🚀 Python & Machine Learning Revision Lab

A structured, hands-on technical repository covering Python Fundamentals → Data Analysis → Machine Learning → Model Evaluation → Ensemble Learning

This repository documents my continuous and disciplined learning journey in Python, Data Analysis, and Machine Learning through structured notebooks, experiments, and real-code implementations.

It is designed as a progressive technical roadmap, moving from core programming foundations to advanced ML concepts, with practical implementation at every step.

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📌 Objective

• Strengthen core Python fundamentals
• Build strong logical and problem-solving ability
• Practice real-world implementation of ML algorithms
• Understand complete machine learning workflows
• Create a structured technical reference for interviews
• Maintain consistent daily technical revision

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🐍 1️⃣ Python Core Foundations

A complete revision of Python fundamentals with structured experimentation.

🔹 Fundamentals

• Variables & Data Types
• Type Casting
• Input / Output Operations
• Keywords & Identifiers

🔹 Control Flow

• Conditional Statements (if, elif, else)
• Looping (for, while)
• Loop Control (break, continue, pass)

🔹 Data Structures

• List – indexing, slicing, methods
• Tuple – immutability & operations
• Set – uniqueness & set operations
• Dictionary – key-value logic & methods

🔹 Strings

• Indexing & slicing
• Built-in string methods
• Palindrome logic
• Conversions & manipulations

🔹 Functions

• User-defined functions
• Positional & keyword arguments
• *args and **kwargs
• Return statements
• Built-in functions

🔹 Object-Oriented Programming

• Classes & Objects
• Constructors (__init__)
• Attributes & Methods
• Encapsulation
• Polymorphism
• Real-world modeling examples

🔹 Exception Handling

• try, except, else, finally
• Handling common runtime errors

🔹 File Handling

• File modes (r, w, a)
• Reading & writing files
• Best practices

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📊 2️⃣ Data Analysis with Python Libraries

Hands-on implementation using industry-standard libraries.

🔹 NumPy

• Array creation & operations
• Numerical computation
• Saving & loading .npy files

🔹 Pandas

• DataFrame creation
• Data cleaning
• Filtering & aggregation
• CSV handling

🔹 Data Visualization

• Matplotlib – core plotting
• Seaborn – statistical visualization
• Plot exporting & analysis

This section builds a strong foundation for Data Analyst and ML roles.

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🤖 3️⃣ Machine Learning Roadmap

A structured and progressive implementation of ML concepts using scikit-learn.

🔹 Machine Learning Workflow

• Introduction to ML
• Feature & Label understanding
• Train-Test Split
• Data preprocessing fundamentals

🔹 Supervised Learning

📈 Regression

• Linear Regression
• Multiple Linear Regression
• Polynomial Regression

📊 Classification

• Logistic Regression (Binary & Multi-class)
• Decision Tree
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Support Vector Machine (SVM)
• Random Forest

🔹 Model Evaluation

• Precision
• Recall
• F1 Score
• Support
• Confusion Matrix

🔹 Model Optimization & Validation

• Underfitting vs Overfitting
• Bias-Variance Understanding
• K-Fold Cross Validation

🔹 Unsupervised Learning

• K-Means Clustering
• Principal Component Analysis (PCA)

🔹 Ensemble Learning

• Bagging
• Random Forest
• Model aggregation concepts

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🧠 What This Repository Demonstrates

✔ Strong Python foundation
✔ Hands-on implementation of ML algorithms
✔ End-to-end ML workflow understanding
✔ Knowledge of evaluation metrics
✔ Understanding of cross-validation techniques
✔ Awareness of bias-variance tradeoff
✔ Practical usage of scikit-learn
✔ Structured and disciplined learning approach

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🛠 Tech Stack

• Python 3
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook
• VS Code

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🎯 Ideal For

• Python beginners
• Data Analyst aspirants
• Machine Learning beginners
• Interview preparation
• Self-learners following a structured roadmap

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🚀 How to Use

git clone https://github.com/rahulgoraksha/python-revision-experiments.git
cd python-revision-experiments