Data Visualization and Machine Learning Projects

Description

In this project, we will explore various data visualization and machine learning tasks using Python libraries like scikit-learn, Seaborn, and Matplotlib.

Part A: Dimensionality Reduction and Visualization

1. Reimplement the Iris dataset clustering visualization from Chapter 15, but this time, perform dimensionality reduction using scikit-learn's TSNE estimator. Visualize the results and compare the clusters to the ones created in the clustering case study.

Part B: Seaborn Pair Plot for California Housing Dataset

2. Create a Seaborn pair plot graph for the California Housing dataset. Explore the Matplotlib features for panning and zooming within the diagram to analyze the data more effectively.

Part C: Classification on Iris Dataset

3. The Iris dataset is labeled, making it suitable for supervised machine learning. Load the Iris dataset and perform classification using the k-nearest neighbors algorithm with a KNeighborsClassifier and the default k value. Report the prediction accuracy.

Part D: Multiple Linear Regression on Diabetes Dataset

4. Investigate the Diabetes dataset bundled with scikit-learn. Reimplement the steps of the multiple linear regression case study from Chapter 15.5. This dataset contains 442 samples, each with 10 features and a label indicating "disease progression one year after baseline."

Part E: Decision Trees on Titanic Disaster Dataset

5. Research and load the Titanic Disaster dataset from the RDatasets repository. Use the DecisionTreeClassifier to build a decision tree for predicting whether a passenger survived or died. Output the decision tree in the DOT graphing language using export_graphviz. Visualize the decision tree using Graphviz.