Welcome! This repository documents my journey in mastering Python, Data Engineering, and Applied Machine Learning.
Focus: Advanced Logic and Local Data Architecture (No Cloud Dependency).
- Languages: Python (Advanced Logic & OOP).
- Data Science: Pandas, NumPy, Scikit-Learn (Machine Learning).
- Visualization: Matplotlib (Advanced Plotting).
- Databases: SQLite (Local Data Architecture).
- Mathematical Foundations: Expert understanding of Probability, Linear Algebra, and Statistics to build logically sound algorithms.
- Pythonic Logic: Crafting clean, high-performance code with a focus on advanced Data Structures and Algorithms.
- Data Mastery (SQL & Analysis): * SQL: Designing and managing local databases to store and retrieve data efficiently.
- Data Analysis: Extracting insights from raw data, cleaning datasets, and performing exploratory data analysis (EDA).
- Predictive Modeling: Leveraging Machine Learning to build models that perform data prediction and trend forecasting.
- Goal: An intelligent task manager using algorithmic sorting to handle complex daily schedules.
- Logic: Built with Python, featuring custom sorting logic and data validation.
- Key Features: Priority-based organization, nested data handling, and clean console interface.
- Code Location:
Smart-To-Do/main.py
- Goal: A professional banking engine with a built-in AI predictor for financial trends.
- Logic: Built with Python & SQLite for persistent data storage (Level 3).
- AI Feature: Uses
Scikit-Learn(Linear Regression) to forecast future balance trends based on history (Level 4). - Data Visualization: Integrated
Matplotlibfor visual financial analytics. - Code Location:
AI-Bank-System/main.py
- Goal: A full-scale data pipeline that automates the journey from raw data generation to AI-driven sales forecasting.
- Logic: Features a modular 4-stage architecture including automated data generation, intelligent cleaning, and predictive modeling.
- Data Mastery: Implements logic-based imputation using Pandas to mathematically calculate missing values and standardize inconsistent datasets.
- AI Forecasting: Utilizes Scikit-Learn (Linear Regression) to analyze 24-month trends and predict future revenue with visual forecasting (Level 4).
- Advanced Visualization: Integrated Matplotlib to generate trend lines, revenue distribution pie charts, and future forecast points.
- Code Location:
Sales-Data-AI-Analyzer/main.py
- Goal: A high-precision model to estimate property values based on socio-economic and geographical data.
- Logic: Implements advanced data preprocessing including
StandardScalerfor feature normalization and correlation analysis. - AI Feature: Uses
Linear Regression(Level 4 Mastery) with multi-variable inputs to forecast real estate prices. - Insights: Includes "Feature Importance" visualization to identify the most critical factors driving market value.
- Code Location:
Real-Estate-Price-Predictor-AI/main.py
- Goal: A basic practice project to try and distinguish between normal and suspicious bank transactions.
- How it works: I used a simple Random Forest model to flag transactions based on basic rules (like the amount and the time of the transaction).
- What I learned: I built this to understand the basics of data classification and how to visualize results using a simple Confusion Matrix.
- Status: This is an initial educational implementation to explore AI security basics.
- Code Location:
AI-Financial-Fraud-Detector/main.py
To explore these projects locally:
- Clone the repository:
git clone https://github.com/PhilopateerDev/My-Projects.git - Install Dependencies:
pip install pandas numpy scikit-learn matplotlib seaborn joblib
I am dedicated to solving data-driven problems through clean code and rigorous logic. Explore my repositories to see my latest local implementations.