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ML Lab Questions

1. Using matplotlib and seaborn to perform data visualization on the standard dataset a. Perform the preprocessing b. Print the no of rows and columns c. Plot box plot d. Heat map e. Scatter plot f. Bubble chart g. Area chart 2. Build a Linear Regression model using Gradient Descent methods in Python for a wine data set 3. Build a Linear Regression model using an ordinary least-squared model in Python for a wine data set  4. Implement quadratic Regression for the wine dataset 5. Implement Logistic Regression for the wine data set 6. Implement classification using SVM for Iris Dataset 7. Implement Decision-tree learning for the Tip Dataset 8. Implement Bagging using Random Forests  9.  Implement K-means Clustering    10.  Implement DBSCAN clustering  11.  Implement the Gaussian Mixture Model  12. Solve the curse of Dimensionality by implementing the PCA algorithm on a high-dimensional 13. Comparison of Classification algorithms  14. Compa...
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Gaussian Mixture Model

A Gaussian Mixture Model (GMM) is a probabilistic model used for clustering and density estimation. It assumes that data is generated from a mixture of several Gaussian distributions, each representing a cluster within the dataset. Unlike K-means, which assigns data points to the nearest cluster centroid deterministically, GMM considers each data point as belonging to each cluster with a certain probability, allowing for soft clustering. GMM is ideal when: Clusters have elliptical shapes or different spreads : GMM captures varying shapes and densities, unlike K-means, which assumes clusters are spherical. Soft clustering is preferred : If you want to know the probability of a data point belonging to each cluster (not a hard assignment). Data has overlapping clusters : GMM allows a point to belong partially to multiple clusters, which is helpful when clusters have significant overlap. Applications of GMM Image Segmentation : Used to segment images into regions, where each region can be...
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Logistic Regression

Logistic regression is a statistical method used for binary classification problems. It's particularly useful when you need to predict the probability of a binary outcome based on one or more predictor variables. Here's a breakdown: What is Logistic Regression? Purpose : It models the probability of a binary outcome (e.g., yes/no, success/failure) using a logistic function (sigmoid function). Function : The logistic function maps predicted values (which are in a range from negative infinity to positive infinity) to a probability range between 0 and 1. Formula : The model is typically expressed as: P ( Y = 1 ∣ X ) = 1 1 + e − ( β 0 + β 1 X ) P(Y = 1 | X) = \frac{1}{1 + e^{-(\beta_0 + \beta_1 X)}} P ( Y = 1∣ X ) = 1 + e − ( β 0 ​ + β 1 ​ X ) 1 ​ Where P ( Y = 1 ∣ X ) P(Y = 1 | X) P ( Y = 1∣ X ) is the probability of the outcome being 1 given predictor X X X , and β 0 \beta_0 β 0 ​ and β 1 \beta_1 β 1 ​ are coefficients estimated during model training. When to Apply Logistic R...
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