SUMMARY:
Guide for building a machine learning model to predict whether a tumor is benign or malignant based on a dataset (cancer.csv). The process uses Google Colab and involves following 37 steps to load the dataset, preprocess it, build a neural network model using TensorFlow/Keras, and train the model on the data.
CODE FOR CANCER Tumor PURPOSE: Predict Benign Vs. Malignant Tumors Model
https://www.youtube.com/watch?v=z1PGJ9quPV8 Video
https://gist.github.com/adameubanks/35a6beea49e5b9ba62797e595a9626c0 Dataset
FIRST LOAD DATASET IN NEW NOTEBOOK (Cancer.csv)
COPY AND PASTE THE FOLLOWING CODE SNIPPETS ONE BY ONE AND RUN THEM TO TRAIN THE MODEL.
CODE SNIPPETS:
1.
import pandas as pd
dataset = pd.read_csv('cancer.csv')
2.
x = dataset.drop(columns=["diagnosis(1=m, 0=b)"])
3.
y = dataset["diagnosis(1=m, 0=b)"]
4.
from sklearn.model_selection import train_test_split
5.
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
6.
import tensorflow as tf
model = tf.keras.models.Sequential()
7.
model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='relu'))
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
8.
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
9.
model.fit(x_train, y_train, epochs=10, batch_size=32)
10.
# prompt: predictions = model.predict(x_test)
predictions = model.predict(x_test)
11.
predictions = model.predict(x_test)
print(predictions)
12.
predictions = model.predict(x_test)
predicted_labels = (predictions > 0.5).astype(int)
13.
# Iterate over y_test and predicted_labels, and print them
for actual, predicted in zip(y_test, predicted_labels):
print(f'Actual: {actual}, Predicted: {predicted[0]}')
14.
from sklearn.metrics import accuracy_score
# Calculate accuracy
accuracy = accuracy_score(y_test, predicted_labels)
print(f'Accuracy: {accuracy}')
15.
from sklearn.metrics import confusion_matrix
# Generate the confusion matrix
cm = confusion_matrix(y_test, predicted_labels)
print('Confusion Matrix:')
print(cm)
16.
from sklearn.metrics import classification_report
# Generate a classification report
report = classification_report(y_test, predicted_labels)
print('Classification Report:')
print(report)
17.
print(y_train.unique())
18.
print(type(y_train))
19.
y_train = y_train.squeeze()
20.
import numpy as np
x_train = np.array(x_train)
y_train = np.array(y_train)
21.
class_weight = {0: 1, 1: 2}
model.fit(x_train, y_train, epochs=100, batch_size=32, class_weight=class_weight)
22.
# Evaluate the model on the test data
test_loss, test_accuracy = model.evaluate(x_test, y_test)
print(f'Test Loss: {test_loss}')
print(f'Test Accuracy: {test_accuracy}')
23.
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
24.
model.add(tf.keras.layers.Dense(512, activation='relu'))
model.add(tf.keras.layers.Dense(256, activation='relu'))
25.
model.add(tf.keras.layers.Dropout(0.5))
26.
# Add the output layer with 1 neuron and sigmoid activation
model.add(tf.keras.layers.Dense(1, activation='sigmoid')) # Binary classification
27.
# Save the entire model to a file
model.save('my_model.h5')
28.
!pip install imbalanced-learn
29.
from imblearn.over_sampling import SMOTE
smote = SMOTE()
x_train_resampled, y_train_resampled = smote.fit_resample(x_train, y_train)
30.
model = tf.keras.models.Sequential()
# Add more neurons to layers and more layers to improve complexity
model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(x_train.shape[1],)))
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid')) # Output layer for binary classification
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
31.
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(x_train.shape[1],)))
model.add(tf.keras.layers.Dropout(0.5)) # Drop 50% of neurons randomly
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
32.
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
33.
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
model.fit(x_train, y_train, epochs=100, batch_size=32, validation_data=(x_test, y_test), callbacks=[early_stopping])
34.
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
35.
model.fit(x_train, y_train, epochs=200, batch_size=64)
36.
!pip install tensorflow scikit-learn
37.
from sklearn.model_selection import KFold
import numpy as np
# Define the number of splits for K-Fold Cross-Validation
kf = KFold(n_splits=5, shuffle=True, random_state=42)
# Create a list to store accuracy scores
accuracy_scores = []
# Define a function to create your model
def create_model():
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(256, activation='relu', input_shape=(x_train.shape[1],)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
# Perform manual cross-validation
for train_index, test_index in kf.split(x_train):
# Split data into train and validation sets for the current fold
x_train_fold, x_val_fold = x_train[train_index], x_train[test_index]
y_train_fold, y_val_fold = y_train[train_index], y_train[test_index]
# Create a new model for each fold
model = create_model()
# Train the model
model.fit(x_train_fold, y_train_fold, epochs=10, batch_size=32, verbose=0)
# Evaluate the model on the validation set
val_loss, val_accuracy = model.evaluate(x_val_fold, y_val_fold, verbose=0)
# Append the accuracy of the current fold to the list
accuracy_scores.append(val_accuracy)
# Print the average accuracy across folds
print(f'Cross-validation accuracy: {np.mean(accuracy_scores)}')
—---------------------------------------------------------------------------------------------------------------------------
RESULTS SHOULD SAY 89% ACCURACY
How to Train Ai Guide
This will train the AI w/ (google colab) to predict within 89% accuracy
benign vs cancerous tumors
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COPY AND PASTE EACH CODE SECTION INDIVIDUALLY
FOLLOWING THESE 37 steps copy and paste code upload cancer.csv to database results in a 89% accuracy
How To Guide Video: https://www.youtube.com/watch?v=z1PGJ9quPV8
Use Cancer.csv file from Github Repo here: https://gist.github.com/adameubanks/35a6beea49e5b9ba62797e595a9626c0
Explanation of what is happening:
This is a guide for building a machine learning model to predict whether a tumor is benign or malignant based on a dataset (`cancer.csv`). The process uses **Google Colab** and involves following 37 steps after you download and load the dataset into a new notebook on https://colab.research.google.com/ , preprocess it, build a neural network model using **TensorFlow/Keras**, and train the model on the data and this is how it's done.
Here’s a breakdown of what's happening:
1. **Data Loading**:
- The dataset (`cancer.csv`) is loaded using `pandas`.
2. **Feature Selection and Target Variable**:
- The features (`x`) are selected by dropping the diagnosis column, and the target variable (`y`) is the diagnosis column that determines whether the tumor is benign (0) or malignant (1).
3. **Data Split**:
- The dataset is split into training and test sets using `train_test_split` from `sklearn`.
4. **Model Construction**:
- A neural network model is created using `Sequential` in **TensorFlow/Keras** with three layers (two hidden layers with 256 neurons and ReLU activation, and one output layer with a sigmoid activation for binary classification).
5. **Model Compilation**:
- The model is compiled using the `Adam` optimizer and `binary_crossentropy` loss function, commonly used for binary classification tasks.
6. **Model Training**:
- The model is trained on the training data (`x_train` and `y_train`) using the `fit()` function.
7. **Prediction**:
- After training, predictions are made on the test set using `model.predict()`.
8. **Evaluation**:
- The model's performance is evaluated by printing predicted values, calculating accuracy, generating confusion matrices, and creating a classification report.
9. **Advanced Steps**:
- Some advanced steps include handling class imbalances using `SMOTE` (Synthetic Minority Over-sampling Technique), adding more layers or dropout layers for regularization, experimenting with different optimizers, and performing K-fold cross-validation to evaluate model performance more robustly.
10. **Saving and Exporting**:
- You save the trained model to a file (`my_model.h5`), which can be loaded later for predictions.
### Key Elements:
- **Machine Learning**: You are using a **neural network** for binary classification (benign vs malignant).
- **TensorFlow/Keras**: The model is built using **TensorFlow**'s **Keras API**.
- **Model Optimization**: Techniques like adding layers, tuning learning rates, and using early stopping are introduced to improve the model's accuracy.
- **Data Resampling**: **SMOTE** is applied to balance the dataset if needed.
- **Cross-Validation**: K-Fold Cross-Validation is used to assess the model's generalization on unseen data.
By following these steps in Google Colab, the model should be able to predict tumor malignancy with about **89% accuracy** after training. The **YouTube video** link and **GitHub dataset** are resources to help understand the process.
You can use these code snippets sequentially in a new Google Colab notebook, ensure the `cancer.csv` dataset is uploaded, and work through the steps for a hands-on experience.