Fluent Essays

Task 1 (50 points): (Intro to Data Science: Survey Response Statistics) Twenty students were asked to rate on a scale of 1 to 5 the quality of the food in the student cafeteria, with 1 being “awful” and 5 being “excellent”. Place the 20 responses in a list. 1, 2, 5, 4, 3, 5, 2, 1, 3, 3, 1, 4, 3, 3, 3, 2, 3, 3, 2, 5 Write a program that does the following: (a) Determine and display the frequency of each rating. (b) Use the built-in functions, statistics module functions and NumPy or Panda functions covered in the course materials to display the following response statistics: minimum, maximum, range, mean, median, variance and standard deviation. (c) Display a bar chart showing the response frequencies and their percentages of the total responses. Grading Rubric – 10 points for defining functions. – 15 points for finishing Task1(a)-(c). – 5 points for appropriate comments. – 10 points for a runnable python program with correct data visualization. – 10 points for screenshots of the program. Task 2 (50 points): (Classification with k-Nearest Neighbors and the Digits Dataset) Read the file “09-02-MachineLearning.pdf” and the python program “CaseStudy1.py” to learn the algorithm of k-Nearest Neighbors with the Digits dataset for recognizing handwritten digits. Re-write the python program by doing the following subtasks: (a) Write code to display the two-dimensional array representing the sample image at index 24 and numeric value of the digit the image represents. (b) Write code to display the image for the sample image at index 24 of the Digits dataset. (c) For the Digits dataset, what numbers of samples would the following statement reserve for training and testing purposes? X train, X test, y train, y test = 1 train test split(digits.data, digits.target, random state=11, test size=0.60) 2 (d) Write code to get and display these numbers. (e) Rewrite the list comprehension in snippet [50] using a for loop. Hint: create an empty list and then use the built-in function “append”. Grading Rubric – 15 points for finishing Task2(a)-(e). – 5 points for appropriate comments. – 20 points for a runnable rewritten python program – 10 points for screenshots of the program. CSCI 333 Final Project 100 points + 10 bonus points Note: This is an individual assignment. Each student MUST complete the work on his/her own. Any code sharing/plagiarism is not tolerated. Overview This project consists of two tasks. The goal is to apply what we have learned to solve real problems in Data Science. Glance at “What to Submit” when you start working on a task so that you know what information to provide from each task. Submission Example csci333-project-XX csci333-project-XX.doc Task1XX.py task2XX.py README.txt What to Submit 1. One doc file “csci333-project-XX.doc” including the text source code and screenshots of the outputs of all programs. Please replace XX with your first name and last name. You can copy/paste the text source code from Pycharm or other IDEs into the doc file. Hopefully, based on the screen snapshots of the output, you can show that your programs passed tests and were well. 2. Python files for all programs. In well-defined programs, proper comments are required. For programs without comments, they will be deducted greatly in grade. 3. Note that if any program or code does not work, you can explain the status of the program or code and then attach your explanation and description in a file “README.txt”. 4. Optional. Anything you want to attract the attention of instructor in grading. Task 1 (50 points): (Intro to Data Science: Survey Response Statistics) Twenty students were asked to rate on a scale of 1 to 5 the quality of the food in the student cafeteria, with 1 being “awful” and 5 being “excellent”. Place the 20 responses in a list. 1, 2, 5, 4, 3, 5, 2, 1, 3, 3, 1, 4, 3, 3, 3, 2, 3, 3, 2, 5 Write a program that does the following: (a) Determine and display the frequency of each rating. (b) Use the built-in functions, statistics module functions and NumPy or Panda functions cov- ered in the course materials to display the following response statistics: minimum, maximum, range, mean, median, variance and standard deviation. (c) Display a bar chart showing the response frequencies and their percentages of the total responses. Grading Rubric – 10 points for defining functions. – 15 points for finishing Task1(a)-(c). – 5 points for appropriate comments. – 10 points for a runnable python program with correct data visualization. – 10 points for screenshots of the program. Task 2 (50 points): (Classification with k-Nearest Neighbors and the Digits Dataset) Read the file “09-02-MachineLearning.pdf” and the python program “CaseStudy1.py” to learn the algorithm of k-Nearest Neighbors with the Digits dataset for recognizing handwritten digits. Re-write the python program by doing the following subtasks: (a) Write code to display the two-dimensional array representing the sample image at index 24 and numeric value of the digit the image represents. (b) Write code to display the image for the sample image at index 24 of the Digits dataset. (c) For the Digits dataset, what numbers of samples would the following statement reserve for training and testing purposes? 1X train, X test, y train, y test = 2train test split(digits.data, digits.target, random state=11, test size=0.60) (d) Write code to get and display these numbers. (e) Rewrite the list comprehension in snippet [50] using a for loop. Hint: create an empty list and then use the built-in function “append”. Grading Rubric – 15 points for finishing Task2(a)-(e). – 5 points for appropriate comments. – 20 points for a runnable rewritten python program – 10 points for screenshots of the program. Challenges in This Project 1. For 10\% extra credit, you are welcome to explore the design of each task. Note: You still have to finish all tasks required by this project. 2. You should configure your machine and PyCharm properly to facilitate the project develop- ment. —————x———— Good Luck ————x————– #!/usr/bin/env python # coding: utf-8 # In[36]: from sklearn.datasets import load_digits # In[37]: digits = load_digits() # In[38]: print(digits.DESCR) # In[39]: digits.target[::100] # target values of every 100th sample # In[40]: digits.images[13] # show array for sample image at index 13 # In[41]: import matplotlib.pyplot as plt # In[42]: plt.imshow(digits.images[13]) # In[43]: figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(6, 4)) for item in zip(axes.ravel(), digits.images, digits.target): axes, image, target = item axes.imshow(image, cmap=plt.cm.gray_r) axes.set_xticks([]) # remove x-axis tick marks axes.set_yticks([]) # remove y-axis tick marks axes.set_title(target) plt.tight_layout() # In[44]: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( digits.data, digits.target, random_state=11, test_size=0.20) # random_state for reproducibility # In[45]: X_train.shape # In[46]: from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier() knn.fit(X=X_train, y=y_train) # In[47]: predicted = knn.predict(X=X_test) expected = y_test predicted[:20] expected[:20] # In[48]: predicted[:20] # In[49]: expected[:20] # In[50]: wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e] # In[51]: wrong # In[59]: print(Model accuracy:\%.2f\%\% \%(knn.score(X_test,y_test)*100)) # In[ ]: # In[ ]: { cells: [ { cell_type: code, execution_count: 36, metadata: {}, outputs: [], source: [ from sklearn.datasets import load_digits ] }, { cell_type: code, execution_count: 37, metadata: {}, outputs: [], source: [ digits = load_digits() ] }, { cell_type: code, execution_count: 38, metadata: { scrolled: true }, outputs: [ { name: stdout, output_type: stream, text: [ .. _digits_dataset:\n, \n, Optical recognition of handwritten digits dataset\n, --------------------------------------------------\n, \n, **Data Set Characteristics:**\n, \n, :Number of Instances: 5620\n, :Number of Attributes: 64\n, :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n, :Missing Attribute Values: None\n, :Creator: E. Alpaydin (alpaydin @ boun.edu.tr)\n, :Date: July; 1998\n, \n, This is a copy of the test set of the UCI ML hand-written digits datasets\n, https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n, \n, The data set contains images of hand-written digits: 10 classes where\n, each class refers to a digit.\n, \n, Preprocessing programs made available by NIST were used to extract\n, normalized bitmaps of handwritten digits from a preprinted form. From a\n, total of 43 people, 30 contributed to the training set and different 13\n, to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n, 4x4 and the number of on pixels are counted in each block. This generates\n, an input matrix of 8x8 where each element is an integer in the range\n, 0..16. This reduces dimensionality and gives invariance to small\n, distortions.\n, \n, For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n, T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n, L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n, 1994.\n, \n, .. topic:: References\n, \n, - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n, Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n, Graduate Studies in Science and Engineering, Bogazici University.\n, - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n, - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n, Linear dimensionalityreduction using relevance weighted LDA. School of\n, Electrical and Electronic Engineering Nanyang Technological University.\n, 2005.\n, - Claudio Gentile. A New Approximate Maximal Margin Classification\n, Algorithm. NIPS. 2000.\n ] } ], source: [ print(digits.DESCR) ] }, { cell_type: code, execution_count: 39, metadata: {}, outputs: [ { data: { text/plain: [ array([0, 4, 1, 7, 4, 8, 2, 2, 4, 4, 1, 9, 7, 3, 2, 1, 2, 5]) ] }, execution_count: 39, metadata: {}, output_type: execute_result } ], source: [ digits.target[::100] # target values of every 100th sample ] }, { cell_type: code, execution_count: 40, metadata: {}, outputs: [ { data: { text/plain: [ array([[ 0., 2., 9., 15., 14., 9., 3., 0.],\n, [ 0., 4., 13., 8., 9., 16., 8., 0.],\n, [ 0., 0., 0., 6., 14., 15., 3., 0.],\n, [ 0., 0., 0., 11., 14., 2., 0., 0.],\n, [ 0., 0., 0., 2., 15., 11., 0., 0.],\n, [ 0., 0., 0., 0., 2., 15., 4., 0.],\n, [ 0., 1., 5., 6., 13., 16., 6., 0.],\n, [ 0., 2., 12., 12., 13., 11., 0., 0.]]) ] }, execution_count: 40, metadata: {}, output_type: execute_result } ], source: [ digits.images[13] # show array for sample image at index 13 ] }, { cell_type: code, execution_count: 41, metadata: {}, outputs: [], source: [ import matplotlib.pyplot as plt ] }, { cell_type: code, execution_count: 42, metadata: {}, outputs: [ { data: { text/plain: [ <matplotlib.image.AxesImage at 0x1b4a17b0> ] }, execution_count: 42, metadata: {}, output_type: execute_result }, { data: { image/png: 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\n, text/plain: [ <Figure size 432x288 with 1 Axes> ] }, metadata: { needs_background: light }, output_type: display_data } ], source: [ plt.imshow(digits.images[13]) ] }, { cell_type: code, execution_count: 43, metadata: {}, outputs: [ { data: { image/png: 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\n, text/plain: [ <Figure size 432x288 with 24 Axes> ] }, metadata: {}, output_type: display_data } ], source: [ figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(6, 4))\n, \n, for item in zip(axes.ravel(), digits.images, digits.target):\n, axes, image, target = item \n, axes.imshow(image, cmap=plt.cm.gray_r)\n, axes.set_xticks([]) # remove x-axis tick marks\n, axes.set_yticks([]) # remove y-axis tick marks\n, axes.set_title(target)\n, plt.tight_layout() ] }, { cell_type: code, execution_count: 44, metadata: {}, outputs: [], source: [ from sklearn.model_selection import train_test_split\n, X_train, X_test, y_train, y_test = train_test_split(\n, digits.data, digits.target, random_state=11, test_size=0.20) \n, # random_state for reproducibility\n ] }, { cell_type: code, execution_count: 45, metadata: {}, outputs: [ { data: { text/plain: [ (1437, 64) ] }, execution_count: 45, metadata: {}, output_type: execute_result } ], source: [ X_train.shape ] }, { cell_type: code, execution_count: 46, metadata: {}, outputs: [ { data: { text/plain: [ KNeighborsClassifier(algorithm=auto, leaf_size=30, metric=minkowski,\n, metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n, weights=uniform) ] }, execution_count: 46, metadata: {}, output_type: execute_result } ], source: [ from sklearn.neighbors import KNeighborsClassifier\n, knn = KNeighborsClassifier()\n, knn.fit(X=X_train, y=y_train)\n ] }, { cell_type: code, execution_count: 47, metadata: { scrolled: true }, outputs: [ { data: { text/plain: [ array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 3, 6]) ] }, execution_count: 47, metadata: {}, output_type: execute_result } ], source: [ predicted = knn.predict(X=X_test)\n, expected = y_test\n, \n, predicted[:20]\n, expected[:20]\n ] }, { cell_type: code, execution_count: 48, metadata: { scrolled: true }, outputs: [ { data: { text/plain: [ array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 5, 6]) ] }, execution_count: 48, metadata: {}, output_type: execute_result } ], source: [ predicted[:20] ] }, { cell_type: code, execution_count: 49, metadata: {}, outputs: [ { data: { text/plain: [ array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 3, 6]) ] }, execution_count: 49, metadata: {}, output_type: execute_result } ], source: [ expected[:20] ] }, { cell_type: code, execution_count: 50, metadata: {}, outputs: [], source: [ wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e] ] }, { cell_type: code, execution_count: 51, metadata: {}, outputs: [ { data: { text/plain: [ [(5, 3), (8, 9), (4, 9), (7, 3), (7, 4)] ] }, execution_count: 51, metadata: {}, output_type: execute_result } ], source: [ wrong ] }, { cell_type: code, execution_count: 59, metadata: {}, outputs: [ { name: stdout, output_type: stream, text: [ Model accuracy:98.61\%\n ] } ], source: [ print(\Model accuracy:\%.2f\%\%\ \%(knn.score(X_test,y_test)*100)) ] }, { cell_type: code, execution_count: null, metadata: {}, outputs: [], source: [] }, { cell_type: code, execution_count: null, metadata: {}, outputs: [], source: [] } ], metadata: { kernelspec: { display_name: Python 3, language: python, name: python3 }, language_info: { codemirror_mode: { name: ipython, version: 3 }, file_extension: .py, mimetype: text/x-python, name: python, nbconvert_exporter: python, pygments_lexer: ipython3, version: 3.7.3 } }, nbformat: 4, nbformat_minor: 2 }