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Machine Learning Lab PCCSL508 Semester 5 KTU CS 2024 Scheme - Dr Binu V P

About Me - Dr Binu V P Syllabus Learn Python well before You start(focus on numpy,pandas,matplotlib )- refer blog Build a strong understanding of the theory before moving on to programming Recommended Tools and Setup for Lab Experiments Regression  Explore Californoa Housing Dataset(learn pandas,scikit-learn and matplotlib ) Simple Linear Regression using Sample Data ( Single variable, Toy Example) Simpe Linear Regression using Sample Data ( using scikit-learn) Simple Linear regression using California Housing Dataset(CSV input) Simple Linear Regression Using California Housing Dataset (using scikit-learn) Simple Linear Regression using Gradient Descent ( Single variable, Toy Example) Simple Linear regression using Gradient Descent on California Housing Dataset Multiple Linear Regression using using Matrix Form ( Toy Dataset) Multiple Linear Regression using scikit-learn for House Price Prediction (synthetic data) Multivariate Linear Regression using Gradient Descent (Toy Dataset) ...
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Explore California Housing Dataset

  California Housing Dataset 🔹 Dataset Characteristics Feature Description Number of Instances                20,640 Number of Attributes                8 numerical predictive attributes + 1 target + 1 categorical Target Variable                Median House Value 🔹 Context This dataset is used in the book “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron . It is widely used as an introductory dataset for machine learning because: Requires basic preprocessing Has clear and interpretable features Is moderate in size (not too small, not too large) 🔹 Dataset Description The dataset contains information about housing in California districts based on the 1990 U.S. Census . Each row represents a census block group , which is: The smallest geographical unit used by the census Typically contains ...
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Recommended Tools and Setup for Lab

  Recommended Tools  Core Stack  Tool Purpose Python           Main language Jupyter Notebook           Interactive lab work NumPy           Numerical computation Pandas           Data handling Matplotlib / Seaborn           Visualization Scikit-learn           ML algorithms Advanced Tools  Tool Use Google Colab      No-install lab  Kaggle Notebooks      datasets + practice TensorFlow / PyTorch           neural networks Streamlit      mini project deployment  Best Offline Setup (Recommended)  Use Anaconda (Easiest) This is the best choice for students . 🔹 Steps: Download Anaconda from  https://www.anaconda.com/download Install it It includes: Python Jupyter Notebook NumPy, ...
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Implementation of Multivariate Linear Regression using Gradient Descent (Toy Dataset)

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  Experiment  Implementation of Multivariate Linear Regression using Gradient Descent (Toy Dataset) 🎯 Aim To implement multivariate linear regression using gradient descent and evaluate its performance. 🎯 Objectives Understand multivariate regression Implement gradient descent manually Train model on sample dataset Compute MSE and R² Visualize cost convergence 📖 Theory 🔹 Multivariate Linear Regression Model y ^ = θ 0 + θ 1 x 1 + θ 2 x 2 + ⋯ + θ n x n \hat{y} = \theta_0 + \theta_1 x_1 + \theta_2 x_2 + \dots + \theta_n x_n ​ Matrix form: y ^ = X θ \hat{y} = X\theta 🔹 Cost Function (MSE) J ( θ ) = 1 n ∑ ( y − y ^ ) 2 J(\theta) = \frac{1}{n} \sum (y - \hat{y})^2 🔹 Gradient Descent Update Rule θ = θ − α ⋅ 2 n X T ( X θ − y ) \theta = \theta - \alpha \cdot \frac{2}{n} X^T (X\theta - y) 🔹 Evaluation Metrics Mean Squared Error (MSE): M S E = 1 n ∑ ( y − y ^ ) 2 MSE = \frac{1}{n} \sum (y - \hat{y})^2 R-squared (R²): R 2 = 1 − S S r e s S S t o ...
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