apply machine learning algorithms learned to real-world datasets, analyze the results, and present findings

Instructions:

1. Select a Dataset:

   • Choose any publicly available dataset of interest. You can explore various sources such as Kaggle or any other reputable data repository. Do not use proprietary or private data
   • Ensure that the dataset is suitable for classification or regression
   • Use the code we used in class. Do not use ANY other code. 

2. Data Exploration and Preprocessing:

   • Preprocess the data as necessary, including handling missing values, encoding categorical variables, scaling numerical features, etc.

3. Algorithm Selection and Implementation:

   • Select any machine learning algorithm(s) that we have covered so far in class. Do not use dimensionality reduction techniques or Neural Networks.
   • Implement the selected algorithm(s) using a machine learning library from scikit-learn only

4. Model Training and Evaluation:

   • Split the dataset into training and testing sets 
   • Train the model on the training set and evaluate its performance on the testing set using appropriate metrics 

5. Results Analysis and Interpretation:

   • Interpret the model’s performance and discuss any insights gained from the analysis.

6. Documentation and Submission:

   • Prepare a jupyter notebook and submit it via Canvas. You do not need to create a report. Just the jupyter notebook.