📊 ML Algorithms Basics – Hands-on Assignments

NOTE: Dataset Links will be updated soon.

This repository contains Jupyter Notebooks covering foundational machine learning algorithms implemented as part of a Pattern Recognition & Machine Learning (PRML) course. Each notebook focuses on a key algorithm or concept, implemented using Python and scikit-learn.

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🔍 Contents

#	Notebook	Topic
1	Household_Energy_Analysis	Time-Series Analysis
2	Bengaluru_Housing_LinearRegression	Linear Regression
3	Bike_Sharing_Demand_Prediction	3D Visualization, Regression
4	KNN_LogReg_SVM_Classification	Classification Models
5	Naive_Bayes_Classifier	Probabilistic Classification
6	KNN_CrossValidation_MNIST	k-NN with Cross Validation
7	Bike_Sharing_Regression_3D	Advanced Linear Regression
8	Customer_Segmentation_KMeans	Clustering
9	Ensemble_Learning_Bagging_Boosting	Bagging & Boosting
10	Regularization_Regression_Lab	Ridge, Lasso Regularization
11	Basic_KMeans_Clustering	Intro to Clustering

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🛠 Tech Stack

• Python 3.x
• Jupyter Notebook
• scikit-learn
• pandas, numpy, matplotlib, seaborn

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🚀 Getting Started

1. Clone the repository
   git clone https://github.com/deena-lad/ML-Algorithms.git
2. Navigate to the folder
   cd ML-Algorithms-Basics
3. Launch Jupyter
   jupyter notebook

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📌 License

This project is for educational and demonstration purposes.