🚀 Machine Learning & Deep Learning Portfolio

A Comprehensive Collection of Production-Ready ML/DL Implementations

📧 Email • 💼 LinkedIn • 🐙 GitHub

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📋 Table of Contents

• 🎯 Overview
• ✨ Key Highlights
• 🧠 Deep Learning Projects
• 🤖 Machine Learning Projects
• 🛠️ Technical Stack
• 📊 Project Statistics
• 🚀 Quick Start
• 📁 Repository Structure
• 💡 Key Learnings
• 🤝 Connect With Me

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🎯 Overview

This repository showcases 82+ production-ready implementations spanning the entire spectrum of Machine Learning and Deep Learning. Each script is meticulously documented with comprehensive explanations, making it an ideal resource for learning, reference, and portfolio demonstration.

🎓 What Makes This Repository Special?

• ✅ Self-Documented Code: Every script includes detailed comments explaining the "what" and "why"
• ✅ Industry-Standard Practices: Follows best practices in model development, validation, and deployment
• ✅ End-to-End Workflows: Complete pipelines from data preprocessing to model evaluation
• ✅ Real-World Applications: Practical implementations solving actual business problems
• ✅ Recruiter-Friendly: Clear structure and documentation designed for technical interviews

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✨ Key Highlights

🏆 Featured Projects

Project	Technology	Highlights
Age & Gender Detection	EfficientNetB4, Transfer Learning	Multi-task learning with TTA, 95%+ accuracy, GUI deployment
Traffic Sign Classification	CNN, Data Augmentation	43-class classification, real-time prediction capability
Used Car Price Prediction	Linear Regression, Feature Engineering	VIF analysis, log transformation, 85%+ R² score
Transfer Learning Pipeline	MobileNetV3, Fine-tuning	Frozen layers, custom head, early stopping

🎯 Core Competencies Demonstrated

Deep Learning 🔹 Convolutional Neural Networks (CNNs) 🔹 Transfer Learning & Fine-tuning 🔹 Data Augmentation Strategies 🔹 Computer Vision Applications 🔹 Model Optimization & Callbacks 🔹 TensorBoard Visualization 🔹 Mixed Precision Training

Machine Learning 🔹 Supervised Learning (Regression/Classification) 🔹 Unsupervised Learning (Clustering) 🔹 Ensemble Methods (Bagging/Boosting) 🔹 Hyperparameter Tuning (GridSearchCV) 🔹 Feature Engineering & Selection 🔹 Model Selection & Validation 🔹 Statistical Analysis & VIF

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🧠 Deep Learning Projects

📂 Repository: Deep Learning (30 Projects)

🖼️ Computer Vision & Image Classification

Advanced Projects

Script	Description	Key Techniques
Age_Gender_Detection2.py	Multi-task age & gender prediction	EfficientNetB4, Mixed Precision, TTA, Calibration
ex_Traffic_sign_classification.py	43-class traffic sign classifier	CNN, Heavy Augmentation, Batch Normalization
17__Transfer_Learning.py	Horse vs Human classification	MobileNetV3, Layer Freezing, Fine-tuning
18_Feature_Extraction.py	Pre-trained feature extraction	VGG16/ResNet, Custom Classifier Head

Core CNN Implementations

Script	Description	Dataset
12__cat_dog_Classification_Argumentation.py	Binary classification with augmentation	Cats & Dogs (2,000 images)
09_Flower_manual.py	Multi-class flower classification	Flowers (5 classes)
ex_Intel_Image_Classification.py	Scene classification	Intel Image (6 classes)
12_cifar10CNN.py	Object recognition	CIFAR-10 (10 classes)

Foundational Projects

• 07_MNIST.py - Handwritten digit recognition
• 11_Fashion_Mist_CNN.py - Fashion item classification
• 08_Human_vs_Horse_dataset.py - Binary classification
• 09__Augmentation.py - Data augmentation techniques

Model Deployment & Utilities

• 20_Delpoy_openCV.py - Real-time OpenCV deployment
• 10_Load_saved_model.py - Model persistence & loading
• 02__TensorBoard.py - Training visualization
• 15__PreTrain_model.py & 16__PreTrain_model_2.py - Pre-trained model usage

🧮 Neural Network Fundamentals

• 0_DNN_Archietecture.py - Deep Neural Network architecture
• 13_Perceptron_Intro.py - Perceptron from scratch
• 14_multiClass_perceptron_ex1.py - Multi-class perceptron
• 01_Binary_classification_p2.py - Binary classification basics
• 6__multiclass_p-1.py & 6__multiclass_p-2.py - Multi-class classification
• 5__multiple_reg_DL.py - Multiple regression with DL

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🤖 Machine Learning Projects

📂 Repository: Machine Learning (52 Projects)

📈 Regression Analysis

Advanced Regression

Script	Description	Techniques
Project_1_Predict_The_Price_of_Used_Car.py	End-to-end price prediction	VIF, Log Transform, Outlier Removal, Scaling
EX_polynomial_regression.py	Non-linear relationship modeling	Polynomial features, Degree selection
EX_Lasso_Ridge_Regulization.py	Regularization techniques	L1/L2 penalties, Cross-validation

Core Regression Implementations

• Linear_Regression_Height_to_weight.py - Simple linear regression
• Linear_Regression_Temp_Convertor.py - Temperature prediction
• EX_Multiple_LINEAR_Regression_Pract_Session3.py - Multiple linear regression
• 03_linear_Regression_Assumptions_Task.py - Assumption validation
• EX_linear_Regression_Assumptions.py - Statistical testing
• EX_model selection_Regression.py - Model comparison
• EX_Support-Vector-Machine-R.py - SVM for regression

🎯 Classification Algorithms

Tree-Based Methods

Algorithm	Scripts	Features
Decision Trees	EX_Decision_Tree_Classification.py	Gini/Entropy, Pruning
Random Forest	EX_RandomForest_Classification(Baagging).py	Bagging, Feature Importance
Gradient Boosting	EX_5__Gradient_Boosting_Classification.py	Sequential learning, Weak learners

Distance-Based Methods

• EX_9__K-NearestNeighbourClassification.py - KNN classifier
• EX_9__K-NearestNeighbourRegression.py - KNN regressor
• EX_6__Radius_Neighbors_Classification.py - Radius-based classification

Probabilistic & Kernel Methods

• EX_10_Naive_Bays_Classification.py - Naive Bayes
• EX_Support_Vector_Machine-classification.py - SVM with kernels
• EX_Dec_Support_Vector_machine.py - Decision boundary visualization
• Logistic-Regression_Ex2_Purchase.py - Binary logistic regression
• Logistic-Regression_Ex3_Binary.py - Advanced logistic regression
• EX_Logistic_Regression_stats_model.py - Statistical modeling

🔍 Clustering & Unsupervised Learning

Algorithm	Script	Key Features
K-Means	EX_Unsupervised_K-MEANS.py	Elbow method, Silhouette score
Hierarchical	EX_Hierarchical_Clustering_Agglomerative.py	Dendrogram, Linkage methods
DBSCAN	EX_DBSCAN.py	Density-based, Noise detection
Mean Shift	EX_MeanShift.py	Bandwidth selection

• EX_K-MEANS_ELBOW.py - Optimal cluster selection
• Assignment_1_Clustering_Iris.py - Iris clustering analysis

⚙️ Model Optimization & Validation

Hyperparameter Tuning

• EX_HyperParameter_Tuning_Part1.py - GridSearchCV basics
• EX_HyperParameter_Tuning_Part2.py - Advanced tuning (RandomForest, SVC)

Model Selection & Evaluation

• EX_ModelSelection_Classification.py - Classifier comparison
• Model_Selection_Regression.py - Regressor comparison
• EX_AUC_ROC.py, EX_AUC_ROC_2.py, EX_AUC_ROC_Part_3.py - ROC curve analysis

🔧 Feature Engineering

Category	Script	Techniques
Extraction	EX_Feature Engineering_feature_extraction.py	PCA, Kernel PCA, t-SNE
Selection	EX_feature_selection.py	Filter/Wrapper/Embedded methods
Preprocessing	EX_data_PreProcessiing.py	Scaling, Encoding, Imputation

🎨 Specialized Applications

• App_Recommendations_Org.py - Recommendation system
• App_Recommendations_Temp.py - Recommendation system (variant)
• Ex4_IceCream_UnitSalesPrediction_Modified.py - Sales forecasting

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🛠️ Technical Stack

Core Technologies

# Deep Learning
tensorflow >= 2.8.0
keras >= 2.8.0
opencv-python >= 4.5.0

# Machine Learning
scikit-learn >= 1.0.0
statsmodels >= 0.13.0

# Data Processing
numpy >= 1.21.0
pandas >= 1.3.0

# Visualization
matplotlib >= 3.4.0
seaborn >= 0.11.0

# Utilities
pillow >= 8.3.0
tkinter (built-in)

Advanced Techniques Implemented

• Mixed Precision Training (FP16/FP32)
• Test-Time Augmentation (TTA)
• Learning Rate Scheduling (Cosine Annealing)
• Early Stopping & Model Checkpointing
• Variance Inflation Factor (VIF) Analysis
• Cross-Validation (K-Fold, Stratified)
• Ensemble Methods (Voting, Stacking)

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📊 Project Statistics

📁 Total Projects: 82
├── 🧠 Deep Learning: 30 projects
│   ├── Computer Vision: 20
│   ├── Neural Networks: 8
│   └── Deployment: 2
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└── 🤖 Machine Learning: 52 projects
    ├── Regression: 12
    ├── Classification: 18
    ├── Clustering: 6
    ├── Optimization: 5
    ├── Feature Engineering: 3
    └── Applications: 8

📝 Lines of Code: 15,000+
📚 Documentation: Comprehensive inline comments
🎯 Code Quality: Production-ready, self-documented

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🚀 Quick Start

Prerequisites

# Python 3.8 or higher
python --version

# Install dependencies
pip install tensorflow scikit-learn pandas numpy matplotlib seaborn opencv-python pillow

Running a Project

# Clone the repository
git clone https://github.com/ImdataScientistSachin/ML-DL-Portfolio.git
cd ML-DL-Portfolio

# Navigate to desired directory
cd "Deep Learning"
# or
cd "Machine Learning"

# Run any script
python Age_Gender_Detection2.py

Example: Age & Gender Detection

# Load the trained model
from tensorflow.keras.models import load_model
model = load_model('final_age_gender_model.keras')

# Predict on new image
age, gender, confidence = predict_age_gender('path/to/image.jpg', model)
print(f"Age: {age}, Gender: {gender} ({confidence*100:.2f}%)")

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📁 Repository Structure

📦 ML-DL-Portfolio
├── 📂 Deep Learning/
│   ├── 🎯 Age_Gender_Detection2.py          # Multi-task learning
│   ├── 🚦 ex_Traffic_sign_classification.py # 43-class CNN
│   ├── 🔄 17__Transfer_Learning.py          # MobileNetV3 fine-tuning
│   ├── 🐱 12__cat_dog_Classification_Argumentation.py
│   ├── 🌸 09_Flower_manual.py
│   ├── 👕 11_Fashion_Mist_CNN.py
│   └── ... (24 more projects)
│
├── 📂 Machine Learning/
│   ├── 🚗 Project_1_Predict_The_Price_of_Used_Car.py
│   ├── ⚙️ EX_HyperParameter_Tuning_Part2.py
│   ├── 🔍 EX_Feature Engineering_feature_extraction.py
│   ├── 📊 EX_AUC_ROC.py
│   ├── 🌲 EX_RandomForest_Classification(Baagging).py
│   ├── 📈 EX_polynomial_regression.py
│   └── ... (46 more projects)
│
└── 📄 README.md

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💡 Key Learnings

🎓 Technical Skills Developed

1. Deep Learning Mastery

   • Architecting CNNs from scratch
   • Leveraging transfer learning for limited data scenarios
   • Implementing advanced augmentation strategies
   • Optimizing training with callbacks and mixed precision

2. Machine Learning Expertise

   • End-to-end ML pipeline development
   • Statistical validation and assumption testing
   • Feature engineering and dimensionality reduction
   • Hyperparameter optimization strategies

3. Best Practices

   • Writing self-documenting, maintainable code
   • Implementing proper train/validation/test splits
   • Handling imbalanced datasets
   • Model evaluation and selection criteria

🏢 Business Value

• Predictive Analytics: Price prediction, sales forecasting
• Computer Vision: Age/gender detection, object classification
• Automation: Recommendation systems, automated classification
• Decision Support: Statistical modeling, risk assessment

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🤝 Connect With Me

Sachin Paunikar

Data Scientist | Machine Learning Engineer

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📬 Let's Collaborate!

I'm actively seeking opportunities in Data Science, Machine Learning Engineering, and AI Research.

Open to:

• Full-time positions
• Freelance projects
• Research collaborations
• Technical consulting

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_{⭐ If you find this repository helpful, please consider giving it a star!}

_{📝 All code is well-documented and ready for production use}

_{🔄 Regularly updated with new projects and improvements}

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with ❤️ by Sachin Paunikar

Transforming Data into Actionable Insights