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{
"cells": [
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from flask import Flask, render_template, request, url_for\n",
"import pickle\n",
"import numpy as np\n",
"\n",
"linreg = pickle.load(open('Models/lr.pkl', 'rb'))\n",
"knn_model = pickle.load(open('Models/knn_model.pkl', 'rb'))\n",
"gaussian_nb = pickle.load(open('Models/nbG_model.pkl', 'rb'))\n",
"multinomial_nb = pickle.load(open('Models/nbM_model.pkl', 'rb'))\n",
"bernoulli_nb = pickle.load(open('Models/nbB_model.pkl', 'rb'))\n",
"\n",
"job_map = {\n",
" 1: 'Junior',\n",
" 2: 'Senior',\n",
" 3: 'Project Manager',\n",
" 4: 'CTO',\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"while True:\n",
" salary = float(input(\"Enter salary: \"))\n",
" print(\"Salary entered: \", salary)\n",
"\n",
" experience = float(input(\"Enter experience: \"))\n",
" print(\"Experience entered: \", experience)\n",
"\n",
" with open('Models/tts.pkl', 'rb') as f:\n",
" data = pickle.load(f)\n",
"\n",
" X=data['X']\n",
" y=data['y']\n",
"\n",
" X = np.vstack((X, np.array([salary, experience])))\n",
" y= np.hstack((y, experience)) # use a new label for the user's input\n",
"\n",
" # Split the data into training and testing sets\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
" # Fit the Naive Bayes models on the training data\n",
" gaussian_nb.fit(X_train, y_train)\n",
" multinomial_nb.fit(X_train, y_train)\n",
" bernoulli_nb.fit(X_train, y_train)\n",
"\n",
" # Evaluate the accuracy of the models on the testing set\n",
" gaussian_accuracy = gaussian_nb.score(X_test, y_test)\n",
" multinomial_accuracy = multinomial_nb.score(X_test, y_test)\n",
" bernoulli_accuracy = bernoulli_nb.score(X_test, y_test)\n",
"\n",
" # Use each Naive Bayes model to make a prediction based on the user's input values\n",
" gaussian_prediction = gaussian_nb.predict([[salary, experience]])[0]\n",
" multinomial_prediction = multinomial_nb.predict([[salary, experience]])[0]\n",
" bernoulli_prediction = bernoulli_nb.predict([[salary, experience]])[0]\n",
"\n",
" # Map the predicted job titles to their corresponding string values\n",
" gaussian_prediction = job_map.get(gaussian_prediction)\n",
" multinomial_prediction = job_map.get(multinomial_prediction)\n",
" bernoulli_prediction = job_map.get(bernoulli_prediction)\n",
"\n",
" # # Print the accuracy and predicted job title for each Naive Bayes model\n",
" # print(\"Gaussian Accuracy: {:.2f}%, Prediction: {}\".format(gaussian_accuracy * 100, gaussian_prediction))\n",
" # print(\"Multinomial Accuracy: {:.2f}%, Prediction: {}\".format(multinomial_accuracy * 100, multinomial_prediction))\n",
" # print(\"Bernoulli Accuracy: {:.2f}%, Prediction: {}\".format(bernoulli_accuracy * 100, bernoulli_prediction))\n",
" # print(\"\\n\")\n",
"\n",
" # # Evaluate the accuracy of the models on the new input\n",
" # gaussian_accuracy_new = gaussian_nb.score([[salary, experience]], [5])\n",
" # multinomial_accuracy_new = multinomial_nb.score([[salary, experience]], [5])\n",
" # bernoulli_accuracy_new = bernoulli_nb.score([[salary, experience]], [5])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" # X=data['X']\n",
" # y=data['y']\n",
"\n",
" # # Combine the user's input values with the existing data\n",
" # X_new = np.vstack((X, np.array([salary, experience])))\n",
" # y_new = np.hstack((y, 5)) # use a new label for the user's input\n",
"\n",
" # n_splits=10\n",
"\n",
" # # Use k-fold cross-validation to generate a new test set for each iteration\n",
" # kf = KFold(n_splits=n_splits, shuffle=False, random_state=None)\n",
"\n",
" # gaussian_accuracy = 0\n",
" # multinomial_accuracy = 0\n",
" # bernoulli_accuracy = 0\n",
"\n",
" # for train_index, test_index in kf.split(X_new):\n",
" # X_train, X_test = X_new[train_index], X_new[test_index]\n",
" # y_train, y_test = y_new[train_index], y_new[test_index]\n",
"\n",
" # # Fit the Naive Bayes models on the training data\n",
" # gaussian_nb.fit(X_train, y_train)\n",
" # multinomial_nb.fit(X_train, y_train)\n",
" # bernoulli_nb.fit(X_train, y_train)\n",
"\n",
" # # Use each Naive Bayes model to make a prediction based on the user's input values\n",
" # gaussian_prediction = gaussian_nb.predict([[salary, experience]])[0]\n",
" # multinomial_prediction = multinomial_nb.predict([[salary, experience]])[0]\n",
" # bernoulli_prediction = bernoulli_nb.predict([[salary, experience]])[0]\n",
"\n",
" # # Update the accuracy scores for each Naive Bayes model\n",
" # gaussian_accuracy += gaussian_nb.score(X_test, y_test)\n",
" # multinomial_accuracy += multinomial_nb.score(X_test, y_test)\n",
" # bernoulli_accuracy += bernoulli_nb.score(X_test, y_test)\n",
"\n",
" # # Calculate the mean accuracy for each Naive Bayes model over all folds\n",
" # gaussian_accuracy = round(gaussian_accuracy / n_splits * 100, 3)\n",
" # multinomial_accuracy = round(multinomial_accuracy / n_splits * 100, 3)\n",
" # bernoulli_accuracy = round(bernoulli_accuracy / n_splits * 100, 3)\n",
"\n",
" # # Map the predicted job titles to their corresponding string values\n",
" # gaussian_prediction = job_map.get(gaussian_prediction)\n",
" # multinomial_prediction = job_map.get(multinomial_prediction)\n",
" # bernoulli_prediction = job_map.get(bernoulli_prediction)\n",
"\n",
" # # Render the results template with the predicted job classification and accuracy scores\n",
" # return render_template('naive.html',\n",
" # gaussian_prediction=gaussian_prediction,\n",
" # multinomial_prediction=multinomial_prediction,\n",
" # bernoulli_prediction=bernoulli_prediction,\n",
" # gaussian_accuracy=str(gaussian_accuracy) + \"%\",\n",
" # multinomial_accuracy=str(multinomial_accuracy) + \"%\",\n",
" # bernoulli_accuracy=str(bernoulli_accuracy) + \"%\",\n",
" # salary=salary,\n",
" # experience=experience,\n",
" # reset=True)\n",
" # else:\n",
" # # Render the job classification form\n",
" # return render_template('naive.html')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" # if request.method == 'POST':\n",
" # # Get the user's input values\n",
" # salary = float(request.form['salary'])\n",
" # experience = float(request.form['experience'])\n",
"\n",
" # with open('Models/tts.pkl', 'rb') as f:\n",
" # data = pickle.load(f)\n",
"\n",
" # X=data['X']\n",
" # y=data['y']\n",
"\n",
"\n",
" # X = np.vstack((X, np.array([salary, experience])))\n",
" # y= np.hstack((y, 5)) # use a new label for the user's input\n",
"\n",
"\n",
" # # Split the data into training and testing sets\n",
" # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
"\n",
" # # Fit the Naive Bayes models on the training data\n",
" # gaussian_nb.fit(X_train, y_train)\n",
" # multinomial_nb.fit(X_train, y_train)\n",
" # bernoulli_nb.fit(X_train, y_train)\n",
"\n",
" # # Use each Naive Bayes model to make a prediction based on the user's input values\n",
" # gaussian_prediction = gaussian_nb.predict([[salary, experience]])[0]\n",
" # multinomial_prediction = multinomial_nb.predict([[salary, experience]])[0]\n",
" # bernoulli_prediction = bernoulli_nb.predict([[salary, experience]])[0]\n",
"\n",
" # # Evaluate the accuracy of the models on the testing set\n",
" # gaussian_accuracy = round(gaussian_nb.score(X_test, y_test) * 100, 3) \n",
" # multinomial_accuracy = round(multinomial_nb.score(X_test, y_test) * 100, 3)\n",
" # bernoulli_accuracy = round(bernoulli_nb.score(X_test, y_test) * 100, 3)\n",
"\n",
" # # Map the predicted job titles to their corresponding string values\n",
" # gaussian_prediction = job_map.get(gaussian_prediction)\n",
" # multinomial_prediction = job_map.get(multinomial_prediction)\n",
" # bernoulli_prediction = job_map.get(bernoulli_prediction)\n",
"\n",
" # # Render the results template with the predicted job classification and accuracy scores\n",
" # return render_template('naive.html',\n",
" # gaussian_prediction=gaussian_prediction,\n",
" # multinomial_prediction=multinomial_prediction,\n",
" # bernoulli_prediction=bernoulli_prediction,\n",
" # gaussian_accuracy=str(gaussian_accuracy) + \"%\",\n",
" # multinomial_accuracy=str(multinomial_accuracy) + \"%\",\n",
" # bernoulli_accuracy=str(bernoulli_accuracy) + \"%\",\n",
" # salary=salary,\n",
" # experience=experience,\n",
" # reset=True)\n",
" # else:\n",
" # # Render the job classification form\n",
" # return render_template('naive.html')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Salary entered: 5000.0\n",
"Experience entered: 5.0\n",
"Gaussian Accuracy: 82.81%, Prediction: None\n",
"Multinomial Accuracy: 26.67%, Prediction: None\n",
"Bernoulli Accuracy: 21.05%, Prediction: Junior\n",
"\n",
"\n",
"Salary entered: 4.0\n",
"Experience entered: 5.0\n",
"Gaussian Accuracy: 82.46%, Prediction: CTO\n",
"Multinomial Accuracy: 25.96%, Prediction: Junior\n",
"Bernoulli Accuracy: 20.70%, Prediction: Junior\n",
"\n",
"\n"
]
},
{
"ename": "ValueError",
"evalue": "could not convert string to float: ''",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[3], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodel_selection\u001b[39;00m \u001b[39mimport\u001b[39;00m KFold\n\u001b[0;32m 2\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m----> 3\u001b[0m salary \u001b[39m=\u001b[39m \u001b[39mfloat\u001b[39m(\u001b[39minput\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mEnter salary: \u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[0;32m 4\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mSalary entered: \u001b[39m\u001b[39m\"\u001b[39m, salary)\n\u001b[0;32m 6\u001b[0m experience \u001b[39m=\u001b[39m \u001b[39mfloat\u001b[39m(\u001b[39minput\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mEnter experience: \u001b[39m\u001b[39m\"\u001b[39m))\n",
"\u001b[1;31mValueError\u001b[0m: could not convert string to float: ''"
]
}
],
"source": [
"# from sklearn.model_selection import KFold\n",
"# while True:\n",
"# salary = float(input(\"Enter salary: \"))\n",
"# print(\"Salary entered: \", salary)\n",
"\n",
"# experience = float(input(\"Enter experience: \"))\n",
"# print(\"Experience entered: \", experience)\n",
"\n",
"# with open('Models/tts.pkl', 'rb') as f:\n",
"# data = pickle.load(f)\n",
"\n",
"# X=data['X']\n",
"# y=data['y']\n",
"\n",
"# # Combine the user's input values with the existing data\n",
"# X_new = np.vstack((X, np.array([salary, experience])))\n",
"# y_new = np.hstack((y, 5)) # use a new label for the user's input\n",
"\n",
"# n_splits=5\n",
"\n",
"# # Use k-fold cross-validation to generate a new test set for each iteration\n",
"# kf = KFold(n_splits=n_splits, shuffle=True, random_state=None)\n",
"\n",
"# gaussian_accuracy = 0\n",
"# multinomial_accuracy = 0\n",
"# bernoulli_accuracy = 0\n",
"\n",
"# for train_index, test_index in kf.split(X_new):\n",
"# X_train, X_test = X_new[train_index], X_new[test_index]\n",
"# y_train, y_test = y_new[train_index], y_new[test_index]\n",
"\n",
"# # Fit the Naive Bayes models on the training data\n",
"# gaussian_nb.fit(X_train, y_train)\n",
"# multinomial_nb.fit(X_train, y_train)\n",
"# bernoulli_nb.fit(X_train, y_train)\n",
"\n",
"# # Use each Naive Bayes model to make a prediction based on the user's input values\n",
"# gaussian_prediction = gaussian_nb.predict([[salary, experience]])[0]\n",
"# multinomial_prediction = multinomial_nb.predict([[salary, experience]])[0]\n",
"# bernoulli_prediction = bernoulli_nb.predict([[salary, experience]])[0]\n",
"\n",
"# # Update the accuracy scores for each Naive Bayes model\n",
"# gaussian_accuracy += gaussian_nb.score(X_test, y_test)\n",
"# multinomial_accuracy += multinomial_nb.score(X_test, y_test)\n",
"# bernoulli_accuracy += bernoulli_nb.score(X_test, y_test)\n",
"\n",
"# # Calculate the mean accuracy for each Naive Bayes model over all folds\n",
"# gaussian_accuracy /= n_splits\n",
"# multinomial_accuracy /= n_splits\n",
"# bernoulli_accuracy /= n_splits\n",
"\n",
"# # Map the predicted job titles to their corresponding string values\n",
"# gaussian_prediction = job_map.get(gaussian_prediction)\n",
"# multinomial_prediction = job_map.get(multinomial_prediction)\n",
"# bernoulli_prediction = job_map.get(bernoulli_prediction)\n",
"\n",
"# # Print the accuracy and predicted job title for each Naive Bayes model\n",
"# print(\"Gaussian Accuracy: {:.2f}%, Prediction: {}\".format(gaussian_accuracy * 100, gaussian_prediction))\n",
"# print(\"Multinomial Accuracy: {:.2f}%, Prediction: {}\".format(multinomial_accuracy * 100, multinomial_prediction))\n",
"# print(\"Bernoulli Accuracy: {:.2f}%, Prediction: {}\".format(bernoulli_accuracy * 100, bernoulli_prediction))\n",
"# print(\"\\n\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @app.route('/predictnaive', methods=['GET', 'POST'])\n",
"# def predictnaive():\n",
"# if request.method == 'POST':\n",
"# # Get the user's input values\n",
"# salary = float(request.form['salary'])\n",
"# experience = float(request.form['experience'])\n",
"\n",
"# # Load the data used to train and test the models\n",
"# with open('Models/tts.pkl', 'rb') as f:\n",
"# data = pickle.load(f)\n",
" \n",
"# # X_user = np.array([[salary, experience]])\n",
"# # y_user = np.array([5])\n",
"# # X_test_combined = np.concatenate((X_test, X_user))\n",
"# # y_test_combined = np.concatenate((y_test, y_user))\n",
"\n",
"# X = np.vstack((data['X'], np.array([salary, experience])))\n",
"# y = np.hstack((data['y'], 5)) # use a new label for the user's input\n",
" \n",
"# from sklearn.model_selection import train_test_split\n",
"# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n",
"\n",
"# # Re-fit models on combined data\n",
"# gaussian_nb.fit(X_train, y_train) \n",
"# multinomial_nb.fit(X_train, y_train)\n",
"# bernoulli_nb.fit(X_train, y_train)\n",
"\n",
"# # Use each Naive Bayes model to make a prediction based on the user's input values\n",
"# gaussian_prediction = gaussian_nb.predict([[salary, experience]])[0]\n",
"# multinomial_prediction = multinomial_nb.predict([[salary, experience]])[0]\n",
"# bernoulli_prediction = bernoulli_nb.predict([[salary, experience]])[0]\n",
"\n",
"\n",
"# # Calculate the accuracy of each Naive Bayes model\n",
"# gaussian_accuracy = round(gaussian_nb.score(X_test, y_test), 3) * 100\n",
"# multinomial_accuracy = round(multinomial_nb.score(X_test, y_test), 3) * 100\n",
"# bernoulli_accuracy = round(bernoulli_nb.score(X_test, y_test), 3) * 100\n",
"\n",
"\n",
"# gaussian_prediction = job_map.get(gaussian_prediction)\n",
"# multinomial_prediction = job_map.get(multinomial_prediction)\n",
"# bernoulli_prediction = job_map.get(bernoulli_prediction)\n",
"\n",
"# # Render the results template with the predicted job classification and accuracy scores\n",
"# return render_template('naive.html', gaussian_prediction=gaussian_prediction, multinomial_prediction=multinomial_prediction, bernoulli_prediction=bernoulli_prediction, gaussian_accuracy=gaussian_accuracy, multinomial_accuracy=multinomial_accuracy, bernoulli_accuracy=bernoulli_accuracy, salary=salary, experience=experience, reset=True)\n",
"# else:\n",
"# # Render the job classification form\n",
"# return render_template('naive.html')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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|