From a01c229b031caa0bba3b3dbcfd0122bfc798ed37 Mon Sep 17 00:00:00 2001 From: Sara Yasser Amur Al Shukaili <71524@omantel.om> Date: Mon, 13 Jan 2025 15:10:15 +0400 Subject: [PATCH] Added Logic for Spotify Music Recommendations --- .../SpotifyRecommendation.ipynb | 321 ++++++++++++++++++ 1 file changed, 321 insertions(+) create mode 100644 jupyter_files/sara_work_area/SpotifyRecommendation.ipynb diff --git a/jupyter_files/sara_work_area/SpotifyRecommendation.ipynb b/jupyter_files/sara_work_area/SpotifyRecommendation.ipynb new file mode 100644 index 00000000..b38ffd39 --- /dev/null +++ b/jupyter_files/sara_work_area/SpotifyRecommendation.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spotify Music Recommendation" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "import json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recommendations using 'data.csv' file only" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the CSV file\n", + "df = pd.read_csv('data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop unnecessary columns\n", + "df = df.drop(['id', 'name', 'release_date'], axis=1, errors='ignore')\n", + "\n", + "# Handle missing values\n", + "df = df.dropna()\n", + "\n", + "# Automatically select numerical columns\n", + "numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "\n", + "# Initialize the scaler\n", + "scaler = MinMaxScaler()\n", + "\n", + "# Apply scaling to numerical columns\n", + "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate cosine similarity\n", + "similarity_matrix = cosine_similarity(df[numerical_columns])\n", + "\n", + "# Function to get recommendations\n", + "def recommend_songs(song_index, num_recommendations=5):\n", + " similarity_scores = list(enumerate(similarity_matrix[song_index]))\n", + " sorted_songs = sorted(similarity_scores, key=lambda x: x[1], reverse=True)\n", + " top_songs = sorted_songs[1:num_recommendations + 1]\n", + " return df.iloc[[i[0] for i in top_songs]]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " danceability energy key loudness mode speechiness \\\n", + "54 0.285539 0.997987 0.727273 0.913713 1.0 0.201484 \n", + "189 0.116422 0.983897 0.545455 0.917595 1.0 0.224912 \n", + "153 0.278186 0.983897 0.545455 0.887314 1.0 0.099961 \n", + "75 0.397059 0.971820 0.545455 0.900989 1.0 0.099375 \n", + "144 0.000000 0.950684 0.727273 0.911033 1.0 0.131199 \n", + "\n", + " acousticness instrumentalness liveness valence tempo \\\n", + "54 0.000034 0.600619 0.289882 0.109770 0.399166 \n", + "189 0.000003 0.887513 0.053676 0.073780 0.458065 \n", + "153 0.000049 0.869969 0.072845 0.352175 0.306995 \n", + "75 0.000014 0.348813 0.023504 0.528951 0.365361 \n", + "144 0.000022 0.379773 0.020337 0.316185 0.003838 \n", + "\n", + " duration_ms time_signature liked \n", + "54 0.294737 0.75 0.0 \n", + "189 0.361073 0.75 0.0 \n", + "153 0.286426 0.75 0.0 \n", + "75 0.299405 0.75 0.0 \n", + "144 0.338735 0.75 0.0 \n" + ] + } + ], + "source": [ + "# Test the recommender\n", + "song_index = 10 # Index of a song to base recommendations on\n", + "recommended_songs = recommend_songs(song_index)\n", + "print(recommended_songs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recommendations using 'data.csv' and json files " + ] + }, + { + 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'id': '3P2zU7fEjnZRunH124QnJL', 'uri': 'spotify:track:3P2zU7fEjnZRunH124QnJL', 'track_href': 'https://api.spotify.com/v1/tracks/3P2zU7fEjnZRunH124QnJL', 'analysis_url': 'https://api.spotify.com/v1/audio-analysis/3P2zU7fEjnZRunH124QnJL', 'duration_ms': 183667, 'time_signature': 4}]}\n" + ] + } + ], + "source": [ + "# Load the JSON files\n", + "with open('good.json', 'r') as file:\n", + " good_data = json.load(file)\n", + "\n", + "with open('dislike.json', 'r') as file:\n", + " disliked_data = json.load(file)\n", + "\n", + "# Display the content to understand its structure\n", + "print(good_data)\n", + "print(disliked_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert JSON data into DataFrames\n", + "good_df = pd.json_normalize(good_data)\n", + "disliked_df = pd.json_normalize(disliked_data)\n", + "\n", + "# Combine both DataFrames\n", + "combined_df = pd.concat([good_df, disliked_df], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the CSV file\n", + "data_csv = pd.read_csv('data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " danceability energy key loudness mode speechiness acousticness \\\n", + "0 0.803 0.6240 7 -6.764 0 0.0477 0.451 \n", + "1 0.762 0.7030 10 -7.951 0 0.3060 0.206 \n", + "2 0.261 0.0149 1 -27.528 1 0.0419 0.992 \n", + "3 0.722 0.7360 3 -6.994 0 0.0585 0.431 \n", + "4 0.787 0.5720 1 -7.516 1 0.2220 0.145 \n", + "\n", + " instrumentalness liveness valence tempo duration_ms time_signature \\\n", + "0 0.000734 0.1000 0.6280 95.968 304524 4 \n", + "1 0.000000 0.0912 0.5190 151.329 247178 4 \n", + "2 0.897000 0.1020 0.0382 75.296 286987 4 \n", + "3 0.000001 0.1230 0.5820 89.860 208920 4 \n", + "4 0.000000 0.0753 0.6470 155.117 179413 4 \n", + "\n", + " liked \n", + "0 0 \n", + "1 1 \n", + "2 0 \n", + "3 1 \n", + "4 1 \n" + ] + } + ], + "source": [ + "# Normalize the 'audio_features' column from combined_df\n", + "audio_features_df = pd.json_normalize(combined_df['audio_features'])\n", + "\n", + "# Combine the normalized audio features with the original CSV data\n", + "final_df = pd.concat([data_csv, audio_features_df], axis=1)\n", + "\n", + "# Normalize all the columns that have nested dictionaries\n", + "for col in final_df.columns:\n", + " if isinstance(final_df[col].iloc[0], dict): # Check if the column contains nested dictionaries\n", + " final_df = pd.concat([final_df.drop(columns=[col]), pd.json_normalize(final_df[col])], axis=1)\n", + "\n", + "final_df = final_df.dropna(axis=1, thresh=int(0.5 * len(final_df)))\n", + "\n", + "# Check the final DataFrame\n", + "print(final_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute pairwise cosine similarity between songs based on audio features\n", + "cosine_sim = cosine_similarity(final_df.drop(['liked'], axis=1))\n", + "\n", + "# Function to recommend songs based on a given song index\n", + "def recommend_song(song_index, top_n=5):\n", + " # Get similarity scores for the song\n", + " sim_scores = list(enumerate(cosine_sim[song_index]))\n", + " \n", + " # Sort songs by similarity\n", + " sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n", + " \n", + " # Get top N most similar songs (excluding the song itself)\n", + " sim_scores = sim_scores[1:top_n+1]\n", + " \n", + " song_indices = [i[0] for i in sim_scores]\n", + " similarity_scores = [i[1] for i in sim_scores]\n", + " \n", + " recommended_songs = final_df.iloc[song_indices]\n", + " \n", + " # Add similarity scores to the output\n", + " recommended_songs['similarity_score'] = similarity_scores\n", + " \n", + " return recommended_songs[['similarity_score', 'danceability', 'energy', 'loudness']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " similarity_score danceability energy loudness\n", + "40 1.0 0.358 0.977 -8.179\n", + "94 1.0 0.908 0.610 -5.735\n", + "114 1.0 0.256 0.952 -8.631\n", + "24 1.0 0.729 0.533 -10.104\n", + "149 1.0 0.829 0.708 -5.050\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\71524\\AppData\\Local\\Temp\\ipykernel_20068\\2503628174.py:21: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " recommended_songs['similarity_score'] = similarity_scores\n" + ] + } + ], + "source": [ + "# Example: recommend songs similar to the first song\n", + "recommended_songs = recommend_song(10, top_n=5)\n", + "print(recommended_songs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}