🚀 Python Data Representation & Analysis Projects

A comprehensive collection of advanced Python projects showcasing signal processing, time series analysis, graph algorithms, and machine learning techniques for real-world data challenges.

📋 Project Overview

This repository contains cutting-edge implementations of data representation and analysis techniques across multiple domains, demonstrating proficiency in Python programming, mathematical modeling, and data science methodologies.

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🎵 A6 Audio Signal Processing & Compression

Exploring the frequency domain of audio signals through advanced DSP techniques

🔧 Key Features:

• Audio File Processing 🎧 - Load and playback WAV files using Python libraries
• Fast Fourier Transform (FFT) 📊 - Convert time-domain signals to frequency representations
• Signal Combination 🔀 - Merge frequency domain representations of multiple audio sources
• Inverse FFT (IFFT) ↩️ - Reconstruct time-domain signals from frequency data
• Short-Time Fourier Transform (STFT) ⏱️ - Analyze time-frequency characteristics
• Spectrogram Visualization 🌈 - Create compelling visual representations of audio data

💡 Technical Highlights:

• Real-time audio processing and playback
• Advanced frequency domain analysis and manipulation
• Time-frequency decomposition for complex audio signals
• Interactive spectrogram interpretation

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📈 A7: Time Series & Graph Analytics

Advanced temporal data analysis and graph theory implementations

📊 Time Series Analysis Features:

• Data Preprocessing 🔄 - Pandas-based datetime parsing and indexing
• Temporal Visualization 📉 - Multi-variable time series plotting with correlation analysis
• Decomposition Analysis 🧩 - Trend and seasonal component extraction
• Signal Smoothing 🌊 - Simple Moving Average (SMA) and Exponential Moving Average (EMA)
• Stationarity Testing 📏 - Augmented Dickey-Fuller (ADF) statistical tests
• Differencing Techniques 🔄 - First-order differencing for stationarity achievement

🌐 Graph Algorithm Implementations:

• Dijkstra's Algorithm 🗺️ - Shortest path computation from Chicago O'Hare (ORD)
• Minimum Spanning Tree 🌳 - Both Prim's and Kruskal's algorithm implementations
• Weighted Graph Processing ⚖️ - Real-world airport network analysis

💻 Technical Stack:

• Libraries: Pandas, NumPy, Matplotlib, NetworkX, Statsmodels
• Algorithms: Advanced graph traversal and optimization techniques
• Statistical Methods: Time series stationarity testing and decomposition

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🤖 A8: Machine Learning for Text & Images

Dual-domain ML applications: NLP sentiment analysis and computer vision

📝 Part 1: Sentiment Classification Engine

• Text Preprocessing Pipeline 🔧 - Tokenization, stopword removal, stemming/lemmatization
• Feature Engineering 📊 - Advanced text vectorization techniques
• Data Visualization 📈 - Word frequency plots, word clouds, distribution analysis
• Binary Classification 🎯 - Logistic Regression vs Support Vector Machine comparison
• Performance Metrics 📏 - Comprehensive accuracy reporting and confusion matrices

🖼️ Part 2: Facial Image Reconstruction

• Computer Vision Processing 👁️ - Olivetti Faces dataset manipulation
• Image Preprocessing 🎨 - Vertical splitting and data preparation
• Support Vector Regression 🧠 - Left-to-right face completion model
• Visual Results 🖥️ - Side-by-side reconstruction comparisons
• Bonus Challenge ⭐ - Random Forest Regression implementation and comparison

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🛠️ Technologies & Libraries Used

# Core Data Science Stack
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Signal Processing
from scipy.fft import fft, ifft
from scipy.signal import stft
import librosa
import soundfile as sf

# Machine Learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import accuracy_score, confusion_matrix

# Time Series Analysis
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller

# Graph Algorithms
import networkx as nx
import heapq

🎯 Skills Demonstrated

• Signal Processing 🔊 - FFT/IFFT, STFT, spectrogram analysis
• Time Series Analysis ⏰ - Decomposition, stationarity, smoothing techniques
• Graph Theory 🕸️ - Shortest path algorithms, MST implementations
• Machine Learning 🤖 - Classification, regression, model comparison
• Data Visualization 📊 - Advanced plotting and interpretation
• Statistical Analysis 📐 - Hypothesis testing, performance evaluation

🚦 Getting Started

1. Clone the repository:

   git clone [repository-url]
   cd python-data-representation-projects

2. Install dependencies:

   pip install -r requirements.txt

3. Run individual assignments:

   python assignment_6_audio_processing.py
   python assignment_7_timeseries_graphs.py
   python assignment_8_ml_classification.py

🏆 Project Outcomes

This repository demonstrates advanced proficiency in:

• Data Science Methodologies - End-to-end analysis pipelines
• Algorithm Implementation - From mathematical concepts to working code
• Real-World Applications - Audio processing, financial time series, NLP, computer vision
• Performance Optimization - Efficient data structures and processing techniques

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📊 Future Enhancements

• Real-time audio processing dashboard
• Interactive time series forecasting models
• Deep learning implementations for image reconstruction
• Graph neural network applications
• Deployment-ready ML model APIs

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