Python Exercise Homework 4 (solution) смотреть онлайн

COHE 6590 Health Data Analytics
Homework 4 (100 points)
Assignment Goals
In this assignment, you will read in data from a database, flatten it, and then analyze it using data analytic and supervised machine learning techniques. Complete the numbered items in the Homework4.ipynb file, execute your code, and answer the questions at the end. Turn in that file to Canvas.

Submission Instructions
Submit the completed Homework4.ipynb file to Canvas.


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On • left-hand side, you could see COURSES- click COHE 6590 Health Data Analytics– Then click on the ASSIGNMENTS, then HOMEWORK 4when you click this, is the instructions for this assignment. There are instructions for the assignments as well as different information to complete them. Download the HOMEWORK 4 zip. The documents popup the HOMEWORK 4-word.docx contains the question for the assignments and others carry the required information for the assignments.

• Only needed to focus on Homework4.ipynb, only answers the one with numbers on it
• Only open this in chrome and on the left-hand side you could see PANOPTO VIDEO, click on that you could see the homework 4 walkthrough, this video has all answers to it you just have to listen to the instructions and do it accordingly, it so easy you have to see this video and answer that’s all.
• These are the questions to be answered
• "# (1) Create column died (13.1 exercises) (5 points)\n",
• "# (2) Drop column discharge_disposition_code (13.1 exercises) (5 points)\n",
• "# (3) Create dummies for race, hispanic, gender, insurance_code, and transport_code (13.1 exercises) (5 points)\n",
• # (4) Write data a file named flattened_encounters_cpt.csv (13.1 exercises) (5 points)\n",
• "# 2. Prepare for Test/Train\n",
• # (1) Remove null values (store as new DataFrame d) and print covid_like_illness value_counts (13.1 exercises) (5 points)\n",
• "# (2) Set X and y (13.1 exercises) (5 points)\n",
• "# (3) Oversample X and y using SMOTE, and over write X and y (11.2 and 13.1 exercises) (5 points)\n",
• "# (4) Perform train-test split using trainTestSplit(X, y) which returns X_train, X_test, y_train, y_test (5 points)\n",
• "# (5) Scale X_train and X_test using scale(X_train, X_test) which returns X_train, X_test (5 points)\n",
• "# 3. Classification Experiments\n",
• "## 3.A. Feature Preserving"
• ### 3.A.1. Logistic Regression Grid Search CV\n",
• "#### Complete the numbered item"
• # (1) Import LogisticRegression and call executeGridSearchCV with parameters \n",
• "# LogisticRegression(), param_grid, k, X_train, X_test, y_train, y_test (6 points)\n",
• "### 3.A.2. KNN Grid Search CV\n",
• "#### Complete the numbered item"
• # Using GridSearchCV here is just too expensive to bother (6 points)\n",
• "from ...\n",
• # (1) Scale X using scale(X) and store as X_scaled (2 points)\n",
• (2) Import PCA and call transform(PCA(n_components=100, whiten='True'), X_scaled, 0.997),\n",
"# storing the results as X_results, then print(X_results) (5 points)\n",
• (3) Perform train-test split using trainTestSplit(X_results, y) \n",
"# which returns X_train, X_test, y_train, y_test (3 points)\n",
"...\n",
• (4) Import MLPClassifer and call executeModel with parameters \n",
"# MLPClassifier(), X_train, X_test, y_train, y_test (3 points)\n",
"from ...\n" "..."
• "# (2) Import LinearDiscriminantAnalysis and call transform(LinearDiscriminantAnalysis(), X_scaled, y),\n",
• "# storing the results as X_results, then print(X_results) (5 points)\n",
• "from ...\n",
• # 4. Questions\n",
Answer the questions below in each cell."
1. Rank the experiments by test accuracy (most to leasts accurate). 3 points"
• ]
• #### (2) Rank the experiments by time in seconds (fastest to slowest time). 3 points"
3. Rank the experiments by the false negatives (fewest to most false negatives). 3 points"
4. Which is the best result overall? Justify. 10 points"

This video guides you on how to use python, and jupyter notebook to find your assignment and homework solution.



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The remaining files can be found attached to the work.