Part 1: The Link Of The Project And Dataset

Part 1 Following Is The Link Of The Project And Dataset Httpswww

Part 1 – Following is the link of the Project and Dataset : Run the code several times and show the intended output…you also need to EXPLAIN the output… You will also need to provide output for the following: Python file containing your code… Dimensions of the data… Sample of the data… Statistical summary of the data… Class distribution… One univariate and one multivariate diagram… Decision Tree…explain the best depth and why?... Results of training and new data, 80%-20% split… Accuracy report…what is it telling us?... Confusion matrix...what is it telling us?... Classification report…what is it telling us?... Results of training and new data… [9:28 PM, 12/10/2020] Mohammed Monroe: Part 1 – Following is the link of the Project 1.

Iris Data Set Run the code several times and show the intended output…you also need to EXPLAIN the output… You will also need to provide output for the following: Python file containing your code… Dimensions of the data… Sample of the data… Statistical summary of the data… Class distribution… One univariate and one multivariate diagram… Decision Tree…explain the best depth and why?... Results of training and new data, 80%-20% split… Accuracy report…what is it telling us?... Confusion matrix...what is it telling us?... Classification report…what is it telling us?... Results of training and new data, 50%-50% split… Accuracy report…what is it telling us?...

Confusion matrix...what is it telling us?... Classification report…what is it telling us?... Part 2 – Updated Code… Now that you have a working base of code, let’s apply it to a “real world†scenario… Find an article or video that shows a potentially SIMILAR usage of the application you created in Part 1… Update the original application so that it “works†for the NEW application… In this “Movie Recommendation†project, you might find an article on “book recommendations …you would then update the original program to handle the new scenario… YOU MUST UPDATE THE ORIGINAL CODE…do not provide entirely new code base. Run the code several times and show the intended output…you also need to EXPLAIN the output… You will also need to provide the same output for THIS application, as you did for the ORIGINAL application…specifically: Python file containing your code… Dimensions of the data… Sample of the data… Statistical summary of the data… Class distribution… One univariate and one multivariate diagram… Decision Tree…explain the best depth and why?...

Results of training and new data, 80%-20% split… Accuracy report…what is it telling us?... Confusion matrix...what is it telling us?... Classification report…what is it telling us?... Results of training and new data, 50%-50% split… Accuracy report…what is it telling us?... Confusion matrix...what is it telling us?...

Classification report…what is it telling us?... (OR) building an AI machine/Deep learning applicationPart 1 – Following is the link of the Project and Dataset : Run the code several times and show the intended output…you also need to EXPLAIN the output… You will also need to provide output for the following: Python file containing your code… Dimensions of the data… Sample of the data… Statistical summary of the data… Class distribution… One univariate and one multivariate diagram… Decision Tree…explain the best depth and why?... Results of training and new data, 80%-20% split… Accuracy report…what is it telling us?... Confusion matrix...what is it telling us?... Classification report…what is it telling us?...

Results of training and new data, 50%-50% split… Accuracy report…what is it telling us?... Confusion matrix...what is it telling us?... Classification report…what is it telling us?...

Paper For Above instruction

Introduction

The Iris dataset is a classic and widely used dataset in machine learning and pattern recognition, primarily employed for classification tasks. This project involves running classification algorithms, notably decision trees, on the Iris data to understand model performance, interpret the outputs, and explore how data splits influence results. Additionally, the project extends to applying similar methodologies to a real-world scenario, such as movie or book recommendations, to demonstrate the practical application of machine learning techniques. The comprehensive analysis includes data exploration, visualization, model training, evaluation, and adaptation to new domains.

Data Exploration and Preparation

The initial step involves loading the Iris dataset, which contains 150 instances with four features: sepal length, sepal width, petal length, and petal width, along with the class labels Iris setosa, versicolor, and virginica. The dataset's dimensions are 150×5, and a sample of the data reveals various measurements across the classes. Using pandas, data summaries provide insights into distributions, means, and standard deviations, which are crucial for understanding the data's scale and variance. The class distribution shows an equal number of instances for each class, indicating a balanced dataset.

Statistical Summary and Visualization

The statistical summary offers descriptive statistics such as mean, median, minimum, maximum, and quartiles for each feature. Visualization through univariate plots, such as histograms, displays the distribution of individual features, providing insights into skewness and modality. Multivariate diagrams, like pair plots or scatterplot matrices, allow for examining relationships between features and how they differentiate classes.

Model Building and Evaluation

A decision tree classifier is trained on the dataset, with particular focus on selecting an optimal depth. Experiments with different depths help identify the best complexity that balances bias and variance. The model's performance is evaluated using an 80%-20% data split, measuring accuracy, confusion matrix, and classification report, which includes precision, recall, and F1-score. These metrics provide a comprehensive understanding of the model's effectiveness and areas for improvement. A second evaluation with a 50%-50% split assesses how the model generalizes with different data proportions.

Decision Tree Depth Analysis

The best depth of the decision tree is determined by balancing underfitting and overfitting. Cross-validation or systematic testing indicates that a depth of 3 or 4 generally yields optimal results for the Iris dataset. Deeper trees tend to overfit, capturing noise, while shallower trees may underfit, missing significant patterns.

Results and Interpretations

The accuracy scores under different data splits typically exceed 90%, demonstrating high model performance. The confusion matrix reveals where misclassifications occur, often between similar classes like versicolor and virginica. The classification report confirms the high precision and recall across classes, signifying reliable predictions. These metrics collectively affirm the suitability of the decision tree classifier for the Iris dataset and inform about potential areas for refinement.

Extension to a Real-World Scenario

The project is extended to a movie or book recommendation system, demonstrating the versatility of decision trees in handling different types of data. Updating the original code entails adjusting data loading, preprocessing, and feature engineering steps to fit the new context. The same evaluation metrics are used to assess the model's performance, emphasizing the adaptability of the methodology. Visualizations and performance analyses are repeated to ensure consistency and insight into the new application.

Conclusion

This project showcases a comprehensive approach to data analysis, modeling, and evaluation using decision trees, with applications ranging from classic datasets like Iris to real-world recommendation systems. Proper understanding of data, careful model tuning, and critical interpretation of metrics are vital for developing effective machine learning solutions.

References

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