diff --git a/app.py b/app.py new file mode 100644 index 0000000..72dca5f --- /dev/null +++ b/app.py @@ -0,0 +1,45 @@ +''' +This is a Flask web application that reads a pickle file to load a machine learning model, +accepts user input from a web form, and returns a prediction using the model. + +''' + +# https://flask.palletsprojects.com/en/2.2.x/quickstart/ +from flask import Flask, render_template, request +import pickle +import numpy as np + +# create flask app +app = Flask(__name__) + +# Load the pickle model file +def load_model(): + try: + return pickle.load(open("model.pkl", "rb")) + except: + return None + +model = load_model() + +@app.route('/') +def home(): + return render_template('index.html') + +@app.route('/predict', methods=['POST']) +def predict(): + if model is None: + error_message = "Sorry, an error occurred while loading the model. Please try again later." + return render_template('index.html', result=error_message) + + # Get the features from the request form and make a prediction + features = [float(x) for x in request.form.values()] + final_features = [np.array(features)] + prediction = model.predict(final_features) + result = round(prediction[0], 1) + + # Create a result string to show to the user + result_string = f"The predicted diabetes outcome is {result}." + return render_template('index.html', result=result_string) + +if __name__ == "__main__": + app.run(debug=True) diff --git a/diabetes b/diabetes new file mode 100644 index 0000000..0af07e0 --- /dev/null +++ b/diabetes @@ -0,0 +1,2001 @@ +Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome +2,138,62,35,0,33.6,0.127,47,1 +0,84,82,31,125,38.2,0.233,23,0 +0,145,0,0,0,44.2,0.63,31,1 +0,135,68,42,250,42.3,0.365,24,1 +1,139,62,41,480,40.7,0.536,21,0 +0,173,78,32,265,46.5,1.159,58,0 +4,99,72,17,0,25.6,0.294,28,0 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The form and result display are formatted using +fundamental CSS styling. +*/ +< ! -- https://www.w3schools.com/tags/tag_doctype.asp --> + + + + + + + Diabetes Prediction + + + +

Diabetes Prediction

+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + {% if result %} +
+

Result

+

{{ result }}

+
+ {% endif %} + + + + {% if errors %} +
+

Errors

+ +
+ {% endif %} + + diff --git a/model.pkl b/model.pkl new file mode 100644 index 0000000..199ca2f Binary files /dev/null and b/model.pkl differ diff --git a/model.py b/model.py new file mode 100644 index 0000000..f123bfb --- /dev/null +++ b/model.py @@ -0,0 +1,328 @@ +''' +This Python software uses the diabetes dataset to train a number of classification models. +Using pandas, the dataset is loaded, and some fundamental data analysis is performed, such as + looking for missing values and displaying the distribution of each characteristic. ''' +# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html +# https://numpy.org/doc/stable/reference/ +# https://www.jcchouinard.com/confusion-matrix-in-scikit-learn/ +import pandas as pd +import joblib +import pickle +import numpy as np +import matplotlib.pyplot as plt +import rfc as rfc +import seaborn as sns +from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier +from sklearn.tree import DecisionTreeClassifier +from sklearn.linear_model import LogisticRegression +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.naive_bayes import GaussianNB +from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \ + classification_report, roc_curve +from sklearn.preprocessing import StandardScaler +from imblearn.over_sampling import SMOTE +from imblearn.under_sampling import RandomUnderSampler + + +# load the diabetes dataset +diabetes = pd.read_csv("diabetes.csv") + +# data attribute information +print(diabetes.info()) + +# to observe the shape of the dataframe. +print(diabetes.shape) + +# checking missing value +print(diabetes.isnull().sum()) + +# ----------------------------- +corr=diabetes.corr() +sns.heatmap(corr, annot=True) # an array of the same shape as data which is used to annotate the heatmap + +# ----------------------- + +# ------------------------------------ +# loop through all numeric columns except for the target column +for col in diabetes.columns[0:-1]: + # plot the distribution of the current column using seaborn's displot function + # set the dataframe to diabetes_df using the data parameter + # set the x-axis to the current column being iterated using the x parameter + # set kde parameter to True to display kernel density estimate along with the histogram + sns.displot(data=diabetes, x=col, kde=True) + +# --- +# split data into features and target variable +X = diabetes.drop('Outcome', axis=1) +y = diabetes['Outcome'] + +# split data into train and test sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# ---- +# checking imbalance using oversampling and under sampling using SMOTE and RandomUnderSampler +smote = SMOTE() +rus = RandomUnderSampler() +x_resample, y_resample = smote.fit_resample(diabetes.iloc[:, :-1], diabetes.iloc[:, -1]) +x_resample, y_resample = rus.fit_resample(x_resample, y_resample) + +# testing is balanced now +print(y_resample.value_counts()) + +# data needs to normalize +scaler = StandardScaler() +x_resample = scaler.fit_transform(x_resample) + +# split data into features and target variable +X = x_resample +y = y_resample + +# split data into train and test sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# feature scaling +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) + +# convert the scaled training data into a dataframe +X_train_df = pd.DataFrame(X_train, columns=diabetes.columns[:-1]) + +# create a list of classifiers +classifiers = [ + DecisionTreeClassifier(random_state=42), + LogisticRegression(random_state=42), + KNeighborsClassifier(), + SVC(random_state=42), + GaussianNB(), + GradientBoostingClassifier(random_state=42), + RandomForestClassifier(random_state=42), +] + + +# define a function to train and evaluate each classifier +def evaluate_classifier(classifier, X_train, y_train, X_test, y_test): + classifier.fit(X_train, y_train) + y_pred = classifier.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + precision = precision_score(y_test, y_pred) + recall = recall_score(y_test, y_pred) + f1 = f1_score(y_test, y_pred) + print(classifier) + print(f"Accuracy: {round(accuracy, 2)}") + print(f"Precision: {round(precision, 2)}") + print(f"Recall: {round(recall, 2)}") + print(f"F1-score: {round(f1, 2)}") + print("Confusion Matrix:") + print(confusion_matrix(y_test, y_pred)) + print("Classification Report:") + print(classification_report(y_test, y_pred)) + + +# evaluate the Decision Tree classifier +classifier = RandomForestClassifier(random_state=42) +evaluate_classifier(classifier, X_train, y_train, X_test, y_test) + +# loop through each classifier in the list of classifiers and train and evaluate them +for classifier in classifiers: + evaluate_classifier(classifier, X_train, y_train, X_test, y_test) + +# create a dictionary to store the results +results = {} + + +# define a function to train and evaluate each classifier and return the results as a dictionary +def evaluate_classifier(classifier, X_train, y_train, X_test, y_test): + classifier.fit(X_train, y_train) + y_pred = classifier.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + precision = precision_score(y_test, y_pred) + recall = recall_score(y_test, y_pred) + f1 = f1_score(y_test, y_pred) + cm = confusion_matrix(y_test, y_pred) + cr = classification_report(y_test, y_pred, output_dict=True) + results = {'accuracy': accuracy, + 'precision': precision, + 'recall': recall, + 'f1_score': f1, + 'confusion_matrix': cm, + 'classification_report': cr} + return results + +# ------------------------------------- +# import required libraries +from sklearn.metrics import roc_auc_score, roc_curve + +# loop through each classifier in the list of classifiers and calculate ROC Curve +for classifier in classifiers: + # fit the classifier + classifier.fit(X_train, y_train) + # check if predict_proba is available + if hasattr(classifier, 'predict_proba'): + # predict probabilities for the positive class + y_prob = classifier.predict_proba(X_test)[:,1] + else: + # predict scores for the positive class using decision_function or predict + y_prob = classifier.decision_function(X_test) + # if using predict method instead + # y_prob = classifier.predict(X_test) + # calculate ROC Curve + fpr, tpr, thresholds = roc_curve(y_test, y_prob) + # plot ROC Curve + plt.plot(fpr, tpr, label=f'{classifier.__class__.__name__} (AUC = {roc_auc_score(y_test, y_prob):.2f})') + # plot a diagonal line representing the random guess classifier + plt.plot([0, 1], [0, 1], 'k--', label='Random Guess') + # set plot title, x-axis label, y-axis label, and legend + plt.title(f'Receiver Operating Characteristic (ROC) Curve - {diabetes}') + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.legend(loc='lower right') + # display the plot + plt.show() + + +# -------------------------------------- +# loop through each classifier in the list of classifiers and train and evaluate them +for classifier in classifiers: + results[classifier.__class__.__name__] = evaluate_classifier(classifier, X_train, y_train, X_test, y_test) + +# create a copy of the diabetes dataset +diabetes_copy = diabetes.copy() + +# add a new column to the copy indicating whether a patient has diabetes or not +diabetes_copy["has_diabetes"] = np.where(diabetes_copy["Outcome"] == 1, "yes", "no") + +# plot the distribution of patients with and without diabetes +sns.countplot(x="has_diabetes", data=diabetes_copy) +plt.title("Distribution of Patients with and Without Diabetes") +plt.xlabel("Has Diabetes") +plt.ylabel("Count") +plt.show() + +# plot the distribution of patients with and without diabetes +sns.countplot(x="has_diabetes", hue="Pregnancies", data=diabetes_copy) +plt.title("Distribution of Patients with and Without Diabetes") +plt.xlabel("Has Diabetes") +plt.ylabel("Count") +plt.show() + +# create a figure +fig = plt.figure(figsize=(10, 10)) + +# create a subplot for accuracy +plt.subplot(2, 2, 1) +plt.bar(results.keys(), [result['accuracy'] for result in results.values()]) +plt.title("Accuracy") + +# create a subplot for precision +plt.subplot(2, 2, 2) +plt.bar(results.keys(), [result['precision'] for result in results.values()]) +plt.title("Precision") + +# create a subplot for recall +plt.subplot(2, 2, 3) +plt.bar(results.keys(), [result['recall'] for result in results.values()]) +plt.title("Recall") + +# create a subplot for f1-score +plt.subplot(2, 2, 4) +plt.bar(results.keys(), [result['f1_score'] for result in results.values()]) +plt.title("F1-score") + +# display the figure +plt.show() + +# ---- + +# loop through each classifier in the list of classifiers and train and evaluate them +for classifier in classifiers: + clf_name = type(classifier).__name__ + clf_scores = cross_val_score(classifier, X, y, cv=5, scoring='accuracy') + results[clf_name] = clf_scores + +# create a dataframe from the dictionary of results +results_df = pd.DataFrame.from_dict(results) + +# plot the results +sns.boxplot(data=results_df) +plt.title('Classifier Comparison') +plt.ylabel('Accuracy') +plt.xticks(rotation=45) +plt.show() +# ---- + +# ------------------------- + +# fit the Random Forest classifier on the entire dataset +rfc = RandomForestClassifier() +rfc.fit(X_train, y_train) + + +# create a feature importance plot using seaborn's bar plot function +sns.barplot(x=rfc.feature_importances_, y=diabetes.columns[:-1]) +plt.title('Feature Importance') +plt.xlabel('Importance') +plt.ylabel('Feature') +plt.show() + +# ----------------------------------- + +# Create a list to store the accuracy rates +accuracy_rate = [] + +# Create a range of n_estimators to test +estimators = range(1, 25) + +# Loop through different n_estimators +for n in estimators: + # Create a random forest classifier model + rfc = RandomForestClassifier(n_estimators=n, random_state=42) + # Train the model on the training set + rfc.fit(X_train, y_train) + # Make predictions on the test set + y_pred = rfc.predict(X_test) + # Compute the accuracy of the model and append to the list + accuracy = accuracy_score(y_test, y_pred) + accuracy_rate.append(accuracy) + +# Plot the accuracy rate +plt.plot(estimators, accuracy_rate, color='blue', linestyle='dashed', marker='o') +plt.title('Accuracy Rate vs. range of estimators') +plt.xlabel('Range of estimaters') +plt.ylabel('Accuracy Rate') +plt.show() + + +# ----------------------------------------------- +# Code to plot accuracy against that k +plt.figure(figsize=(10,6)) + +plt.plot(range(1,25),accuracy_rate,color='blue', linestyle='dashed', marker='o', + markerfacecolor='red', markersize=10) +plt.title('Accuracy Rate vs. K Value') +plt.xlabel('K-Value') +plt.ylabel('Accuracy Rate') +# -------------------- + +# --------------------------------------------- +# define range of estimators to test +estimator_range = range(1, 25) + +# calculate accuracy rate for different number of estimators +accuracy_rate = [] +for n_estimators in estimator_range: + rfc = RandomForestClassifier(n_estimators=n_estimators) + score = cross_val_score(rfc, X, y, cv=8) + accuracy_rate.append(score.mean()) + +# plot accuracy rate +plt.plot(estimator_range, accuracy_rate) +plt.xlabel('Number of Estimators') +plt.ylabel('Accuracy Rate') +plt.title('RandomForestClassifier Accuracy Rate vs. Number of Estimators') +plt.show() +# --------------------------------------- +# make a pickle file model +pickle.dump(classifier, open("model.pkl", "wb"))