a>
<div class="project-date">Jun 2024 - Mar 2025</div>
</div>
</div>
<!-- 5. Employee Performance AI -->
<div
class="project-card-new compact"
data-category="ml"
data-topics="python, data-science, machine-learning, scikit-learn, jupyter-notebook, pandas, xgboost, classification, lightgbm, data-analysis, feature-engineering, predictive-analytics, performance-prediction, multiclass-classification, catboost, shap, model-interpretability, hr-analytics, employee-performance, workforce-analytics"
>
<div class="card-header-new">
<div class="project-icon-new ml">
<i class="fas fa-users-cog"></i>
</div>
<h3 class="project-title-new">INX Employee Performance Analysis</h3>
</div>
<div class="card-body-new">
<p class="project-description-new">
Multiclass classification (3 classes) for employee performance using XGBoost with
<strong>92.5% accuracy</strong>, <strong>0.9756 ROC AUC</strong>, and
<strong>SHAP interpretability</strong>. Analyzes 1,200 records across 11 features
with class weights for imbalance. Top drivers: environment satisfaction, salary hike,
promotion history. Includes department-wise analysis and HR recommendations.
</p>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/6/69/XGBoost_logo.png"
alt="XGBoost"
class="tech-logo"
/>
XGBoost
</span>
<span class="tech-badge">
<img
src="https://raw.githubusercontent.com/shap/shap/master/docs/artwork/shap_header.svg"
alt="SHAP"
class="tech-logo"
/>
SHAP
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg"
alt="Scikit-learn"
class="tech-logo"
/>
Scikit-learn
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a
href="https://github.com/dhaneshbb/INX-Employee-Performance-Analysis"
target="_blank"
rel="noopener"
>
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/INX-Employee-Performance-Analysis"
alt="INX Employee Performance Analysis Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<a
href="https://github.com/dhaneshbb/INX-Employee-Performance-Analysis"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> Code
</a>
<div class="project-date">Mar 2025 - Apr 2025</div>
</div>
</div>
<!-- 6. Sales Effectiveness Analysis -->
<div
class="project-card-new compact"
data-category="ml"
data-topics="python, machine-learning, scikit-learn, jupyter-notebook, cross-validation, pandas, statistical-analysis, xgboost, classification, data-analysis, feature-engineering, hyperparameter-tuning, imbalanced-data, model-evaluation, gradient-boosting, lead-scoring, sales-analytics, frequency-encoding, b2b-sales, threshold-optimization"
>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-chart-pie"></i>
</div>
<h3 class="project-title-new">FicZon Sales Effectiveness</h3>
</div>
<div class="card-body-new">
<p class="project-description-new">
Sales lead quality prediction using XGBoost with <strong>81.06% ROC AUC</strong> and
<strong>84.74% recall</strong> on 7,420 leads. Handles 24% missing data, 26-category
high cardinality via <strong>frequency encoding</strong>, and 1.6:1 class imbalance.
Business impact: <strong>$142K cost savings</strong>, <strong>$380K revenue gains</strong>,
45% junk lead reduction. Grade A evaluation.
</p>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/6/69/XGBoost_logo.png"
alt="XGBoost"
class="tech-logo"
/>
XGBoost
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg"
alt="Scikit-learn"
class="tech-logo"
/>
Scikit-learn
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/jupyter/jupyter-original.svg"
alt="Jupyter"
class="tech-logo"
/>
Jupyter
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a
href="https://github.com/dhaneshbb/FicZon-Sales-Effectiveness"
target="_blank"
rel="noopener"
>
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/FicZon-Sales-Effectiveness"
alt="FicZon Sales Effectiveness Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<a
href="https://github.com/dhaneshbb/FicZon-Sales-Effectiveness"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> View Code
</a>
<div class="project-date">Feb 2025 - Mar 2025</div>
</div>
</div>
<!-- 8. Bike Rental Demand -->
<div
class="project-card-new compact"
data-category="ml"
data-topics="python, data-science, machine-learning, scikit-learn, jupyter-notebook, cross-validation, regression, statistical-analysis, xgboost, data-analysis, feature-engineering, ridge-regression, bike-sharing, hyperparameter-tuning, multicollinearity, capital-bikeshare, demand-forecasting, gradient-boosting, model-comparison, uci-dataset"
>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-bicycle"></i>
</div>
<h3 class="project-title-new">Capital Bikeshare Demand Forecaster</h3>
</div>
<div class="card-body-new">
<p class="project-description-new">
Bike rental demand prediction using Ridge Regression on Capital Bikeshare (731 days).
<strong>R²=0.832</strong>, CV <strong>R²=0.815±0.032</strong> with only
<strong>0.5% overfitting gap</strong> (vs XGBoost 9.4%). Resolves VIF 662→37
multicollinearity. 10 algorithms compared, 72x faster than XGBoost. Provides fleet
rebalancing recommendations. <strong>Grade A+</strong> evaluation.
</p>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<i class="fas fa-chart-line"></i>
Ridge Regression
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg"
alt="Scikit-learn"
class="tech-logo"
/>
Scikit-learn
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/jupyter/jupyter-original.svg"
alt="Jupyter"
class="tech-logo"
/>
Jupyter
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a href="https://github.com/dhaneshbb/BikeRental" target="_blank" rel="noopener">
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/BikeRental"
alt="Capital Bikeshare Demand Forecaster Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<a
href="https://github.com/dhaneshbb/BikeRental"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> View Code
</a>
<a
href="https://www.kaggle.com/code/dhaneshbb/prcp-1018-bikerental"
target="_blank"
rel="noopener"
class="btn-link kaggle"
>
<i class="fab fa-kaggle"></i> Kaggle
</a>
<div class="project-date">Jun 2024 - Mar 2025</div>
</div>
</div>
<!-- 9. Auto Price Prediction -->
<div
class="project-card-new compact"
data-category="ml"
data-topics="python, data-science, machine-learning, scikit-learn, jupyter-notebook, cross-validation, regression, pandas, statistical-analysis, xgboost, pca, data-analysis, feature-engineering, hyperparameter-tuning, multicollinearity, lasso-regression, model-comparison, model-interpretability, uci-dataset, automobile-pricing-algorithm"
>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-car"></i>
</div>
<h3 class="project-title-new">Automobile Price Predictor</h3>
</div>
<div class="card-body-new">
<p class="project-description-new">
1985 automobile price prediction using Lasso on UCI dataset (200 samples, 42 features).
<strong>91.7% R²</strong> with 29 sparse coefficients, <strong>600x faster inference</strong>
than XGBoost. Resolves extreme multicollinearity (<strong>VIF 16,676→8.36</strong>) via
PCA. 10 algorithms compared with 2.3-point CV-test gap vs XGBoost's 8.3-point gap.
<strong>Grade A+</strong> evaluation.
</p>
<div class="tech-stack-new">
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<i class="fas fa-project-diagram"></i>
Lasso
</span>
<span class="tech-badge">
<i class="fas fa-compress-arrows-alt"></i>
PCA
</span>
<span class="tech-badge">
<img
src="https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg"
alt="Scikit-learn"
class="tech-logo"
/>
Scikit-learn
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a href="https://github.com/dhaneshbb/AutoPricePred" target="_blank" rel="noopener">
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/AutoPricePred"
alt="Automobile Price Predictor Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<a
href="https://github.com/dhaneshbb/AutoPricePred"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> View Code
</a>
<a
href="https://www.kaggle.com/code/dhaneshbb/prcp-1017-autopricepred"
target="_blank"
rel="noopener"
class="btn-link kaggle"
>
<i class="fab fa-kaggle"></i> Kaggle
</a>
<div class="project-date">Jun 2024 - Mar 2025</div>
</div>
</div>
</div>
<!-- Development Tools Section -->
<div class="section-header mt-5">
<h2 class="section-title">Development Tools & Automation</h2>
<p class="section-subtitle">CLI tools, desktop apps, and automation frameworks</p>
</div>
<div class="projects-grid-new">
<!-- 10. Spectratact -->
<div
class="project-card-new compact"
data-category="tools desktop"
data-topics="electron, python, windows, productivity, automation, application-launcher, desktop-application, pywin32, desktop-automation, multi-monitor, task-automation, workspace-manager, window-management, layout-management, window-orchestration"
>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-window-restore"></i>
</div>
<h3 class="project-title-new">SpectraTact</h3>
</div>
<div class="card-body-new">
<p class="project-description-new">
Hybrid Python + Electron app for <strong>intelligent window orchestration</strong>
on Windows. Features <strong>grid/side-by-side layouts</strong>, multi-monitor support
(span, distribute, overflow modes), profile-based management, keyboard shortcuts,
dark/light themes, and first-run onboarding with JSON-RPC IPC, two-tier caching, and
live config reload.
</p>
<div class="tech-stack-new">
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/electron/electron-original.svg"
alt="Electron"
class="tech-logo"
/>
Electron
</span>
<span class="tech-badge python">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/python/python-original.svg"
alt="Python"
class="tech-logo"
/>
Python
</span>
<span class="tech-badge">
<img
src="https://cdn.jsdelivr.net/gh/devicons/devicon@v2.16.0/icons/windows8/windows8-original.svg"
alt="Windows"
class="tech-logo"
/>
Windows
</span>
<span class="tech-badge">
<i class="fas fa-cogs"></i>
Automation
</span>
</div>
</div>
<div class="card-footer-new">
<div class="repo-card-container">
<a href="https://github.com/dhaneshbb/spectratact" target="_blank" rel="noopener">
<img
src="https://opengraph.githubassets.com/1/dhaneshbb/spectratact"
alt="SpectraTact Repository"
class="repo-card"
loading="lazy"
/>
</a>
</div>
<a
href="https://github.com/dhaneshbb/spectratact"
target="_blank"
rel="noopener"
class="btn-link github"
>
<i class="fab fa-github"></i> View Code
</a>
<div class="project-date">Aug 2025 - Present</div>
</div>
</div>
<!-- 11. AutoCSV Profiler (PyPI) -->
<div
class="project-card-new compact"
data-category="packages tools"
data-topics="csv, data-analysis, profiling, statistics, pandas, data-quality, exploratory-analysis, visualization, statistical-analysis, data-profiling, automated-analysis, csv-analysis, data-exploration"
>
<div class="card-ribbon" style="background: linear-gradient(135deg, #4c1bd3, #7c3aed); box-shadow: 0 2px 8px rgba(76, 27, 211, 0.3);">PyPI</div>
<div class="card-header-new">
<div class="project-icon-new">
<i class="fas fa-file-csv"></i>
</div>
<h3 class="project-title-new">
AutoCSV Profiler
<a
href="https://pepy.tech/projects/autocsv-profiler"
target="_blank"
rel="noopener"
class="downloads-inline-badge"
>