diff --git a/anit961/gitUpdate.bat b/anit961/gitUpdate.bat new file mode 100644 index 00000000..a6629e2b --- /dev/null +++ b/anit961/gitUpdate.bat @@ -0,0 +1,7 @@ +git status . + +@pause + +git add . +git commit -m"update anit961," +start git push \ No newline at end of file diff --git a/anit961/meta.md b/anit961/meta.md new file mode 100644 index 00000000..e69de29b diff --git a/anit961/task1/Assignment2-1.docx b/anit961/task1/Assignment2-1.docx new file mode 100644 index 00000000..a9e81226 Binary files /dev/null and b/anit961/task1/Assignment2-1.docx differ diff --git a/anit961/task1/Assignment2_template.ipynb b/anit961/task1/Assignment2_template.ipynb new file mode 100644 index 00000000..5afb6f5a --- /dev/null +++ b/anit961/task1/Assignment2_template.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c9eac7d0-4f4c-49e7-a335-b8ca8de34f20", + "metadata": {}, + "source": [ + "# Assignment 2:\n", + "In this assignment, you are given a dataset about bodyfat measure collected from a clinic. As a nutritionist, you want to use regression model to check whether your patients are in danger of high bodyfat.\n", + "The data contains physical measurements of patients:\n", + "- explantory variables: Age (in year), Weight (in pounds), Height (in inches), and BMI (Body Mass Index).\n", + "- response variable: bodyfat (in %)\n", + "\n", + "Complete the given tasks below. (Notice each of the missing code to be filled is a single line command, more than one command line will be downgraded)" + ] + }, + { + "cell_type": "markdown", + "id": "0f79e7b9-1fff-4bef-9fe8-8e3eb54c68cb", + "metadata": { + "tags": [] + }, + "source": [ + "

Author

\n", + "\n", + " \n", + "- Name: \n", + "- Student ID: " + ] + }, + { + "cell_type": "markdown", + "id": "d4725f61-cb83-4e3c-bc31-1a3992dadd28", + "metadata": {}, + "source": [ + "## Pre-loaded packages and functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7e90f45", + "metadata": {}, + "outputs": [], + "source": [ + "#### Pandas is for using data structures\n", + "import pandas as pd\n", + "# statsmodels contain modules for regression and time series analysis\n", + "import statsmodels.api as sm\n", + "# numpy is for numerical computing of array and matrix\n", + "import numpy as np\n", + "# Matplotlib, Seaborn: plotting package\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns \n", + "# matplotlib Showing the plot right after the current code \n", + "%matplotlib inline\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "# basic statistics package\n", + "import scipy.stats as stats\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + "import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8983cbf-0937-443f-ba43-d18956ad4a3e", + "metadata": {}, + "outputs": [], + "source": [ + "def outlier(dataframe,model,Type='all'):\n", + " A = dataframe.copy()\n", + " A = A.dropna()\n", + " A.index = range(1,A.shape[0]+1)\n", + " #A.index = range(0,A.shape[0])\n", + " studentized_residuals = model.get_influence().resid_studentized_internal\n", + " if Type == 'neg':\n", + " return(A[studentized_residuals<-2])\n", + " elif Type == 'posi':\n", + " return(A[studentized_residuals>2])\n", + " else:\n", + " return(A[np.abs(studentized_residuals)>2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b47cb1ec-efe7-4124-a653-a9e942ae5e54", + "metadata": { + "id": "7NIZKrveWDbd" + }, + "outputs": [], + "source": [ + "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + "def getvif(X):\n", + " X = sm.add_constant(X)\n", + " vif = pd.DataFrame()\n", + " vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n", + " vif[\"Predictors\"] = X.columns\n", + " return(vif.drop(index = 0).round(2)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bd86624-4cc8-4790-85d5-826a91ef02aa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "760c8202-fcaa-4e5f-bbb1-35f972fb4fd6", + "metadata": {}, + "source": [ + "## Getting the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16326102", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"https://drive.google.com/uc?export=download&id=1r6Za0azxHvJpjUA6lgMJyBqYIWRljmz8\")\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fd1d118", + "metadata": {}, + "outputs": [], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "id": "9496e740-2f7c-45cc-9409-a029d242a732", + "metadata": {}, + "source": [ + "## Preliminary study" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52d51f09-1993-47c5-83c8-4d874d0ddaf1", + "metadata": {}, + "outputs": [], + "source": [ + "# scatter plot matrix of the whole data\n", + "sns_plot = sns.pairplot(data)" + ] + }, + { + "cell_type": "markdown", + "id": "931e81f6-e2f6-4f07-ab1c-485f598f8a93", + "metadata": {}, + "source": [ + "### Task 1: Compute the correlation matrix; which variable has the strongest correlation with bodyfat? [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fe53540-95ce-4286-a0b4-0c35437dbe0c", + "metadata": {}, + "outputs": [], + "source": [ + "$$$Code_Here$$$" + ] + }, + { + "cell_type": "markdown", + "id": "7d5d3504-6a3c-486f-afa2-639a4f85e015", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a155616-4e5b-4992-8a04-467c85ae2977", + "metadata": {}, + "source": [ + "### Answer:" + ] + }, + { + "cell_type": "markdown", + "id": "7688f2a9-8c5e-4fae-83f3-8b4b1ef7a799", + "metadata": {}, + "source": [ + "### Task 2: Split the data into train and test set by random (use 25 as the random seed/state) [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ae78c9d-deaa-4998-8928-5437c437e285", + "metadata": {}, + "outputs": [], + "source": [ + "train = $$$Code_Here$$$\n", + "test = $$$Code_Here$$$\n", + "train.shape, test.shape" + ] + }, + { + "cell_type": "markdown", + "id": "1d82fae0-ce3c-4e4b-92f8-69657b421f74", + "metadata": {}, + "source": [ + "## Model Building" + ] + }, + { + "cell_type": "markdown", + "id": "e5d20382-fd4c-4030-9d43-677f267b447b", + "metadata": {}, + "source": [ + "### Task 3: Using the train set, fit a simple regression model for bodyfat by the variable from Task1 (with max correlation) [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4994daf5-b40b-41b3-93fd-ab3f90568c91", + "metadata": {}, + "outputs": [], + "source": [ + "Y = train[\"bodyfat\"]\n", + "X = $$$Code_Here$$$\n", + "SLR = $$$Code_Here$$$\n", + "print(SLR.summary())" + ] + }, + { + "cell_type": "markdown", + "id": "8ba21618-7c3a-4baf-861f-cbdbb7cc7f05", + "metadata": {}, + "source": [ + "### Task 4: Using the trian set, fit a multiple regression model for bodyfat by all of the explanatory variables (i.e. age, weight, height and bmi) [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "500ee96e-c8db-4053-ad0b-4097ae291696", + "metadata": {}, + "outputs": [], + "source": [ + "Y = train[\"bodyfat\"]\n", + "X = $$$Code_Here$$$\n", + "MLR_all = $$$Code_Here$$$\n", + "print(MLR_all.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af58180f-45a0-4cc9-b042-3845996d683e", + "metadata": {}, + "outputs": [], + "source": [ + "# VIF of all predictors\n", + "getvif(X)" + ] + }, + { + "cell_type": "markdown", + "id": "c2cebb64-f6e0-41b6-96be-09f218ba05a3", + "metadata": {}, + "source": [ + "### Task 5: Multicollinearity is reflected from above. Does bmi cause the problem? Briefly explain using your understanding about bmi.[5pts]" + ] + }, + { + "cell_type": "markdown", + "id": "87def1eb-ba5c-49a9-bfdf-48b3af5e1a81", + "metadata": {}, + "source": [ + "### Answer: " + ] + }, + { + "cell_type": "markdown", + "id": "dc60e40f-72b8-43a5-8b70-40fec8cc4f4d", + "metadata": {}, + "source": [ + "### Task 6: Using the train set, fit a multiple regression model for bodyfat by all of the explanatory variables except bmi [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73499ab4-b937-4b30-a647-aeedc20d929e", + "metadata": {}, + "outputs": [], + "source": [ + "Y = train[\"bodyfat\"]\n", + "X = $$$Code_Here$$$\n", + "MLR = $$$Code_Here$$$\n", + "print(MLR.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "883671ff-79ce-4c80-941c-957b0c8bcde2", + "metadata": {}, + "outputs": [], + "source": [ + "getvif(X)" + ] + }, + { + "cell_type": "markdown", + "id": "742ef24b-3b03-439d-9d18-47c1626efe05", + "metadata": {}, + "source": [ + "## Compare the predictive power between SLR and MLR using the test set. " + ] + }, + { + "cell_type": "markdown", + "id": "708e9eed-0b88-49d1-93a1-fba94254b597", + "metadata": {}, + "source": [ + "### Task 7: Compute the RMSE for MLR [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "469672cd-2db3-49eb-b137-c6d63033a55f", + "metadata": {}, + "outputs": [], + "source": [ + "Test_X_SLR = test['bmi']\n", + "Test_X_MLR = $$$Code_Here$$$\n", + "\n", + "Test_Y_SLR = SLR.predict(sm.add_constant(Test_X_SLR))\n", + "Test_Y_MLR = $$$Code_Here$$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f077e2cf-4cad-4578-90bb-a63c32ddda18", + "metadata": {}, + "outputs": [], + "source": [ + "Test_Y = test[\"bodyfat\"]\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "rmse_SLR = np.sqrt(mean_squared_error(Test_Y, Test_Y_SLR))\n", + "rmse_MLR = $$$Code_Here$$$\n", + "print(\"RMSE for test set (SLR): \", rmse_SLR)\n", + "print(\"RMSE for test set (MLR): \", rmse_MLR)" + ] + }, + { + "cell_type": "markdown", + "id": "1659a34e-8080-4e92-be34-717acf81c82c", + "metadata": {}, + "source": [ + "## Final Model and application" + ] + }, + { + "cell_type": "markdown", + "id": "89256526-a134-4e7d-9a84-1f7dc331bfc2", + "metadata": {}, + "source": [ + "### Task 8: Refit the better model from Task7 using the full dataset. [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84da494e-36bf-4f96-9376-3fb43e8df704", + "metadata": {}, + "outputs": [], + "source": [ + "Y = data[\"bodyfat\"]\n", + "X = $$$Code_Here$$$\n", + "Final = $$$Code_Here$$$\n", + "print(Final.summary())" + ] + }, + { + "cell_type": "markdown", + "id": "539487cb-0e72-4a2b-85cd-6dbac1d2e321", + "metadata": { + "tags": [] + }, + "source": [ + "### Task 9: Predict the body fat for a new patient using the final model from Task 8: age=35, weight=170 pounds. Height=72 inches, BMI=23 [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71aacafa-0cf1-40ed-8ee9-d5d09ebf26ab", + "metadata": {}, + "outputs": [], + "source": [ + "$$$Code_Here$$$" + ] + }, + { + "cell_type": "markdown", + "id": "37f640ce-dfd6-42c9-90d5-13b05769448f", + "metadata": { + "tags": [] + }, + "source": [ + "### Task 10: by using residuals (from final model) outlier analysis, report those patients are in danger (i.e. % of fat is much higher than what we expected from the final model) [5pts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f06aa2bd-9836-46c2-8e8d-4a876c9ae253", + "metadata": {}, + "outputs": [], + "source": [ + "$$$Code_Here$$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "149424ee-f928-479b-8b07-19c3ae88f751", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/anit961/task1/google_drive.csv b/anit961/task1/google_drive.csv new file mode 100644 index 00000000..2031e5f6 --- /dev/null +++ b/anit961/task1/google_drive.csv @@ -0,0 +1,250 @@ +bodyfat,age,weight,height,bmi +11.8,27,168,71.25,23.2645614 +22.2,69,177.75,68.5,26.63077415 +10.6,57,147.75,65.75,24.02654368 +47.5,51,219,64,37.58715821 +24.2,40,202.25,70,29.01668367 +23.3,52,167,67.75,25.57721164 +26,54,230,72.25,30.97472492 +9,47,184.25,74.5,23.3372821 +4,47,127.5,66.75,20.1169886 +22.1,43,150,69.25,21.98907845 +9.4,26,152.25,69,22.48093888 +16.7,40,158,69.25,23.1618293 +29.9,65,189.75,65.75,30.85642412 +11.7,23,198.25,73.5,25.79846361 +25.8,61,178,67,27.87569615 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