sarkaftghareeb / prostate-cancer-

most important genes expressed in aggressive cancer

Prostate-cancer-

most important genes expressed in aggressive cancer omplete machine learning pipeline: This code snippet performs various machine learning tasks such as data preprocessing, feature selection, model training, and evaluation. key components:

1. Data Loading and Preprocessing:

   • The code starts by importing necessary libraries and loading an Excel dataset named 'TUMOR.xlsx' using pandas.
   • It transposes the data and extracts specific columns for further analysis.
   • It prepares the target variable by binarizing it based on a predefined threshold, which is the median in this case.
   • Splits the dataset into training and testing sets and performs feature scaling using StandardScaler.
   • Selects top 20 features using SelectKBest method with ANOVA F-value as the score function.

2. Model Training and Evaluation:

   • It trains and evaluates three models: Logistic Regression, Multi-Layer Perceptron (MLP) classifier, and a Convolutional Neural Network (CNN) using Keras.
   • Models are trained on the scaled features, and their accuracy and ROC-AUC scores are calculated for evaluation.
   • The CNN architecture includes Convolutional, Flatten, and Dense layers.
   • Grid search is performed to tune hyperparameters for Logistic Regression model using GridSearchCV.

3. Visualization:

   • The code generates a bar plot showing the accuracy and ROC-AUC scores of the Logistic Regression and MLP models.
   • The plot helps in comparing the performance of the two models visually.

4. Overall:

   • The code demonstrates a complete machine learning pipeline including data preprocessing, model training, evaluation, hyperparameter tuning, and visualization.
   • It utilizes libraries like pandas, scikit-learn, Keras, and Matplotlib for different tasks.

Please note that for the neural network part, the code assumes that the data has been reshaped appropriately for the input layer based on the number of features. Also, some of the variable names, such as logreg_pred, mlp_pred, and cnn_pred_probs used in the visualization part need to be defined earlier in the code for it to execute successfully.