In [ ]:
plt.figure(figsize=(5,5))
plt.pie(df['Outcome'].value_counts(), labels=['No Diabetes', 'Diabetes'], autopct='%1.1f%%', shadow=False, startangle=90)
plt.title('Diabetes Outcome')
plt.show()
The aim of this project to analyze the medical factors of a patient such as Glucose Level, Blood Pressure, Skin Thickness, Insulin Level and many others to predict whether the patient has diabetes or not.
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
| Feature | Description |
|---|---|
| Pregnancies | Number of times pregnant |
| Glucose | Plasma glucose concentration a 2 hours in an oral glucose tolerance test |
| BloodPressure | Diastolic blood pressure (mm Hg) |
| SkinThickness | Triceps skin fold thickness (mm) |
| Insulin | 2-Hour serum insulin (mu U/ml) |
| BMI | Body mass index (weight in kg/(height in m)^2) |
| DiabetesPedigreeFunction | Diabetes pedigree function |
| Age | Age (years) |
| Outcome | Class variable (0 or 1) |
#importing the libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
#loading the dataset
df = pd.read_csv("diabetes.csv")
df.head()
| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | 1 |
#shape of the dataset
df.shape
(768, 9)
Checking the unique values for each variable in the dataset
#checking unique values
variables = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age','Outcome']
for i in variables:
print(df[i].unique())
[ 6 1 8 0 5 3 10 2 4 7 9 11 13 15 17 12 14] [148 85 183 89 137 116 78 115 197 125 110 168 139 189 166 100 118 107 103 126 99 196 119 143 147 97 145 117 109 158 88 92 122 138 102 90 111 180 133 106 171 159 146 71 105 101 176 150 73 187 84 44 141 114 95 129 79 0 62 131 112 113 74 83 136 80 123 81 134 142 144 93 163 151 96 155 76 160 124 162 132 120 173 170 128 108 154 57 156 153 188 152 104 87 75 179 130 194 181 135 184 140 177 164 91 165 86 193 191 161 167 77 182 157 178 61 98 127 82 72 172 94 175 195 68 186 198 121 67 174 199 56 169 149 65 190] [ 72 66 64 40 74 50 0 70 96 92 80 60 84 30 88 90 94 76 82 75 58 78 68 110 56 62 85 86 48 44 65 108 55 122 54 52 98 104 95 46 102 100 61 24 38 106 114] [35 29 0 23 32 45 19 47 38 30 41 33 26 15 36 11 31 37 42 25 18 24 39 27 21 34 10 60 13 20 22 28 54 40 51 56 14 17 50 44 12 46 16 7 52 43 48 8 49 63 99] [ 0 94 168 88 543 846 175 230 83 96 235 146 115 140 110 245 54 192 207 70 240 82 36 23 300 342 304 142 128 38 100 90 270 71 125 176 48 64 228 76 220 40 152 18 135 495 37 51 99 145 225 49 50 92 325 63 284 119 204 155 485 53 114 105 285 156 78 130 55 58 160 210 318 44 190 280 87 271 129 120 478 56 32 744 370 45 194 680 402 258 375 150 67 57 116 278 122 545 75 74 182 360 215 184 42 132 148 180 205 85 231 29 68 52 255 171 73 108 43 167 249 293 66 465 89 158 84 72 59 81 196 415 275 165 579 310 61 474 170 277 60 14 95 237 191 328 250 480 265 193 79 86 326 188 106 65 166 274 77 126 330 600 185 25 41 272 321 144 15 183 91 46 440 159 540 200 335 387 22 291 392 178 127 510 16 112] [33.6 26.6 23.3 28.1 43.1 25.6 31. 35.3 30.5 0. 37.6 38. 27.1 30.1 25.8 30. 45.8 29.6 43.3 34.6 39.3 35.4 39.8 29. 36.6 31.1 39.4 23.2 22.2 34.1 36. 31.6 24.8 19.9 27.6 24. 33.2 32.9 38.2 37.1 34. 40.2 22.7 45.4 27.4 42. 29.7 28. 39.1 19.4 24.2 24.4 33.7 34.7 23. 37.7 46.8 40.5 41.5 25. 25.4 32.8 32.5 42.7 19.6 28.9 28.6 43.4 35.1 32. 24.7 32.6 43.2 22.4 29.3 24.6 48.8 32.4 38.5 26.5 19.1 46.7 23.8 33.9 20.4 28.7 49.7 39. 26.1 22.5 39.6 29.5 34.3 37.4 33.3 31.2 28.2 53.2 34.2 26.8 55. 42.9 34.5 27.9 38.3 21.1 33.8 30.8 36.9 39.5 27.3 21.9 40.6 47.9 50. 25.2 40.9 37.2 44.2 29.9 31.9 28.4 43.5 32.7 67.1 45. 34.9 27.7 35.9 22.6 33.1 30.4 52.3 24.3 22.9 34.8 30.9 40.1 23.9 37.5 35.5 42.8 42.6 41.8 35.8 37.8 28.8 23.6 35.7 36.7 45.2 44. 46.2 35. 43.6 44.1 18.4 29.2 25.9 32.1 36.3 40. 25.1 27.5 45.6 27.8 24.9 25.3 37.9 27. 26. 38.7 20.8 36.1 30.7 32.3 52.9 21. 39.7 25.5 26.2 19.3 38.1 23.5 45.5 23.1 39.9 36.8 21.8 41. 42.2 34.4 27.2 36.5 29.8 39.2 38.4 36.2 48.3 20. 22.3 45.7 23.7 22.1 42.1 42.4 18.2 26.4 45.3 37. 24.5 32.2 59.4 21.2 26.7 30.2 46.1 41.3 38.8 35.2 42.3 40.7 46.5 33.5 37.3 30.3 26.3 21.7 36.4 28.5 26.9 38.6 31.3 19.5 20.1 40.8 23.4 28.3 38.9 57.3 35.6 49.6 44.6 24.1 44.5 41.2 49.3 46.3] [0.627 0.351 0.672 0.167 2.288 0.201 0.248 0.134 0.158 0.232 0.191 0.537 1.441 0.398 0.587 0.484 0.551 0.254 0.183 0.529 0.704 0.388 0.451 0.263 0.205 0.257 0.487 0.245 0.337 0.546 0.851 0.267 0.188 0.512 0.966 0.42 0.665 0.503 1.39 0.271 0.696 0.235 0.721 0.294 1.893 0.564 0.586 0.344 0.305 0.491 0.526 0.342 0.467 0.718 0.962 1.781 0.173 0.304 0.27 0.699 0.258 0.203 0.855 0.845 0.334 0.189 0.867 0.411 0.583 0.231 0.396 0.14 0.391 0.37 0.307 0.102 0.767 0.237 0.227 0.698 0.178 0.324 0.153 0.165 0.443 0.261 0.277 0.761 0.255 0.13 0.323 0.356 0.325 1.222 0.179 0.262 0.283 0.93 0.801 0.207 0.287 0.336 0.247 0.199 0.543 0.192 0.588 0.539 0.22 0.654 0.223 0.759 0.26 0.404 0.186 0.278 0.496 0.452 0.403 0.741 0.361 1.114 0.457 0.647 0.088 0.597 0.532 0.703 0.159 0.268 0.286 0.318 0.272 0.572 0.096 1.4 0.218 0.085 0.399 0.432 1.189 0.687 0.137 0.637 0.833 0.229 0.817 0.204 0.368 0.743 0.722 0.256 0.709 0.471 0.495 0.18 0.542 0.773 0.678 0.719 0.382 0.319 0.19 0.956 0.084 0.725 0.299 0.244 0.745 0.615 1.321 0.64 0.142 0.374 0.383 0.578 0.136 0.395 0.187 0.905 0.15 0.874 0.236 0.787 0.407 0.605 0.151 0.289 0.355 0.29 0.375 0.164 0.431 0.742 0.514 0.464 1.224 1.072 0.805 0.209 0.666 0.101 0.198 0.652 2.329 0.089 0.645 0.238 0.394 0.293 0.479 0.686 0.831 0.582 0.446 0.402 1.318 0.329 1.213 0.427 0.282 0.143 0.38 0.284 0.249 0.926 0.557 0.092 0.655 1.353 0.612 0.2 0.226 0.997 0.933 1.101 0.078 0.24 1.136 0.128 0.422 0.251 0.677 0.296 0.454 0.744 0.881 0.28 0.259 0.619 0.808 0.34 0.434 0.757 0.613 0.692 0.52 0.412 0.84 0.839 0.156 0.215 0.326 1.391 0.875 0.313 0.433 0.626 1.127 0.315 0.345 0.129 0.527 0.197 0.731 0.148 0.123 0.127 0.122 1.476 0.166 0.932 0.343 0.893 0.331 0.472 0.673 0.389 0.485 0.349 0.279 0.346 0.252 0.243 0.58 0.559 0.302 0.569 0.378 0.385 0.499 0.306 0.234 2.137 1.731 0.545 0.225 0.816 0.528 0.509 1.021 0.821 0.947 1.268 0.221 0.66 0.239 0.949 0.444 0.463 0.803 1.6 0.944 0.196 0.241 0.161 0.135 0.376 1.191 0.702 0.674 1.076 0.534 1.095 0.554 0.624 0.219 0.507 0.561 0.421 0.516 0.264 0.328 0.233 0.108 1.138 0.147 0.727 0.435 0.497 0.23 0.955 2.42 0.658 0.33 0.51 0.285 0.415 0.381 0.832 0.498 0.212 0.364 1.001 0.46 0.733 0.416 0.705 1.022 0.269 0.6 0.571 0.607 0.17 0.21 0.126 0.711 0.466 0.162 0.419 0.63 0.365 0.536 1.159 0.629 0.292 0.145 1.144 0.174 0.547 0.163 0.738 0.314 0.968 0.409 0.297 0.525 0.154 0.771 0.107 0.493 0.717 0.917 0.501 1.251 0.735 0.804 0.661 0.549 0.825 0.423 1.034 0.16 0.341 0.68 0.591 0.3 0.121 0.502 0.401 0.601 0.748 0.338 0.43 0.892 0.813 0.693 0.575 0.371 0.206 0.417 1.154 0.925 0.175 1.699 0.682 0.194 0.4 0.1 1.258 0.482 0.138 0.593 0.878 0.157 1.282 0.141 0.246 1.698 1.461 0.347 0.362 0.393 0.144 0.732 0.115 0.465 0.649 0.871 0.149 0.695 0.303 0.61 0.73 0.447 0.455 0.133 0.155 1.162 1.292 0.182 1.394 0.217 0.631 0.88 0.614 0.332 0.366 0.181 0.828 0.335 0.856 0.886 0.439 0.253 0.598 0.904 0.483 0.565 0.118 0.177 0.176 0.295 0.441 0.352 0.826 0.97 0.595 0.317 0.265 0.646 0.426 0.56 0.515 0.453 0.785 0.734 1.174 0.488 0.358 1.096 0.408 1.182 0.222 1.057 0.766 0.171] [50 31 32 21 33 30 26 29 53 54 34 57 59 51 27 41 43 22 38 60 28 45 35 46 56 37 48 40 25 24 58 42 44 39 36 23 61 69 62 55 65 47 52 66 49 63 67 72 81 64 70 68] [1 0]
In the dataset the variables except Pregnancies and Outcome cannot have value as 0, because it is not possible to have 0 Glucose Level or to have 0 Blood Pressure. So, this will be counted as incorrect information
Checking the count of value 0 in the variables
variables = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age',]
for i in variables:
c = 0
for x in (df[i]):
if x == 0:
c = c + 1
print(i,c)
Glucose 5 BloodPressure 35 SkinThickness 227 Insulin 374 BMI 11 DiabetesPedigreeFunction 0 Age 0
Now, I have count of incoorect values in the variables, I will be replacing these values
#replacing the missing values with the mean
variables = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
for i in variables:
df[i].replace(0,df[i].mean(),inplace=True)
#checking to make sure that incorrect values are replace
for i in variables:
c = 0
for x in (df[i]):
if x == 0:
c = c + 1
print(i,c)
Glucose 0 BloodPressure 0 SkinThickness 0 Insulin 0 BMI 0
Now, I have replace the incorrect values
#missing values
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 768 entries, 0 to 767 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Pregnancies 768 non-null int64 1 Glucose 768 non-null float64 2 BloodPressure 768 non-null float64 3 SkinThickness 768 non-null float64 4 Insulin 768 non-null float64 5 BMI 768 non-null float64 6 DiabetesPedigreeFunction 768 non-null float64 7 Age 768 non-null int64 8 Outcome 768 non-null int64 dtypes: float64(6), int64(3) memory usage: 54.1 KB
Descriptive Statistics
#checking descriptive statistics
df.describe()
| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome | |
|---|---|---|---|---|---|---|---|---|---|
| count | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 |
| mean | 3.845052 | 121.681605 | 72.254807 | 26.606479 | 118.660163 | 32.450805 | 0.471876 | 33.240885 | 0.348958 |
| std | 3.369578 | 30.436016 | 12.115932 | 9.631241 | 93.080358 | 6.875374 | 0.331329 | 11.760232 | 0.476951 |
| min | 0.000000 | 44.000000 | 24.000000 | 7.000000 | 14.000000 | 18.200000 | 0.078000 | 21.000000 | 0.000000 |
| 25% | 1.000000 | 99.750000 | 64.000000 | 20.536458 | 79.799479 | 27.500000 | 0.243750 | 24.000000 | 0.000000 |
| 50% | 3.000000 | 117.000000 | 72.000000 | 23.000000 | 79.799479 | 32.000000 | 0.372500 | 29.000000 | 0.000000 |
| 75% | 6.000000 | 140.250000 | 80.000000 | 32.000000 | 127.250000 | 36.600000 | 0.626250 | 41.000000 | 1.000000 |
| max | 17.000000 | 199.000000 | 122.000000 | 99.000000 | 846.000000 | 67.100000 | 2.420000 | 81.000000 | 1.000000 |
df.head()
| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 148.0 | 72.0 | 35.000000 | 79.799479 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85.0 | 66.0 | 29.000000 | 79.799479 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183.0 | 64.0 | 20.536458 | 79.799479 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89.0 | 66.0 | 23.000000 | 94.000000 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137.0 | 40.0 | 35.000000 | 168.000000 | 43.1 | 2.288 | 33 | 1 |
In the exploratory data analysis, I will be looking at the distribution of the data, the correlation between the features, and the relationship between the features and the target variable. I will start by looking at the distribution of the data, followed by relationship between the target variable and independent variables.
plt.figure(figsize=(5,5))
plt.pie(df['Outcome'].value_counts(), labels=['No Diabetes', 'Diabetes'], autopct='%1.1f%%', shadow=False, startangle=90)
plt.title('Diabetes Outcome')
plt.show()
sns.catplot(x="Outcome", y="Age", kind="swarm", data=df)
C:\Users\DELL\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\seaborn\categorical.py:3544: UserWarning: 6.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
<seaborn.axisgrid.FacetGrid at 0x2c9111178d0>
C:\Users\DELL\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\seaborn\categorical.py:3544: UserWarning: 22.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
From the graph, it is quite clear that majority of the patients are adult within the age group of 20-30 years. Patients in the age range 40-55 years are more prone to diabetes, as compared to other age groups. Since the number adults in the age group 20-30 years is more, the number of patients with diabetes is also more as compared of other age groups.
fig,ax = plt.subplots(1,2,figsize=(15,5))
sns.boxplot(x='Outcome',y='Pregnancies',data=df,ax=ax[0])
sns.violinplot(x='Outcome',y='Pregnancies',data=df,ax=ax[1])
<Axes: xlabel='Outcome', ylabel='Pregnancies'>
Both boxplot and violinplot shows strange relation between the number of preganacies and diabetes. According to the graphs the increased number of pregnancies highlights increased risk of diabetes.
sns.boxplot(x='Outcome', y='Glucose', data=df).set_title('Glucose vs Diabetes')
Text(0.5, 1.0, 'Glucose vs Diabetes')
Glucose level plays a major role in determine whether the patient is diabetic or not. The patients with median gluocse level less than 120 are more likely to be non-diabetic. The patients with median gluocse level greather than 140 are more likely to be diabetic. Therefore, high gluocose levels is a good indicator of diabetes.
fig,ax = plt.subplots(1,2,figsize=(15,5))
sns.boxplot(x='Outcome', y='BloodPressure', data=df, ax=ax[0]).set_title('BloodPressure vs Diabetes')
sns.violinplot(x='Outcome', y='BloodPressure', data=df, ax=ax[1]).set_title('BloodPressure vs Diabetes')
Text(0.5, 1.0, 'BloodPressure vs Diabetes')
Both the boxplot and voilinplot provides clear understanding of the realtion between the blood pressure and diabetes. The boxplot shows that the median of the blood pressure for the diabetic patients is slightly higher than the non-diabetic patients. The voilinplot shows that the distribution of the blood pressure for the diabetic patients is slightly higher than the non-diabetic patients. But there has been not enough evidence to conclude that the blood pressure is a good predictor of diabetes.
fig,ax = plt.subplots(1,2,figsize=(15,5))
sns.boxplot(x='Outcome', y='SkinThickness', data=df,ax=ax[0]).set_title('SkinThickness vs Diabetes')
sns.violinplot(x='Outcome', y='SkinThickness', data=df,ax=ax[1]).set_title('SkinThickness vs Diabetes')
Text(0.5, 1.0, 'SkinThickness vs Diabetes')
Here both the boxplot and violinplot reveals the effect of diabetes on skin thickness. As obserevd in the boxplot, the median of skin thickness is higher for the diabetic patients than the non-diabetic patients, where non diabetic patients have median skin thickness near 20 in comparison to skin thickness nearly 30 in diabetic patients. The voilinpplot shows the distribution of patients' skin thickness amoung the patients, where the non diabetic ones have greater distribution near 20 and diabetic much less distribution near 20 and increased distribution near 30. Therefore, skin thickness can be a indicator of diabetes.
fig,ax = plt.subplots(1,2,figsize=(15,5))
sns.boxplot(x='Outcome',y='Insulin',data=df,ax=ax[0]).set_title('Insulin vs Diabetes')
sns.violinplot(x='Outcome',y='Insulin',data=df,ax=ax[1]).set_title('Insulin vs Diabetes')
Text(0.5, 1.0, 'Insulin vs Diabetes')
Insulin is a major body hormone that regulates glucose metabolism. Insulin is required for the body to efficiently use sugars, fats and proteins. Any change in insulin amount in the body would result in change glucose levels as well. Here the boxplot and violinplot shows the distribution of insulin level in patients. In non diabetic patients the insulin level is near to 100, whereas in diabetic patients the insulin level is near to 200. In the voilinplot we can see that the distribution of insulin level in non diabetic patients is more spread out near 100, whereas in diabetic patients the distribution is contracted and shows a little bit spread in higher insulin levels. This shows that the insulin level is a good indicator of diabetes.
fig,ax = plt.subplots(1,2,figsize=(15,5))
sns.boxplot(x='Outcome',y='BMI',data=df,ax=ax[0])
sns.violinplot(x='Outcome',y='BMI',data=df,ax=ax[1])
<Axes: xlabel='Outcome', ylabel='BMI'>
Both graphs highlights the role of BMI in diabetes prediction. Non diabetic patients have a normal BMI within the range of 25-35 whereas the diabetic patients have a BMI greater than 35. The violinplot reveals the BMI distribution, where the non dibetic patients have a increased spread from 25 to 35 with narrows after 35. However in diabetic patients there is increased spread at 35 and increased spread 45-50 as compared to non diabetic patients.Therefore BMI is a good predictor of diabetes and obese people are more likely to be diabetic.
fig,ax = plt.subplots(1,2,figsize=(15,5))
sns.boxplot(x='Outcome',y='DiabetesPedigreeFunction',data=df,ax=ax[0]).set_title('Diabetes Pedigree Function')
sns.violinplot(x='Outcome',y='DiabetesPedigreeFunction',data=df,ax=ax[1]).set_title('Diabetes Pedigree Function')
Text(0.5, 1.0, 'Diabetes Pedigree Function')
Diabetes Pedigree Function (DPF) calculates diabetes likelihood depending on the subject's age and his/her diabetic family history. From the boxplot, the patients with lower DPF, are much less likely to have diabetes. The patients with higher DPF, are much more likely to have diabetes. In the violinplot, majority of the non diabetic patients have a DPF of 0.25-0.35, whereas the diabetic patients have a increased DPF, which is shown by the their distribution in the violinplot where there is a increased spread in the DPF from 0.5 -1.5. Therefore the DPF is a good indicator of diabetes.
#correlation heatmap
plt.figure(figsize=(12,12))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm').set_title('Correlation Heatmap')
Text(0.5, 1.0, 'Correlation Heatmap')
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(df.drop('Outcome',axis=1),df['Outcome'],test_size=0.2,random_state=42)
For predictiong the diabetes, I will be using the following algorithms:
#building model
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr
LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LogisticRegression()
#training the model
lr.fit(X_train,y_train)
#training accuracy
lr.score(X_train,y_train)
C:\Users\DELL\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
0.7719869706840391
#predicted outcomes
lr_pred = lr.predict(X_test)
#buidling model
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=100,random_state=42)
rfc
RandomForestClassifier(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(random_state=42)
#training model
rfc.fit(X_train, y_train)
#training accuracy
rfc.score(X_train, y_train)
1.0
#predicted outcomes
rfc_pred = rfc.predict(X_test)
#building model
from sklearn.svm import SVC
svm = SVC(kernel='linear', random_state=0)
svm
SVC(kernel='linear', random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC(kernel='linear', random_state=0)
#training the model
svm.fit(X_train, y_train)
#training the model
svm.score(X_test, y_test)
0.7597402597402597
#predicting outcomes
svm_pred = svm.predict(X_test)
from sklearn.metrics import confusion_matrix
sns.heatmap(confusion_matrix(y_test, lr_pred), annot=True, cmap='Blues')
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Confusion Matrix for Logistic Regression')
plt.show()
The diagonal boxes shows the count of true positives for each class. The predicted value is given on top while the actual value is given on the left side. The off-diagonal boxes shows the count of false positives.
ax = sns.distplot(y_test, color='r', label='Actual Value',hist=False)
sns.distplot(lr_pred, color='b', label='Predicted Value',hist=False,ax=ax)
plt.title('Actual vs Predicted Value Logistic Regression')
plt.xlabel('Outcome')
plt.ylabel('Count')
C:\Users\DELL\AppData\Local\Temp\ipykernel_14240\2460762733.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 ax = sns.distplot(y_test, color='r', label='Actual Value',hist=False) C:\Users\DELL\AppData\Local\Temp\ipykernel_14240\2460762733.py:2: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(lr_pred, color='b', label='Predicted Value',hist=False,ax=ax)
Text(0, 0.5, 'Count')
These distribution plot clearly visualizes the accuracy of the model. The red color represents the actual values and the blue color represents the predicted values. The more the overlapping of the two colors, the more accurate the model is.
from sklearn.metrics import classification_report
print(classification_report(y_test, lr_pred))
precision recall f1-score support
0 0.82 0.85 0.83 99
1 0.71 0.65 0.68 55
accuracy 0.78 154
macro avg 0.76 0.75 0.76 154
weighted avg 0.78 0.78 0.78 154
The model has as an average f1 score of 0.755 and acuuracy of 78%.
from sklearn.metrics import accuracy_score,mean_absolute_error,mean_squared_error,r2_score
print('Accuracy Score: ',accuracy_score(y_test,lr_pred))
print('Mean Absolute Error: ',mean_absolute_error(y_test,lr_pred))
print('Mean Squared Error: ',mean_squared_error(y_test,lr_pred))
print('R2 Score: ',r2_score(y_test,lr_pred))
Accuracy Score: 0.7792207792207793 Mean Absolute Error: 0.22077922077922077 Mean Squared Error: 0.22077922077922077 R2 Score: 0.038383838383838076
sns.heatmap(confusion_matrix(y_test, rfc_pred), annot=True, cmap='Blues')
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Confusion Matrix for Logistic Regression')
plt.show()
The diagonal boxes shows the count of true positives for each class. The predicted value is given on top while the actual value is given on the left side. The off-diagonal boxes shows the count of false positives.
ax = sns.distplot(y_test, color='r', label='Actual Value',hist=False)
sns.distplot(rfc_pred, color='b', label='Predicted Value',hist=False,ax=ax)
plt.title('Actual vs Predicted Value Logistic Regression')
plt.xlabel('Outcome')
plt.ylabel('Count')
C:\Users\DELL\AppData\Local\Temp\ipykernel_14240\821443087.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 ax = sns.distplot(y_test, color='r', label='Actual Value',hist=False) C:\Users\DELL\AppData\Local\Temp\ipykernel_14240\821443087.py:2: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(rfc_pred, color='b', label='Predicted Value',hist=False,ax=ax)
Text(0, 0.5, 'Count')
These distribution plot clearly visualizes the accuracy of the model. The red color represents the actual values and the blue color represents the predicted values. The more the overlapping of the two colors, the more accurate the model is.
print(classification_report(y_test, rfc_pred))
precision recall f1-score support
0 0.83 0.80 0.81 99
1 0.66 0.71 0.68 55
accuracy 0.77 154
macro avg 0.75 0.75 0.75 154
weighted avg 0.77 0.77 0.77 154
The model has as an average f1 score of 0.745 and acuuracy of 77% which less in comparison to Logistic Regression model.
print('Accuracy Score: ',accuracy_score(y_test,rfc_pred))
print('Mean Absolute Error: ',mean_absolute_error(y_test,rfc_pred))
print('Mean Squared Error: ',mean_squared_error(y_test,rfc_pred))
print('R2 Score: ',r2_score(y_test,rfc_pred))
Accuracy Score: 0.7662337662337663 Mean Absolute Error: 0.23376623376623376 Mean Squared Error: 0.23376623376623376 R2 Score: -0.01818181818181852
sns.heatmap(confusion_matrix(y_test, svm_pred), annot=True, cmap='Blues')
plt.xlabel('Predicted Values')
plt.ylabel('Actual Values')
plt.title('Confusion Matrix for Logistic Regression')
plt.show()
The diagonal boxes shows the count of true positives for each class. The predicted value is given on top while the actual value is given on the left side. The off-diagonal boxes shows the count of false positives.
ax = sns.distplot(y_test, color='r', label='Actual Value',hist=False)
sns.distplot(svm_pred, color='b', label='Predicted Value',hist=False,ax=ax)
plt.title('Actual vs Predicted Value Logistic Regression')
plt.xlabel('Outcome')
plt.ylabel('Count')
C:\Users\DELL\AppData\Local\Temp\ipykernel_14240\3198536253.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 ax = sns.distplot(y_test, color='r', label='Actual Value',hist=False) C:\Users\DELL\AppData\Local\Temp\ipykernel_14240\3198536253.py:2: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(svm_pred, color='b', label='Predicted Value',hist=False,ax=ax)
Text(0, 0.5, 'Count')
These distribution plot clearly visualizes the accuracy of the model. The red color represents the actual values and the blue color represents the predicted values. The more the overlapping of the two colors, the more accurate the model is.
print(classification_report(y_test, rfc_pred))
precision recall f1-score support
0 0.83 0.80 0.81 99
1 0.66 0.71 0.68 55
accuracy 0.77 154
macro avg 0.75 0.75 0.75 154
weighted avg 0.77 0.77 0.77 154
The model has as an average f1 score of 0.745 and acuuracy of 77% which is equivalent to previous model.
print('Accuracy Score: ',accuracy_score(y_test,svm_pred))
print('Mean Absolute Error: ',mean_absolute_error(y_test,svm_pred))
print('Mean Squared Error: ',mean_squared_error(y_test,svm_pred))
print('R2 Score: ',r2_score(y_test,svm_pred))
Accuracy Score: 0.7597402597402597 Mean Absolute Error: 0.24025974025974026 Mean Squared Error: 0.24025974025974026 R2 Score: -0.046464646464646764
#comparing the accuracy of different models
sns.barplot(x=['Logistic Regression', 'RandomForestClassifier', 'SVM'], y=[0.7792207792207793,0.7662337662337663,0.7597402597402597])
plt.xlabel('Classifier Models')
plt.ylabel('Accuracy')
plt.title('Comparison of different models')
Text(0.5, 1.0, 'Comparison of different models')
From the exploratory data analysis, I have concluded that the risk of diabetes depends upon the following factors:
With in increase in Glucose level, insulin level, BMI and number of pregnancies, the risk of diabetes increases. However, the number of pregnancies have strange effect of risk of diabetes which couldn't be explained by the data. The risk of diabetes also increases with increase in skin thickness.
Coming to the classification models, Logistic Regression outperformed Random Forest and SVM with 78% accuracy. The accuracy of the model can be improved by increasing the size of the dataset. The dataset used for this project was very small and had only 768 rows.