Kaggle Courses: A comprehensive archive of completed Data Science, Machine Learning, and Data Manipulation courses. This repository documents mastery of the Python ecosystem through structured Kaggle exercises and certifications.
Source Code · Amey's Kaggle · Mega's Kaggle · Certifications
Authors · Overview · Courses · Structure · Certifications · Quick Start · Usage Guidelines · License · About · Acknowledgments
Important
Special thanks to Mega Satish for her meaningful contributions, guidance, and support that helped shape this work.
This repository was created as part of the Internship at Technocolabs Softwares. Curated by Amey Thakur and Mega Satish, this project documents our journey of learning and acquiring essential Data Science and Machine Learning skills through 7 official Kaggle courses. We successfully completed all course material, from data cleaning fundamentals to machine learning explainability, earning recognized certifications as a record of our achievement.
The project demonstrates a disciplined approach to upskilling in Data Science, leveraging the Python Ecosystem (Pandas, Numpy, Scikit-Learn, XGBoost) to solve real-world analytical problems.
The curriculum is governed by strict computational data science principles:
- Data Wrangling: Mastering
pandasfor efficient data manipulation, cleaning, and transformation. - Visual Analytics: Utilizing
matplotlibandseabornfor exploratory data analysis and publication-quality visualizations. - Machine Learning: Implementing robust models from decision trees to gradient boosting and understanding model explainability.
Tip
Kaggle Course Completion
This repository represents the successful completion of 7 Kaggle Courses. Courses successfully completed with Mega Satish. Each directory corresponds to specific course milestones, ensuring a linear and verifiable progression of skills.
- Exercise 1 - Creating, Reading and Writing
- Exercise 2 - Indexing, Selecting & Assigning
- Exercise 3 - Summary Functions and Maps
- Exercise 4 - Grouping and Sorting
- Exercise 5 - Data Types and Missing Values
- Exercise 1 - Handling Missing Values
- Exercise 2 - Scaling and Normalization
- Exercise 3 - Parsing Dates
- Exercise 4 - Character Encodings
- Exercise 5 - Inconsistent Data Entry
- Exercise 1 - Hello, Seaborn
- Exercise 2 - Line Charts
- Exercise 3 - Bar Charts and Heatmaps
- Exercise 4 - Scatter Plots
- Exercise 5 - Distributions
- Exercise 6 - Choosing Plot Types and Custom Styles
- Exercise 7 - Final Project
- Exercise 1 - Explore Your Data
- Exercise 2 - Your First Machine Learning Model
- Exercise 3 - Model Validation
- Exercise 4 - Underfitting and Overfitting
- Exercise 5 - Random Forests
- Exercise 6 - Machine Learning Competitions
- Exercise 1 - Mutual Information
- Exercise 2 - Creating Features
- Exercise 3 - Clustering With K-Means
- Exercise 4 - Principal Component Analysis
- Exercise 5 - Target Encoding
- Exercise 1 - Introduction
- Exercise 2 - Missing Values
- Exercise 3 - Categorical Variables
- Exercise 4 - Pipelines
- Exercise 5 - Cross-Validation
- Exercise 6 - XGBoost
- Exercise 7 - Data Leakage
- Exercise 1 - Permutation Importance
- Exercise 2 - Partial Plots
- Exercise 3 - SHAP Values
- Exercise 4 - Advanced Uses of SHAP Values
Note
Detailed code and notebooks for every single course are available in the repository structure. Refer to the directory tree below to navigate to specific topics.
KAGGLE/
│
├── docs/ # Documentation Layer
│ └── SPECIFICATION.md # Technical Architecture
│
├── Mega/ # Attribution Assets
│ ├── Filly.jpg # Companion (Filly)
│ └── Mega.png # Profile Image (Mega Satish)
│
├── Source Code/ # Core Learning Modules
│ ├── Data Cleaning/ # Missing Values, Dates, Encodings
│ ├── Data Manipulation/ # Pandas Mastery
│ ├── Data Visualization/ # Seaborn, Matplotlib
│ ├── Feature Engineering/ # PCA, Clustering, Encoding
│ ├── Machine Learning.../ # Intro & Intermediate ML
│ └── Machine Learning Explainability/ # SHAP, Permutation
│
├── Certificates/ # Course Completion Credentials
│ ├── Kaggle Pandas.png
│ ├── Kaggle Data Cleaning.png
│ ├── Kaggle Data Visualization.png
│ ├── Kaggle Intro to Machine Learning.png
│ ├── Kaggle Feature Engineering.png
│ ├── Kaggle Intermediate Machine Learning.png
│ └── Kaggle Machine Learning Explainability.png
│
├── .gitattributes # Git LFS/Attribute Configuration
├── .gitignore # Project Ignore Patterns
├── CITATION.cff # Project Citation Manifest
├── codemeta.json # Metadata Standard
├── LICENSE # MIT License
├── README.md # Project Entrance
└── SECURITY.md # Security ProtocolsCertified completion of comprehensive Data Science curriculum through Kaggle Learn.
- Python (3.7+): Core runtime environment. Download Python
- Jupyter Notebook: Interactive computing environment. Install Jupyter
Warning
Runtime Environment Guard
Python notebooks rely on relative file paths for datasets. Ensure you set your working directory to the repository root or the specific notebook folder before execution to prevent FileNotFoundError during data loading phases.
Open your terminal and clone the repository:
git clone https://github.com/Amey-Thakur/KAGGLE-COURSES.git
cd KAGGLE-COURSESEnsure all required Python libraries are installed. Open your terminal and run:
pip install pandas numpy matplotlib seaborn scikit-learn xgboost shapNavigate to the Source Code directory and launch Jupyter:
jupyter notebookTip
Experience the complete Kaggle Learning Ecosystem directly through this centralized Internship Archive. This hub serves as a scholarly gateway that orchestrates the mastery of seven core data science disciplines, providing a visual demonstration of skill evolution, credential validation, and featured project integration across the modern analytical landscape.
This repository is openly shared to support learning and knowledge exchange across the data science community.
For Students
Utilize this repository as a definitive roadmap for mastering the Kaggle learning platform. The structured course progression offers a rigorous, measurable pathway to transition from data cleaning fundamentals to advanced machine learning explainability.
For Educators
Adopt this curriculum architecture as a modular template for designing intensive data science workshops or accelerated machine learning bootcamps, providing a proven pedagogical framework for technical capability building.
For Researchers
Reference these artifacts as a verifiable case study in self-paced technical education, demonstrating the efficacy of structured Kaggle courses in rapid skill acquisition and applied machine learning.
This repository and all its creative and technical assets are made available under the MIT License. See the LICENSE file for complete terms.
Note
Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original authors.
Copyright © 2021 Amey Thakur & Mega Satish
Created & Maintained by: Amey Thakur & Mega Satish
This repository was created as part of the Internship at Technocolabs Softwares. Together with Mega Satish, we embarked on a journey to learn and acquire essential Data Science and Machine Learning skills through structured Kaggle courses. This repository serves as a record of our learning experience and the certifications we earned along the way. It documents our progression from data cleaning and manipulation fundamentals to advanced feature engineering and model explainability.
Connect: GitHub · LinkedIn · ORCID
Grateful acknowledgment to Mega Satish for her exceptional collaboration and scholarly partnership during these Kaggle courses. Her intellectual agility, a veritable superpower to rapidly synthesize complex logic and articulate it with clarity, was the driving force behind the successful mastery of this intensive curriculum. She navigated advanced data science and machine learning concepts with remarkable speed, clarifying intricate details in a way that made the learning process reciprocal and effortless. Her engagement was essential for the completion of these course milestones; this educational journey would not have been possible without her steady discipline, ability to simplify the complex, and constant encouragement. Thank you, Mega, for everything you shared and taught along the way.
Special thanks to Technocolabs Softwares for facilitating this internship experience and providing the structured learning environment that made these achievements possible.
Authors · Overview · Courses · Structure · Certifications · Quick Start · Usage Guidelines · License · About · Acknowledgments
🏆 Kaggle
Computer Engineering (B.E.) - University of Mumbai
Semester-wise curriculum, laboratories, projects, and academic notes.