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Boosterboy12 / repository
My 30-Day Data Science & Machine Learning Grind! Day-by-day implementation of core libraries, starting from NumPy matrix manipulation to advanced ML models. Fast-paced, efficient, and 100% hands-on. 🚀
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This repository documents my high-intensity 30-day journey into the depths of Data Analytics, Advanced Python, and Algorithmic Logic. This is not just about writing scripts; it's about building scalable pipeline logic, engineering custom data visualizations, and maintaining strict version control discipline.
| Day | Core Engineering Focus | Status | Deliverables & Insights |
|---|---|---|---|
| 01 | Basic Syntax & Algorithmic Logic | ✅ Done | Built foundational control flows and basic logic blocks. |
| 02 | Advanced Git Workflows & Structure | ✅ Done | Modular repository mapping and structured clean file layouts. |
| 03 | Pandas: Data Manipulation & Cleaning | ✅ Done | Ingested hardware datasets from Kaggle; executed deep cleaning pipelines. |
| 04 | Advanced Pandas & Matrix Operations | ✅ Done | Handled aggregations, indexing, and generated relational Heatmaps. |
| 05 | Matplotlib: Core Visual Foundations | ✅ Done | Categorical plotting (Vertical/Horizontal Bars, Scatters, Lines). |
| 06 | Matplotlib Destruction & Custom Art | ✅ Done | Mastered plt.style. Engineering Combo & Dual Y-Axis Dashboards. |
| 07 | Essence Of Linear Algebra | ✅ Done | Completed The 3 Blue 1 Brown PLaylist Essence Of Linear Algebra |
| 08 | Started Linear Regression | ✅ Done | Made A Simple Linear Regression Model |
| 09 | Linear Regression Finished | ✅ Done | Completed Linear Regression And Started Logistic Regression |
| 10 | Finished Logistic Regression | ✅ Done |
To run these data pipelines smoothly, the following system configurations are recommended:
| Resource | Minimum Requirement | Recommended Configuration |
|---|---|---|
| Python Version | Python 3.8 | Python 3.11+ |
| RAM | 4 GB | 8 GB or above (For large Kaggle Dataframes) |
| IDE | Terminal / IDLE | VS Code (With Jupyter Notebook Extensions) |
| Core Libraries | pandas, numpy | pandas, numpy, matplotlib, seaborn |
git clone [https://github.com/Boosterboy12/30-Days-Extreme-Challenge.git](https://github.com/Boosterboy12/30-Days-Extreme-Challenge.git)
cd 30-Days-Extreme-Challenge
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| Completed Logistic Regression And Started Decision Tree |
| 11 | Finished Decision Tree | ✅ Done | Did Some Practice Of Decision Trees & Finished It |
| 12 | Data Preprocessing | ✅ Done | Learned Onehot Encoder, Standard Scaler & Label Encoder |
| 13 | Over & UnderFitting | ✅ Done | Understood Concepts Of Underfitting, Overfitting, Bias & Variance |
| 14 | Ml Pipeline 1 | ✅ Done | Build An Complete Ml Pipeline On Housing Data |
| 15 | Ml Pipeline 2 | ✅ Done | Build An Complete Ml Pipeline On Heart Disease Data |
| 16 | Perceptron | ✅ Done | Introduction To Deep Learning: Understoond Concepts Like Perceptron & Forward Propogation (NO CODE) |
| 17 | Backward Propogation | ✅ Done | Finished The Concept Of Backward Propogation (NO CODE) |
| 18 | Pytorch | ✅ Done | Started Pytorch, Understood What is A Tensor & Tensor Operations |
| 19 | ANN | ✅ Done | Made A Simple ANN Model |
| 20 | CNN | ✅ Done | Made A CNN Model With A 99% Accuracy |
| 21 | NLP | ✅ Done | Understood the basic concepts of NLP and made an NLP model |
| 22 | Attention Mechanics | ✅ Done | Understood the concepts of Attention Mechanics (NO CODE ) |
| 23 | LLM | ✅ Done | Understood the concepts of LLMMs (NO CODE ) |
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