diabetes.cvs

python machine learning

Use Logistic Regression to predict the outcome for the attached dataset. The outcome refers to: a patient has diabetes (1) or no diabetes (0) 1. What type of learning problem is this: supervised/unsupervised? Regression/classification? Justify your answer. 2. Discuss the correlation between all the variables 3. Which features have the highest impact on the label? 4. Estimate the Accuracy of your model Show all your work, including your code and submit your work as an attachment (doc, ppt, pdf). Justify all your answers. The dataset consists of several medical predictor variables and one target variable, Outcome. Predictor variables include the number of pregnancies the patient has had their BMI, insulin level, age, and so on. Dataset: diabetes.csv diabetes.csv B Data description 1. Pregnancies:Number of times pregnant 2. Glucose:Plasma glucose concentration a 2 hours in an oral glucose tolerance test 3. Blood Pressure:Diastolic blood pressure (mm Hg) 4. SkinThickness:Triceps skin fold thickness (mm) 5. Insulin:2-Hour serum insulin (mu U/ml) 6. BMI:Body mass index (weight in kg/(height in m)^2) 7. DiabetesPedigree Function:Diabetes pedigree function 9. Outcome:1 if diabetes, O if no diabetes File Home Insert Page Layout Formulas Data Review View Help Share Comments 47 o 5 y Insert Calibri 11 A A == 22 General 3X Delete Paste BIUD A Cell = == = Conditional Format as $ % -28 Format Formatting Table Styles Clipboard Font Alignment Number Styles Cells POSSIBLE DATA LOSS Some features might be lost if you save this workbook in the comma-delimited (.csv) format. To preserve these features, save it in an Excel file format. Sort & Find & Filter Select Editing Don't show again Analyze Sensitivity Data Analysis Sensitivity Save As... X A1 y pregnant Formula Bar C F G H J L M N O P 0 R T U D skin E insulin 1 | label bmi 72 35 0 50 1 0 66 29 0 31 0 0 32 1 64 66 23 94 21 0 1 40 35 168 pedigree age 33.6 0.627 26.6 0.351 23.3 0.672 28.1 0.167 43.1 2.288 25.6 0.201 31 0.248 35.3 0.134 30.5 0.158 0 0.232 33 74 0 0 30 0 50 88 26 1 32 0 0 0 29 A B 1 pregnant lglucose bp 2 6 148 3 1 85 4 8 183 5 1 89 6 137 7 5 116 8 3 78 9 10 115 10 2 197 11 8 125 12 4 110 13 10 168 14 10 139 15 1 189 16 5 166 17 7 100 18 118 19 7 107 20 1 103 0 70 45 53 1 543 0 96 0 54 1 92 0 0 30 0 WAVON BACON WOCO 37.6 38 0.191 0.537 74 0 0 34 1 0 0 27.1 1.441 0 80 60 57 59 23 846 0.398 1 30.1 25.8 72 19 175 0.587 51 1 0 0 0 30 0.484 32 1 84 47 230 45.8 0.551 31 1 74 0 0 31 1 29.6 43.3 0.254 0.183 30 38 83 33 0 21 1 115 70 30 0.529 1 96 235 32 27 22 41 34.6 39.3 35.4 126 99 0 88 84 0.704 0.388 23 8 0 0 50 0 diabetes (+ W 1 + 100% Use Logistic Regression to predict the outcome for the attached dataset. The outcome refers to: a patient has diabetes (1) or no diabetes (0) 1. What type of learning problem is this: supervised/unsupervised? Regression/classification? Justify your answer. 2. Discuss the correlation between all the variables 3. Which features have the highest impact on the label? 4. Estimate the Accuracy of your model Show all your work, including your code and submit your work as an attachment (doc, ppt, pdf). Justify all your answers. The dataset consists of several medical predictor variables and one target variable, Outcome. Predictor variables include the number of pregnancies the patient has had their BMI, insulin level, age, and so on. Dataset: diabetes.csv diabetes.csv B Data description 1. Pregnancies:Number of times pregnant 2. Glucose:Plasma glucose concentration a 2 hours in an oral glucose tolerance test 3. Blood Pressure:Diastolic blood pressure (mm Hg) 4. SkinThickness:Triceps skin fold thickness (mm) 5. Insulin:2-Hour serum insulin (mu U/ml) 6. BMI:Body mass index (weight in kg/(height in m)^2) 7. DiabetesPedigree Function:Diabetes pedigree function 9. Outcome:1 if diabetes, O if no diabetes File Home Insert Page Layout Formulas Data Review View Help Share Comments 47 o 5 y Insert Calibri 11 A A == 22 General 3X Delete Paste BIUD A Cell = == = Conditional Format as $ % -28 Format Formatting Table Styles Clipboard Font Alignment Number Styles Cells POSSIBLE DATA LOSS Some features might be lost if you save this workbook in the comma-delimited (.csv) format. To preserve these features, save it in an Excel file format. Sort & Find & Filter Select Editing Don't show again Analyze Sensitivity Data Analysis Sensitivity Save As... X A1 y pregnant Formula Bar C F G H J L M N O P 0 R T U D skin E insulin 1 | label bmi 72 35 0 50 1 0 66 29 0 31 0 0 32 1 64 66 23 94 21 0 1 40 35 168 pedigree age 33.6 0.627 26.6 0.351 23.3 0.672 28.1 0.167 43.1 2.288 25.6 0.201 31 0.248 35.3 0.134 30.5 0.158 0 0.232 33 74 0 0 30 0 50 88 26 1 32 0 0 0 29 A B 1 pregnant lglucose bp 2 6 148 3 1 85 4 8 183 5 1 89 6 137 7 5 116 8 3 78 9 10 115 10 2 197 11 8 125 12 4 110 13 10 168 14 10 139 15 1 189 16 5 166 17 7 100 18 118 19 7 107 20 1 103 0 70 45 53 1 543 0 96 0 54 1 92 0 0 30 0 WAVON BACON WOCO 37.6 38 0.191 0.537 74 0 0 34 1 0 0 27.1 1.441 0 80 60 57 59 23 846 0.398 1 30.1 25.8 72 19 175 0.587 51 1 0 0 0 30 0.484 32 1 84 47 230 45.8 0.551 31 1 74 0 0 31 1 29.6 43.3 0.254 0.183 30 38 83 33 0 21 1 115 70 30 0.529 1 96 235 32 27 22 41 34.6 39.3 35.4 126 99 0 88 84 0.704 0.388 23 8 0 0 50 0 diabetes (+ W 1 + 100%