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Add recommendation.ipynb notebook
This commit is contained in:
Jatin Kumar
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Recommendation System for Apna_Vaidya\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1. Rule-Based System"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"user_profile = None\n",
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"user_scenario = None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"def rule_based_recommendation(user_profile, user_scenario):\n",
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" if user_scenario == 'Patient with Symptoms':\n",
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" return [\"Talk to a Doctor\", \"Verify with a Doctor\", \"Symptom Checker (AI-based)\"]\n",
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" elif user_scenario == 'Chronic Condition Management':\n",
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" return [\"Get a Prescription\", \"Order Medication\", \"Talk to a Doctor\", \"Talk to a Pharmacist\", \"Health Coach Consultation\"]\n",
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" elif user_scenario == 'New Medication Inquiry':\n",
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" return [\"Talk to a Doctor\", \"Talk to a Pharmacist\", \"Medication Information (AI-based)\"]\n",
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" elif user_scenario == 'Diagnostic Needs':\n",
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" return [\"Connect with a Diagnostic Center\", \"Verify with a Doctor\", \"Home Sample Collection\"]\n",
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" elif user_scenario == 'Prescription Refill':\n",
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" return [\"Order Medication\", \"Get a Prescription\", \"Talk to a Pharmacist\", \"Auto-Refill Subscription\"]\n",
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" elif user_scenario == 'Preventive Care':\n",
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" return [\"Schedule a Check-up\", \"Health Coach Consultation\", \"Vaccination Booking\"]\n",
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" elif user_scenario == 'Post-Treatment Follow-up':\n",
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" return [\"Talk to a Doctor\", \"Schedule a Follow-up\", \"Physical Therapy Consultation\"]\n",
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" else:\n",
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" return [\"Talk to a Doctor\"]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['Talk to a Doctor', 'Verify with a Doctor', 'Symptom Checker (AI-based)']\n"
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]
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}
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],
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"source": [
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"# Example usage\n",
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"user_profile = 'Chronic Condition Management'\n",
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"user_scenario = 'Patient with Symptoms'\n",
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"recommendation = rule_based_recommendation(user_profile, user_scenario)\n",
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"print(recommendation)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 2. Collaborative Filtering"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.metrics.pairwise import cosine_similarity"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example user-item interaction matrix (rows: users, columns: options)\n",
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"interaction_matrix = np.array([\n",
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" [1, 1, 0, 0, 0, 0, 0, 0, 0], # User 1\n",
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" [0, 1, 1, 0, 0, 0, 0, 0, 0], # User 2\n",
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" [0, 0, 1, 1, 0, 0, 0, 0, 0], # User 3\n",
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" [0, 0, 0, 1, 1, 1, 0, 0, 0], # User 4\n",
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" [0, 0, 0, 0, 1, 1, 1, 0, 0], # User 5\n",
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" [0, 0, 0, 0, 0, 1, 1, 1, 0], # User 6\n",
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"])\n",
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"\n",
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"# Calculate similarity between users\n",
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"user_similarity = cosine_similarity(interaction_matrix)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Recommend based on the most similar user's interactions\n",
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"def collaborative_filtering_recommendation(user_index, interaction_matrix, user_similarity):\n",
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" similar_user_index = np.argmax(user_similarity[user_index])\n",
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" recommendations = np.where(interaction_matrix[similar_user_index] == 1)[0]\n",
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" return recommendations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example usage\n",
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"user_index = 0 # User 1\n",
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"recommendations = collaborative_filtering_recommendation(user_index, interaction_matrix, user_similarity)\n",
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"print(recommendations) # Output the indices of the recommended options"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 3. Content-Based Filtering"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"\n",
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"# Example option descriptions\n",
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"options = [\n",
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" \"Talk to a Doctor for immediate consultation\",\n",
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" \"Verify with a Doctor for second opinion\",\n",
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" \"Use Symptom Checker to self-diagnose\",\n",
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" \"Get a Prescription for chronic condition\",\n",
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" \"Order Medication online\",\n",
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" \"Talk to a Pharmacist for medication advice\",\n",
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" \"Health Coach Consultation for chronic condition management\",\n",
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" \"Medication Information for new prescriptions\",\n",
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" \"Medication Interaction Checker\",\n",
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" \"Connect with a Diagnostic Center for tests\",\n",
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" \"Home Sample Collection for diagnostics\",\n",
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" \"Book Lab Tests Online\",\n",
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" \"Schedule a Check-up for preventive care\",\n",
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" \"Health Screening Packages for preventive care\",\n",
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" \"Vaccination Booking for preventive care\",\n",
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" \"Schedule a Follow-up for post-treatment\",\n",
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" \"Physical Therapy Consultation for recovery\",\n",
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" \"Remote Monitoring Services for follow-up\",\n",
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" \"Talk to a Therapist for mental health support\",\n",
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" \"Join a Support Group for mental health\",\n",
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" \"Mental Health Self-assessment (AI-based)\",\n",
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" \"Meditation and Mindfulness Resources\"\n",
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"]\n",
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"\n",
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"# User profile description\n",
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"user_profile_description = \"Patient with chronic condition needing regular medication and follow-ups\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Vectorize the descriptions\n",
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"vectorizer = TfidfVectorizer()\n",
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"option_vectors = vectorizer.fit_transform(options)\n",
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"user_vector = vectorizer.transform([user_profile_description])\n",
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"\n",
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"similarity = cosine_similarity(user_vector, option_vectors)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Recommend based on the highest similarity scores\n",
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"def content_based_recommendation(similarity, options):\n",
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" recommendations = np.argsort(similarity[0])[::-1]\n",
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" return [options[i] for i in recommendations[:5]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example usage\n",
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"recommendations = content_based_recommendation(similarity, options)\n",
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"print(recommendations)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 4. Machine Learning Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# using a decision tree classifier\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example dataset (features: user profile data, labels: recommended options)\n",
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"# This is a simplified example; in practice, you would have a more complex dataset\n",
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"X = np.array([\n",
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" [25, 1, 0, 1], # User 1: age, chronic condition, acute symptoms, preventive care\n",
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" [45, 1, 1, 0], # User 2\n",
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" [60, 0, 0, 1], # User 3\n",
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" [35, 1, 0, 1], # User 4\n",
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" [50, 0, 1, 0], # User 5\n",
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"])\n",
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"y = np.array([\n",
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" [0, 1, 1, 0], # Recommended options for User 1\n",
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" [1, 1, 0, 1], # Recommended options for User 2\n",
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" [0, 0, 1, 1], # Recommended options for User 3\n",
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" [1, 1, 0, 0], # Recommended options for User 4\n",
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" [0, 0, 1, 1], # Recommended options for User 5\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Split the data into training and testing sets\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Train the decision tree classifier\n",
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"clf = DecisionTreeClassifier()\n",
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"clf.fit(X_train, y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Predict recommendations for a new user\n",
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"new_user = np.array([[40, 1, 1, 0]]) # New user profile\n",
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"predictions = clf.predict(new_user)\n",
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"print(predictions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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