目的：

本项目的目的是

  1. 关于恶性和良性癌症的研究。
  2. 关于探索性数据分析的研究。
  3. 研究不同的机器学习模型并进行比较。

描述：

创建了一个机器学习模型，通过分析数据集中给出的因素和变量来预测乳腺癌。
通过创建数据帧并输入准确率分数和相应的机器学习模型，比较了 4 种机器学习模型的预测。

 

屏幕截图 （135）

图 I：这是定义恶性癌症和良性癌症计数的计数图。


 

屏幕截图 （136）

图 II：这是定义数据集中给定的不同变量/因素之间相关性的热图。

特征

该项目的特点如下：

  1. 使每行代码与解释保持一致
  2. 通过代码注释获得其他知识
  3. 每行说明
  4. 基本图形（Countplot 和 Heatmap）
  5. 同一项目中有 4 种不同的机器学习模型

选择这个项目的原因：

选择这个项目的一些原因如下：

  1. 癌症是值得研究的话题，因为它是全球最常见的疾病之一
  2. 研究同一项目中的不同模型

1. 导入库

导入 requirements.txt 文件中
给出的所需元器件库所需的元器件库也列在 Libraries Used （使用的元器件库） 下方
：

  1. Numpy
  2. 熊猫
  3. 海生
  4. 马特普洛特库
  5. Scikit-学习

2. 导入数据集

从 kaggle 或 ML datasets 文件夹中
提供的 Dataset 导入数据集Kaggle数据集

3. 数据预处理

删除数据集中不需要的列：'Unnamed： 32'，'id'。
将列分类为 mean、se 和 worst。

4. 数据可视化

（数据图如上所示）
创建所需的 countplot 和 heatmap 以可视化数据：

  1. 计数图：用于检查 Malignant 和 Benign Cancer 的值。
  2. Heatmap：用于根据图表中颜色的强度可视化数据。
    热图定义了数据集中给定的不同变量/因素之间的相关性。

5. 模型构建和预测

我们想使用不同的其他因素找出癌症的类型。
命名：恶性癌症 == 1 和良性癌症 == 0
为了预测“诊断”列，我们使用了其他列/因子。
测试训练拆分如下： 训练（398 行） ： 测试（171 行） ：： 70 ： 30
使用准确率分数来预测给定预测的模型准确性。
使用不同机器学习模型的结果：

  1. Logistic 回归：通过分析一个或多个现有自变量之间的关系来预测依赖关系。
  2. 决策树分类器：通过学习从先前数据（训练数据）推断的简单决策规则来预测目标变量的类别或值。
  3. 随机森林分类器：元估计器，在数据集的各个子样本上拟合多个决策树分类器，并使用平均来提高预测准确性和控制过拟合。
  4. 支持向量分类器：预测分析数据分类算法，用于将新数据元素分配给其中一个标记的类别。

6. 结论和结果

不同机器学习算法给出的准确率结果：

  1. Logistic 回归 = 0.982456
  2. 决策树分类器 = 0.923977
  3. 随机森林分类器 = 0.959064
  4. 支持向量分类器 = 0.964912

7. 实现代码

import warnings
warnings.filterwarnings('ignore') #ignore the warnings due to time lack or timeline

In [18]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns #use to plot functions as graphs

In [19]:

df = pd.read_csv('https://raw.githubusercontent.com/Aneeshcoder/ML-Breast-Cancer-Prediction/main/data.csv') #reading raw data

In [20]:

df.head() #checking data with their heads or titles & df is data frame

Out[20]:

id diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst Unnamed: 32
0 842302 M 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399 0.04904 0.05373 0.01587 0.03003 0.006193 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890 NaN
1 842517 M 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 0.5435 0.7339 3.398 74.08 0.005225 0.01308 0.01860 0.01340 0.01389 0.003532 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902 NaN
2 84300903 M 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 0.7456 0.7869 4.585 94.03 0.006150 0.04006 0.03832 0.02058 0.02250 0.004571 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758 NaN
3 84348301 M 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 0.4956 1.1560 3.445 27.23 0.009110 0.07458 0.05661 0.01867 0.05963 0.009208 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300 NaN
4 84358402 M 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 0.7572 0.7813 5.438 94.44 0.011490 0.02461 0.05688 0.01885 0.01756 0.005115 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678 NaN

In [21]:

df.columns #name of the columns

Out[21]:

Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',
       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],
      dtype='object')

In [22]:

df.info() #checking data types

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
 32  Unnamed: 32              0 non-null      float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB

In [23]:

#drop column axis=1 and row axis=0
#we need to drop unnamed column
df['Unnamed: 32']

Out[23]:

0     NaN
1     NaN
2     NaN
3     NaN
4     NaN
       ..
564   NaN
565   NaN
566   NaN
567   NaN
568   NaN
Name: Unnamed: 32, Length: 569, dtype: float64

In [24]:

df = df.drop("Unnamed: 32", axis=1)

In [25]:

df.head()

Out[25]:

id diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 842302 M 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399 0.04904 0.05373 0.01587 0.03003 0.006193 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 842517 M 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 0.5435 0.7339 3.398 74.08 0.005225 0.01308 0.01860 0.01340 0.01389 0.003532 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 84300903 M 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 0.7456 0.7869 4.585 94.03 0.006150 0.04006 0.03832 0.02058 0.02250 0.004571 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 84348301 M 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 0.4956 1.1560 3.445 27.23 0.009110 0.07458 0.05661 0.01867 0.05963 0.009208 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 84358402 M 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 0.7572 0.7813 5.438 94.44 0.011490 0.02461 0.05688 0.01885 0.01756 0.005115 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

In [26]:

df.columns

Out[26]:

Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',
       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst'],
      dtype='object')

In [27]:

#id column not providing any useful data
df.drop("id",axis=1, inplace=True) 
# df=df.drop('id',axis=1) ,we can also use this

In [28]:

df.columns

Out[28]:

Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',
       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst'],
      dtype='object')

In [29]:

type(df.columns) #data type of df

Out[29]:

pandas.core.indexes.base.Index

In [30]:

l = list(df.columns) #convert columns into list
print(l)

['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']

In [31]:

#now we can use index location that is 0,1,2,....
features_mean = l[1:11] #in python start index is inclusive and end index is exclusive

features_se = l[11:21]

features_worst = l[21:] #feature is known as column in ML

In [32]:

print(features_mean)

['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']

In [33]:

print(features_se)

['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se']

In [34]:

print(features_worst)

['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']

In [35]:

df.head(2)

Out[35]:

diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 M 17.99 10.38 122.8 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399 0.04904 0.05373 0.01587 0.03003 0.006193 25.38 17.33 184.6 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 M 20.57 17.77 132.9 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 0.5435 0.7339 3.398 74.08 0.005225 0.01308 0.01860 0.01340 0.01389 0.003532 24.99 23.41 158.8 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902

In [36]:

df['diagnosis'].unique()
# M = Malignant cancer and B = Benign cancer

Out[36]:

array(['M', 'B'], dtype=object)

In [37]:

sns.countplot(df['diagnosis'], label="count",);

In [38]:

df['diagnosis'].value_counts()
#value count function tells us about values of M and B in data set

Out[38]:

B    357
M    212
Name: diagnosis, dtype: int64

In [39]:

df.shape #569 no. of rows and 31 no. of columns

Out[39]:

(569, 31)

In [40]:

#Explore the Data

In [41]:

df.describe()
#summary of all the numeric columns
#std is standard deviation

Out[41]:

radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
count 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000
mean 14.127292 19.289649 91.969033 654.889104 0.096360 0.104341 0.088799 0.048919 0.181162 0.062798 0.405172 1.216853 2.866059 40.337079 0.007041 0.025478 0.031894 0.011796 0.020542 0.003795 16.269190 25.677223 107.261213 880.583128 0.132369 0.254265 0.272188 0.114606 0.290076 0.083946
std 3.524049 4.301036 24.298981 351.914129 0.014064 0.052813 0.079720 0.038803 0.027414 0.007060 0.277313 0.551648 2.021855 45.491006 0.003003 0.017908 0.030186 0.006170 0.008266 0.002646 4.833242 6.146258 33.602542 569.356993 0.022832 0.157336 0.208624 0.065732 0.061867 0.018061
min 6.981000 9.710000 43.790000 143.500000 0.052630 0.019380 0.000000 0.000000 0.106000 0.049960 0.111500 0.360200 0.757000 6.802000 0.001713 0.002252 0.000000 0.000000 0.007882 0.000895 7.930000 12.020000 50.410000 185.200000 0.071170 0.027290 0.000000 0.000000 0.156500 0.055040
25% 11.700000 16.170000 75.170000 420.300000 0.086370 0.064920 0.029560 0.020310 0.161900 0.057700 0.232400 0.833900 1.606000 17.850000 0.005169 0.013080 0.015090 0.007638 0.015160 0.002248 13.010000 21.080000 84.110000 515.300000 0.116600 0.147200 0.114500 0.064930 0.250400 0.071460
50% 13.370000 18.840000 86.240000 551.100000 0.095870 0.092630 0.061540 0.033500 0.179200 0.061540 0.324200 1.108000 2.287000 24.530000 0.006380 0.020450 0.025890 0.010930 0.018730 0.003187 14.970000 25.410000 97.660000 686.500000 0.131300 0.211900 0.226700 0.099930 0.282200 0.080040
75% 15.780000 21.800000 104.100000 782.700000 0.105300 0.130400 0.130700 0.074000 0.195700 0.066120 0.478900 1.474000 3.357000 45.190000 0.008146 0.032450 0.042050 0.014710 0.023480 0.004558 18.790000 29.720000 125.400000 1084.000000 0.146000 0.339100 0.382900 0.161400 0.317900 0.092080
max 28.110000 39.280000 188.500000 2501.000000 0.163400 0.345400 0.426800 0.201200 0.304000 0.097440 2.873000 4.885000 21.980000 542.200000 0.031130 0.135400 0.396000 0.052790 0.078950 0.029840 36.040000 49.540000 251.200000 4254.000000 0.222600 1.058000 1.252000 0.291000 0.663800 0.207500

In [42]:

#Create Correlation Plot  ,, Correlation values[-1,1]
# +ve correlation = if 1st variable increase then 2nd variable also increases
# -ve correlation = if 1st variable increase then 2nd variable decreases
# no correlation = zero or nothing

In [43]:

len(df.columns)

Out[43]:

31

In [44]:

#correlation plot 
corr = df.corr()
corr

Out[44]:

radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
radius_mean 1.000000 0.323782 0.997855 0.987357 0.170581 0.506124 0.676764 0.822529 0.147741 -0.311631 0.679090 -0.097317 0.674172 0.735864 -0.222600 0.206000 0.194204 0.376169 -0.104321 -0.042641 0.969539 0.297008 0.965137 0.941082 0.119616 0.413463 0.526911 0.744214 0.163953 0.007066
texture_mean 0.323782 1.000000 0.329533 0.321086 -0.023389 0.236702 0.302418 0.293464 0.071401 -0.076437 0.275869 0.386358 0.281673 0.259845 0.006614 0.191975 0.143293 0.163851 0.009127 0.054458 0.352573 0.912045 0.358040 0.343546 0.077503 0.277830 0.301025 0.295316 0.105008 0.119205
perimeter_mean 0.997855 0.329533 1.000000 0.986507 0.207278 0.556936 0.716136 0.850977 0.183027 -0.261477 0.691765 -0.086761 0.693135 0.744983 -0.202694 0.250744 0.228082 0.407217 -0.081629 -0.005523 0.969476 0.303038 0.970387 0.941550 0.150549 0.455774 0.563879 0.771241 0.189115 0.051019
area_mean 0.987357 0.321086 0.986507 1.000000 0.177028 0.498502 0.685983 0.823269 0.151293 -0.283110 0.732562 -0.066280 0.726628 0.800086 -0.166777 0.212583 0.207660 0.372320 -0.072497 -0.019887 0.962746 0.287489 0.959120 0.959213 0.123523 0.390410 0.512606 0.722017 0.143570 0.003738
smoothness_mean 0.170581 -0.023389 0.207278 0.177028 1.000000 0.659123 0.521984 0.553695 0.557775 0.584792 0.301467 0.068406 0.296092 0.246552 0.332375 0.318943 0.248396 0.380676 0.200774 0.283607 0.213120 0.036072 0.238853 0.206718 0.805324 0.472468 0.434926 0.503053 0.394309 0.499316
compactness_mean 0.506124 0.236702 0.556936 0.498502 0.659123 1.000000 0.883121 0.831135 0.602641 0.565369 0.497473 0.046205 0.548905 0.455653 0.135299 0.738722 0.570517 0.642262 0.229977 0.507318 0.535315 0.248133 0.590210 0.509604 0.565541 0.865809 0.816275 0.815573 0.510223 0.687382
concavity_mean 0.676764 0.302418 0.716136 0.685983 0.521984 0.883121 1.000000 0.921391 0.500667 0.336783 0.631925 0.076218 0.660391 0.617427 0.098564 0.670279 0.691270 0.683260 0.178009 0.449301 0.688236 0.299879 0.729565 0.675987 0.448822 0.754968 0.884103 0.861323 0.409464 0.514930
concave points_mean 0.822529 0.293464 0.850977 0.823269 0.553695 0.831135 0.921391 1.000000 0.462497 0.166917 0.698050 0.021480 0.710650 0.690299 0.027653 0.490424 0.439167 0.615634 0.095351 0.257584 0.830318 0.292752 0.855923 0.809630 0.452753 0.667454 0.752399 0.910155 0.375744 0.368661
symmetry_mean 0.147741 0.071401 0.183027 0.151293 0.557775 0.602641 0.500667 0.462497 1.000000 0.479921 0.303379 0.128053 0.313893 0.223970 0.187321 0.421659 0.342627 0.393298 0.449137 0.331786 0.185728 0.090651 0.219169 0.177193 0.426675 0.473200 0.433721 0.430297 0.699826 0.438413
fractal_dimension_mean -0.311631 -0.076437 -0.261477 -0.283110 0.584792 0.565369 0.336783 0.166917 0.479921 1.000000 0.000111 0.164174 0.039830 -0.090170 0.401964 0.559837 0.446630 0.341198 0.345007 0.688132 -0.253691 -0.051269 -0.205151 -0.231854 0.504942 0.458798 0.346234 0.175325 0.334019 0.767297
radius_se 0.679090 0.275869 0.691765 0.732562 0.301467 0.497473 0.631925 0.698050 0.303379 0.000111 1.000000 0.213247 0.972794 0.951830 0.164514 0.356065 0.332358 0.513346 0.240567 0.227754 0.715065 0.194799 0.719684 0.751548 0.141919 0.287103 0.380585 0.531062 0.094543 0.049559
texture_se -0.097317 0.386358 -0.086761 -0.066280 0.068406 0.046205 0.076218 0.021480 0.128053 0.164174 0.213247 1.000000 0.223171 0.111567 0.397243 0.231700 0.194998 0.230283 0.411621 0.279723 -0.111690 0.409003 -0.102242 -0.083195 -0.073658 -0.092439 -0.068956 -0.119638 -0.128215 -0.045655
perimeter_se 0.674172 0.281673 0.693135 0.726628 0.296092 0.548905 0.660391 0.710650 0.313893 0.039830 0.972794 0.223171 1.000000 0.937655 0.151075 0.416322 0.362482 0.556264 0.266487 0.244143 0.697201 0.200371 0.721031 0.730713 0.130054 0.341919 0.418899 0.554897 0.109930 0.085433
area_se 0.735864 0.259845 0.744983 0.800086 0.246552 0.455653 0.617427 0.690299 0.223970 -0.090170 0.951830 0.111567 0.937655 1.000000 0.075150 0.284840 0.270895 0.415730 0.134109 0.127071 0.757373 0.196497 0.761213 0.811408 0.125389 0.283257 0.385100 0.538166 0.074126 0.017539
smoothness_se -0.222600 0.006614 -0.202694 -0.166777 0.332375 0.135299 0.098564 0.027653 0.187321 0.401964 0.164514 0.397243 0.151075 0.075150 1.000000 0.336696 0.268685 0.328429 0.413506 0.427374 -0.230691 -0.074743 -0.217304 -0.182195 0.314457 -0.055558 -0.058298 -0.102007 -0.107342 0.101480
compactness_se 0.206000 0.191975 0.250744 0.212583 0.318943 0.738722 0.670279 0.490424 0.421659 0.559837 0.356065 0.231700 0.416322 0.284840 0.336696 1.000000 0.801268 0.744083 0.394713 0.803269 0.204607 0.143003 0.260516 0.199371 0.227394 0.678780 0.639147 0.483208 0.277878 0.590973
concavity_se 0.194204 0.143293 0.228082 0.207660 0.248396 0.570517 0.691270 0.439167 0.342627 0.446630 0.332358 0.194998 0.362482 0.270895 0.268685 0.801268 1.000000 0.771804 0.309429 0.727372 0.186904 0.100241 0.226680 0.188353 0.168481 0.484858 0.662564 0.440472 0.197788 0.439329
concave points_se 0.376169 0.163851 0.407217 0.372320 0.380676 0.642262 0.683260 0.615634 0.393298 0.341198 0.513346 0.230283 0.556264 0.415730 0.328429 0.744083 0.771804 1.000000 0.312780 0.611044 0.358127 0.086741 0.394999 0.342271 0.215351 0.452888 0.549592 0.602450 0.143116 0.310655
symmetry_se -0.104321 0.009127 -0.081629 -0.072497 0.200774 0.229977 0.178009 0.095351 0.449137 0.345007 0.240567 0.411621 0.266487 0.134109 0.413506 0.394713 0.309429 0.312780 1.000000 0.369078 -0.128121 -0.077473 -0.103753 -0.110343 -0.012662 0.060255 0.037119 -0.030413 0.389402 0.078079
fractal_dimension_se -0.042641 0.054458 -0.005523 -0.019887 0.283607 0.507318 0.449301 0.257584 0.331786 0.688132 0.227754 0.279723 0.244143 0.127071 0.427374 0.803269 0.727372 0.611044 0.369078 1.000000 -0.037488 -0.003195 -0.001000 -0.022736 0.170568 0.390159 0.379975 0.215204 0.111094 0.591328
radius_worst 0.969539 0.352573 0.969476 0.962746 0.213120 0.535315 0.688236 0.830318 0.185728 -0.253691 0.715065 -0.111690 0.697201 0.757373 -0.230691 0.204607 0.186904 0.358127 -0.128121 -0.037488 1.000000 0.359921 0.993708 0.984015 0.216574 0.475820 0.573975 0.787424 0.243529 0.093492
texture_worst 0.297008 0.912045 0.303038 0.287489 0.036072 0.248133 0.299879 0.292752 0.090651 -0.051269 0.194799 0.409003 0.200371 0.196497 -0.074743 0.143003 0.100241 0.086741 -0.077473 -0.003195 0.359921 1.000000 0.365098 0.345842 0.225429 0.360832 0.368366 0.359755 0.233027 0.219122
perimeter_worst 0.965137 0.358040 0.970387 0.959120 0.238853 0.590210 0.729565 0.855923 0.219169 -0.205151 0.719684 -0.102242 0.721031 0.761213 -0.217304 0.260516 0.226680 0.394999 -0.103753 -0.001000 0.993708 0.365098 1.000000 0.977578 0.236775 0.529408 0.618344 0.816322 0.269493 0.138957
area_worst 0.941082 0.343546 0.941550 0.959213 0.206718 0.509604 0.675987 0.809630 0.177193 -0.231854 0.751548 -0.083195 0.730713 0.811408 -0.182195 0.199371 0.188353 0.342271 -0.110343 -0.022736 0.984015 0.345842 0.977578 1.000000 0.209145 0.438296 0.543331 0.747419 0.209146 0.079647
smoothness_worst 0.119616 0.077503 0.150549 0.123523 0.805324 0.565541 0.448822 0.452753 0.426675 0.504942 0.141919 -0.073658 0.130054 0.125389 0.314457 0.227394 0.168481 0.215351 -0.012662 0.170568 0.216574 0.225429 0.236775 0.209145 1.000000 0.568187 0.518523 0.547691 0.493838 0.617624
compactness_worst 0.413463 0.277830 0.455774 0.390410 0.472468 0.865809 0.754968 0.667454 0.473200 0.458798 0.287103 -0.092439 0.341919 0.283257 -0.055558 0.678780 0.484858 0.452888 0.060255 0.390159 0.475820 0.360832 0.529408 0.438296 0.568187 1.000000 0.892261 0.801080 0.614441 0.810455
concavity_worst 0.526911 0.301025 0.563879 0.512606 0.434926 0.816275 0.884103 0.752399 0.433721 0.346234 0.380585 -0.068956 0.418899 0.385100 -0.058298 0.639147 0.662564 0.549592 0.037119 0.379975 0.573975 0.368366 0.618344 0.543331 0.518523 0.892261 1.000000 0.855434 0.532520 0.686511
concave points_worst 0.744214 0.295316 0.771241 0.722017 0.503053 0.815573 0.861323 0.910155 0.430297 0.175325 0.531062 -0.119638 0.554897 0.538166 -0.102007 0.483208 0.440472 0.602450 -0.030413 0.215204 0.787424 0.359755 0.816322 0.747419 0.547691 0.801080 0.855434 1.000000 0.502528 0.511114
symmetry_worst 0.163953 0.105008 0.189115 0.143570 0.394309 0.510223 0.409464 0.375744 0.699826 0.334019 0.094543 -0.128215 0.109930 0.074126 -0.107342 0.277878 0.197788 0.143116 0.389402 0.111094 0.243529 0.233027 0.269493 0.209146 0.493838 0.614441 0.532520 0.502528 1.000000 0.537848
fractal_dimension_worst 0.007066 0.119205 0.051019 0.003738 0.499316 0.687382 0.514930 0.368661 0.438413 0.767297 0.049559 -0.045655 0.085433 0.017539 0.101480 0.590973 0.439329 0.310655 0.078079 0.591328 0.093492 0.219122 0.138957 0.079647 0.617624 0.810455 0.686511 0.511114 0.537848 1.000000

In [45]:

corr.shape

Out[45]:

(30, 30)

In [46]:

plt.figure(figsize=(8,8)) #(8,8) is the size of graph i.e. 8X8
sns.heatmap(corr);

In [47]:

df.head()

Out[47]:

diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 M 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399 0.04904 0.05373 0.01587 0.03003 0.006193 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 M 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 0.5435 0.7339 3.398 74.08 0.005225 0.01308 0.01860 0.01340 0.01389 0.003532 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 M 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 0.7456 0.7869 4.585 94.03 0.006150 0.04006 0.03832 0.02058 0.02250 0.004571 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 M 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 0.4956 1.1560 3.445 27.23 0.009110 0.07458 0.05661 0.01867 0.05963 0.009208 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 M 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 0.7572 0.7813 5.438 94.44 0.011490 0.02461 0.05688 0.01885 0.01756 0.005115 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

In [48]:

#M==1 and B==0
df['diagnosis']=df['diagnosis'].map({'M':1, 'B':0})

In [49]:

df.head()

Out[49]:

diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 1 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399 0.04904 0.05373 0.01587 0.03003 0.006193 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 0.5435 0.7339 3.398 74.08 0.005225 0.01308 0.01860 0.01340 0.01389 0.003532 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 1 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 0.7456 0.7869 4.585 94.03 0.006150 0.04006 0.03832 0.02058 0.02250 0.004571 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 1 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 0.4956 1.1560 3.445 27.23 0.009110 0.07458 0.05661 0.01867 0.05963 0.009208 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 1 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 0.7572 0.7813 5.438 94.44 0.011490 0.02461 0.05688 0.01885 0.01756 0.005115 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

In [50]:

df['diagnosis'].unique()

Out[50]:

array([1, 0])

In [51]:

X = df.drop('diagnosis',axis=1)  #Capital X is preferred
X.head()

Out[51]:

radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se smoothness_se compactness_se concavity_se concave points_se symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 1.0950 0.9053 8.589 153.40 0.006399 0.04904 0.05373 0.01587 0.03003 0.006193 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 0.5435 0.7339 3.398 74.08 0.005225 0.01308 0.01860 0.01340 0.01389 0.003532 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 0.7456 0.7869 4.585 94.03 0.006150 0.04006 0.03832 0.02058 0.02250 0.004571 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 0.4956 1.1560 3.445 27.23 0.009110 0.07458 0.05661 0.01867 0.05963 0.009208 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 0.7572 0.7813 5.438 94.44 0.011490 0.02461 0.05688 0.01885 0.01756 0.005115 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

In [52]:

y = df['diagnosis']  #Small y is preferred
y.head()

Out[52]:

0    1
1    1
2    1
3    1
4    1
Name: diagnosis, dtype: int64

In [53]:

#0. fit()
#1. Study(X_train,y_train)
#2. Brain(intelligence)
#3. How good is preparation X_test
#4. y_pred i.e. predicted values
#5. Comparison of y_pred and y_test => how good is model prepared

In [54]:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

In [55]:

df.shape

Out[55]:

(569, 31)

In [56]:

X_train.shape

Out[56]:

(398, 30)

In [57]:

X_test.shape

Out[57]:

(171, 30)

In [58]:

y_train.shape

Out[58]:

(398,)

In [59]:

y_test.shape

Out[59]:

(171,)

In [60]:

from sklearn.preprocessing import StandardScaler
ss = StandardScaler() #scaling near to 0
X_train = ss.fit_transform(X_train)  #fit and immediately transform
X_test = ss.transform(X_test) #dont need fit as fit is equivalent to studying

In [61]:

X_train

Out[61]:

array([[-1.14855258,  0.03289996, -1.14449945, ..., -1.41275479,
        -1.03771554, -0.87408813],
       [-0.39236814,  2.2678828 , -0.44529805, ..., -0.76228731,
        -0.8073702 , -1.00779198],
       [ 1.64440453,  0.48035079,  1.58878064, ...,  1.02200017,
         0.24586401, -0.0900031 ],
       ...,
       [-0.48508041, -0.68938108, -0.38219323, ...,  0.29315944,
         0.50162677,  0.99051064],
       [-0.56330638,  0.24413309, -0.61904665, ..., -0.62002614,
        -0.35144528, -0.80360857],
       [-0.72555286, -0.28735673, -0.65438535, ...,  0.40599253,
        -0.40545729,  0.43600239]])

In [62]:

# Machine Learning Models

In [63]:

# Logistic Regression

In [64]:

from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)

Out[64]:

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

In [65]:

y_pred =lr.predict(X_test)

In [66]:

y_pred

Out[66]:

array([1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,
       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0])

In [67]:

y_test

Out[67]:

8      1
62     1
478    0
238    0
488    0
      ..
243    0
350    0
16     1
531    0
298    0
Name: diagnosis, Length: 171, dtype: int64

In [68]:

# accuracy_score gives performance of function

In [69]:

from sklearn.metrics import accuracy_score
print(accuracy_score(y_test,y_pred))  # approx 98.245%

0.9883040935672515

In [70]:

lr_acc = accuracy_score(y_test, y_pred)
print(lr_acc)

0.9883040935672515

In [71]:

results = pd.DataFrame()
results  #empty dataframe

Out[71]:

In [72]:

tempResults = pd.DataFrame({'Algorithm':['Logistic Regression Method'], 'Accuracy':[lr_acc]}) #Headings Algorithm and Accuracy
results = pd.concat([results,tempResults])
results = results[['Algorithm','Accuracy']]
results

Out[72]:

Algorithm Accuracy
0 Logistic Regression Method 0.988304

In [73]:

# Decision Tree Classifier //more powerful algorithm than LR

In [74]:

from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)

Out[74]:

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=None, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

In [75]:

y_pred = dtc.predict(X_test)
y_pred

Out[75]:

array([1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,
       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0])

In [76]:

from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))

0.9473684210526315

In [77]:

dtc_acc = accuracy_score(y_test, y_pred)
print(dtc_acc)

0.9473684210526315

In [78]:

tempResults = pd.DataFrame({'Algorithm':['Decision Tree Classifier Method'], 'Accuracy':[dtc_acc]}) #Headings Algorithm and Accuracy
results = pd.concat([results,tempResults])
results = results[['Algorithm','Accuracy']]
results

Out[78]:

Algorithm Accuracy
0 Logistic Regression Method 0.988304
0 Decision Tree Classifier Method 0.947368

In [79]:

# Random Forest Classifier //most powerful algorithm

In [80]:

from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

Out[80]:

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

In [81]:

y_pred = rfc.predict(X_test)
y_pred

Out[81]:

array([1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,
       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0])

In [82]:

from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))

0.9824561403508771

In [83]:

rfc_acc = accuracy_score(y_test, y_pred)
print(rfc_acc)

0.9824561403508771

In [84]:

tempResults = pd.DataFrame({'Algorithm':['Random Forest Classifier Method'], 'Accuracy':[rfc_acc]}) #Headings Algorithm and Accuracy
results = pd.concat([results,tempResults])
results = results[['Algorithm','Accuracy']]
results

Out[84]:

Algorithm Accuracy
0 Logistic Regression Method 0.988304
0 Decision Tree Classifier Method 0.947368
0 Random Forest Classifier Method 0.982456

In [85]:

# Support Vector Classifier

In [86]:

from sklearn import svm
svc = svm.SVC()
svc.fit(X_train, y_train)

Out[86]:

SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

In [87]:

y_pred = svc.predict(X_test)
y_pred

Out[87]:

array([1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,
       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
       1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0])

In [88]:

from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))

0.9883040935672515

In [89]:

svm_acc = accuracy_score(y_test, y_pred)
print(svm_acc)

0.9883040935672515

In [90]:

tempResults = pd.DataFrame({'Algorithm':['Support Vector Classifier Method'], 'Accuracy':[svm_acc]}) #Headings Algorithm and Accuracy
results = pd.concat([results,tempResults])
results = results[['Algorithm','Accuracy']]
results

Out[90]:

Algorithm Accuracy
0 Logistic Regression Method 0.988304
0 Decision Tree Classifier Method 0.947368
0 Random Forest Classifier Method 0.982456
0 Support Vector Classifier Method 0.988304

最后，我得出结论，对于我们的预测，准确率得分达到了 98.2456%。
Logistic Regression 最适合这个特定的机器学习模型。

8.代码项目联系

点击这里看最后

# python # 深度学习 # 机器学习
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