Written response_ ArashKarimi _ 301303157

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Centennial College *

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247

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Statistics

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Apr 3, 2024

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docx

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Uploaded by DrCrownDove37

Exercise 1: 2d) Data Visualization: Utilizing libraries such as Pandas, Matplotlib, and Seaborn, we aim to generate a series of plots (three to five) to visualize the dataset from various perspectives. The types of plots include: Distribution plots: To showcase the distribution of continuous variables within the dataset. Boxplots: Useful for visualizing the spread and centers of data, as well as outliers. Heatmaps for correlation: To represent the pairwise correlations between variables. These visualizations will facilitate a better understanding of the data, highlighting underlying patterns, distributions, and potential relationships that warrant further investigation. 6) Data Preparation: In this step, the 'ID' column is removed from the dataset using the Pandas library. This is a common practice when the 'ID' is merely a unique identifier that does not contribute predictive power to the model. 10) Model Evaluation: We evaluate the performance of a Support Vector Machine (SVM) model with a linear kernel by computing accuracy scores for both the training set (X_train, y_train) and the test set (X_test, y_test). The results of these evaluations provide insights into the model's ability to fit the data and its predictive performance on unseen data. 11) Accuracy Matrix: The term 'accuracy matrix' likely refers to the confusion matrix, a critical tool for assessing the performance of a classification model. It details the true positives, false positives, true negatives, and false negatives, enabling a nuanced assessment of the model's performance. Interpretation of Outputs:

First Image (Distribution Plot): The plot displays the distribution of the 'ID' column. Typically, 'ID' columns contain unique identifiers, which are not useful for distribution analysis and thus the plot may not hold valuable insights. Second Image (Correlation Heatmap): This heatmap depicts the Pearson correlation coefficients among the dataset's variables. Correlation coefficients range from -1 to 1, where values close to the extremes suggest a strong linear relationship and values near zero suggest no linear relationship. Exercise 2 9) 11)

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13) Output )

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Comparison : Exercise 1: I load a dataset related to breast cancer and perform an initial exploration to understand the data types, identify missing values, and review the statistical summary. Then preprocess the data by addressing missing values and converting certain object columns to numeric. To visualize the data distribution, I use histograms and a correlation heatmap. Next, I prepare the data for modeling by separating the features and the target variable 'class'. I split the data into training and test sets and proceeded to train SVM classifiers with different kernels: linear, RBF, polynomial, and sigmoid. I specifically adjust the regularization parameter C for the linear kernel. The performance of each kernel is evaluated based on training and testing accuracy, and I also compute confusion matrices to understand the models' predictive capabilities in more detail.

Exercise 2: Building on my previous work, I load the same breast cancer dataset and carry out additional preprocessing steps. Specifically, I handle the 'bare' column's missing values by imputing the median and drop the 'ID' column as it's not a feature relevant to the analysis. For a more comprehensive understanding of the dataset, I create various visualizations including pair plots, correlation heatmaps, box plots, and histograms. These visualizations offer insights into the feature distributions and the relationships between them. The data is then prepared for machine learning by separating it into features and the target class, followed by a train-test split. I proceed to train SVM classifiers with different kernels and record their accuracies. To refine the model, I use a pipeline with preprocessing steps such as imputation and standardization, followed by an SVM classifier. I utilize GridSearchCV to perform an exhaustive search over specified parameter values for the SVM. After identifying the best parameters, I evaluate the best estimator's performance on the test set.