Learning Core Machine Learning using Scikit-learn, feature engineering, model evaluation, and deployment. This repository tracks my complete Phase 2 journey — one commit every day.
A structured self-study roadmap covering supervised machine learning, classification algorithms, feature engineering, model evaluation, SQL for ML, and real-world deployment projects.
week1/ → Scikit-learn Pipelines, Data Preprocessing, Gradient Descent, Logistic Regression Theory
week2/ → Logistic Regression Implementation, Evaluation Metrics, Overfitting & Underfitting
week3/ → Feature Engineering, Correlation Analysis, Decision Trees
week4/ → SQL for ML, Regularization, Random Forest, Imbalanced Data
week5/ → Major Project — Hyderabad Rental Price Estimator (Coming Soon)
Python 3.11 • NumPy • Pandas • Matplotlib • Seaborn • Scikit-learn • SQL • Streamlit
B.Tech CSE (AI & ML) @ Keshav Memorial College of Engineering (KMCE), Hyderabad
🚀 Machine Learning Enthusiast • Python Developer • Open Source Learner
✅ Week 1 — Pipelines + Gradient Descent + Logistic Regression Theory
⬜ Week 2 — Logistic Regression + Evaluation Metrics
⬜ Week 3 — Feature Engineering + Decision Trees
⬜ Week 4 — Random Forest + SQL + Regularization
⬜ Week 5 — Hyderabad Rental Price Estimator
By completing this repository, I will gain hands-on experience with:
- Data Preprocessing
- Machine Learning Pipelines
- Logistic Regression
- Feature Engineering
- Decision Trees
- Random Forest
- Model Evaluation
- SQL for Data Analysis
- End-to-End Machine Learning Projects
- Streamlit Deployment
STARTING SOON
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