{"id":26609265,"url":"https://github.com/mdalamin5/data-science-machine-learning-basics","last_synced_at":"2026-05-15T01:03:05.639Z","repository":{"id":210718197,"uuid":"726131922","full_name":"MDalamin5/Data-Science-Machine-Learning-Basics","owner":"MDalamin5","description":"This repository is a comprehensive guide to Machine Learning algorithms, Python OOP, data preprocessing, and visualization using Pandas, NumPy, Seaborn, Scikit-learn, and more. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **Comprehensive Guide to Machine Learning \u0026 Python OOP**  \n\n## **Overview**  \nThis repository serves as a **comprehensive resource** for understanding **Machine Learning algorithms**, **Python Object-Oriented Programming (OOP)**, **data preprocessing**, and **visualization techniques** using industry-standard tools.  \n\n\n\n## **Topics Covered**  \n\n### 🔹 **Machine Learning Algorithms**  \n✔ **Supervised Learning:** Linear Regression, Logistic Regression, Decision Trees, SVM, KNN  \n✔ **Unsupervised Learning:** K-Means Clustering, PCA, DBSCAN  \n✔ **Ensemble Methods:** Random Forest, Gradient \n✔ **Deep Learning (Basic):** Neural Networks, CNN, RNN (Intro)  \n\n### 🔹 **Data Preprocessing Techniques**  \n✔ Handling **Missing Values** (Mean/Mode Imputation, Interpolation)  \n✔ **Feature Scaling:** Min-Max Scaling, Standardization  \n✔ **Categorical Encoding:** One-Hot Encoding, Label Encoding  \n✔ **Feature Selection:** Correlation Analysis, Recursive Feature Elimination (RFE)  \n\n### 🔹 **Visualization Techniques**  \n✔ **Seaborn \u0026 Matplotlib:** Histograms, Pair Plots, Heatmaps  \n✔ **Pandas Profiling:** Automated EDA  \n✔ **Plotly \u0026 Interactive Visuals:** Scatter Plots, Line Graphs, 3D Plots  \n\n### 🔹 **Python OOP in Machine Learning**  \n✔ **DataPreprocessor Class** (Handles missing values, encoding, scaling)  \n✔ **ModelTrainer Class** (Fits and evaluates ML models)  \n✔ **Visualizer Class** (Generates charts \u0026 plots for analysis)  \n✔ **Pipeline Implementation** (Combining preprocessing, training, and evaluation)  \n\n## **Installation**  \nTo set up the environment, install dependencies with:  \n```\npip install -r requirements.txt\n```\n\n## **Future Enhancements**  \n🚀 Implement Deep Learning models for advanced tasks  \n🚀 Add more real-world datasets for hands-on learning  \n🚀 Expand visualization techniques with interactive tools  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