In this section, we plan to predict whether breast cancer is malignant or benign by specific parameters.
Dataset Story Breast cancer is the most common cancer among women in the world. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.
The key challenge against its detection is how to classify tumors into malignant (cancerous) or benign(non-cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (with SVMs) and the Breast Cancer Wisconsin (Diagnostic) Dataset.
Required libraries and some settings for this section are:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
pd.set_option("display.width", 500)
pd.set_option("display.float_format", lambda x: "%.4f" % x)
from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import joblib
import warnings
warnings.filterwarnings("ignore")
First, we import the dataset breast-cancer.csv into the pandas DataFrame.
As we want to check the data to have a general opinion about it, we create and use a function called check_df(dataframe, head=5, tail=5) that prints the referred functions:
dataframe.head(head)
dataframe.tail(tail)
dataframe.shape
dataframe.dtypes
dataframe.size
dataframe.isnull().sum()
dataframe.describe([0, 0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99, 1]).T
df = df.drop(columns=['id'], axis=1)
By this expression, we eliminate id feature since it is irrelevant to us.
After checking the data frame, we need to define and separate columns as categorical and numerical. We define a function called grab_col_names for separation that benefits from multiple list comprehensions as follows:
cat_cols = [col for col in dataframe.columns if str(dataframe[col].dtypes) in ['category', 'object', 'bool']]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes in ['uint8', 'int64', 'int32', 'float64']]
cat_but_car = [col for col in df.columns if df[col].nunique() > car_th and str(df[col].dtypes) in ['object', 'category']]
cat_cols = cat_cols + num_but_cat
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in ['uint8', 'int64', 'float64']]
num_cols = [col for col in num_cols if col not in cat_cols]
cat_th and car_th are the threshold parameters to decide the column type.
Categorical Columns:
- diagnosis
Numerical Columns:
Rest of the features are numerical
To summarize and visualize the referred column we create two other functions called cat_summary and num_summary.
For example, categorical column diagnosis:
############### diagnosis ###############
| diagnosis | diagnosis count | Ratio |
|---|---|---|
| 0 | 500 | 65.1042 |
| 1 | 268 | 34.8958 |
Another example is, the numerical column radius_mean:
############### radius_mean ###############
| Process | Result |
|---|---|
| count | 569.0000 |
| mean | 14.1273 |
| std | 3.5240 |
| min | 6.9810 |
| 1% | 8.4584 |
| 5% | 9.5292 |
| 10% | 10.2600 |
| 20% | 11.3660 |
| 30% | 12.0120 |
| 40% | 12.7260 |
| 50% | 13.3700 |
| 60% | 14.0580 |
| 70% | 15.0560 |
| 80% | 17.0680 |
| 90% | 19.5300 |
| 95% | 20.5760 |
| 99% | 24.3716 |
| max | 28.1100 |
Name: radius_mean, dtype: float64
With the help of a for loop we apply these functions to all columns in the data frame.
We create another plot function called plot_num_summary(dataframe) to see the whole summary of numerical columns due to the high quantity of them:
We create another function called target_summary_with_cat(dataframe, target, categorical_col) to examine the target by categorical features.
For instance radius_mean
################ diagnosis --> radius_mean #################
| diagnosis | radius_mean |
|---|---|
| 0 | 12.1465 |
| 1 | 17.4628 |
To analyze correlations between numerical columns we create a function called correlated_cols(dataframe):
We analyze the correlations once more with a threshold of 90%:
High correlated features:
- perimeter_mean
- area_mean
- concave points_mean
- perimeter_se
- area_se
- radius_worst
- texture_worst
- perimeter_worst
- area_worst
- concave points_worst
We check the data to designate the missing values in it, dataframe.isnull().sum():
- diagnosis 0
- radius_mean 0
- texture_mean 0
- perimeter_mean 0
- area_mean 0
- smoothness_mean 0
- compactness_mean 0
- concavity_mean 0
- concave points_mean 0
- symmetry_mean 0
- fractal_dimension_mean 0
- radius_se 0
- texture_se 0
- perimeter_se 0
- area_se 0
- smoothness_se 0
- compactness_se 0
- concavity_se 0
- concave points_se 0
- symmetry_se 0
- fractal_dimension_se 0
- radius_worst 0
- texture_worst 0
- perimeter_worst 0
- area_worst 0
- smoothness_worst 0
- compactness_worst 0
- concavity_worst 0
- concave points_worst 0
- symmetry_worst 0
- fractal_dimension_worst 0
dtype: int64
At last, we create our model and see the results:
******************** Accuracy & Results ********************
Accuracy Train: 1.000
Accuracy Test: 0.971
R2 Train: 1.000
R2 Test: 0.971
Cross Validation Train: 0.950
Cross Validation Test: 0.959
Cross Validation (Accuracy): 0.956
Cross Validation (F1): 0.940
Cross Validation (Precision): 0.953
Cross Validation (Recall): 0.930
Cross Validation (ROC Auc): 0.991
Via joblib we can save and/or load our model:
def load_model(pklfile):
model_disc = joblib.load(pklfile)
return model_disc
model_disc = load_model("rf_model.pkl")
Now we can make predictions with our model:
X = df.drop("diagnosis", axis=1)
x = X.sample(1).values.tolist()
model_disc.predict(pd.DataFrame(X))[0]
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sample2 = [13, 8, 125, 1000, 0.12, 0.25, 0.4, 0.2, 0.16, 0.07, 1, 0.8, 9, 153, 0.005, 0.05, 0.06, 0.02, 0.02, 0.005, 26, 18, 185, 2020, 0.17, 0.5, 0.9, 0.28, 0.44, 0.12]
model_disc.predict(pd.DataFrame(sample2).T)[0]
1