A collection of Machine Learning algorithm implementations in Python using libraries like scikit-learn, pandas, numpy, matplotlib, and seaborn. Covers Regression, Classification, Clustering, and Reinforcement Learning with practical datasets like Titanic, Boston Housing, Iris, and Mall Customers.
Data Preprocessing: Handle missing values, encoding, scaling, normalization, and feature selection. Regression Algorithms: Linear Regression, Ridge, Lasso, Polynomial Regression, and evaluation using RMSE, R², and MSE. Classification Algorithms: Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Decision Trees, and Random Forests. Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN. Evaluation & Visualization: Confusion matrices, ROC curves, learning curves, feature importance, cluster visualization. Reinforcement Learning: Basic Q-Learning implementations on grid-based environments.
#Datasets
Projects use real-world structured datasets, including: Titanic Survival Dataset Boston Housing Dataset Iris Flower Dataset Adult Census Dataset Mall Customer Dataset Bank Marketing Dataset
End-to-end ML workflow: data exploration → preprocessing → model training → evaluation → visualization. Focus on practical understanding and experimentation with Python ML libraries. Suitable for beginners to intermediate learners wanting to strengthen ML skills.
Getting Started Clone the repository: git clone https://github.com/your-username/Specialization-TT.git
Open the notebooks in Google Colab or Jupyter Notebook to run and experiment with the code.